Interactions of small platinum clusters with the TiC(001) surface

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mao, Jianjun; Li, Shasha; Chu, Xingli

2015-11-14

Density functional theory calculations are used to elucidate the interactions of small platinum clusters (Pt{sub n}, n = 1–5) with the TiC(001) surface. The results are analyzed in terms of geometric, energetic, and electronic properties. It is found that a single Pt atom prefers to be adsorbed at the C-top site, while a Pt{sub 2} cluster prefers dimerization and a Pt{sub 3} cluster forms a linear structure on the TiC(001). As for the Pt{sub 4} cluster, the three-dimensional distorted tetrahedral structure and the two-dimensional square structure almost have equal stability. In contrast with the two-dimensional isolated Pt{sub 5} cluster, the adsorbed Pt{submore » 5} cluster prefers a three-dimensional structure on TiC(001). Substantial charge transfer takes place from TiC(001) surface to the adsorbed Pt{sub n} clusters, resulting in the negatively charged Pt{sub n} clusters. At last, the d-band centers of the absorbed Pt atoms and their implications in the catalytic activity are discussed.« less

Importance of many-body dispersion and temperature effects on gas-phase gold cluster (meta)stability

NASA Astrophysics Data System (ADS)

Goldsmith, Bryan R.; Gruene, Philipp; Lyon, Jonathan T.; Rayner, David M.; Fielicke, André; Scheffler, Matthias; Ghiringhelli, Luca M.

Gold clusters in the gas phase exhibit many structural isomers that are shown to intercovert frequently, even at room temperature. We performed ab initio replica-exchange molecular dynamics (REMD) calculations on gold clusters (of sizes 5-14 atoms) to identify metastable states and their relative populations at finite temperature, as well as to examine the importance of temperature and van der Waals (vdW) on their isomer energetic ordering. Free energies of the gold cluster isomers are optimally estimated using the Multistate Bennett Acceptance Ratio. The distribution of bond coordination numbers and radius of gyration are used to address the challenge of discriminating isomers along their dynamical trajectories. Dispersion effects are important for stabilizing three-dimensional structures relative to planar structures and brings isomer energetic predictions to closer quantitative agreement compared with RPA@PBE calculations. We find that higher temperatures typically stabilize metastable three-dimensional structures relative to planar/quasiplanar structures. Computed IR spectra of low free energy Au9, Au10, and Au12 isomers are in agreement with experimental spectra obtained by far-IR multiple photon dissociation in a molecular beam at 100 K.

A cluster analysis investigation of workaholism as a syndrome.

PubMed

Aziz, Shahnaz; Zickar, Michael J

2006-01-01

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

Study of cluster behavior in the riser of CFB by the DSMC method

NASA Astrophysics Data System (ADS)

Liu, H. P.; Liu, D. Y.; Liu, H.

2010-03-01

The flow behaviors of clusters in the riser of a two-dimensional (2D) circulating fluidized bed was numerically studied based on the Euler-Lagrangian approach. Gas turbulence was modeled by means of Large Eddy Simulation (LES). Particle collision was modeled by means of the direct simulation Monte Carlo (DSMC) method. Clusters' hydrodynamic characteristics are obtained using a cluster identification method proposed by sharrma et al. (2000). The descending clusters near the wall region and the up- and down-flowing clusters in the core were studied separately due to their different flow behaviors. The effects of superficial gas velocity on the cluster behavior were analyzed. Simulated results showed that near wall clusters flow downward and the descent velocity is about -45 cm/s. The occurrence frequency of the up-flowing cluster is higher than that of down-flowing cluster in the core of riser. With the increase of superficial gas velocity, the solid concentration and occurrence frequency of clusters decrease, while the cluster axial velocity increase. Simulated results were in agreement with experimental data. The stochastic method used in present paper is feasible for predicting the cluster flow behavior in CFBs.

Systematic exploration of unsupervised methods for mapping behavior

NASA Astrophysics Data System (ADS)

Todd, Jeremy G.; Kain, Jamey S.; de Bivort, Benjamin L.

2017-02-01

To fully understand the mechanisms giving rise to behavior, we need to be able to precisely measure it. When coupled with large behavioral data sets, unsupervised clustering methods offer the potential of unbiased mapping of behavioral spaces. However, unsupervised techniques to map behavioral spaces are in their infancy, and there have been few systematic considerations of all the methodological options. We compared the performance of seven distinct mapping methods in clustering a wavelet-transformed data set consisting of the x- and y-positions of the six legs of individual flies. Legs were automatically tracked by small pieces of fluorescent dye, while the fly was tethered and walking on an air-suspended ball. We find that there is considerable variation in the performance of these mapping methods, and that better performance is attained when clustering is done in higher dimensional spaces (which are otherwise less preferable because they are hard to visualize). High dimensionality means that some algorithms, including the non-parametric watershed cluster assignment algorithm, cannot be used. We developed an alternative watershed algorithm which can be used in high-dimensional spaces when a probability density estimate can be computed directly. With these tools in hand, we examined the behavioral space of fly leg postural dynamics and locomotion. We find a striking division of behavior into modes involving the fore legs and modes involving the hind legs, with few direct transitions between them. By computing behavioral clusters using the data from all flies simultaneously, we show that this division appears to be common to all flies. We also identify individual-to-individual differences in behavior and behavioral transitions. Lastly, we suggest a computational pipeline that can achieve satisfactory levels of performance without the taxing computational demands of a systematic combinatorial approach.

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

PubMed

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

2017-12-10

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

A strategy for analysis of (molecular) equilibrium simulations: Configuration space density estimation, clustering, and visualization

NASA Astrophysics Data System (ADS)

Hamprecht, Fred A.; Peter, Christine; Daura, Xavier; Thiel, Walter; van Gunsteren, Wilfred F.

2001-02-01

We propose an approach for summarizing the output of long simulations of complex systems, affording a rapid overview and interpretation. First, multidimensional scaling techniques are used in conjunction with dimension reduction methods to obtain a low-dimensional representation of the configuration space explored by the system. A nonparametric estimate of the density of states in this subspace is then obtained using kernel methods. The free energy surface is calculated from that density, and the configurations produced in the simulation are then clustered according to the topography of that surface, such that all configurations belonging to one local free energy minimum form one class. This topographical cluster analysis is performed using basin spanning trees which we introduce as subgraphs of Delaunay triangulations. Free energy surfaces obtained in dimensions lower than four can be visualized directly using iso-contours and -surfaces. Basin spanning trees also afford a glimpse of higher-dimensional topographies. The procedure is illustrated using molecular dynamics simulations on the reversible folding of peptide analoga. Finally, we emphasize the intimate relation of density estimation techniques to modern enhanced sampling algorithms.

Approximate cluster analysis method and three-dimensional diagram of optical characteristics of lunar surface

NASA Astrophysics Data System (ADS)

Yevsyukov, N. N.

1985-09-01

An approximate isolation algorithm for the isolation of multidimensional clusters is developed and applied in the construction of a three-dimensional diagram of the optical characteristics of the lunar surface. The method is somewhat analogous to that of Koontz and Fukunaga (1972) and involves isolating two-dimensional clusters, adding a new characteristic, and linearizing, a cycle which is repeated a limited number of times. The lunar-surface parameters analyzed are the 620-nm albedo, the 620/380-nm color index, and the 950/620-nm index. The results are presented graphically; the reliability of the cluster-isolation process is discussed; and some correspondences between known lunar morphology and the cluster maps are indicated.

Lipid-Mediated Clusters of Guest Molecules in Model Membranes and Their Dissolving in the Presence of Lipid Rafts.

PubMed

Kardash, Maria E; Dzuba, Sergei A

2017-05-25

The clustering of molecules is an important feature of plasma membrane organization. It is challenging to develop methods for quantifying membrane heterogeneities because of their transient nature and small size. Here, we obtained evidence that transient membrane heterogeneities can be frozen at cryogenic temperatures which allows the application of solid-state experimental techniques sensitive to the nanoscale distance range. We employed the pulsed version of electron paramagnetic resonance (EPR) spectroscopy, the electron spin echo (ESE) technique, for spin-labeled molecules in multilamellar lipid bilayers. ESE decays were refined for pure contribution of spin-spin magnetic dipole-dipolar interaction between the labels; these interactions manifest themselves at a nanometer distance range. The bilayers were prepared from different types of saturated and unsaturated lipids and cholesterol (Chol); in all cases, a small amount of guest spin-labeled substances 5-doxyl-stearic-acid (5-DSA) or 3β-doxyl-5α-cholestane (DChl) was added. The local concentration found of 5-DSA and DChl molecules was remarkably higher than the mean concentration in the bilayer, evidencing the formation of lipid-mediated clusters of these molecules. To our knowledge, formation of nanoscale clusters of guest amphiphilic molecules in biological membranes is a new phenomenon suggested only recently. Two-dimensional 5-DSA molecular clusters were found, whereas flat DChl molecules were found to be clustered into stacked one-dimensional structures. These clusters disappear when the Chol content is varied between the boundaries known for lipid raft formation at room temperatures. The room temperature EPR evidenced entrapping of DChl molecules in the rafts.

Formation of a percolating cluster in films prepared by cathodic electrodeposition of a mixture of lower and higher molecular weight epoxy-amine adducts.

PubMed

Ranjbar, Zahra; Moradian, Siamak; Rastegar, Saeed

2003-08-15

The electrodeposition behavior of blends of primary dispersions of a lower and a higher molecular weight epoxy-amine adduct has been investigated. The throwing power of the above-mentioned blends showed a voltage-dependent critical composition at which the throwing power dropped to a much lower value. This was assigned to the formation of an infinite conducting cluster, the extension of which is dependent on the rate of the electrocoagulation process at the cathode boundary. The random resistor network approach of Stauffer (RRNS) and the random resistor network approach of Miller and Abrahams (RRNMA) were applied to the experimental data with high correlations (r2=0.9314 and 0.9699). The percolating cluster formed within the film, however, gave a critical exponent of conductivity equal to 1.1028, much less than expected from a classical three-dimensional lattice (i.e., 1.5-2.0). This discrepancy was explained in terms of the changed behavior of the film resulting from the bubbles formed near the cathode and its effect on the infinite conducting cluster.

Graphical Representations and Cluster Algorithms for Ice Rule Vertex Models.

NASA Astrophysics Data System (ADS)

Shtengel, Kirill; Chayes, L.

2002-03-01

We introduce a new class of polymer models which is closely related to loop models, recently a topic of intensive studies. These particular models arise as graphical representations for ice-rule vertex models. The associated cluster algorithms provide a unification and generalisation of most of the existing algorithms. For many lattices, percolation in the polymer models evidently indicates first order phase transitions in the vertex models. Critical phases can be understood as being susceptible to colour symmetry breaking in the polymer models. The analysis includes, but is certainly not limited to the square lattice six-vertex model. In particular, analytic criteria can be found for low temperature phases in other even coordinated 2D lattices such as the triangular lattice, or higher dimensional lattices such as the hyper-cubic lattices of arbitrary dimensionality. Finally, our approach can be generalised to the vertex models that do not obey the ice rule, such as the eight-vertex model.

Noise-robust unsupervised spike sorting based on discriminative subspace learning with outlier handling.

PubMed

Keshtkaran, Mohammad Reza; Yang, Zhi

2017-06-01

Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.

Noise-robust unsupervised spike sorting based on discriminative subspace learning with outlier handling

NASA Astrophysics Data System (ADS)

Keshtkaran, Mohammad Reza; Yang, Zhi

2017-06-01

Objective. Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. Approach. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Main results. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. Significance. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.

The method of approximate cluster analysis and the three-dimensional diagram of optical characteristics of the lunar surface

NASA Astrophysics Data System (ADS)

Evsyukov, N. N.

1984-12-01

An approximate isolation algorithm for the isolation of multidimensional clusters is developed and applied in the construction of a three-dimensional diagram of the optical characteristics of the lunar surface. The method is somewhat analogous to that of Koontz and Fukunaga (1972) and involves isolating two-dimensional clusters, adding a new characteristic, and linearizing, a cycle which is repeated a limited number of times. The lunar-surface parameters analyzed are the 620-nm albedo, the 620/380-nm color index, and the 950/620-nm index. The results are presented graphically; the reliability of the cluster-isolation process is discussed; and some correspondences between known lunar morphology and the cluster maps are indicated.

Charge carrier localised in zero-dimensional (CH3NH3)3Bi2I9 clusters.

PubMed

Ni, Chengsheng; Hedley, Gordon; Payne, Julia; Svrcek, Vladimir; McDonald, Calum; Jagadamma, Lethy Krishnan; Edwards, Paul; Martin, Robert; Jain, Gunisha; Carolan, Darragh; Mariotti, Davide; Maguire, Paul; Samuel, Ifor; Irvine, John

2017-08-01

A metal-organic hybrid perovskite (CH 3 NH 3 PbI 3 ) with three-dimensional framework of metal-halide octahedra has been reported as a low-cost, solution-processable absorber for a thin-film solar cell with a power-conversion efficiency over 20%. Low-dimensional layered perovskites with metal halide slabs separated by the insulating organic layers are reported to show higher stability, but the efficiencies of the solar cells are limited by the confinement of excitons. In order to explore the confinement and transport of excitons in zero-dimensional metal-organic hybrid materials, a highly orientated film of (CH 3 NH 3 ) 3 Bi 2 I 9 with nanometre-sized core clusters of Bi 2 I 9 3- surrounded by insulating CH 3 NH 3 + was prepared via solution processing. The (CH 3 NH 3 ) 3 Bi 2 I 9 film shows highly anisotropic photoluminescence emission and excitation due to the large proportion of localised excitons coupled with delocalised excitons from intercluster energy transfer. The abrupt increase in photoluminescence quantum yield at excitation energy above twice band gap could indicate a quantum cutting due to the low dimensionality.Understanding the confinement and transport of excitons in low dimensional systems will aid the development of next generation photovoltaics. Via photophysical studies Ni et al. observe 'quantum cutting' in 0D metal-organic hybrid materials based on methylammonium bismuth halide (CH 3 NH 3 )3Bi 2 I 9 .

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

NASA Astrophysics Data System (ADS)

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

2016-10-01

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

Electrodynamic tailoring of self-assembled three-dimensional electrospun constructs

NASA Astrophysics Data System (ADS)

Reis, Tiago C.; Correia, Ilídio J.; Aguiar-Ricardo, Ana

2013-07-01

The rational design of three-dimensional electrospun constructs (3DECs) can lead to striking topographies and tailored shapes of electrospun materials. This new generation of materials is suppressing some of the current limitations of the usual 2D non-woven electrospun fiber mats, such as small pore sizes or only flat shaped constructs. Herein, we pursued an explanation for the self-assembly of 3DECs based on electrodynamic simulations and experimental validation. We concluded that the self-assembly process is driven by the establishment of attractive electrostatic forces between the positively charged aerial fibers and the already collected ones, which tend to acquire a negatively charged network oriented towards the nozzle. The in situ polarization degree is strengthened by higher amounts of clustered fibers, and therefore the initial high density fibrous regions are the preliminary motifs for the self-assembly mechanism. As such regions increase their in situ polarization electrostatic repulsive forces will appear, favoring a competitive growth of these self-assembled fibrous clusters. Highly polarized regions will evidence higher distances between consecutive micro-assembled fibers (MAFs). Different processing parameters - deposition time, electric field intensity, concentration of polymer solution, environmental temperature and relative humidity - were evaluated in an attempt to control material's design.The rational design of three-dimensional electrospun constructs (3DECs) can lead to striking topographies and tailored shapes of electrospun materials. This new generation of materials is suppressing some of the current limitations of the usual 2D non-woven electrospun fiber mats, such as small pore sizes or only flat shaped constructs. Herein, we pursued an explanation for the self-assembly of 3DECs based on electrodynamic simulations and experimental validation. We concluded that the self-assembly process is driven by the establishment of attractive electrostatic forces between the positively charged aerial fibers and the already collected ones, which tend to acquire a negatively charged network oriented towards the nozzle. The in situ polarization degree is strengthened by higher amounts of clustered fibers, and therefore the initial high density fibrous regions are the preliminary motifs for the self-assembly mechanism. As such regions increase their in situ polarization electrostatic repulsive forces will appear, favoring a competitive growth of these self-assembled fibrous clusters. Highly polarized regions will evidence higher distances between consecutive micro-assembled fibers (MAFs). Different processing parameters - deposition time, electric field intensity, concentration of polymer solution, environmental temperature and relative humidity - were evaluated in an attempt to control material's design. Electronic supplementary information (ESI) available. See DOI: 10.1039/c3nr01668d

Prescribed nanoparticle cluster architectures and low-dimensional arrays built using octahedral DNA origami frames

NASA Astrophysics Data System (ADS)

Tian, Ye; Wang, Tong; Liu, Wenyan; Xin, Huolin L.; Li, Huilin; Ke, Yonggang; Shih, William M.; Gang, Oleg

2015-07-01

Three-dimensional mesoscale clusters that are formed from nanoparticles spatially arranged in pre-determined positions can be thought of as mesoscale analogues of molecules. These nanoparticle architectures could offer tailored properties due to collective effects, but developing a general platform for fabricating such clusters is a significant challenge. Here, we report a strategy for assembling three-dimensional nanoparticle clusters that uses a molecular frame designed with encoded vertices for particle placement. The frame is a DNA origami octahedron and can be used to fabricate clusters with various symmetries and particle compositions. Cryo-electron microscopy is used to uncover the structure of the DNA frame and to reveal that the nanoparticles are spatially coordinated in the prescribed manner. We show that the DNA frame and one set of nanoparticles can be used to create nanoclusters with different chiroptical activities. We also show that the octahedra can serve as programmable interparticle linkers, allowing one- and two-dimensional arrays to be assembled with designed particle arrangements.

Universal dynamical properties preclude standard clustering in a large class of biochemical data.

PubMed

Gomez, Florian; Stoop, Ralph L; Stoop, Ruedi

2014-09-01

Clustering of chemical and biochemical data based on observed features is a central cognitive step in the analysis of chemical substances, in particular in combinatorial chemistry, or of complex biochemical reaction networks. Often, for reasons unknown to the researcher, this step produces disappointing results. Once the sources of the problem are known, improved clustering methods might revitalize the statistical approach of compound and reaction search and analysis. Here, we present a generic mechanism that may be at the origin of many clustering difficulties. The variety of dynamical behaviors that can be exhibited by complex biochemical reactions on variation of the system parameters are fundamental system fingerprints. In parameter space, shrimp-like or swallow-tail structures separate parameter sets that lead to stable periodic dynamical behavior from those leading to irregular behavior. We work out the genericity of this phenomenon and demonstrate novel examples for their occurrence in realistic models of biophysics. Although we elucidate the phenomenon by considering the emergence of periodicity in dependence on system parameters in a low-dimensional parameter space, the conclusions from our simple setting are shown to continue to be valid for features in a higher-dimensional feature space, as long as the feature-generating mechanism is not too extreme and the dimension of this space is not too high compared with the amount of available data. For online versions of super-paramagnetic clustering see http://stoop.ini.uzh.ch/research/clustering. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Improving 3d Spatial Queries Search: Newfangled Technique of Space Filling Curves in 3d City Modeling

NASA Astrophysics Data System (ADS)

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

2013-09-01

The advantages of three dimensional (3D) city models can be seen in various applications including photogrammetry, urban and regional planning, computer games, etc.. They expand the visualization and analysis capabilities of Geographic Information Systems on cities, and they can be developed using web standards. However, these 3D city models consume much more storage compared to two dimensional (2D) spatial data. They involve extra geometrical and topological information together with semantic data. Without a proper spatial data clustering method and its corresponding spatial data access method, retrieving portions of and especially searching these 3D city models, will not be done optimally. Even though current developments are based on an open data model allotted by the Open Geospatial Consortium (OGC) called CityGML, its XML-based structure makes it challenging to cluster the 3D urban objects. In this research, we propose an opponent data constellation technique of space-filling curves (3D Hilbert curves) for 3D city model data representation. Unlike previous methods, that try to project 3D or n-dimensional data down to 2D or 3D using Principal Component Analysis (PCA) or Hilbert mappings, in this research, we extend the Hilbert space-filling curve to one higher dimension for 3D city model data implementations. The query performance was tested using a CityGML dataset of 1,000 building blocks and the results are presented in this paper. The advantages of implementing space-filling curves in 3D city modeling will improve data retrieval time by means of optimized 3D adjacency, nearest neighbor information and 3D indexing. The Hilbert mapping, which maps a subinterval of the [0, 1] interval to the corresponding portion of the d-dimensional Hilbert's curve, preserves the Lebesgue measure and is Lipschitz continuous. Depending on the applications, several alternatives are possible in order to cluster spatial data together in the third dimension compared to its clustering in 2D.

Information extraction from dynamic PS-InSAR time series using machine learning

NASA Astrophysics Data System (ADS)

van de Kerkhof, B.; Pankratius, V.; Chang, L.; van Swol, R.; Hanssen, R. F.

2017-12-01

Due to the increasing number of SAR satellites, with shorter repeat intervals and higher resolutions, SAR data volumes are exploding. Time series analyses of SAR data, i.e. Persistent Scatterer (PS) InSAR, enable the deformation monitoring of the built environment at an unprecedented scale, with hundreds of scatterers per km2, updated weekly. Potential hazards, e.g. due to failure of aging infrastructure, can be detected at an early stage. Yet, this requires the operational data processing of billions of measurement points, over hundreds of epochs, updating this data set dynamically as new data come in, and testing whether points (start to) behave in an anomalous way. Moreover, the quality of PS-InSAR measurements is ambiguous and heterogeneous, which will yield false positives and false negatives. Such analyses are numerically challenging. Here we extract relevant information from PS-InSAR time series using machine learning algorithms. We cluster (group together) time series with similar behaviour, even though they may not be spatially close, such that the results can be used for further analysis. First we reduce the dimensionality of the dataset in order to be able to cluster the data, since applying clustering techniques on high dimensional datasets often result in unsatisfying results. Our approach is to apply t-distributed Stochastic Neighbor Embedding (t-SNE), a machine learning algorithm for dimensionality reduction of high-dimensional data to a 2D or 3D map, and cluster this result using Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The results show that we are able to detect and cluster time series with similar behaviour, which is the starting point for more extensive analysis into the underlying driving mechanisms. The results of the methods are compared to conventional hypothesis testing as well as a Self-Organising Map (SOM) approach. Hypothesis testing is robust and takes the stochastic nature of the observations into account, but is time consuming. Therefore, we successively apply our machine learning approach with the hypothesis testing approach in order to benefit from both the reduced computation time of the machine learning approach as from the robust quality metrics of hypothesis testing. We acknowledge support from NASA AISTNNX15AG84G (PI V. Pankratius)

Hyper-spectral image segmentation using spectral clustering with covariance descriptors

NASA Astrophysics Data System (ADS)

Kursun, Olcay; Karabiber, Fethullah; Koc, Cemalettin; Bal, Abdullah

2009-02-01

Image segmentation is an important and difficult computer vision problem. Hyper-spectral images pose even more difficulty due to their high-dimensionality. Spectral clustering (SC) is a recently popular clustering/segmentation algorithm. In general, SC lifts the data to a high dimensional space, also known as the kernel trick, then derive eigenvectors in this new space, and finally using these new dimensions partition the data into clusters. We demonstrate that SC works efficiently when combined with covariance descriptors that can be used to assess pixelwise similarities rather than in the high-dimensional Euclidean space. We present the formulations and some preliminary results of the proposed hybrid image segmentation method for hyper-spectral images.

Nucleation and growth of Ag on Sb-terminated Ge( 1 0 0 )

NASA Astrophysics Data System (ADS)

Chan, L. H.; Altman, E. I.

2002-06-01

The effect of Sb on Ag growth on Ge(1 0 0) was characterized using scanning tunneling microscopy, low energy electron diffraction, and Auger electron spectroscopy. Silver was found to immediately form three-dimensional clusters on the Sb-covered surface over the entire temperature range studied (320-570 K), thus the growth was Volmer-Weber. Regardless of the deposition conditions, there was no evidence that Sb segregated to the Ag surface, despite Sb having a lower surface tension than either Ag or Ge. The failure of Sb to segregate to the surface could be understood in terms of the much stronger interaction between Sb and Ge versus Ag and Ge creating a driving force to maintain an Sb-Ge interface. Silver nucleation on Sb/Ge(1 0 0) was characterized by measuring the Ag cluster density as a function of deposition rate. The results revealed that the cluster density was nearly independent of the deposition rate below 420 K, indicating that heterogeneous nucleation at defects in the Sb-terminated surface competed with homogeneous nucleation. At higher temperatures, the defects were less effective in trapping diffusing Ag atoms and the dependence of the cluster density on deposition rate suggested a critical size of at least two. For temperatures above 420 K, the Ag diffusion barrier plus the dissociation energy of the critical cluster was estimated by measuring the cluster density as a function of temperature; the results suggested a value of 0.84±0.1 eV which is significantly higher than values reported for Ag nucleation on Sb-free surfaces. In comparison to the bare Ge surface, Ag formed a higher density of smaller, lower clusters when Sb was present. Below 420 K the higher cluster density could be attributed to nucleation at defects in the Sb layer while at higher temperatures the high diffusion barrier restricted the cluster size and density. Although Sb does not act as a surfactant in this system since it does not continuously float to the surface and the growth is not layer-by-layer, adding Sb was found to be useful in limiting the Ag cluster size and height which led to smoother, more continuous Ag films and in preventing the formation of metastable Ag-Ge surface alloys.

Design of hybrid radial basis function neural networks (HRBFNNs) realized with the aid of hybridization of fuzzy clustering method (FCM) and polynomial neural networks (PNNs).

PubMed

Huang, Wei; Oh, Sung-Kwun; Pedrycz, Witold

2014-12-01

In this study, we propose Hybrid Radial Basis Function Neural Networks (HRBFNNs) realized with the aid of fuzzy clustering method (Fuzzy C-Means, FCM) and polynomial neural networks. Fuzzy clustering used to form information granulation is employed to overcome a possible curse of dimensionality, while the polynomial neural network is utilized to build local models. Furthermore, genetic algorithm (GA) is exploited here to optimize the essential design parameters of the model (including fuzzification coefficient, the number of input polynomial fuzzy neurons (PFNs), and a collection of the specific subset of input PFNs) of the network. To reduce dimensionality of the input space, principal component analysis (PCA) is considered as a sound preprocessing vehicle. The performance of the HRBFNNs is quantified through a series of experiments, in which we use several modeling benchmarks of different levels of complexity (different number of input variables and the number of available data). A comparative analysis reveals that the proposed HRBFNNs exhibit higher accuracy in comparison to the accuracy produced by some models reported previously in the literature. Copyright © 2014 Elsevier Ltd. All rights reserved.

Interfacial energetics of two-dimensional colloidal clusters generated with a tunable anharmonic interaction potential

NASA Astrophysics Data System (ADS)

Hilou, Elaa; Du, Di; Kuei, Steve; Biswal, Sibani Lisa

2018-02-01

Interfacial characteristics are critical to various properties of two-dimensional (2D) materials such as band alignment at a heterojunction and nucleation kinetics in a 2D crystal. Despite the desire to harness these enhanced interfacial properties for engineering new materials, unexpected phase transitions and defects, unique to the 2D morphology, have left a number of open questions. In particular, the effects of configurational anisotropy, which are difficult to isolate experimentally, and their influence on interfacial properties are not well understood. In this work, we begin to probe this structure-thermodynamic relationship, using a rotating magnetic field to generate an anharmonic interaction potential in a 2D system of paramagnetic particles. At low magnetic field strengths, weakly interacting colloidal particles form non-close-packed, fluidlike droplets, whereas, at higher field strengths, crystallites with hexagonal ordering are observed. We examine spatial and interfacial properties of these 2D colloidal clusters by measuring the local bond orientation order parameter and interfacial stiffness as a function of the interaction strength. To our knowledge, this is the first study to measure the tunable interfacial stiffness of a 2D colloidal cluster by controlling particle interactions using external fields.

Information jet: Handling noisy big data from weakly disconnected network

NASA Astrophysics Data System (ADS)

Aurongzeb, Deeder

Sudden aggregation (information jet) of large amount of data is ubiquitous around connected social networks, driven by sudden interacting and non-interacting events, network security threat attacks, online sales channel etc. Clustering of information jet based on time series analysis and graph theory is not new but little work is done to connect them with particle jet statistics. We show pre-clustering based on context can element soft network or network of information which is critical to minimize time to calculate results from noisy big data. We show difference between, stochastic gradient boosting and time series-graph clustering. For disconnected higher dimensional information jet, we use Kallenberg representation theorem (Kallenberg, 2005, arXiv:1401.1137) to identify and eliminate jet similarities from dense or sparse graph.

Local matrix learning in clustering and applications for manifold visualization.

PubMed

Arnonkijpanich, Banchar; Hasenfuss, Alexander; Hammer, Barbara

2010-05-01

Electronic data sets are increasing rapidly with respect to both, size of the data sets and data resolution, i.e. dimensionality, such that adequate data inspection and data visualization have become central issues of data mining. In this article, we present an extension of classical clustering schemes by local matrix adaptation, which allows a better representation of data by means of clusters with an arbitrary spherical shape. Unlike previous proposals, the method is derived from a global cost function. The focus of this article is to demonstrate the applicability of this matrix clustering scheme to low-dimensional data embedding for data inspection. The proposed method is based on matrix learning for neural gas and manifold charting. This provides an explicit mapping of a given high-dimensional data space to low dimensionality. We demonstrate the usefulness of this method for data inspection and manifold visualization. 2009 Elsevier Ltd. All rights reserved.

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

NASA Astrophysics Data System (ADS)

Anokhina, Ekaterina V.

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

Application of fuzzy c-means clustering to PRTR chemicals uncovering their release and toxicity characteristics.

PubMed

Xue, Mianqiang; Zhou, Liang; Kojima, Naoya; Dos Muchangos, Leticia Sarmento; Machimura, Takashi; Tokai, Akihiro

2018-05-01

Increasing manufacture and usage of chemicals have not been matched by the increase in our understanding of their risks. Pollutant release and transfer register (PRTR) is becoming a popular measure for collecting chemical data and enhancing the public right to know. However, these data are usually in high dimensionality which restricts their wider use. The present study partitions Japanese PRTR chemicals into five fuzzy clusters by fuzzy c-mean clustering (FCM) to explore the implicit information. Each chemical with membership degrees belongs to each cluster. Cluster I features high releases from non-listed industries and the household sector and high environmental toxicity. Cluster II is characterized by high reported releases and transfers from 24 listed industries above the threshold, mutagenicity, and high environmental toxicity. Chemicals in cluster III have characteristics of high releases from non-listed industries and low toxicity. Cluster IV is characterized by high reported releases and transfers from 24 listed industries above the threshold and extremely high environmental toxicity. Cluster V is characterized by low releases yet mutagenicity and high carcinogenicity. Chemicals with the highest membership degree were identified as representatives for each cluster. For the highest membership degree, half of the chemicals have a value higher than 0.74. If we look at both the highest and the second highest membership degrees simultaneously, about 94% of the chemicals have a value higher than 0.5. FCM can serve as an approach to uncover the implicit information of highly complex chemical dataset, which subsequently supports the strategy development for efficient and effective chemical management. Copyright © 2017 Elsevier B.V. All rights reserved.

Dimensionality effects on magnetic properties of FexCo1-x nanoclusters on Pt(1 1 1)

NASA Astrophysics Data System (ADS)

Miranda, I. P.; Igarashi, R. N.; Klautau, A. B.; Petrilli, H. M.

2017-11-01

The behavior of local magnetic moments and exchange coupling parameters of FexCo1-x nanostructures (nanowires and compact clusters) on the fcc Pt(1 1 1) surface is here investigated using the first-principles real-space RS-LMTO-ASA method, in the framework of the DFT. Different configurations of FexCo1-x trimers and heptamers on Pt(1 1 1) are considered, varying the positions and the concentration of Fe or Co atoms. We discuss the influence of dimensionality and stoichiometry changes on the magnetic properties, specially on the orbital moments, which are very important in establishing a nanoscopic understanding of delocalized electron systems. We demonstrate the existence of a strictly decreasing nonlinear trend of the average orbital moments with the Fe concentration for the compact clusters, different from what was found for FexCo1-x nanowires on Pt(1 1 1) and also for corresponding higher-dimensional systems (FexCo1-x monolayer on Pt(1 1 1) and FexCo1-x bulk). The average spin moments, however, are invariably described by a linear function with respect to stoichiometry. In all studied cases, the nearest neighbors exchange couplings have shown to be strongly ferromagnetic.

Temperature Map of the Perseus Cluster of Galaxies Observed with ASCA

NASA Technical Reports Server (NTRS)

Furusho, T.; Yamasaki, N. Y.; Ohashi, T.; Shibata, R.; Ezawa, H.; White, Nicholas E. (Technical Monitor)

2000-01-01

We present two-dimensional temperature map of the Perseus cluster based on multi-pointing observations with the Advanced Spacecraft for Cosmology Astrophysics (ASCA) Gas Imaging Spectrometer (GIS), covering a region with a diameter of approximately 2 deg. By correcting for the effect of the X-ray telescope response, the temperatures were estimated from hardness ratios and the complete temperature structure of the cluster with a spatial resolution of about 100 kpc was obtained for the first time. There is an extended cool region with a diameter of approximately 20 arcmin and kT approx. 5 keV at about 20 arcmin east from the cluster center. This region also shows higher surface brightness and is surrounded by a large ring-like hot region with kT approx. > 7 keV, and likely to be a remnant of a merger with a poor cluster. Another extended cool region is extending outward from the IC 310 subcluster. These features and the presence of several other hot and cool blobs suggest that this rich cluster has been formed as a result of a repetition of many subcluster mergers.

Sine-Gordon Equation in (1+2) and (1+3) dimensions: Existence and Classification of Traveling-Wave Solutions.

PubMed

Zarmi, Yair

2015-01-01

The (1+1)-dimensional Sine-Gordon equation passes integrability tests commonly applied to nonlinear evolution equations. Its kink solutions (one-dimensional fronts) are obtained by a Hirota algorithm. In higher space-dimensions, the equation does not pass these tests. Although it has been derived over the years for quite a few physical systems that have nothing to do with Special Relativity, the Sine-Gordon equation emerges as a non-linear relativistic wave equation. This opens the way for exploiting the tools of the Theory of Special Relativity. Using no more than the relativistic kinematics of tachyonic momentum vectors, from which the solutions are constructed through the Hirota algorithm, the existence and classification of N-moving-front solutions of the (1+2)- and (1+3)-dimensional equations for all N ≥ 1 are presented. In (1+2) dimensions, each multi-front solution propagates rigidly at one velocity. The solutions are divided into two subsets: Solutions whose velocities are lower than a limiting speed, c = 1, or are greater than or equal to c. To connect with concepts of the Theory of Special Relativity, c will be called "the speed of light." In (1+3)-dimensions, multi-front solutions are characterized by spatial structure and by velocity composition. The spatial structure is either planar (rotated (1+2)-dimensional solutions), or genuinely three-dimensional--branes. Planar solutions, propagate rigidly at one velocity, which is lower than, equal to, or higher than c. Branes must contain clusters of fronts whose speed exceeds c = 1. Some branes are "hybrids": different clusters of fronts propagate at different velocities. Some velocities may be lower than c but some must be equal to, or exceed, c. Finally, the speed of light cannot be approached from within the subset of slower-than-light solutions in both (1+2) and (1+3) dimensions.

Categorical clustering of the neural representation of color.

PubMed

Brouwer, Gijs Joost; Heeger, David J

2013-09-25

Cortical activity was measured with functional magnetic resonance imaging (fMRI) while human subjects viewed 12 stimulus colors and performed either a color-naming or diverted attention task. A forward model was used to extract lower dimensional neural color spaces from the high-dimensional fMRI responses. The neural color spaces in two visual areas, human ventral V4 (V4v) and VO1, exhibited clustering (greater similarity between activity patterns evoked by stimulus colors within a perceptual category, compared to between-category colors) for the color-naming task, but not for the diverted attention task. Response amplitudes and signal-to-noise ratios were higher in most visual cortical areas for color naming compared to diverted attention. But only in V4v and VO1 did the cortical representation of color change to a categorical color space. A model is presented that induces such a categorical representation by changing the response gains of subpopulations of color-selective neurons.

Invasion percolation between two sites in two, three, and four dimensions

NASA Astrophysics Data System (ADS)

Lee, Sang Bub

2009-06-01

The mass distribution of invaded clusters in non-trapping invasion percolation between an injection site and an extraction site has been studied, in two, three, and four dimensions. This study is an extension of the recent study focused on two dimensions by Araújo et al. [A.D. Araújo, T.F. Vasconcelos, A.A. Moreira, L.S. Lucena, J.S. Andrade Jr., Phys. Rev. E 72 (2005) 041404] with respect to higher dimensions. The mass distribution exhibits a power-law behavior, P(m)∝m. It has been found that the index α for pe

Categorical Clustering of the Neural Representation of Color

PubMed Central

Heeger, David J.

2013-01-01

Cortical activity was measured with functional magnetic resonance imaging (fMRI) while human subjects viewed 12 stimulus colors and performed either a color-naming or diverted attention task. A forward model was used to extract lower dimensional neural color spaces from the high-dimensional fMRI responses. The neural color spaces in two visual areas, human ventral V4 (V4v) and VO1, exhibited clustering (greater similarity between activity patterns evoked by stimulus colors within a perceptual category, compared to between-category colors) for the color-naming task, but not for the diverted attention task. Response amplitudes and signal-to-noise ratios were higher in most visual cortical areas for color naming compared to diverted attention. But only in V4v and VO1 did the cortical representation of color change to a categorical color space. A model is presented that induces such a categorical representation by changing the response gains of subpopulations of color-selective neurons. PMID:24068814

Two-dimensional and three-dimensional Coulomb clusters in parabolic traps

DOE Office of Scientific and Technical Information (OSTI.GOV)

D'yachkov, L. G., E-mail: dyachk@mail.ru; Myasnikov, M. I., E-mail: miasnikovmi@mail.ru; Petrov, O. F.

2014-09-15

We consider the shell structure of Coulomb clusters in an axially symmetric parabolic trap exhibiting a confining potential U{sub c}(ρ,z)=(mω{sup 2}/2)(ρ{sup 2}+αz{sup 2}). Assuming an anisotropic parameter α = 4 (corresponding to experiments employing a cusp magnetic trap under microgravity conditions), we have calculated cluster configurations for particle numbers N = 3 to 30. We have shown that clusters with N ≤ 12 initially remain flat, transitioning to three-dimensional configurations as N increases. For N = 8, we have calculated the configurations of minimal potential energy for all values of α and found the points of configuration transitions. For N = 13 and 23, we discuss the influence of bothmore » the shielding and anisotropic parameter on potential energy, cluster size, and shell structure.« less

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

NASA Astrophysics Data System (ADS)

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

2018-04-01

Similarity metrics, kernels and similarity-based algorithms have gained much attention due to their increasing applications in information retrieval, data mining, pattern recognition and machine learning. Similarity Graphs are often adopted as the underlying representation of similarity matrices and are at the origin of known clustering algorithms such as spectral clustering. Similarity matrices offer the advantage of working in object-object (two-dimensional) space where visualization of clusters similarities is available instead of object-features (multi-dimensional) space. In this paper, sparse ɛ-similarity graphs are constructed and decomposed into strong components using appropriate methods such as Dulmage-Mendelsohn permutation (DMperm) and/or Reverse Cuthill-McKee (RCM) algorithms. The obtained strong components correspond to groups (clusters) in the input (feature) space. Parameter ɛi is estimated locally, at each data point i from a corresponding narrow range of the number of nearest neighbors. Although more advanced clustering techniques are available, our method has the advantages of simplicity, better complexity and direct visualization of the clusters similarities in a two-dimensional space. Also, no prior information about the number of clusters is needed. We conducted our experiments on two and three dimensional, low and high-sized synthetic datasets as well as on an astronomical real-dataset. The results are verified graphically and analyzed using gap statistics over a range of neighbors to verify the robustness of the algorithm and the stability of the results. Combining the proposed algorithm with gap statistics provides a promising tool for solving clustering problems. An astronomical application is conducted for confirming the existence of 45 galaxy clusters around the X-ray positions of galaxy clusters in the redshift range [0.1..0.8]. We re-estimate the photometric redshifts of the identified galaxy clusters and obtain acceptable values compared to published spectroscopic redshifts with a 0.029 standard deviation of their differences.

ICM: a web server for integrated clustering of multi-dimensional biomedical data.

PubMed

He, Song; He, Haochen; Xu, Wenjian; Huang, Xin; Jiang, Shuai; Li, Fei; He, Fuchu; Bo, Xiaochen

2016-07-08

Large-scale efforts for parallel acquisition of multi-omics profiling continue to generate extensive amounts of multi-dimensional biomedical data. Thus, integrated clustering of multiple types of omics data is essential for developing individual-based treatments and precision medicine. However, while rapid progress has been made, methods for integrated clustering are lacking an intuitive web interface that facilitates the biomedical researchers without sufficient programming skills. Here, we present a web tool, named Integrated Clustering of Multi-dimensional biomedical data (ICM), that provides an interface from which to fuse, cluster and visualize multi-dimensional biomedical data and knowledge. With ICM, users can explore the heterogeneity of a disease or a biological process by identifying subgroups of patients. The results obtained can then be interactively modified by using an intuitive user interface. Researchers can also exchange the results from ICM with collaborators via a web link containing a Project ID number that will directly pull up the analysis results being shared. ICM also support incremental clustering that allows users to add new sample data into the data of a previous study to obtain a clustering result. Currently, the ICM web server is available with no login requirement and at no cost at http://biotech.bmi.ac.cn/icm/. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

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

Convex clustering: an attractive alternative to hierarchical clustering.

PubMed

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

2015-05-01

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/.

Optimum Particle Size for Gold-Catalyzed CO Oxidation

PubMed Central

2018-01-01

The structure sensitivity of gold-catalyzed CO oxidation is presented by analyzing in detail the dependence of CO oxidation rate on particle size. Clusters with less than 14 gold atoms adopt a planar structure, whereas larger ones adopt a three-dimensional structure. The CO and O2 adsorption properties depend strongly on particle structure and size. All of the reaction barriers relevant to CO oxidation display linear scaling relationships with CO and O2 binding strengths as main reactivity descriptors. Planar and three-dimensional gold clusters exhibit different linear scaling relationship due to different surface topologies and different coordination numbers of the surface atoms. On the basis of these linear scaling relationships, first-principles microkinetics simulations were conducted to determine CO oxidation rates and possible rate-determining step of Au particles. Planar Au9 and three-dimensional Au79 clusters present the highest CO oxidation rates for planar and three-dimensional clusters, respectively. The planar Au9 cluster is much more active than the optimum Au79 cluster. A common feature of optimum CO oxidation performance is the intermediate binding strengths of CO and O2, resulting in intermediate coverages of CO, O2, and O. Both these optimum particles present lower performance than maximum Sabatier performance, indicating that there is sufficient room for improvement of gold catalysts for CO oxidation. PMID:29707098

Effects of additional data on Bayesian clustering.

PubMed

Yamazaki, Keisuke

2017-10-01

Hierarchical probabilistic models, such as mixture models, are used for cluster analysis. These models have two types of variables: observable and latent. In cluster analysis, the latent variable is estimated, and it is expected that additional information will improve the accuracy of the estimation of the latent variable. Many proposed learning methods are able to use additional data; these include semi-supervised learning and transfer learning. However, from a statistical point of view, a complex probabilistic model that encompasses both the initial and additional data might be less accurate due to having a higher-dimensional parameter. The present paper presents a theoretical analysis of the accuracy of such a model and clarifies which factor has the greatest effect on its accuracy, the advantages of obtaining additional data, and the disadvantages of increasing the complexity. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

PubMed

Dashti, Ali; Komarov, Ivan; D'Souza, Roshan M

2013-01-01

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

A density functional global optimisation study of neutral 8-atom Cu-Ag and Cu-Au clusters

NASA Astrophysics Data System (ADS)

Heard, Christopher J.; Johnston, Roy L.

2013-02-01

The effect of doping on the energetics and dimensionality of eight atom coinage metal subnanometre particles is fully resolved using a genetic algorithm in tandem with on the fly density functional theory calculations to determine the global minima (GM) for Cu n Ag(8- n) and Cu n Au(8- n) clusters. Comparisons are made to previous ab initio work on mono- and bimetallic clusters, with excellent agreement found. Charge transfer and geometric arguments are considered to rationalise the stability of the particular permutational isomers found. An interesting transition between three dimensional and two dimensional GM structures is observed for copper-gold clusters, which is sharper and appears earlier in the doping series than is known for gold-silver particles.

Clustering and assembly dynamics of a one-dimensional microphase former.

PubMed

Hu, Yi; Charbonneau, Patrick

2018-05-23

Both ordered and disordered microphases ubiquitously form in suspensions of particles that interact through competing short-range attraction and long-range repulsion (SALR). While ordered microphases are more appealing materials targets, understanding the rich structural and dynamical properties of their disordered counterparts is essential to controlling their mesoscale assembly. Here, we study the disordered regime of a one-dimensional (1D) SALR model, whose simplicity enables detailed analysis by transfer matrices and Monte Carlo simulations. We first characterize the signature of the clustering process on macroscopic observables, and then assess the equilibration dynamics of various simulation algorithms. We notably find that cluster moves markedly accelerate the mixing time, but that event chains are of limited help in the clustering regime. These insights will inspire further study of three-dimensional microphase formers.

The correlation function for density perturbations in an expanding universe. II - Nonlinear theory

NASA Technical Reports Server (NTRS)

Mcclelland, J.; Silk, J.

1977-01-01

A formalism is developed to find the two-point and higher-order correlation functions for a given distribution of sizes and shapes of perturbations which are randomly placed in three-dimensional space. The perturbations are described by two parameters such as central density and size, and the two-point correlation function is explicitly related to the luminosity function of groups and clusters of galaxies

Fast dimension reduction and integrative clustering of multi-omics data using low-rank approximation: application to cancer molecular classification.

PubMed

Wu, Dingming; Wang, Dongfang; Zhang, Michael Q; Gu, Jin

2015-12-01

One major goal of large-scale cancer omics study is to identify molecular subtypes for more accurate cancer diagnoses and treatments. To deal with high-dimensional cancer multi-omics data, a promising strategy is to find an effective low-dimensional subspace of the original data and then cluster cancer samples in the reduced subspace. However, due to data-type diversity and big data volume, few methods can integrative and efficiently find the principal low-dimensional manifold of the high-dimensional cancer multi-omics data. In this study, we proposed a novel low-rank approximation based integrative probabilistic model to fast find the shared principal subspace across multiple data types: the convexity of the low-rank regularized likelihood function of the probabilistic model ensures efficient and stable model fitting. Candidate molecular subtypes can be identified by unsupervised clustering hundreds of cancer samples in the reduced low-dimensional subspace. On testing datasets, our method LRAcluster (low-rank approximation based multi-omics data clustering) runs much faster with better clustering performances than the existing method. Then, we applied LRAcluster on large-scale cancer multi-omics data from TCGA. The pan-cancer analysis results show that the cancers of different tissue origins are generally grouped as independent clusters, except squamous-like carcinomas. While the single cancer type analysis suggests that the omics data have different subtyping abilities for different cancer types. LRAcluster is a very useful method for fast dimension reduction and unsupervised clustering of large-scale multi-omics data. LRAcluster is implemented in R and freely available via http://bioinfo.au.tsinghua.edu.cn/software/lracluster/ .

Nonlinear dimensionality reduction of data lying on the multicluster manifold.

PubMed

Meng, Deyu; Leung, Yee; Fung, Tung; Xu, Zongben

2008-08-01

A new method, which is called decomposition-composition (D-C) method, is proposed for the nonlinear dimensionality reduction (NLDR) of data lying on the multicluster manifold. The main idea is first to decompose a given data set into clusters and independently calculate the low-dimensional embeddings of each cluster by the decomposition procedure. Based on the intercluster connections, the embeddings of all clusters are then composed into their proper positions and orientations by the composition procedure. Different from other NLDR methods for multicluster data, which consider associatively the intracluster and intercluster information, the D-C method capitalizes on the separate employment of the intracluster neighborhood structures and the intercluster topologies for effective dimensionality reduction. This, on one hand, isometrically preserves the rigid-body shapes of the clusters in the embedding process and, on the other hand, guarantees the proper locations and orientations of all clusters. The theoretical arguments are supported by a series of experiments performed on the synthetic and real-life data sets. In addition, the computational complexity of the proposed method is analyzed, and its efficiency is theoretically analyzed and experimentally demonstrated. Related strategies for automatic parameter selection are also examined.

Rigidity of transmembrane proteins determines their cluster shape

NASA Astrophysics Data System (ADS)

Jafarinia, Hamidreza; Khoshnood, Atefeh; Jalali, Mir Abbas

2016-01-01

Protein aggregation in cell membrane is vital for the majority of biological functions. Recent experimental results suggest that transmembrane domains of proteins such as α -helices and β -sheets have different structural rigidities. We use molecular dynamics simulation of a coarse-grained model of protein-embedded lipid membranes to investigate the mechanisms of protein clustering. For a variety of protein concentrations, our simulations under thermal equilibrium conditions reveal that the structural rigidity of transmembrane domains dramatically affects interactions and changes the shape of the cluster. We have observed stable large aggregates even in the absence of hydrophobic mismatch, which has been previously proposed as the mechanism of protein aggregation. According to our results, semiflexible proteins aggregate to form two-dimensional clusters, while rigid proteins, by contrast, form one-dimensional string-like structures. By assuming two probable scenarios for the formation of a two-dimensional triangular structure, we calculate the lipid density around protein clusters and find that the difference in lipid distribution around rigid and semiflexible proteins determines the one- or two-dimensional nature of aggregates. It is found that lipids move faster around semiflexible proteins than rigid ones. The aggregation mechanism suggested in this paper can be tested by current state-of-the-art experimental facilities.

Evaluation of Solute Clusters Associated with Bake-Hardening Response in Isothermal Aged Al-Mg-Si Alloys Using a Three-Dimensional Atom Probe

NASA Astrophysics Data System (ADS)

Aruga, Yasuhiro; Kozuka, Masaya; Takaki, Yasuo; Sato, Tatsuo

2014-12-01

Temporal changes in the number density, size distribution, and chemical composition of clusters formed during natural aging at room temperature and pre-aging at 363 K (90 °C) in an Al-0.62Mg-0.93Si (mass pct) alloy were evaluated using atom probe tomography. More than 10 million atoms were examined in the cluster analysis, in which about 1000 clusters were obtained for each material after various aging treatments. The statistically proven records show that both number density and the average radius of clusters in pre-aged materials are larger than in naturally aged materials. It was revealed that the fraction of clusters with a low Mg/Si ratio after natural aging for a short time is higher than with other aging treatments, regardless of cluster size. This indicates that Si-rich clusters form more easily after short-period natural aging, and that Mg atoms can diffuse into the clusters or possibly form another type of Mg-Si cluster after prolonged natural aging. The formation of large clusters with a uniform Mg/Si ratio is encouraged by pre-aging. It can be concluded that an increase of small clusters with various Mg/Si ratios does not promote the bake-hardening (BH) response, whereas large clusters with a uniform Mg/Si ratio play an important role in hardening during the BH treatment at 443 K (170 °C).

Application of diffusion maps to identify human factors of self-reported anomalies in aviation.

PubMed

Andrzejczak, Chris; Karwowski, Waldemar; Mikusinski, Piotr

2012-01-01

A study investigating what factors are present leading to pilots submitting voluntary anomaly reports regarding their flight performance was conducted. Diffusion Maps (DM) were selected as the method of choice for performing dimensionality reduction on text records for this study. Diffusion Maps have seen successful use in other domains such as image classification and pattern recognition. High-dimensionality data in the form of narrative text reports from the NASA Aviation Safety Reporting System (ASRS) were clustered and categorized by way of dimensionality reduction. Supervised analyses were performed to create a baseline document clustering system. Dimensionality reduction techniques identified concepts or keywords within records, and allowed the creation of a framework for an unsupervised document classification system. Results from the unsupervised clustering algorithm performed similarly to the supervised methods outlined in the study. The dimensionality reduction was performed on 100 of the most commonly occurring words within 126,000 text records describing commercial aviation incidents. This study demonstrates that unsupervised machine clustering and organization of incident reports is possible based on unbiased inputs. Findings from this study reinforced traditional views on what factors contribute to civil aviation anomalies, however, new associations between previously unrelated factors and conditions were also found.

Localized Ambient Solidity Separation Algorithm Based Computer User Segmentation.

PubMed

Sun, Xiao; Zhang, Tongda; Chai, Yueting; Liu, Yi

2015-01-01

Most of popular clustering methods typically have some strong assumptions of the dataset. For example, the k-means implicitly assumes that all clusters come from spherical Gaussian distributions which have different means but the same covariance. However, when dealing with datasets that have diverse distribution shapes or high dimensionality, these assumptions might not be valid anymore. In order to overcome this weakness, we proposed a new clustering algorithm named localized ambient solidity separation (LASS) algorithm, using a new isolation criterion called centroid distance. Compared with other density based isolation criteria, our proposed centroid distance isolation criterion addresses the problem caused by high dimensionality and varying density. The experiment on a designed two-dimensional benchmark dataset shows that our proposed LASS algorithm not only inherits the advantage of the original dissimilarity increments clustering method to separate naturally isolated clusters but also can identify the clusters which are adjacent, overlapping, and under background noise. Finally, we compared our LASS algorithm with the dissimilarity increments clustering method on a massive computer user dataset with over two million records that contains demographic and behaviors information. The results show that LASS algorithm works extremely well on this computer user dataset and can gain more knowledge from it.

Localized Ambient Solidity Separation Algorithm Based Computer User Segmentation

PubMed Central

Sun, Xiao; Zhang, Tongda; Chai, Yueting; Liu, Yi

2015-01-01

Most of popular clustering methods typically have some strong assumptions of the dataset. For example, the k-means implicitly assumes that all clusters come from spherical Gaussian distributions which have different means but the same covariance. However, when dealing with datasets that have diverse distribution shapes or high dimensionality, these assumptions might not be valid anymore. In order to overcome this weakness, we proposed a new clustering algorithm named localized ambient solidity separation (LASS) algorithm, using a new isolation criterion called centroid distance. Compared with other density based isolation criteria, our proposed centroid distance isolation criterion addresses the problem caused by high dimensionality and varying density. The experiment on a designed two-dimensional benchmark dataset shows that our proposed LASS algorithm not only inherits the advantage of the original dissimilarity increments clustering method to separate naturally isolated clusters but also can identify the clusters which are adjacent, overlapping, and under background noise. Finally, we compared our LASS algorithm with the dissimilarity increments clustering method on a massive computer user dataset with over two million records that contains demographic and behaviors information. The results show that LASS algorithm works extremely well on this computer user dataset and can gain more knowledge from it. PMID:26221133

A Separable Two-Dimensional Random Field Model of Binary Response Data from Multi-Day Behavioral Experiments.

PubMed

Malem-Shinitski, Noa; Zhang, Yingzhuo; Gray, Daniel T; Burke, Sara N; Smith, Anne C; Barnes, Carol A; Ba, Demba

2018-04-18

The study of learning in populations of subjects can provide insights into the changes that occur in the brain with aging, drug intervention, and psychiatric disease. We introduce a separable two-dimensional (2D) random field (RF) model for analyzing binary response data acquired during the learning of object-reward associations across multiple days. The method can quantify the variability of performance within a day and across days, and can capture abrupt changes in learning. We apply the method to data from young and aged macaque monkeys performing a reversal-learning task. The method provides an estimate of performance within a day for each age group, and a learning rate across days for each monkey. We find that, as a group, the older monkeys require more trials to learn the object discriminations than do the young monkeys, and that the cognitive flexibility of the younger group is higher. We also use the model estimates of performance as features for clustering the monkeys into two groups. The clustering results in two groups that, for the most part, coincide with those formed by the age groups. Simulation studies suggest that clustering captures inter-individual differences in performance levels. In comparison with generalized linear models, this method is better able to capture the inherent two-dimensional nature of the data and find between group differences. Applied to binary response data from groups of individuals performing multi-day behavioral experiments, the model discriminates between-group differences and identifies subgroups. Copyright © 2018. Published by Elsevier B.V.

Correlation between centre offsets and gas velocity dispersion of galaxy clusters in cosmological simulations

NASA Astrophysics Data System (ADS)

Li, Ming-Hua; Zhu, Weishan; Zhao, Dong

2018-05-01

The gas is the dominant component of baryonic matter in most galaxy groups and clusters. The spatial offsets of gas centre from the halo centre could be an indicator of the dynamical state of cluster. Knowledge of such offsets is important for estimate the uncertainties when using clusters as cosmological probes. In this paper, we study the centre offsets roff between the gas and that of all the matter within halo systems in ΛCDM cosmological hydrodynamic simulations. We focus on two kinds of centre offsets: one is the three-dimensional PB offsets between the gravitational potential minimum of the entire halo and the barycentre of the ICM, and the other is the two-dimensional PX offsets between the potential minimum of the halo and the iterative centroid of the projected synthetic X-ray emission of the halo. Haloes at higher redshifts tend to have larger values of rescaled offsets roff/r200 and larger gas velocity dispersion σ v^gas/σ _{200}. For both types of offsets, we find that the correlation between the rescaled centre offsets roff/r200 and the rescaled 3D gas velocity dispersion, σ _v^gas/σ _{200} can be approximately described by a quadratic function as r_{off}/r_{200} ∝ (σ v^gas/σ _{200} - k_2)2. A Bayesian analysis with MCMC method is employed to estimate the model parameters. Dependence of the correlation relation on redshifts and the gas mass fraction are also investigated.

Prescribed nanoparticle cluster architectures and low-dimensional arrays built using octahedral DNA origami frames

DOE PAGES

Tian, Ye; Wang, Tong; Liu, Wenyan; ...

2015-05-25

Three-dimensional mesoscale clusters that are formed from nanoparticles spatially arranged in pre-determined positions can be thought of as mesoscale analogues of molecules. These nanoparticle architectures could offer tailored properties due to collective effects, but developing a general platform for fabricating such clusters is a significant challenge. Here, we report a strategy for assembling 3D nanoparticle clusters that uses a molecular frame designed with encoded vertices for particle placement. The frame is a DNA origami octahedron and can be used to fabricate clusters with various symmetries and particle compositions. Cryo-electron microscopy is used to uncover the structure of the DNA framemore » and to reveal that the nanoparticles are spatially coordinated in the prescribed manner. We show that the DNA frame and one set of nanoparticles can be used to create nanoclusters with different chiroptical activities. We also show that the octahedra can serve as programmable interparticle linkers, allowing one- and two-dimensional arrays to be assembled that have designed particle arrangements.« less

Nonconventional screening of the Coulomb interaction in FexOy clusters: An ab initio study

NASA Astrophysics Data System (ADS)

Peters, L.; Şaşıoǧlu, E.; Rossen, S.; Friedrich, C.; Blügel, S.; Katsnelson, M. I.

2017-04-01

From microscopic point-dipole model calculations of the screening of the Coulomb interaction in nonpolar systems by polarizable atoms, it is known that screening strongly depends on dimensionality. For example, in one-dimensional systems, the short-range interaction is screened, while the long-range interaction is antiscreened. This antiscreening is also observed in some zero-dimensional structures, i.e., molecular systems. By means of ab initio calculations in conjunction with the random-phase approximation (RPA) within the FLAPW method, we study screening of the Coulomb interaction in FexOy clusters. For completeness, these results are compared with their bulk counterpart magnetite. It appears that the on-site Coulomb interaction is very well screened both in the clusters and bulk. On the other hand, for the intersite Coulomb interaction, the important observation is made that it is almost constant throughout the clusters, while for the bulk it is almost completely screened. More precisely and interestingly, in the clusters antiscreening is observed by means of ab initio calculations.

The Three-Dimensional Power Spectrum Of Galaxies from the Sloan Digital Sky Survey

DTIC Science & Technology

2004-05-10

aspects of the three-dimensional clustering of a much larger data set involving over 200,000 galaxies with redshifts. This paper is focused on measuring... papers , we will constrain galaxy bias empirically by using clustering measurements on smaller scales (e.g., I. Zehavi et al. 2004, in preparation...minimum-variance measurements in 22 k-bands of both the clustering power and its anisotropy due to redshift-space distortions, with narrow and well

Structures of undecagold clusters: Ligand effect

NASA Astrophysics Data System (ADS)

Spivey, Kasi; Williams, Joseph I.; Wang, Lichang

2006-12-01

The most stable structure of undecagold, or Au 11, clusters was predicted from our DFT calculations to be planar [L. Xiao, L. Wang, Chem. Phys. Lett. 392 (2004) 452; L. Xiao, B. Tollberg, X. Hu, L. Wang, J. Chem. Phys. 124 (2005) 114309.]. The structures of ligand protected undecagold clusters were shown to be three-dimensional experimentally. In this work, we used DFT calculations to study the ligand effect on the structures of Au 11 clusters. Our results show that the most stable structure of Au 11 is in fact three-dimensional when SCH 3 ligands are attached. This indicates that the structures of small gold clusters are altered substantially in the presence of ligands.

Coulomb double helical structure

NASA Astrophysics Data System (ADS)

Kamimura, Tetsuo; Ishihara, Osamu

2012-01-01

Structures of Coulomb clusters formed by dust particles in a plasma are studied by numerical simulation. Our study reveals the presence of various types of self-organized structures of a cluster confined in a prolate spheroidal electrostatic potential. The stable configurations depend on a prolateness parameter for the confining potential as well as on the number of dust particles in a cluster. One-dimensional string, two-dimensional zigzag structure and three-dimensional double helical structure are found as a result of the transition controlled by the prolateness parameter. The formation of stable double helical structures resulted from the transition associated with the instability of angular perturbations on double strings. Analytical perturbation study supports the findings of numerical simulations.

Scaling Properties of Dimensionality Reduction for Neural Populations and Network Models

PubMed Central

Cowley, Benjamin R.; Doiron, Brent; Kohn, Adam

2016-01-01

Recent studies have applied dimensionality reduction methods to understand how the multi-dimensional structure of neural population activity gives rise to brain function. It is unclear, however, how the results obtained from dimensionality reduction generalize to recordings with larger numbers of neurons and trials or how these results relate to the underlying network structure. We address these questions by applying factor analysis to recordings in the visual cortex of non-human primates and to spiking network models that self-generate irregular activity through a balance of excitation and inhibition. We compared the scaling trends of two key outputs of dimensionality reduction—shared dimensionality and percent shared variance—with neuron and trial count. We found that the scaling properties of networks with non-clustered and clustered connectivity differed, and that the in vivo recordings were more consistent with the clustered network. Furthermore, recordings from tens of neurons were sufficient to identify the dominant modes of shared variability that generalize to larger portions of the network. These findings can help guide the interpretation of dimensionality reduction outputs in regimes of limited neuron and trial sampling and help relate these outputs to the underlying network structure. PMID:27926936

A hybrid intelligent method for three-dimensional short-term prediction of dissolved oxygen content in aquaculture.

PubMed

Chen, Yingyi; Yu, Huihui; Cheng, Yanjun; Cheng, Qianqian; Li, Daoliang

2018-01-01

A precise predictive model is important for obtaining a clear understanding of the changes in dissolved oxygen content in crab ponds. Highly accurate interval forecasting of dissolved oxygen content is fundamental to reduce risk, and three-dimensional prediction can provide more accurate results and overall guidance. In this study, a hybrid three-dimensional (3D) dissolved oxygen content prediction model based on a radial basis function (RBF) neural network, K-means and subtractive clustering was developed and named the subtractive clustering (SC)-K-means-RBF model. In this modeling process, K-means and subtractive clustering methods were employed to enhance the hyperparameters required in the RBF neural network model. The comparison of the predicted results of different traditional models validated the effectiveness and accuracy of the proposed hybrid SC-K-means-RBF model for three-dimensional prediction of dissolved oxygen content. Consequently, the proposed model can effectively display the three-dimensional distribution of dissolved oxygen content and serve as a guide for feeding and future studies.

Function Clustering Self-Organization Maps (FCSOMs) for mining differentially expressed genes in Drosophila and its correlation with the growth medium.

PubMed

Liu, L L; Liu, M J; Ma, M

2015-09-28

The central task of this study was to mine the gene-to-medium relationship. Adequate knowledge of this relationship could potentially improve the accuracy of differentially expressed gene mining. One of the approaches to differentially expressed gene mining uses conventional clustering algorithms to identify the gene-to-medium relationship. Compared to conventional clustering algorithms, self-organization maps (SOMs) identify the nonlinear aspects of the gene-to-medium relationships by mapping the input space into another higher dimensional feature space. However, SOMs are not suitable for huge datasets consisting of millions of samples. Therefore, a new computational model, the Function Clustering Self-Organization Maps (FCSOMs), was developed. FCSOMs take advantage of the theory of granular computing as well as advanced statistical learning methodologies, and are built specifically for each information granule (a function cluster of genes), which are intelligently partitioned by the clustering algorithm provided by the DAVID_6.7 software platform. However, only the gene functions, and not their expression values, are considered in the fuzzy clustering algorithm of DAVID. Compared to the clustering algorithm of DAVID, these experimental results show a marked improvement in the accuracy of classification with the application of FCSOMs. FCSOMs can handle huge datasets and their complex classification problems, as each FCSOM (modeled for each function cluster) can be easily parallelized.

Clustering cancer gene expression data by projective clustering ensemble

PubMed Central

Yu, Xianxue; Yu, Guoxian

2017-01-01

Gene expression data analysis has paramount implications for gene treatments, cancer diagnosis and other domains. Clustering is an important and promising tool to analyze gene expression data. Gene expression data is often characterized by a large amount of genes but with limited samples, thus various projective clustering techniques and ensemble techniques have been suggested to combat with these challenges. However, it is rather challenging to synergy these two kinds of techniques together to avoid the curse of dimensionality problem and to boost the performance of gene expression data clustering. In this paper, we employ a projective clustering ensemble (PCE) to integrate the advantages of projective clustering and ensemble clustering, and to avoid the dilemma of combining multiple projective clusterings. Our experimental results on publicly available cancer gene expression data show PCE can improve the quality of clustering gene expression data by at least 4.5% (on average) than other related techniques, including dimensionality reduction based single clustering and ensemble approaches. The empirical study demonstrates that, to further boost the performance of clustering cancer gene expression data, it is necessary and promising to synergy projective clustering with ensemble clustering. PCE can serve as an effective alternative technique for clustering gene expression data. PMID:28234920

A Structure-Based Distance Metric for High-Dimensional Space Exploration with Multi-Dimensional Scaling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, Hyun Jung; McDonnell, Kevin T.; Zelenyuk, Alla

2014-03-01

Although the Euclidean distance does well in measuring data distances within high-dimensional clusters, it does poorly when it comes to gauging inter-cluster distances. This significantly impacts the quality of global, low-dimensional space embedding procedures such as the popular multi-dimensional scaling (MDS) where one can often observe non-intuitive layouts. We were inspired by the perceptual processes evoked in the method of parallel coordinates which enables users to visually aggregate the data by the patterns the polylines exhibit across the dimension axes. We call the path of such a polyline its structure and suggest a metric that captures this structure directly inmore » high-dimensional space. This allows us to better gauge the distances of spatially distant data constellations and so achieve data aggregations in MDS plots that are more cognizant of existing high-dimensional structure similarities. Our MDS plots also exhibit similar visual relationships as the method of parallel coordinates which is often used alongside to visualize the high-dimensional data in raw form. We then cast our metric into a bi-scale framework which distinguishes far-distances from near-distances. The coarser scale uses the structural similarity metric to separate data aggregates obtained by prior classification or clustering, while the finer scale employs the appropriate Euclidean distance.« less

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

PubMed

Jeong, Ji-Wook; Chae, Seung-Hoon; Chae, Eun Young; Kim, Hak Hee; Choi, Young-Wook; Lee, Sooyeul

2016-01-01

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

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

NASA Technical Reports Server (NTRS)

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

2002-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2018-02-01

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

Theoretical investigation of the He4Br2 conformers.

PubMed

Valdés, Álvaro; Prosmiti, Rita; Villarreal, Pablo; Delgado-Barrio, Gerardo

2012-07-05

Full dimensional quantum dynamics calculations of the three lowest isomers of the He(4)Br(2) van der Waals molecule in its ground electronic state are reported. The calculations are performed using the multiconfiguration time-dependent Hartree (MCTDH) method and a realistic potential form that includes the sum of three body ab initio coupled-cluster single double triple [CCSD(T)] He-Br(2) interactions plus the He-He and Br-Br interactions. This potential exhibits several multiple minima, with the three lowest ones lying very close in energy, just within 2 cm(-1). Such small differences are also found in the calculated binding energies of the three most stable conformers, indicating the floppiness of the system and, thus, the need of accurate potential forms and quantum full dynamics methods to treat this kind of complexes. The 12 dimensional results reported in this work present benchmark data and, thus, can serve to evaluate approximate methods aiming to describe higher order rare gas-dihalogen (N > 4) complexes. A comparison with previous studies using different potential forms and approaches to the energetics for the He(4)Br(2) cluster is also presented.

Cluster analysis based on dimensional information with applications to feature selection and classification

NASA Technical Reports Server (NTRS)

Eigen, D. J.; Fromm, F. R.; Northouse, R. A.

1974-01-01

A new clustering algorithm is presented that is based on dimensional information. The algorithm includes an inherent feature selection criterion, which is discussed. Further, a heuristic method for choosing the proper number of intervals for a frequency distribution histogram, a feature necessary for the algorithm, is presented. The algorithm, although usable as a stand-alone clustering technique, is then utilized as a global approximator. Local clustering techniques and configuration of a global-local scheme are discussed, and finally the complete global-local and feature selector configuration is shown in application to a real-time adaptive classification scheme for the analysis of remote sensed multispectral scanner data.

Non-uniform breaking of molecular bonds, peripheral morphology and releasable adhesion by elastic anisotropy in bio-adhesive contacts

PubMed Central

Liu, Yan; Gao, Yanfei

2015-01-01

Biological adhesive contacts are usually of hierarchical structures, such as the clustering of hundreds of sub-micrometre spatulae on keratinous hairs of gecko feet, or the clustering of molecular bonds into focal contacts in cell adhesion. When separating these interfaces, releasable adhesion can be accomplished by asymmetric alignment of the lowest scale discrete bonds (such as the inclined spatula that leads to different peeling force when loading in different directions) or by elastic anisotropy. However, only two-dimensional contact has been analysed for the latter method (Chen & Gao 2007 J. Mech. Phys. Solids 55, 1001–1015 (doi:10.1016/j.jmps.2006.10.008)). Important questions such as the three-dimensional contact morphology, the maximum to minimum pull-off force ratio and the tunability of releasable adhesion cannot be answered. In this work, we developed a three-dimensional cohesive interface model with fictitious viscosity that is capable of simulating the de-adhesion instability and the peripheral morphology before and after the onset of instability. The two-dimensional prediction is found to significantly overestimate the maximum to minimum pull-off force ratio. Based on an interface fracture mechanics analysis, we conclude that (i) the maximum and minimum pull-off forces correspond to the largest and smallest contact stiffness, i.e. ‘stiff-adhere and compliant-release’, (ii) the fracture toughness is sensitive to the crack morphology and the initial contact shape can be designed to attain a significantly higher maximum-to-minimum pull-off force ratio than a circular contact, and (iii) since the adhesion is accomplished by clustering of discrete bonds or called bridged crack in terms of fracture mechanics terminology, the above conclusions can only be achieved when the bridging zone is significantly smaller than the contact size. This adhesion-fracture analogy study leads to mechanistic predictions that can be readily used to design biomimetics and releasable adhesives. PMID:25392403

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Griffin, Rhiannon D.; Dai, Xinyu; Kochanek, Christopher S.

2016-01-15

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

Reversible Electrochemical Lithium-Ion Insertion into the Rhenium Cluster Chalcogenide-Halide Re6Se8Cl2.

PubMed

Bruck, Andrea M; Yin, Jiefu; Tong, Xiao; Takeuchi, Esther S; Takeuchi, Kenneth J; Szczepura, Lisa F; Marschilok, Amy C

2018-05-07

The cluster-based material Re 6 Se 8 Cl 2 is a two-dimensional ternary material with cluster-cluster bonding across the a and b axes capable of multiple electron transfer accompanied by ion insertion across the c axis. The Li/Re 6 Se 8 Cl 2 system showed reversible electron transfer from 1 to 3 electron equivalents (ee) at high current densities (88 mA/g). Upon cycling to 4 ee, there was evidence of capacity degradation over 50 cycles associated with the formation of an organic solid-electrolyte interface (between 1.45 and 1 V vs Li/Li + ). This investigation highlights the ability of cluster-based materials with two-dimensional cluster bonding to be used in applications such as energy storage, showing structural stability and high rate capability.

Interval data clustering using self-organizing maps based on adaptive Mahalanobis distances.

PubMed

Hajjar, Chantal; Hamdan, Hani

2013-10-01

The self-organizing map is a kind of artificial neural network used to map high dimensional data into a low dimensional space. This paper presents a self-organizing map for interval-valued data based on adaptive Mahalanobis distances in order to do clustering of interval data with topology preservation. Two methods based on the batch training algorithm for the self-organizing maps are proposed. The first method uses a common Mahalanobis distance for all clusters. In the second method, the algorithm starts with a common Mahalanobis distance per cluster and then switches to use a different distance per cluster. This process allows a more adapted clustering for the given data set. The performances of the proposed methods are compared and discussed using artificial and real interval data sets. Copyright © 2013 Elsevier Ltd. All rights reserved.

Understanding 3D human torso shape via manifold clustering

NASA Astrophysics Data System (ADS)

Li, Sheng; Li, Peng; Fu, Yun

2013-05-01

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

State estimation and prediction using clustered particle filters.

PubMed

Lee, Yoonsang; Majda, Andrew J

2016-12-20

Particle filtering is an essential tool to improve uncertain model predictions by incorporating noisy observational data from complex systems including non-Gaussian features. A class of particle filters, clustered particle filters, is introduced for high-dimensional nonlinear systems, which uses relatively few particles compared with the standard particle filter. The clustered particle filter captures non-Gaussian features of the true signal, which are typical in complex nonlinear dynamical systems such as geophysical systems. The method is also robust in the difficult regime of high-quality sparse and infrequent observations. The key features of the clustered particle filtering are coarse-grained localization through the clustering of the state variables and particle adjustment to stabilize the method; each observation affects only neighbor state variables through clustering and particles are adjusted to prevent particle collapse due to high-quality observations. The clustered particle filter is tested for the 40-dimensional Lorenz 96 model with several dynamical regimes including strongly non-Gaussian statistics. The clustered particle filter shows robust skill in both achieving accurate filter results and capturing non-Gaussian statistics of the true signal. It is further extended to multiscale data assimilation, which provides the large-scale estimation by combining a cheap reduced-order forecast model and mixed observations of the large- and small-scale variables. This approach enables the use of a larger number of particles due to the computational savings in the forecast model. The multiscale clustered particle filter is tested for one-dimensional dispersive wave turbulence using a forecast model with model errors.

State estimation and prediction using clustered particle filters

PubMed Central

Lee, Yoonsang; Majda, Andrew J.

2016-01-01

Particle filtering is an essential tool to improve uncertain model predictions by incorporating noisy observational data from complex systems including non-Gaussian features. A class of particle filters, clustered particle filters, is introduced for high-dimensional nonlinear systems, which uses relatively few particles compared with the standard particle filter. The clustered particle filter captures non-Gaussian features of the true signal, which are typical in complex nonlinear dynamical systems such as geophysical systems. The method is also robust in the difficult regime of high-quality sparse and infrequent observations. The key features of the clustered particle filtering are coarse-grained localization through the clustering of the state variables and particle adjustment to stabilize the method; each observation affects only neighbor state variables through clustering and particles are adjusted to prevent particle collapse due to high-quality observations. The clustered particle filter is tested for the 40-dimensional Lorenz 96 model with several dynamical regimes including strongly non-Gaussian statistics. The clustered particle filter shows robust skill in both achieving accurate filter results and capturing non-Gaussian statistics of the true signal. It is further extended to multiscale data assimilation, which provides the large-scale estimation by combining a cheap reduced-order forecast model and mixed observations of the large- and small-scale variables. This approach enables the use of a larger number of particles due to the computational savings in the forecast model. The multiscale clustered particle filter is tested for one-dimensional dispersive wave turbulence using a forecast model with model errors. PMID:27930332

A meta-classifier for detecting prostate cancer by quantitative integration of in vivo magnetic resonance spectroscopy and magnetic resonance imaging

NASA Astrophysics Data System (ADS)

Viswanath, Satish; Tiwari, Pallavi; Rosen, Mark; Madabhushi, Anant

2008-03-01

Recently, in vivo Magnetic Resonance Imaging (MRI) and Magnetic Resonance Spectroscopy (MRS) have emerged as promising new modalities to aid in prostate cancer (CaP) detection. MRI provides anatomic and structural information of the prostate while MRS provides functional data pertaining to biochemical concentrations of metabolites such as creatine, choline and citrate. We have previously presented a hierarchical clustering scheme for CaP detection on in vivo prostate MRS and have recently developed a computer-aided method for CaP detection on in vivo prostate MRI. In this paper we present a novel scheme to develop a meta-classifier to detect CaP in vivo via quantitative integration of multimodal prostate MRS and MRI by use of non-linear dimensionality reduction (NLDR) methods including spectral clustering and locally linear embedding (LLE). Quantitative integration of multimodal image data (MRI and PET) involves the concatenation of image intensities following image registration. However multimodal data integration is non-trivial when the individual modalities include spectral and image intensity data. We propose a data combination solution wherein we project the feature spaces (image intensities and spectral data) associated with each of the modalities into a lower dimensional embedding space via NLDR. NLDR methods preserve the relationships between the objects in the original high dimensional space when projecting them into the reduced low dimensional space. Since the original spectral and image intensity data are divorced from their original physical meaning in the reduced dimensional space, data at the same spatial location can be integrated by concatenating the respective embedding vectors. Unsupervised consensus clustering is then used to partition objects into different classes in the combined MRS and MRI embedding space. Quantitative results of our multimodal computer-aided diagnosis scheme on 16 sets of patient data obtained from the ACRIN trial, for which corresponding histological ground truth for spatial extent of CaP is known, show a marginally higher sensitivity, specificity, and positive predictive value compared to corresponding CAD results with the individual modalities.

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

PubMed Central

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

2017-01-01

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

Morphology of size-selected Ptn clusters on CeO2(111)

NASA Astrophysics Data System (ADS)

Shahed, Syed Mohammad Fakruddin; Beniya, Atsushi; Hirata, Hirohito; Watanabe, Yoshihide

2018-03-01

Supported Pt catalysts and ceria are well known for their application in automotive exhaust catalysts. Size-selected Pt clusters supported on a CeO2(111) surface exhibit distinct physical and chemical properties. We investigated the morphology of the size-selected Ptn (n = 5-13) clusters on a CeO2(111) surface using scanning tunneling microscopy at room temperature. Ptn clusters prefer a two-dimensional morphology for n = 5 and a three-dimensional (3D) morphology for n ≥ 6. We further observed the preference for a 3D tri-layer structure when n ≥ 10. For each cluster size, we quantitatively estimated the relative fraction of the clusters for each type of morphology. Size-dependent morphology of the Ptn clusters on the CeO2(111) surface was attributed to the Pt-Pt interaction in the cluster and the Pt-O interaction between the cluster and CeO2(111) surface. The results obtained herein provide a clear understanding of the size-dependent morphology of the Ptn clusters on a CeO2(111) surface.

Morphology of size-selected Ptn clusters on CeO2(111).

PubMed

Shahed, Syed Mohammad Fakruddin; Beniya, Atsushi; Hirata, Hirohito; Watanabe, Yoshihide

2018-03-21

Supported Pt catalysts and ceria are well known for their application in automotive exhaust catalysts. Size-selected Pt clusters supported on a CeO 2 (111) surface exhibit distinct physical and chemical properties. We investigated the morphology of the size-selected Pt n (n = 5-13) clusters on a CeO 2 (111) surface using scanning tunneling microscopy at room temperature. Pt n clusters prefer a two-dimensional morphology for n = 5 and a three-dimensional (3D) morphology for n ≥ 6. We further observed the preference for a 3D tri-layer structure when n ≥ 10. For each cluster size, we quantitatively estimated the relative fraction of the clusters for each type of morphology. Size-dependent morphology of the Pt n clusters on the CeO 2 (111) surface was attributed to the Pt-Pt interaction in the cluster and the Pt-O interaction between the cluster and CeO 2 (111) surface. The results obtained herein provide a clear understanding of the size-dependent morphology of the Pt n clusters on a CeO 2 (111) surface.

Principal Cluster Axes: A Projection Pursuit Index for the Preservation of Cluster Structures in the Presence of Data Reduction

ERIC Educational Resources Information Center

Steinley, Douglas; Brusco, Michael J.; Henson, Robert

2012-01-01

A measure of "clusterability" serves as the basis of a new methodology designed to preserve cluster structure in a reduced dimensional space. Similar to principal component analysis, which finds the direction of maximal variance in multivariate space, principal cluster axes find the direction of maximum clusterability in multivariate space.…

Sensory over responsivity and obsessive compulsive symptoms: A cluster analysis.

PubMed

Ben-Sasson, Ayelet; Podoly, Tamar Yonit

2017-02-01

Several studies have examined the sensory component in Obsesseive Compulsive Disorder (OCD) and described an OCD subtype which has a unique profile, and that Sensory Phenomena (SP) is a significant component of this subtype. SP has some commonalities with Sensory Over Responsivity (SOR) and might be in part a characteristic of this subtype. Although there are some studies that have examined SOR and its relation to Obsessive Compulsive Symptoms (OCS), literature lacks sufficient data on this interplay. First to further examine the correlations between OCS and SOR, and to explore the correlations between SOR modalities (i.e. smell, touch, etc.) and OCS subscales (i.e. washing, ordering, etc.). Second, to investigate the cluster analysis of SOR and OCS dimensions in adults, that is, to classify the sample using the sensory scores to find whether a sensory OCD subtype can be specified. Our third goal was to explore the psychometric features of a new sensory questionnaire: the Sensory Perception Quotient (SPQ). A sample of non clinical adults (n=350) was recruited via e-mail, social media and social networks. Participants completed questionnaires for measuring SOR, OCS, and anxiety. SOR and OCI-F scores were moderately significantly correlated (n=274), significant correlations between all SOR modalities and OCS subscales were found with no specific higher correlation between one modality to one OCS subscale. Cluster analysis revealed four distinct clusters: (1) No OC and SOR symptoms (NONE; n=100), (2) High OC and SOR symptoms (BOTH; n=28), (3) Moderate OC symptoms (OCS; n=63), (4) Moderate SOR symptoms (SOR; n=83). The BOTH cluster had significantly higher anxiety levels than the other clusters, and shared OC subscales scores with the OCS cluster. The BOTH cluster also reported higher SOR scores across tactile, vision, taste and olfactory modalities. The SPQ was found reliable and suitable to detect SOR, the sample SPQ scores was normally distributed (n=350). SOR is a dimensional feature that can influence the severity of OCS and may characterize a unique sensory OCD subtype. Copyright © 2016 Elsevier Inc. All rights reserved.

Induced formation and maturation of acetylcholine receptor clusters in a defined 3D bio-artificial muscle.

PubMed

Wang, Lin; Shansky, Janet; Vandenburgh, Herman

2013-12-01

Dysfunction of the neuromuscular junction is involved in a wide range of muscular diseases. The development of neuromuscular junction through which skeletal muscle is innervated requires the functional modulation of acetylcholine receptor (AchR) clustering on myofibers. However, studies on AchR clustering in vitro are mostly done on monolayer muscle cell culture, which lacks a three-dimensional (3D) structure, a prominent limitation of the two-dimensional (2D) system. To enable a better understanding on the structure-function correlation underlying skeletal muscle innervation, a muscle system with a well-defined geometry mimicking the in vivo muscular setting is needed. Here, we report a 3D bio-artificial muscle (BAM) bioengineered from green fluorescent protein-transduced C3H murine myoblasts as a novel in vitro tissue-based model for muscle innervation studies. Our cell biological and molecular analysis showed that this BAM is structurally similar to in vivo muscle tissue and can reach the perinatal differentiation stage, higher than does 2D culture. Effective clustering and morphological maturation of AchRs on BAMs induced by agrin and laminin indicate the functional activity and plasticity of this BAM system toward innervation. Taken together, our results show that the BAM provides a favorable 3D environment that at least partially recapitulates real physiological skeletal muscle with regard to innervation. With a convenience of fabrication and manipulation, this 3D in vitro system offers a novel model for studying mechanisms underlying skeletal muscle innervation and testing therapeutic strategies for relevant nervous and muscular diseases.

Single exposure three-dimensional imaging of dusty plasma clusters.

PubMed

Hartmann, Peter; Donkó, István; Donkó, Zoltán

2013-02-01

We have worked out the details of a single camera, single exposure method to perform three-dimensional imaging of a finite particle cluster. The procedure is based on the plenoptic imaging principle and utilizes a commercial Lytro light field still camera. We demonstrate the capabilities of our technique on a single layer particle cluster in a dusty plasma, where the camera is aligned and inclined at a small angle to the particle layer. The reconstruction of the third coordinate (depth) is found to be accurate and even shadowing particles can be identified.

Clustering of Multi-Temporal Fully Polarimetric L-Band SAR Data for Agricultural Land Cover Mapping

NASA Astrophysics Data System (ADS)

Tamiminia, H.; Homayouni, S.; Safari, A.

2015-12-01

Recently, the unique capabilities of Polarimetric Synthetic Aperture Radar (PolSAR) sensors make them an important and efficient tool for natural resources and environmental applications, such as land cover and crop classification. The aim of this paper is to classify multi-temporal full polarimetric SAR data using kernel-based fuzzy C-means clustering method, over an agricultural region. This method starts with transforming input data into the higher dimensional space using kernel functions and then clustering them in the feature space. Feature space, due to its inherent properties, has the ability to take in account the nonlinear and complex nature of polarimetric data. Several SAR polarimetric features extracted using target decomposition algorithms. Features from Cloude-Pottier, Freeman-Durden and Yamaguchi algorithms used as inputs for the clustering. This method was applied to multi-temporal UAVSAR L-band images acquired over an agricultural area near Winnipeg, Canada, during June and July in 2012. The results demonstrate the efficiency of this approach with respect to the classical methods. In addition, using multi-temporal data in the clustering process helped to investigate the phenological cycle of plants and significantly improved the performance of agricultural land cover mapping.

Metal-superconductor transition in low-dimensional superconducting clusters embedded in two-dimensional electron systems

NASA Astrophysics Data System (ADS)

Bucheli, D.; Caprara, S.; Castellani, C.; Grilli, M.

2013-02-01

Motivated by recent experimental data on thin film superconductors and oxide interfaces, we propose a random-resistor network apt to describe the occurrence of a metal-superconductor transition in a two-dimensional electron system with disorder on the mesoscopic scale. We consider low-dimensional (e.g. filamentary) structures of a superconducting cluster embedded in the two-dimensional network and we explore the separate effects and the interplay of the superconducting structure and of the statistical distribution of local critical temperatures. The thermal evolution of the resistivity is determined by a numerical calculation of the random-resistor network and, for comparison, a mean-field approach called effective medium theory (EMT). Our calculations reveal the relevance of the distribution of critical temperatures for clusters with low connectivity. In addition, we show that the presence of spatial correlations requires a modification of standard EMT to give qualitative agreement with the numerical results. Applying the present approach to an LaTiO3/SrTiO3 oxide interface, we find that the measured resistivity curves are compatible with a network of spatially dense but loosely connected superconducting islands.

A hybrid intelligent method for three-dimensional short-term prediction of dissolved oxygen content in aquaculture

PubMed Central

Yu, Huihui; Cheng, Yanjun; Cheng, Qianqian; Li, Daoliang

2018-01-01

A precise predictive model is important for obtaining a clear understanding of the changes in dissolved oxygen content in crab ponds. Highly accurate interval forecasting of dissolved oxygen content is fundamental to reduce risk, and three-dimensional prediction can provide more accurate results and overall guidance. In this study, a hybrid three-dimensional (3D) dissolved oxygen content prediction model based on a radial basis function (RBF) neural network, K-means and subtractive clustering was developed and named the subtractive clustering (SC)-K-means-RBF model. In this modeling process, K-means and subtractive clustering methods were employed to enhance the hyperparameters required in the RBF neural network model. The comparison of the predicted results of different traditional models validated the effectiveness and accuracy of the proposed hybrid SC-K-means-RBF model for three-dimensional prediction of dissolved oxygen content. Consequently, the proposed model can effectively display the three-dimensional distribution of dissolved oxygen content and serve as a guide for feeding and future studies. PMID:29466394

Harnessing Sparse and Low-Dimensional Structures for Robust Clustering of Imagery Data

ERIC Educational Resources Information Center

Rao, Shankar Ramamohan

2009-01-01

We propose a robust framework for clustering data. In practice, data obtained from real measurement devices can be incomplete, corrupted by gross errors, or not correspond to any assumed model. We show that, by properly harnessing the intrinsic low-dimensional structure of the data, these kinds of practical problems can be dealt with in a uniform…

Progeny Clustering: A Method to Identify Biological Phenotypes

PubMed Central

Hu, Chenyue W.; Kornblau, Steven M.; Slater, John H.; Qutub, Amina A.

2015-01-01

Estimating the optimal number of clusters is a major challenge in applying cluster analysis to any type of dataset, especially to biomedical datasets, which are high-dimensional and complex. Here, we introduce an improved method, Progeny Clustering, which is stability-based and exceptionally efficient in computing, to find the ideal number of clusters. The algorithm employs a novel Progeny Sampling method to reconstruct cluster identity, a co-occurrence probability matrix to assess the clustering stability, and a set of reference datasets to overcome inherent biases in the algorithm and data space. Our method was shown successful and robust when applied to two synthetic datasets (datasets of two-dimensions and ten-dimensions containing eight dimensions of pure noise), two standard biological datasets (the Iris dataset and Rat CNS dataset) and two biological datasets (a cell phenotype dataset and an acute myeloid leukemia (AML) reverse phase protein array (RPPA) dataset). Progeny Clustering outperformed some popular clustering evaluation methods in the ten-dimensional synthetic dataset as well as in the cell phenotype dataset, and it was the only method that successfully discovered clinically meaningful patient groupings in the AML RPPA dataset. PMID:26267476

Exploratory Item Classification Via Spectral Graph Clustering

PubMed Central

Chen, Yunxiao; Li, Xiaoou; Liu, Jingchen; Xu, Gongjun; Ying, Zhiliang

2017-01-01

Large-scale assessments are supported by a large item pool. An important task in test development is to assign items into scales that measure different characteristics of individuals, and a popular approach is cluster analysis of items. Classical methods in cluster analysis, such as the hierarchical clustering, K-means method, and latent-class analysis, often induce a high computational overhead and have difficulty handling missing data, especially in the presence of high-dimensional responses. In this article, the authors propose a spectral clustering algorithm for exploratory item cluster analysis. The method is computationally efficient, effective for data with missing or incomplete responses, easy to implement, and often outperforms traditional clustering algorithms in the context of high dimensionality. The spectral clustering algorithm is based on graph theory, a branch of mathematics that studies the properties of graphs. The algorithm first constructs a graph of items, characterizing the similarity structure among items. It then extracts item clusters based on the graphical structure, grouping similar items together. The proposed method is evaluated through simulations and an application to the revised Eysenck Personality Questionnaire. PMID:29033476

Effects of the bipartite structure of a network on performance of recommenders

NASA Astrophysics Data System (ADS)

Wang, Qing-Xian; Li, Jian; Luo, Xin; Xu, Jian-Jun; Shang, Ming-Sheng

2018-02-01

Recommender systems aim to predict people's preferences for online items by analyzing their historical behaviors. A recommender can be modeled as a high-dimensional and sparse bipartite network, where the key issue is to understand the relation between the network structure and a recommender's performance. To address this issue, we choose three network characteristics, clustering coefficient, network density and user-item ratio, as the analyzing targets. For the cluster coefficient, we adopt the Degree-preserving rewiring algorithm to obtain a series of bipartite network with varying cluster coefficient, while the degree of user and item keep unchanged. Furthermore, five state-of-the-art recommenders are applied on two real datasets. The performances of recommenders are measured by both numerical and physical metrics. These results show that a recommender's performance is positively related to the clustering coefficient of a bipartite network. Meanwhile, higher density of a bipartite network can provide more accurate but less diverse or novel recommendations. Furthermore, the user-item ratio is positively correlated with the accuracy metrics but negatively correlated with the diverse and novel metrics.

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

PubMed Central

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

2012-01-01

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

Three subgroups of pain profiles identified in 227 women with arthritis: a latent class analysis.

PubMed

de Luca, Katie; Parkinson, Lynne; Downie, Aron; Blyth, Fiona; Byles, Julie

2017-03-01

The objectives were to identify subgroups of women with arthritis based upon the multi-dimensional nature of their pain experience and to compare health and socio-demographic variables between subgroups. A latent class analysis of 227 women with self-reported arthritis was used to identify clusters of women based upon the sensory, affective, and cognitive dimensions of the pain experience. Multivariate multinomial logistic regression analysis was used to determine the relationship between cluster membership and health and sociodemographic characteristics. A three-class cluster model was most parsimonious. 39.5 % of women had a unidimensional pain profile; 38.6 % of women had moderate multidimensional pain profile that included additional pain symptomatology such as sensory qualities and pain catastrophizing; and 21.9 % of women had severe multidimensional pain profile that included prominent pain symptomatology such as sensory and affective qualities of pain, pain catastrophizing, and neuropathic pain. Women with severe multidimensional pain profile have a 30.5 % higher risk of poorer quality of life and a 7.3 % higher risk of suffering depression, and women with moderate multidimensional pain profile have a 6.4 % higher risk of poorer quality of life when compared to women with unidimensional pain. This study identified three distinct subgroups of pain profiles in older women with arthritis. Women had very different experiences of pain, and cluster membership impacted significantly on health-related quality of life. These preliminary findings provide a stronger understanding of profiles of pain and may contribute to the development of tailored treatment options in arthritis.

Solid State Digital Propulsion "Cluster Thrusters" For Small Satellites Using High Performance Electrically Controlled Extinguishable Solid Propellants (ECESP)

NASA Technical Reports Server (NTRS)

Sawka, Wayne N.; Katzakian, Arthur; Grix, Charles

2005-01-01

Electrically controlled extinguishable solid propellants (ESCSP) are capable of multiple ignitions, extinguishments and throttle control by the application of electrical power. Both core and end burning no moving parts ECESP grains/motors to three inches in diameter have now been tested. Ongoing research has led to a newer family of even higher performance ECESP providing up to 10% higher performance, manufacturing ease, and significantly higher electrical conduction. The high conductivity was not found to be desirable for larger motors; however it is ideal for downward scaling to micro and pico- propulsion applications with a web thickness of less than 0.125 inch/ diameter. As a solid solution propellant, this ECESP is molecularly uniform, having no granular structure. Because of this homogeneity and workable viscosity it can be directly cast into thin layers or vacuum cast into complex geometries. Both coaxial and grain stacks have been demonstrated. Combining individual propellant coaxial grains and/or grain stacks together form three-dimensional arrays yield modular cluster thrusters. Adoption of fabless manufacturing methods and standards from the electronics industry will provide custom, highly reproducible micro-propulsion arrays and clusters at low costs. These stack and cluster thruster designs provide a small footprint saving spacecraft surface area for solar panels and/or experiments. The simplicity of these thrusters will enable their broad use on micro-pico satellites for primary propulsion, ACS and formation flying applications. Larger spacecraft may find uses for ECESP thrusters on extended booms, on-orbit refueling, pneumatic actuators, and gas generators.

From globally coupled maps to complex-systems biology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kaneko, Kunihiko, E-mail: kaneko@complex.c.u-tokyo.ac.jp

Studies of globally coupled maps, introduced as a network of chaotic dynamics, are briefly reviewed with an emphasis on novel concepts therein, which are universal in high-dimensional dynamical systems. They include clustering of synchronized oscillations, hierarchical clustering, chimera of synchronization and desynchronization, partition complexity, prevalence of Milnor attractors, chaotic itinerancy, and collective chaos. The degrees of freedom necessary for high dimensionality are proposed to equal the number in which the combinatorial exceeds the exponential. Future analysis of high-dimensional dynamical systems with regard to complex-systems biology is briefly discussed.

Nuclear Potential Clustering As a New Tool to Detect Patterns in High Dimensional Datasets

NASA Astrophysics Data System (ADS)

Tonkova, V.; Paulus, D.; Neeb, H.

2013-02-01

We present a new approach for the clustering of high dimensional data without prior assumptions about the structure of the underlying distribution. The proposed algorithm is based on a concept adapted from nuclear physics. To partition the data, we model the dynamic behaviour of nucleons interacting in an N-dimensional space. An adaptive nuclear potential, comprised of a short-range attractive (strong interaction) and a long-range repulsive term (Coulomb force) is assigned to each data point. By modelling the dynamics, nucleons that are densely distributed in space fuse to build nuclei (clusters) whereas single point clusters repel each other. The formation of clusters is completed when the system reaches the state of minimal potential energy. The data are then grouped according to the particles' final effective potential energy level. The performance of the algorithm is tested with several synthetic datasets showing that the proposed method can robustly identify clusters even when complex configurations are present. Furthermore, quantitative MRI data from 43 multiple sclerosis patients were analyzed, showing a reasonable splitting into subgroups according to the individual patients' disease grade. The good performance of the algorithm on such highly correlated non-spherical datasets, which are typical for MRI derived image features, shows that Nuclear Potential Clustering is a valuable tool for automated data analysis, not only in the MRI domain.

A highly accurate ab initio potential energy surface for methane.

PubMed

Owens, Alec; Yurchenko, Sergei N; Yachmenev, Andrey; Tennyson, Jonathan; Thiel, Walter

2016-09-14

A new nine-dimensional potential energy surface (PES) for methane has been generated using state-of-the-art ab initio theory. The PES is based on explicitly correlated coupled cluster calculations with extrapolation to the complete basis set limit and incorporates a range of higher-level additive energy corrections. These include core-valence electron correlation, higher-order coupled cluster terms beyond perturbative triples, scalar relativistic effects, and the diagonal Born-Oppenheimer correction. Sub-wavenumber accuracy is achieved for the majority of experimentally known vibrational energy levels with the four fundamentals of (12)CH4 reproduced with a root-mean-square error of 0.70 cm(-1). The computed ab initio equilibrium C-H bond length is in excellent agreement with previous values despite pure rotational energies displaying minor systematic errors as J (rotational excitation) increases. It is shown that these errors can be significantly reduced by adjusting the equilibrium geometry. The PES represents the most accurate ab initio surface to date and will serve as a good starting point for empirical refinement.

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

PubMed

Nagaoka, Tomoaki; Watanabe, Soichi

2012-01-01

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

Data-driven cluster reinforcement and visualization in sparsely-matched self-organizing maps.

PubMed

Manukyan, Narine; Eppstein, Margaret J; Rizzo, Donna M

2012-05-01

A self-organizing map (SOM) is a self-organized projection of high-dimensional data onto a typically 2-dimensional (2-D) feature map, wherein vector similarity is implicitly translated into topological closeness in the 2-D projection. However, when there are more neurons than input patterns, it can be challenging to interpret the results, due to diffuse cluster boundaries and limitations of current methods for displaying interneuron distances. In this brief, we introduce a new cluster reinforcement (CR) phase for sparsely-matched SOMs. The CR phase amplifies within-cluster similarity in an unsupervised, data-driven manner. Discontinuities in the resulting map correspond to between-cluster distances and are stored in a boundary (B) matrix. We describe a new hierarchical visualization of cluster boundaries displayed directly on feature maps, which requires no further clustering beyond what was implicitly accomplished during self-organization in SOM training. We use a synthetic benchmark problem and previously published microbial community profile data to demonstrate the benefits of the proposed methods.

Quantum computational universality of the Cai-Miyake-Duer-Briegel two-dimensional quantum state from Affleck-Kennedy-Lieb-Tasaki quasichains

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wei, Tzu-Chieh; C. N. Yang Institute for Theoretical Physics, State University of New York at Stony Brook, Stony Brook, New York 11794-3840; Raussendorf, Robert

2011-10-15

Universal quantum computation can be achieved by simply performing single-qubit measurements on a highly entangled resource state, such as cluster states. Cai, Miyake, Duer, and Briegel recently constructed a ground state of a two-dimensional quantum magnet by combining multiple Affleck-Kennedy-Lieb-Tasaki quasichains of mixed spin-3/2 and spin-1/2 entities and by mapping pairs of neighboring spin-1/2 particles to individual spin-3/2 particles [Phys. Rev. A 82, 052309 (2010)]. They showed that this state enables universal quantum computation by single-spin measurements. Here, we give an alternative understanding of how this state gives rise to universal measurement-based quantum computation: by local operations, each quasichain canmore » be converted to a one-dimensional cluster state and entangling gates between two neighboring logical qubits can be implemented by single-spin measurements. We further argue that a two-dimensional cluster state can be distilled from the Cai-Miyake-Duer-Briegel state.« less

Exponents of non-linear clustering in scale-free one-dimensional cosmological simulations

NASA Astrophysics Data System (ADS)

Benhaiem, David; Joyce, Michael; Sicard, François

2013-03-01

One-dimensional versions of dissipationless cosmological N-body simulations have been shown to share many qualitative behaviours of the three-dimensional problem. Their interest lies in the fact that they can resolve a much greater range of time and length scales, and admit exact numerical integration. We use such models here to study how non-linear clustering depends on initial conditions and cosmology. More specifically, we consider a family of models which, like the three-dimensional Einstein-de Sitter (EdS) model, lead for power-law initial conditions to self-similar clustering characterized in the strongly non-linear regime by power-law behaviour of the two-point correlation function. We study how the corresponding exponent γ depends on the initial conditions, characterized by the exponent n of the power spectrum of initial fluctuations, and on a single parameter κ controlling the rate of expansion. The space of initial conditions/cosmology divides very clearly into two parts: (1) a region in which γ depends strongly on both n and κ and where it agrees very well with a simple generalization of the so-called stable clustering hypothesis in three dimensions; and (2) a region in which γ is more or less independent of both the spectrum and the expansion of the universe. The boundary in (n, κ) space dividing the `stable clustering' region from the `universal' region is very well approximated by a `critical' value of the predicted stable clustering exponent itself. We explain how this division of the (n, κ) space can be understood as a simple physical criterion which might indeed be expected to control the validity of the stable clustering hypothesis. We compare and contrast our findings to results in three dimensions, and discuss in particular the light they may throw on the question of `universality' of non-linear clustering in this context.

Characterization of the velocity anisotropy of accreted globular clusters

NASA Astrophysics Data System (ADS)

Bianchini, P.; Sills, A.; Miholics, M.

2017-10-01

Galactic globular clusters (GCs) are believed to have formed in situ in the Galaxy as well as in dwarf galaxies later accreted on to the Milky Way. However, to date, there is no unambiguous signature to distinguish accreted GCs. Using specifically designed N-body simulations of GCs evolving in a variety of time-dependent tidal fields (describing the potential of a dwarf galaxy-Milky Way merger), we analyse the effects imprinted on the internal kinematics of an accreted GC. In particular, we look at the evolution of the velocity anisotropy. Our simulations show that at early phases, the velocity anisotropy is determined by the tidal field of the dwarf galaxy and subsequently the clusters will adapt to the new tidal environment, losing any signature of their original environment in a few relaxation times. At 10 Gyr, GCs exhibit a variety of velocity anisotropy profiles, namely, isotropic velocity distribution in the inner regions and either isotropy or radial/tangential anisotropy in the intermediate and outer regions. Independent of an accreted origin, the velocity anisotropy primarily depends on the strength of the tidal field cumulatively experienced by a cluster. Tangentially anisotropic clusters correspond to systems that have experienced stronger tidal fields and are characterized by higher tidal filling factor, r50/rj ≳ 0.17, higher mass-loss ≳ 60 per cent and relaxation times trel ≲ 109 Gyr. Interestingly, we demonstrate that the presence of tidal tails can significantly contaminate the measurements of velocity anisotropy when a cluster is observed in projection. Our characterization of the velocity anisotropy profiles in different tidal environments provides a theoretical benchmark for the interpretation of the unprecedented amount of three-dimensional kinematic data progressively available for Galactic GCs.

Stimuli Reduce the Dimensionality of Cortical Activity

PubMed Central

Mazzucato, Luca; Fontanini, Alfredo; La Camera, Giancarlo

2016-01-01

The activity of ensembles of simultaneously recorded neurons can be represented as a set of points in the space of firing rates. Even though the dimension of this space is equal to the ensemble size, neural activity can be effectively localized on smaller subspaces. The dimensionality of the neural space is an important determinant of the computational tasks supported by the neural activity. Here, we investigate the dimensionality of neural ensembles from the sensory cortex of alert rats during periods of ongoing (inter-trial) and stimulus-evoked activity. We find that dimensionality grows linearly with ensemble size, and grows significantly faster during ongoing activity compared to evoked activity. We explain these results using a spiking network model based on a clustered architecture. The model captures the difference in growth rate between ongoing and evoked activity and predicts a characteristic scaling with ensemble size that could be tested in high-density multi-electrode recordings. Moreover, we present a simple theory that predicts the existence of an upper bound on dimensionality. This upper bound is inversely proportional to the amount of pair-wise correlations and, compared to a homogeneous network without clusters, it is larger by a factor equal to the number of clusters. The empirical estimation of such bounds depends on the number and duration of trials and is well predicted by the theory. Together, these results provide a framework to analyze neural dimensionality in alert animals, its behavior under stimulus presentation, and its theoretical dependence on ensemble size, number of clusters, and correlations in spiking network models. PMID:26924968

Stimuli Reduce the Dimensionality of Cortical Activity.

PubMed

Mazzucato, Luca; Fontanini, Alfredo; La Camera, Giancarlo

2016-01-01

The activity of ensembles of simultaneously recorded neurons can be represented as a set of points in the space of firing rates. Even though the dimension of this space is equal to the ensemble size, neural activity can be effectively localized on smaller subspaces. The dimensionality of the neural space is an important determinant of the computational tasks supported by the neural activity. Here, we investigate the dimensionality of neural ensembles from the sensory cortex of alert rats during periods of ongoing (inter-trial) and stimulus-evoked activity. We find that dimensionality grows linearly with ensemble size, and grows significantly faster during ongoing activity compared to evoked activity. We explain these results using a spiking network model based on a clustered architecture. The model captures the difference in growth rate between ongoing and evoked activity and predicts a characteristic scaling with ensemble size that could be tested in high-density multi-electrode recordings. Moreover, we present a simple theory that predicts the existence of an upper bound on dimensionality. This upper bound is inversely proportional to the amount of pair-wise correlations and, compared to a homogeneous network without clusters, it is larger by a factor equal to the number of clusters. The empirical estimation of such bounds depends on the number and duration of trials and is well predicted by the theory. Together, these results provide a framework to analyze neural dimensionality in alert animals, its behavior under stimulus presentation, and its theoretical dependence on ensemble size, number of clusters, and correlations in spiking network models.

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.

Three-dimensional study of grain boundary engineering effects on intergranular stress corrosion cracking of 316 stainless steel in high temperature water

NASA Astrophysics Data System (ADS)

Liu, Tingguang; Xia, Shuang; Bai, Qin; Zhou, Bangxin; Zhang, Lefu; Lu, Yonghao; Shoji, Tetsuo

2018-01-01

The intergranular cracks and grain boundary (GB) network of a GB-engineered 316 stainless steel after stress corrosion cracking (SCC) test in high temperature high pressure water of reactor environment were investigated by two-dimensional and three-dimensional (3D) characterization in order to expose the mechanism that GB-engineering mitigates intergranular SCC. The 3D microstructure shown that the essential characteristic of the GB-engineered microstructure is formation of many large twin-boundaries as a result of multiple-twinning, which results in the formation of large grain-clusters. The large grain-clusters played a key role to the improvement of intergranular SCC resistance by GB-engineering. The main intergranular cracks propagated in a zigzag along the outer boundaries of these large grain-clusters because all inner boundaries of the grain-clusters were twin-boundaries (∑3) or twin-related boundaries (∑3n) which had much lower susceptibility to SCC than random boundaries. These large grain-clusters had tree-ring-shaped topology structure and very complex morphology. They got tangled so that difficult to be separated during SCC, resulting in some large crack-bridges retained in the crack surface.

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

PubMed Central

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

2009-01-01

Background Uncovering subtypes of disease from microarray samples has important clinical implications such as survival time and sensitivity of individual patients to specific therapies. Unsupervised clustering methods have been used to classify this type of data. However, most existing methods focus on clusters with compact shapes and do not reflect the geometric complexity of the high dimensional microarray clusters, which limits their performance. Results We present a cluster-number-based ensemble clustering algorithm, called MULTI-K, for microarray sample classification, which demonstrates remarkable accuracy. The method amalgamates multiple k-means runs by varying the number of clusters and identifies clusters that manifest the most robust co-memberships of elements. In addition to the original algorithm, we newly devised the entropy-plot to control the separation of singletons or small clusters. MULTI-K, unlike the simple k-means or other widely used methods, was able to capture clusters with complex and high-dimensional structures accurately. MULTI-K outperformed other methods including a recently developed ensemble clustering algorithm in tests with five simulated and eight real gene-expression data sets. Conclusion The geometric complexity of clusters should be taken into account for accurate classification of microarray data, and ensemble clustering applied to the number of clusters tackles the problem very well. The C++ code and the data sets tested are available from the authors. PMID:19698124

Copula based flexible modeling of associations between clustered event times.

PubMed

Geerdens, Candida; Claeskens, Gerda; Janssen, Paul

2016-07-01

Multivariate survival data are characterized by the presence of correlation between event times within the same cluster. First, we build multi-dimensional copulas with flexible and possibly symmetric dependence structures for such data. In particular, clustered right-censored survival data are modeled using mixtures of max-infinitely divisible bivariate copulas. Second, these copulas are fit by a likelihood approach where the vast amount of copula derivatives present in the likelihood is approximated by finite differences. Third, we formulate conditions for clustered right-censored survival data under which an information criterion for model selection is either weakly consistent or consistent. Several of the familiar selection criteria are included. A set of four-dimensional data on time-to-mastitis is used to demonstrate the developed methodology.

Image Recommendation Algorithm Using Feature-Based Collaborative Filtering

NASA Astrophysics Data System (ADS)

Kim, Deok-Hwan

As the multimedia contents market continues its rapid expansion, the amount of image contents used in mobile phone services, digital libraries, and catalog service is increasing remarkably. In spite of this rapid growth, users experience high levels of frustration when searching for the desired image. Even though new images are profitable to the service providers, traditional collaborative filtering methods cannot recommend them. To solve this problem, in this paper, we propose feature-based collaborative filtering (FBCF) method to reflect the user's most recent preference by representing his purchase sequence in the visual feature space. The proposed approach represents the images that have been purchased in the past as the feature clusters in the multi-dimensional feature space and then selects neighbors by using an inter-cluster distance function between their feature clusters. Various experiments using real image data demonstrate that the proposed approach provides a higher quality recommendation and better performance than do typical collaborative filtering and content-based filtering techniques.

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

PubMed

Ji, Shuiwang

2013-07-11

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

Density-functional theory study of the geometries, stabilities, and electronic properties of Au n Rb (n = 1-10) clusters: comparison with pure gold clusters

NASA Astrophysics Data System (ADS)

Hu, Yan-Fei; Jiang, Gang; Meng, Da-Qiao

2012-01-01

The density functional method with the relativistic effective core potential has been employed to investigate systematically the geometric structures, relative stabilities, growth-pattern behavior, and electronic properties of small bimetallic Au n Rb (n = 1-10) and pure gold Au n (n ≤ 11) clusters. For the geometric structures of the Au n Rb (n = 1-10) clusters, the dominant growth pattern is for a Rb-substituted Au n +1 cluster or one Au atom capped on a Au n -1Rb cluster, and the turnover point from a two-dimensional to a three-dimensional structure occurs at n = 4. Moreover, the stability of the ground-state structures of these clusters has been examined via an analysis of the average atomic binding energies, fragmentation energies, and the second-order difference of energies as a function of cluster size. The results exhibit a pronounced even-odd alternation phenomenon. The same pronounced even-odd alternations are found for the HOMO-LUMO gap, VIPs, VEAs, and the chemical hardness. In addition, about one electron charge transfers from the Au n host to the Rb atom in each corresponding Au n Rb cluster.

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

PubMed

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

2018-05-01

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

Cluster stability in the analysis of mass cytometry data.

PubMed

Melchiotti, Rossella; Gracio, Filipe; Kordasti, Shahram; Todd, Alan K; de Rinaldis, Emanuele

2017-01-01

Manual gating has been traditionally applied to cytometry data sets to identify cells based on protein expression. The advent of mass cytometry allows for a higher number of proteins to be simultaneously measured on cells, therefore providing a means to define cell clusters in a high dimensional expression space. This enhancement, whilst opening unprecedented opportunities for single cell-level analyses, makes the incremental replacement of manual gating with automated clustering a compelling need. To this aim many methods have been implemented and their successful applications demonstrated in different settings. However, the reproducibility of automatically generated clusters is proving challenging and an analytical framework to distinguish spurious clusters from more stable entities, and presumably more biologically relevant ones, is still missing. One way to estimate cell clusters' stability is the evaluation of their consistent re-occurrence within- and between-algorithms, a metric that is commonly used to evaluate results from gene expression. Herein we report the usage and importance of cluster stability evaluations, when applied to results generated from three popular clustering algorithms - SPADE, FLOCK and PhenoGraph - run on four different data sets. These algorithms were shown to generate clusters with various degrees of statistical stability, many of them being unstable. By comparing the results of automated clustering with manually gated populations, we illustrate how information on cluster stability can assist towards a more rigorous and informed interpretation of clustering results. We also explore the relationships between statistical stability and other properties such as clusters' compactness and isolation, demonstrating that whilst cluster stability is linked to other properties it cannot be reliably predicted by any of them. Our study proposes the introduction of cluster stability as a necessary checkpoint for cluster interpretation and contributes to the construction of a more systematic and standardized analytical framework for the assessment of cytometry clustering results. © 2016 International Society for Advancement of Cytometry. © 2016 International Society for Advancement of Cytometry.

ON HELIUM MIXING IN QUASI-GLOBAL SIMULATIONS OF THE INTRACLUSTER MEDIUM

DOE Office of Scientific and Technical Information (OSTI.GOV)

Berlok, Thomas; Pessah, Martin E., E-mail: berlok@nbi.dk, E-mail: mpessah@nbi.dk

The assumption of a spatially uniform helium distribution in the intracluster medium (ICM) can lead to biases in the estimates of key cluster parameters if composition gradients are present. The helium concentration profile in galaxy clusters is unfortunately not directly observable. Current models addressing the putative sedimentation are one-dimensional and parametrize the presence of magnetic fields in a crude way, ignoring the weakly collisional, magnetized nature of the medium. When these effects are considered, a wide variety of instabilities can play an important role in the plasma dynamics. In a series of recent papers, we have developed the local, linearmore » theory of these instabilities and addressed their nonlinear development with a modified version of Athena. Here, we extend our study by developing a quasi-global approach that we use to simulate the mixing of helium as induced by generalizations of the heat-flux-driven buoyancy instability (HBI) and the magnetothermal instability, which feed off thermal and composition gradients. In the inner region of the ICM, mixing can occur over a few gigayears, after which the average magnetic field inclination angle is ∼30°–50°, resulting in an averaged Spitzer parameter higher by about 20% than the value obtained in homogeneous simulations. In the cluster outskirts the instabilities are rather inefficient, due to the shallow gradients. This suggests that composition gradients in cluster cores might be shallower than one-dimensional models predict. More quantitative statements demand more refined models that can incorporate the physics driving the sedimentation process and simultaneously account for the weakly collisional nature of the plasma.« less

Study on Data Clustering and Intelligent Decision Algorithm of Indoor Localization

NASA Astrophysics Data System (ADS)

Liu, Zexi

2018-01-01

Indoor positioning technology enables the human beings to have the ability of positional perception in architectural space, and there is a shortage of single network coverage and the problem of location data redundancy. So this article puts forward the indoor positioning data clustering algorithm and intelligent decision-making research, design the basic ideas of multi-source indoor positioning technology, analyzes the fingerprint localization algorithm based on distance measurement, position and orientation of inertial device integration. By optimizing the clustering processing of massive indoor location data, the data normalization pretreatment, multi-dimensional controllable clustering center and multi-factor clustering are realized, and the redundancy of locating data is reduced. In addition, the path is proposed based on neural network inference and decision, design the sparse data input layer, the dynamic feedback hidden layer and output layer, low dimensional results improve the intelligent navigation path planning.

Stable dissipative optical vortex clusters by inhomogeneous effective diffusion.

PubMed

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

2017-10-30

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

Ultracold few fermionic atoms in needle-shaped double wells: spin chains and resonating spin clusters from microscopic Hamiltonians emulated via antiferromagnetic Heisenberg and t-J models

NASA Astrophysics Data System (ADS)

Yannouleas, Constantine; Brandt, Benedikt B.; Landman, Uzi

2016-07-01

Advances with trapped ultracold atoms intensified interest in simulating complex physical phenomena, including quantum magnetism and transitions from itinerant to non-itinerant behavior. Here we show formation of antiferromagnetic ground states of few ultracold fermionic atoms in single and double well (DW) traps, through microscopic Hamiltonian exact diagonalization for two DW arrangements: (i) two linearly oriented one-dimensional, 1D, wells, and (ii) two coupled parallel wells, forming a trap of two-dimensional, 2D, nature. The spectra and spin-resolved conditional probabilities reveal for both cases, under strong repulsion, atomic spatial localization at extemporaneously created sites, forming quantum molecular magnetic structures with non-itinerant character. These findings usher future theoretical and experimental explorations into the highly correlated behavior of ultracold strongly repelling fermionic atoms in higher dimensions, beyond the fermionization physics that is strictly applicable only in the 1D case. The results for four atoms are well described with finite Heisenberg spin-chain and cluster models. The numerical simulations of three fermionic atoms in symmetric DWs reveal the emergent appearance of coupled resonating 2D Heisenberg clusters, whose emulation requires the use of a t-J-like model, akin to that used in investigations of high T c superconductivity. The highly entangled states discovered in the microscopic and model calculations of controllably detuned, asymmetric, DWs suggest three-cold-atom DW quantum computing qubits.

Ar(n)HF van der Waals clusters revisited: II. Energetics and HF vibrational frequency shifts from diffusion Monte Carlo calculations on additive and nonadditive potential-energy surfaces for n=1-12.

PubMed

Jiang, Hao; Xu, Minzhong; Hutson, Jeremy M; Bacić, Zlatko

2005-08-01

The ground-state energies and HF vibrational frequency shifts of Ar(n)HF clusters have been calculated on the nonadditive potential-energy surfaces (PESs) for n=2-7 and on the pairwise-additive PESs for the clusters with n=1-12, using the diffusion Monte Carlo (DMC) method. For n>3, the calculations have been performed for the lowest-energy isomer and several higher-lying isomers which are the closest in energy. They provide information about the isomer dependence of the HF redshift, and enable direct comparison with the experimental data recently obtained in helium nanodroplets. The agreement between theory and experiment is excellent, in particular, for the nonadditive DMC redshifts. The relative, incremental redshifts are reproduced accurately even at the lower level of theory, i.e., the DMC and quantum five-dimensional (rigid Ar(n)) calculations on the pairwise-additive PESs. The nonadditive interactions make a significant contribution to the frequency shift, on the order of 10%-12%, and have to be included in the PESs in order for the theory to yield accurate magnitude of the HF redshift. The energy gaps between the DMC ground states of the cluster isomers are very different from the energy separation of their respective minima on the PES, due to the considerable variations in the intermolecular zero-point energy of different Ar(n)HF isomers.

Vibrational Spectroscopy of BENZENE-(WATER)_N Clusters with N=6,7

NASA Astrophysics Data System (ADS)

Tabor, Daniel P.; Sibert, Edwin; Kusaka, Ryoji; Walsh, Patrick S.; Zwier, Timothy S.

2015-06-01

The investigation of benzene-water clusters (Bz-(H_2O)_n) provides insight into the relative importance π-hydrogen bond interactions in cluster formation. Taking advantage of the higher resolution of current IR sources, isomer-specific resonant ion-dip infrared (RIDIR) spectra were recorded in the OH stretch region (3000-3750 cm-1). A local mode Hamiltonian for describing the OH stretch vibrations of water clusters is applied to Bz-(H_2O)_6 and Bz-(H_2O)_7 and compared with the RIDIR spectra. These clusters are the smallest water clusters in which three-dimensional H-bonded networks containing three-coordinate water molecules begin to be formed, and are therefore particularly susceptible to re-ordering or re-shaping in response to the presence of a benzene molecule. The spectrum of Bz-(H_2O)_6 is assigned to an inverted book structure while the major conformer of Bz-(H_2O)_7 is assigned to an S_4-derived inserted cubic structure in which the benzene occupies one corner of the cube. The local mode model is used to extract monomer Hamiltonians for individual water molecules, including stretch-bend Fermi resonance and intra-monomer couplings. The monomer Hamiltonians divide into sub-groups based on their local H-bonding architecture (DA, DDA, DAA) and the nature of their interaction with benzene.

A cluster analysis method for identification of subpopulations of cells in flow cytometric list-mode arrays

NASA Technical Reports Server (NTRS)

Li, Z. K.

1985-01-01

A specialized program was developed for flow cytometric list-mode data using an heirarchical tree method for identifying and enumerating individual subpopulations, the method of principal components for a two-dimensional display of 6-parameter data array, and a standard sorting algorithm for characterizing subpopulations. The program was tested against a published data set subjected to cluster analysis and experimental data sets from controlled flow cytometry experiments using a Coulter Electronics EPICS V Cell Sorter. A version of the program in compiled BASIC is usable on a 16-bit microcomputer with the MS-DOS operating system. It is specialized for 6 parameters and up to 20,000 cells. Its two-dimensional display of Euclidean distances reveals clusters clearly, as does its 1-dimensional display. The identified subpopulations can, in suitable experiments, be related to functional subpopulations of cells.

Atomically resolved structure of ligand-protected Au{sub 9} clusters on TiO{sub 2} nanosheets using aberration-corrected STEM

DOE Office of Scientific and Technical Information (OSTI.GOV)

Al Qahtani, Hassan S.; Andersson, Gunther G., E-mail: gunther.andersson@flinders.edu.au, E-mail: nakayama.tomonobu@nims.go.jp; Kimoto, Koji

2016-03-21

Triphenylphosphine ligand-protected Au{sub 9} clusters deposited onto titania nanosheets show three different atomic configurations as observed by scanning transmission electron microscopy. The configurations observed are a 3-dimensional structure, corresponding to the previously proposed Au{sub 9} core of the clusters, and two pseudo-2-dimensional (pseudo-2D) structures, newly found by this work. With the help of density functional theory (DFT) calculations, the observed pseudo-2D structures are attributed to the low energy, de-ligated structures formed through interaction with the substrate. The combination of scanning transmission electron microscopy with DFT calculations thus allows identifying whether or not the deposited Au{sub 9} clusters have been de-ligatedmore » in the deposition process.« less

FAST TRACK COMMUNICATION Critical exponents of domain walls in the two-dimensional Potts model

NASA Astrophysics Data System (ADS)

Dubail, Jérôme; Lykke Jacobsen, Jesper; Saleur, Hubert

2010-12-01

We address the geometrical critical behavior of the two-dimensional Q-state Potts model in terms of the spin clusters (i.e. connected domains where the spin takes a constant value). These clusters are different from the usual Fortuin-Kasteleyn clusters, and are separated by domain walls that can cross and branch. We develop a transfer matrix technique enabling the formulation and numerical study of spin clusters even when Q is not an integer. We further identify geometrically the crossing events which give rise to conformal correlation functions. This leads to an infinite series of fundamental critical exponents h_{\\ell _1-\\ell _2,2\\ell _1}, valid for 0 <= Q <= 4, that describe the insertion of ell1 thin and ell2 thick domain walls.

X-ray clusters from a high-resolution hydrodynamic PPM simulation of the cold dark matter universe

NASA Technical Reports Server (NTRS)

Bryan, Greg L.; Cen, Renyue; Norman, Michael L.; Ostriker, Jermemiah P.; Stone, James M.

1994-01-01

A new three-dimensional hydrodynamic code based on the piecewise parabolic method (PPM) is utilized to compute the distribution of hot gas in the standard Cosmic Background Explorer (COBE)-normalized cold dark matter (CDM) universe. Utilizing periodic boundary conditions, a box with size 85 h(exp-1) Mpc, having cell size 0.31 h(exp-1) Mpc, is followed in a simulation with 270(exp 3)=10(exp 7.3) cells. Adopting standard parameters determined from COBE and light-element nucleosynthesis, Sigma(sub 8)=1.05, Omega(sub b)=0.06, we find the X-ray-emitting clusters, compute the luminosity function at several wavelengths, the temperature distribution, and estimated sizes, as well as the evolution of these quantities with redshift. The results, which are compared with those obtained in the preceding paper (Kang et al. 1994a), may be used in conjuction with ROSAT and other observational data sets. Overall, the results of the two computations are qualitatively very similar with regard to the trends of cluster properties, i.e., how the number density, radius, and temeprature depend on luminosity and redshift. The total luminosity from clusters is approximately a factor of 2 higher using the PPM code (as compared to the 'total variation diminishing' (TVD) code used in the previous paper) with the number of bright clusters higher by a similar factor. The primary conclusions of the prior paper, with regard to the power spectrum of the primeval density perturbations, are strengthened: the standard CDM model, normalized to the COBE microwave detection, predicts too many bright X-ray emitting clusters, by a factor probably in excess of 5. The comparison between observations and theoretical predictions for the evolution of cluster properties, luminosity functions, and size and temperature distributions should provide an important discriminator among competing scenarios for the development of structure in the universe.

Synthesis of borophenes: Anisotropic, two-dimensional boron polymorphs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mannix, A. J.; Zhou, X. -F.; Kiraly, B.

At the atomic-cluster scale, pure boron is markedly similar to carbon, forming simple planar molecules and cage-like fullerenes. Theoretical studies predict that two-dimensional (2D) boron sheets will adopt an atomic configuration similar to that of boron atomic clusters. We synthesized atomically thin, crystalline 2D boron sheets (i.e., borophene) on silver surfaces under ultrahigh-vacuum conditions. Atomic-scale characterization, supported by theoretical calculations, revealed structures reminiscent of fused boron clusters with multiple scales of anisotropic, out-of-plane buckling. Unlike bulk boron allotropes, borophene shows metallic characteristics that are consistent with predictions of a highly anisotropic, 2D metal.

The quest for inorganic fullerenes

NASA Astrophysics Data System (ADS)

Pietsch, Susanne; Dollinger, Andreas; Strobel, Christoph H.; Park, Eun Ji; Ganteför, Gerd; Seo, Hyun Ook; Kim, Young Dok; Idrobo, Juan-Carlos; Pennycook, Stephen J.

2015-10-01

Experimental results of the search for inorganic fullerenes are presented. MonSm- and WnSm- clusters are generated with a pulsed arc cluster ion source equipped with an annealing stage. This is known to enhance fullerene formation in the case of carbon. Analogous to carbon, the mass spectra of the metal chalcogenide clusters produced in this way exhibit a bimodal structure. The species in the first maximum at low mass are known to be platelets. Here, the structure of the species in the second maximum is studied by anion photoelectron spectroscopy, scanning transmission electron microscopy, and scanning tunneling microcopy. All experimental results indicate a two-dimensional structure of these species and disagree with a three-dimensional fullerene-like geometry. A possible explanation for this preference of two-dimensional structures is the ability of a two-element material to saturate the dangling bonds at the edges of a platelet by excess atoms of one element. A platelet consisting of a single element only cannot do this. Accordingly, graphite and boron might be the only materials forming nano-spheres because they are the only single element materials assuming two-dimensional structures.

Quantum computational universality of the Cai-Miyake-Dür-Briegel two-dimensional quantum state from Affleck-Kennedy-Lieb-Tasaki quasichains

NASA Astrophysics Data System (ADS)

Wei, Tzu-Chieh; Raussendorf, Robert; Kwek, Leong Chuan

2011-10-01

Universal quantum computation can be achieved by simply performing single-qubit measurements on a highly entangled resource state, such as cluster states. Cai, Miyake, Dür, and Briegel recently constructed a ground state of a two-dimensional quantum magnet by combining multiple Affleck-Kennedy-Lieb-Tasaki quasichains of mixed spin-3/2 and spin-1/2 entities and by mapping pairs of neighboring spin-1/2 particles to individual spin-3/2 particles [Phys. Rev. APLRAAN1050-294710.1103/PhysRevA.82.052309 82, 052309 (2010)]. They showed that this state enables universal quantum computation by single-spin measurements. Here, we give an alternative understanding of how this state gives rise to universal measurement-based quantum computation: by local operations, each quasichain can be converted to a one-dimensional cluster state and entangling gates between two neighboring logical qubits can be implemented by single-spin measurements. We further argue that a two-dimensional cluster state can be distilled from the Cai-Miyake-Dür-Briegel state.

Quantum trilogy: discrete Toda, Y-system and chaos

NASA Astrophysics Data System (ADS)

Yamazaki, Masahito

2018-02-01

We discuss a discretization of the quantum Toda field theory associated with a semisimple finite-dimensional Lie algebra or a tamely-laced infinite-dimensional Kac-Moody algebra G, generalizing the previous construction of discrete quantum Liouville theory for the case G  =  A 1. The model is defined on a discrete two-dimensional lattice, whose spatial direction is of length L. In addition we also find a ‘discretized extra dimension’ whose width is given by the rank r of G, which decompactifies in the large r limit. For the case of G  =  A N or AN-1(1) , we find a symmetry exchanging L and N under appropriate spatial boundary conditions. The dynamical time evolution rule of the model is quantizations of the so-called Y-system, and the theory can be well described by the quantum cluster algebra. We discuss possible implications for recent discussions of quantum chaos, and comment on the relation with the quantum higher Teichmüller theory of type A N .

Effect of palladium doping on the stability and fragmentation patterns of cationic gold clusters

NASA Astrophysics Data System (ADS)

Ferrari, P.; Hussein, H. A.; Heard, C. J.; Vanbuel, J.; Johnston, R. L.; Lievens, P.; Janssens, E.

2018-05-01

We analyze in detail how the interplay between electronic structure and cluster geometry determines the stability and the fragmentation channels of single Pd-doped cationic Au clusters, PdA uN-1+ (N =2 -20 ). For this purpose, a combination of photofragmentation experiments and density functional theory calculations was employed. A remarkable agreement between the experiment and the calculations is obtained. Pd doping is found to modify the structure of the Au clusters, in particular altering the two-dimensional to three-dimensional transition size, with direct consequences on the stability of the clusters. Analysis of the electronic density of states of the clusters shows that depending on cluster size, Pd delocalizes one 4 d electron, giving an enhanced stability to PdA u6 + , or remains with all 4 d10 electrons localized, closing an electronic shell in PdA u9 + . Furthermore, it is observed that for most clusters, Au evaporation is the lowest-energy decay channel, although for some sizes Pd evaporation competes. In particular, PdA u7 + and PdA u9 + decay by Pd evaporation due to the high stability of the A u7 + and A u9 + fragmentation products.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Krause, Josua; Dasgupta, Aritra; Fekete, Jean-Daniel

Dealing with the curse of dimensionality is a key challenge in high-dimensional data visualization. We present SeekAView to address three main gaps in the existing research literature. First, automated methods like dimensionality reduction or clustering suffer from a lack of transparency in letting analysts interact with their outputs in real-time to suit their exploration strategies. The results often suffer from a lack of interpretability, especially for domain experts not trained in statistics and machine learning. Second, exploratory visualization techniques like scatter plots or parallel coordinates suffer from a lack of visual scalability: it is difficult to present a coherent overviewmore » of interesting combinations of dimensions. Third, the existing techniques do not provide a flexible workflow that allows for multiple perspectives into the analysis process by automatically detecting and suggesting potentially interesting subspaces. In SeekAView we address these issues using suggestion based visual exploration of interesting patterns for building and refining multidimensional subspaces. Compared to the state-of-the-art in subspace search and visualization methods, we achieve higher transparency in showing not only the results of the algorithms, but also interesting dimensions calibrated against different metrics. We integrate a visually scalable design space with an iterative workflow guiding the analysts by choosing the starting points and letting them slice and dice through the data to find interesting subspaces and detect correlations, clusters, and outliers. We present two usage scenarios for demonstrating how SeekAView can be applied in real-world data analysis scenarios.« less

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

NASA Astrophysics Data System (ADS)

Bischi, G. I.; Tramontana, F.

2010-10-01

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

The effects of self-interstitial clusters on cascade defect evolution beyond the primary damage state

DOE Office of Scientific and Technical Information (OSTI.GOV)

Heinisch, H.L.

1997-04-01

The intracascade evolution of the defect distributions of cascades in copper is investigated using stochastic annealing simulations applied to cascades generated with molecular dynamics (MD). The temperature and energy dependencies of annihilation, clustering and free defect production are determined for individual cascades. The annealing simulation results illustrate the strong influence on intracascade evolution of the defect configuration existing in the primary damage state. Another factor significantly affecting the evolution of the defect distribution is the rapid one-dimensional diffusion of small, glissile interstitial loops produced directly in cascades. This phenomenon introduces a cascade energy dependence of defect evolution that is apparentmore » only beyond the primary damage state, amplifying the need for further study of the annealing phase of cascade evolution and for performing many more MD cascade simulations at higher energies.« less

Semi-Supervised Clustering for High-Dimensional and Sparse Features

ERIC Educational Resources Information Center

Yan, Su

2010-01-01

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

TimesVector: a vectorized clustering approach to the analysis of time series transcriptome data from multiple phenotypes.

PubMed

Jung, Inuk; Jo, Kyuri; Kang, Hyejin; Ahn, Hongryul; Yu, Youngjae; Kim, Sun

2017-12-01

Identifying biologically meaningful gene expression patterns from time series gene expression data is important to understand the underlying biological mechanisms. To identify significantly perturbed gene sets between different phenotypes, analysis of time series transcriptome data requires consideration of time and sample dimensions. Thus, the analysis of such time series data seeks to search gene sets that exhibit similar or different expression patterns between two or more sample conditions, constituting the three-dimensional data, i.e. gene-time-condition. Computational complexity for analyzing such data is very high, compared to the already difficult NP-hard two dimensional biclustering algorithms. Because of this challenge, traditional time series clustering algorithms are designed to capture co-expressed genes with similar expression pattern in two sample conditions. We present a triclustering algorithm, TimesVector, specifically designed for clustering three-dimensional time series data to capture distinctively similar or different gene expression patterns between two or more sample conditions. TimesVector identifies clusters with distinctive expression patterns in three steps: (i) dimension reduction and clustering of time-condition concatenated vectors, (ii) post-processing clusters for detecting similar and distinct expression patterns and (iii) rescuing genes from unclassified clusters. Using four sets of time series gene expression data, generated by both microarray and high throughput sequencing platforms, we demonstrated that TimesVector successfully detected biologically meaningful clusters of high quality. TimesVector improved the clustering quality compared to existing triclustering tools and only TimesVector detected clusters with differential expression patterns across conditions successfully. The TimesVector software is available at http://biohealth.snu.ac.kr/software/TimesVector/. sunkim.bioinfo@snu.ac.kr. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

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

PubMed

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

2017-05-15

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

Use of advanced particle methods in modeling space propulsion and its supersonic expansions

NASA Astrophysics Data System (ADS)

Borner, Arnaud

This research discusses the use of advanced kinetic particle methods such as Molecular Dynamics (MD) and direct simulation Monte Carlo (DSMC) to model space propulsion systems such as electrospray thrusters and their supersonic expansions. MD simulations are performed to model an electrospray thruster for the ionic liquid (IL) EMIM--BF4 using coarse-grained (CG) potentials. The model is initially featuring a constant electric field applied in the longitudinal direction. Two coarse-grained potentials are compared, and the effective-force CG (EFCG) potential is found to predict the formation of the Taylor cone, the cone-jet, and other extrusion modes for similar electric fields and mass flow rates observed in experiments of a IL fed capillary-tip-extractor system better than the simple CG potential. Later, one-dimensional and fully transient three-dimensional electric fields, the latter solving Poisson's equation to take into account the electric field due to space charge at each timestep, are computed by coupling the MD model to a Poisson solver. It is found that the inhomogeneous electric field as well as that of the IL space-charge improve agreement between modeling and experiment. The boundary conditions (BCs) are found to have a substantial impact on the potential and electric field, and the tip BC is introduced and compared to the two previous BCs, named plate and needle, showing good improvement by reducing unrealistically high radial electric fields generated in the vicinity of the capillary tip. The influence of the different boundary condition models on charged species currents as a function of the mass flow rate is studied, and it is found that a constant electric field model gives similar agreement to the more rigorous and computationally expensive tip boundary condition at lower flow rates. However, at higher mass flow rates the MD simulations with the constant electric field produces extruded particles with higher Coulomb energy per ion, consistent with droplet formation. Supersonic expansions to vacuum produce clusters of sufficiently small size that properties such as heat capacities and latent heat of evaporation cannot be described by bulk vapor thermodynamic values. Therefore, MD simulations are performed to compute the evaporation rate of small water clusters as a function of temperature and size and the rates are found to agree with Unimolecular Dissociation Theory (UDT) and Classical Nucleation Theory (CNT). The heat capacities and latent heat of vaporization obtained from Monte-Carlo Canonical-Ensemble (MCCE) simulations are used in DSMC simulations of two experiments that measured Rayleigh scattering and terminal dimer mole fraction of supersonic water-jet expansions. Water-cluster temperature and size are found to be influenced by the use of kinetic rather than thermodynamic heat-capacity and latent-heat values as well as the nucleation model. Additionally, MD simulations of water condensation in a one-dimensional free expansion are performed to simulate the conditions in the core of a plume. We find that the internal structure of the clusters formed depends on the stagnation temperature conditions. Clusters of sizes 21 and 324 are studied in detail, and their radial distribution functions (RDF) are computed and compared to reported RDFs for solid amorphous ice clusters. Dielectric properties of liquid water and water clusters are investigated, and the static dielectric constant, dipole moment autocorrelation function and relative permittivity are computed by means of MD simulations.

SU-G-TeP3-14: Three-Dimensional Cluster Model in Inhomogeneous Dose Distribution

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wei, J; Penagaricano, J; Narayanasamy, G

2016-06-15

Purpose: We aim to investigate 3D cluster formation in inhomogeneous dose distribution to search for new models predicting radiation tissue damage and further leading to new optimization paradigm for radiotherapy planning. Methods: The aggregation of higher dose in the organ at risk (OAR) than a preset threshold was chosen as the cluster whose connectivity dictates the cluster structure. Upon the selection of the dose threshold, the fractional density defined as the fraction of voxels in the organ eligible to be part of the cluster was determined according to the dose volume histogram (DVH). A Monte Carlo method was implemented tomore » establish a case pertinent to the corresponding DVH. Ones and zeros were randomly assigned to each OAR voxel with the sampling probability equal to the fractional density. Ten thousand samples were randomly generated to ensure a sufficient number of cluster sets. A recursive cluster searching algorithm was developed to analyze the cluster with various connectivity choices like 1-, 2-, and 3-connectivity. The mean size of the largest cluster (MSLC) from the Monte Carlo samples was taken to be a function of the fractional density. Various OARs from clinical plans were included in the study. Results: Intensive Monte Carlo study demonstrates the inverse relationship between the MSLC and the cluster connectivity as anticipated and the cluster size does not change with fractional density linearly regardless of the connectivity types. An initially-slow-increase to exponential growth transition of the MSLC from low to high density was observed. The cluster sizes were found to vary within a large range and are relatively independent of the OARs. Conclusion: The Monte Carlo study revealed that the cluster size could serve as a suitable index of the tissue damage (percolation cluster) and the clinical outcome of the same DVH might be potentially different.« less

Creating Quasi Two-Dimensional Cluster-Assembled Materials through Self-Assembly of a Janus Polyoxometalate-Silsesquioxane Co-Cluster.

PubMed

Wu, Han; Zhang, Yu-Qi; Hu, Min-Biao; Ren, Li-Jun; Lin, Yue; Wang, Wei

2017-05-30

Clusters are an important class of nanoscale molecules or superatoms that exhibit an amazing diversity in structure, chemical composition, shape, and functionality. Assembling two types of clusters is creating emerging cluster-assembled materials (CAMs). In this paper, we report an effective approach to produce quasi two-dimensional (2D) CAMs of two types of spherelike clusters, polyhedral oligomeric silsesquioxanes (POSS), and polyoxometalates (POM). To avoid macrophase separation between the two clusters, they are covalently linked to form a POM-POSS cocluster with Janus characteristics and a dumbbell shape. This Janus characteristics enables the cocluster to self-assemble into diverse nanoaggregates, as conventional amphiphilic molecules and macromolecules do, in selective solvents. In our study, we obtained micelles, vesicles, nanosheets, and nanoribbons by tuning the n-hexane content in mixed solvents of acetone and n-hexane. Ordered packing of clusters in the nanosheets and nanoribbons were directly visualized using high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) technique. We infer that the increase of packing order results in the vesicle-to-sheet transition and the change in packing mode causes the sheet-to-ribbon transitions. Our findings have verified the effectivity of creating quasi 2D cluster-assembled materials though the cocluster self-assembly as a new approach to produce novel CAMs.

First assembly times and equilibration in stochastic coagulation-fragmentation

DOE Office of Scientific and Technical Information (OSTI.GOV)

D’Orsogna, Maria R.; Department of Mathematics, CSUN, Los Angeles, California 91330-8313; Lei, Qi

2015-07-07

We develop a fully stochastic theory for coagulation and fragmentation (CF) in a finite system with a maximum cluster size constraint. The process is modeled using a high-dimensional master equation for the probabilities of cluster configurations. For certain realizations of total mass and maximum cluster sizes, we find exact analytical results for the expected equilibrium cluster distributions. If coagulation is fast relative to fragmentation and if the total system mass is indivisible by the mass of the largest allowed cluster, we find a mean cluster-size distribution that is strikingly broader than that predicted by the corresponding mass-action equations. Combinations ofmore » total mass and maximum cluster size under which equilibration is accelerated, eluding late-stage coarsening, are also delineated. Finally, we compute the mean time it takes particles to first assemble into a maximum-sized cluster. Through careful state-space enumeration, the scaling of mean assembly times is derived for all combinations of total mass and maximum cluster size. We find that CF accelerates assembly relative to monomer kinetic only in special cases. All of our results hold in the infinite system limit and can be only derived from a high-dimensional discrete stochastic model, highlighting how classical mass-action models of self-assembly can fail.« less

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

PubMed Central

2013-01-01

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

Prevalence and severity of categorical and dimensional personality disorders in adolescents with eating disorders.

PubMed

Magallón-Neri, Ernesto; González, Esther; Canalda, Gloria; Forns, Maria; De La Fuente, J Eugenio; Martínez, Estebán; García, Raquel; Lara, Anais; Vallès, Antoni; Castro-Fornieles, Josefina

2014-05-01

The objective of this study is to explore and compare the prevalence of categorical and dimensional personality disorders (PDs) and their severity in Spanish adolescents with Eating Disorders (EDs). Diagnostic and Statistical Manual of Mental Disorders Fourth Edition and International Classification of Diseases, Tenth Revision-10 modules of the International Personality Disorder Examination were administered to a sample of 100 female adolescents with EDs (mean age=15.8 years, SD=0.9). 'Thirty-three per cent of the sample had at least one PD, in most cases a simple PD. The rate of PDs was 64-76% in bulimia patients, 22-28% in anorexia and 25% in EDs not otherwise specified. The highest dimensional scores were observed in bulimia, [corrected] mainly in borderline and histrionic PDs, and higher scores for anankastic PD in anorexia than in the other ED diagnoses. Overall, purging type EDs had higher cluster B personality pathology scores than restrictive type.' [corrected] The Publisher would like to apologize for this error and any confusion it may have caused. [corrected]. Adolescent female patients with ED have a risk of presenting a comorbid PD, especially patients with bulimia and purging type EDs. Copyright © 2013 John Wiley & Sons, Ltd and Eating Disorders Association.

Higher-order clustering in networks

NASA Astrophysics Data System (ADS)

Yin, Hao; Benson, Austin R.; Leskovec, Jure

2018-05-01

A fundamental property of complex networks is the tendency for edges to cluster. The extent of the clustering is typically quantified by the clustering coefficient, which is the probability that a length-2 path is closed, i.e., induces a triangle in the network. However, higher-order cliques beyond triangles are crucial to understanding complex networks, and the clustering behavior with respect to such higher-order network structures is not well understood. Here we introduce higher-order clustering coefficients that measure the closure probability of higher-order network cliques and provide a more comprehensive view of how the edges of complex networks cluster. Our higher-order clustering coefficients are a natural generalization of the traditional clustering coefficient. We derive several properties about higher-order clustering coefficients and analyze them under common random graph models. Finally, we use higher-order clustering coefficients to gain new insights into the structure of real-world networks from several domains.

Vortex clustering and universal scaling laws in two-dimensional quantum turbulence.

PubMed

Skaugen, Audun; Angheluta, Luiza

2016-03-01

We investigate numerically the statistics of quantized vortices in two-dimensional quantum turbulence using the Gross-Pitaevskii equation. We find that a universal -5/3 scaling law in the turbulent energy spectrum is intimately connected with the vortex statistics, such as number fluctuations and vortex velocity, which is also characterized by a similar scaling behavior. The -5/3 scaling law appearing in the power spectrum of vortex number fluctuations is consistent with the scenario of passive advection of isolated vortices by a turbulent superfluid velocity generated by like-signed vortex clusters. The velocity probability distribution of clustered vortices is also sensitive to spatial configurations, and exhibits a power-law tail distribution with a -5/3 exponent.

Synthesis of borophenes: Anisotropic, two-dimensional boron polymorphs.

PubMed

Mannix, Andrew J; Zhou, Xiang-Feng; Kiraly, Brian; Wood, Joshua D; Alducin, Diego; Myers, Benjamin D; Liu, Xiaolong; Fisher, Brandon L; Santiago, Ulises; Guest, Jeffrey R; Yacaman, Miguel Jose; Ponce, Arturo; Oganov, Artem R; Hersam, Mark C; Guisinger, Nathan P

2015-12-18

At the atomic-cluster scale, pure boron is markedly similar to carbon, forming simple planar molecules and cage-like fullerenes. Theoretical studies predict that two-dimensional (2D) boron sheets will adopt an atomic configuration similar to that of boron atomic clusters. We synthesized atomically thin, crystalline 2D boron sheets (i.e., borophene) on silver surfaces under ultrahigh-vacuum conditions. Atomic-scale characterization, supported by theoretical calculations, revealed structures reminiscent of fused boron clusters with multiple scales of anisotropic, out-of-plane buckling. Unlike bulk boron allotropes, borophene shows metallic characteristics that are consistent with predictions of a highly anisotropic, 2D metal. Copyright © 2015, American Association for the Advancement of Science.

Cluster sizes in a classical Lennard-Jones chain

NASA Astrophysics Data System (ADS)

Lee-Dadswell, G. R.; Barrett, Nicholas; Power, Michael

2017-09-01

The definitions of breaks and clusters in a one-dimensional chain in equilibrium are discussed. Analytical expressions are obtained for the expected cluster length, 〈K 〉 , as a function of temperature and pressure in a one-dimensional Lennard-Jones chain. These expressions are compared with results from molecular dynamics simulations. It is found that 〈K 〉 increases exponentially with β =1 /kBT and with pressure, P in agreement with previous results in the literature. A method is illustrated for using 〈K 〉(β ,P ) to generate a "phase diagram" for the Lennard-Jones chain. Some implications for the study of heat transport in Lennard-Jones chains are discussed.

Topology for Dominance for Network of Multi-Agent System

NASA Astrophysics Data System (ADS)

Szeto, K. Y.

2007-05-01

The resource allocation problem in evolving two-dimensional point patterns is investigated for the existence of good strategies for the construction of initial configuration that leads to fast dominance of the pattern by one single species, which can be interpreted as market dominance by a company in the context of multi-agent systems in econophysics. For hexagonal lattice, certain special topological arrangements of the resource in two-dimensions, such as rings, lines and clusters have higher probability of dominance, compared to random pattern. For more complex networks, a systematic way to search for a stable and dominant strategy of resource allocation in the changing environment is found by means of genetic algorithm. Five typical features can be summarized by means of the distribution function for the local neighborhood of friends and enemies as well as the local clustering coefficients: (1) The winner has more triangles than the loser has. (2) The winner likes to form clusters as the winner tends to connect with other winner rather than with losers; while the loser tends to connect with winners rather than losers. (3) The distribution function of friends as well as enemies for the winner is broader than the corresponding distribution function for the loser. (4) The connectivity at which the peak of the distribution of friends for the winner occurs is larger than that of the loser; while the peak values for friends for winners is lower. (5) The connectivity at which the peak of the distribution of enemies for the winner occurs is smaller than that of the loser; while the peak values for enemies for winners is lower. These five features appear to be general, at least in the context of two-dimensional hexagonal lattices of various sizes, hierarchical lattice, Voronoi diagrams, as well as high-dimensional random networks. These general local topological properties of networks are relevant to strategists aiming at dominance in evolving patterns when the interaction between the agents is local.

Density functional study on structure and stability of bimetallic AuNZn (N<=6) clusters and their cations

NASA Astrophysics Data System (ADS)

Tanaka, Hiromasa; Neukermans, Sven; Janssens, Ewald; Silverans, Roger E.; Lievens, Peter

2003-10-01

A systematic study on the structure and stability of zinc doped gold clusters has been performed by density functional theory calculations. All the lowest-energy isomers found have a planar structure and resemble pure gold clusters in shape. Stable isomers tend to equally delocalize valence s electrons of the constituent atoms over the entire structure and maximize the number of Au-Zn bonds in the structure. This is because the Au-Zn bond is stronger than the Au-Au bond and gives an extra σ-bonding interaction by the overlap between vacant Zn 4p and valence Au 6s(5d) orbitals. No three-dimensional isomers were found for Au5Zn+ and Au4Zn clusters containing six delocalized valence electrons. This result reflects that these clusters have a magic number of delocalized electrons for two-dimensional systems. Calculated vertical ionization energies and dissociation energies as a function of the cluster size show odd-even behavior, in agreement with recent mass spectrometric observations [Tanaka et al., J. Am. Chem. Soc. 125, 2862 (2003)].

A clustering algorithm for determining community structure in complex networks

NASA Astrophysics Data System (ADS)

Jin, Hong; Yu, Wei; Li, ShiJun

2018-02-01

Clustering algorithms are attractive for the task of community detection in complex networks. DENCLUE is a representative density based clustering algorithm which has a firm mathematical basis and good clustering properties allowing for arbitrarily shaped clusters in high dimensional datasets. However, this method cannot be directly applied to community discovering due to its inability to deal with network data. Moreover, it requires a careful selection of the density parameter and the noise threshold. To solve these issues, a new community detection method is proposed in this paper. First, we use a spectral analysis technique to map the network data into a low dimensional Euclidean Space which can preserve node structural characteristics. Then, DENCLUE is applied to detect the communities in the network. A mathematical method named Sheather-Jones plug-in is chosen to select the density parameter which can describe the intrinsic clustering structure accurately. Moreover, every node on the network is meaningful so there were no noise nodes as a result the noise threshold can be ignored. We test our algorithm on both benchmark and real-life networks, and the results demonstrate the effectiveness of our algorithm over other popularity density based clustering algorithms adopted to community detection.

Discrete Cosine Transform Image Coding With Sliding Block Codes

NASA Astrophysics Data System (ADS)

Divakaran, Ajay; Pearlman, William A.

1989-11-01

A transform trellis coding scheme for images is presented. A two dimensional discrete cosine transform is applied to the image followed by a search on a trellis structured code. This code is a sliding block code that utilizes a constrained size reproduction alphabet. The image is divided into blocks by the transform coding. The non-stationarity of the image is counteracted by grouping these blocks in clusters through a clustering algorithm, and then encoding the clusters separately. Mandela ordered sequences are formed from each cluster i.e identically indexed coefficients from each block are grouped together to form one dimensional sequences. A separate search ensues on each of these Mandela ordered sequences. Padding sequences are used to improve the trellis search fidelity. The padding sequences absorb the error caused by the building up of the trellis to full size. The simulations were carried out on a 256x256 image ('LENA'). The results are comparable to any existing scheme. The visual quality of the image is enhanced considerably by the padding and clustering.

Clustering and Dimensionality Reduction to Discover Interesting Patterns in Binary Data

NASA Astrophysics Data System (ADS)

Palumbo, Francesco; D'Enza, Alfonso Iodice

The attention towards binary data coding increased consistently in the last decade due to several reasons. The analysis of binary data characterizes several fields of application, such as market basket analysis, DNA microarray data, image mining, text mining and web-clickstream mining. The paper illustrates two different approaches exploiting a profitable combination of clustering and dimensionality reduction for the identification of non-trivial association structures in binary data. An application in the Association Rules framework supports the theory with the empirical evidence.

Zeldovich pancakes in observational data are cold

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brinckmann, Thejs; Lindholmer, Mikkel; Hansen, Steen

The present day universe consists of galaxies, galaxy clusters, one-dimensional filaments and two-dimensional sheets or pancakes, all of which combine to form the cosmic web. The so called ''Zeldovich pancakes' are very difficult to observe, because their overdensity is only slightly greater than the average density of the universe. Falco et al. [1] presented a method to identify Zeldovich pancakes in observational data, and these were used as a tool for estimating the mass of galaxy clusters. Here we expand and refine that observational detection method. We study two pancakes on scales of 10 Mpc, identified from spectroscopically observed galaxiesmore » near the Coma cluster, and compare with twenty numerical pancakes.We find that the observed structures have velocity dispersions of about 100 km/sec, which is relatively low compared to typical groups and filaments. These velocity dispersions are consistent with those found for the numerical pancakes. We also confirm that the identified structures are in fact two-dimensional structures. Finally, we estimate the stellar to total mass of the observational pancakes to be 2 · 10{sup −4}, within one order of magnitude, which is smaller than that of clusters of galaxies.« less

Spike sorting based upon machine learning algorithms (SOMA).

PubMed

Horton, P M; Nicol, A U; Kendrick, K M; Feng, J F

2007-02-15

We have developed a spike sorting method, using a combination of various machine learning algorithms, to analyse electrophysiological data and automatically determine the number of sampled neurons from an individual electrode, and discriminate their activities. We discuss extensions to a standard unsupervised learning algorithm (Kohonen), as using a simple application of this technique would only identify a known number of clusters. Our extra techniques automatically identify the number of clusters within the dataset, and their sizes, thereby reducing the chance of misclassification. We also discuss a new pre-processing technique, which transforms the data into a higher dimensional feature space revealing separable clusters. Using principal component analysis (PCA) alone may not achieve this. Our new approach appends the features acquired using PCA with features describing the geometric shapes that constitute a spike waveform. To validate our new spike sorting approach, we have applied it to multi-electrode array datasets acquired from the rat olfactory bulb, and from the sheep infero-temporal cortex, and using simulated data. The SOMA sofware is available at http://www.sussex.ac.uk/Users/pmh20/spikes.

Nature of phase transitions in Axelrod-like coupled Potts models in two dimensions

NASA Astrophysics Data System (ADS)

Gandica, Yerali; Chiacchiera, Silvia

2016-03-01

We study F coupled q -state Potts models in a two-dimensional square lattice. The interaction between the different layers is attractive to favor a simultaneous alignment in all of them, and its strength is fixed. The nature of the phase transition for zero field is numerically determined for F =2 ,3 . Using the Lee-Kosterlitz method, we find that it is continuous for F =2 and q =2 , whereas it is abrupt for higher values of q and/or F . When a continuous or a weakly first-order phase transition takes place, we also analyze the properties of the geometrical clusters. This allows us to determine the fractal dimension D of the incipient infinite cluster and to examine the finite-size scaling of the cluster number density via data collapse. A mean-field approximation of the model, from which some general trends can be determined, is presented too. Finally, since this lattice model has been recently considered as a thermodynamic counterpart of the Axelrod model of social dynamics, we discuss our results in connection with this one.

Nature of phase transitions in Axelrod-like coupled Potts models in two dimensions.

PubMed

Gandica, Yerali; Chiacchiera, Silvia

2016-03-01

We study F coupled q-state Potts models in a two-dimensional square lattice. The interaction between the different layers is attractive to favor a simultaneous alignment in all of them, and its strength is fixed. The nature of the phase transition for zero field is numerically determined for F = 2,3. Using the Lee-Kosterlitz method, we find that it is continuous for F = 2 and q = 2, whereas it is abrupt for higher values of q and/or F. When a continuous or a weakly first-order phase transition takes place, we also analyze the properties of the geometrical clusters. This allows us to determine the fractal dimension D of the incipient infinite cluster and to examine the finite-size scaling of the cluster number density via data collapse. A mean-field approximation of the model, from which some general trends can be determined, is presented too. Finally, since this lattice model has been recently considered as a thermodynamic counterpart of the Axelrod model of social dynamics, we discuss our results in connection with this one.

Structural, energetic, and electronic trends in low-dimensional late-transition-metal systems

NASA Astrophysics Data System (ADS)

Hu, C. H.; Chizallet, C.; Toulhoat, H.; Raybaud, P.

2009-05-01

Using first-principles calculations, we present a comprehensive investigation of the structural trends of low dimensionality late 4d (from Tc to Ag) and 5d (from Re to Au) transition-metal systems including 13-atom clusters. Energetically favorable clusters not being reported previously are discovered by molecular-dynamics simulation based on the simulated annealing method. They allow a better agreement between experiments and theory for their magnetic properties. The structural periodic trend exhibits a nonmonotonic variation of the ratio of square to triangular facets for the two rows, with a maximum for Rh13 and Ir13 . By a comparative analysis of the relevant energetic and electronic properties performed on other metallic systems with reduced dimensionalities such as four-atom planar clusters, one-dimensional (1D) scales, double scales, 1D cylinders, monatomic films, two and seven layer slabs, we highlight that this periodic trend can be generalized. Hence, it appears that 1D-metallic nanocylinders or 1D-double nanoscales (with similar binding energies as TM13 ) also favor square facets for Rh and Ir. We finally propose an interpretation based on the evolution of the width of the valence band and of the Coulombic repulsions of the bonding basins.

The quest for inorganic fullerenes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pietsch, Susanne; Dollinger, Andreas; Strobel, Christoph H.

2015-10-02

Experimental results of the search for inorganic fullerenes are presented. Mo nS m - and W nS m - clusters are generated with a pulsed arc cluster ion source equipped with an annealing stage. This is known to enhance fullerene formation in the case of carbon. Analogous to carbon, the mass spectra of the metal chalcogenide clusters produced in this way exhibit a bimodal structure. Moreover, the species in the first maximum at low mass are known to be platelets. The structure of the species in the second maximum is studied by anion photoelectron spectroscopy, scanning transmission electron microscopy,more » and scanning tunneling microcopy. All experimental results indicate a two-dimensional structure of these species and disagree with a three-dimensional fullerene-like geometry. A possible explanation for this preference of two-dimensional structures is the ability of a two-element material to saturate the dangling bonds at the edges of a platelet by excess atoms of one element. A platelet consisting of a single element only cannot do this. Likewise, graphite and boron might be the only materials forming nano-spheres because they are the only single element materials assuming two-dimensional structures.« less

The quest for inorganic fullerenes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pietsch, Susanne; Dollinger, Andreas; Strobel, Christoph H.

2015-10-07

Experimental results of the search for inorganic fullerenes are presented. Mo{sub n}S{sub m}{sup −} and W{sub n}S{sub m}{sup −} clusters are generated with a pulsed arc cluster ion source equipped with an annealing stage. This is known to enhance fullerene formation in the case of carbon. Analogous to carbon, the mass spectra of the metal chalcogenide clusters produced in this way exhibit a bimodal structure. The species in the first maximum at low mass are known to be platelets. Here, the structure of the species in the second maximum is studied by anion photoelectron spectroscopy, scanning transmission electron microscopy, andmore » scanning tunneling microcopy. All experimental results indicate a two-dimensional structure of these species and disagree with a three-dimensional fullerene-like geometry. A possible explanation for this preference of two-dimensional structures is the ability of a two-element material to saturate the dangling bonds at the edges of a platelet by excess atoms of one element. A platelet consisting of a single element only cannot do this. Accordingly, graphite and boron might be the only materials forming nano-spheres because they are the only single element materials assuming two-dimensional structures.« less

Quantum Computational Universality of the 2D Cai-Miyake-D"ur-Briegel Quantum State

NASA Astrophysics Data System (ADS)

Wei, Tzu-Chieh; Raussendorf, Robert; Kwek, Leong Chuan

2012-02-01

Universal quantum computation can be achieved by simply performing single-qubit measurements on a highly entangled resource state, such as cluster states. Cai, Miyake, D"ur, and Briegel recently constructed a ground state of a two-dimensional quantum magnet by combining multiple Affleck-Kennedy-Lieb-Tasaki quasichains of mixed spin-3/2 and spin-1/2 entities and by mapping pairs of neighboring spin-1/2 particles to individual spin-3/2 particles [Phys. Rev. A 82, 052309 (2010)]. They showed that this state enables universal quantum computation by constructing single- and two-qubit universal gates. Here, we give an alternative understanding of how this state gives rise to universal measurement-based quantum computation: by local operations, each quasichain can be converted to a one-dimensional cluster state and entangling gates between two neighboring logical qubits can be implemented by single-spin measurements. Furthermore, a two-dimensional cluster state can be distilled from the Cai-Miyake-D"ur-Briegel state.

Development of a Three-Dimensional PSE Code for Compressible Flows: Stability of Three-Dimensional Compressible Boundary Layers

NASA Technical Reports Server (NTRS)

Balakumar, P.; Jeyasingham, Samarasingham

1999-01-01

A program is developed to investigate the linear stability of three-dimensional compressible boundary layer flows over bodies of revolutions. The problem is formulated as a two dimensional (2D) eigenvalue problem incorporating the meanflow variations in the normal and azimuthal directions. Normal mode solutions are sought in the whole plane rather than in a line normal to the wall as is done in the classical one dimensional (1D) stability theory. The stability characteristics of a supersonic boundary layer over a sharp cone with 50 half-angle at 2 degrees angle of attack is investigated. The 1D eigenvalue computations showed that the most amplified disturbances occur around x(sub 2) = 90 degrees and the azimuthal mode number for the most amplified disturbances range between m = -30 to -40. The frequencies of the most amplified waves are smaller in the middle region where the crossflow dominates the instability than the most amplified frequencies near the windward and leeward planes. The 2D eigenvalue computations showed that due to the variations in the azimuthal direction, the eigenmodes are clustered into isolated confined regions. For some eigenvalues, the eigenfunctions are clustered in two regions. Due to the nonparallel effect in the azimuthal direction, the eigenmodes are clustered into isolated confined regions. For some eigenvalues, the eigenfunctions are clustered in two regions. Due to the nonparallel effect in the azimuthal direction, the most amplified disturbances are shifted to 120 degrees compared to 90 degrees for the parallel theory. It is also observed that the nonparallel amplification rates are smaller than that is obtained from the parallel theory.

High-dimensional cluster analysis with the Masked EM Algorithm

PubMed Central

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

2014-01-01

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

Application of 2D and 3D image technologies to characterise morphological attributes of grapevine clusters.

PubMed

Tello, Javier; Cubero, Sergio; Blasco, José; Tardaguila, Javier; Aleixos, Nuria; Ibáñez, Javier

2016-10-01

Grapevine cluster morphology influences the quality and commercial value of wine and table grapes. It is routinely evaluated by subjective and inaccurate methods that do not meet the requirements set by the food industry. Novel two-dimensional (2D) and three-dimensional (3D) machine vision technologies emerge as promising tools for its automatic and fast evaluation. The automatic evaluation of cluster length, width and elongation was successfully achieved by the analysis of 2D images, significant and strong correlations with the manual methods being found (r = 0.959, 0.861 and 0.852, respectively). The classification of clusters according to their shape can be achieved by evaluating their conicity in different sections of the cluster. The geometric reconstruction of the morphological volume of the cluster from 2D features worked better than the direct 3D laser scanning system, showing a high correlation (r = 0.956) with the manual approach (water displacement method). In addition, we constructed and validated a simple linear regression model for cluster compactness estimation. It showed a high predictive capacity for both the training and validation subsets of clusters (R(2)  = 84.5 and 71.1%, respectively). The methodologies proposed in this work provide continuous and accurate data for the fast and objective characterisation of cluster morphology. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

Discriminative validity of the MacAndrew Alcoholism Scale with Cluster B personality disorders.

PubMed

Smith, S R; Hilsenroth, M J

2001-06-01

This study was designed to assess the ability of the Minnesota Multiphasic Personality Inventory (MMPI-2) MacAndrew Alcoholism Scale (MAC-R) to differentiate between outpatients with personality disorders with Substance-Related Disorders (SRDs) and without SRDs. MMPI-2 validity, clinical, and MAC-R scale scores were compared in an SRD Cluster B group (comprised of Narcissistic, Antisocial, Borderline, and Histrionic; n = 15), a non-SRD Cluster B group (n = 33), and a non-SRD group with personality disorders from Clusters A and C (n = 18). Results revealed that the substance-abusing Cluster B group scored significantly higher on the MAC-R ( p <.0001) as well as the Psychopathic Deviate scale ( p <.01). Dimensional analyses illustrated that MAC-R scores were related to the presence of an SRD diagnosis (rpb =.70, p <.0001) and diagnostic criteria for Antisocial Personality Disorder (r =.60, p <.0001). Stepwise regression revealed that (in order of magnitude) the presence of a substance-abuse diagnosis followed by diagnostic criteria for Antisocial and Histrionic Personality Disorders were most related to MAC-R scores (R =.78, R(2) =.60). This indicates that the MAC-R may be more related to the presence of an SRD than has been suggested, and when used in outpatient settings as MacAndrew (1965) intended, the MAC-R may be useful as a screening device for assessing SRD among outpatients with Axis II psychopathology.

Spreading of correlations in the Falicov-Kimball model

NASA Astrophysics Data System (ADS)

Herrmann, Andreas J.; Antipov, Andrey E.; Werner, Philipp

2018-04-01

We study dynamical properties of the one- and two-dimensional Falicov-Kimball model using lattice Monte Carlo simulations. In particular, we calculate the spreading of charge correlations in the equilibrium model and after an interaction quench. The results show a reduction of the light-cone velocity with interaction strength at low temperature, while the phase velocity increases. At higher temperature, the initial spreading is determined by the Fermi velocity of the noninteracting system and the maximum range of the correlations decreases with increasing interaction strength. Charge order correlations in the disorder potential enhance the range of the correlations. We also use the numerically exact lattice Monte Carlo results to benchmark the accuracy of equilibrium and nonequilibrium dynamical cluster approximation calculations. It is shown that the bias introduced by the mapping to a periodized cluster is substantial, and that from a numerical point of view, it is more efficient to simulate the lattice model directly.

On the Partitioning of Squared Euclidean Distance and Its Applications in Cluster Analysis.

ERIC Educational Resources Information Center

Carter, Randy L.; And Others

1989-01-01

The partitioning of squared Euclidean--E(sup 2)--distance between two vectors in M-dimensional space into the sum of squared lengths of vectors in mutually orthogonal subspaces is discussed. Applications to specific cluster analysis problems are provided (i.e., to design Monte Carlo studies for performance comparisons of several clustering methods…

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

PubMed

Wang, Haizhou; Song, Mingzhou

2011-12-01

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

Interpretable dimensionality reduction of single cell transcriptome data with deep generative models.

PubMed

Ding, Jiarui; Condon, Anne; Shah, Sohrab P

2018-05-21

Single-cell RNA-sequencing has great potential to discover cell types, identify cell states, trace development lineages, and reconstruct the spatial organization of cells. However, dimension reduction to interpret structure in single-cell sequencing data remains a challenge. Existing algorithms are either not able to uncover the clustering structures in the data or lose global information such as groups of clusters that are close to each other. We present a robust statistical model, scvis, to capture and visualize the low-dimensional structures in single-cell gene expression data. Simulation results demonstrate that low-dimensional representations learned by scvis preserve both the local and global neighbor structures in the data. In addition, scvis is robust to the number of data points and learns a probabilistic parametric mapping function to add new data points to an existing embedding. We then use scvis to analyze four single-cell RNA-sequencing datasets, exemplifying interpretable two-dimensional representations of the high-dimensional single-cell RNA-sequencing data.

AMOEBA clustering revisited. [cluster analysis, classification, and image display program

NASA Technical Reports Server (NTRS)

Bryant, Jack

1990-01-01

A description of the clustering, classification, and image display program AMOEBA is presented. Using a difficult high resolution aircraft-acquired MSS image, the steps the program takes in forming clusters are traced. A number of new features are described here for the first time. Usage of the program is discussed. The theoretical foundation (the underlying mathematical model) is briefly presented. The program can handle images of any size and dimensionality.

West Virginia US Department of Energy experimental program to stimulate competitive research. Section 2: Human resource development; Section 3: Carbon-based structural materials research cluster; Section 3: Data parallel algorithms for scientific computing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1994-02-02

This report consists of three separate but related reports. They are (1) Human Resource Development, (2) Carbon-based Structural Materials Research Cluster, and (3) Data Parallel Algorithms for Scientific Computing. To meet the objectives of the Human Resource Development plan, the plan includes K--12 enrichment activities, undergraduate research opportunities for students at the state`s two Historically Black Colleges and Universities, graduate research through cluster assistantships and through a traineeship program targeted specifically to minorities, women and the disabled, and faculty development through participation in research clusters. One research cluster is the chemistry and physics of carbon-based materials. The objective of thismore » cluster is to develop a self-sustaining group of researchers in carbon-based materials research within the institutions of higher education in the state of West Virginia. The projects will involve analysis of cokes, graphites and other carbons in order to understand the properties that provide desirable structural characteristics including resistance to oxidation, levels of anisotropy and structural characteristics of the carbons themselves. In the proposed cluster on parallel algorithms, research by four WVU faculty and three state liberal arts college faculty are: (1) modeling of self-organized critical systems by cellular automata; (2) multiprefix algorithms and fat-free embeddings; (3) offline and online partitioning of data computation; and (4) manipulating and rendering three dimensional objects. This cluster furthers the state Experimental Program to Stimulate Competitive Research plan by building on existing strengths at WVU in parallel algorithms.« less

The MUSIC of CLASH: Predictions on the Concentration-Mass Relation

NASA Astrophysics Data System (ADS)

Meneghetti, M.; Rasia, E.; Vega, J.; Merten, J.; Postman, M.; Yepes, G.; Sembolini, F.; Donahue, M.; Ettori, S.; Umetsu, K.; Balestra, I.; Bartelmann, M.; Benítez, N.; Biviano, A.; Bouwens, R.; Bradley, L.; Broadhurst, T.; Coe, D.; Czakon, N.; De Petris, M.; Ford, H.; Giocoli, C.; Gottlöber, S.; Grillo, C.; Infante, L.; Jouvel, S.; Kelson, D.; Koekemoer, A.; Lahav, O.; Lemze, D.; Medezinski, E.; Melchior, P.; Mercurio, A.; Molino, A.; Moscardini, L.; Monna, A.; Moustakas, J.; Moustakas, L. A.; Nonino, M.; Rhodes, J.; Rosati, P.; Sayers, J.; Seitz, S.; Zheng, W.; Zitrin, A.

2014-12-01

We present an analysis of the MUSIC-2 N-body/hydrodynamical simulations aimed at estimating the expected concentration-mass relation for the CLASH (Cluster Lensing and Supernova Survey with Hubble) cluster sample. We study nearly 1,400 halos simulated at high spatial and mass resolution. We study the shape of both their density and surface-density profiles and fit them with a variety of radial functions, including the Navarro-Frenk-White (NFW), the generalized NFW, and the Einasto density profiles. We derive concentrations and masses from these fits. We produce simulated Chandra observations of the halos, and we use them to identify objects resembling the X-ray morphologies and masses of the clusters in the CLASH X-ray-selected sample. We also derive a concentration-mass relation for strong-lensing clusters. We find that the sample of simulated halos that resembles the X-ray morphology of the CLASH clusters is composed mainly of relaxed halos, but it also contains a significant fraction of unrelaxed systems. For such a heterogeneous sample we measure an average two-dimensional concentration that is ~11% higher than is found for the full sample of simulated halos. After accounting for projection and selection effects, the average NFW concentrations of CLASH clusters are expected to be intermediate between those predicted in three dimensions for relaxed and super-relaxed halos. Matching the simulations to the individual CLASH clusters on the basis of the X-ray morphology, we expect that the NFW concentrations recovered from the lensing analysis of the CLASH clusters are in the range [3-6], with an average value of 3.87 and a standard deviation of 0.61.

The music of clash: predictions on the concentration-mass relation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Meneghetti, M.; Rasia, E.; Vega, J.

We present an analysis of the MUSIC-2 N-body/hydrodynamical simulations aimed at estimating the expected concentration-mass relation for the CLASH (Cluster Lensing and Supernova Survey with Hubble) cluster sample. We study nearly 1,400 halos simulated at high spatial and mass resolution. We study the shape of both their density and surface-density profiles and fit them with a variety of radial functions, including the Navarro-Frenk-White (NFW), the generalized NFW, and the Einasto density profiles. We derive concentrations and masses from these fits. We produce simulated Chandra observations of the halos, and we use them to identify objects resembling the X-ray morphologies andmore » masses of the clusters in the CLASH X-ray-selected sample. We also derive a concentration-mass relation for strong-lensing clusters. We find that the sample of simulated halos that resembles the X-ray morphology of the CLASH clusters is composed mainly of relaxed halos, but it also contains a significant fraction of unrelaxed systems. For such a heterogeneous sample we measure an average two-dimensional concentration that is ∼11% higher than is found for the full sample of simulated halos. After accounting for projection and selection effects, the average NFW concentrations of CLASH clusters are expected to be intermediate between those predicted in three dimensions for relaxed and super-relaxed halos. Matching the simulations to the individual CLASH clusters on the basis of the X-ray morphology, we expect that the NFW concentrations recovered from the lensing analysis of the CLASH clusters are in the range [3-6], with an average value of 3.87 and a standard deviation of 0.61.« less

Cluster self-organization of TR-containing germanate systems: Suprapolyhedral precursors and self-assembly of the crystal structures of the LiNdGeO{sub 4} and CeGeO{sub 4} compounds

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ilyushin, G. D., E-mail: ilyushin@nc.cryst.ras.ru; Dem'yanets, L. N.

2007-07-15

A combinatorial-topological analysis of the orthogermanates LiNdGeO{sub 4} (space group Pbcn) and CeGeO{sub 4} (space group I 4{sub 1}/a, the scheelite structure type), which have MT frameworks composed of polyhedral structural units in the form of M dodecahedra (NdO{sub 8} and CeO{sub 8}) and T tetrahedra (GeO{sub 4}), is performed using the method of coordination sequences with the TOPOS program package. It is established that the structures of both orthogermanates are characterized by equivalent crystal-forming nets 4444. The cluster precursors of the M{sub 2}T{sub 2} cyclic type are identified by the method of two-color decomposition. The local symmetry of four-polyhedralmore » clusters corresponds to the point group 2. In the precursor of the LiNdGeO{sub 4} orthogermanate, the Li atom is located above the M{sub 2}T{sub 2} ring. The number of Li-O bonds in this precursor is 4. The cluster precursors M{sub 2}T{sub 2} and LiM{sub 2}T{sub 2} are responsible for the formation of crystal-forming clusters of a higher level according to the mechanism of matrix self-assembly. The coordination numbers of the cluster precursors in two-dimensional nets for these structures are found to be equal to 4. The equivalent bilayer TR,Ge stacks that consist of eight cluster precursors are revealed in the structures under investigation. It is demonstrated that there exist three types of translational interlayer arrangements of cluster precursors upon the formation of macrostructures of the orthogermanates.« less

Greedy subspace clustering.

DOT National Transportation Integrated Search

2016-09-01

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

Model-based Clustering of High-Dimensional Data in Astrophysics

NASA Astrophysics Data System (ADS)

Bouveyron, C.

2016-05-01

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

Cluster Analysis and Gaussian Mixture Estimation of Correlated Time-Series by Means of Multi-dimensional Scaling

NASA Astrophysics Data System (ADS)

Ibuki, Takero; Suzuki, Sei; Inoue, Jun-ichi

We investigate cross-correlations between typical Japanese stocks collected through Yahoo!Japan website ( http://finance.yahoo.co.jp/ ). By making use of multi-dimensional scaling (MDS) for the cross-correlation matrices, we draw two-dimensional scattered plots in which each point corresponds to each stock. To make a clustering for these data plots, we utilize the mixture of Gaussians to fit the data set to several Gaussian densities. By minimizing the so-called Akaike Information Criterion (AIC) with respect to parameters in the mixture, we attempt to specify the best possible mixture of Gaussians. It might be naturally assumed that all the two-dimensional data points of stocks shrink into a single small region when some economic crisis takes place. The justification of this assumption is numerically checked for the empirical Japanese stock data, for instance, those around 11 March 2011.

Self-organizing neural networks--an alternative way of cluster analysis in clinical chemistry.

PubMed

Reibnegger, G; Wachter, H

1996-04-15

Supervised learning schemes have been employed by several workers for training neural networks designed to solve clinical problems. We demonstrate that unsupervised techniques can also produce interesting and meaningful results. Using a data set on the chemical composition of milk from 22 different mammals, we demonstrate that self-organizing feature maps (Kohonen networks) as well as a modified version of error backpropagation technique yield results mimicking conventional cluster analysis. Both techniques are able to project a potentially multi-dimensional input vector onto a two-dimensional space whereby neighborhood relationships remain conserved. Thus, these techniques can be used for reducing dimensionality of complicated data sets and for enhancing comprehensibility of features hidden in the data matrix.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, Benjamin; Tan, Jonathan C.; Christie, Duncan

We study giant molecular cloud (GMC) collisions and their ability to trigger star cluster formation. We further develop our three-dimensional magnetized, turbulent, colliding GMC simulations by implementing star formation subgrid models. Two such models are explored: (1) “Density-Regulated,” i.e., fixed efficiency per free-fall time above a set density threshold and (2) “Magnetically Regulated,” i.e., fixed efficiency per free-fall time in regions that are magnetically supercritical. Variations of parameters associated with these models are also explored. In the non-colliding simulations, the overall level of star formation is sensitive to model parameter choices that relate to effective density thresholds. In the GMCmore » collision simulations, the final star formation rates and efficiencies are relatively independent of these parameters. Between the non-colliding and colliding cases, we compare the morphologies of the resulting star clusters, properties of star-forming gas, time evolution of the star formation rate (SFR), spatial clustering of the stars, and resulting kinematics of the stars in comparison to the natal gas. We find that typical collisions, by creating larger amounts of dense gas, trigger earlier and enhanced star formation, resulting in 10 times higher SFRs and efficiencies. The star clusters formed from GMC collisions show greater spatial substructure and more disturbed kinematics.« less

PCA based clustering for brain tumor segmentation of T1w MRI images.

PubMed

Kaya, Irem Ersöz; Pehlivanlı, Ayça Çakmak; Sekizkardeş, Emine Gezmez; Ibrikci, Turgay

2017-03-01

Medical images are huge collections of information that are difficult to store and process consuming extensive computing time. Therefore, the reduction techniques are commonly used as a data pre-processing step to make the image data less complex so that a high-dimensional data can be identified by an appropriate low-dimensional representation. PCA is one of the most popular multivariate methods for data reduction. This paper is focused on T1-weighted MRI images clustering for brain tumor segmentation with dimension reduction by different common Principle Component Analysis (PCA) algorithms. Our primary aim is to present a comparison between different variations of PCA algorithms on MRIs for two cluster methods. Five most common PCA algorithms; namely the conventional PCA, Probabilistic Principal Component Analysis (PPCA), Expectation Maximization Based Principal Component Analysis (EM-PCA), Generalize Hebbian Algorithm (GHA), and Adaptive Principal Component Extraction (APEX) were applied to reduce dimensionality in advance of two clustering algorithms, K-Means and Fuzzy C-Means. In the study, the T1-weighted MRI images of the human brain with brain tumor were used for clustering. In addition to the original size of 512 lines and 512 pixels per line, three more different sizes, 256 × 256, 128 × 128 and 64 × 64, were included in the study to examine their effect on the methods. The obtained results were compared in terms of both the reconstruction errors and the Euclidean distance errors among the clustered images containing the same number of principle components. According to the findings, the PPCA obtained the best results among all others. Furthermore, the EM-PCA and the PPCA assisted K-Means algorithm to accomplish the best clustering performance in the majority as well as achieving significant results with both clustering algorithms for all size of T1w MRI images. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Measurement of entanglement entropy in the two-dimensional Potts model using wavelet analysis.

PubMed

Tomita, Yusuke

2018-05-01

A method is introduced to measure the entanglement entropy using a wavelet analysis. Using this method, the two-dimensional Haar wavelet transform of a configuration of Fortuin-Kasteleyn (FK) clusters is performed. The configuration represents a direct snapshot of spin-spin correlations since spin degrees of freedom are traced out in FK representation. A snapshot of FK clusters loses image information at each coarse-graining process by the wavelet transform. It is shown that the loss of image information measures the entanglement entropy in the Potts model.

Self-assembled three-dimensional chiral colloidal architecture

NASA Astrophysics Data System (ADS)

Ben Zion, Matan Yah; He, Xiaojin; Maass, Corinna C.; Sha, Ruojie; Seeman, Nadrian C.; Chaikin, Paul M.

2017-11-01

Although stereochemistry has been a central focus of the molecular sciences since Pasteur, its province has previously been restricted to the nanometric scale. We have programmed the self-assembly of micron-sized colloidal clusters with structural information stemming from a nanometric arrangement. This was done by combining DNA nanotechnology with colloidal science. Using the functional flexibility of DNA origami in conjunction with the structural rigidity of colloidal particles, we demonstrate the parallel self-assembly of three-dimensional microconstructs, evincing highly specific geometry that includes control over position, dihedral angles, and cluster chirality.

Correlation buildup during recrystallization in three-dimensional dusty plasma clusters

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schella, André; Mulsow, Matthias; Melzer, André

2014-05-15

The recrystallization process of finite three-dimensional dust clouds after laser heating is studied experimentally. The time-dependent Coulomb coupling parameter is presented, showing that the recrystallization starts with an exponential cooling phase where cooling is slower than damping by the neutral gas friction. At later times, the coupling parameter oscillates into equilibrium. It is found that a large fraction of cluster states after recrystallization experiments is in metastable states. The temporal evolution of the correlation buildup shows that correlation occurs on even slower time scale than cooling.

Unsupervised Learning and Pattern Recognition of Biological Data Structures with Density Functional Theory and Machine Learning.

PubMed

Chen, Chien-Chang; Juan, Hung-Hui; Tsai, Meng-Yuan; Lu, Henry Horng-Shing

2018-01-11

By introducing the methods of machine learning into the density functional theory, we made a detour for the construction of the most probable density function, which can be estimated by learning relevant features from the system of interest. Using the properties of universal functional, the vital core of density functional theory, the most probable cluster numbers and the corresponding cluster boundaries in a studying system can be simultaneously and automatically determined and the plausibility is erected on the Hohenberg-Kohn theorems. For the method validation and pragmatic applications, interdisciplinary problems from physical to biological systems were enumerated. The amalgamation of uncharged atomic clusters validated the unsupervised searching process of the cluster numbers and the corresponding cluster boundaries were exhibited likewise. High accurate clustering results of the Fisher's iris dataset showed the feasibility and the flexibility of the proposed scheme. Brain tumor detections from low-dimensional magnetic resonance imaging datasets and segmentations of high-dimensional neural network imageries in the Brainbow system were also used to inspect the method practicality. The experimental results exhibit the successful connection between the physical theory and the machine learning methods and will benefit the clinical diagnoses.

One dimensional motion of interstitial clusters and void growth in Ni and Ni alloys

NASA Astrophysics Data System (ADS)

Yoshiie, T.; Ishizaki, T.; Xu, Q.; Satoh, Y.; Kiritani, M.

2002-12-01

One dimensional (1-D) motion of interstitial clusters is important for the microstructural evolution in metals. In this paper, the effect of 2 at.% alloying with elements Si (volume size factor to Ni: -5.81%), Cu (7.18%), Ge (14.76%) and Sn (74.08%) in Ni on 1-D motion of interstitial clusters and void growth was studied. In neutron irradiated pure Ni, Ni-Cu and Ni-Ge, well developed dislocation networks and voids in the matrix, and no defects near grain boundaries were observed at 573 K to a dose of 0.4 dpa by transmission electron microscopy. No voids were formed and only interstitial type dislocation loops were observed near grain boundaries in Ni-Si and Ni-Sn. The reaction kinetics analysis which included the point defect flow into planar sink revealed the existence of 1-D motion of interstitial clusters in Ni, Ni-Cu and Ni-Ge, and lack of such motion in Ni-Si and Ni-Sn. In Ni-Sn and Ni-Si, the alloying elements will trap interstitial clusters and thereby reduce the cluster mobility, which lead to the reduction in void growth.

Slider thickness promotes lubricity: from 2D islands to 3D clusters

NASA Astrophysics Data System (ADS)

Guerra, Roberto; Tosatti, Erio; Vanossi, Andrea

2016-05-01

The sliding of three-dimensional clusters and two-dimensional islands adsorbed on crystal surfaces represents an important test case to understand friction. Even for the same material, monoatomic islands and thick clusters will not as a rule exhibit the same friction, but specific differences have not been explored. Through realistic molecular dynamics simulations of the static friction of gold on graphite, an experimentally relevant system, we uncover as a function of gold thickness a progressive drop of static friction from monolayer islands, that are easily pinned, towards clusters, that slide more readily. The main ingredient contributing to this thickness-induced lubricity appears to be the increased effective rigidity of the atomic contact, acting to reduce the cluster interdigitation with the substrate. A second element which plays a role is the lateral contact size, which can accommodate the solitons typical of the incommensurate interface only above a critical contact diameter, which is larger for monolayer islands than for thick clusters. The two effects concur to make clusters more lubric than islands, and large sizes more lubric than smaller ones. These conclusions are expected to be of broader applicability in diverse nanotribological systems, where the role played by static, and dynamic, friction is generally quite important.

Slider thickness promotes lubricity: from 2D islands to 3D clusters.

PubMed

Guerra, Roberto; Tosatti, Erio; Vanossi, Andrea

2016-06-07

The sliding of three-dimensional clusters and two-dimensional islands adsorbed on crystal surfaces represents an important test case to understand friction. Even for the same material, monoatomic islands and thick clusters will not as a rule exhibit the same friction, but specific differences have not been explored. Through realistic molecular dynamics simulations of the static friction of gold on graphite, an experimentally relevant system, we uncover as a function of gold thickness a progressive drop of static friction from monolayer islands, that are easily pinned, towards clusters, that slide more readily. The main ingredient contributing to this thickness-induced lubricity appears to be the increased effective rigidity of the atomic contact, acting to reduce the cluster interdigitation with the substrate. A second element which plays a role is the lateral contact size, which can accommodate the solitons typical of the incommensurate interface only above a critical contact diameter, which is larger for monolayer islands than for thick clusters. The two effects concur to make clusters more lubric than islands, and large sizes more lubric than smaller ones. These conclusions are expected to be of broader applicability in diverse nanotribological systems, where the role played by static, and dynamic, friction is generally quite important.

Identification of crystalline structures in jet-cooled acetylene large clusters studied by two-dimensional correlation infrared spectroscopy

NASA Astrophysics Data System (ADS)

Matsumoto, Yoshiteru; Yoshiura, Ryuto; Honma, Kenji

2017-07-01

We investigated the crystalline structures of jet-cooled acetylene (C2H2) large clusters by laser spectroscopy and chemometrics. The CH stretching vibrations of the C2H2 large clusters were observed by infrared (IR) cavity ringdown spectroscopy. The IR spectra of C2H2 clusters were measured under the conditions of various concentrations of C2H2/He mixture gas for supersonic jets. Upon increasing the gas concentration from 1% to 10%, we observed a rapid intensity enhancement for a band in the IR spectra. The strong dependence of the intensity on the gas concentration indicates that the band was assigned to CH stretching vibrations of the large clusters. An analysis of the IR spectra by two-dimensional correlation spectroscopy revealed that the IR absorption due to the C2H2 large cluster is decomposed into two CH stretching vibrations. The vibrational frequencies of the two bands are almost equivalent to the IR absorption of the pure- and poly-crystalline orthorhombic structures in the aerosol particles. The characteristic temperature behavior of the IR spectra implies the existence of the other large cluster, which is discussed in terms of the phase transition of a bulk crystal.

Manifold Learning in MR spectroscopy using nonlinear dimensionality reduction and unsupervised clustering.

PubMed

Yang, Guang; Raschke, Felix; Barrick, Thomas R; Howe, Franklyn A

2015-09-01

To investigate whether nonlinear dimensionality reduction improves unsupervised classification of (1) H MRS brain tumor data compared with a linear method. In vivo single-voxel (1) H magnetic resonance spectroscopy (55 patients) and (1) H magnetic resonance spectroscopy imaging (MRSI) (29 patients) data were acquired from histopathologically diagnosed gliomas. Data reduction using Laplacian eigenmaps (LE) or independent component analysis (ICA) was followed by k-means clustering or agglomerative hierarchical clustering (AHC) for unsupervised learning to assess tumor grade and for tissue type segmentation of MRSI data. An accuracy of 93% in classification of glioma grade II and grade IV, with 100% accuracy in distinguishing tumor and normal spectra, was obtained by LE with unsupervised clustering, but not with the combination of k-means and ICA. With (1) H MRSI data, LE provided a more linear distribution of data for cluster analysis and better cluster stability than ICA. LE combined with k-means or AHC provided 91% accuracy for classifying tumor grade and 100% accuracy for identifying normal tissue voxels. Color-coded visualization of normal brain, tumor core, and infiltration regions was achieved with LE combined with AHC. The LE method is promising for unsupervised clustering to separate brain and tumor tissue with automated color-coding for visualization of (1) H MRSI data after cluster analysis. © 2014 Wiley Periodicals, Inc.

SAIL: Summation-bAsed Incremental Learning for Information-Theoretic Text Clustering.

PubMed

Cao, Jie; Wu, Zhiang; Wu, Junjie; Xiong, Hui

2013-04-01

Information-theoretic clustering aims to exploit information-theoretic measures as the clustering criteria. A common practice on this topic is the so-called Info-Kmeans, which performs K-means clustering with KL-divergence as the proximity function. While expert efforts on Info-Kmeans have shown promising results, a remaining challenge is to deal with high-dimensional sparse data such as text corpora. Indeed, it is possible that the centroids contain many zero-value features for high-dimensional text vectors, which leads to infinite KL-divergence values and creates a dilemma in assigning objects to centroids during the iteration process of Info-Kmeans. To meet this challenge, in this paper, we propose a Summation-bAsed Incremental Learning (SAIL) algorithm for Info-Kmeans clustering. Specifically, by using an equivalent objective function, SAIL replaces the computation of KL-divergence by the incremental computation of Shannon entropy. This can avoid the zero-feature dilemma caused by the use of KL-divergence. To improve the clustering quality, we further introduce the variable neighborhood search scheme and propose the V-SAIL algorithm, which is then accelerated by a multithreaded scheme in PV-SAIL. Our experimental results on various real-world text collections have shown that, with SAIL as a booster, the clustering performance of Info-Kmeans can be significantly improved. Also, V-SAIL and PV-SAIL indeed help improve the clustering quality at a lower cost of computation.

Discriminative clustering on manifold for adaptive transductive classification.

PubMed

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

2017-10-01

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

Classification of holter registers by dynamic clustering using multi-dimensional particle swarm optimization.

PubMed

Kiranyaz, Serkan; Ince, Turker; Pulkkinen, Jenni; Gabbouj, Moncef

2010-01-01

In this paper, we address dynamic clustering in high dimensional data or feature spaces as an optimization problem where multi-dimensional particle swarm optimization (MD PSO) is used to find out the true number of clusters, while fractional global best formation (FGBF) is applied to avoid local optima. Based on these techniques we then present a novel and personalized long-term ECG classification system, which addresses the problem of labeling the beats within a long-term ECG signal, known as Holter register, recorded from an individual patient. Due to the massive amount of ECG beats in a Holter register, visual inspection is quite difficult and cumbersome, if not impossible. Therefore the proposed system helps professionals to quickly and accurately diagnose any latent heart disease by examining only the representative beats (the so called master key-beats) each of which is representing a cluster of homogeneous (similar) beats. We tested the system on a benchmark database where the beats of each Holter register have been manually labeled by cardiologists. The selection of the right master key-beats is the key factor for achieving a highly accurate classification and the proposed systematic approach produced results that were consistent with the manual labels with 99.5% average accuracy, which basically shows the efficiency of the system.

Mining High-Dimensional Data

NASA Astrophysics Data System (ADS)

Wang, Wei; Yang, Jiong

With the rapid growth of computational biology and e-commerce applications, high-dimensional data becomes very common. Thus, mining high-dimensional data is an urgent problem of great practical importance. However, there are some unique challenges for mining data of high dimensions, including (1) the curse of dimensionality and more crucial (2) the meaningfulness of the similarity measure in the high dimension space. In this chapter, we present several state-of-art techniques for analyzing high-dimensional data, e.g., frequent pattern mining, clustering, and classification. We will discuss how these methods deal with the challenges of high dimensionality.

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

PubMed

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

2015-10-01

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

Applications of conformal field theory to problems in 2D percolation

NASA Astrophysics Data System (ADS)

Simmons, Jacob Joseph Harris

This thesis explores critical two-dimensional percolation in bounded regions in the continuum limit. The main method which we employ is conformal field theory (CFT). Our specific results follow from the null-vector structure of the c = 0 CFT that applies to critical two-dimensional percolation. We also make use of the duality symmetry obeyed at the percolation point, and the fact that percolation may be understood as the q-state Potts model in the limit q → 1. Our first results describe the correlations between points in the bulk and boundary intervals or points, i.e. the probability that the various points or intervals are in the same percolation cluster. These quantities correspond to order-parameter profiles under the given conditions, or cluster connection probabilities. We consider two specific cases: an anchoring interval, and two anchoring points. We derive results for these and related geometries using the CFT null-vectors for the corresponding boundary condition changing (bcc) operators. In addition, we exhibit several exact relationships between these probabilities. These relations between the various bulk-boundary connection probabilities involve parameters of the CFT called operator product expansion (OPE) coefficients. We then compute several of these OPE coefficients, including those arising in our new probability relations. Beginning with the familiar CFT operator φ1,2, which corresponds to a free-fixed spin boundary change in the q-state Potts model, we then develop physical interpretations of the bcc operators. We argue that, when properly normalized, higher-order bcc operators correspond to successive fusions of multiple φ1,2, operators. Finally, by identifying the derivative of φ1,2 with the operator φ1,4, we derive several new quantities called first crossing densities. These new results are then combined and integrated to obtain the three previously known crossing quantities in a rectangle: the probability of a horizontal crossing cluster, the probability of a cluster crossing both horizontally and vertically, and the expected number of horizontal crossing clusters. These three results were known to be solutions to a certain fifth-order differential equation, but until now no physically meaningful explanation had appeared. This differential equation arises naturally in our derivation.

Reconstruction of the mass distribution of galaxy clusters from the inversion of the thermal Sunyaev-Zel'dovich effect

NASA Astrophysics Data System (ADS)

Majer, C. L.; Meyer, S.; Konrad, S.; Sarli, E.; Bartelmann, M.

2016-07-01

This paper continues a series in which we intend to show how all observables of galaxy clusters can be combined to recover the two-dimensional, projected gravitational potential of individual clusters. Our goal is to develop a non-parametric algorithm for joint cluster reconstruction taking all cluster observables into account. For this reason we focus on the line-of-sight projected gravitational potential, proportional to the lensing potential, in order to extend existing reconstruction algorithms. In this paper, we begin with the relation between the Compton-y parameter and the Newtonian gravitational potential, assuming hydrostatic equilibrium and a polytropic stratification of the intracluster gas. Extending our first publication we now consider a spheroidal rather than a spherical cluster symmetry. We show how a Richardson-Lucy deconvolution can be used to convert the intensity change of the CMB due to the thermal Sunyaev-Zel'dovich effect into an estimate for the two-dimensional gravitational potential. We apply our reconstruction method to a cluster based on an N-body/hydrodynamical simulation processed with the characteristics (resolution and noise) of the ALMA interferometer for which we achieve a relative error of ≲20 per cent for a large fraction of the virial radius. We further apply our method to an observation of the galaxy cluster RXJ1347 for which we can reconstruct the potential with a relative error of ≲20 per cent for the observable cluster range.

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

PubMed

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

2011-10-28

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

CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets

PubMed Central

Nowicka, Malgorzata; Krieg, Carsten; Weber, Lukas M.; Hartmann, Felix J.; Guglietta, Silvia; Becher, Burkhard; Levesque, Mitchell P.; Robinson, Mark D.

2017-01-01

High dimensional mass and flow cytometry (HDCyto) experiments have become a method of choice for high throughput interrogation and characterization of cell populations.Here, we present an R-based pipeline for differential analyses of HDCyto data, largely based on Bioconductor packages. We computationally define cell populations using FlowSOM clustering, and facilitate an optional but reproducible strategy for manual merging of algorithm-generated clusters. Our workflow offers different analysis paths, including association of cell type abundance with a phenotype or changes in signaling markers within specific subpopulations, or differential analyses of aggregated signals. Importantly, the differential analyses we show are based on regression frameworks where the HDCyto data is the response; thus, we are able to model arbitrary experimental designs, such as those with batch effects, paired designs and so on. In particular, we apply generalized linear mixed models to analyses of cell population abundance or cell-population-specific analyses of signaling markers, allowing overdispersion in cell count or aggregated signals across samples to be appropriately modeled. To support the formal statistical analyses, we encourage exploratory data analysis at every step, including quality control (e.g. multi-dimensional scaling plots), reporting of clustering results (dimensionality reduction, heatmaps with dendrograms) and differential analyses (e.g. plots of aggregated signals). PMID:28663787

Reconstruction of a digital core containing clay minerals based on a clustering algorithm.

PubMed

He, Yanlong; Pu, Chunsheng; Jing, Cheng; Gu, Xiaoyu; Chen, Qingdong; Liu, Hongzhi; Khan, Nasir; Dong, Qiaoling

2017-10-01

It is difficult to obtain a core sample and information for digital core reconstruction of mature sandstone reservoirs around the world, especially for an unconsolidated sandstone reservoir. Meanwhile, reconstruction and division of clay minerals play a vital role in the reconstruction of the digital cores, although the two-dimensional data-based reconstruction methods are specifically applicable as the microstructure reservoir simulation methods for the sandstone reservoir. However, reconstruction of clay minerals is still challenging from a research viewpoint for the better reconstruction of various clay minerals in the digital cores. In the present work, the content of clay minerals was considered on the basis of two-dimensional information about the reservoir. After application of the hybrid method, and compared with the model reconstructed by the process-based method, the digital core containing clay clusters without the labels of the clusters' number, size, and texture were the output. The statistics and geometry of the reconstruction model were similar to the reference model. In addition, the Hoshen-Kopelman algorithm was used to label various connected unclassified clay clusters in the initial model and then the number and size of clay clusters were recorded. At the same time, the K-means clustering algorithm was applied to divide the labeled, large connecting clusters into smaller clusters on the basis of difference in the clusters' characteristics. According to the clay minerals' characteristics, such as types, textures, and distributions, the digital core containing clay minerals was reconstructed by means of the clustering algorithm and the clay clusters' structure judgment. The distributions and textures of the clay minerals of the digital core were reasonable. The clustering algorithm improved the digital core reconstruction and provided an alternative method for the simulation of different clay minerals in the digital cores.

An unsupervised classification approach for analysis of Landsat data to monitor land reclamation in Belmont county, Ohio

NASA Technical Reports Server (NTRS)

Brumfield, J. O.; Bloemer, H. H. L.; Campbell, W. J.

1981-01-01

Two unsupervised classification procedures for analyzing Landsat data used to monitor land reclamation in a surface mining area in east central Ohio are compared for agreement with data collected from the corresponding locations on the ground. One procedure is based on a traditional unsupervised-clustering/maximum-likelihood algorithm sequence that assumes spectral groupings in the Landsat data in n-dimensional space; the other is based on a nontraditional unsupervised-clustering/canonical-transformation/clustering algorithm sequence that not only assumes spectral groupings in n-dimensional space but also includes an additional feature-extraction technique. It is found that the nontraditional procedure provides an appreciable improvement in spectral groupings and apparently increases the level of accuracy in the classification of land cover categories.

Finding SDSS Galaxy Clusters in 4-dimensional Color Space Using the False Discovery Rate

NASA Astrophysics Data System (ADS)

Nichol, R. C.; Miller, C. J.; Reichart, D.; Wasserman, L.; Genovese, C.; SDSS Collaboration

2000-12-01

We describe a recently developed statistical technique that provides a meaningful cut-off in probability-based decision making. We are concerned with multiple testing, where each test produces a well-defined probability (or p-value). By well-known, we mean that the null hypothesis used to determine the p-value is fully understood and appropriate. The method is entitled False Discovery Rate (FDR) and its largest advantage over other measures is that it allows one to specify a maximal amount of acceptable error. As an example of this tool, we apply FDR to a four-dimensional clustering algorithm using SDSS data. For each galaxy (or test galaxy), we count the number of neighbors that fit within one standard deviation of a four dimensional Gaussian centered on that test galaxy. The mean and standard deviation of that Gaussian are determined from the colors and errors of the test galaxy. We then take that same Gaussian and place it on a random selection of n galaxies and make a similar count. In the limit of large n, we expect the median count around these random galaxies to represent a typical field galaxy. For every test galaxy we determine the probability (or p-value) that it is a field galaxy based on these counts. A low p-value implies that the test galaxy is in a cluster environment. Once we have a p-value for every galaxy, we use FDR to determine at what level we should make our probability cut-off. Once this cut-off is made, we have a final sample of galaxies that are cluster-like galaxies. Using FDR, we also know the maximum amount of field contamination in our cluster galaxy sample. We present our preliminary galaxy clustering results using these methods.

Automated modal parameter estimation using correlation analysis and bootstrap sampling

NASA Astrophysics Data System (ADS)

Yaghoubi, Vahid; Vakilzadeh, Majid K.; Abrahamsson, Thomas J. S.

2018-02-01

The estimation of modal parameters from a set of noisy measured data is a highly judgmental task, with user expertise playing a significant role in distinguishing between estimated physical and noise modes of a test-piece. Various methods have been developed to automate this procedure. The common approach is to identify models with different orders and cluster similar modes together. However, most proposed methods based on this approach suffer from high-dimensional optimization problems in either the estimation or clustering step. To overcome this problem, this study presents an algorithm for autonomous modal parameter estimation in which the only required optimization is performed in a three-dimensional space. To this end, a subspace-based identification method is employed for the estimation and a non-iterative correlation-based method is used for the clustering. This clustering is at the heart of the paper. The keys to success are correlation metrics that are able to treat the problems of spatial eigenvector aliasing and nonunique eigenvectors of coalescent modes simultaneously. The algorithm commences by the identification of an excessively high-order model from frequency response function test data. The high number of modes of this model provides bases for two subspaces: one for likely physical modes of the tested system and one for its complement dubbed the subspace of noise modes. By employing the bootstrap resampling technique, several subsets are generated from the same basic dataset and for each of them a model is identified to form a set of models. Then, by correlation analysis with the two aforementioned subspaces, highly correlated modes of these models which appear repeatedly are clustered together and the noise modes are collected in a so-called Trashbox cluster. Stray noise modes attracted to the mode clusters are trimmed away in a second step by correlation analysis. The final step of the algorithm is a fuzzy c-means clustering procedure applied to a three-dimensional feature space to assign a degree of physicalness to each cluster. The proposed algorithm is applied to two case studies: one with synthetic data and one with real test data obtained from a hammer impact test. The results indicate that the algorithm successfully clusters similar modes and gives a reasonable quantification of the extent to which each cluster is physical.

A method of using cluster analysis to study statistical dependence in multivariate data

NASA Technical Reports Server (NTRS)

Borucki, W. J.; Card, D. H.; Lyle, G. C.

1975-01-01

A technique is presented that uses both cluster analysis and a Monte Carlo significance test of clusters to discover associations between variables in multidimensional data. The method is applied to an example of a noisy function in three-dimensional space, to a sample from a mixture of three bivariate normal distributions, and to the well-known Fisher's Iris data.

Order statistics applied to the most massive and most distant galaxy clusters

NASA Astrophysics Data System (ADS)

Waizmann, J.-C.; Ettori, S.; Bartelmann, M.

2013-06-01

In this work, we present an analytic framework for calculating the individual and joint distributions of the nth most massive or nth highest redshift galaxy cluster for a given survey characteristic allowing us to formulate Λ cold dark matter (ΛCDM) exclusion criteria. We show that the cumulative distribution functions steepen with increasing order, giving them a higher constraining power with respect to the extreme value statistics. Additionally, we find that the order statistics in mass (being dominated by clusters at lower redshifts) is sensitive to the matter density and the normalization of the matter fluctuations, whereas the order statistics in redshift is particularly sensitive to the geometric evolution of the Universe. For a fixed cosmology, both order statistics are efficient probes of the functional shape of the mass function at the high-mass end. To allow a quick assessment of both order statistics, we provide fits as a function of the survey area that allow percentile estimation with an accuracy better than 2 per cent. Furthermore, we discuss the joint distributions in the two-dimensional case and find that for the combination of the largest and the second largest observation, it is most likely to find them to be realized with similar values with a broadly peaked distribution. When combining the largest observation with higher orders, it is more likely to find a larger gap between the observations and when combining higher orders in general, the joint probability density function peaks more strongly. Having introduced the theory, we apply the order statistical analysis to the Southpole Telescope (SPT) massive cluster sample and metacatalogue of X-ray detected clusters of galaxies catalogue and find that the 10 most massive clusters in the sample are consistent with ΛCDM and the Tinker mass function. For the order statistics in redshift, we find a discrepancy between the data and the theoretical distributions, which could in principle indicate a deviation from the standard cosmology. However, we attribute this deviation to the uncertainty in the modelling of the SPT survey selection function. In turn, by assuming the ΛCDM reference cosmology, order statistics can also be utilized for consistency checks of the completeness of the observed sample and of the modelling of the survey selection function.

A nationwide hospital survey on patient safety culture in Belgian hospitals: setting priorities at the launch of a 5-year patient safety plan.

PubMed

Vlayen, Annemie; Hellings, Johan; Claes, Neree; Peleman, Hilde; Schrooten, Ward

2012-09-01

To measure patient safety culture in Belgian hospitals and to examine the homogeneous grouping of underlying safety culture dimensions. The Hospital Survey on Patient Safety Culture was distributed organisation-wide in 180 Belgian hospitals participating in the federal program on quality and safety between 2007 and 2009. Participating hospitals were invited to submit their data to a comparative database. Homogeneous groups of underlying safety culture dimensions were sought by hierarchical cluster analysis. 90 acute, 42 psychiatric and 11 long-term care hospitals submitted their data for comparison to other hospitals. The benchmark database included 55 225 completed questionnaires (53.7% response rate). Overall dimensional scores were low, although scores were found to be higher for psychiatric and long-term care hospitals than for acute hospitals. The overall perception of patient safety was lower in French-speaking hospitals. Hierarchical clustering of dimensions resulted in two distinct clusters. Cluster I grouped supervisor/manager expectations and actions promoting safety, organisational learning-continuous improvement, teamwork within units and communication openness, while Cluster II included feedback and communication about error, overall perceptions of patient safety, non-punitive response to error, frequency of events reported, teamwork across units, handoffs and transitions, staffing and management support for patient safety. The nationwide safety culture assessment confirms the need for a long-term national initiative to improve patient safety culture and provides each hospital with a baseline patient safety culture profile to direct an intervention plan. The identification of clusters of safety culture dimensions indicates the need for a different approach and context towards the implementation of interventions aimed at improving the safety culture. Certain clusters require unit level improvements, whereas others demand a hospital-wide policy.

Deeper Insights into the Circumgalactic Medium using Multivariate Analysis Methods

NASA Astrophysics Data System (ADS)

Lewis, James; Churchill, Christopher W.; Nielsen, Nikole M.; Kacprzak, Glenn

2017-01-01

Drawing from a database of galaxies whose surrounding gas has absorption from MgII, called the MgII-Absorbing Galaxy Catalog (MAGIICAT, Neilsen et al 2013), we studied the circumgalactic medium (CGM) for a sample of 47 galaxies. Using multivariate analysis, in particular the k-means clustering algorithm, we determined that simultaneously examining column density (N), rest-frame B-K color, virial mass, and azimuthal angle (the projected angle between the galaxy major axis and the quasar line of sight) yields two distinct populations: (1) bluer, lower mass galaxies with higher column density along the minor axis, and (2) redder, higher mass galaxies with lower column density along the major axis. We support this grouping by running (i) two-sample, two-dimensional Kolmogorov-Smirnov (KS) tests on each of the six bivariate planes and (ii) two-sample KS tests on each of the four variables to show that the galaxies significantly cluster into two independent populations. To account for the fact that 16 of our 47 galaxies have upper limits on N, we performed Monte-Carlo tests whereby we replaced upper limits with random deviates drawn from a Schechter distribution fit, f(N). These tests strengthen the results of the KS tests. We examined the behavior of the MgII λ2796 absorption line equivalent width and velocity width for each galaxy population. We find that equivalent width and velocity width do not show similar characteristic distinctions between the two galaxy populations. We discuss the k-means clustering algorithm for optimizing the analysis of populations within datasets as opposed to using arbitrary bivariate subsample cuts. We also discuss the power of the k-means clustering algorithm in extracting deeper physical insight into the CGM in relationship to host galaxies.

Analysis of 3D vortex motion in a dusty plasma

NASA Astrophysics Data System (ADS)

Mulsow, M.; Himpel, M.; Melzer, A.

2017-12-01

Dust clusters of about 50-1000 particles have been confined near the sheath region of a gaseous radio-frequency plasma discharge. These compact clusters exhibit a vortex motion which has been reconstructed in full three dimensions from stereoscopy. Smaller clusters are found to show a competition between solid-like cluster structure and vortex motion, whereas larger clusters feature very pronounced vortices. From the three-dimensional analysis, the dust flow field has been found to be nearly incompressible. The vortices in all observed clusters are essentially poloidal. The dependence of the vorticity on the cluster size is discussed. Finally, the vortex motion has been quantitatively attributed to radial gradients of the ion drag force.

Automated flow cytometric analysis across large numbers of samples and cell types.

PubMed

Chen, Xiaoyi; Hasan, Milena; Libri, Valentina; Urrutia, Alejandra; Beitz, Benoît; Rouilly, Vincent; Duffy, Darragh; Patin, Étienne; Chalmond, Bernard; Rogge, Lars; Quintana-Murci, Lluis; Albert, Matthew L; Schwikowski, Benno

2015-04-01

Multi-parametric flow cytometry is a key technology for characterization of immune cell phenotypes. However, robust high-dimensional post-analytic strategies for automated data analysis in large numbers of donors are still lacking. Here, we report a computational pipeline, called FlowGM, which minimizes operator input, is insensitive to compensation settings, and can be adapted to different analytic panels. A Gaussian Mixture Model (GMM)-based approach was utilized for initial clustering, with the number of clusters determined using Bayesian Information Criterion. Meta-clustering in a reference donor permitted automated identification of 24 cell types across four panels. Cluster labels were integrated into FCS files, thus permitting comparisons to manual gating. Cell numbers and coefficient of variation (CV) were similar between FlowGM and conventional gating for lymphocyte populations, but notably FlowGM provided improved discrimination of "hard-to-gate" monocyte and dendritic cell (DC) subsets. FlowGM thus provides rapid high-dimensional analysis of cell phenotypes and is amenable to cohort studies. Copyright © 2015. Published by Elsevier Inc.

Visualizing nD Point Clouds as Topological Landscape Profiles to Guide Local Data Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Oesterling, Patrick; Heine, Christian; Weber, Gunther H.

2012-05-04

Analyzing high-dimensional point clouds is a classical challenge in visual analytics. Traditional techniques, such as projections or axis-based techniques, suffer from projection artifacts, occlusion, and visual complexity.We propose to split data analysis into two parts to address these shortcomings. First, a structural overview phase abstracts data by its density distribution. This phase performs topological analysis to support accurate and non-overlapping presentation of the high-dimensional cluster structure as a topological landscape profile. Utilizing a landscape metaphor, it presents clusters and their nesting as hills whose height, width, and shape reflect cluster coherence, size, and stability, respectively. A second local analysis phasemore » utilizes this global structural knowledge to select individual clusters or point sets for further, localized data analysis. Focusing on structural entities significantly reduces visual clutter in established geometric visualizations and permits a clearer, more thorough data analysis. In conclusion, this analysis complements the global topological perspective and enables the user to study subspaces or geometric properties, such as shape.« less

MRI quantification of pancreas motion as a function of patient setup for particle therapy —a preliminary study

PubMed Central

Riboldi, Marco; Gianoli, Chiara; Chirvase, Cezarina I.; Villa, Gaetano; Paganelli, Chiara; Summers, Paul E.; Tagaste, Barbara; Pella, Andrea; Fossati, Piero; Ciocca, Mario; Baroni, Guido; Valvo, Francesca; Orecchia, Roberto

2016-01-01

Particle therapy (PT) has shown positive therapeutic results in local control of locally advanced pancreatic lesions. PT effectiveness is highly influenced by target localization accuracy both in space, since the pancreas is located in proximity to radiosensitive vital organs, and in time as it is subject to substantial breathing‐related motion. The purpose of this preliminary study was to quantify pancreas range of motion under typical PT treatment conditions. Three common immobilization devices (vacuum cushion, thermoplastic mask, and compressor belt) were evaluated on five male patients in prone and supine positions. Retrospective four‐dimensional magnetic resonance imaging data were reconstructed for each condition and the pancreas was manually segmented on each of six breathing phases. A k‐means algorithm was then applied on the manually segmented map in order to obtain clusters representative of the three pancreas segments: head, body, and tail. Centers of mass (COM) for the pancreas and its segments were computed, as well as their displacements with respect to a reference breathing phase (beginning exhalation). The median three‐dimensional COM displacements were in the range of 3 mm. Latero–lateral and superior–inferior directions had a higher range of motion than the anterior–posterior direction. Motion analysis of the pancreas segments showed slightly lower COM displacements for the head cluster compared to the tail cluster, especially in prone position. Statistically significant differences were found within patients among the investigated setups. Hence a patient‐specific approach, rather than a general strategy, is suggested to define the optimal treatment setup in the frame of a millimeter positioning accuracy. PACS number(s): 87.55.‐x, 87.57.nm, 87.61 PMID:27685119

Radio jet propagation and wide-angle tailed radio sources in merging galaxy cluster environments

NASA Technical Reports Server (NTRS)

Loken, Chris; Roettiger, Kurt; Burns, Jack O.; Norman, Michael

1995-01-01

The intracluster medium (ICM) within merging clusters of galaxies is likely to be in a violent or turbulent dynamical state which may have a significant effect on the evolution of cluster radio sources. We present results from a recent gas + N-body simulation of a cluster merger, suggesting that mergers can result in long-lived, supersonic bulk flows, as well as shocks, within a few hundred kiloparsecs of the core of the dominant cluster. These results have motivated our new two-dimensional and three-dimensional simulations of jet propagation in such environments. The first set of simulations models the ISM/ICM transition as a contact discontinuity with a strong velocity shear. A supersonic (M(sub j) = 6) jet crossing this discontinuity into an ICM with a transverse, supersonic wind bends continuously, becomes 'naked' on the upwind side, and forms a distended cocoon on the downwind side. In the case of a mildly supersonic jet (M(sub j) = 3), however, a shock is driven into the ISM and ISM material is pulled along with the jet into the ICM. Instabilities excited at the ISM/ICM interface result in the jet repeatedly pinching off and reestablishing itself in a series of 'disconnection events.' The second set of simulations deals with a jet encountering a shock in the merging cluster environment. A series of relatively high-resolution two-dimensional calculations is used to confirm earlier analysis predicting that the jet will not disrupt when the jet Mach number is greater than the shock Mach number. A jet which survives the encounter with the shock will decrease in radius and disrupt shortly thereafter as a result of the growth of Kelvin-Helmholtz instabilities. We also find, in disagreement with predictions, that the jet flaring angle decreases with increasing jet density. Finally, a three-dimensional simulation of a jet crossing an oblique shock gives rise to a morphology which resembles a wide-angle tailed radio source with the jet flaring at the shock and disrupting to form a long, turbulent tail which is dragged downstream by the preshock wind.

The [(AI 2O 3) 2] - Anion Cluster: Electron Localization-Delocalization Isomerism

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sierka, Marek; Dobler, Jens; Sauer, Joachim

2009-10-05

Three-dimensional bulk alumina and its two-dimensional thin films show great structural diversity, posing considerable challenges to their experimental structural characterization and computational modeling. Recently, structural diversity has also been demonstrated for zerodimensional gas phase aluminum oxide clusters. Mass-selected clusters not only make systematic studies of the structural and electronic properties as a function of size possible, but lately have also emerged as powerful molecular models of complex surfaces and solid catalysts. In particular, the [(Al 2O 3) 3-5] + clusters were the first example of polynuclear maingroup metal oxide cluster that are able to thermally activate CH 4. Over themore » past decades gas phase aluminum oxide clusters have been extensively studied both experimentally and computationally, but definitive structural assignments were made for only a handful of them: the planar [Al 3O 3] - and [Al 5O 4] - cluster anions, and the [(Al 2O 3) 1-4(AlO)] + cluster cations. For stoichiometric clusters only the atomic structures of [(Al 2O 3) 4] +/0 have been nambiguously resolved. Here we report on the structures of the [(Al 2O 3) 2] -/0 clusters combining photoelectron spectroscopy (PES) and quantum chemical calculations employing a genetic algorithm as a global optimization technique. The [(Al 2O 3) 2] - cluster anion show energetically close lying but structurally distinct cage and sheet-like isomers which differ by delocalization/localization of the extra electron. The experimental results are crucial for benchmarking the different computational methods applied with respect to a proper description of electron localization and the relative energies for the isomers which will be of considerable value for future computational studies of aluminum oxide and related systems.« less

Fractal geometry in an expanding, one-dimensional, Newtonian universe.

PubMed

Miller, Bruce N; Rouet, Jean-Louis; Le Guirriec, Emmanuel

2007-09-01

Observations of galaxies over large distances reveal the possibility of a fractal distribution of their positions. The source of fractal behavior is the lack of a length scale in the two body gravitational interaction. However, even with new, larger, sample sizes from recent surveys, it is difficult to extract information concerning fractal properties with confidence. Similarly, three-dimensional N-body simulations with a billion particles only provide a thousand particles per dimension, far too small for accurate conclusions. With one-dimensional models these limitations can be overcome by carrying out simulations with on the order of a quarter of a million particles without compromising the computation of the gravitational force. Here the multifractal properties of two of these models that incorporate different features of the dynamical equations governing the evolution of a matter dominated universe are compared. For each model at least two scaling regions are identified. By employing criteria from dynamical systems theory it is shown that only one of them can be geometrically significant. The results share important similarities with galaxy observations, such as hierarchical clustering and apparent bifractal geometry. They also provide insights concerning possible constraints on length and time scales for fractal structure. They clearly demonstrate that fractal geometry evolves in the mu (position, velocity) space. The observed patterns are simply a shadow (projection) of higher-dimensional structure.

Density-based clustering: A 'landscape view' of multi-channel neural data for inference and dynamic complexity analysis.

PubMed

Baglietto, Gabriel; Gigante, Guido; Del Giudice, Paolo

2017-01-01

Two, partially interwoven, hot topics in the analysis and statistical modeling of neural data, are the development of efficient and informative representations of the time series derived from multiple neural recordings, and the extraction of information about the connectivity structure of the underlying neural network from the recorded neural activities. In the present paper we show that state-space clustering can provide an easy and effective option for reducing the dimensionality of multiple neural time series, that it can improve inference of synaptic couplings from neural activities, and that it can also allow the construction of a compact representation of the multi-dimensional dynamics, that easily lends itself to complexity measures. We apply a variant of the 'mean-shift' algorithm to perform state-space clustering, and validate it on an Hopfield network in the glassy phase, in which metastable states are largely uncorrelated from memories embedded in the synaptic matrix. In this context, we show that the neural states identified as clusters' centroids offer a parsimonious parametrization of the synaptic matrix, which allows a significant improvement in inferring the synaptic couplings from the neural activities. Moving to the more realistic case of a multi-modular spiking network, with spike-frequency adaptation inducing history-dependent effects, we propose a procedure inspired by Boltzmann learning, but extending its domain of application, to learn inter-module synaptic couplings so that the spiking network reproduces a prescribed pattern of spatial correlations; we then illustrate, in the spiking network, how clustering is effective in extracting relevant features of the network's state-space landscape. Finally, we show that the knowledge of the cluster structure allows casting the multi-dimensional neural dynamics in the form of a symbolic dynamics of transitions between clusters; as an illustration of the potential of such reduction, we define and analyze a measure of complexity of the neural time series.

Illuminating the clinical significance of alexithymia subtypes: A cluster analysis of alexithymic traits and psychiatric symptoms.

PubMed

Kajanoja, J; Scheinin, N M; Karlsson, L; Karlsson, H; Karukivi, M

2017-06-01

Alexithymia is a personality construct involving difficulties identifying and verbalizing feelings, and an externally oriented thinking style. There is preliminary evidence for alexithymia subtypes that may carry different risk profiles for psychiatric illness. The aim of this study was to gain support for the existence of alexithymia subtypes and further characterize their clinical relevance. To identify possible subtypes, a cluster analysis was conducted for individuals with high alexithymic traits (N=113). Current depressive and anxiety symptoms, self-reported psychiatric medical history, and self-reported early life adversity were compared between subtypes. The cluster analysis was replicated with the low (N=2471) and moderate (N=290) alexithymia groups. We identified two alexithymia subtypes. Compared to type A, type B alexithymia was associated with higher levels of difficulties in identifying feelings, and was more strongly associated with current depressive (Cohen's d=0.77, p<0.001) and anxiety symptoms (Cohen's d=0.82, p<0.001), and self-reported early life adversity (Cohen's d 0.42, p=0.048). Compared to type A, type B alexithymia was also associated with a higher prevalence of self-reported diagnosis of major depressive- (30.2% vs. 8.3%) and anxiety disorder (18.9% vs. 3.3%). The results of this study support the hypothesis of alexithymia subtypes, and add support to the growing evidence showing that alexithymia is likely a heterogeneous and dimensional phenomenon. The subtype (type B) with most pronounced difficulties in identifying feelings may be associated with a higher risk for psychiatric illness compared to type A alexithymia, and may exhibit a more severe history of early life adversity. Copyright © 2017 Elsevier Inc. All rights reserved.

FUSE: a profit maximization approach for functional summarization of biological networks.

PubMed

Seah, Boon-Siew; Bhowmick, Sourav S; Dewey, C Forbes; Yu, Hanry

2012-03-21

The availability of large-scale curated protein interaction datasets has given rise to the opportunity to investigate higher level organization and modularity within the protein interaction network (PPI) using graph theoretic analysis. Despite the recent progress, systems level analysis of PPIS remains a daunting task as it is challenging to make sense out of the deluge of high-dimensional interaction data. Specifically, techniques that automatically abstract and summarize PPIS at multiple resolutions to provide high level views of its functional landscape are still lacking. We present a novel data-driven and generic algorithm called FUSE (Functional Summary Generator) that generates functional maps of a PPI at different levels of organization, from broad process-process level interactions to in-depth complex-complex level interactions, through a pro t maximization approach that exploits Minimum Description Length (MDL) principle to maximize information gain of the summary graph while satisfying the level of detail constraint. We evaluate the performance of FUSE on several real-world PPIS. We also compare FUSE to state-of-the-art graph clustering methods with GO term enrichment by constructing the biological process landscape of the PPIS. Using AD network as our case study, we further demonstrate the ability of FUSE to quickly summarize the network and identify many different processes and complexes that regulate it. Finally, we study the higher-order connectivity of the human PPI. By simultaneously evaluating interaction and annotation data, FUSE abstracts higher-order interaction maps by reducing the details of the underlying PPI to form a functional summary graph of interconnected functional clusters. Our results demonstrate its effectiveness and superiority over state-of-the-art graph clustering methods with GO term enrichment.

Application of Fuzzy c-Means and Joint-Feature-Clustering to Detect Redundancies of Image-Features in Drug Combinations Studies of Breast Cancer

NASA Astrophysics Data System (ADS)

Brandl, Miriam B.; Beck, Dominik; Pham, Tuan D.

2011-06-01

The high dimensionality of image-based dataset can be a drawback for classification accuracy. In this study, we propose the application of fuzzy c-means clustering, cluster validity indices and the notation of a joint-feature-clustering matrix to find redundancies of image-features. The introduced matrix indicates how frequently features are grouped in a mutual cluster. The resulting information can be used to find data-derived feature prototypes with a common biological meaning, reduce data storage as well as computation times and improve the classification accuracy.

Diffusion maps, clustering and fuzzy Markov modeling in peptide folding transitions

NASA Astrophysics Data System (ADS)

Nedialkova, Lilia V.; Amat, Miguel A.; Kevrekidis, Ioannis G.; Hummer, Gerhard

2014-09-01

Using the helix-coil transitions of alanine pentapeptide as an illustrative example, we demonstrate the use of diffusion maps in the analysis of molecular dynamics simulation trajectories. Diffusion maps and other nonlinear data-mining techniques provide powerful tools to visualize the distribution of structures in conformation space. The resulting low-dimensional representations help in partitioning conformation space, and in constructing Markov state models that capture the conformational dynamics. In an initial step, we use diffusion maps to reduce the dimensionality of the conformational dynamics of Ala5. The resulting pretreated data are then used in a clustering step. The identified clusters show excellent overlap with clusters obtained previously by using the backbone dihedral angles as input, with small—but nontrivial—differences reflecting torsional degrees of freedom ignored in the earlier approach. We then construct a Markov state model describing the conformational dynamics in terms of a discrete-time random walk between the clusters. We show that by combining fuzzy C-means clustering with a transition-based assignment of states, we can construct robust Markov state models. This state-assignment procedure suppresses short-time memory effects that result from the non-Markovianity of the dynamics projected onto the space of clusters. In a comparison with previous work, we demonstrate how manifold learning techniques may complement and enhance informed intuition commonly used to construct reduced descriptions of the dynamics in molecular conformation space.

Diffusion maps, clustering and fuzzy Markov modeling in peptide folding transitions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nedialkova, Lilia V.; Amat, Miguel A.; Kevrekidis, Ioannis G., E-mail: yannis@princeton.edu, E-mail: gerhard.hummer@biophys.mpg.de

Using the helix-coil transitions of alanine pentapeptide as an illustrative example, we demonstrate the use of diffusion maps in the analysis of molecular dynamics simulation trajectories. Diffusion maps and other nonlinear data-mining techniques provide powerful tools to visualize the distribution of structures in conformation space. The resulting low-dimensional representations help in partitioning conformation space, and in constructing Markov state models that capture the conformational dynamics. In an initial step, we use diffusion maps to reduce the dimensionality of the conformational dynamics of Ala5. The resulting pretreated data are then used in a clustering step. The identified clusters show excellent overlapmore » with clusters obtained previously by using the backbone dihedral angles as input, with small—but nontrivial—differences reflecting torsional degrees of freedom ignored in the earlier approach. We then construct a Markov state model describing the conformational dynamics in terms of a discrete-time random walk between the clusters. We show that by combining fuzzy C-means clustering with a transition-based assignment of states, we can construct robust Markov state models. This state-assignment procedure suppresses short-time memory effects that result from the non-Markovianity of the dynamics projected onto the space of clusters. In a comparison with previous work, we demonstrate how manifold learning techniques may complement and enhance informed intuition commonly used to construct reduced descriptions of the dynamics in molecular conformation space.« less

Visualization and unsupervised predictive clustering of high-dimensional multimodal neuroimaging data.

PubMed

Mwangi, Benson; Soares, Jair C; Hasan, Khader M

2014-10-30

Neuroimaging machine learning studies have largely utilized supervised algorithms - meaning they require both neuroimaging scan data and corresponding target variables (e.g. healthy vs. diseased) to be successfully 'trained' for a prediction task. Noticeably, this approach may not be optimal or possible when the global structure of the data is not well known and the researcher does not have an a priori model to fit the data. We set out to investigate the utility of an unsupervised machine learning technique; t-distributed stochastic neighbour embedding (t-SNE) in identifying 'unseen' sample population patterns that may exist in high-dimensional neuroimaging data. Multimodal neuroimaging scans from 92 healthy subjects were pre-processed using atlas-based methods, integrated and input into the t-SNE algorithm. Patterns and clusters discovered by the algorithm were visualized using a 2D scatter plot and further analyzed using the K-means clustering algorithm. t-SNE was evaluated against classical principal component analysis. Remarkably, based on unlabelled multimodal scan data, t-SNE separated study subjects into two very distinct clusters which corresponded to subjects' gender labels (cluster silhouette index value=0.79). The resulting clusters were used to develop an unsupervised minimum distance clustering model which identified 93.5% of subjects' gender. Notably, from a neuropsychiatric perspective this method may allow discovery of data-driven disease phenotypes or sub-types of treatment responders. Copyright © 2014 Elsevier B.V. All rights reserved.

Diffusion maps, clustering and fuzzy Markov modeling in peptide folding transitions

PubMed Central

Nedialkova, Lilia V.; Amat, Miguel A.; Kevrekidis, Ioannis G.; Hummer, Gerhard

2014-01-01

Using the helix-coil transitions of alanine pentapeptide as an illustrative example, we demonstrate the use of diffusion maps in the analysis of molecular dynamics simulation trajectories. Diffusion maps and other nonlinear data-mining techniques provide powerful tools to visualize the distribution of structures in conformation space. The resulting low-dimensional representations help in partitioning conformation space, and in constructing Markov state models that capture the conformational dynamics. In an initial step, we use diffusion maps to reduce the dimensionality of the conformational dynamics of Ala5. The resulting pretreated data are then used in a clustering step. The identified clusters show excellent overlap with clusters obtained previously by using the backbone dihedral angles as input, with small—but nontrivial—differences reflecting torsional degrees of freedom ignored in the earlier approach. We then construct a Markov state model describing the conformational dynamics in terms of a discrete-time random walk between the clusters. We show that by combining fuzzy C-means clustering with a transition-based assignment of states, we can construct robust Markov state models. This state-assignment procedure suppresses short-time memory effects that result from the non-Markovianity of the dynamics projected onto the space of clusters. In a comparison with previous work, we demonstrate how manifold learning techniques may complement and enhance informed intuition commonly used to construct reduced descriptions of the dynamics in molecular conformation space. PMID:25240340

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

PubMed

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

2018-02-05

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

Electric-field-induced association of colloidal particles

NASA Astrophysics Data System (ADS)

Fraden, Seth; Hurd, Alan J.; Meyer, Robert B.

1989-11-01

Dilute suspensions of micron diameter dielectric spheres confined to two dimensions are induced to aggregate linearly by application of an electric field. The growth of the average cluster size agrees well with the Smoluchowski equation, but the evolution of the measured cluster size distribution exhibits significant departures from theory at large times due to the formation of long linear clusters which effectively partition space into isolated one-dimensional strips.

Quasirandom geometric networks from low-discrepancy sequences

NASA Astrophysics Data System (ADS)

Estrada, Ernesto

2017-08-01

We define quasirandom geometric networks using low-discrepancy sequences, such as Halton, Sobol, and Niederreiter. The networks are built in d dimensions by considering the d -tuples of digits generated by these sequences as the coordinates of the vertices of the networks in a d -dimensional Id unit hypercube. Then, two vertices are connected by an edge if they are at a distance smaller than a connection radius. We investigate computationally 11 network-theoretic properties of two-dimensional quasirandom networks and compare them with analogous random geometric networks. We also study their degree distribution and their spectral density distributions. We conclude from this intensive computational study that in terms of the uniformity of the distribution of the vertices in the unit square, the quasirandom networks look more random than the random geometric networks. We include an analysis of potential strategies for generating higher-dimensional quasirandom networks, where it is know that some of the low-discrepancy sequences are highly correlated. In this respect, we conclude that up to dimension 20, the use of scrambling, skipping and leaping strategies generate quasirandom networks with the desired properties of uniformity. Finally, we consider a diffusive process taking place on the nodes and edges of the quasirandom and random geometric graphs. We show that the diffusion time is shorter in the quasirandom graphs as a consequence of their larger structural homogeneity. In the random geometric graphs the diffusion produces clusters of concentration that make the process more slow. Such clusters are a direct consequence of the heterogeneous and irregular distribution of the nodes in the unit square in which the generation of random geometric graphs is based on.

Carbohydrate Cluster Microarrays Fabricated on 3-Dimensional Dendrimeric Platforms for Functional Glycomics Exploration

PubMed Central

Zhou, Xichun; Turchi, Craig; Wang, Denong

2009-01-01

We reported here a novel, ready-to-use bioarray platform and methodology for construction of sensitive carbohydrate cluster microarrays. This technology utilizes a 3-dimensional (3-D) poly(amidoamine) starburst dendrimer monolayer assembled on glass surface, which is functionalized with terminal aminooxy and hydrazide groups for site-specific coupling of carbohydrates. A wide range of saccharides, including monosaccharides, oligosaccharides and polysaccharides of diverse structures, are applicable for the 3-D bioarray platform without prior chemical derivatization. The process of carbohydrate coupling is effectively accelerated by microwave radiation energy. The carbohydrate concentration required for microarray fabrication is substantially reduced using this technology. Importantly, this bioarray platform presents sugar chains in defined orientation and cluster configurations. It is, thus, uniquely useful for exploration of the structural and conformational diversities of glyco-epitope and their functional properties. PMID:19791771

The Immersive Virtual Reality Experience: A Typology of Users Revealed Through Multiple Correspondence Analysis Combined with Cluster Analysis Technique.

PubMed

Rosa, Pedro J; Morais, Diogo; Gamito, Pedro; Oliveira, Jorge; Saraiva, Tomaz

2016-03-01

Immersive virtual reality is thought to be advantageous by leading to higher levels of presence. However, and despite users getting actively involved in immersive three-dimensional virtual environments that incorporate sound and motion, there are individual factors, such as age, video game knowledge, and the predisposition to immersion, that may be associated with the quality of virtual reality experience. Moreover, one particular concern for users engaged in immersive virtual reality environments (VREs) is the possibility of side effects, such as cybersickness. The literature suggests that at least 60% of virtual reality users report having felt symptoms of cybersickness, which reduces the quality of the virtual reality experience. The aim of this study was thus to profile the right user to be involved in a VRE through head-mounted display. To examine which user characteristics are associated with the most effective virtual reality experience (lower cybersickness), a multiple correspondence analysis combined with cluster analysis technique was performed. Results revealed three distinct profiles, showing that the PC gamer profile is more associated with higher levels of virtual reality effectiveness, that is, higher predisposition to be immersed and reduced cybersickness symptoms in the VRE than console gamer and nongamer. These findings can be a useful orientation in clinical practice and future research as they help identify which users are more predisposed to benefit from immersive VREs.

Open framework metal chalcogenides as efficient photocatalysts for reduction of CO2 into renewable hydrocarbon fuel.

PubMed

Sasan, Koroush; Lin, Qipu; Mao, Chengyu; Feng, Pingyun

2016-06-07

Open framework metal chalcogenides are a family of porous semiconducting materials with diverse chemical compositions. Here we show that these materials containing covalent three-dimensional superlattices of nanosized supertetrahedral clusters can function as efficient photocatalysts for the reduction of CO2 to CH4. Unlike dense semiconductors, metal cations are successfully incorporated into the channels of the porous semiconducting materials to further tune the physical properties of the materials such as electrical conductivity and band gaps. In terms of the photocatalytic properties, the metal-incorporated porous chalcogenides demonstrated enhanced solar energy absorption and higher electrical conductivity and improved photocatalytic activity.

A Photometrically Detected Forming Cluster of Galaxies at Redshift 1.6 in the GOODS Field

NASA Astrophysics Data System (ADS)

Castellano, M.; Salimbeni, S.; Trevese, D.; Grazian, A.; Pentericci, L.; Fiore, F.; Fontana, A.; Giallongo, E.; Santini, P.; Cristiani, S.; Nonino, M.; Vanzella, E.

2007-12-01

We report the discovery of a localized overdensity at z~1.6 in the GOODS-South field, presumably a poor cluster in the process of formation. The three-dimensional galaxy density has been estimated on the basis of well-calibrated photometric redshifts from the multiband photometric GOODS-MUSIC catalog using the (2+1)-dimensional technique. The density peak is embedded in the larger scale overdensity of galaxies known to exist at z=1.61 in the area. The properties of the member galaxies are compared to those of the surrounding field, and we find that the two populations are significantly different, supporting the reality of the structure. The reddest galaxies, once evolved according to their best-fit models, have colors consistent with the red sequence of lower redshift clusters. The estimated M200 total mass of the cluster is in the range 1.3×1014-5.7×1014 Msolar, depending on the assumed bias factor b. An upper limit for the 2-10 keV X-ray luminosity, based on the 1 Ms Chandra observations, is LX=0.5×1043 erg s-1, suggesting that the cluster has not yet reached the virial equilibrium.

Self-organization of cosmic radiation pressure instability. II - One-dimensional simulations

NASA Technical Reports Server (NTRS)

Hogan, Craig J.; Woods, Jorden

1992-01-01

The clustering of statistically uniform discrete absorbing particles moving solely under the influence of radiation pressure from uniformly distributed emitters is studied in a simple one-dimensional model. Radiation pressure tends to amplify statistical clustering in the absorbers; the absorbing material is swept into empty bubbles, the biggest bubbles grow bigger almost as they would in a uniform medium, and the smaller ones get crushed and disappear. Numerical simulations of a one-dimensional system are used to support the conjecture that the system is self-organizing. Simple statistics indicate that a wide range of initial conditions produce structure approaching the same self-similar statistical distribution, whose scaling properties follow those of the attractor solution for an isolated bubble. The importance of the process for large-scale structuring of the interstellar medium is briefly discussed.

Sparsity enabled cluster reduced-order models for control

NASA Astrophysics Data System (ADS)

Kaiser, Eurika; Morzyński, Marek; Daviller, Guillaume; Kutz, J. Nathan; Brunton, Bingni W.; Brunton, Steven L.

2018-01-01

Characterizing and controlling nonlinear, multi-scale phenomena are central goals in science and engineering. Cluster-based reduced-order modeling (CROM) was introduced to exploit the underlying low-dimensional dynamics of complex systems. CROM builds a data-driven discretization of the Perron-Frobenius operator, resulting in a probabilistic model for ensembles of trajectories. A key advantage of CROM is that it embeds nonlinear dynamics in a linear framework, which enables the application of standard linear techniques to the nonlinear system. CROM is typically computed on high-dimensional data; however, access to and computations on this full-state data limit the online implementation of CROM for prediction and control. Here, we address this key challenge by identifying a small subset of critical measurements to learn an efficient CROM, referred to as sparsity-enabled CROM. In particular, we leverage compressive measurements to faithfully embed the cluster geometry and preserve the probabilistic dynamics. Further, we show how to identify fewer optimized sensor locations tailored to a specific problem that outperform random measurements. Both of these sparsity-enabled sensing strategies significantly reduce the burden of data acquisition and processing for low-latency in-time estimation and control. We illustrate this unsupervised learning approach on three different high-dimensional nonlinear dynamical systems from fluids with increasing complexity, with one application in flow control. Sparsity-enabled CROM is a critical facilitator for real-time implementation on high-dimensional systems where full-state information may be inaccessible.

CLUMP-3D: Three-dimensional Shape and Structure of 20 CLASH Galaxy Clusters from Combined Weak and Strong Lensing

NASA Astrophysics Data System (ADS)

Chiu, I.-Non; Umetsu, Keiichi; Sereno, Mauro; Ettori, Stefano; Meneghetti, Massimo; Merten, Julian; Sayers, Jack; Zitrin, Adi

2018-06-01

We perform a three-dimensional triaxial analysis of 16 X-ray regular and 4 high-magnification galaxy clusters selected from the CLASH survey by combining two-dimensional weak-lensing and central strong-lensing constraints. In a Bayesian framework, we constrain the intrinsic structure and geometry of each individual cluster assuming a triaxial Navarro–Frenk–White halo with arbitrary orientations, characterized by the mass {M}200{{c}}, halo concentration {c}200{{c}}, and triaxial axis ratios ({q}{{a}}≤slant {q}{{b}}), and investigate scaling relations between these halo structural parameters. From triaxial modeling of the X-ray-selected subsample, we find that the halo concentration decreases with increasing cluster mass, with a mean concentration of {c}200{{c}}=4.82+/- 0.30 at the pivot mass {M}200{{c}}={10}15{M}ȯ {h}-1. This is consistent with the result from spherical modeling, {c}200{{c}}=4.51+/- 0.14. Independently of the priors, the minor-to-major axis ratio {q}{{a}} of our full sample exhibits a clear deviation from the spherical configuration ({q}{{a}}=0.52+/- 0.04 at {10}15{M}ȯ {h}-1 with uniform priors), with a weak dependence on the cluster mass. Combining all 20 clusters, we obtain a joint ensemble constraint on the minor-to-major axis ratio of {q}{{a}}={0.652}-0.078+0.162 and a lower bound on the intermediate-to-major axis ratio of {q}{{b}}> 0.63 at the 2σ level from an analysis with uniform priors. Assuming priors on the axis ratios derived from numerical simulations, we constrain the degree of triaxiality for the full sample to be { \\mathcal T }=0.79+/- 0.03 at {10}15{M}ȯ {h}-1, indicating a preference for a prolate geometry of cluster halos. We find no statistical evidence for an orientation bias ({f}geo}=0.93+/- 0.07), which is insensitive to the priors and in agreement with the theoretical expectation for the CLASH clusters.

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

PubMed Central

Liu, Jingxian; Wu, Kefeng

2017-01-01

The Shipboard Automatic Identification System (AIS) is crucial for navigation safety and maritime surveillance, data mining and pattern analysis of AIS information have attracted considerable attention in terms of both basic research and practical applications. Clustering of spatio-temporal AIS trajectories can be used to identify abnormal patterns and mine customary route data for transportation safety. Thus, the capacities of navigation safety and maritime traffic monitoring could be enhanced correspondingly. However, trajectory clustering is often sensitive to undesirable outliers and is essentially more complex compared with traditional point clustering. To overcome this limitation, a multi-step trajectory clustering method is proposed in this paper for robust AIS trajectory clustering. In particular, the Dynamic Time Warping (DTW), a similarity measurement method, is introduced in the first step to measure the distances between different trajectories. The calculated distances, inversely proportional to the similarities, constitute a distance matrix in the second step. Furthermore, as a widely-used dimensional reduction method, Principal Component Analysis (PCA) is exploited to decompose the obtained distance matrix. In particular, the top k principal components with above 95% accumulative contribution rate are extracted by PCA, and the number of the centers k is chosen. The k centers are found by the improved center automatically selection algorithm. In the last step, the improved center clustering algorithm with k clusters is implemented on the distance matrix to achieve the final AIS trajectory clustering results. In order to improve the accuracy of the proposed multi-step clustering algorithm, an automatic algorithm for choosing the k clusters is developed according to the similarity distance. Numerous experiments on realistic AIS trajectory datasets in the bridge area waterway and Mississippi River have been implemented to compare our proposed method with traditional spectral clustering and fast affinity propagation clustering. Experimental results have illustrated its superior performance in terms of quantitative and qualitative evaluations. PMID:28777353

Topic modeling for cluster analysis of large biological and medical datasets

PubMed Central

2014-01-01

Background The big data moniker is nowhere better deserved than to describe the ever-increasing prodigiousness and complexity of biological and medical datasets. New methods are needed to generate and test hypotheses, foster biological interpretation, and build validated predictors. Although multivariate techniques such as cluster analysis may allow researchers to identify groups, or clusters, of related variables, the accuracies and effectiveness of traditional clustering methods diminish for large and hyper dimensional datasets. Topic modeling is an active research field in machine learning and has been mainly used as an analytical tool to structure large textual corpora for data mining. Its ability to reduce high dimensionality to a small number of latent variables makes it suitable as a means for clustering or overcoming clustering difficulties in large biological and medical datasets. Results In this study, three topic model-derived clustering methods, highest probable topic assignment, feature selection and feature extraction, are proposed and tested on the cluster analysis of three large datasets: Salmonella pulsed-field gel electrophoresis (PFGE) dataset, lung cancer dataset, and breast cancer dataset, which represent various types of large biological or medical datasets. All three various methods are shown to improve the efficacy/effectiveness of clustering results on the three datasets in comparison to traditional methods. A preferable cluster analysis method emerged for each of the three datasets on the basis of replicating known biological truths. Conclusion Topic modeling could be advantageously applied to the large datasets of biological or medical research. The three proposed topic model-derived clustering methods, highest probable topic assignment, feature selection and feature extraction, yield clustering improvements for the three different data types. Clusters more efficaciously represent truthful groupings and subgroupings in the data than traditional methods, suggesting that topic model-based methods could provide an analytic advancement in the analysis of large biological or medical datasets. PMID:25350106

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

PubMed

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

2014-01-01

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

Dimensionality in Language Learners' Personal Epistemologies

ERIC Educational Resources Information Center

Nikitina, Larisa; Furuoka, Fumitaka

2018-01-01

This study aimed to examine dimensionality in language learners' epistemic beliefs. To achieve this, a survey was conducted using a newly-developed research instrument-"Language Learners' Epistemic Beliefs" (LLEB) questionnaire. Based on a review of literature, it was proposed that language learners' epistemic beliefs would cluster in…

Version 4.0 of code Java for 3D simulation of the CCA model

NASA Astrophysics Data System (ADS)

Fan, Linyu; Liao, Jianwei; Zuo, Junsen; Zhang, Kebo; Li, Chao; Xiong, Hailing

2018-07-01

This paper presents a new version Java code for the three-dimensional simulation of Cluster-Cluster Aggregation (CCA) model to replace the previous version. Many redundant traverses of clusters-list in the program were totally avoided, so that the consumed simulation time is significantly reduced. In order to show the aggregation process in a more intuitive way, we have labeled different clusters with varied colors. Besides, a new function is added for outputting the particle's coordinates of aggregates in file to benefit coupling our model with other models.

A three-dimensional hydrodynamic treatment of the hot dark matter cosmological scenario

NASA Technical Reports Server (NTRS)

Cen, Renyue; Ostriker, Jeremiah P.

1992-01-01

The study computes the evolution of the hot dark matter (HDM) model containing both baryonic matter and dark matter for a post recombination Friedmann-Robertson-Walker universe. A locally valid Newtonian approximation is used to model a representative piece of the universe with size much less than the horizon. For the HDM model with the present chosen normalization, the hard X-ray (1-10 keV) radiation intensity is less than that in the observations (Wu et al., 1991) by a factor of 30. In agreement with other work, it is found that baryonic matter is slightly antibiased over dark matter on the cell scale, 0.5/h Mpc = 667 kpc. The HDM model with the present chosen parameters does not overproduce X-ray-luminous clusters, and there is a negative evolution in the late epochs in the sense that the number density of X-ray clusters was higher at 0.5 redshift than at 0 redshift at the brightest end.

Identification of individual coherent sets associated with flow trajectories using coherent structure coloring

NASA Astrophysics Data System (ADS)

Schlueter-Kuck, Kristy L.; Dabiri, John O.

2017-09-01

We present a method for identifying the coherent structures associated with individual Lagrangian flow trajectories even where only sparse particle trajectory data are available. The method, based on techniques in spectral graph theory, uses the Coherent Structure Coloring vector and associated eigenvectors to analyze the distance in higher-dimensional eigenspace between a selected reference trajectory and other tracer trajectories in the flow. By analyzing this distance metric in a hierarchical clustering, the coherent structure of which the reference particle is a member can be identified. This algorithm is proven successful in identifying coherent structures of varying complexities in canonical unsteady flows. Additionally, the method is able to assess the relative coherence of the associated structure in comparison to the surrounding flow. Although the method is demonstrated here in the context of fluid flow kinematics, the generality of the approach allows for its potential application to other unsupervised clustering problems in dynamical systems such as neuronal activity, gene expression, or social networks.

Ram Pressure Stripping: Observations Meet Simulations

NASA Astrophysics Data System (ADS)

Past, Matthew; Ruszkowski, Mateusz; Sharon, Keren

2017-01-01

Ram pressure stripping occurs when a galaxy falls into the potential well of a cluster, removing gas and dust as the galaxy travels through the intracluster medium. This interaction leads to filamentary gas tails stretching behind the galaxy and plays an important role in galaxy evolution. Previously, these “jellyfish” galaxies had only been observed in nearby clusters, but recently, higher redshift (z > 0.3) examples have been found from HST data imaging.Recent work has shown that cosmic rays injected by supernovae can cause galactic disks to thicken due to cosmic ray pressure. We run three-dimensional magneto-hydrodynamical simulations of ram pressure stripping including cosmic rays to compare to previous models. We study how the efficiency of the ram pressure stripping of the gas, and the morphology of the filamentary tails, depend on the magnitude of the cosmic ray pressure support. We generate mock X-ray images and radio polarization data. Simultaneously, we perform an exhaustive search of the HST archive to increase the sample of jellyfish galaxies and compare selected cases to simulations.

Measurement-based quantum computation on two-body interacting qubits with adiabatic evolution.

PubMed

Kyaw, Thi Ha; Li, Ying; Kwek, Leong-Chuan

2014-10-31

A cluster state cannot be a unique ground state of a two-body interacting Hamiltonian. Here, we propose the creation of a cluster state of logical qubits encoded in spin-1/2 particles by adiabatically weakening two-body interactions. The proposal is valid for any spatial dimensional cluster states. Errors induced by thermal fluctuations and adiabatic evolution within finite time can be eliminated ensuring fault-tolerant quantum computing schemes.

Statistical Exploration of Electronic Structure of Molecules from Quantum Monte-Carlo Simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Prabhat, Mr; Zubarev, Dmitry; Lester, Jr., William A.

In this report, we present results from analysis of Quantum Monte Carlo (QMC) simulation data with the goal of determining internal structure of a 3N-dimensional phase space of an N-electron molecule. We are interested in mining the simulation data for patterns that might be indicative of the bond rearrangement as molecules change electronic states. We examined simulation output that tracks the positions of two coupled electrons in the singlet and triplet states of an H2 molecule. The electrons trace out a trajectory, which was analyzed with a number of statistical techniques. This project was intended to address the following scientificmore » questions: (1) Do high-dimensional phase spaces characterizing electronic structure of molecules tend to cluster in any natural way? Do we see a change in clustering patterns as we explore different electronic states of the same molecule? (2) Since it is hard to understand the high-dimensional space of trajectories, can we project these trajectories to a lower dimensional subspace to gain a better understanding of patterns? (3) Do trajectories inherently lie in a lower-dimensional manifold? Can we recover that manifold? After extensive statistical analysis, we are now in a better position to respond to these questions. (1) We definitely see clustering patterns, and differences between the H2 and H2tri datasets. These are revealed by the pamk method in a fairly reliable manner and can potentially be used to distinguish bonded and non-bonded systems and get insight into the nature of bonding. (2) Projecting to a lower dimensional subspace ({approx}4-5) using PCA or Kernel PCA reveals interesting patterns in the distribution of scalar values, which can be related to the existing descriptors of electronic structure of molecules. Also, these results can be immediately used to develop robust tools for analysis of noisy data obtained during QMC simulations (3) All dimensionality reduction and estimation techniques that we tried seem to indicate that one needs 4 or 5 components to account for most of the variance in the data, hence this 5D dataset does not necessarily lie on a well-defined, low dimensional manifold. In terms of specific clustering techniques, K-means was generally useful in exploring the dataset. The partition around medoids (pam) technique produced the most definitive results for our data showing distinctive patterns for both a sample of the complete data and time-series. The gap statistic with tibshirani criteria did not provide any distinction across the 2 dataset. The gap statistic w/DandF criteria, Model based clustering and hierarchical modeling simply failed to run on our datasets. Thankfully, the vanilla PCA technique was successful in handling our entire dataset. PCA revealed some interesting patterns for the scalar value distribution. Kernel PCA techniques (vanilladot, RBF, Polynomial) and MDS failed to run on the entire dataset, or even a significant fraction of the dataset, and we resorted to creating an explicit feature map followed by conventional PCA. Clustering using K-means and PAM in the new basis set seems to produce promising results. Understanding the new basis set in the scientific context of the problem is challenging, and we are currently working to further examine and interpret the results.« less

The Formation of Filamentary Structures in Radiative Cluster Winds

NASA Astrophysics Data System (ADS)

Rodríguez-González, Ary; Esquivel, Alejandro; Raga, Alejandro C.; Cantó, Jorge

We explore the dynamics of a "cluster wind" flow in the regime in which the shocks resulting from the interaction of winds from nearby stars are radiative. We show that for a cluster with low-intermedia mass stars, the wind interactions are indeed likely to be radiative. We then compute three dimensional, radiative simulations of a cluster of 75 young stars, exploring the effects of varying the wind parameters and the density of the initial ISM that permeates the volume of the cluster. These simulations show that the ISM is compressed by the action of the winds into a structure of dense knots and filaments.

Strongest Earthquake-Prone Areas in Kamchatka

NASA Astrophysics Data System (ADS)

Dzeboev, B. A.; Agayan, S. M.; Zharkikh, Yu. I.; Krasnoperov, R. I.; Barykina, Yu. V.

2018-03-01

The paper continues the series of our works on recognizing the areas prone to the strongest, strong, and significant earthquakes with the use of the Formalized Clustering And Zoning (FCAZ) intellectual clustering system. We recognized the zones prone to the probable emergence of epicenters of the strongest ( M ≥ 74/3) earthquakes on the Pacific Coast of Kamchatka. The FCAZ-zones are compared to the zones that were recognized in 1984 by the classical recognition method for Earthquake-Prone Areas (EPA) by transferring the criteria of high seismicity from the Andes mountain belt to the territory of Kamchatka. The FCAZ recognition was carried out with two-dimensional and three-dimensional objects of recognition.

Chemical Distances for Percolation of Planar Gaussian Free Fields and Critical Random Walk Loop Soups

NASA Astrophysics Data System (ADS)

Ding, Jian; Li, Li

2018-05-01

We initiate the study on chemical distances of percolation clusters for level sets of two-dimensional discrete Gaussian free fields as well as loop clusters generated by two-dimensional random walk loop soups. One of our results states that the chemical distance between two macroscopic annuli away from the boundary for the random walk loop soup at the critical intensity is of dimension 1 with positive probability. Our proof method is based on an interesting combination of a theorem of Makarov, isomorphism theory, and an entropic repulsion estimate for Gaussian free fields in the presence of a hard wall.

Chemical Distances for Percolation of Planar Gaussian Free Fields and Critical Random Walk Loop Soups

NASA Astrophysics Data System (ADS)

Ding, Jian; Li, Li

2018-06-01

We initiate the study on chemical distances of percolation clusters for level sets of two-dimensional discrete Gaussian free fields as well as loop clusters generated by two-dimensional random walk loop soups. One of our results states that the chemical distance between two macroscopic annuli away from the boundary for the random walk loop soup at the critical intensity is of dimension 1 with positive probability. Our proof method is based on an interesting combination of a theorem of Makarov, isomorphism theory, and an entropic repulsion estimate for Gaussian free fields in the presence of a hard wall.

Homogeneity Pursuit

PubMed Central

Ke, Tracy; Fan, Jianqing; Wu, Yichao

2014-01-01

This paper explores the homogeneity of coefficients in high-dimensional regression, which extends the sparsity concept and is more general and suitable for many applications. Homogeneity arises when regression coefficients corresponding to neighboring geographical regions or a similar cluster of covariates are expected to be approximately the same. Sparsity corresponds to a special case of homogeneity with a large cluster of known atom zero. In this article, we propose a new method called clustering algorithm in regression via data-driven segmentation (CARDS) to explore homogeneity. New mathematics are provided on the gain that can be achieved by exploring homogeneity. Statistical properties of two versions of CARDS are analyzed. In particular, the asymptotic normality of our proposed CARDS estimator is established, which reveals better estimation accuracy for homogeneous parameters than that without homogeneity exploration. When our methods are combined with sparsity exploration, further efficiency can be achieved beyond the exploration of sparsity alone. This provides additional insights into the power of exploring low-dimensional structures in high-dimensional regression: homogeneity and sparsity. Our results also shed lights on the properties of the fussed Lasso. The newly developed method is further illustrated by simulation studies and applications to real data. Supplementary materials for this article are available online. PMID:26085701

Sequential updating of multimodal hydrogeologic parameter fields using localization and clustering techniques

NASA Astrophysics Data System (ADS)

Sun, Alexander Y.; Morris, Alan P.; Mohanty, Sitakanta

2009-07-01

Estimated parameter distributions in groundwater models may contain significant uncertainties because of data insufficiency. Therefore, adaptive uncertainty reduction strategies are needed to continuously improve model accuracy by fusing new observations. In recent years, various ensemble Kalman filters have been introduced as viable tools for updating high-dimensional model parameters. However, their usefulness is largely limited by the inherent assumption of Gaussian error statistics. Hydraulic conductivity distributions in alluvial aquifers, for example, are usually non-Gaussian as a result of complex depositional and diagenetic processes. In this study, we combine an ensemble Kalman filter with grid-based localization and a Gaussian mixture model (GMM) clustering techniques for updating high-dimensional, multimodal parameter distributions via dynamic data assimilation. We introduce innovative strategies (e.g., block updating and dimension reduction) to effectively reduce the computational costs associated with these modified ensemble Kalman filter schemes. The developed data assimilation schemes are demonstrated numerically for identifying the multimodal heterogeneous hydraulic conductivity distributions in a binary facies alluvial aquifer. Our results show that localization and GMM clustering are very promising techniques for assimilating high-dimensional, multimodal parameter distributions, and they outperform the corresponding global ensemble Kalman filter analysis scheme in all scenarios considered.

Three-dimensional P-wave velocity structure derived from local earthquakes at the Katmai group of volcanoes, Alaska

USGS Publications Warehouse

Jolly, A.D.; Moran, S.C.; McNutt, S.R.; Stone, D.B.

2007-01-01

The three-dimensional P-wave velocity structure beneath the Katmai group of volcanoes is determined by inversion of more than 10,000 rays from over 1000 earthquakes recorded on a local 18 station short-period network between September 1996 and May 2001. The inversion is well constrained from sea level to about 6??km below sea level and encompasses all of the Katmai volcanoes; Martin, Mageik, Trident, Griggs, Novarupta, Snowy, and Katmai caldera. The inversion reduced the average RMS travel-time error from 0.22??s for locations from the standard one-dimensional model to 0.13??s for the best three-dimensional model. The final model, from the 6th inversion step, reveals a prominent low velocity zone (3.6-5.0??km/s) centered at Katmai Pass and extending from Mageik to Trident volcanoes. The anomaly has values about 20-25% slower than velocities outboard of the region (5.0-6.5??km/s). Moderately low velocities (4.5-6.0??km/s) are observed along the volcanic axis between Martin and Katmai Caldera. Griggs volcano, located about 10??km behind (northwest of) the volcanic axis, has unremarkable velocities (5.0-5.7??km/s) compared to non-volcanic regions. The highest velocities are observed between Snowy and Griggs volcanoes (5.5-6.5??km/s). Relocated hypocenters for the best 3-D model are shifted significantly relative to the standard model with clusters of seismicity at Martin volcano shifting systematically deeper by about 1??km to depths of 0 to 4??km below sea level. Hypocenters for the Katmai Caldera are more tightly clustered, relocating beneath the 1912 scarp walls. The relocated hypocenters allow us to compare spatial frequency-size distributions (b-values) using one-dimensional and three-dimensional models. We find that the distribution of b is significantly changed for Martin volcano, which was characterized by variable values (0.8 < b < 2.0) with standard locations and more uniform values (0.8 < b < 1.2) after relocation. Other seismic clusters at Mageik (1.2 < b < 2.2), Trident (0.5 < b < 1.5) and Katmai Caldera (0.8 < b < 1.8) had stable b-values indicating the robustness of the observations. The strong high b-value region at Mageik volcano is mainly associated with an earthquake swarm in October, 1996 that possibly indicates a shallow intrusion or influx of gas. The new velocity and spatial b-value results, in conjunction with prior gravity (Bouguer anomalies up to - 40??mgal) and interferometry (several cm uplift) data, provide strong evidence in favor of partially molten rock at shallow depths beneath the Mageik-Katmai-Novarupta region. Moderately low velocities beneath Martin and Katmai suggest that old, mostly solidified intrusions exist beneath these volcanoes. Higher relative velocities beneath the Griggs and Snowy vents suggest that no magma is resident in the shallow crust beneath these volcanoes. ?? 2006 Elsevier B.V.

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

NASA Astrophysics Data System (ADS)

Curotto, E.; Mella, M.

2018-03-01

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

Cluster state generation in one-dimensional Kitaev honeycomb model via shortcut to adiabaticity

NASA Astrophysics Data System (ADS)

Kyaw, Thi Ha; Kwek, Leong-Chuan

2018-04-01

We propose a mean to obtain computationally useful resource states also known as cluster states, for measurement-based quantum computation, via transitionless quantum driving algorithm. The idea is to cool the system to its unique ground state and tune some control parameters to arrive at computationally useful resource state, which is in one of the degenerate ground states. Even though there is set of conserved quantities already present in the model Hamiltonian, which prevents the instantaneous state to go to any other eigenstate subspaces, one cannot quench the control parameters to get the desired state. In that case, the state will not evolve. With involvement of the shortcut Hamiltonian, we obtain cluster states in fast-forward manner. We elaborate our proposal in the one-dimensional Kitaev honeycomb model, and show that the auxiliary Hamiltonian needed for the counterdiabatic driving is of M-body interaction.

Crystallization process of a three-dimensional complex plasma

NASA Astrophysics Data System (ADS)

Steinmüller, Benjamin; Dietz, Christopher; Kretschmer, Michael; Thoma, Markus H.

2018-05-01

Characteristic timescales and length scales for phase transitions of real materials are in ranges where a direct visualization is unfeasible. Therefore, model systems can be useful. Here, the crystallization process of a three-dimensional complex plasma under gravity conditions is considered where the system ranges up to a large extent into the bulk plasma. Time-resolved measurements exhibit the process down to a single-particle level. Primary clusters, consisting of particles in the solid state, grow vertically and, secondarily, horizontally. The box-counting method shows a fractal dimension of df≈2.72 for the clusters. This value gives a hint that the formation process is a combination of local epitaxial and diffusion-limited growth. The particle density and the interparticle distance to the nearest neighbor remain constant within the clusters during crystallization. All results are in good agreement with former observations of a single-particle layer.

Nonlinear dimensionality reduction methods for synthetic biology biobricks' visualization.

PubMed

Yang, Jiaoyun; Wang, Haipeng; Ding, Huitong; An, Ning; Alterovitz, Gil

2017-01-19

Visualizing data by dimensionality reduction is an important strategy in Bioinformatics, which could help to discover hidden data properties and detect data quality issues, e.g. data noise, inappropriately labeled data, etc. As crowdsourcing-based synthetic biology databases face similar data quality issues, we propose to visualize biobricks to tackle them. However, existing dimensionality reduction methods could not be directly applied on biobricks datasets. Hereby, we use normalized edit distance to enhance dimensionality reduction methods, including Isomap and Laplacian Eigenmaps. By extracting biobricks from synthetic biology database Registry of Standard Biological Parts, six combinations of various types of biobricks are tested. The visualization graphs illustrate discriminated biobricks and inappropriately labeled biobricks. Clustering algorithm K-means is adopted to quantify the reduction results. The average clustering accuracy for Isomap and Laplacian Eigenmaps are 0.857 and 0.844, respectively. Besides, Laplacian Eigenmaps is 5 times faster than Isomap, and its visualization graph is more concentrated to discriminate biobricks. By combining normalized edit distance with Isomap and Laplacian Eigenmaps, synthetic biology biobircks are successfully visualized in two dimensional space. Various types of biobricks could be discriminated and inappropriately labeled biobricks could be determined, which could help to assess crowdsourcing-based synthetic biology databases' quality, and make biobricks selection.

Spatial model of the gecko foot hair: functional significance of highly specialized non-uniform geometry.

PubMed

Filippov, Alexander E; Gorb, Stanislav N

2015-02-06

One of the important problems appearing in experimental realizations of artificial adhesives inspired by gecko foot hair is so-called clusterization. If an artificially produced structure is flexible enough to allow efficient contact with natural rough surfaces, after a few attachment-detachment cycles, the fibres of the structure tend to adhere one to another and form clusters. Normally, such clusters are much larger than original fibres and, because they are less flexible, form much worse adhesive contacts especially with the rough surfaces. Main problem here is that the forces responsible for the clusterization are the same intermolecular forces which attract fibres to fractal surface of the substrate. However, arrays of real gecko setae are much less susceptible to this problem. One of the possible reasons for this is that ends of the seta have more sophisticated non-uniformly distributed three-dimensional structure than that of existing artificial systems. In this paper, we simulated three-dimensional spatial geometry of non-uniformly distributed branches of nanofibres of the setal tip numerically, studied its attachment-detachment dynamics and discussed its advantages versus uniformly distributed geometry.

Method and system for data clustering for very large databases

NASA Technical Reports Server (NTRS)

Livny, Miron (Inventor); Zhang, Tian (Inventor); Ramakrishnan, Raghu (Inventor)

1998-01-01

Multi-dimensional data contained in very large databases is efficiently and accurately clustered to determine patterns therein and extract useful information from such patterns. Conventional computer processors may be used which have limited memory capacity and conventional operating speed, allowing massive data sets to be processed in a reasonable time and with reasonable computer resources. The clustering process is organized using a clustering feature tree structure wherein each clustering feature comprises the number of data points in the cluster, the linear sum of the data points in the cluster, and the square sum of the data points in the cluster. A dense region of data points is treated collectively as a single cluster, and points in sparsely occupied regions can be treated as outliers and removed from the clustering feature tree. The clustering can be carried out continuously with new data points being received and processed, and with the clustering feature tree being restructured as necessary to accommodate the information from the newly received data points.

The formation of magnetic silicide Fe3Si clusters during ion implantation

NASA Astrophysics Data System (ADS)

Balakirev, N.; Zhikharev, V.; Gumarov, G.

2014-05-01

A simple two-dimensional model of the formation of magnetic silicide Fe3Si clusters during high-dose Fe ion implantation into silicon has been proposed and the cluster growth process has been computer simulated. The model takes into account the interaction between the cluster magnetization and magnetic moments of Fe atoms random walking in the implanted layer. If the clusters are formed in the presence of the external magnetic field parallel to the implanted layer, the model predicts the elongation of the growing cluster in the field direction. It has been proposed that the cluster elongation results in the uniaxial magnetic anisotropy in the plane of the implanted layer, which is observed in iron silicide films ion-beam synthesized in the external magnetic field.

Mechanical properties of Fe rich Fe-Si alloys: ab initio local bulk-modulus viewpoint

NASA Astrophysics Data System (ADS)

Bhattacharya, Somesh Kr; Kohyama, Masanori; Tanaka, Shingo; Shiihara, Yoshinori; Saengdeejing, Arkapol; Chen, Ying; Mohri, Tetsuo

2017-11-01

Fe-rich Fe-Si alloys show peculiar bulk-modulus changes depending on the Si concentration in the range of 0-15 at.%Si. In order to clarify the origin of this phenomenon, we have performed density-functional theory calculations of supercells of Fe-Si alloy models with various Si concentrations. We have applied our recent techniques of ab initio local energy and local stress, by which we can obtain a local bulk modulus of each atom or atomic group as a local constituent of the cell-averaged bulk modulus. A2-phase alloy models are constructed by introducing Si substitution into bcc Fe as uniformly as possible so as to prevent mutual neighboring, while higher Si concentrations over 6.25 at.%Si lead to contacts between SiFe8 cubic clusters via sharing corner Fe atoms. For 12.5 at.%Si, in addition to an A2 model, we deal with partial D03 models containing local D03-like layers consisting of edge-shared SiFe8 cubic clusters. For the cell-averaged bulk modulus, we have successfully reproduced the Si-concentration dependence as a monotonic decrease until 11.11 at.%Si and a recovery at 12.5 at.%Si. The analysis of local bulk moduli of SiFe8 cubic clusters and Fe regions is effective to understand the variations of the cell-averaged bulk modulus. The local bulk moduli of Fe regions become lower for increasing Si concentration, due to the suppression of bulk-like d-d bonding states in narrow Fe regions. For higher Si concentrations till 11.11 at.%Si, corner-shared contacts or 1D chains of SiFe8 clusters lead to remarkable reduction of local bulk moduli of the clusters. At 12 at.%Si, on the other hand, two- or three-dimensional arrangements of corner- or edge-shared SiFe8 cubic clusters show greatly enhanced local bulk moduli, due to quite different bonding nature with much stronger p-d hybridization. The relation among the local bulk moduli, local electronic and magnetic structures, and local configurations such as connectivity of SiFe8 clusters and Fe-region sizes has been analyzed. The ab initio local stress has opened the way for obtaining accurate local elastic properties reflecting local valence-electron behaviors.

Spontaneous assembly of chemically encoded two-dimensional coacervate droplet arrays by acoustic wave patterning

PubMed Central

Tian, Liangfei; Martin, Nicolas; Bassindale, Philip G.; Patil, Avinash J.; Li, Mei; Barnes, Adrian; Drinkwater, Bruce W.; Mann, Stephen

2016-01-01

The spontaneous assembly of chemically encoded, molecularly crowded, water-rich micro-droplets into periodic defect-free two-dimensional arrays is achieved in aqueous media by a combination of an acoustic standing wave pressure field and in situ complex coacervation. Acoustically mediated coalescence of primary droplets generates single-droplet per node micro-arrays that exhibit variable surface-attachment properties, spontaneously uptake dyes, enzymes and particles, and display spatial and time-dependent fluorescence outputs when exposed to a reactant diffusion gradient. In addition, coacervate droplet arrays exhibiting dynamical behaviour and exchange of matter are prepared by inhibiting coalescence to produce acoustically trapped lattices of droplet clusters that display fast and reversible changes in shape and spatial configuration in direct response to modulations in the acoustic frequencies and fields. Our results offer a novel route to the design and construction of ‘water-in-water' micro-droplet arrays with controllable spatial organization, programmable signalling pathways and higher order collective behaviour. PMID:27708286

STAR CLUSTERS IN A NUCLEAR STAR FORMING RING: THE DISAPPEARING STRING OF PEARLS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Väisänen, Petri; Barway, Sudhanshu; Randriamanakoto, Zara, E-mail: petri@saao.ac.za

2014-12-20

An analysis of the star cluster population in a low-luminosity early-type galaxy, NGC 2328, is presented. The clusters are found in a tight star forming nuclear spiral/ring pattern and we also identify a bar from structural two-dimensional decomposition. These massive clusters are forming very efficiently in the circumnuclear environment and they are young, possibly all less than 30 Myr of age. The clusters indicate an azimuthal age gradient, consistent with a ''pearls-on-a-string'' formation scenario, suggesting bar-driven gas inflow. The cluster mass function has a robust down turn at low masses at all age bins. Assuming clusters are born with a power-lawmore » distribution, this indicates extremely rapid disruption at timescales of just several million years. If found to be typical, it means that clusters born in dense circumnuclear rings do not survive to become old globular clusters in non-interacting systems.« less

Porphyrin network materials: Chemical exploration in the supramolecular solid-state

NASA Astrophysics Data System (ADS)

Kosal, Margaret Elizabeth

Rational design of solid-state materials from molecular building blocks possessing desired physical and chemical characteristics remains among the most challenging tasks for the synthetic chemist. Using p-carboxylic acid tetraphenyl porphyrin molecules, H2T(p-CO 2H)PP, as the organic building block, the synthesis of novel microporous coordination framework materials has been pursued for this work. The self-assembly of the anionic carboxylate with divalent alkaline earth or transition metal cations yielded clathrate, lamellar and three-dimensional network materials. The solvothermal synthesis, characterization, and selective sorption properties of a 3-dimensional metalloporphyrin network solid, [CoT( p-CO2)PPCo1.5], named PIZA-1 for Porphyrinic Illinois Zeolite Analogue 1, have been investigated. The extended structure reveals a single, independent, neutral network with large, bi-directional oval-shaped channels (9 x 7 A) along the crystallographic b - and c-axes and another set of channels (14 x 7 A) along the a-axis. At the intersection of channels, an internal chamber (31 x 31 x 10 A) is realized. Channel-shape is attributable to ruffling of the metalloporphyrin macrocycles when coordinated to the bridging trinuclear Co(II)-carboxylate clusters. The void volume of the stable, thermally robust, solvate-free material is calculated to be 74% of the total unit cell volume. Size-, shape- and functional-group-selective sorption indicates a preference for water and amines. This organic zeolite analogue also demonstrates remarkable ability as a molecular sieve for removal of water from common organic solvents. By powder X-ray diffraction, BET gas adsorption studies and FTIR, this material has been shown to maintain its porous structure as a guest-free solid when heated under vacuum to 250°C. PIZA-1 demonstrates extremely high capacity for repeated selective sorption of water. In comparison to 4A molecular sieves, PIZA-1 exhibits higher capacity and faster response for the selective adsorption of water from common organic solvents. Molecular modeling of corroborates experimental results. The large internal cavities of PIZA-1 are a consequence of the trinuclear Co(II)carboxylate cluster forcing the ruffling of the porphyrin building blocks. The linear trinuclear metal-carboxylate cluster of PIZA-1 is contrasted with the bent trinuclear M(II) carboxylate clusters (M = Co, Mn) of isostructural 3-dimensional frameworks: PIZA-2 and PIZA-3. Containing near-planar metalloporphyrin macrocycles, PIZA-2 and PIZA-3 manifest lower void volumes (56%).

Human vocal tract resonances and the corresponding mode shapes investigated by three-dimensional finite-element modelling based on CT measurement.

PubMed

Vampola, Tomáš; Horáček, Jaromír; Laukkanen, Anne-Maria; Švec, Jan G

2015-04-01

Resonance frequencies of the vocal tract have traditionally been modelled using one-dimensional models. These cannot accurately represent the events in the frequency region of the formant cluster around 2.5-4.5 kHz, however. Here, the vocal tract resonance frequencies and their mode shapes are studied using a three-dimensional finite element model obtained from computed tomography measurements of a subject phonating on vowel [a:]. Instead of the traditional five, up to eight resonance frequencies of the vocal tract were found below the prominent antiresonance around 4.7 kHz. The three extra resonances were found to correspond to modes which were axially asymmetric and involved the piriform sinuses, valleculae, and transverse vibrations in the oral cavity. The results therefore suggest that the phenomenon of speaker's and singer's formant clustering may be more complex than originally thought.

Three-Dimensional Modeling of Fracture Clusters in Geothermal Reservoirs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ghassemi, Ahmad

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

Fast Multipole Methods for Three-Dimensional N-body Problems

NASA Technical Reports Server (NTRS)

Koumoutsakos, P.

1995-01-01

We are developing computational tools for the simulations of three-dimensional flows past bodies undergoing arbitrary motions. High resolution viscous vortex methods have been developed that allow for extended simulations of two-dimensional configurations such as vortex generators. Our objective is to extend this methodology to three dimensions and develop a robust computational scheme for the simulation of such flows. A fundamental issue in the use of vortex methods is the ability of employing efficiently large numbers of computational elements to resolve the large range of scales that exist in complex flows. The traditional cost of the method scales as Omicron (N(sup 2)) as the N computational elements/particles induce velocities at each other, making the method unacceptable for simulations involving more than a few tens of thousands of particles. In the last decade fast methods have been developed that have operation counts of Omicron (N log N) or Omicron (N) (referred to as BH and GR respectively) depending on the details of the algorithm. These methods are based on the observation that the effect of a cluster of particles at a certain distance may be approximated by a finite series expansion. In order to exploit this observation we need to decompose the element population spatially into clusters of particles and build a hierarchy of clusters (a tree data structure) - smaller neighboring clusters combine to form a cluster of the next size up in the hierarchy and so on. This hierarchy of clusters allows one to determine efficiently when the approximation is valid. This algorithm is an N-body solver that appears in many fields of engineering and science. Some examples of its diverse use are in astrophysics, molecular dynamics, micro-magnetics, boundary element simulations of electromagnetic problems, and computer animation. More recently these N-body solvers have been implemented and applied in simulations involving vortex methods. Koumoutsakos and Leonard (1995) implemented the GR scheme in two dimensions for vector computer architectures allowing for simulations of bluff body flows using millions of particles. Winckelmans presented three-dimensional, viscous simulations of interacting vortex rings, using vortons and an implementation of a BH scheme for parallel computer architectures. Bhatt presented a vortex filament method to perform inviscid vortex ring interactions, with an alternative implementation of a BH scheme for a Connection Machine parallel computer architecture.

Asteroid clusters similar to asteroid pairs

NASA Astrophysics Data System (ADS)

Pravec, Petr; Vokrouhlicky, David; Fatka, Petr; Kusnirák, Peter; Hornoch, Kamil; Galád, Adrián

2016-10-01

We study five small, tight and young clusters of asteroids. They are placed around following largest (primary) bodies: (11842) Kap'bos, (14627) Emilkowalski, (16598) 1992 YC2, (21509) Lucascavin and (39991) 1998 HR37. Each cluster has 2-4 secondaries that are tightly clustered around the primary body, with distance in the 5-dimensional space of mean orbital elements mostly within 10 m/s, and always < 23 m/s. Backward orbital integrations indicate that they formed between 105 and 106 yr ago. In the P1-q space, where P1 is the primary's spin period and q = Σ Mj/M1 is the total secondary-to-primary mass ratio, the clusters lie in the same range as asteroid pairs formed by rotational fission. We have extended the model of a proto-system separation after rotational fission by Pravec et al. (2010) for application to systems with more than one secondary and found a perfect match for the five tight clusters. We find these clusters to be similar to asteroid pairs and we suggest that they are "extended pairs", having 2-4 escaped secondaries rather than just one secondary as in the case of an asteroid pair. We compare them to six young mini-families (1270) Datura, (2384) Schulhof, (3152) Jones, (6825) Irvine, (10321) Rampo and (20674) 1999 VT1. These mini-families have similar ages, but they have a higher number of members and/or they show a significantly larger spread in the mean orbital elements (dmean on an order of tens m/s) than the five tight clusters. In the P1-q space, all but one of the mini-families lie in the same range as asteroid pairs and the tight clusters; the exception is the mini-family of (3152) Jones which appears to be a collisional family. A possibility that the other five mini-families were also formed by rotational fission as we suggest for the tight clusters ("extended asteroid pairs") is being explored.Reference:Pravec, P., et al. Formation of asteroid pairs by rotational fission. Nature 466, 1085-1088.

Structural disorder in the decagonal Al-Co-Ni. I. Patterson analysis of diffuse x-ray scattering data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kobas, Miroslav; Weber, Thomas; Steurer, Walter

The three-dimensional (3D) difference Patterson (autocorrelation) function of a disordered quasicrystal (Edagawa phase) has been analyzed. 3D diffuse x-ray diffraction data were collected in situ at 300, 1070, and 1120 K. A method, the punch-and-fill technique, has been developed for separating diffuse scattering and Bragg reflections. Its potential and limits are discussed in detail. The different Patterson maps are interpreted in terms of intercluster correlations as a function of temperature. Both at high and low temperatures, the clusters decorate the vertices of the same quasiperiodic covering. At low temperatures, for the disordered part of the structure, short-range intercluster correlations aremore » present, whereas at higher temperatures, medium-range intercluster correlations are formed. This indicates disorder mainly inside clusters at low temperatures, whereas at higher temperatures disorder takes place inside larger superclusters. Qualitatively, the Patterson maps may be interpreted by intercluster correlations mainly inside pentagonal superclusters below 1120 K, and inside the larger decagonal superclusters at 1120 K. The results of our diffraction study are published in two parts. Part I focuses on the 3D Patterson analysis based on experimental data, Part II reports modeling of structural disorder in decagonal Al-Co-Ni.« less

Band structures in coupled-cluster singles-and-doubles Green's function (GFCCSD)

NASA Astrophysics Data System (ADS)

Furukawa, Yoritaka; Kosugi, Taichi; Nishi, Hirofumi; Matsushita, Yu-ichiro

2018-05-01

We demonstrate that the coupled-cluster singles-and-doubles Green's function (GFCCSD) method is a powerful and prominent tool drawing the electronic band structures and the total energies, which many theoretical techniques struggle to reproduce. We have calculated single-electron energy spectra via the GFCCSD method for various kinds of systems, ranging from ionic to covalent and van der Waals, for the first time: the one-dimensional LiH chain, one-dimensional C chain, and one-dimensional Be chain. We have found that the bandgap becomes narrower than in HF due to the correlation effect. We also show that the band structures obtained from the GFCCSD method include both quasiparticle and satellite peaks successfully. Besides, taking one-dimensional LiH as an example, we discuss the validity of restricting the active space to suppress the computational cost of the GFCCSD method. We show that the calculated results without bands that do not contribute to the chemical bonds are in good agreement with full-band calculations. With the GFCCSD method, we can calculate the total energies and spectral functions for periodic systems in an explicitly correlated manner.

Symptom Dimensions of the Psychotic Symptom Rating Scales in Psychosis: A Multisite Study

PubMed Central

Woodward, Todd S.; Jung, Kwanghee; Hwang, Heungsun; Yin, John; Taylor, Laura; Menon, Mahesh; Peters, Emmanuelle; Kuipers, Elizabeth; Waters, Flavie; Lecomte, Tania; Sommer, Iris E.; Daalman, Kirstin; van Lutterveld, Remko; Hubl, Daniela; Kindler, Jochen; Homan, Philipp; Badcock, Johanna C.; Chhabra, Saruchi; Cella, Matteo; Keedy, Sarah; Allen, Paul; Mechelli, Andrea; Preti, Antonio; Siddi, Sara; Erickson, David

2014-01-01

The Psychotic Symptom Rating Scales (PSYRATS) is an instrument designed to quantify the severity of delusions and hallucinations and is typically used in research studies and clinical settings focusing on people with psychosis and schizophrenia. It is comprised of the auditory hallucinations (AHS) and delusions subscales (DS), but these subscales do not necessarily reflect the psychological constructs causing intercorrelation between clusters of scale items. Identification of these constructs is important in some clinical and research contexts because item clustering may be caused by underlying etiological processes of interest. Previous attempts to identify these constructs have produced conflicting results. In this study, we compiled PSYRATS data from 12 sites in 7 countries, comprising 711 participants for AHS and 520 for DS. We compared previously proposed and novel models of underlying constructs using structural equation modeling. For the AHS, a novel 4-dimensional model provided the best fit, with latent variables labeled Distress (negative content, distress, and control), Frequency (frequency, duration, and disruption), Attribution (location and origin of voices), and Loudness (loudness item only). For the DS, a 2-dimensional solution was confirmed, with latent variables labeled Distress (amount/intensity) and Frequency (preoccupation, conviction, and disruption). The within-AHS and within-DS dimension intercorrelations were higher than those between subscales, with the exception of the AHS and DS Distress dimensions, which produced a correlation that approached the range of the within-scale correlations. Recommendations are provided for integrating these underlying constructs into research and clinical applications of the PSYRATS. PMID:24936086

Elucidation of Seventeen Human Peripheral Blood B cell Subsets and Quantification of the Tetanus Response Using a Density-Based Method for the Automated Identification of Cell Populations in Multidimensional Flow Cytometry Data

PubMed Central

Qian, Yu; Wei, Chungwen; Lee, F. Eun-Hyung; Campbell, John; Halliley, Jessica; Lee, Jamie A.; Cai, Jennifer; Kong, Megan; Sadat, Eva; Thomson, Elizabeth; Dunn, Patrick; Seegmiller, Adam C.; Karandikar, Nitin J.; Tipton, Chris; Mosmann, Tim; Sanz, Iñaki; Scheuermann, Richard H.

2011-01-01

Background Advances in multi-parameter flow cytometry (FCM) now allow for the independent detection of larger numbers of fluorochromes on individual cells, generating data with increasingly higher dimensionality. The increased complexity of these data has made it difficult to identify cell populations from high-dimensional FCM data using traditional manual gating strategies based on single-color or two-color displays. Methods To address this challenge, we developed a novel program, FLOCK (FLOw Clustering without K), that uses a density-based clustering approach to algorithmically identify biologically relevant cell populations from multiple samples in an unbiased fashion, thereby eliminating operator-dependent variability. Results FLOCK was used to objectively identify seventeen distinct B cell subsets in a human peripheral blood sample and to identify and quantify novel plasmablast subsets responding transiently to tetanus and other vaccinations in peripheral blood. FLOCK has been implemented in the publically available Immunology Database and Analysis Portal – ImmPort (http://www.immport.org) for open use by the immunology research community. Conclusions FLOCK is able to identify cell subsets in experiments that use multi-parameter flow cytometry through an objective, automated computational approach. The use of algorithms like FLOCK for FCM data analysis obviates the need for subjective and labor intensive manual gating to identify and quantify cell subsets. Novel populations identified by these computational approaches can serve as hypotheses for further experimental study. PMID:20839340

Price Formation Based on Particle-Cluster Aggregation

NASA Astrophysics Data System (ADS)

Wang, Shijun; Zhang, Changshui

In the present work, we propose a microscopic model of financial markets based on particle-cluster aggregation on a two-dimensional small-world information network in order to simulate the dynamics of the stock markets. "Stylized facts" of the financial market time series, such as fat-tail distribution of returns, volatility clustering and multifractality, are observed in the model. The results of the model agree with empirical data taken from historical records of the daily closures of the NYSE composite index.

Fusion And Inference From Multiple And Massive Disparate Distributed Dynamic Data Sets

DTIC Science & Technology

2017-07-01

principled methodology for two-sample graph testing; designed a provably almost-surely perfect vertex clustering algorithm for block model graphs; proved...3.7 Semi-Supervised Clustering Methodology ...................................................................... 9 3.8 Robust Hypothesis Testing...dimensional Euclidean space – allows the full arsenal of statistical and machine learning methodology for multivariate Euclidean data to be deployed for

Greenhouse tomato limited cluster production systems: crop management practices affect yield

NASA Technical Reports Server (NTRS)

Logendra, L. S.; Gianfagna, T. J.; Specca, D. R.; Janes, H. W.

2001-01-01

Limited-cluster production systems may be a useful strategy to increase crop production and profitability for the greenhouse tomato (Lycopersicon esculentum Mill). In this study, using an ebb-and-flood hydroponics system, we modified plant architecture and spacing and determined the effects on fruit yield and harvest index at two light levels. Single-cluster plants pruned to allow two leaves above the cluster had 25% higher fruit yields than did plants pruned directly above the cluster; this was due to an increase in fruit weight, not fruit number. Both fruit yield and harvest index were greater for all single-cluster plants at the higher light level because of increases in both fruit weight and fruit number. Fruit yield for two-cluster plants was 30% to 40% higher than for single-cluster plants, and there was little difference in the dates or length of the harvest period. Fruit yield for three-cluster plants was not significantly different from that of two-cluster plants; moreover, the harvest period was delayed by 5 days. Plant density (5.5, 7.4, 9.2 plants/m2) affected fruit yield/plant, but not fruit yield/unit area. Given the higher costs for materials and labor associated with higher plant densities, a two-cluster crop at 5.5 plants/m2 with two leaves above the cluster was the best of the production system strategies tested.

Self-assembled three-dimensional chiral colloidal architecture.

PubMed

Ben Zion, Matan Yah; He, Xiaojin; Maass, Corinna C; Sha, Ruojie; Seeman, Nadrian C; Chaikin, Paul M

2017-11-03

Although stereochemistry has been a central focus of the molecular sciences since Pasteur, its province has previously been restricted to the nanometric scale. We have programmed the self-assembly of micron-sized colloidal clusters with structural information stemming from a nanometric arrangement. This was done by combining DNA nanotechnology with colloidal science. Using the functional flexibility of DNA origami in conjunction with the structural rigidity of colloidal particles, we demonstrate the parallel self-assembly of three-dimensional microconstructs, evincing highly specific geometry that includes control over position, dihedral angles, and cluster chirality. Copyright © 2017 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

Spectral-element simulation of two-dimensional elastic wave propagation in fully heterogeneous media on a GPU cluster

NASA Astrophysics Data System (ADS)

Rudianto, Indra; Sudarmaji

2018-04-01

We present an implementation of the spectral-element method for simulation of two-dimensional elastic wave propagation in fully heterogeneous media. We have incorporated most of realistic geological features in the model, including surface topography, curved layer interfaces, and 2-D wave-speed heterogeneity. To accommodate such complexity, we use an unstructured quadrilateral meshing technique. Simulation was performed on a GPU cluster, which consists of 24 core processors Intel Xeon CPU and 4 NVIDIA Quadro graphics cards using CUDA and MPI implementation. We speed up the computation by a factor of about 5 compared to MPI only, and by a factor of about 40 compared to Serial implementation.

Self-confinement of finite dust clusters in isotropic plasmas.

PubMed

Miloshevsky, G V; Hassanein, A

2012-05-01

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

The Use of Multiple Correspondence Analysis to Explore Associations between Categories of Qualitative Variables in Healthy Ageing.

PubMed

Costa, Patrício Soares; Santos, Nadine Correia; Cunha, Pedro; Cotter, Jorge; Sousa, Nuno

2013-01-01

The main focus of this study was to illustrate the applicability of multiple correspondence analysis (MCA) in detecting and representing underlying structures in large datasets used to investigate cognitive ageing. Principal component analysis (PCA) was used to obtain main cognitive dimensions, and MCA was used to detect and explore relationships between cognitive, clinical, physical, and lifestyle variables. Two PCA dimensions were identified (general cognition/executive function and memory), and two MCA dimensions were retained. Poorer cognitive performance was associated with older age, less school years, unhealthier lifestyle indicators, and presence of pathology. The first MCA dimension indicated the clustering of general/executive function and lifestyle indicators and education, while the second association was between memory and clinical parameters and age. The clustering analysis with object scores method was used to identify groups sharing similar characteristics. The weaker cognitive clusters in terms of memory and executive function comprised individuals with characteristics contributing to a higher MCA dimensional mean score (age, less education, and presence of indicators of unhealthier lifestyle habits and/or clinical pathologies). MCA provided a powerful tool to explore complex ageing data, covering multiple and diverse variables, showing if a relationship exists and how variables are related, and offering statistical results that can be seen both analytically and visually.

Design and synthesis of an exceptionally stable and highly porous metal-organic framework

NASA Astrophysics Data System (ADS)

Li, Hailian; Eddaoudi, Mohamed; O'Keeffe, M.; Yaghi, O. M.

1999-11-01

Open metal-organic frameworks are widely regarded as promising materials for applications in catalysis, separation, gas storage and molecular recognition. Compared to conventionally used microporous inorganic materials such as zeolites, these organic structures have the potential for more flexible rational design, through control of the architecture and functionalization of the pores. So far, the inability of these open frameworks to support permanent porosity and to avoid collapsing in the absence of guest molecules, such as solvents, has hindered further progress in the field. Here we report the synthesis of a metal-organic framework which remains crystalline, as evidenced by X-ray single-crystal analyses, and stable when fully desolvated and when heated up to 300°C. This synthesis is achieved by borrowing ideas from metal carboxylate cluster chemistry, where an organic dicarboxylate linker is used in a reaction that gives supertetrahedron clusters when capped with monocarboxylates. The rigid and divergent character of the added linker allows the articulation of the clusters into a three-dimensional framework resulting in a structure with higher apparent surface area and pore volume than most porous crystalline zeolites. This simple and potentially universal design strategy is currently being pursued in the synthesis of new phases and composites, and for gas-storage applications.

Topological cell clustering in the ATLAS calorimeters and its performance in LHC Run 1

NASA Astrophysics Data System (ADS)

Aad, G.; Abbott, B.; Abdallah, J.; Abdinov, O.; Aben, R.; Abolins, M.; AbouZeid, O. S.; Abramowicz, H.; Abreu, H.; Abreu, R.; Abulaiti, Y.; Acharya, B. S.; Adamczyk, L.; Adams, D. L.; Adelman, J.; Adomeit, S.; Adye, T.; Affolder, A. A.; Agatonovic-Jovin, T.; Agricola, J.; Aguilar-Saavedra, J. A.; Ahlen, S. P.; Ahmadov, F.; Aielli, G.; Akerstedt, H.; Åkesson, T. P. A.; Akimov, A. V.; Alberghi, G. L.; Albert, J.; Albrand, S.; Verzini, M. J. Alconada; Aleksa, M.; Aleksandrov, I. N.; Alexa, C.; Alexander, G.; Alexopoulos, T.; Alhroob, M.; Alimonti, G.; Alio, L.; Alison, J.; Alkire, S. P.; Allbrooke, B. M. M.; Allport, P. P.; Aloisio, A.; Alonso, A.; Alonso, F.; Alpigiani, C.; Altheimer, A.; Gonzalez, B. Alvarez; Piqueras, D. Álvarez; Alviggi, M. G.; Amadio, B. T.; Amako, K.; Coutinho, Y. Amaral; Amelung, C.; Amidei, D.; Santos, S. P. Amor Dos; Amorim, A.; Amoroso, S.; Amram, N.; Amundsen, G.; Anastopoulos, C.; Ancu, L. S.; Andari, N.; Andeen, T.; Anders, C. F.; Anders, G.; Anders, J. K.; Anderson, K. J.; Andreazza, A.; Andrei, V.; Angelidakis, S.; Angelozzi, I.; Anger, P.; Angerami, A.; Anghinolfi, F.; Anisenkov, A. V.; Anjos, N.; Annovi, A.; Antonelli, M.; Antonov, A.; Antos, J.; Anulli, F.; Aoki, M.; Bella, L. Aperio; Arabidze, G.; Arai, Y.; Araque, J. P.; Arce, A. T. H.; Arduh, F. A.; Arguin, J.-F.; Argyropoulos, S.; Arik, M.; Armbruster, A. J.; Arnaez, O.; Arnold, H.; Arratia, M.; Arslan, O.; Artamonov, A.; Artoni, G.; Artz, S.; Asai, S.; Asbah, N.; Ashkenazi, A.; Åsman, B.; Asquith, L.; Assamagan, K.; Astalos, R.; Atkinson, M.; Atlay, N. B.; Augsten, K.; Aurousseau, M.; Avolio, G.; Axen, B.; Ayoub, M. K.; Azuelos, G.; Baak, M. A.; Baas, A. E.; Baca, M. J.; Bacci, C.; Bachacou, H.; Bachas, K.; Backes, M.; Backhaus, M.; Bagiacchi, P.; Bagnaia, P.; Bai, Y.; Bain, T.; Baines, J. T.; Baker, O. K.; Baldin, E. M.; Balek, P.; Balestri, T.; Balli, F.; Balunas, W. K.; Banas, E.; Banerjee, Sw.; Bannoura, A. A. E.; Barak, L.; Barberio, E. L.; Barberis, D.; Barbero, M.; Barillari, T.; Barisonzi, M.; Barklow, T.; Barlow, N.; Barnes, S. L.; Barnett, B. M.; Barnett, R. M.; Barnovska, Z.; Baroncelli, A.; Barone, G.; Barr, A. J.; Barreiro, F.; da Costa, J. Barreiro Guimarães; Bartoldus, R.; Barton, A. E.; Bartos, P.; Basalaev, A.; Bassalat, A.; Basye, A.; Bates, R. L.; Batista, S. J.; Batley, J. R.; Battaglia, M.; Bauce, M.; Bauer, F.; Bawa, H. S.; Beacham, J. B.; Beattie, M. D.; Beau, T.; Beauchemin, P. H.; Beccherle, R.; Bechtle, P.; Beck, H. P.; Becker, K.; Becker, M.; Beckingham, M.; Becot, C.; Beddall, A. J.; Beddall, A.; Bednyakov, V. A.; Bee, C. P.; Beemster, L. J.; Beermann, T. A.; Begel, M.; Behr, J. K.; Belanger-Champagne, C.; Bell, W. H.; Bella, G.; Bellagamba, L.; Bellerive, A.; Bellomo, M.; Belotskiy, K.; Beltramello, O.; Benary, O.; Benchekroun, D.; Bender, M.; Bendtz, K.; Benekos, N.; Benhammou, Y.; Noccioli, E. Benhar; Garcia, J. A. Benitez; Benjamin, D. P.; Bensinger, J. R.; Bentvelsen, S.; Beresford, L.; Beretta, M.; Berge, D.; Kuutmann, E. Bergeaas; Berger, N.; Berghaus, F.; Beringer, J.; Bernard, C.; Bernard, N. R.; Bernius, C.; Bernlochner, F. U.; Berry, T.; Berta, P.; Bertella, C.; Bertoli, G.; Bertolucci, F.; Bertsche, C.; Bertsche, D.; Besana, M. I.; Besjes, G. J.; Bylund, O. Bessidskaia; Bessner, M.; Besson, N.; Betancourt, C.; Bethke, S.; Bevan, A. J.; Bhimji, W.; Bianchi, R. M.; Bianchini, L.; Bianco, M.; Biebel, O.; Biedermann, D.; Biesuz, N. V.; Biglietti, M.; De Mendizabal, J. Bilbao; Bilokon, H.; Bindi, M.; Binet, S.; Bingul, A.; Bini, C.; Biondi, S.; Bjergaard, D. M.; Black, C. W.; Black, J. E.; Black, K. M.; Blackburn, D.; Blair, R. E.; Blanchard, J.-B.; Blanco, J. E.; Blazek, T.; Bloch, I.; Blocker, C.; Blum, W.; Blumenschein, U.; Blunier, S.; Bobbink, G. J.; Bobrovnikov, V. S.; Bocchetta, S. S.; Bocci, A.; Bock, C.; Boehler, M.; Bogaerts, J. A.; Bogavac, D.; Bogdanchikov, A. G.; Bohm, C.; Boisvert, V.; Bold, T.; Boldea, V.; Boldyrev, A. S.; Bomben, M.; Bona, M.; Boonekamp, M.; Borisov, A.; Borissov, G.; Borroni, S.; Bortfeldt, J.; Bortolotto, V.; Bos, K.; Boscherini, D.; Bosman, M.; Boudreau, J.; Bouffard, J.; Bouhova-Thacker, E. V.; Boumediene, D.; Bourdarios, C.; Bousson, N.; Boutle, S. K.; Boveia, A.; Boyd, J.; Boyko, I. R.; Bozic, I.; Bracinik, J.; Brandt, A.; Brandt, G.; Brandt, O.; Bratzler, U.; Brau, B.; Brau, J. E.; Braun, H. M.; Madden, W. D. Breaden; Brendlinger, K.; Brennan, A. J.; Brenner, L.; Brenner, R.; Bressler, S.; Bristow, T. M.; Britton, D.; Britzger, D.; Brochu, F. M.; Brock, I.; Brock, R.; Bronner, J.; Brooijmans, G.; Brooks, T.; Brooks, W. K.; Brosamer, J.; Brost, E.; de Renstrom, P. A. Bruckman; Bruncko, D.; Bruneliere, R.; Bruni, A.; Bruni, G.; Bruschi, M.; Bruscino, N.; Bryngemark, L.; Buanes, T.; Buat, Q.; Buchholz, P.; Buckley, A. G.; Budagov, I. A.; Buehrer, F.; Bugge, L.; Bugge, M. K.; Bulekov, O.; Bullock, D.; Burckhart, H.; Burdin, S.; Burgard, C. D.; Burghgrave, B.; Burke, S.; Burmeister, I.; Busato, E.; Büscher, D.; Büscher, V.; Bussey, P.; Butler, J. M.; Butt, A. I.; Buttar, C. M.; Butterworth, J. M.; Butti, P.; Buttinger, W.; Buzatu, A.; Buzykaev, A. R.; Urbán, S. Cabrera; Caforio, D.; Cairo, V. M.; Cakir, O.; Calace, N.; Calafiura, P.; Calandri, A.; Calderini, G.; Calfayan, P.; Caloba, L. P.; Calvet, D.; Calvet, S.; Toro, R. Camacho; Camarda, S.; Camarri, P.; Cameron, D.; Armadans, R. Caminal; Campana, S.; Campanelli, M.; Campoverde, A.; Canale, V.; Canepa, A.; Bret, M. Cano; Cantero, J.; Cantrill, R.; Cao, T.; Garrido, M. D. M. Capeans; Caprini, I.; Caprini, M.; Capua, M.; Caputo, R.; Carbone, R. M.; Cardarelli, R.; Cardillo, F.; Carli, T.; Carlino, G.; Carminati, L.; Caron, S.; Carquin, E.; Carrillo-Montoya, G. D.; Carter, J. R.; Carvalho, J.; Casadei, D.; Casado, M. P.; Casolino, M.; Casper, D. W.; Castaneda-Miranda, E.; Castelli, A.; Gimenez, V. Castillo; Castro, N. F.; Catastini, P.; Catinaccio, A.; Catmore, J. R.; Cattai, A.; Caudron, J.; Cavaliere, V.; Cavalli, D.; Cavalli-Sforza, M.; Cavasinni, V.; Ceradini, F.; Alberich, L. Cerda; Cerio, B. C.; Cerny, K.; Cerqueira, A. S.; Cerri, A.; Cerrito, L.; Cerutti, F.; Cerv, M.; Cervelli, A.; Cetin, S. A.; Chafaq, A.; Chakraborty, D.; Chalupkova, I.; Chan, Y. L.; Chang, P.; Chapman, J. D.; Charlton, D. G.; Chau, C. C.; Barajas, C. A. Chavez; Che, S.; Cheatham, S.; Chegwidden, A.; Chekanov, S.; Chekulaev, S. V.; Chelkov, G. A.; Chelstowska, M. A.; Chen, C.; Chen, H.; Chen, K.; Chen, L.; Chen, S.; Chen, S.; Chen, X.; Chen, Y.; Cheng, H. C.; Cheng, Y.; Cheplakov, A.; Cheremushkina, E.; Moursli, R. Cherkaoui El; Chernyatin, V.; Cheu, E.; Chevalier, L.; Chiarella, V.; Chiarelli, G.; Chiodini, G.; Chisholm, A. S.; Chislett, R. T.; Chitan, A.; Chizhov, M. V.; Choi, K.; Chouridou, S.; Chow, B. K. B.; Christodoulou, V.; Chromek-Burckhart, D.; Chudoba, J.; Chuinard, A. J.; Chwastowski, J. J.; Chytka, L.; Ciapetti, G.; Ciftci, A. K.; Cinca, D.; Cindro, V.; Cioara, I. A.; Ciocio, A.; Cirotto, F.; Citron, Z. H.; Ciubancan, M.; Clark, A.; Clark, B. L.; Clark, P. J.; Clarke, R. N.; Clement, C.; Coadou, Y.; Cobal, M.; Coccaro, A.; Cochran, J.; Coffey, L.; Cogan, J. G.; Colasurdo, L.; Cole, B.; Cole, S.; Colijn, A. P.; Collot, J.; Colombo, T.; Compostella, G.; Muiño, P. Conde; Coniavitis, E.; Connell, S. H.; Connelly, I. A.; Consorti, V.; Constantinescu, S.; Conta, C.; Conti, G.; Conventi, F.; Cooke, M.; Cooper, B. D.; Cooper-Sarkar, A. M.; Cornelissen, T.; Corradi, M.; Corriveau, F.; Corso-Radu, A.; Cortes-Gonzalez, A.; Cortiana, G.; Costa, G.; Costa, M. J.; Costanzo, D.; Côté, D.; Cottin, G.; Cowan, G.; Cox, B. E.; Cranmer, K.; Cree, G.; Crépé-Renaudin, S.; Crescioli, F.; Cribbs, W. A.; Ortuzar, M. Crispin; Cristinziani, M.; Croft, V.; Crosetti, G.; Donszelmann, T. 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2017-07-01

The reconstruction of the signal from hadrons and jets emerging from the proton-proton collisions at the Large Hadron Collider (LHC) and entering the ATLAS calorimeters is based on a three-dimensional topological clustering of individual calorimeter cell signals. The cluster formation follows cell signal-significance patterns generated by electromagnetic and hadronic showers. In this, the clustering algorithm implicitly performs a topological noise suppression by removing cells with insignificant signals which are not in close proximity to cells with significant signals. The resulting topological cell clusters have shape and location information, which is exploited to apply a local energy calibration and corrections depending on the nature of the cluster. Topological cell clustering is established as a well-performing calorimeter signal definition for jet and missing transverse momentum reconstruction in ATLAS.

Clustering XML Documents Using Frequent Subtrees

NASA Astrophysics Data System (ADS)

Kutty, Sangeetha; Tran, Tien; Nayak, Richi; Li, Yuefeng

This paper presents an experimental study conducted over the INEX 2008 Document Mining Challenge corpus using both the structure and the content of XML documents for clustering them. The concise common substructures known as the closed frequent subtrees are generated using the structural information of the XML documents. The closed frequent subtrees are then used to extract the constrained content from the documents. A matrix containing the term distribution of the documents in the dataset is developed using the extracted constrained content. The k-way clustering algorithm is applied to the matrix to obtain the required clusters. In spite of the large number of documents in the INEX 2008 Wikipedia dataset, the proposed frequent subtree-based clustering approach was successful in clustering the documents. This approach significantly reduces the dimensionality of the terms used for clustering without much loss in accuracy.

Topological cell clustering in the ATLAS calorimeters and its performance in LHC Run 1.

PubMed

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Schaetzel, S; Schäfer, U; Schaffer, A C; Schaile, D; Schamberger, R D; Scharf, V; Schegelsky, V A; Scheirich, D; Schernau, M; Schiavi, C; Schillo, C; Schioppa, M; Schlenker, S; Schmieden, K; Schmitt, C; Schmitt, S; Schmitt, S; Schmitz, S; Schneider, B; Schnellbach, Y J; Schnoor, U; Schoeffel, L; Schoening, A; Schoenrock, B D; Schopf, E; Schorlemmer, A L S; Schott, M; Schouten, D; Schovancova, J; Schramm, S; Schreyer, M; Schuh, N; Schultens, M J; Schultz-Coulon, H-C; Schulz, H; Schumacher, M; Schumm, B A; Schune, Ph; Schwanenberger, C; Schwartzman, A; Schwarz, T A; Schwegler, Ph; Schweiger, H; Schwemling, Ph; Schwienhorst, R; Schwindling, J; Schwindt, T; Scifo, E; Sciolla, G; Scuri, F; Scutti, F; Searcy, J; Sedov, G; Sedykh, E; Seema, P; Seidel, S C; Seiden, A; Seifert, F; Seixas, J M; Sekhniaidze, G; Sekhon, K; Sekula, S J; Seliverstov, D M; Semprini-Cesari, N; Serfon, C; Serin, L; Serkin, L; Serre, T; Sessa, M; Seuster, R; Severini, H; Sfiligoj, T; Sforza, F; Sfyrla, A; Shabalina, E; Shamim, M; Shan, L Y; Shang, R; Shank, J T; Shapiro, M; Shatalov, P B; Shaw, K; Shaw, S M; Shcherbakova, A; Shehu, C Y; Sherwood, P; Shi, L; Shimizu, S; Shimmin, C O; Shimojima, M; Shiyakova, M; Shmeleva, A; Saadi, D Shoaleh; Shochet, M J; Shojaii, S; Shrestha, S; Shulga, E; Shupe, M A; Sicho, P; Sidebo, P E; Sidiropoulou, O; Sidorov, D; Sidoti, A; Siegert, F; Sijacki, Dj; Silva, J; Silver, Y; Silverstein, S B; Simak, V; Simard, O; Simic, Lj; Simion, S; Simioni, E; Simmons, B; Simon, D; Simon, M; Sinervo, P; Sinev, N B; Sioli, M; Siragusa, G; Sisakyan, A N; Sivoklokov, S Yu; Sjölin, J; Sjursen, T B; Skinner, M B; Skottowe, H P; Skubic, P; Slater, M; Slavicek, T; Slawinska, M; Sliwa, K; Smakhtin, V; Smart, B H; Smestad, L; Smirnov, S Yu; Smirnov, Y; Smirnova, L N; Smirnova, O; Smith, M N K; Smith, R W; Smizanska, M; Smolek, K; Snesarev, A A; Snidero, G; Snyder, S; Sobie, R; Socher, F; Soffer, A; Soh, D A; Sokhrannyi, G; Sanchez, C A Solans; Solar, M; Solc, J; Soldatov, E Yu; Soldevila, U; Solodkov, A A; Soloshenko, A; Solovyanov, O V; Solovyev, V; Sommer, P; Song, H Y; Soni, N; Sood, A; Sopczak, A; Sopko, B; Sopko, V; Sorin, V; Sosa, D; Sosebee, M; Sotiropoulou, C L; Soualah, R; Soukharev, A M; South, D; Sowden, B C; Spagnolo, S; Spalla, M; Spangenberg, M; Spanò, F; Spearman, W R; Sperlich, D; Spettel, F; Spighi, R; Spigo, G; Spiller, L A; Spousta, M; Denis, R D St; Stabile, A; Staerz, S; Stahlman, J; Stamen, R; Stamm, S; Stanecka, E; Stanek, R W; Stanescu, C; Stanescu-Bellu, M; Stanitzki, M M; Stapnes, S; Starchenko, E A; Stark, J; Staroba, P; Starovoitov, P; Staszewski, R; Steinberg, P; Stelzer, B; Stelzer, H J; Stelzer-Chilton, O; Stenzel, H; Stewart, G A; Stillings, J A; Stockton, M C; Stoebe, M; Stoicea, G; Stolte, P; Stonjek, S; Stradling, A R; Straessner, A; Stramaglia, M E; Strandberg, J; Strandberg, S; Strandlie, A; Strauss, E; Strauss, M; Strizenec, P; Ströhmer, R; Strom, D M; Stroynowski, R; Strubig, A; Stucci, S A; Stugu, B; Styles, N A; Su, D; Su, J; Subramaniam, R; Succurro, A; Suchek, S; Sugaya, Y; Suk, M; Sulin, V V; Sultansoy, S; Sumida, T; Sun, S; Sun, X; Sundermann, J E; Suruliz, K; Susinno, G; Sutton, M R; Suzuki, S; Svatos, M; Swiatlowski, M; Sykora, I; Sykora, T; Ta, D; Taccini, C; Tackmann, K; Taenzer, J; Taffard, A; Tafirout, R; Taiblum, N; Takai, H; Takashima, R; Takeda, H; Takeshita, T; Takubo, Y; Talby, M; Talyshev, A A; Tam, J Y C; Tan, K G; Tanaka, J; Tanaka, R; Tanaka, S; Tannenwald, B B; Araya, S Tapia; Tapprogge, S; Tarem, S; Tarrade, F; Tartarelli, G F; Tas, P; Tasevsky, M; Tashiro, T; Tassi, E; Delgado, A Tavares; Tayalati, Y; Taylor, A C; Taylor, F E; Taylor, G N; Taylor, P T E; Taylor, W; Teischinger, F A; Teixeira-Dias, P; Temming, K K; Temple, D; Kate, H Ten; Teng, P K; Teoh, J J; Tepel, F; Terada, S; Terashi, K; Terron, J; Terzo, S; Testa, M; Teuscher, R J; Theveneaux-Pelzer, T; Thomas, J P; Thomas-Wilsker, J; Thompson, E N; Thompson, P D; Thompson, R J; Thompson, A S; Thomsen, L A; Thomson, E; Thomson, M; Thun, R P; Tibbetts, M J; Torres, R E Ticse; Tikhomirov, V O; Tikhonov, Yu A; Timoshenko, S; Tiouchichine, E; Tipton, P; Tisserant, S; Todome, K; Todorov, T; Todorova-Nova, S; Tojo, J; Tokár, S; Tokushuku, K; Tollefson, K; Tolley, E; Tomlinson, L; Tomoto, M; Tompkins, L; Toms, K; Torrence, E; Torres, H; Pastor, E Torró; Toth, J; Touchard, F; Tovey, D R; Trefzger, T; Tremblet, L; Tricoli, A; Trigger, I M; Trincaz-Duvoid, S; Tripiana, M F; Trischuk, W; Trocmé, B; Troncon, C; Trottier-McDonald, M; Trovatelli, M; Truong, L; Trzebinski, M; Trzupek, A; Tsarouchas, C; Tseng, J C-L; Tsiareshka, P V; Tsionou, D; Tsipolitis, G; Tsirintanis, N; Tsiskaridze, S; Tsiskaridze, V; Tskhadadze, E G; Tsui, K M; Tsukerman, I I; Tsulaia, V; Tsuno, S; Tsybychev, D; Tudorache, A; Tudorache, V; Tuna, A N; Tupputi, S A; Turchikhin, S; Turecek, D; Turra, R; Turvey, A J; Tuts, P M; Tykhonov, A; Tylmad, M; Tyndel, M; Ueda, I; Ueno, R; Ughetto, M; Ukegawa, F; Unal, G; Undrus, A; Unel, G; Ungaro, F C; Unno, Y; Unverdorben, C; Urban, J; Urquijo, P; Urrejola, P; Usai, G; Usanova, A; Vacavant, L; Vacek, V; Vachon, B; Valderanis, C; Valencic, N; Valentinetti, S; Valero, A; Valery, L; Valkar, S; Vallecorsa, S; Ferrer, J A Valls; Van Den Wollenberg, W; Van Der Deijl, P C; van der Geer, R; van der Graaf, H; van Eldik, N; van Gemmeren, P; Van Nieuwkoop, J; van Vulpen, I; van Woerden, M C; Vanadia, M; Vandelli, W; Vanguri, R; Vaniachine, A; Vannucci, F; Vardanyan, G; Vari, R; Varnes, E W; Varol, T; Varouchas, D; Vartapetian, A; Varvell, K E; Vazeille, F; Schroeder, T Vazquez; Veatch, J; Veloce, L M; Veloso, F; Velz, T; Veneziano, S; Ventura, A; Ventura, D; Venturi, M; Venturi, N; Venturini, A; Vercesi, V; Verducci, M; Verkerke, W; Vermeulen, J C; Vest, A; Vetterli, M C; Viazlo, O; Vichou, I; Vickey, T; Boeriu, O E Vickey; Viehhauser, G H A; Viel, S; Vigne, R; Villa, M; Perez, M Villaplana; Vilucchi, E; Vincter, M G; Vinogradov, V B; Vivarelli, I; Vlachos, S; Vladoiu, D; Vlasak, M; Vogel, M; Vokac, P; Volpi, G; Volpi, M; von der Schmitt, H; von Radziewski, H; von Toerne, E; Vorobel, V; Vorobev, K; Vos, M; Voss, R; Vossebeld, J H; Vranjes, N; Milosavljevic, M Vranjes; Vrba, V; Vreeswijk, M; Vuillermet, R; Vukotic, I; Vykydal, Z; Wagner, P; Wagner, W; Wahlberg, H; Wahrmund, S; Wakabayashi, J; Walder, J; Walker, R; Walkowiak, W; Wang, C; Wang, F; Wang, H; Wang, H; Wang, J; Wang, J; Wang, K; Wang, R; Wang, S M; Wang, T; Wang, T; Wang, X; Wanotayaroj, C; Warburton, A; Ward, C P; Wardrope, D R; Washbrook, A; Wasicki, C; Watkins, P M; Watson, A T; Watson, I J; Watson, M F; Watts, G; Watts, S; Waugh, B M; Webb, S; Weber, M S; Weber, S W; Webster, J S; Weidberg, A R; Weinert, B; Weingarten, J; Weiser, C; Weits, H; Wells, P S; Wenaus, T; Wengler, T; Wenig, S; Wermes, N; Werner, M; Werner, P; Wessels, M; Wetter, J; Whalen, K; Wharton, A M; White, A; White, M J; White, R; White, S; Whiteson, D; Wickens, F J; Wiedenmann, W; Wielers, M; Wienemann, P; Wiglesworth, C; Wiik-Fuchs, L A M; Wildauer, A; Wilkens, H G; Williams, H H; Williams, S; Willis, C; Willocq, S; Wilson, A; Wilson, J A; Wingerter-Seez, I; Winklmeier, F; Winter, B T; Wittgen, M; Wittkowski, J; Wollstadt, S J; Wolter, M W; Wolters, H; Wosiek, B K; Wotschack, J; Woudstra, M J; Wozniak, K W; Wu, M; Wu, M; Wu, S L; Wu, X; Wu, Y; Wyatt, T R; Wynne, B M; Xella, S; Xu, D; Xu, L; Yabsley, B; Yacoob, S; Yakabe, R; Yamada, M; Yamaguchi, D; Yamaguchi, Y; Yamamoto, A; Yamamoto, S; Yamanaka, T; Yamauchi, K; Yamazaki, Y; Yan, Z; Yang, H; Yang, H; Yang, Y; Yao, W-M; Yap, Y C; Yasu, Y; Yatsenko, E; Wong, K H Yau; Ye, J; Ye, S; Yeletskikh, I; Yen, A L; Yildirim, E; Yorita, K; Yoshida, R; Yoshihara, K; Young, C; Young, C J S; Youssef, S; Yu, D R; Yu, J; Yu, J M; Yu, J; Yuan, L; Yuen, S P Y; Yurkewicz, A; Yusuff, I; Zabinski, B; Zaidan, R; Zaitsev, A M; Zalieckas, J; Zaman, A; Zambito, S; Zanello, L; Zanzi, D; Zeitnitz, C; Zeman, M; Zemla, A; Zeng, J C; Zeng, Q; Zengel, K; Zenin, O; Ženiš, T; Zerwas, D; Zhang, D; Zhang, F; Zhang, G; Zhang, H; Zhang, J; Zhang, L; Zhang, R; Zhang, X; Zhang, Z; Zhao, X; Zhao, Y; Zhao, Z; Zhemchugov, A; Zhong, J; Zhou, B; Zhou, C; Zhou, L; Zhou, L; Zhou, M; Zhou, N; Zhu, C G; Zhu, H; Zhu, J; Zhu, Y; Zhuang, X; Zhukov, K; Zibell, A; Zieminska, D; Zimine, N I; Zimmermann, C; Zimmermann, S; Zinonos, Z; Zinser, M; Ziolkowski, M; Živković, L; Zobernig, G; Zoccoli, A; Nedden, M Zur; Zurzolo, G; Zwalinski, L

2017-01-01

The reconstruction of the signal from hadrons and jets emerging from the proton-proton collisions at the Large Hadron Collider (LHC) and entering the ATLAS calorimeters is based on a three-dimensional topological clustering of individual calorimeter cell signals. The cluster formation follows cell signal-significance patterns generated by electromagnetic and hadronic showers. In this, the clustering algorithm implicitly performs a topological noise suppression by removing cells with insignificant signals which are not in close proximity to cells with significant signals. The resulting topological cell clusters have shape and location information, which is exploited to apply a local energy calibration and corrections depending on the nature of the cluster. Topological cell clustering is established as a well-performing calorimeter signal definition for jet and missing transverse momentum reconstruction in ATLAS.

Topological cell clustering in the ATLAS calorimeters and its performance in LHC Run 1

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aad, G.; Abbott, B.; Abdallah, J.

The reconstruction of the signal from hadrons and jets emerging from the proton–proton collisions at the Large Hadron Collider (LHC) and entering the ATLAS calorimeters is based on a three-dimensional topological clustering of individual calorimeter cell signals. The cluster formation follows cell signal-significance patterns generated by electromagnetic and hadronic showers. In this, the clustering algorithm implicitly performs a topological noise suppression by removing cells with insignificant signals which are not in close proximity to cells with significant signals. The resulting topological cell clusters have shape and location information, which is exploited to apply a local energy calibration and corrections dependingmore » on the nature of the cluster. Lastly, topological cell clustering is established as a well-performing calorimeter signal definition for jet and missing transverse momentum reconstruction in ATLAS.« less

Topological cell clustering in the ATLAS calorimeters and its performance in LHC Run 1

DOE PAGES

Aad, G.; Abbott, B.; Abdallah, J.; ...

2017-07-24

The reconstruction of the signal from hadrons and jets emerging from the proton–proton collisions at the Large Hadron Collider (LHC) and entering the ATLAS calorimeters is based on a three-dimensional topological clustering of individual calorimeter cell signals. The cluster formation follows cell signal-significance patterns generated by electromagnetic and hadronic showers. In this, the clustering algorithm implicitly performs a topological noise suppression by removing cells with insignificant signals which are not in close proximity to cells with significant signals. The resulting topological cell clusters have shape and location information, which is exploited to apply a local energy calibration and corrections dependingmore » on the nature of the cluster. Lastly, topological cell clustering is established as a well-performing calorimeter signal definition for jet and missing transverse momentum reconstruction in ATLAS.« less

Big Data Clustering via Community Detection and Hyperbolic Network Embedding in IoT Applications.

PubMed

Karyotis, Vasileios; Tsitseklis, Konstantinos; Sotiropoulos, Konstantinos; Papavassiliou, Symeon

2018-04-15

In this paper, we present a novel data clustering framework for big sensory data produced by IoT applications. Based on a network representation of the relations among multi-dimensional data, data clustering is mapped to node clustering over the produced data graphs. To address the potential very large scale of such datasets/graphs that test the limits of state-of-the-art approaches, we map the problem of data clustering to a community detection one over the corresponding data graphs. Specifically, we propose a novel computational approach for enhancing the traditional Girvan-Newman (GN) community detection algorithm via hyperbolic network embedding. The data dependency graph is embedded in the hyperbolic space via Rigel embedding, allowing more efficient computation of edge-betweenness centrality needed in the GN algorithm. This allows for more efficient clustering of the nodes of the data graph in terms of modularity, without sacrificing considerable accuracy. In order to study the operation of our approach with respect to enhancing GN community detection, we employ various representative types of artificial complex networks, such as scale-free, small-world and random geometric topologies, and frequently-employed benchmark datasets for demonstrating its efficacy in terms of data clustering via community detection. Furthermore, we provide a proof-of-concept evaluation by applying the proposed framework over multi-dimensional datasets obtained from an operational smart-city/building IoT infrastructure provided by the Federated Interoperable Semantic IoT/cloud Testbeds and Applications (FIESTA-IoT) testbed federation. It is shown that the proposed framework can be indeed used for community detection/data clustering and exploited in various other IoT applications, such as performing more energy-efficient smart-city/building sensing.

Correlation Functions in Two-Dimensional Critical Systems with Conformal Symmetry

NASA Astrophysics Data System (ADS)

Flores, Steven Miguel

This thesis presents a study of certain conformal field theory (CFT) correlation functions that describe physical observables in conform ally invariant two-dimensional critical systems. These are typically continuum limits of critical lattice models in a domain within the complex plane and with a boundary. Certain clusters, called boundary clusters, anchor to the boundary of the domain, and many of their features are governed by a conformally invariant probability measure. For example, percolaion is an example of a critical lattice model, and when it is confined to a domain with a boundary, connected clusters of activated bonds that touch that boundary are the boundary clusters. This thesis is concerned with how the boundary clusters interact with each other according to that measure. One question that it considers are "how likely are these clusters to repel each other or to connect with one another in a certain topological configuration?" Chapter one non-rigorously derives an already well-known elliptic system of differential equations closely tied to this matter by using standard techniques of CFT, chapters two and three rigorously infer certain properties concerning the solution space of this system, and chapter four uses some of those results to predict an answer to this question. This thesis also considers local variations of this question such as "what regions of the domain do the perimeters of the boundary clusters explore," and "how often will several boundary clusters connect at just a single, specified point in the domain?" Chapter five predicts precise answers to these questions. All of these answers are quantitative predictions that we verify via high-precision computer simulation. Chapters four and five also present these simulation results. Further material that supplements chapter one is included in two appendices.

Big Data Clustering via Community Detection and Hyperbolic Network Embedding in IoT Applications

PubMed Central

Sotiropoulos, Konstantinos

2018-01-01

In this paper, we present a novel data clustering framework for big sensory data produced by IoT applications. Based on a network representation of the relations among multi-dimensional data, data clustering is mapped to node clustering over the produced data graphs. To address the potential very large scale of such datasets/graphs that test the limits of state-of-the-art approaches, we map the problem of data clustering to a community detection one over the corresponding data graphs. Specifically, we propose a novel computational approach for enhancing the traditional Girvan–Newman (GN) community detection algorithm via hyperbolic network embedding. The data dependency graph is embedded in the hyperbolic space via Rigel embedding, allowing more efficient computation of edge-betweenness centrality needed in the GN algorithm. This allows for more efficient clustering of the nodes of the data graph in terms of modularity, without sacrificing considerable accuracy. In order to study the operation of our approach with respect to enhancing GN community detection, we employ various representative types of artificial complex networks, such as scale-free, small-world and random geometric topologies, and frequently-employed benchmark datasets for demonstrating its efficacy in terms of data clustering via community detection. Furthermore, we provide a proof-of-concept evaluation by applying the proposed framework over multi-dimensional datasets obtained from an operational smart-city/building IoT infrastructure provided by the Federated Interoperable Semantic IoT/cloud Testbeds and Applications (FIESTA-IoT) testbed federation. It is shown that the proposed framework can be indeed used for community detection/data clustering and exploited in various other IoT applications, such as performing more energy-efficient smart-city/building sensing. PMID:29662043

GDPC: Gravitation-based Density Peaks Clustering algorithm

NASA Astrophysics Data System (ADS)

Jiang, Jianhua; Hao, Dehao; Chen, Yujun; Parmar, Milan; Li, Keqin

2018-07-01

The Density Peaks Clustering algorithm, which we refer to as DPC, is a novel and efficient density-based clustering approach, and it is published in Science in 2014. The DPC has advantages of discovering clusters with varying sizes and varying densities, but has some limitations of detecting the number of clusters and identifying anomalies. We develop an enhanced algorithm with an alternative decision graph based on gravitation theory and nearby distance to identify centroids and anomalies accurately. We apply our method to some UCI and synthetic data sets. We report comparative clustering performances using F-Measure and 2-dimensional vision. We also compare our method to other clustering algorithms, such as K-Means, Affinity Propagation (AP) and DPC. We present F-Measure scores and clustering accuracies of our GDPC algorithm compared to K-Means, AP and DPC on different data sets. We show that the GDPC has the superior performance in its capability of: (1) detecting the number of clusters obviously; (2) aggregating clusters with varying sizes, varying densities efficiently; (3) identifying anomalies accurately.

Fractal Clustering and Knowledge-driven Validation Assessment for Gene Expression Profiling.

PubMed

Wang, Lu-Yong; Balasubramanian, Ammaiappan; Chakraborty, Amit; Comaniciu, Dorin

2005-01-01

DNA microarray experiments generate a substantial amount of information about the global gene expression. Gene expression profiles can be represented as points in multi-dimensional space. It is essential to identify relevant groups of genes in biomedical research. Clustering is helpful in pattern recognition in gene expression profiles. A number of clustering techniques have been introduced. However, these traditional methods mainly utilize shape-based assumption or some distance metric to cluster the points in multi-dimension linear Euclidean space. Their results shows poor consistence with the functional annotation of genes in previous validation study. From a novel different perspective, we propose fractal clustering method to cluster genes using intrinsic (fractal) dimension from modern geometry. This method clusters points in such a way that points in the same clusters are more self-affine among themselves than to the points in other clusters. We assess this method using annotation-based validation assessment for gene clusters. It shows that this method is superior in identifying functional related gene groups than other traditional methods.

Partially supervised speaker clustering.

PubMed

Tang, Hao; Chu, Stephen Mingyu; Hasegawa-Johnson, Mark; Huang, Thomas S

2012-05-01

Content-based multimedia indexing, retrieval, and processing as well as multimedia databases demand the structuring of the media content (image, audio, video, text, etc.), one significant goal being to associate the identity of the content to the individual segments of the signals. In this paper, we specifically address the problem of speaker clustering, the task of assigning every speech utterance in an audio stream to its speaker. We offer a complete treatment to the idea of partially supervised speaker clustering, which refers to the use of our prior knowledge of speakers in general to assist the unsupervised speaker clustering process. By means of an independent training data set, we encode the prior knowledge at the various stages of the speaker clustering pipeline via 1) learning a speaker-discriminative acoustic feature transformation, 2) learning a universal speaker prior model, and 3) learning a discriminative speaker subspace, or equivalently, a speaker-discriminative distance metric. We study the directional scattering property of the Gaussian mixture model (GMM) mean supervector representation of utterances in the high-dimensional space, and advocate exploiting this property by using the cosine distance metric instead of the euclidean distance metric for speaker clustering in the GMM mean supervector space. We propose to perform discriminant analysis based on the cosine distance metric, which leads to a novel distance metric learning algorithm—linear spherical discriminant analysis (LSDA). We show that the proposed LSDA formulation can be systematically solved within the elegant graph embedding general dimensionality reduction framework. Our speaker clustering experiments on the GALE database clearly indicate that 1) our speaker clustering methods based on the GMM mean supervector representation and vector-based distance metrics outperform traditional speaker clustering methods based on the “bag of acoustic features” representation and statistical model-based distance metrics, 2) our advocated use of the cosine distance metric yields consistent increases in the speaker clustering performance as compared to the commonly used euclidean distance metric, 3) our partially supervised speaker clustering concept and strategies significantly improve the speaker clustering performance over the baselines, and 4) our proposed LSDA algorithm further leads to state-of-the-art speaker clustering performance.

Synthesis of nanoparticles through x-ray radiolysis using synchrotron radiation

NASA Astrophysics Data System (ADS)

Yamaguchi, A.; Okada, I.; Fukuoka, T.; Ishihara, M.; Sakurai, I.; Utsumi, Y.

2016-09-01

The synthesis and deposition of nanoparticles consisting of Cu and Au in a CuSO4 solution with some kinds of alcohol and electroplating solution containing gold (I) trisodium disulphite under synchrotron X-ray radiation was investigated. The functional group of alcohol plays an important in nucleation, growth and aggregation process of copper and cupric oxide particles. We found that the laboratory X-ray source also enables us to synthesize the NPs from the metallic solution. As increasing X-ray exposure time, the full length at half width of particle size distribution is broader and higher-order nanostructure containing NPs clusters is formed. The surface-enhanced Raman scattering (SERS) of 4, 4'-bipyridine (4bpy) in aqueous solution was measured using higher-order nanostructure immobilized on silicon substrates under systematically-varied X-ray exposure. This demonstration provide a clue to develop a three-dimensional printing and sensor for environmental analyses and molecular detection through simple SERS measurements.

Meta-atom cluster acoustic metamaterial with broadband negative effective mass density

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Huaijun; Zhai, Shilong; Ding, Changlin

2014-02-07

We design a resonant meta-atom cluster, via which a two-dimensional (2D) acoustic metamaterial (AM) with broadband negative effective mass density from 1560 Hz to 5580 Hz is fabricated. Experimental results confirm that there is only weak interaction among the meta-atoms in the cluster. And then the meta-atoms in the cluster independently resonate, resulting in the cluster becoming equivalent to a broadband resonance unit. Extracted effective refractive indices from reflection and transmission measurements of the 2D AM appear to be negative from 1500 Hz to 5480 Hz. The broadband negative refraction has also been demonstrated by our further experiments. We expectmore » that this meta-atom cluster AM will significantly contribute to the design of broadband negative effective mass density AM.« less

Role of Hydrophobic Clusters and Long-Range Contact Networks in the Folding of (α/β)8 Barrel Proteins

PubMed Central

Selvaraj, S.; Gromiha, M. Michael

2003-01-01

Analysis on the three dimensional structures of (α/β)8 barrel proteins provides ample light to understand the factors that are responsible for directing and maintaining their common fold. In this work, the hydrophobically enriched clusters are identified in 92% of the considered (α/β)8 barrel proteins. The residue segments with hydrophobic clusters have high thermal stability. Further, these clusters are formed and stabilized through long-range interactions. Specifically, a network of long-range contacts connects adjacent β-strands of the (α/β)8 barrel domain and the hydrophobic clusters. The implications of hydrophobic clusters and long-range networks in providing a feasible common mechanism for the folding of (α/β)8 barrel proteins are proposed. PMID:12609894

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

PubMed Central

Curtis, Andrew J

2008-01-01

Background An epidemic may exhibit different spatial patterns with a change in geographic scale, with each scale having different conduits and impediments to disease spread. Mapping disease at each of these scales often reveals different cluster patterns. This paper will consider this change of geographic scale in an analysis of yellow fever deaths for New Orleans in 1878. Global clustering for the whole city, will be followed by a focus on the French Quarter, then clusters of that area, and finally street-level patterns of a single cluster. The three-dimensional visualization capabilities of a GIS will be used as part of a cluster creation process that incorporates physical buildings in calculating mortality-to-mortality distance. Including nativity of the deceased will also capture cultural connection. Results Twenty-two yellow fever clusters were identified for the French Quarter. These generally mirror the results of other global cluster and density surfaces created for the entire epidemic in New Orleans. However, the addition of building-distance, and disease specific time frame between deaths reveal that disease spread contains a cultural component. Same nativity mortality clusters emerge in a similar time frame irrespective of proximity. Italian nativity mortalities were far more densely grouped than any of the other cohorts. A final examination of mortalities for one of the nativity clusters reveals that further sub-division is present, and that this pattern would only be revealed at this scale (street level) of investigation. Conclusion Disease spread in an epidemic is complex resulting from a combination of geographic distance, geographic distance with specific connection to the built environment, disease-specific time frame between deaths, impediments such as herd immunity, and social or cultural connection. This research has shown that the importance of cultural connection may be more important than simple proximity, which in turn might mean traditional quarantine measures should be re-evaluated. PMID:18721469

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

PubMed

Curtis, Andrew J

2008-08-22

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

The Psychology of Yoga Practitioners: A Cluster Analysis.

PubMed

Genovese, Jeremy E C; Fondran, Kristine M

2017-11-01

Yoga practitioners (N = 261) completed the revised Expression of Spirituality Inventory (ESI) and the Multidimensional Body-Self Relations Questionnaire. Cluster analysis revealed three clusters: Cluster A scored high on all four spiritual constructs. They had high positive evaluations of their appearance, but a lower orientation towards their appearance. They tended to have a high evaluation of their fitness and health, and higher body satisfaction. Cluster B showed lower scores on the spiritual constructs. Like Cluster A, members of Cluster B tended to show high positive evaluations of appearance and fitness. They also had higher body satisfaction. Members of Cluster B had a higher fitness orientation and a higher appearance orientation than members of Cluster A. Members of Cluster C had low scores for all spiritual constructs. They had a low evaluation of, and unhappiness with, their appearance. They were unhappy with the size and appearance of their bodies. They tended to see themselves as overweight. There was a significant difference in years of practice between the three groups (Kruskall -Wallis, p = .0041). Members of Cluster A have the most years of yoga experience and members of Cluster B have more yoga experience than members of Cluster C. These results suggest the possible existence of a developmental trajectory for yoga practitioners. Such a developmental sequence may have important implications for yoga practice and instruction.

The Psychology of Yoga Practitioners: A Cluster Analysis.

PubMed

Genovese, Jeremy E C; Fondran, Kristine M

2017-03-30

Yoga practitioners (N = 261) completed the revised Expression of Spirituality Inventory (ESI) and the Multidimensional Body-Self Relations Questionnaire. Cluster analysis revealed three clusters: Cluster A scored high on all four spiritual constructs. They had high positive evaluations of their appearance, but a lower orientation towards their appearance. They tended to have a high evaluation of their fitness and health, and higher body satisfaction. Cluster B showed lower scores on the spiritual constructs. Like Cluster A, members of Cluster B tended to show high positive evaluations of appearance and fitness. They also had higher body satisfaction. Members of Cluster B had a higher fitness orientation and a higher appearance orientation than members of Cluster A. Members of Cluster C had low scores for all spiritual constructs. They had a low evaluation of, and unhappiness with, their appearance. They were unhappy with the size and appearance of their bodies. They tended to see themselves as overweight. There was a significant difference in years of practice between the three groups (Kruskall-Wallis, p = .0041). Members of Cluster A have the most years of yoga experience and members of Cluster B have more yoga experience than members of Cluster C. These results suggest the possible existence of a developmental trajectory for yoga practitioners. Such a developmental sequence may have important implications for yoga practice and instruction.

A spectral clustering search algorithm for predicting shallow landslide size and location

Treesearch

Dino Bellugi; David G. Milledge; William E. Dietrich; Jim A. McKean; J. Taylor Perron; Erik B. Sudderth; Brian Kazian

2015-01-01

The potential hazard and geomorphic significance of shallow landslides depend on their location and size. Commonly applied one-dimensional stability models do not include lateral resistances and cannot predict landslide size. Multi-dimensional models must be applied to specific geometries, which are not known a priori, and testing all possible geometries is...

Joint spatial-spectral hyperspectral image clustering using block-diagonal amplified affinity matrix

NASA Astrophysics Data System (ADS)

Fan, Lei; Messinger, David W.

2018-03-01

The large number of spectral channels in a hyperspectral image (HSI) produces a fine spectral resolution to differentiate between materials in a scene. However, difficult classes that have similar spectral signatures are often confused while merely exploiting information in the spectral domain. Therefore, in addition to spectral characteristics, the spatial relationships inherent in HSIs should also be considered for incorporation into classifiers. The growing availability of high spectral and spatial resolution of remote sensors provides rich information for image clustering. Besides the discriminating power in the rich spectrum, contextual information can be extracted from the spatial domain, such as the size and the shape of the structure to which one pixel belongs. In recent years, spectral clustering has gained popularity compared to other clustering methods due to the difficulty of accurate statistical modeling of data in high dimensional space. The joint spatial-spectral information could be effectively incorporated into the proximity graph for spectral clustering approach, which provides a better data representation by discovering the inherent lower dimensionality from the input space. We embedded both spectral and spatial information into our proposed local density adaptive affinity matrix, which is able to handle multiscale data by automatically selecting the scale of analysis for every pixel according to its neighborhood of the correlated pixels. Furthermore, we explored the "conductivity method," which aims at amplifying the block diagonal structure of the affinity matrix to further improve the performance of spectral clustering on HSI datasets.

Surface passivation for tight-binding calculations of covalent solids.

PubMed

Bernstein, N

2007-07-04

Simulation of a cluster representing a finite portion of a larger covalently bonded system requires the passivation of the cluster surface. We compute the effects of an explicit hybrid orbital passivation (EHOP) on the atomic structure in a model bulk, three-dimensional, narrow gap semiconductor, which is very different from the wide gap, quasi-one-dimensional organic molecules where most passivation schemes have been studied in detail. The EHOP approach is directly applicable to minimal atomic orbital basis methods such as tight-binding. Each broken bond is passivated by a hybrid created from an explicitly expressed linear combination of basis orbitals, chosen to represent the contribution of the missing neighbour, e.g. a sp(3) hybrid for a single bond. The method is tested by computing the forces on atoms near a point defect as a function of cluster geometry. We show that, compared to alternatives such as pseudo-hydrogen passivation, the force on an atom converges to the correct bulk limit more quickly as a function of cluster radius, and that the force is more stable with respect to perturbations in the position of the cluster centre. The EHOP method also obviates the need for parameterizing the interactions between the system atoms and the passivating atoms. The method is useful for cluster calculations of non-periodic defects in large systems and for hybrid schemes that simulate large systems by treating finite regions with a quantum-mechanical model, coupled to an interatomic potential description of the rest of the system.

Surface passivation for tight-binding calculations of covalent solids

NASA Astrophysics Data System (ADS)

Bernstein, N.

2007-07-01

Simulation of a cluster representing a finite portion of a larger covalently bonded system requires the passivation of the cluster surface. We compute the effects of an explicit hybrid orbital passivation (EHOP) on the atomic structure in a model bulk, three-dimensional, narrow gap semiconductor, which is very different from the wide gap, quasi-one-dimensional organic molecules where most passivation schemes have been studied in detail. The EHOP approach is directly applicable to minimal atomic orbital basis methods such as tight-binding. Each broken bond is passivated by a hybrid created from an explicitly expressed linear combination of basis orbitals, chosen to represent the contribution of the missing neighbour, e.g. a sp3 hybrid for a single bond. The method is tested by computing the forces on atoms near a point defect as a function of cluster geometry. We show that, compared to alternatives such as pseudo-hydrogen passivation, the force on an atom converges to the correct bulk limit more quickly as a function of cluster radius, and that the force is more stable with respect to perturbations in the position of the cluster centre. The EHOP method also obviates the need for parameterizing the interactions between the system atoms and the passivating atoms. The method is useful for cluster calculations of non-periodic defects in large systems and for hybrid schemes that simulate large systems by treating finite regions with a quantum-mechanical model, coupled to an interatomic potential description of the rest of the system.

Challenge Online Time Series Clustering For Demand Response A Theory to Break the ‘Curse of Dimensionality'

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pal, Ranjan; Chelmis, Charalampos; Aman, Saima

The advent of smart meters and advanced communication infrastructures catalyzes numerous smart grid applications such as dynamic demand response, and paves the way to solve challenging research problems in sustainable energy consumption. The space of solution possibilities are restricted primarily by the huge amount of generated data requiring considerable computational resources and efficient algorithms. To overcome this Big Data challenge, data clustering techniques have been proposed. Current approaches however do not scale in the face of the “increasing dimensionality” problem where a cluster point is represented by the entire customer consumption time series. To overcome this aspect we first rethinkmore » the way cluster points are created and designed, and then design an efficient online clustering technique for demand response (DR) in order to analyze high volume, high dimensional energy consumption time series data at scale, and on the fly. Our online algorithm is randomized in nature, and provides optimal performance guarantees in a computationally efficient manner. Unlike prior work we (i) study the consumption properties of the whole population simultaneously rather than developing individual models for each customer separately, claiming it to be a ‘killer’ approach that breaks the “curse of dimensionality” in online time series clustering, and (ii) provide tight performance guarantees in theory to validate our approach. Our insights are driven by the field of sociology, where collective behavior often emerges as the result of individual patterns and lifestyles.« less

Submorphotypes of the maxillary first molar and their effects on alignment and rotation.

PubMed

Kim, Hong-Kyun; Kwon, Ho Beom; Hyun, Hong-Keun; Jung, Min-Ho; Han, Seong Ho; Park, Young-Seok

2014-09-01

The aim of this study was to explore the shape differences in maxillary first molars with orthographic measurements using 3-dimensional virtual models to assess whether there is variability in morphology that could affect the alignment results when treated by straight-wire appliance systems. A total of 175 maxillary first molars with 4 cusps were selected for classification. With 3-dimensional laser scanning and reconstruction software, virtual casts were constructed. After performing several linear and angular measurements on the virtual occlusal plane, the teeth were clustered into 2 groups by the method of partitioning around medoids. To visualize the 2 groups, occlusal polygons were constructed using the average data of these groups. The resultant 2 clusters showed statistically significant differences in the measurements describing the cusp locations and the buccal and lingual outlines. The rotation along the centers made the 2 cluster polygons look similar, but there was a difference in the direction of the midsagittal lines. There was considerable variability in morphology according to 2 clusters in the population of this study. The occlusal polygons showed that the outlines of the 2 clusters were similar, but the midsagittal line directions and inner geometries were different. The difference between the morphologies of the 2 clusters could result in occlusal contact differences, which might be considered for better alignment of the maxillary posterior segment. Copyright © 2014 American Association of Orthodontists. Published by Elsevier Inc. All rights reserved.

Spatiotemporal analysis of the agricultural drought risk in Heilongjiang Province, China

NASA Astrophysics Data System (ADS)

Pei, Wei; Fu, Qiang; Liu, Dong; Li, Tian-xiao; Cheng, Kun; Cui, Song

2017-06-01

Droughts are natural disasters that pose significant threats to agricultural production as well as living conditions, and a spatial-temporal difference analysis of agricultural drought risk can help determine the spatial distribution and temporal variation of the drought risk within a region. Moreover, this type of analysis can provide a theoretical basis for the identification, prevention, and mitigation of drought disasters. In this study, the overall dispersion and local aggregation of projection points were based on research by Friedman and Tukey (IEEE Trans on Computer 23:881-890, 1974). In this work, high-dimensional samples were clustered by cluster analysis. The clustering results were represented by the clustering matrix, which determined the local density in the projection index. This method avoids the problem of determining a cutoff radius. An improved projection pursuit model is proposed that combines cluster analysis and the projection pursuit model, which offer advantages for classification and assessment, respectively. The improved model was applied to analyze the agricultural drought risk of 13 cities in Heilongjiang Province over 6 years (2004, 2006, 2008, 2010, 2012, and 2014). The risk of an agricultural drought disaster was characterized by 14 indicators and the following four aspects: hazard, exposure, sensitivity, and resistance capacity. The spatial distribution and temporal variation characteristics of the agricultural drought risk in Heilongjiang Province were analyzed. The spatial distribution results indicated that Suihua, Qigihar, Daqing, Harbin, and Jiamusi are located in high-risk areas, Daxing'anling and Yichun are located in low-risk areas, and the differences among the regions were primarily caused by the aspects exposure and resistance capacity. The temporal variation results indicated that the risk of agricultural drought in most areas presented an initially increasing and then decreasing trend. A higher value for the exposure aspect increased the risk of drought, whereas a higher value for the resistance capacity aspect reduced the risk of drought. Over the long term, the exposure level of the region presented limited increases, whereas the resistance capacity presented considerable increases. Therefore, the risk of agricultural drought in Heilongjiang Province will continue to exhibit a decreasing trend.

A Latent Class Multidimensional Scaling Model for Two-Way One-Mode Continuous Rating Dissimilarity Data

ERIC Educational Resources Information Center

Vera, J. Fernando; Macias, Rodrigo; Heiser, Willem J.

2009-01-01

In this paper, we propose a cluster-MDS model for two-way one-mode continuous rating dissimilarity data. The model aims at partitioning the objects into classes and simultaneously representing the cluster centers in a low-dimensional space. Under the normal distribution assumption, a latent class model is developed in terms of the set of…

Spin-canting magnetization in an unusual Co4 cluster-based layer compound from a 2,3-dihydroxyquinoxaline ligand.

PubMed

Yang, Chen-I; Chuang, Po-Hsiang; Lee, Gene-Hsiang; Peng, Shie-Ming; Lu, Kuang-Lieh

2012-01-16

The self-assembly of Co(O(2)CPh)(2) with a 2,3-dihydroxyquinoxaline (H(2)dhq) linker has revealed a new two-dimensional cluster-based compound, [Co(4)(OMe)(2)(O(2)CPh)(2)(dhq)(2)(MeOH)(2)](n), which shows spin-canted magnetization and a definite magnetic hysteresis loop.

M-Isomap: Orthogonal Constrained Marginal Isomap for Nonlinear Dimensionality Reduction.

PubMed

Zhang, Zhao; Chow, Tommy W S; Zhao, Mingbo

2013-02-01

Isomap is a well-known nonlinear dimensionality reduction (DR) method, aiming at preserving geodesic distances of all similarity pairs for delivering highly nonlinear manifolds. Isomap is efficient in visualizing synthetic data sets, but it usually delivers unsatisfactory results in benchmark cases. This paper incorporates the pairwise constraints into Isomap and proposes a marginal Isomap (M-Isomap) for manifold learning. The pairwise Cannot-Link and Must-Link constraints are used to specify the types of neighborhoods. M-Isomap computes the shortest path distances over constrained neighborhood graphs and guides the nonlinear DR through separating the interclass neighbors. As a result, large margins between both interand intraclass clusters are delivered and enhanced compactness of intracluster points is achieved at the same time. The validity of M-Isomap is examined by extensive simulations over synthetic, University of California, Irvine, and benchmark real Olivetti Research Library, YALE, and CMU Pose, Illumination, and Expression databases. The data visualization and clustering power of M-Isomap are compared with those of six related DR methods. The visualization results show that M-Isomap is able to deliver more separate clusters. Clustering evaluations also demonstrate that M-Isomap delivers comparable or even better results than some state-of-the-art DR algorithms.

Role of radial nonuniformities in the interaction of an intense laser with atomic clusters

DOE Office of Scientific and Technical Information (OSTI.GOV)

Holkundkar, Amol R.; Gupta, N. K.

A model for the interaction of an intense laser with atomic clusters is presented. The model takes into account the spatial nonuniformities of the cluster as it evolves in time. The cluster is treated as a stratified sphere having an arbitrary number of layers. Electric and magnetic fields are obtained by solving the vector Helmholtz equation coupled with one-dimensional Lagrangian hydrodynamics. Results are compared with the uniform density nanoplasma model. Enhancement in the amount of energy absorbed is seen over the uniform density model. In some cases the absorbed energy increases by as much as a factor of 40.

Hybrid Assembly of Different-Sized Supertetrahedral Clusters into a Unique Non-Interpenetrated Mn-In-S Open Framework with Large Cavity.

PubMed

Wang, Hongxiang; Wang, Wei; Hu, Dandan; Luo, Min; Xue, Chaozhuang; Li, Dongsheng; Wu, Tao

2018-06-04

Reported here is a unique crystalline semiconductor open-framework material built from the large-sized supertetrahedral T4 and T5 clusters with the Mn-In-S compositions. The hybrid assembly between T4 and T5 clusters by sharing terminal μ 2 -S 2- is for the first time observed among the cluster-based chalcogenide open frameworks. Such three-dimensional structure displays non-interpenetrated diamond-type topology with extra-large nonframework volume of 82%. Moreover, ion exchange, CO 2 adsorption, as well as photoluminescence properties of the title compound are also investigated.

Self-Organizing Hidden Markov Model Map (SOHMMM): Biological Sequence Clustering and Cluster Visualization.

PubMed

Ferles, Christos; Beaufort, William-Scott; Ferle, Vanessa

2017-01-01

The present study devises mapping methodologies and projection techniques that visualize and demonstrate biological sequence data clustering results. The Sequence Data Density Display (SDDD) and Sequence Likelihood Projection (SLP) visualizations represent the input symbolical sequences in a lower-dimensional space in such a way that the clusters and relations of data elements are depicted graphically. Both operate in combination/synergy with the Self-Organizing Hidden Markov Model Map (SOHMMM). The resulting unified framework is in position to analyze automatically and directly raw sequence data. This analysis is carried out with little, or even complete absence of, prior information/domain knowledge.

Online clustering algorithms for radar emitter classification.

PubMed

Liu, Jun; Lee, Jim P Y; Senior; Li, Lingjie; Luo, Zhi-Quan; Wong, K Max

2005-08-01

Radar emitter classification is a special application of data clustering for classifying unknown radar emitters from received radar pulse samples. The main challenges of this task are the high dimensionality of radar pulse samples, small sample group size, and closely located radar pulse clusters. In this paper, two new online clustering algorithms are developed for radar emitter classification: One is model-based using the Minimum Description Length (MDL) criterion and the other is based on competitive learning. Computational complexity is analyzed for each algorithm and then compared. Simulation results show the superior performance of the model-based algorithm over competitive learning in terms of better classification accuracy, flexibility, and stability.

Spectral properties near the Mott transition in the two-dimensional t-J model with next-nearest-neighbor hopping

NASA Astrophysics Data System (ADS)

Kohno, Masanori

2018-05-01

The single-particle spectral properties of the two-dimensional t-J model with next-nearest-neighbor hopping are investigated near the Mott transition by using cluster perturbation theory. The spectral features are interpreted by considering the effects of the next-nearest-neighbor hopping on the shift of the spectral-weight distribution of the two-dimensional t-J model. Various anomalous features observed in hole-doped and electron-doped high-temperature cuprate superconductors are collectively explained in the two-dimensional t-J model with next-nearest-neighbor hopping near the Mott transition.

Earthquake Clustering in Noisy Viscoelastic Systems

NASA Astrophysics Data System (ADS)

Dicaprio, C. J.; Simons, M.; Williams, C. A.; Kenner, S. J.

2006-12-01

Geologic studies show evidence for temporal clustering of earthquakes on certain fault systems. Since post- seismic deformation may result in a variable loading rate on a fault throughout the inter-seismic period, it is reasonable to expect that the rheology of the non-seismogenic lower crust and mantle lithosphere may play a role in controlling earthquake recurrence times. Previously, the role of rheology of the lithosphere on the seismic cycle had been studied with a one-dimensional spring-dashpot-slider model (Kenner and Simons [2005]). In this study we use the finite element code PyLith to construct a two-dimensional continuum model a strike-slip fault in an elastic medium overlying one or more linear Maxwell viscoelastic layers loaded in the far field by a constant velocity boundary condition. Taking advantage of the linear properties of the model, we use the finite element solution to one earthquake as a spatio-temporal Green's function. Multiple Green's function solutions, scaled by the size of each earthquake, are then summed to form an earthquake sequence. When the shear stress on the fault reaches a predefined yield stress it is allowed to slip, relieving all accumulated shear stress. Random variation in the fault yield stress from one earthquake to the next results in a temporally clustered earthquake sequence. The amount of clustering depends on a non-dimensional number, W, called the Wallace number. For models with one viscoelastic layer, W is equal to the standard deviation of the earthquake stress drop divided by the viscosity times the tectonic loading rate. This definition of W is modified from the original one used in Kenner and Simons [2005] by using the standard deviation of the stress drop instead of the mean stress drop. We also use a new, more appropriate, metric to measure the amount of temporal clustering of the system. W is the ratio of the viscoelastic relaxation rate of the system to the tectonic loading rate of the system. For values of W greater than the critical value of about 10, the clustered earthquake behavior is due to the rapid reloading of the fault due to viscoelastic recycling of stress. A model with multiple viscoelastic layers has more complex clustering behavior than a system with only one viscosity. In this case, multiple clustering modes exist; the size and mean period of which are influenced by the viscosities and relative thicknesses of the viscoelastic layers. Kenner, S.J. and Simons, M., (2005), Temporal cluster of major earthquakes along individual faults due to post-seismic reloading, Geophysical Journal International, 160, 179-194.

Analysis of three-dimensional SAR distributions emitted by mobile phones in an epidemiological perspective.

PubMed

Deltour, Isabelle; Wiart, Joe; Taki, Masao; Wake, Kanako; Varsier, Nadège; Mann, Simon; Schüz, Joachim; Cardis, Elisabeth

2011-12-01

The three-dimensional distribution of the specific absorption rate of energy (SAR) in phantom models was analysed to detect clusters of mobile phones producing similar spatial deposition of energy in the head. The clusters' characteristics were described from the phones external features, frequency band and communication protocol. Compliance measurements with phones in cheek and tilt positions, and on the left and right side of a physical phantom were used. Phones used the Personal Digital Cellular (PDC), Code division multiple access One (CdmaOne), Global System for Mobile Communications (GSM) and Nordic Mobile Telephony (NMT) communication systems, in the 800, 900, 1500 and 1800 MHz bands. Each phone's measurements were summarised by the half-ellipsoid in which the SAR values were above half the maximum value. Cluster analysis used the Partitioning Around Medoids algorithm. The dissimilarity measure was based on the overlap of the ellipsoids, and the Manhattan distance was used for robustness analysis. Within the 800 MHz frequency band, and in part within the 900 MHz and the 1800 MHz frequency bands, weak clustering was obtained for the handset shape (bar phone, flip with top and flip with central antennas), but only in specific positions (tilt or cheek). On measurements of 120 phones, the three-dimensional distribution of SAR in phantom models did not appear to be related to particular external phone characteristics or measurement characteristics, which could be used for refining the assessment of exposure to radiofrequency energy within the brain in epidemiological studies such as the Interphone. Copyright © 2011 Wiley Periodicals, Inc.

Graphical classification of DNA sequences of HLA alleles by deep learning.

PubMed

Miyake, Jun; Kaneshita, Yuhei; Asatani, Satoshi; Tagawa, Seiichi; Niioka, Hirohiko; Hirano, Takashi

2018-04-01

Alleles of human leukocyte antigen (HLA)-A DNAs are classified and expressed graphically by using artificial intelligence "Deep Learning (Stacked autoencoder)". Nucleotide sequence data corresponding to the length of 822 bp, collected from the Immuno Polymorphism Database, were compressed to 2-dimensional representation and were plotted. Profiles of the two-dimensional plots indicate that the alleles can be classified as clusters are formed. The two-dimensional plot of HLA-A DNAs gives a clear outlook for characterizing the various alleles.

Cluster and principal component analysis based on SSR markers of Amomum tsao-ko in Jinping County of Yunnan Province

NASA Astrophysics Data System (ADS)

Ma, Mengli; Lei, En; Meng, Hengling; Wang, Tiantao; Xie, Linyan; Shen, Dong; Xianwang, Zhou; Lu, Bingyue

2017-08-01

Amomum tsao-ko is a commercial plant that used for various purposes in medicinal and food industries. For the present investigation, 44 germplasm samples were collected from Jinping County of Yunnan Province. Clusters analysis and 2-dimensional principal component analysis (PCA) was used to represent the genetic relations among Amomum tsao-ko by using simple sequence repeat (SSR) markers. Clustering analysis clearly distinguished the samples groups. Two major clusters were formed; first (Cluster I) consisted of 34 individuals, the second (Cluster II) consisted of 10 individuals, Cluster I as the main group contained multiple sub-clusters. PCA also showed 2 groups: PCA Group 1 included 29 individuals, PCA Group 2 included 12 individuals, consistent with the results of cluster analysis. The purpose of the present investigation was to provide information on genetic relationship of Amomum tsao-ko germplasm resources in main producing areas, also provide a theoretical basis for the protection and utilization of Amomum tsao-ko resources.

Clustering PPI data by combining FA and SHC method.

PubMed

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

2015-01-01

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

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

A new clustering algorithm applicable to multispectral and polarimetric SAR images

NASA Technical Reports Server (NTRS)

Wong, Yiu-Fai; Posner, Edward C.

1993-01-01

We describe an application of a scale-space clustering algorithm to the classification of a multispectral and polarimetric SAR image of an agricultural site. After the initial polarimetric and radiometric calibration and noise cancellation, we extracted a 12-dimensional feature vector for each pixel from the scattering matrix. The clustering algorithm was able to partition a set of unlabeled feature vectors from 13 selected sites, each site corresponding to a distinct crop, into 13 clusters without any supervision. The cluster parameters were then used to classify the whole image. The classification map is much less noisy and more accurate than those obtained by hierarchical rules. Starting with every point as a cluster, the algorithm works by melting the system to produce a tree of clusters in the scale space. It can cluster data in any multidimensional space and is insensitive to variability in cluster densities, sizes and ellipsoidal shapes. This algorithm, more powerful than existing ones, may be useful for remote sensing for land use.

Graph-based analysis of kinetics on multidimensional potential-energy surfaces.

PubMed

Okushima, T; Niiyama, T; Ikeda, K S; Shimizu, Y

2009-09-01

The aim of this paper is twofold: one is to give a detailed description of an alternative graph-based analysis method, which we call saddle connectivity graph, for analyzing the global topography and the dynamical properties of many-dimensional potential-energy landscapes and the other is to give examples of applications of this method in the analysis of the kinetics of realistic systems. A Dijkstra-type shortest path algorithm is proposed to extract dynamically dominant transition pathways by kinetically defining transition costs. The applicability of this approach is first confirmed by an illustrative example of a low-dimensional random potential. We then show that a coarse-graining procedure tailored for saddle connectivity graphs can be used to obtain the kinetic properties of 13- and 38-atom Lennard-Jones clusters. The coarse-graining method not only reduces the complexity of the graphs, but also, with iterative use, reveals a self-similar hierarchical structure in these clusters. We also propose that the self-similarity is common to many-atom Lennard-Jones clusters.

The void spectrum in two-dimensional numerical simulations of gravitational clustering

NASA Technical Reports Server (NTRS)

Kauffmann, Guinevere; Melott, Adrian L.

1992-01-01

An algorithm for deriving a spectrum of void sizes from two-dimensional high-resolution numerical simulations of gravitational clustering is tested, and it is verified that it produces the correct results where those results can be anticipated. The method is used to study the growth of voids as clustering proceeds. It is found that the most stable indicator of the characteristic void 'size' in the simulations is the mean fractional area covered by voids of diameter d, in a density field smoothed at its correlation length. Very accurate scaling behavior is found in power-law numerical models as they evolve. Eventually, this scaling breaks down as the nonlinearity reaches larger scales. It is shown that this breakdown is a manifestation of the undesirable effect of boundary conditions on simulations, even with the very large dynamic range possible here. A simple criterion is suggested for deciding when simulations with modest large-scale power may systematically underestimate the frequency of larger voids.

Effective traffic features selection algorithm for cyber-attacks samples

NASA Astrophysics Data System (ADS)

Li, Yihong; Liu, Fangzheng; Du, Zhenyu

2018-05-01

By studying the defense scheme of Network attacks, this paper propose an effective traffic features selection algorithm based on k-means++ clustering to deal with the problem of high dimensionality of traffic features which extracted from cyber-attacks samples. Firstly, this algorithm divide the original feature set into attack traffic feature set and background traffic feature set by the clustering. Then, we calculates the variation of clustering performance after removing a certain feature. Finally, evaluating the degree of distinctiveness of the feature vector according to the result. Among them, the effective feature vector is whose degree of distinctiveness exceeds the set threshold. The purpose of this paper is to select out the effective features from the extracted original feature set. In this way, it can reduce the dimensionality of the features so as to reduce the space-time overhead of subsequent detection. The experimental results show that the proposed algorithm is feasible and it has some advantages over other selection algorithms.

Clustering of arc volcanoes caused by temperature perturbations in the back-arc mantle

PubMed Central

Lee, Changyeol; Wada, Ikuko

2017-01-01

Clustering of arc volcanoes in subduction zones indicates along-arc variation in the physical condition of the underlying mantle where majority of arc magmas are generated. The sub-arc mantle is brought in from the back-arc largely by slab-driven mantle wedge flow. Dynamic processes in the back-arc, such as small-scale mantle convection, are likely to cause lateral variations in the back-arc mantle temperature. Here we use a simple three-dimensional numerical model to quantify the effects of back-arc temperature perturbations on the mantle wedge flow pattern and sub-arc mantle temperature. Our model calculations show that relatively small temperature perturbations in the back-arc result in vigorous inflow of hotter mantle and subdued inflow of colder mantle beneath the arc due to the temperature dependence of the mantle viscosity. This causes a three-dimensional mantle flow pattern that amplifies the along-arc variations in the sub-arc mantle temperature, providing a simple mechanism for volcano clustering. PMID:28660880

Clustering of arc volcanoes caused by temperature perturbations in the back-arc mantle.

PubMed

Lee, Changyeol; Wada, Ikuko

2017-06-29

Clustering of arc volcanoes in subduction zones indicates along-arc variation in the physical condition of the underlying mantle where majority of arc magmas are generated. The sub-arc mantle is brought in from the back-arc largely by slab-driven mantle wedge flow. Dynamic processes in the back-arc, such as small-scale mantle convection, are likely to cause lateral variations in the back-arc mantle temperature. Here we use a simple three-dimensional numerical model to quantify the effects of back-arc temperature perturbations on the mantle wedge flow pattern and sub-arc mantle temperature. Our model calculations show that relatively small temperature perturbations in the back-arc result in vigorous inflow of hotter mantle and subdued inflow of colder mantle beneath the arc due to the temperature dependence of the mantle viscosity. This causes a three-dimensional mantle flow pattern that amplifies the along-arc variations in the sub-arc mantle temperature, providing a simple mechanism for volcano clustering.

Clustering in the SDSS Redshift Survey

NASA Astrophysics Data System (ADS)

Zehavi, I.; Blanton, M. R.; Frieman, J. A.; Weinberg, D. H.; SDSS Collaboration

2002-05-01

We present measurements of clustering in the Sloan Digital Sky Survey (SDSS) galaxy redshift survey. Our current sample consists of roughly 80,000 galaxies with redshifts in the range 0.02 < z < 0.2, covering about 1200 square degrees. We measure the clustering in redshift space and in real space. The two-dimensional correlation function ξ (rp,π ) shows clear signatures of redshift distortions, both the small-scale ``fingers-of-God'' effect and the large-scale compression. The inferred real-space correlation function is well described by a power law. The SDSS is especially suitable for investigating the dependence of clustering on galaxy properties, due to the wealth of information in the photometric survey. We focus on the dependence of clustering on color and on luminosity.

Attenuation and source properties at the Coso Geothermal area, California

USGS Publications Warehouse

Hough, S.E.; Lees, J.M.; Monastero, F.

1999-01-01

We use a multiple-empirical Green's function method to determine source properties of small (M -0.4 to 1.3) earthquakes and P- and S-wave attenuation at the Coso Geothermal Field, California. Source properties of a previously identified set of clustered events from the Coso geothermal region are first analyzed using an empirical Green's function (EGF) method. Stress-drop values of at least 0.5-1 MPa are inferred for all of the events; in many cases, the corner frequency is outside the usable bandwidth, and the stress drop can only be constrained as being higher than 3 MPa. P- and S-wave stress-drop estimates are identical to the resolution limits of the data. These results are indistinguishable from numerous EGF studies of M 2-5 earthquakes, suggesting a similarity in rupture processes that extends to events that are both tiny and induced, providing further support for Byerlee's Law. Whole-path Q estimates for P and S waves are determined using the multiple-empirical Green's function (MEGF) method of Hough (1997), whereby spectra from clusters of colocated events at a given station are inverted for a single attenuation parameter, ??, with source parameters constrained from EGF analysis. The ?? estimates, which we infer to be resolved to within 0.01 sec or better, exhibit almost as much scatter as a function of hypocentral distance as do values from previous single-spectrum studies for which much higher uncertainties in individual ?? estimates are expected. The variability in ?? estimates determined here therefore suggests real lateral variability in Q structure. Although the ray-path coverage is too sparse to yield a complete three-dimensional attenuation tomographic image, we invert the inferred ?? value for three-dimensional structure using a damped least-squares method, and the results do reveal significant lateral variability in Q structure. The inferred attenuation variability corresponds to the heat-flow variations within the geothermal region. A central low-Q region corresponds well with the central high-heat flow region; additional detailed structure is also suggested.

Cluster-level statistical inference in fMRI datasets: The unexpected behavior of random fields in high dimensions.

PubMed

Bansal, Ravi; Peterson, Bradley S

2018-06-01

Identifying regional effects of interest in MRI datasets usually entails testing a priori hypotheses across many thousands of brain voxels, requiring control for false positive findings in these multiple hypotheses testing. Recent studies have suggested that parametric statistical methods may have incorrectly modeled functional MRI data, thereby leading to higher false positive rates than their nominal rates. Nonparametric methods for statistical inference when conducting multiple statistical tests, in contrast, are thought to produce false positives at the nominal rate, which has thus led to the suggestion that previously reported studies should reanalyze their fMRI data using nonparametric tools. To understand better why parametric methods may yield excessive false positives, we assessed their performance when applied both to simulated datasets of 1D, 2D, and 3D Gaussian Random Fields (GRFs) and to 710 real-world, resting-state fMRI datasets. We showed that both the simulated 2D and 3D GRFs and the real-world data contain a small percentage (<6%) of very large clusters (on average 60 times larger than the average cluster size), which were not present in 1D GRFs. These unexpectedly large clusters were deemed statistically significant using parametric methods, leading to empirical familywise error rates (FWERs) as high as 65%: the high empirical FWERs were not a consequence of parametric methods failing to model spatial smoothness accurately, but rather of these very large clusters that are inherently present in smooth, high-dimensional random fields. In fact, when discounting these very large clusters, the empirical FWER for parametric methods was 3.24%. Furthermore, even an empirical FWER of 65% would yield on average less than one of those very large clusters in each brain-wide analysis. Nonparametric methods, in contrast, estimated distributions from those large clusters, and therefore, by construct rejected the large clusters as false positives at the nominal FWERs. Those rejected clusters were outlying values in the distribution of cluster size but cannot be distinguished from true positive findings without further analyses, including assessing whether fMRI signal in those regions correlates with other clinical, behavioral, or cognitive measures. Rejecting the large clusters, however, significantly reduced the statistical power of nonparametric methods in detecting true findings compared with parametric methods, which would have detected most true findings that are essential for making valid biological inferences in MRI data. Parametric analyses, in contrast, detected most true findings while generating relatively few false positives: on average, less than one of those very large clusters would be deemed a true finding in each brain-wide analysis. We therefore recommend the continued use of parametric methods that model nonstationary smoothness for cluster-level, familywise control of false positives, particularly when using a Cluster Defining Threshold of 2.5 or higher, and subsequently assessing rigorously the biological plausibility of the findings, even for large clusters. Finally, because nonparametric methods yielded a large reduction in statistical power to detect true positive findings, we conclude that the modest reduction in false positive findings that nonparametric analyses afford does not warrant a re-analysis of previously published fMRI studies using nonparametric techniques. Copyright © 2018 Elsevier Inc. All rights reserved.

Cluster formation of network-modifier cations in cesium silicate glasses

NASA Astrophysics Data System (ADS)

Jardón-Álvarez, Daniel; Sanders, Kevin J.; Phyo, Pyae; Baltisberger, Jay H.; Grandinetti, Philip J.

2018-03-01

Natural abundance 29Si two-dimensional magic-angle flipping (2D MAF) NMR spectra were measured in a series of ten cesium silicate glass compositions xCs2O.(1 - x)SiO2, where x is 0.067, 0.113, 0.175, 0.179, 0.218, 0.234, 0.263, 0.298, 0.31, and 0.36. The Q3 shielding anisotropy decreases with increasing Cs content—interpreted as an increase in the non-bridging oxygen (NBO) bond length from increasing Cs coordination (clustering) around the NBO. The 29Si 2D MAF spectra for four glass compositions x = 0.218, 0.234, 0.263, 0.298 exhibit a second co-existing and distinctly smaller shielding anisotropy corresponding to a significantly longer Si-NBO length arising from a higher degree of Cs clustering around the NBO. This second Q3 site appears at a Cs2O mole fraction close to the critical mole fraction of x = 0.24 associated with the percolation threshold of non-bridging oxygen in random close packing of oxygen, thus suggesting that the longer Si-NBO length is associated with an infinite size spanning cluster while the sites with larger anisotropies are associated with shorter Si-NBO lengths and belong to finite size clusters. The equilibrium constant of the Q3 disproportionation reaction was determined as k3 = 0.005, indicating a Qn anionic species distribution close to a binary model as expected for a low field strength modifier such as cesium. It is also found that evolution of the isotropic Q4 and line shapes with increasing Cs content are consistent with a random connectivity model between Qn of differing number of bridging oxygen, n.

Geomorphological analysis of boulders and polygons on Martian periglacial patterned ground terrains

NASA Astrophysics Data System (ADS)

Orloff, Travis C.

Images from the High Resolution Imaging Science Experiment Camera onboard the Mars Reconnaisance Orbiter show the surface in higher detail than previously capable. I look at a landscape on Mars called permafrost patterned ground which covers ˜10 million square kilometers of the surface at high latitudes (>50°). Using the new high resolution images available we objectively characterize permafrost patterned ground terrains as an alternative to observational surveys which while detailed suffer from subjective bias. I take two dimensional Fourier transforms of individual images of Martian permafrost patterned ground to find the scale most representative of the terrain. This scale acts as a proxy for the size of the polygons themselves. Then I look at the distribution of spectral scales in the northern hemisphere between 50-70° and find correlations to previous studies and with the extent of ground ice in the surface. The high resolution images also show boulders clustering with respect to the underlying pattern. I make the first detailed observations of these clustered boulders and use crater counting to place constraints on the time it takes for boulders to cluster. Finally, I present a potential mechanism for the process that clusters the boulders that takes the specifics of the Martian environment to account. Boulders lying on the surface get trapped in seasonal CO2 frost while ice in the near surface contracts in the winter. The CO2 frost sublimates in spring/summer allowing the boulders to move when the near surface ice expands in summer. Repeated iterations lead to boulders that cluster in the polygon edges. Using a thermal model of the subsurface with Mars conditions and an elastic model of a polygon I show boulders could move as much as ˜0.1mm per year in the present day.

Carbon atom and cluster sputtering under low-energy noble gas plasma bombardment

NASA Astrophysics Data System (ADS)

Oyarzabal, E.; Doerner, R. P.; Shimada, M.; Tynan, G. R.

2008-08-01

Exit-angle resolved carbon atom and cluster (C2 and C3) sputtering yields are measured during different noble gas (Xe, Kr, Ar, Ne, and He) ion bombardments from a plasma, for low incident energies (75-225 eV). A quadrupole mass spectrometer (QMS) is used to detect the fraction of sputtered neutrals that is ionized in the plasma and to obtain the angular distribution by changing the angle between the target normal and the QMS aperture. A one-dimensional Monte Carlo code is used to simulate the interaction of the plasma and the sputtered particles in the region between the sample and the QMS. The effective elastic scattering cross sections of C, C2, and C3 with the different bombarding gas neutrals are obtained by varying the distance between the sample and the QMS and by performing a best fit of the simulation results to the experimental results. The total sputtering yield (C+C2+C3) for each bombarding gas is obtained from weight-loss measurements and the sputtering yield for C, C2, and C3 is then calculated from the integration of the measured angular distribution, taking into account the scattering and ionization of the sputtered particles between the sample and the QMS. We observe undercosine angular distributions of the sputtered atoms and clusters for all the studied bombarding gases and a clear decrease of the atom to cluster (C2 and C3) sputtering ratio as the incident ion mass increases, changing from a carbon atom preferential erosion for the lower incident ion masses (He, Ne, and Ar) to a cluster preferential erosion for the higher incident ion masses (Kr and Xe).

Coarse Point Cloud Registration by Egi Matching of Voxel Clusters

NASA Astrophysics Data System (ADS)

Wang, Jinhu; Lindenbergh, Roderik; Shen, Yueqian; Menenti, Massimo

2016-06-01

Laser scanning samples the surface geometry of objects efficiently and records versatile information as point clouds. However, often more scans are required to fully cover a scene. Therefore, a registration step is required that transforms the different scans into a common coordinate system. The registration of point clouds is usually conducted in two steps, i.e. coarse registration followed by fine registration. In this study an automatic marker-free coarse registration method for pair-wise scans is presented. First the two input point clouds are re-sampled as voxels and dimensionality features of the voxels are determined by principal component analysis (PCA). Then voxel cells with the same dimensionality are clustered. Next, the Extended Gaussian Image (EGI) descriptor of those voxel clusters are constructed using significant eigenvectors of each voxel in the cluster. Correspondences between clusters in source and target data are obtained according to the similarity between their EGI descriptors. The random sampling consensus (RANSAC) algorithm is employed to remove outlying correspondences until a coarse alignment is obtained. If necessary, a fine registration is performed in a final step. This new method is illustrated on scan data sampling two indoor scenarios. The results of the tests are evaluated by computing the point to point distance between the two input point clouds. The presented two tests resulted in mean distances of 7.6 mm and 9.5 mm respectively, which are adequate for fine registration.

a Probabilistic Embedding Clustering Method for Urban Structure Detection

NASA Astrophysics Data System (ADS)

Lin, X.; Li, H.; Zhang, Y.; Gao, L.; Zhao, L.; Deng, M.

2017-09-01

Urban structure detection is a basic task in urban geography. Clustering is a core technology to detect the patterns of urban spatial structure, urban functional region, and so on. In big data era, diverse urban sensing datasets recording information like human behaviour and human social activity, suffer from complexity in high dimension and high noise. And unfortunately, the state-of-the-art clustering methods does not handle the problem with high dimension and high noise issues concurrently. In this paper, a probabilistic embedding clustering method is proposed. Firstly, we come up with a Probabilistic Embedding Model (PEM) to find latent features from high dimensional urban sensing data by "learning" via probabilistic model. By latent features, we could catch essential features hidden in high dimensional data known as patterns; with the probabilistic model, we can also reduce uncertainty caused by high noise. Secondly, through tuning the parameters, our model could discover two kinds of urban structure, the homophily and structural equivalence, which means communities with intensive interaction or in the same roles in urban structure. We evaluated the performance of our model by conducting experiments on real-world data and experiments with real data in Shanghai (China) proved that our method could discover two kinds of urban structure, the homophily and structural equivalence, which means clustering community with intensive interaction or under the same roles in urban space.

Mapping the Indonesian territory, based on pollution, social demography and geographical data, using self organizing feature map

NASA Astrophysics Data System (ADS)

Hernawati, Kuswari; Insani, Nur; Bambang S. H., M.; Nur Hadi, W.; Sahid

2017-08-01

This research aims to mapping the 33 (thirty-three) provinces in Indonesia, based on the data on air, water and soil pollution, as well as social demography and geography data, into a clustered model. The method used in this study was unsupervised method that combines the basic concept of Kohonen or Self-Organizing Feature Maps (SOFM). The method is done by providing the design parameters for the model based on data related directly/ indirectly to pollution, which are the demographic and social data, pollution levels of air, water and soil, as well as the geographical situation of each province. The parameters used consists of 19 features/characteristics, including the human development index, the number of vehicles, the availability of the plant's water absorption and flood prevention, as well as geographic and demographic situation. The data used were secondary data from the Central Statistics Agency (BPS), Indonesia. The data are mapped into SOFM from a high-dimensional vector space into two-dimensional vector space according to the closeness of location in term of Euclidean distance. The resulting outputs are represented in clustered grouping. Thirty-three provinces are grouped into five clusters, where each cluster has different features/characteristics and level of pollution. The result can used to help the efforts on prevention and resolution of pollution problems on each cluster in an effective and efficient way.

On three-dimensional misorientation spaces.

PubMed

Krakow, Robert; Bennett, Robbie J; Johnstone, Duncan N; Vukmanovic, Zoja; Solano-Alvarez, Wilberth; Lainé, Steven J; Einsle, Joshua F; Midgley, Paul A; Rae, Catherine M F; Hielscher, Ralf

2017-10-01

Determining the local orientation of crystals in engineering and geological materials has become routine with the advent of modern crystallographic mapping techniques. These techniques enable many thousands of orientation measurements to be made, directing attention towards how such orientation data are best studied. Here, we provide a guide to the visualization of misorientation data in three-dimensional vector spaces, reduced by crystal symmetry, to reveal crystallographic orientation relationships. Domains for all point group symmetries are presented and an analysis methodology is developed and applied to identify crystallographic relationships, indicated by clusters in the misorientation space, in examples from materials science and geology. This analysis aids the determination of active deformation mechanisms and evaluation of cluster centres and spread enables more accurate description of transformation processes supporting arguments regarding provenance.

Viscosity of confined two-dimensional Yukawa liquids: A nonequilibrium method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Landmann, S.; Kählert, H.; Thomsen, H.

2015-09-15

We present a nonequilibrium method that allows one to determine the viscosity of two-dimensional dust clusters in an isotropic confinement. By applying a tangential external force to the outer parts of the cluster (e.g., with lasers), a sheared velocity profile is created. The decay of the angular velocity towards the center of the confinement potential is determined by a balance between internal (viscosity) and external friction (neutral gas damping). The viscosity can then be calculated from a fit of the measured velocity profile to a solution of the Navier-Stokes equation. Langevin dynamics simulations are used to demonstrate the feasibility ofmore » the method. We find good agreement of the measured viscosity with previous results for macroscopic Yukawa plasmas.« less

On three-dimensional misorientation spaces

NASA Astrophysics Data System (ADS)

Krakow, Robert; Bennett, Robbie J.; Johnstone, Duncan N.; Vukmanovic, Zoja; Solano-Alvarez, Wilberth; Lainé, Steven J.; Einsle, Joshua F.; Midgley, Paul A.; Rae, Catherine M. F.; Hielscher, Ralf

2017-10-01

Determining the local orientation of crystals in engineering and geological materials has become routine with the advent of modern crystallographic mapping techniques. These techniques enable many thousands of orientation measurements to be made, directing attention towards how such orientation data are best studied. Here, we provide a guide to the visualization of misorientation data in three-dimensional vector spaces, reduced by crystal symmetry, to reveal crystallographic orientation relationships. Domains for all point group symmetries are presented and an analysis methodology is developed and applied to identify crystallographic relationships, indicated by clusters in the misorientation space, in examples from materials science and geology. This analysis aids the determination of active deformation mechanisms and evaluation of cluster centres and spread enables more accurate description of transformation processes supporting arguments regarding provenance.

The three-dimensional Multi-Block Advanced Grid Generation System (3DMAGGS)

NASA Technical Reports Server (NTRS)

Alter, Stephen J.; Weilmuenster, Kenneth J.

1993-01-01

As the size and complexity of three dimensional volume grids increases, there is a growing need for fast and efficient 3D volumetric elliptic grid solvers. Present day solvers are limited by computational speed and do not have all the capabilities such as interior volume grid clustering control, viscous grid clustering at the wall of a configuration, truncation error limiters, and convergence optimization residing in one code. A new volume grid generator, 3DMAGGS (Three-Dimensional Multi-Block Advanced Grid Generation System), which is based on the 3DGRAPE code, has evolved to meet these needs. This is a manual for the usage of 3DMAGGS and contains five sections, including the motivations and usage, a GRIDGEN interface, a grid quality analysis tool, a sample case for verifying correct operation of the code, and a comparison to both 3DGRAPE and GRIDGEN3D. Since it was derived from 3DGRAPE, this technical memorandum should be used in conjunction with the 3DGRAPE manual (NASA TM-102224).

Saddle-node bifurcation to jammed state for quasi-one-dimensional counter-chemotactic flow.

PubMed

Fujii, Masashi; Awazu, Akinori; Nishimori, Hiraku

2010-07-01

The transition of a counter-chemotactic particle flow from a free-flow state to a jammed state in a quasi-one-dimensional path is investigated. One of the characteristic features of such a flow is that the constituent particles spontaneously form a cluster that blocks the path, called a path-blocking cluster (PBC), and causes a jammed state when the particle density is greater than a threshold value. Near the threshold value, the PBC occasionally collapses on itself to recover the free flow. In other words, the time evolution of the size of the PBC governs the flux of a counter-chemotactic flow. In this Rapid Communication, on the basis of numerical results of a stochastic cellular automata (SCA) model, we introduce a Langevin equation model for the size evolution of the PBC that reproduces the qualitative characteristics of the SCA model. The results suggest that the emergence of the jammed state in a quasi-one-dimensional counterflow is caused by a saddle-node bifurcation.

Diffusion dynamics of the Li+ ion on a model surface of amorphous carbon: a direct molecular orbital dynamics study.

PubMed

Tachikawa, Hiroto; Shimizu, Akira

2005-07-14

Diffusion processes of the Li+ ion on a model surface of amorphous carbon (Li+C96H24 system) have been investigated by means of the direct molecular orbital (MO) dynamics method at the semiempirical AM1 level. The total energy and energy gradient on the full-dimensional AM1 potential energy surface were calculated at each time step in the dynamics calculation. The optimized structure, where Li+ is located in the center of the cluster, was used as the initial structure at time zero. The dynamics calculation was carried out in the temperature range 100-1000 K. The calculations showed that the Li+ ion vibrates around the equilibrium point below 200 K, while the Li+ ion moves on the surface above 250 K. At intermediate temperatures (300 K < T < 400 K), the ion moves on the surface and falls in the edge regions of the cluster. At higher temperatures (600 K < T), the Li+ ion transfers freely on the surface and edge regions. The diffusion pathway of the Li+ ion was discussed on the basis of theoretical results.

Personality Disorders in Obsessive-Compulsive Disorder: A Comparative Study versus Other Anxiety Disorders

PubMed Central

Pena-Garijo, Josep; Edo Villamón, Silvia; Ruipérez, M. Ángeles

2013-01-01

Objective. The purpose of this paper is to provide evidence for the relationship between personality disorders (PDs), obsessive compulsive disorder (OCD), and other anxiety disorders different from OCD (non-OCD) symptomatology. Method. The sample consisted of a group of 122 individuals divided into three groups (41 OCD; 40 non-OCD, and 41 controls) matched by sex, age, and educational level. All the individuals answered the IPDE questionnaire and were evaluated by means of the SCID-I and SCID-II interviews. Results. Patients with OCD and non-OCD present a higher presence of PD. There was an increase in cluster C diagnoses in both groups, with no statistically significant differences between them. Conclusions. Presenting anxiety disorder seems to cause a specific vulnerability for PD. Most of the PDs that were presented belonged to cluster C. Obsessive Compulsive Personality Disorder (OCPD) is the most common among OCD. However, it does not occur more frequently among OCD patients than among other anxious patients, which does not confirm the continuum between obsessive personality and OCD. Implications for categorical and dimensional diagnoses are discussed. PMID:24453917

Cluster redshifts in five suspected superclusters

NASA Technical Reports Server (NTRS)

Ciardullo, R.; Ford, H.; Harms, R.

1985-01-01

Redshift surveys for rich superclusters were carried out in five regions of the sky containing surface-density enhancements of Abell clusters. While several superclusters are identified, projection effects dominate each field, and no system contains more than five rich clusters. Two systems are found to be especially interesting. The first, field 0136 10, is shown to contain a superposition of at least four distinct superclusters, with the richest system possessing a small velocity dispersion. The second system, 2206 - 22, though a region of exceedingly high Abell cluster surface density, appears to be a remarkable superposition of 23 rich clusters almost uniformly distributed in redshift space between 0.08 and 0.24. The new redshifts significantly increase the three-dimensional information available for the distance class 5 and 6 Abell clusters and allow the spatial correlation function around rich superclusters to be estimated.

Categorical and Dimensional Structure of Autism Spectrum Disorders: The Nosologic Validity of Asperger Syndrome

ERIC Educational Resources Information Center

Kamp-Becker, Inge; Smidt, Judith; Ghahreman, Mardjan; Heinzel-Gutenbrunner, Monika; Becker, Katja; Remschmidt, Helmut

2010-01-01

There is an ongoing debate whether a differentiation of autistic subtypes, especially between Asperger Syndrome (AS) and high-functioning-autism (HFA) is possible and if so, whether it is a categorical or dimensional one. The aim of this study was to examine the possible clustering of responses in different symptom domains without making any…

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

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.

Late Diagenetic Cements in the Murray Formation, Gale Crater, Mars: Implications for Postdepositional Fluid Flow

NASA Astrophysics Data System (ADS)

Kah, L. C.; Kronyak, R. E.; Van Beek, J.; Nachon, M.; Mangold, N.; Thompson, L. M.; Wiens, R. C.; Grotzinger, J. P.; Schieber, J.

2015-12-01

The Murray formation in its type section at Pahrump Hills, consists of approximately 14 meters of recessive-weathering mudstone interbedded with decimeter-scale cross-bedded sandstone in the upper portions of the exposed section. Mudstone textures vary from massive, to poorly laminated, to well laminated. Unusual 3-dimensional crystal clusters and dendrites occur in the lowermost part of the section and are erosionally resistant with respect to the host rock. Crystal clusters consist of elongate lathes that occur within individual blocks of the fractured substrate. Individual lathes show tabular morphologies with a pseudo-rectangular cross-section and the three dimensional morphology of the crystal clusters cross-cut host rock lamination with little or no deformation. Dendritic structures are typically larger and show predominantly planar growth aligned with bedding planes. Individual lathes within the dendrites are elongate and pseudo-rectangular in cross-section. Unlike crystal clusters, dendritic morphologies appear to nucleate at bedrock fractures and near mineralized veins. Here we show evidence that crystal clusters and dendrites are post-depositional, potentially burial diagenetic features. Association of features with through-going fractures suggests that fractures may have been a primary transport pathway for ions responsible for dendrite growth. Even where dendrites do not occur, enhanced cementation suggests that fluids permeated the rock matrix. We suggest that growth of clusters proceeded as inter-particle crystal growth, wherein mineral growth within inter-particle spaces resulted in cementation and porosity loss, with little further effect on the rock matrix. Crystal clusters and dendrites are most likely to form when mineral saturation states are highest, for instance with initial intrusion of fracture-borne fluids and mixing with ambient pore fluids, and thus emphasize the importance of fractures in ion transport during late diagenesis.

Growth mode and structures of magnetic Mn clusters on graphene

DOE PAGES

Liu, Xiaojie; Wang, Cai-Zhuang

2016-06-22

We present a systematic study of Mn clusters on graphene by first-principles calculations. We show that the growth of Mn on graphene follows a three-dimensional (3D) mode. Both adsorption and attachment energies show that (Mn) 3 and (Mn) 6 on graphene are energetically favorable in the size range (Mn) 1-7. Moreover, larger formation energy for Mn cluster on graphene implies the incoming Mn atoms are likely to nucleate and grow into bigger and bigger Mn clusters on graphene. The magnetic moments of (Mn) 1,5,7 on graphene are enhanced by 11%, 186%, and 26% from their values at free-standing clusters, respectively.more » By contrast, the net magnetic moment of (Mn) 2,3,4,6 on graphene is reduced from that of the corresponding free-standing clusters. The origin of the magnetic moment changes can be attributed to the charge transfer within the Mn clusters and between the clusters and graphene upon adsorption.« less

Applied anatomic site study of palatal anchorage implants using cone beam computed tomography.

PubMed

Lai, Ren-fa; Zou, Hui; Kong, Wei-dong; Lin, Wei

2010-06-01

The purpose of this study was to conduct quantitative research on bone height and bone mineral density of palatal implant sites for implantation, and to provide reference sites for safe and stable palatal implants. Three-dimensional reformatting images were reconstructed by cone beam computed tomography (CBCT) in 34 patients, aged 18 to 35 years, using EZ Implant software. Bone height was measured at 20 sites of interest on the palate. Bone mineral density was measured at the 10 sites with the highest implantation rate, classified using K-mean cluster analysis based on bone height and bone mineral density. According to the cluster analysis, 10 sites were classified into three clusters. Significant differences in bone height and bone mineral density were detected between these three clusters (P<0.05). The greatest bone height was obtained in cluster 2, followed by cluster 1 and cluster 3. The highest bone mineral density was found in cluster 3, followed by cluster 1 and cluster 2. CBCT plays an important role in pre-surgical treatment planning. CBCT is helpful in identifying safe and stable implantation sites for palatal anchorage.

Statistical Significance for Hierarchical Clustering

PubMed Central

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

2017-01-01

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

Higher-order gravity in higher dimensions: geometrical origins of four-dimensional cosmology?

NASA Astrophysics Data System (ADS)

Troisi, Antonio

2017-03-01

Determining the cosmological field equations is still very much debated and led to a wide discussion around different theoretical proposals. A suitable conceptual scheme could be represented by gravity models that naturally generalize Einstein theory like higher-order gravity theories and higher-dimensional ones. Both of these two different approaches allow one to define, at the effective level, Einstein field equations equipped with source-like energy-momentum tensors of geometrical origin. In this paper, the possibility is discussed to develop a five-dimensional fourth-order gravity model whose lower-dimensional reduction could provide an interpretation of cosmological four-dimensional matter-energy components. We describe the basic concepts of the model, the complete field equations formalism and the 5-D to 4-D reduction procedure. Five-dimensional f( R) field equations turn out to be equivalent, on the four-dimensional hypersurfaces orthogonal to the extra coordinate, to an Einstein-like cosmological model with three matter-energy tensors related with higher derivative and higher-dimensional counter-terms. By considering the gravity model with f(R)=f_0R^n the possibility is investigated to obtain five-dimensional power law solutions. The effective four-dimensional picture and the behaviour of the geometrically induced sources are finally outlined in correspondence to simple cases of such higher-dimensional solutions.

Generalized quantum kinetic expansion: Higher-order corrections to multichromophoric Förster theory

NASA Astrophysics Data System (ADS)

Wu, Jianlan; Gong, Zhihao; Tang, Zhoufei

2015-08-01

For a general two-cluster energy transfer network, a new methodology of the generalized quantum kinetic expansion (GQKE) method is developed, which predicts an exact time-convolution equation for the cluster population evolution under the initial condition of the local cluster equilibrium state. The cluster-to-cluster rate kernel is expanded over the inter-cluster couplings. The lowest second-order GQKE rate recovers the multichromophoric Förster theory (MCFT) rate. The higher-order corrections to the MCFT rate are systematically included using the continued fraction resummation form, resulting in the resummed GQKE method. The reliability of the GQKE methodology is verified in two model systems, revealing the relevance of higher-order corrections.

Is Statistical Learning Constrained by Lower Level Perceptual Organization?

PubMed Central

Emberson, Lauren L.; Liu, Ran; Zevin, Jason D.

2013-01-01

In order for statistical information to aid in complex developmental processes such as language acquisition, learning from higher-order statistics (e.g. across successive syllables in a speech stream to support segmentation) must be possible while perceptual abilities (e.g. speech categorization) are still developing. The current study examines how perceptual organization interacts with statistical learning. Adult participants were presented with multiple exemplars from novel, complex sound categories designed to reflect some of the spectral complexity and variability of speech. These categories were organized into sequential pairs and presented such that higher-order statistics, defined based on sound categories, could support stream segmentation. Perceptual similarity judgments and multi-dimensional scaling revealed that participants only perceived three perceptual clusters of sounds and thus did not distinguish the four experimenter-defined categories, creating a tension between lower level perceptual organization and higher-order statistical information. We examined whether the resulting pattern of learning is more consistent with statistical learning being “bottom-up,” constrained by the lower levels of organization, or “top-down,” such that higher-order statistical information of the stimulus stream takes priority over the perceptual organization, and perhaps influences perceptual organization. We consistently find evidence that learning is constrained by perceptual organization. Moreover, participants generalize their learning to novel sounds that occupy a similar perceptual space, suggesting that statistical learning occurs based on regions of or clusters in perceptual space. Overall, these results reveal a constraint on learning of sound sequences, such that statistical information is determined based on lower level organization. These findings have important implications for the role of statistical learning in language acquisition. PMID:23618755

Clogging and jamming transitions in periodic obstacle arrays

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nguyen, Hong; Reichhardt, Charles; Olson Reichhardt, Cynthia Jane

2017-03-29

We numerically examine clogging transitions for bidisperse disks flowing through a two-dimensional periodic obstacle array. Here, we show that clogging is a probabilistic event that occurs through a transition from a homogeneous flowing state to a heterogeneous or phase-separated jammed state where the disks form dense connected clusters. The probability for clogging to occur during a fixed time increases with increasing particle packing and obstacle number. For driving at different angles with respect to the symmetry direction of the obstacle array, we show that certain directions have a higher clogging susceptibility. It is also possible to have a size-specific cloggingmore » transition in which one disk size becomes completely immobile while the other disk size continues to flow.« less

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

PubMed

Cunha, Conceição

2015-01-01

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

Quantum phase transition between cluster and antiferromagnetic states

NASA Astrophysics Data System (ADS)

Son, W.; Amico, L.; Fazio, R.; Hamma, A.; Pascazio, S.; Vedral, V.

2011-09-01

We study a Hamiltonian system describing a three-spin-1/2 cluster-like interaction competing with an Ising-like exchange. We show that the ground state in the cluster phase possesses symmetry protected topological order. A continuous quantum phase transition occurs as result of the competition between the cluster and Ising terms. At the critical point the Hamiltonian is self-dual. The geometric entanglement is also studied and used to investigate the quantum phase transition. Our findings in one dimension corroborate the analysis of the two-dimensional generalization of the system, indicating, at a mean-field level, the presence of a direct transition between an antiferromagnetic and a valence bond solid ground state.

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

PubMed

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

2015-11-05

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. Copyright © 2015 Elsevier Ltd. All rights reserved.

Genetic and environmental influences on dimensional representations of DSM-IV cluster C personality disorders: a population-based multivariate twin study.

PubMed

Reichborn-Kjennerud, Ted; Czajkowski, Nikolai; Neale, Michael C; Ørstavik, Ragnhild E; Torgersen, Svenn; Tambs, Kristian; Røysamb, Espen; Harris, Jennifer R; Kendler, Kenneth S

2007-05-01

The DSM-IV cluster C Axis II disorders include avoidant (AVPD), dependent (DEPD) and obsessive-compulsive (OCPD) personality disorders. We aimed to estimate the genetic and environmental influences on dimensional representations of these disorders and examine the validity of the cluster C construct by determining to what extent common familial factors influence the individual PDs. PDs were assessed using the Structured Interview for DSM-IV Personality (SIDP-IV) in a sample of 1386 young adult twin pairs from the Norwegian Institute of Public Health Twin Panel (NIPHTP). A single-factor independent pathway multivariate model was applied to the number of endorsed criteria for the three cluster C disorders, using the statistical modeling program Mx. The best-fitting model included genetic and unique environmental factors only, and equated parameters for males and females. Heritability ranged from 27% to 35%. The proportion of genetic variance explained by a common factor was 83, 48 and 15% respectively for AVPD, DEPD and OCPD. Common genetic and environmental factors accounted for 54% and 64% respectively of the variance in AVPD and DEPD but only 11% of the variance in OCPD. Cluster C PDs are moderately heritable. No evidence was found for shared environmental or sex effects. Common genetic and individual environmental factors account for a substantial proportion of the variance in AVPD and DEPD. However, OCPD appears to be largely etiologically distinct from the other two PDs. The results do not support the validity of the DSM-IV cluster C construct in its present form.

Fast Constrained Spectral Clustering and Cluster Ensemble with Random Projection

PubMed Central

Liu, Wenfen

2017-01-01

Constrained spectral clustering (CSC) method can greatly improve the clustering accuracy with the incorporation of constraint information into spectral clustering and thus has been paid academic attention widely. In this paper, we propose a fast CSC algorithm via encoding landmark-based graph construction into a new CSC model and applying random sampling to decrease the data size after spectral embedding. Compared with the original model, the new algorithm has the similar results with the increase of its model size asymptotically; compared with the most efficient CSC algorithm known, the new algorithm runs faster and has a wider range of suitable data sets. Meanwhile, a scalable semisupervised cluster ensemble algorithm is also proposed via the combination of our fast CSC algorithm and dimensionality reduction with random projection in the process of spectral ensemble clustering. We demonstrate by presenting theoretical analysis and empirical results that the new cluster ensemble algorithm has advantages in terms of efficiency and effectiveness. Furthermore, the approximate preservation of random projection in clustering accuracy proved in the stage of consensus clustering is also suitable for the weighted k-means clustering and thus gives the theoretical guarantee to this special kind of k-means clustering where each point has its corresponding weight. PMID:29312447

Somatotyping using 3D anthropometry: a cluster analysis.

PubMed

Olds, Tim; Daniell, Nathan; Petkov, John; David Stewart, Arthur

2013-01-01

Somatotyping is the quantification of human body shape, independent of body size. Hitherto, somatotyping (including the most popular method, the Heath-Carter system) has been based on subjective visual ratings, sometimes supported by surface anthropometry. This study used data derived from three-dimensional (3D) whole-body scans as inputs for cluster analysis to objectively derive clusters of similar body shapes. Twenty-nine dimensions normalised for body size were measured on a purposive sample of 301 adults aged 17-56 years who had been scanned using a Vitus Smart laser scanner. K-means Cluster Analysis with v-fold cross-validation was used to determine shape clusters. Three male and three female clusters emerged, and were visualised using those scans closest to the cluster centroid and a caricature defined by doubling the difference between the average scan and the cluster centroid. The male clusters were decidedly endomorphic (high fatness), ectomorphic (high linearity), and endo-mesomorphic (a mixture of fatness and muscularity). The female clusters were clearly endomorphic, ectomorphic, and the ecto-mesomorphic (a mixture of linearity and muscularity). An objective shape quantification procedure combining 3D scanning and cluster analysis yielded shape clusters strikingly similar to traditional somatotyping.

High-order harmonic generation from a two-dimensional band structure

NASA Astrophysics Data System (ADS)

Jin, Jian-Zhao; Xiao, Xiang-Ru; Liang, Hao; Wang, Mu-Xue; Chen, Si-Ge; Gong, Qihuang; Peng, Liang-You

2018-04-01

In the past few years, harmonic generation in solids has attracted tremendous attention. Recently, some experiments of two-dimensional (2D) monolayer or few-layer materials have been carried out. These studies demonstrated that harmonic generation in the 2D case shows a strong dependence on the laser's orientation and ellipticity, which calls for a quantitative theoretical interpretation. In this work, we carry out a systematic study on the harmonic generation from a 2D band structure based on a numerical solution to the time-dependent Schrödinger equation. By comparing with the 1D case, we find that the generation dynamics can have a significant difference due to the existence of many crossing points in the 2D band structure. In particular, the higher conduction bands can be excited step by step via these crossing points and the total contribution of the harmonic is given by the mixing of transitions between different clusters of conduction bands to the valence band. We also present the orientation dependence of the harmonic yield on the laser polarization direction.

D Geomarketing Segmentation: a Higher Spatial Dimension Planning Perspective

NASA Astrophysics Data System (ADS)

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

2016-09-01

Geomarketing is a discipline which uses geographic information in the process of planning and implementation of marketing activities. It can be used in any aspect of the marketing such as price, promotion or geo targeting. The analysis of geomarketing data use a huge data pool such as location residential areas, topography, it also analyzes demographic information such as age, genre, annual income and lifestyle. This information can help users to develop successful promotional campaigns in order to achieve marketing goals. One of the common activities in geomarketing is market segmentation. The segmentation clusters the data into several groups based on its geographic criteria. To refine the search operation during analysis, we proposed an approach to cluster the data using a clustering algorithm. However, with the huge data pool, overlap among clusters may happen and leads to inefficient analysis. Moreover, geomarketing is usually active in urban areas and requires clusters to be organized in a three-dimensional (3D) way (i.e. multi-level shop lots, residential apartments). This is a constraint with the current Geographic Information System (GIS) framework. To avoid this issue, we proposed a combination of market segmentation based on geographic criteria and clustering algorithm for 3D geomarketing data management. The proposed approach is capable in minimizing the overlap region during market segmentation. In this paper, geomarketing in urban area is used as a case study. Based on the case study, several locations of customers and stores in 3D are used in the test. The experiments demonstrated in this paper substantiated that the proposed approach is capable of minimizing overlapping segmentation and reducing repetitive data entries. The structure is also tested for retrieving the spatial records from the database. For marketing purposes, certain radius of point is used to analyzing marketing targets. Based on the presented tests in this paper, we strongly believe that the structure is capable in handling and managing huge pool of geomarketing data. For future outlook, this paper also discusses the possibilities of expanding the structure.

Determination System Of Food Vouchers For the Poor Based On Fuzzy C-Means Method

NASA Astrophysics Data System (ADS)

Anamisa, D. R.; Yusuf, M.; Syakur, M. A.

2018-01-01

Food vouchers are government programs to tackle the poverty of rural communities. This program aims to help the poor group in getting enough food and nutrients from carbohydrates. There are several factors that influence to receive the food voucher, such as: job, monthly income, Taxes, electricity bill, size of house, number of family member, education certificate and amount of rice consumption every week. In the execution for the distribution of vouchers is often a lot of problems, such as: the distribution of food vouchers has been misdirected and someone who receives is still subjective. Some of the solutions to decision making have not been done. The research aims to calculating the change of each partition matrix and each cluster using Fuzzy C-Means method. Hopefully this research makes contribution by providing higher result using Fuzzy C-Means comparing to other method for this case study. In this research, decision making is done by using Fuzzy C-Means method. The Fuzzy C-Means method is a clustering method that has an organized and scattered cluster structure with regular patterns on two-dimensional datasets. Furthermore, Fuzzy C-Means method used for calculates the change of each partition matrix. Each cluster will be sorted by the proximity of the data element to the centroid of the cluster to get the ranking. Various trials were conducted for grouping and ranking of proposed data that received food vouchers based on the quota of each village. This testing by Fuzzy C-Means method, is developed and abled for determining the recipient of the food voucher with satisfaction results. Fulfillment of the recipient of the food voucher is 80% to 90% and this testing using data of 115 Family Card from 6 Villages. The quality of success affected, has been using the number of iteration factors is 20 and the number of clusters is 3

A Fast Exact k-Nearest Neighbors Algorithm for High Dimensional Search Using k-Means Clustering and Triangle Inequality.

PubMed

Wang, Xueyi

2012-02-08

The k-nearest neighbors (k-NN) algorithm is a widely used machine learning method that finds nearest neighbors of a test object in a feature space. We present a new exact k-NN algorithm called kMkNN (k-Means for k-Nearest Neighbors) that uses the k-means clustering and the triangle inequality to accelerate the searching for nearest neighbors in a high dimensional space. The kMkNN algorithm has two stages. In the buildup stage, instead of using complex tree structures such as metric trees, kd-trees, or ball-tree, kMkNN uses a simple k-means clustering method to preprocess the training dataset. In the searching stage, given a query object, kMkNN finds nearest training objects starting from the nearest cluster to the query object and uses the triangle inequality to reduce the distance calculations. Experiments show that the performance of kMkNN is surprisingly good compared to the traditional k-NN algorithm and tree-based k-NN algorithms such as kd-trees and ball-trees. On a collection of 20 datasets with up to 10(6) records and 10(4) dimensions, kMkNN shows a 2-to 80-fold reduction of distance calculations and a 2- to 60-fold speedup over the traditional k-NN algorithm for 16 datasets. Furthermore, kMkNN performs significant better than a kd-tree based k-NN algorithm for all datasets and performs better than a ball-tree based k-NN algorithm for most datasets. The results show that kMkNN is effective for searching nearest neighbors in high dimensional spaces.

Partial Discharge Spectral Characterization in HF, VHF and UHF Bands Using Particle Swarm Optimization.

PubMed

Robles, Guillermo; Fresno, José Manuel; Martínez-Tarifa, Juan Manuel; Ardila-Rey, Jorge Alfredo; Parrado-Hernández, Emilio

2018-03-01

The measurement of partial discharge (PD) signals in the radio frequency (RF) range has gained popularity among utilities and specialized monitoring companies in recent years. Unfortunately, in most of the occasions the data are hidden by noise and coupled interferences that hinder their interpretation and renders them useless especially in acquisition systems in the ultra high frequency (UHF) band where the signals of interest are weak. This paper is focused on a method that uses a selective spectral signal characterization to feature each signal, type of partial discharge or interferences/noise, with the power contained in the most representative frequency bands. The technique can be considered as a dimensionality reduction problem where all the energy information contained in the frequency components is condensed in a reduced number of UHF or high frequency (HF) and very high frequency (VHF) bands. In general, dimensionality reduction methods make the interpretation of results a difficult task because the inherent physical nature of the signal is lost in the process. The proposed selective spectral characterization is a preprocessing tool that facilitates further main processing. The starting point is a clustering of signals that could form the core of a PD monitoring system. Therefore, the dimensionality reduction technique should discover the best frequency bands to enhance the affinity between signals in the same cluster and the differences between signals in different clusters. This is done maximizing the minimum Mahalanobis distance between clusters using particle swarm optimization (PSO). The tool is tested with three sets of experimental signals to demonstrate its capabilities in separating noise and PDs with low signal-to-noise ratio and separating different types of partial discharges measured in the UHF and HF/VHF bands.

CLUMP-3D: Testing ΛCDM with Galaxy Cluster Shapes

NASA Astrophysics Data System (ADS)

Sereno, Mauro; Umetsu, Keiichi; Ettori, Stefano; Sayers, Jack; Chiu, I.-Non; Meneghetti, Massimo; Vega-Ferrero, Jesús; Zitrin, Adi

2018-06-01

The ΛCDM model of structure formation makes strong predictions on the concentration and shape of dark matter (DM) halos, which are determined by mass accretion processes. Comparison between predicted shapes and observations provides a geometric test of the ΛCDM model. Accurate and precise measurements needs a full three-dimensional (3D) analysis of the cluster mass distribution. We accomplish this with a multi-probe 3D analysis of the X-ray regular Cluster Lensing and Supernova survey with Hubble (CLASH) clusters combining strong and weak lensing, X-ray photometry and spectroscopy, and the Sunyaev–Zel’dovich effect (SZe). The cluster shapes and concentrations are consistent with ΛCDM predictions. The CLASH clusters are randomly oriented, as expected given the sample selection criteria. Shapes agree with numerical results for DM-only halos, which hints at baryonic physics being less effective in making halos rounder.

Formation of fivefold axes in the FCC-metal nanoclusters

NASA Astrophysics Data System (ADS)

Myasnichenko, Vladimir S.; Starostenkov, Mikhail D.

2012-11-01

Formation of atomistic structures of metallic Cu, Au, Ag clusters and bimetallic Cu-Au clusters was studied with the help of molecular dynamics using the many-body tight-binding interatomic potential. The simulation of the crystallization process of clusters with the number of atoms ranging from 300 to 1092 was carried out. The most stable configurations of atoms in the system, corresponding to the minimum of potential energy, was found during super-fast cooling from 1000 K. Atoms corresponding to fcc, hcp, and Ih phases were identified by the method of common neighbor analysis. Incomplete icosahedral core can be discovered at the intersection of one of the Ih axes with the surface of monometallic cluster. The decahedron-shaped structure of bimetallic Cu-Au cluster with seven completed icosahedral cores was obtained. The principles of the construction of small bimetallic clusters with icosahedral symmetry and increased fractal dimensionality were offered.

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

PubMed

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

2015-01-01

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

Cluster adsorption on amorphous and crystalline surfaces - A molecular dynamics study of model Pt on Cu and model Pd on Pt

NASA Technical Reports Server (NTRS)

Garofalini, S. H.; Halicioglu, T.; Pound, G. M.

1981-01-01

Molecular dynamics was used to study the structure, dispersion and short-time behavior of ten-atom clusters adsorbed onto amorphous and crystalline substrates, in which the cluster atoms differed from the substrate atoms. Two adatom-substrate model systems were chosen; one, in which the interaction energy between adatom pairs was greater than that between substrate pairs, and the other, in which the reverse was true. At relatively low temperature ranges, increased dispersion of cluster atoms occurred: (a) on the amorphous substrate as compared to the FCC(100) surface, (b) with increasing reduced temperature, and (c) with adatom-substrate interaction energy stronger than adatom-adatom interaction. Two-dimensional clusters (rafts) on the FCC(100) surface displayed migration of edge atoms only, indicating a mechanism for the cluster rotation and shape changes found in experimental studies.

Computational gene expression profiling under salt stress reveals patterns of co-expression

PubMed Central

Sanchita; Sharma, Ashok

2016-01-01

Plants respond differently to environmental conditions. Among various abiotic stresses, salt stress is a condition where excess salt in soil causes inhibition of plant growth. To understand the response of plants to the stress conditions, identification of the responsible genes is required. Clustering is a data mining technique used to group the genes with similar expression. The genes of a cluster show similar expression and function. We applied clustering algorithms on gene expression data of Solanum tuberosum showing differential expression in Capsicum annuum under salt stress. The clusters, which were common in multiple algorithms were taken further for analysis. Principal component analysis (PCA) further validated the findings of other cluster algorithms by visualizing their clusters in three-dimensional space. Functional annotation results revealed that most of the genes were involved in stress related responses. Our findings suggest that these algorithms may be helpful in the prediction of the function of co-expressed genes. PMID:26981411

A review on the multivariate statistical methods for dimensional reduction studies

NASA Astrophysics Data System (ADS)

Aik, Lim Eng; Kiang, Lam Chee; Mohamed, Zulkifley Bin; Hong, Tan Wei

2017-05-01

In this research study we have discussed multivariate statistical methods for dimensional reduction, which has been done by various researchers. The reduction of dimensionality is valuable to accelerate algorithm progression, as well as really may offer assistance with the last grouping/clustering precision. A lot of boisterous or even flawed info information regularly prompts a not exactly alluring algorithm progression. Expelling un-useful or dis-instructive information segments may for sure help the algorithm discover more broad grouping locales and principles and generally speaking accomplish better exhibitions on new data set.

Stereo Imaging Velocimetry

NASA Technical Reports Server (NTRS)

McDowell, Mark (Inventor); Glasgow, Thomas K. (Inventor)

1999-01-01

A system and a method for measuring three-dimensional velocities at a plurality of points in a fluid employing at least two cameras positioned approximately perpendicular to one another. The cameras are calibrated to accurately represent image coordinates in world coordinate system. The two-dimensional views of the cameras are recorded for image processing and centroid coordinate determination. Any overlapping particle clusters are decomposed into constituent centroids. The tracer particles are tracked on a two-dimensional basis and then stereo matched to obtain three-dimensional locations of the particles as a function of time so that velocities can be measured therefrom The stereo imaging velocimetry technique of the present invention provides a full-field. quantitative, three-dimensional map of any optically transparent fluid which is seeded with tracer particles.

On characterizing population commonalities and subject variations in brain networks.

PubMed

Ghanbari, Yasser; Bloy, Luke; Tunc, Birkan; Shankar, Varsha; Roberts, Timothy P L; Edgar, J Christopher; Schultz, Robert T; Verma, Ragini

2017-05-01

Brain networks based on resting state connectivity as well as inter-regional anatomical pathways obtained using diffusion imaging have provided insight into pathology and development. Such work has underscored the need for methods that can extract sub-networks that can accurately capture the connectivity patterns of the underlying population while simultaneously describing the variation of sub-networks at the subject level. We have designed a multi-layer graph clustering method that extracts clusters of nodes, called 'network hubs', which display higher levels of connectivity within the cluster than to the rest of the brain. The method determines an atlas of network hubs that describes the population, as well as weights that characterize subject-wise variation in terms of within- and between-hub connectivity. This lowers the dimensionality of brain networks, thereby providing a representation amenable to statistical analyses. The applicability of the proposed technique is demonstrated by extracting an atlas of network hubs for a population of typically developing controls (TDCs) as well as children with autism spectrum disorder (ASD), and using the structural and functional networks of a population to determine the subject-level variation of these hubs and their inter-connectivity. These hubs are then used to compare ASD and TDCs. Our method is generalizable to any population whose connectivity (structural or functional) can be captured via non-negative network graphs. Copyright © 2015 Elsevier B.V. All rights reserved.

On three-dimensional misorientation spaces

PubMed Central

Bennett, Robbie J.; Vukmanovic, Zoja; Solano-Alvarez, Wilberth; Lainé, Steven J.; Einsle, Joshua F.; Midgley, Paul A.; Rae, Catherine M. F.; Hielscher, Ralf

2017-01-01

Determining the local orientation of crystals in engineering and geological materials has become routine with the advent of modern crystallographic mapping techniques. These techniques enable many thousands of orientation measurements to be made, directing attention towards how such orientation data are best studied. Here, we provide a guide to the visualization of misorientation data in three-dimensional vector spaces, reduced by crystal symmetry, to reveal crystallographic orientation relationships. Domains for all point group symmetries are presented and an analysis methodology is developed and applied to identify crystallographic relationships, indicated by clusters in the misorientation space, in examples from materials science and geology. This analysis aids the determination of active deformation mechanisms and evaluation of cluster centres and spread enables more accurate description of transformation processes supporting arguments regarding provenance. PMID:29118660

Predictive Rate-Distortion for Infinite-Order Markov Processes

NASA Astrophysics Data System (ADS)

Marzen, Sarah E.; Crutchfield, James P.

2016-06-01

Predictive rate-distortion analysis suffers from the curse of dimensionality: clustering arbitrarily long pasts to retain information about arbitrarily long futures requires resources that typically grow exponentially with length. The challenge is compounded for infinite-order Markov processes, since conditioning on finite sequences cannot capture all of their past dependencies. Spectral arguments confirm a popular intuition: algorithms that cluster finite-length sequences fail dramatically when the underlying process has long-range temporal correlations and can fail even for processes generated by finite-memory hidden Markov models. We circumvent the curse of dimensionality in rate-distortion analysis of finite- and infinite-order processes by casting predictive rate-distortion objective functions in terms of the forward- and reverse-time causal states of computational mechanics. Examples demonstrate that the resulting algorithms yield substantial improvements.

Metal-chelate dye-controlled organization of Cd32S14(SPh)40(4-) nanoclusters into three-dimensional molecular and covalent open architecture.

PubMed

Zheng, Nanfeng; Lu, Haiwei; Bu, Xianhui; Feng, Pingyun

2006-04-12

Chalcogenide II-VI nanoclusters are usually prepared as isolated clusters and have defied numerous efforts to join them into covalent open-framework architecture with conventional templating methods such as protonated amines or inorganic cations commonly used to direct the formation of porous frameworks. Herein, we report the first templated synthesis of II-VI covalent superlattices from large II-VI tetrahedral clusters (i.e., [Cd32S14(SPh)38]2-). Our method takes advantage of low charge density of metal-chelate dyes that is a unique match with three-dimensional II-VI semiconductor frameworks in charge density, surface hydrophilicity-hydrophobicity, and spatial organization. In addition, metal-chelate dyes also serve to tune the optical properties of resulting dye semiconductor composite materials.

Scaffold Architecture Controls Insulinoma Clustering, Viability, and Insulin Production

PubMed Central

Blackstone, Britani N.; Palmer, Andre F.; Rilo, Horacio R.

2014-01-01

Recently, in vitro diagnostic tools have shifted focus toward personalized medicine by incorporating patient cells into traditional test beds. These cell-based platforms commonly utilize two-dimensional substrates that lack the ability to support three-dimensional cell structures seen in vivo. As monolayer cell cultures have previously been shown to function differently than cells in vivo, the results of such in vitro tests may not accurately reflect cell response in vivo. It is therefore of interest to determine the relationships between substrate architecture, cell structure, and cell function in 3D cell-based platforms. To investigate the effect of substrate architecture on insulinoma organization and function, insulinomas were seeded onto 2D gelatin substrates and 3D fibrous gelatin scaffolds with three distinct fiber diameters and fiber densities. Cell viability and clustering was assessed at culture days 3, 5, and 7 with baseline insulin secretion and glucose-stimulated insulin production measured at day 7. Small, closely spaced gelatin fibers promoted the formation of large, rounded insulinoma clusters, whereas monolayer organization and large fibers prevented cell clustering and reduced glucose-stimulated insulin production. Taken together, these data show that scaffold properties can be used to control the organization and function of insulin-producing cells and may be useful as a 3D test bed for diabetes drug development. PMID:24410263

Scrutinizing human MHC polymorphism: Supertype analysis using Poisson-Boltzmann electrostatics and clustering.

PubMed

Mumtaz, Shahzad; Nabney, Ian T; Flower, Darren R

2017-10-01

Peptide-binding MHC proteins are thought the most variable across the human population; the extreme MHC polymorphism observed is functionally important and results from constrained divergent evolution. MHCs have vital functions in immunology and homeostasis: cell surface MHC class I molecules report cell status to CD8+ T cells, NKT cells and NK cells, thus playing key roles in pathogen defence, as well as mediating smell recognition, mate choice, Adverse Drug Reactions, and transplantation rejection. MHC peptide specificity falls into several supertypes exhibiting commonality of binding. It seems likely that other supertypes exist relevant to other functions. Since comprehensive experimental characterization is intractable, structure-based bioinformatics is the only viable solution. We modelled functional MHC proteins by homology and used calculated Poisson-Boltzmann electrostatics projected from the top surface of the MHC as multi-dimensional descriptors, analysing them using state-of-the-art dimensionality reduction techniques and clustering algorithms. We were able to recover the 3 MHC loci as separate clusters and identify clear sub-groups within them, vindicating unequivocally our choice of both data representation and clustering strategy. We expect this approach to make a profound contribution to the study of MHC polymorphism and its functional consequences, and, by extension, other burgeoning structural systems, such as GPCRs. Copyright © 2017 Elsevier Inc. All rights reserved.

Why do gallium clusters have a higher melting point than the bulk?

PubMed

Chacko, S; Joshi, Kavita; Kanhere, D G; Blundell, S A

2004-04-02

Density functional molecular dynamical simulations have been performed on Ga17 and Ga13 clusters to understand the recently observed higher-than-bulk melting temperatures in small gallium clusters [Phys. Rev. Lett. 91, 215508 (2003)

Higher (odd) dimensional quantum Hall effect and extended dimensional hierarchy

NASA Astrophysics Data System (ADS)

Hasebe, Kazuki

2017-07-01

We demonstrate dimensional ladder of higher dimensional quantum Hall effects by exploiting quantum Hall effects on arbitrary odd dimensional spheres. Non-relativistic and relativistic Landau models are analyzed on S 2 k - 1 in the SO (2 k - 1) monopole background. The total sub-band degeneracy of the odd dimensional lowest Landau level is shown to be equal to the winding number from the base-manifold S 2 k - 1 to the one-dimension higher SO (2 k) gauge group. Based on the chiral Hopf maps, we clarify the underlying quantum Nambu geometry for odd dimensional quantum Hall effect and the resulting quantum geometry is naturally embedded also in one-dimension higher quantum geometry. An origin of such dimensional ladder connecting even and odd dimensional quantum Hall effects is illuminated from a viewpoint of the spectral flow of Atiyah-Patodi-Singer index theorem in differential topology. We also present a BF topological field theory as an effective field theory in which membranes with different dimensions undergo non-trivial linking in odd dimensional space. Finally, an extended version of the dimensional hierarchy for higher dimensional quantum Hall liquids is proposed, and its relationship to quantum anomaly and D-brane physics is discussed.

Theory of the vortex-clustering transition in a confined two-dimensional quantum fluid

NASA Astrophysics Data System (ADS)

Yu, Xiaoquan; Billam, Thomas P.; Nian, Jun; Reeves, Matthew T.; Bradley, Ashton S.

2016-08-01

Clustering of like-sign vortices in a planar bounded domain is known to occur at negative temperature, a phenomenon that Onsager demonstrated to be a consequence of bounded phase space. In a confined superfluid, quantized vortices can support such an ordered phase, provided they evolve as an almost isolated subsystem containing sufficient energy. A detailed theoretical understanding of the statistical mechanics of such states thus requires a microcanonical approach. Here we develop an analytical theory of the vortex clustering transition in a neutral system of quantum vortices confined to a two-dimensional disk geometry, within the microcanonical ensemble. The choice of ensemble is essential for identifying the correct thermodynamic limit of the system, enabling a rigorous description of clustering in the language of critical phenomena. As the system energy increases above a critical value, the system develops global order via the emergence of a macroscopic dipole structure from the homogeneous phase of vortices, spontaneously breaking the Z2 symmetry associated with invariance under vortex circulation exchange, and the rotational SO (2 ) symmetry due to the disk geometry. The dipole structure emerges characterized by the continuous growth of the macroscopic dipole moment which serves as a global order parameter, resembling a continuous phase transition. The critical temperature of the transition, and the critical exponent associated with the dipole moment, are obtained exactly within mean-field theory. The clustering transition is shown to be distinct from the final state reached at high energy, known as supercondensation. The dipole moment develops via two macroscopic vortex clusters and the cluster locations are found analytically, both near the clustering transition and in the supercondensation limit. The microcanonical theory shows excellent agreement with Monte Carlo simulations, and signatures of the transition are apparent even for a modest system of 100 vortices, accessible in current Bose-Einstein condensate experiments.

Kinetic energy distribution of multiply charged ions in Coulomb explosion of Xe clusters.

PubMed

Heidenreich, Andreas; Jortner, Joshua

2011-02-21

We report on the calculations of kinetic energy distribution (KED) functions of multiply charged, high-energy ions in Coulomb explosion (CE) of an assembly of elemental Xe(n) clusters (average size (n) = 200-2171) driven by ultra-intense, near-infrared, Gaussian laser fields (peak intensities 10(15) - 4 × 10(16) W cm(-2), pulse lengths 65-230 fs). In this cluster size and pulse parameter domain, outer ionization is incomplete∕vertical, incomplete∕nonvertical, or complete∕nonvertical, with CE occurring in the presence of nanoplasma electrons. The KEDs were obtained from double averaging of single-trajectory molecular dynamics simulation ion kinetic energies. The KEDs were doubly averaged over a log-normal cluster size distribution and over the laser intensity distribution of a spatial Gaussian beam, which constitutes either a two-dimensional (2D) or a three-dimensional (3D) profile, with the 3D profile (when the cluster beam radius is larger than the Rayleigh length) usually being experimentally realized. The general features of the doubly averaged KEDs manifest the smearing out of the structure corresponding to the distribution of ion charges, a marked increase of the KEDs at very low energies due to the contribution from the persistent nanoplasma, a distortion of the KEDs and of the average energies toward lower energy values, and the appearance of long low-intensity high-energy tails caused by the admixture of contributions from large clusters by size averaging. The doubly averaged simulation results account reasonably well (within 30%) for the experimental data for the cluster-size dependence of the CE energetics and for its dependence on the laser pulse parameters, as well as for the anisotropy in the angular distribution of the energies of the Xe(q+) ions. Possible applications of this computational study include a control of the ion kinetic energies by the choice of the laser intensity profile (2D∕3D) in the laser-cluster interaction volume.

Adsorption and diffusion of mono, di, and trivalent ions on two-dimensional TiS2

NASA Astrophysics Data System (ADS)

Samad, Abdus; Shafique, Aamir; Shin, Young-Han

2017-04-01

A comparative study of the monovalent (Li, Na, and K) and multivalent (Be, Mg, Ca, and Al) metal ion adsorption and diffusion on an electronically semi-metallic two-dimensional nanosheet of 1T structured TiS2 is presented here to contribute to the search for abundant, cheap, and nontoxic ingredients for efficient rechargeable metal ion batteries. The total formation energy of the metal ion adsorption and the Bader charge analysis show that the divalent Mg and Ca ions can have a charge storage density double that of the monovalent Li, Na, and K ions, while the Be and Al ions form metallic clusters even at a low adsorption density because of their high bulk energies. The adsorption of Mg ions shows the lowest averaged open circuit voltage (0.13 V). The activation energy barriers for the diffusion of metal ions on the surface of the monolayer successively decrease from Li to K and Be to Ca. Mg and Ca, being divalent, are capable of storing a higher power density than Li while K and Na have a higher rate capability than the Li ions. Therefore, rechargeable Li ion batteries can be totally or partially replaceable by Mg ion batteries, where high power density and high cell voltage are required, while the abundant, cheap, and fast Na ions can be used for green grid applications.

Electronic and molecular structure of carbon grains

NASA Technical Reports Server (NTRS)

Almloef, Jan; Luethi, Hans-Peter

1990-01-01

Clusters of carbon atoms have been studied with large-scale ab initio calculations. Planar, single-sheet graphite fragments with 6 to 54 atoms were investigated, as well as the spherical C(sub 60) Buckminsterfullerene molecule. Polycyclic aromatic hydrocarbons (PAHs) have also been considered. Thermodynamic differences between diamond- and graphite-like grains have been studied in particular. Saturation of the peripheral bonds with hydrogen is found to provide a smooth and uniform convergence of the properties with increasing cluster size. For the graphite-like clusters the convergence to bulk values is much slower than for the three-dimensional complexes.

Cluster-based control of a separating flow over a smoothly contoured ramp

NASA Astrophysics Data System (ADS)

Kaiser, Eurika; Noack, Bernd R.; Spohn, Andreas; Cattafesta, Louis N.; Morzyński, Marek

2017-12-01

The ability to manipulate and control fluid flows is of great importance in many scientific and engineering applications. The proposed closed-loop control framework addresses a key issue of model-based control: The actuation effect often results from slow dynamics of strongly nonlinear interactions which the flow reveals at timescales much longer than the prediction horizon of any model. Hence, we employ a probabilistic approach based on a cluster-based discretization of the Liouville equation for the evolution of the probability distribution. The proposed methodology frames high-dimensional, nonlinear dynamics into low-dimensional, probabilistic, linear dynamics which considerably simplifies the optimal control problem while preserving nonlinear actuation mechanisms. The data-driven approach builds upon a state space discretization using a clustering algorithm which groups kinematically similar flow states into a low number of clusters. The temporal evolution of the probability distribution on this set of clusters is then described by a control-dependent Markov model. This Markov model can be used as predictor for the ergodic probability distribution for a particular control law. This probability distribution approximates the long-term behavior of the original system on which basis the optimal control law is determined. We examine how the approach can be used to improve the open-loop actuation in a separating flow dominated by Kelvin-Helmholtz shedding. For this purpose, the feature space, in which the model is learned, and the admissible control inputs are tailored to strongly oscillatory flows.

Shape and dynamics of thermoregulating honey bee clusters.

PubMed

Sumpter, D J; Broomhead, D S

2000-05-07

A model of simple algorithmic "agents" acting in a discrete temperature field is used to investigate the movement of individuals in thermoregulating honey bee (Apis mellifera) clusters. Thermoregulation in over-wintering clusters is thought to be the result of individual bees attempting to regulate their own body temperatures. At ambient temperatures above 0( degrees )C, a clustering bee will move relative to its neighbours so as to put its local temperature within some ideal range. The proposed model incorporates this behaviour into an algorithm for bee agents moving on a two-dimensional lattice. Heat transport on the lattice is modelled by a discrete diffusion process. Computer simulation of this model demonstrates qualitative behaviour which agrees with that of real honey bee clusters. In particular, we observe the formation of both disc- and ring-like cluster shapes. The simulation also suggests that at lower ambient temperatures, clusters do not always have a stable shape but can oscillate between insulating rings of different sizes and densities. Copyright 2000 Academic Press.

Robust continuous clustering

PubMed Central

Shah, Sohil Atul

2017-01-01

Clustering is a fundamental procedure in the analysis of scientific data. It is used ubiquitously across the sciences. Despite decades of research, existing clustering algorithms have limited effectiveness in high dimensions and often require tuning parameters for different domains and datasets. We present a clustering algorithm that achieves high accuracy across multiple domains and scales efficiently to high dimensions and large datasets. The presented algorithm optimizes a smooth continuous objective, which is based on robust statistics and allows heavily mixed clusters to be untangled. The continuous nature of the objective also allows clustering to be integrated as a module in end-to-end feature learning pipelines. We demonstrate this by extending the algorithm to perform joint clustering and dimensionality reduction by efficiently optimizing a continuous global objective. The presented approach is evaluated on large datasets of faces, hand-written digits, objects, newswire articles, sensor readings from the Space Shuttle, and protein expression levels. Our method achieves high accuracy across all datasets, outperforming the best prior algorithm by a factor of 3 in average rank. PMID:28851838

Finding Hidden HIV Clusters to Support Geographic-Oriented HIV Interventions in Kenya.

PubMed

Waruru, Anthony; Achia, Thomas N O; Tobias, James L; Ngʼangʼa, James; Mwangi, Mary; Wamicwe, Joyce; Zielinski-Gutierrez, Emily; Oluoch, Tom; Muthama, Evelyn; Tylleskär, Thorkild

2018-06-01

In a spatially well known and dispersed HIV epidemic, identifying geographic clusters with significantly higher HIV prevalence is important for focusing interventions for people living with HIV (PLHIV). We used Kulldorff spatial-scan Poisson model to identify clusters with high numbers of HIV-infected persons 15-64 years old. We classified PLHIV as belonging to either higher prevalence or lower prevalence (HP/LP) clusters, then assessed distributions of sociodemographic and biobehavioral HIV risk factors and associations with clustering. About half of survey locations, 112/238 (47%) had high rates of HIV (HP clusters), with 1.1-4.6 times greater PLHIV adults observed than expected. Richer persons compared with respondents in lowest wealth index had higher odds of belonging to a HP cluster, adjusted odds ratio (aOR) 1.61 [95% confidence interval (CI): 1.13 to 2.3], aOR 1.66 (95% CI: 1.09 to 2.53), aOR 3.2 (95% CI: 1.82 to 5.65), and aOR 2.28 (95% CI: 1.09 to 4.78) in second, middle, fourth, and highest quintiles, respectively. Respondents who perceived themselves to have greater HIV risk or were already HIV-infected had higher odds of belonging to a HP cluster, aOR 1.96 (95% CI: 1.13 to 3.4) and aOR 5.51 (95% CI: 2.42 to 12.55), respectively; compared with perceived low risk. Men who had ever been clients of female sex worker had higher odds of belonging to a HP cluster than those who had never been, aOR 1.47 (95% CI: 1.04 to 2.08); and uncircumcised men vs circumcised, aOR 3.2 (95% CI: 1.74 to 5.8). HIV infection in Kenya exhibits localized geographic clustering associated with sociodemographic and behavioral factors, suggesting disproportionate exposure to higher HIV risk. Identification of these clusters reveals the right places for targeting priority-tailored HIV interventions.

Three-dimensional kinematics of the lumbar spine during gait using marker-based systems: a systematic review.

PubMed

Needham, Robert; Stebbins, Julie; Chockalingam, Nachiappan

2016-01-01

To review the current scientific literature on the assessment of three-dimensional movement of the lumbar spine with a focus on the utilisation of a 3D cluster. Electronic databases PubMed, OVID, CINAHL, The Cochrance Library, ScienceDirect, ProQuest and Web of Knowledge were searched between 1966 and March 2015. The reference lists of the articles that met the inclusion criteria were also searched. From the 1530 articles identified through an initial search, 16 articles met the inclusion criteria. All information relating to methodology and kinematic modelling of the lumbar segment along with the outcome measures were extracted from the studies identified for synthesis. Guidelines detailing 3D cluster construction were limited in the identified articles and the lack of information presented makes it difficult to assess the external validity of this technique. Scarce information was presented detailing time-series angle data of the lumbar spine during gait. Further developments of the 3D cluster technique are required and it is essential that the authors provide clear instruction, definitions and standards in their manuscript to improve clarity and reproducibility.

Exploring multicollinearity using a random matrix theory approach.

PubMed

Feher, Kristen; Whelan, James; Müller, Samuel

2012-01-01

Clustering of gene expression data is often done with the latent aim of dimension reduction, by finding groups of genes that have a common response to potentially unknown stimuli. However, what is poorly understood to date is the behaviour of a low dimensional signal embedded in high dimensions. This paper introduces a multicollinear model which is based on random matrix theory results, and shows potential for the characterisation of a gene cluster's correlation matrix. This model projects a one dimensional signal into many dimensions and is based on the spiked covariance model, but rather characterises the behaviour of the corresponding correlation matrix. The eigenspectrum of the correlation matrix is empirically examined by simulation, under the addition of noise to the original signal. The simulation results are then used to propose a dimension estimation procedure of clusters from data. Moreover, the simulation results warn against considering pairwise correlations in isolation, as the model provides a mechanism whereby a pair of genes with `low' correlation may simply be due to the interaction of high dimension and noise. Instead, collective information about all the variables is given by the eigenspectrum.

Gender differences in psychiatric disorders and clusters of self-esteem among detained adolescents.

PubMed

Van Damme, Lore; Colins, Olivier F; Vanderplasschen, Wouter

2014-12-30

Detained minors display substantial mental health needs. This study focused on two features (psychopathology and self-esteem) that have received considerable attention in the literature and clinical work, but have rarely been studied simultaneously in detained youths. The aims of this study were to examine gender differences in psychiatric disorders and clusters of self-esteem, and to test the hypothesis that the cluster of adolescents with lower (versus higher) levels of self-esteem have higher rates of psychiatric disorders. The prevalence of psychiatric disorders was assessed in 440 Belgian, detained adolescents using the Diagnostic Interview Schedule for Children-IV. Self-esteem was assessed using the Self-perception Profile for Adolescents. Model-based cluster analyses were performed to identify youths with lower and/or higher levels of self-esteem across several domains. Girls have higher rates for most psychiatric disorders and lower levels of self-esteem than boys. A higher number of clusters was identified in boys (four) than girls (three). Generally, the cluster of adolescents with lower (versus higher) levels of self-esteem had a higher prevalence of psychiatric disorders. These results suggest that the detection of low levels of self-esteem in adolescents, especially girls, might help clinicians to identify a subgroup of detained adolescents with the highest prevalence of psychopathology.

Composition, morphology, and growth of clusters in a gas of particles with random interactions

NASA Astrophysics Data System (ADS)

Azizi, Itay; Rabin, Yitzhak

2018-03-01

We use Langevin dynamics simulations to study the growth kinetics and the steady-state properties of condensed clusters in a dilute two-dimensional system of particles that are all different (APD) in the sense that each particle is characterized by a randomly chosen interaction parameter. The growth exponents, the transition temperatures, and the steady-state properties of the clusters and of the surrounding gas phase are obtained and compared with those of one-component systems. We investigate the fractionation phenomenon, i.e., how particles of different identities are distributed between the coexisting mother (gas) and daughter (clusters) phases. We study the local organization of particles inside clusters, according to their identity—neighbourhood identity ordering (NIO)—and compare the results with those of previous studies of NIO in dense APD systems.

Synaptic Bistability Due to Nucleation and Evaporation of Receptor Clusters

NASA Astrophysics Data System (ADS)

Burlakov, V. M.; Emptage, N.; Goriely, A.; Bressloff, P. C.

2012-01-01

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

Quantitative analysis of voids in percolating structures in two-dimensional N-body simulations

NASA Technical Reports Server (NTRS)

Harrington, Patrick M.; Melott, Adrian L.; Shandarin, Sergei F.

1993-01-01

We present in this paper a quantitative method for defining void size in large-scale structure based on percolation threshold density. Beginning with two-dimensional gravitational clustering simulations smoothed to the threshold of nonlinearity, we perform percolation analysis to determine the large scale structure. The resulting objective definition of voids has a natural scaling property, is topologically interesting, and can be applied immediately to redshift surveys.

A k-space method for acoustic propagation using coupled first-order equations in three dimensions.

PubMed

Tillett, Jason C; Daoud, Mohammad I; Lacefield, James C; Waag, Robert C

2009-09-01

A previously described two-dimensional k-space method for large-scale calculation of acoustic wave propagation in tissues is extended to three dimensions. The three-dimensional method contains all of the two-dimensional method features that allow accurate and stable calculation of propagation. These features are spectral calculation of spatial derivatives, temporal correction that produces exact propagation in a homogeneous medium, staggered spatial and temporal grids, and a perfectly matched boundary layer. Spectral evaluation of spatial derivatives is accomplished using a fast Fourier transform in three dimensions. This computational bottleneck requires all-to-all communication; execution time in a parallel implementation is therefore sensitive to node interconnect latency and bandwidth. Accuracy of the three-dimensional method is evaluated through comparisons with exact solutions for media having spherical inhomogeneities. Large-scale calculations in three dimensions were performed by distributing the nearly 50 variables per voxel that are used to implement the method over a cluster of computers. Two computer clusters used to evaluate method accuracy are compared. Comparisons of k-space calculations with exact methods including absorption highlight the need to model accurately the medium dispersion relationships, especially in large-scale media. Accurately modeled media allow the k-space method to calculate acoustic propagation in tissues over hundreds of wavelengths.

Understanding boron through size-selected clusters: structure, chemical bonding, and fluxionality.

PubMed

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

2014-04-15

Boron is an interesting element with unusual polymorphism. While three-dimensional (3D) structural motifs are prevalent in bulk boron, atomic boron clusters are found to have planar or quasi-planar structures, stabilized by localized two-center-two-electron (2c-2e) σ bonds on the periphery and delocalized multicenter-two-electron (nc-2e) bonds in both σ and π frameworks. Electron delocalization is a result of boron's electron deficiency and leads to fluxional behavior, which has been observed in B13(+) and B19(-). A unique capability of the in-plane rotation of the inner atoms against the periphery of the cluster in a chosen direction by employing circularly polarized infrared radiation has been suggested. Such fluxional behaviors in boron clusters are interesting and have been proposed as molecular Wankel motors. The concepts of aromaticity and antiaromaticity have been extended beyond organic chemistry to planar boron clusters. The validity of these concepts in understanding the electronic structures of boron clusters is evident in the striking similarities of the π-systems of planar boron clusters to those of polycyclic aromatic hydrocarbons, such as benzene, naphthalene, coronene, anthracene, or phenanthrene. Chemical bonding models developed for boron clusters not only allowed the rationalization of the stability of boron clusters but also lead to the design of novel metal-centered boron wheels with a record-setting planar coordination number of 10. The unprecedented highly coordinated borometallic molecular wheels provide insights into the interactions between transition metals and boron and expand the frontier of boron chemistry. Another interesting feature discovered through cluster studies is boron transmutation. Even though it is well-known that B(-), formed by adding one electron to boron, is isoelectronic to carbon, cluster studies have considerably expanded the possibilities of new structures and new materials using the B(-)/C analogy. It is believed that the electronic transmutation concept will be effective and valuable in aiding the design of new boride materials with predictable properties. The study of boron clusters with intermediate properties between those of individual atoms and bulk solids has given rise to a unique opportunity to broaden the frontier of boron chemistry. Understanding boron clusters has spurred experimentalists and theoreticians to find new boron-based nanomaterials, such as boron fullerenes, nanotubes, two-dimensional boron, and new compounds containing boron clusters as building blocks. Here, a brief and timely overview is presented addressing the recent progress made on boron clusters and the approaches used in the authors' laboratories to determine the structure, stability, and chemical bonding of size-selected boron clusters by joint photoelectron spectroscopy and theoretical studies. Specifically, key findings on all-boron hydrocarbon analogues, metal-centered boron wheels, and electronic transmutation in boron clusters are summarized.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sergeeva, Alina P.; Popov, Ivan A.; Piazza, Zachary A.

Conspectus Boron is an interesting element with unusual polymorphism. While three-dimensional (3D) structural motifs are prevalent in bulk boron, atomic boron clusters are found to have planar or quasi-planar structures, stabilized by localized two-center–two-electron (2c–2e) σ bonds on the periphery and delocalized multicenter–two-electron (nc–2e) bonds in both σ and π frameworks. Electron delocalization is a result of boron’s electron deficiency and leads to fluxional behavior, which has been observed in B13+ and B19–. A unique capability of the in-plane rotation of the inner atoms against the periphery of the cluster in a chosen direction by employing circularly polarized infrared radiationmore » has been suggested. Such fluxional behaviors in boron clusters are interesting and have been proposed as molecular Wankel motors. The concepts of aromaticity and antiaromaticity have been extended beyond organic chemistry to planar boron clusters. The validity of these concepts in understanding the electronic structures of boron clusters is evident in the striking similarities of the π-systems of planar boron clusters to those of polycyclic aromatic hydrocarbons, such as benzene, naphthalene, coronene, anthracene, or phenanthrene. Chemical bonding models developed for boron clusters not only allowed the rationalization of the stability of boron clusters but also lead to the design of novel metal-centered boron wheels with a record-setting planar coordination number of 10. The unprecedented highly coordinated borometallic molecular wheels provide insights into the interactions between transition metals and boron and expand the frontier of boron chemistry. Another interesting feature discovered through cluster studies is boron transmutation. Even though it is well-known that B–, formed by adding one electron to boron, is isoelectronic to carbon, cluster studies have considerably expanded the possibilities of new structures and new materials using the B–/C analogy. It is believed that the electronic transmutation concept will be effective and valuable in aiding the design of new boride materials with predictable properties. The study of boron clusters with intermediate properties between those of individual atoms and bulk solids has given rise to a unique opportunity to broaden the frontier of boron chemistry. Understanding boron clusters has spurred experimentalists and theoreticians to find new boron-based nanomaterials, such as boron fullerenes, nanotubes, two-dimensional boron, and new compounds containing boron clusters as building blocks. Here, a brief and timely overview is presented addressing the recent progress made on boron clusters and the approaches used in the authors’ laboratories to determine the structure, stability, and chemical bonding of size-selected boron clusters by joint photoelectron spectroscopy and theoretical studies. Specifically, key findings on all-boron hydrocarbon analogues, metal-centered boron wheels, and electronic transmutation in boron clusters are summarized.« less

AHIMSA - Ad hoc histogram information measure sensing algorithm for feature selection in the context of histogram inspired clustering techniques

NASA Technical Reports Server (NTRS)

Dasarathy, B. V.

1976-01-01

An algorithm is proposed for dimensionality reduction in the context of clustering techniques based on histogram analysis. The approach is based on an evaluation of the hills and valleys in the unidimensional histograms along the different features and provides an economical means of assessing the significance of the features in a nonparametric unsupervised data environment. The method has relevance to remote sensing applications.

Multilingual Data Selection for Low Resource Speech Recognition

DTIC Science & Technology

2016-09-12

Figure 1: Identification of language clusters using scores from an LID system training languages used in the Base and OP1 evaluation periods of the Babel...the posterior scores over frames. For a set of languages that are used to train the lan- guage identification (LID) network, pairs of languages that...which are combined during test time to produce 10 dimensional language 3854 Figure 3: Identification of language clusters using scores from individually

Graph Based Models for Unsupervised High Dimensional Data Clustering and Network Analysis

DTIC Science & Technology

2015-01-01

ApprovedOMB No. 0704-0188 Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for...algorithms we proposed improve the time e ciency signi cantly for large scale datasets. In the last chapter, we also propose an incremental reseeding...plume detection in hyper-spectral video data. These graph based clustering algorithms we proposed improve the time efficiency significantly for large

Dimensional structure of DSM-5 posttraumatic stress disorder symptoms: results from the National Health and Resilience in Veterans Study.

PubMed

Tsai, Jack; Harpaz-Rotem, Ilan; Armour, Cherie; Southwick, Steven M; Krystal, John H; Pietrzak, Robert H

2015-05-01

To evaluate the prevalence of DSM-5 posttraumatic stress disorder (PTSD) and factor structure of PTSD symptomatology in a nationally representative sample of US veterans and examine how PTSD symptom clusters are related to depression, anxiety, suicidal ideation, hostility, physical and mental health-related functioning, and quality of life. Data were analyzed from the National Health and Resilience in Veterans Study, a nationally representative survey of 1,484 US veterans conducted from September through October 2013. Confirmatory factor analyses were conducted to evaluate the factor structure of PTSD symptoms, and structural equation models were constructed to examine the association between PTSD symptom clusters and external correlates. 12.0% of veterans screened positive for lifetime PTSD and 5.2% for past-month PTSD. A 5-factor dysphoric arousal model and a newly proposed 6-factor model both fit the data significantly better than the 4-factor model of DSM-5. The 6-factor model fit the data best in the full sample, as well as in subsamples of female veterans and veterans with lifetime PTSD. The emotional numbing symptom cluster was more strongly related to depression (P < .001) and worse mental health-related functioning (P < .001) than other symptom clusters, while the externalizing behavior symptom cluster was more strongly related to hostility (P < .001). A total of 5.2% of US veterans screened positive for past-month DSM-5 PTSD. A 6-factor model of DSM-5 PTSD symptoms, which builds on extant models and includes a sixth externalizing behavior factor, provides the best dimensional representation of DSM-5 PTSD symptom clusters and demonstrates validity in assessing health outcomes of interest in this population. © Copyright 2015 Physicians Postgraduate Press, Inc.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Data Analysis and Visualization; nternational Research Training Group ``Visualization of Large and Unstructured Data Sets,'' University of Kaiserslautern, Germany; Computational Research Division, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley, CA 94720, USA

2008-05-12

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

Buried landmine detection using multivariate normal clustering

NASA Astrophysics Data System (ADS)

Duston, Brian M.

2001-10-01

A Bayesian classification algorithm is presented for discriminating buried land mines from buried and surface clutter in Ground Penetrating Radar (GPR) signals. This algorithm is based on multivariate normal (MVN) clustering, where feature vectors are used to identify populations (clusters) of mines and clutter objects. The features are extracted from two-dimensional images created from ground penetrating radar scans. MVN clustering is used to determine the number of clusters in the data and to create probability density models for target and clutter populations, producing the MVN clustering classifier (MVNCC). The Bayesian Information Criteria (BIC) is used to evaluate each model to determine the number of clusters in the data. An extension of the MVNCC allows the model to adapt to local clutter distributions by treating each of the MVN cluster components as a Poisson process and adaptively estimating the intensity parameters. The algorithm is developed using data collected by the Mine Hunter/Killer Close-In Detector (MH/K CID) at prepared mine lanes. The Mine Hunter/Killer is a prototype mine detecting and neutralizing vehicle developed for the U.S. Army to clear roads of anti-tank mines.

A phase cell cluster expansion for Euclidean field theories

NASA Astrophysics Data System (ADS)

Battle, Guy A., III; Federbush, Paul

1982-08-01

We adapt the cluster expansion first used to treat infrared problems for lattice models (a mass zero cluster expansion) to the usual field theory situation. The field is expanded in terms of special block spin functions and the cluster expansion given in terms of the expansion coefficients (phase cell variables); the cluster expansion expresses correlation functions in terms of contributions from finite coupled subsets of these variables. Most of the present work is carried through in d space time dimensions (for φ24 the details of the cluster expansion are pursued and convergence is proven). Thus most of the results in the present work will apply to a treatment of φ34 to which we hope to return in a succeeding paper. Of particular interest in this paper is a substitute for the stability of the vacuum bound appropriate to this cluster expansion (for d = 2 and d = 3), and a new method for performing estimates with tree graphs. The phase cell cluster expansions have the renormalization group incorporated intimately into their structure. We hope they will be useful ultimately in treating four dimensional field theories.

Reconstruction of the two-dimensional gravitational potential of galaxy clusters from X-ray and Sunyaev-Zel'dovich measurements

NASA Astrophysics Data System (ADS)

Tchernin, C.; Bartelmann, M.; Huber, K.; Dekel, A.; Hurier, G.; Majer, C. L.; Meyer, S.; Zinger, E.; Eckert, D.; Meneghetti, M.; Merten, J.

2018-06-01

Context. The mass of galaxy clusters is not a direct observable, nonetheless it is commonly used to probe cosmological models. Based on the combination of all main cluster observables, that is, the X-ray emission, the thermal Sunyaev-Zel'dovich (SZ) signal, the velocity dispersion of the cluster galaxies, and gravitational lensing, the gravitational potential of galaxy clusters can be jointly reconstructed. Aims: We derive the two main ingredients required for this joint reconstruction: the potentials individually reconstructed from the observables and their covariance matrices, which act as a weight in the joint reconstruction. We show here the method to derive these quantities. The result of the joint reconstruction applied to a real cluster will be discussed in a forthcoming paper. Methods: We apply the Richardson-Lucy deprojection algorithm to data on a two-dimensional (2D) grid. We first test the 2D deprojection algorithm on a β-profile. Assuming hydrostatic equilibrium, we further reconstruct the gravitational potential of a simulated galaxy cluster based on synthetic SZ and X-ray data. We then reconstruct the projected gravitational potential of the massive and dynamically active cluster Abell 2142, based on the X-ray observations collected with XMM-Newton and the SZ observations from the Planck satellite. Finally, we compute the covariance matrix of the projected reconstructed potential of the cluster Abell 2142 based on the X-ray measurements collected with XMM-Newton. Results: The gravitational potentials of the simulated cluster recovered from synthetic X-ray and SZ data are consistent, even though the potential reconstructed from X-rays shows larger deviations from the true potential. Regarding Abell 2142, the projected gravitational cluster potentials recovered from SZ and X-ray data reproduce well the projected potential inferred from gravitational-lensing observations. We also observe that the covariance matrix of the potential for Abell 2142 reconstructed from XMM-Newton data sensitively depends on the resolution of the deprojected grid and on the smoothing scale used in the deprojection. Conclusions: We show that the Richardson-Lucy deprojection method can be effectively applied on a grid and that the projected potential is well recovered from real and simulated data based on X-ray and SZ signal. The comparison between the reconstructed potentials from the different observables provides additional information on the validity of the assumptions as function of the projected radius.

Prediction of chemotherapeutic response in bladder cancer using k-means clustering of DCE-MRI pharmacokinetic parameters

PubMed Central

Nguyen, Huyen T.; Jia, Guang; Shah, Zarine K.; Pohar, Kamal; Mortazavi, Amir; Zynger, Debra L.; Wei, Lai; Yang, Xiangyu; Clark, Daniel; Knopp, Michael V.

2015-01-01

Purpose To apply k-means clustering of two pharmacokinetic parameters derived from 3T DCE-MRI to predict chemotherapeutic response in bladder cancer at the mid-cycle time-point. Materials and Methods With the pre-determined number of 3 clusters, k-means clustering was performed on non-dimensionalized Amp and kep estimates of each bladder tumor. Three cluster volume fractions (VFs) were calculated for each tumor at baseline and mid-cycle. The changes of three cluster VFs from baseline to mid-cycle were correlated with the tumor’s chemotherapeutic response. Receiver-operating-characteristics curve analysis was used to evaluate the performance of each cluster VF change as a biomarker of chemotherapeutic response in bladder cancer. Results k-means clustering partitioned each bladder tumor into cluster 1 (low kep and low Amp), cluster 2 (low kep and high Amp), cluster 3 (high kep and low Amp). The changes of all three cluster VFs were found to be associated with bladder tumor response to chemotherapy. The VF change of cluster 2 presented with the highest area-under-the-curve value (0.96) and the highest sensitivity/specificity/accuracy (96%/100%/97%) with a selected cutoff value. Conclusion k-means clustering of the two DCE-MRI pharmacokinetic parameters can characterize the complex microcirculatory changes within a bladder tumor to enable early prediction of the tumor’s chemotherapeutic response. PMID:24943272

Properties of a new small-world network with spatially biased random shortcuts

NASA Astrophysics Data System (ADS)

Matsuzawa, Ryo; Tanimoto, Jun; Fukuda, Eriko

2017-11-01

This paper introduces a small-world (SW) network with a power-law distance distribution that differs from conventional models in that it uses completely random shortcuts. By incorporating spatial constraints, we analyze the divergence of the proposed model from conventional models in terms of fundamental network properties such as clustering coefficient, average path length, and degree distribution. We find that when the spatial constraint more strongly prohibits a long shortcut, the clustering coefficient is improved and the average path length increases. We also analyze the spatial prisoner's dilemma (SPD) games played on our new SW network in order to understand its dynamical characteristics. Depending on the basis graph, i.e., whether it is a one-dimensional ring or a two-dimensional lattice, and the parameter controlling the prohibition of long-distance shortcuts, the emergent results can vastly differ.

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

PubMed

Fronczak, Agata

2013-02-01

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

Prediction (early recognition) of emerging flu strain clusters

NASA Astrophysics Data System (ADS)

Li, X.; Phillips, J. C.

2017-08-01

Early detection of incipient dominant influenza strains is one of the key steps in the design and manufacture of an effective annual influenza vaccine. Here we report the most current results for pandemic H3N2 flu vaccine design. A 2006 model of dimensional reduction (compaction) of viral mutational complexity derives two-dimensional Cartesian mutational maps (2DMM) that exhibit an emergent dominant strain as a small and distinct cluster of as few as 10 strains. We show that recent extensions of this model can detect incipient strains one year or more in advance of their dominance in the human population. Our structural interpretation of our unexpectedly rich 2DMM involves sialic acid, and is based on nearly 6000 strains in a series of recent 3-year time windows. Vaccine effectiveness is predicted best by analyzing dominant mutational epitopes.

Spatial clusters of daytime sleepiness and association with nighttime noise levels in a Swiss general population (GeoHypnoLaus).

PubMed

Joost, Stéphane; Haba-Rubio, José; Himsl, Rebecca; Vollenweider, Peter; Preisig, Martin; Waeber, Gérard; Marques-Vidal, Pedro; Heinzer, Raphaël; Guessous, Idris

2018-05-31

Daytime sleepiness is highly prevalent in the general adult population and has been linked to an increased risk of workplace and vehicle accidents, lower professional performance and poorer health. Despite the established relationship between noise and daytime sleepiness, little research has explored the individual-level spatial distribution of noise-related sleep disturbances. We assessed the spatial dependence of daytime sleepiness and tested whether clusters of individuals exhibiting higher daytime sleepiness were characterized by higher nocturnal noise levels than other clusters. Population-based cross-sectional study, in the city of Lausanne, Switzerland. Sleepiness was measured using the Epworth Sleepiness Scale (ESS) for 3697 georeferenced individuals from the CoLaus|PsyCoLaus cohort (period = 2009-2012). We used the sonBASE georeferenced database produced by the Swiss Federal Office for the Environment to characterize nighttime road traffic noise exposure throughout the city. We used the GeoDa software program to calculate the Getis-Ord G i * statistics for unadjusted and adjusted ESS in order to detect spatial clusters of high and low ESS values. Modeled nighttime noise exposure from road and rail traffic was compared across ESS clusters. Daytime sleepiness was not randomly distributed and showed a significant spatial dependence. The median nighttime traffic noise exposure was significantly different across the three ESS Getis cluster classes (p < 0.001). The mean nighttime noise exposure in the high ESS cluster class was 47.6, dB(A) 5.2 dB(A) higher than in low clusters (p < 0.001) and 2.1 dB(A) higher than in the neutral class (p < 0.001). These associations were independent of major potential confounders including body mass index and neighborhood income level. Clusters of higher daytime sleepiness in adults are associated with higher median nighttime noise levels. The identification of these clusters can guide tailored public health interventions. Copyright © 2018 The Authors. Published by Elsevier GmbH.. All rights reserved.

Higher frequencies of HLA DQB1*05:01 and anti-glycosphingolipid antibodies in a cluster of severe Guillain-Barré syndrome.

PubMed

Schirmer, L; Worthington, V; Solloch, U; Loleit, V; Grummel, V; Lakdawala, N; Grant, D; Wassmuth, R; Schmidt, A H; Gebhardt, F; Andlauer, T F M; Sauter, J; Berthele, A; Lunn, M P; Hemmer, Bernhard

2016-10-01

Few regional and seasonal Guillain-Barré syndrome (GBS) clusters have been reported so far. It is unknown whether patients suffering from sporadic GBS differ from GBS clusters with respect to clinical and paraclinical parameters, HLA association and antibody response to glycosphingolipids and Campylobacter jejuni (Cj). We examined 40 consecutive patients with GBS from the greater Munich area in Germany with 14 of those admitted within a period of 3 months in fall 2010 defining a cluster of GBS. Sequencing-based HLA typing of the HLA genes DRB1, DQB1, and DPB1 was performed, and ELISA for anti-glycosphingolipid antibodies was carried out. Clinical and paraclinical findings (Cj seroreactivity, cerebrospinal fluid parameters, and electrophysiology) were obtained and analyzed. GBS cluster patients were characterized by a more severe clinical phenotype with more patients requiring mechanical ventilation and higher frequencies of autoantibodies against sulfatide, GalC and certain ganglioside epitopes (54 %) as compared to sporadic GBS cases (13 %, p = 0.017). Cj seropositivity tended to be higher within GBS cluster patients (69 %) as compared to sporadic cases (46 %, p = 0.155). We noted higher frequencies of HLA class II allele DQB1*05:01 in the cluster cohort (23 %) as compared to sporadic GBS patients (3 %, p = 0.019). Cluster of severe GBS was defined by higher frequencies of autoantibodies against glycosphingolipids. HLA class II allele DQB1*05:01 might contribute to clinical worsening in the cluster patients.

Nonlinear modulation of the HI power spectrum on ultra-large scales. I

DOE Office of Scientific and Technical Information (OSTI.GOV)

Umeh, Obinna; Maartens, Roy; Santos, Mario, E-mail: umeobinna@gmail.com, E-mail: roy.maartens@gmail.com, E-mail: mgrsantos@uwc.ac.za

2016-03-01

Intensity mapping of the neutral hydrogen brightness temperature promises to provide a three-dimensional view of the universe on very large scales. Nonlinear effects are typically thought to alter only the small-scale power, but we show how they may bias the extraction of cosmological information contained in the power spectrum on ultra-large scales. For linear perturbations to remain valid on large scales, we need to renormalize perturbations at higher order. In the case of intensity mapping, the second-order contribution to clustering from weak lensing dominates the nonlinear contribution at high redshift. Renormalization modifies the mean brightness temperature and therefore the evolutionmore » bias. It also introduces a term that mimics white noise. These effects may influence forecasting analysis on ultra-large scales.« less

Multiple Imputation based Clustering Validation (MIV) for Big Longitudinal Trial Data with Missing Values in eHealth.

PubMed

Zhang, Zhaoyang; Fang, Hua; Wang, Honggang

2016-06-01

Web-delivered trials are an important component in eHealth services. These trials, mostly behavior-based, generate big heterogeneous data that are longitudinal, high dimensional with missing values. Unsupervised learning methods have been widely applied in this area, however, validating the optimal number of clusters has been challenging. Built upon our multiple imputation (MI) based fuzzy clustering, MIfuzzy, we proposed a new multiple imputation based validation (MIV) framework and corresponding MIV algorithms for clustering big longitudinal eHealth data with missing values, more generally for fuzzy-logic based clustering methods. Specifically, we detect the optimal number of clusters by auto-searching and -synthesizing a suite of MI-based validation methods and indices, including conventional (bootstrap or cross-validation based) and emerging (modularity-based) validation indices for general clustering methods as well as the specific one (Xie and Beni) for fuzzy clustering. The MIV performance was demonstrated on a big longitudinal dataset from a real web-delivered trial and using simulation. The results indicate MI-based Xie and Beni index for fuzzy-clustering are more appropriate for detecting the optimal number of clusters for such complex data. The MIV concept and algorithms could be easily adapted to different types of clustering that could process big incomplete longitudinal trial data in eHealth services.

Multiple Imputation based Clustering Validation (MIV) for Big Longitudinal Trial Data with Missing Values in eHealth

PubMed Central

Zhang, Zhaoyang; Wang, Honggang

2016-01-01

Web-delivered trials are an important component in eHealth services. These trials, mostly behavior-based, generate big heterogeneous data that are longitudinal, high dimensional with missing values. Unsupervised learning methods have been widely applied in this area, however, validating the optimal number of clusters has been challenging. Built upon our multiple imputation (MI) based fuzzy clustering, MIfuzzy, we proposed a new multiple imputation based validation (MIV) framework and corresponding MIV algorithms for clustering big longitudinal eHealth data with missing values, more generally for fuzzy-logic based clustering methods. Specifically, we detect the optimal number of clusters by auto-searching and -synthesizing a suite of MI-based validation methods and indices, including conventional (bootstrap or cross-validation based) and emerging (modularity-based) validation indices for general clustering methods as well as the specific one (Xie and Beni) for fuzzy clustering. The MIV performance was demonstrated on a big longitudinal dataset from a real web-delivered trial and using simulation. The results indicate MI-based Xie and Beni index for fuzzy-clustering is more appropriate for detecting the optimal number of clusters for such complex data. The MIV concept and algorithms could be easily adapted to different types of clustering that could process big incomplete longitudinal trial data in eHealth services. PMID:27126063

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

PubMed

Alarcón, Francisco; Valeriani, Chantal; Pagonabarraga, Ignacio

2017-01-25

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

Clustervision: Visual Supervision of Unsupervised Clustering.

PubMed

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

2018-01-01

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

Modeling tensional homeostasis in multicellular clusters.

PubMed

Tam, Sze Nok; Smith, Michael L; Stamenović, Dimitrije

2017-03-01

Homeostasis of mechanical stress in cells, or tensional homeostasis, is essential for normal physiological function of tissues and organs and is protective against disease progression, including atherosclerosis and cancer. Recent experimental studies have shown that isolated cells are not capable of maintaining tensional homeostasis, whereas multicellular clusters are, with stability increasing with the size of the clusters. Here, we proposed simple mathematical models to interpret experimental results and to obtain insight into factors that determine homeostasis. Multicellular clusters were modeled as one-dimensional arrays of linearly elastic blocks that were either jointed or disjointed. Fluctuating forces that mimicked experimentally measured cell-substrate tractions were obtained from Monte Carlo simulations. These forces were applied to the cluster models, and the corresponding stress field in the cluster was calculated by solving the equilibrium equation. It was found that temporal fluctuations of the cluster stress field became attenuated with increasing cluster size, indicating that the cluster approached tensional homeostasis. These results were consistent with previously reported experimental data. Furthermore, the models revealed that key determinants of tensional homeostasis in multicellular clusters included the cluster size, the distribution of traction forces, and mechanical coupling between adjacent cells. Based on these findings, we concluded that tensional homeostasis was a multicellular phenomenon. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Probing the structural and electronic properties of cationic rubidium-gold clusters: [AunRb]+ (n = 1-10)

NASA Astrophysics Data System (ADS)

Zhao, Ya-Ru; Zhang, Hai-Rong; Qian, Yu; Duan, Xu-Chao; Hu, Yan-Fei

2016-03-01

Density functional theory has been applied to study the geometric structures, relative stabilities, and electronic properties of cationic [AunRb]+ and Aun + 1+ (n = 1-10) clusters. For the lowest energy structures of [AunRb]+ clusters, the planar to three-dimensional transformation is found to occur at cluster size n = 4 and the Rb atoms prefer being located at the most highly coordinated position. The trends of the averaged atomic binding energies, fragmentation energies, second-order difference of energies, and energy gaps show pronounced even-odd alternations. It indicated that the clusters containing odd number of atoms maintain greater stability than the clusters in the vicinity. In particular, the [Au6Rb]+ clusters are the most stable isomer for [AunRb]+ clusters in the region of n = 1-10. The charges in [AunRb]+ clusters transfer from the Rb atoms to Aun host. Density of states revealed that the Au-5d, Au-5p, and Rb-4p orbitals hardly participated in bonding. In addition, it is found that the most favourable channel of the [AunRb]+ clusters is Rb+ cation ejection. The electronic localisation function (ELF) analysis of the [AunRb]+ clusters shown that strong interactions are not revealed in this study.

Method for discovering relationships in data by dynamic quantum clustering

DOEpatents

Weinstein, Marvin; Horn, David

2017-05-09

Data clustering is provided according to a dynamical framework based on quantum mechanical time evolution of states corresponding to data points. To expedite computations, we can approximate the time-dependent Hamiltonian formalism by a truncated calculation within a set of Gaussian wave-functions (coherent states) centered around the original points. This allows for analytic evaluation of the time evolution of all such states, opening up the possibility of exploration of relationships among data-points through observation of varying dynamical-distances among points and convergence of points into clusters. This formalism may be further supplemented by preprocessing, such as dimensional reduction through singular value decomposition and/or feature filtering.

Method for discovering relationships in data by dynamic quantum clustering

DOEpatents

Weinstein, Marvin; Horn, David

2014-10-28

Data clustering is provided according to a dynamical framework based on quantum mechanical time evolution of states corresponding to data points. To expedite computations, we can approximate the time-dependent Hamiltonian formalism by a truncated calculation within a set of Gaussian wave-functions (coherent states) centered around the original points. This allows for analytic evaluation of the time evolution of all such states, opening up the possibility of exploration of relationships among data-points through observation of varying dynamical-distances among points and convergence of points into clusters. This formalism may be further supplemented by preprocessing, such as dimensional reduction through singular value decomposition and/or feature filtering.

Spectral-clustering approach to Lagrangian vortex detection.

PubMed

Hadjighasem, Alireza; Karrasch, Daniel; Teramoto, Hiroshi; Haller, George

2016-06-01

One of the ubiquitous features of real-life turbulent flows is the existence and persistence of coherent vortices. Here we show that such coherent vortices can be extracted as clusters of Lagrangian trajectories. We carry out the clustering on a weighted graph, with the weights measuring pairwise distances of fluid trajectories in the extended phase space of positions and time. We then extract coherent vortices from the graph using tools from spectral graph theory. Our method locates all coherent vortices in the flow simultaneously, thereby showing high potential for automated vortex tracking. We illustrate the performance of this technique by identifying coherent Lagrangian vortices in several two- and three-dimensional flows.

Modified Cheeger and Ratio Cut Methods Using the Ginzburg-Landau Functional for Classification of High-Dimensional Data

DTIC Science & Technology

2016-02-01

Modified Cheeger and Ratio Cut Methods Using the Ginzburg-Landau Functional for Classification of High-Dimensional Data Ekaterina Merkurjev*, Andrea...bertozzi@math.ucla.edu, xiaoran@isi.edu, lerman@isi.edu. Abstract Recent advances in clustering have included continuous relaxations of the Cheeger cut ...fully nonlinear Cheeger cut problem, as well as the ratio cut optimization task. Both problems are connected to total variation minimization, and the

A data-driven feature extraction framework for predicting the severity of condition of congestive heart failure patients.

PubMed

Sideris, Costas; Alshurafa, Nabil; Pourhomayoun, Mohammad; Shahmohammadi, Farhad; Samy, Lauren; Sarrafzadeh, Majid

2015-01-01

In this paper, we propose a novel methodology for utilizing disease diagnostic information to predict severity of condition for Congestive Heart Failure (CHF) patients. Our methodology relies on a novel, clustering-based, feature extraction framework using disease diagnostic information. To reduce the dimensionality we identify disease clusters using cooccurence frequencies. We then utilize these clusters as features to predict patient severity of condition. We build our clustering and feature extraction algorithm using the 2012 National Inpatient Sample (NIS), Healthcare Cost and Utilization Project (HCUP) which contains 7 million discharge records and ICD-9-CM codes. The proposed framework is tested on Ronald Reagan UCLA Medical Center Electronic Health Records (EHR) from 3041 patients. We compare our cluster-based feature set with another that incorporates the Charlson comorbidity score as a feature and demonstrate an accuracy improvement of up to 14% in the predictability of the severity of condition.

Ground-state geometries and stability of impurity doped clusters: LinBe and LinMg (n=1-12)

NASA Astrophysics Data System (ADS)

Deshpande, M.; Dhavale, A.; Zope, R. R.; Chacko, S.; Kanhere, D. G.

2000-12-01

We have investigated the ground-state geometries of LinBe and LinMg (n=1-12) clusters using ab initio molecular dynamics. These divalent impurities Be and Mg induce different geometries and follow a different growth path for n>5. LinMg clusters are significantly different from the host geometries while LinBe clusters can be approximately viewed as Be occupying an interstitial site in the host. Our results indicate that Be gets trapped inside the Li cage, while Mg remains on the surface of the cluster. Mg-induced geometries become three-dimensional earlier at n=4 as compared to the Be system. In spite of a distinct arrangement of atoms in both cases the character of the wave functions in the d manifold is remarkably similar. In both cases an eight valence electron system has been found to be the most stable, in conformity with the spherical jellium model.

Enhanced peculiar velocities in brane-induced gravity

NASA Astrophysics Data System (ADS)

Wyman, Mark; Khoury, Justin

2010-08-01

The mounting evidence for anomalously large peculiar velocities in our Universe presents a challenge for the ΛCDM paradigm. The recent estimates of the large-scale bulk flow by Watkins et al. are inconsistent at the nearly 3σ level with ΛCDM predictions. Meanwhile, Lee and Komatsu have recently estimated that the occurrence of high-velocity merging systems such as the bullet cluster (1E0657-57) is unlikely at a 6.5-5.8σ level, with an estimated probability between 3.3×10-11 and 3.6×10-9 in ΛCDM cosmology. We show that these anomalies are alleviated in a broad class of infrared-modifed gravity theories, called brane-induced gravity, in which gravity becomes higher-dimensional at ultralarge distances. These theories include additional scalar forces that enhance gravitational attraction and therefore speed up structure formation at late times and on sufficiently large scales. The peculiar velocities are enhanced by 24-34% compared to standard gravity, with the maximal enhancement nearly consistent at the 2σ level with bulk flow observations. The occurrence of the bullet cluster in these theories is ≈104 times more probable than in ΛCDM cosmology.

Enhanced peculiar velocities in brane-induced gravity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wyman, Mark; Khoury, Justin

The mounting evidence for anomalously large peculiar velocities in our Universe presents a challenge for the {Lambda}CDM paradigm. The recent estimates of the large-scale bulk flow by Watkins et al. are inconsistent at the nearly 3{sigma} level with {Lambda}CDM predictions. Meanwhile, Lee and Komatsu have recently estimated that the occurrence of high-velocity merging systems such as the bullet cluster (1E0657-57) is unlikely at a 6.5-5.8{sigma} level, with an estimated probability between 3.3x10{sup -11} and 3.6x10{sup -9} in {Lambda}CDM cosmology. We show that these anomalies are alleviated in a broad class of infrared-modifed gravity theories, called brane-induced gravity, in which gravitymore » becomes higher-dimensional at ultralarge distances. These theories include additional scalar forces that enhance gravitational attraction and therefore speed up structure formation at late times and on sufficiently large scales. The peculiar velocities are enhanced by 24-34% compared to standard gravity, with the maximal enhancement nearly consistent at the 2{sigma} level with bulk flow observations. The occurrence of the bullet cluster in these theories is {approx_equal}10{sup 4} times more probable than in {Lambda}CDM cosmology.« less

Diffusion-limited aggregation in two dimensions

NASA Astrophysics Data System (ADS)

Hurd, Alan J.; Schaefer, Dale W.

1985-03-01

We have studied the aggregation of silica microspheres confined to two dimensions at an air-water interface. Under microscopic observation, both monomers and clusters are seen to aggregate by a diffusion-limited process. The clusters' fractal dimension is 1.20+/-0.15, smaller than values obtained from current models of aggregation. We propose that anisotropic repulsive interactions account for the low dimensionality by more effectively repelling particles from the side of an existing dendrite than from the end.

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.

Two-Dimensional Animal-Like Fractals in Thin Films

NASA Astrophysics Data System (ADS)

Gao, Hong-jun; Xue, Zeng-quan; Wu, Quan-de; Pang, Shi-jin

1996-02-01

We present a few unique animal-like fractal patterns in ionized-cluster-beam deposited fullerene-tetracyanoquinodimethane thin films. The fractal patterns consisting of animal-like aggregates such as "fishes" and "quasi-seahorses" have been characterized by transmission electron microscopy. The results indicate that the small aggregates of the animal-like body are composed of many single crystals whose crystalline directions are generally different. The formation of the fractal patterns can be attributed to the cluster-diffusion-limited aggregation.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hua, Xiu-Ni; Qin, Lan; Yan, Xiao-Zhi

Hydrothermal reactions of N-auxiliary flexible exo-bidentate ligand 1,3-bis(4-pyridyl)propane (bpp) and carboxylates ligands naphthalene-2,6-dicarboxylic acid (2,6-H{sub 2}ndc) or 4,4′-(hydroxymethylene)dibenzoic acid (H{sub 2}hmdb), in the presence of cadmium(II) salts have given rise to two novel metal-organic frameworks based on flexible ligands (FL-MOFs), namely, [Cd{sub 2}(2,6-ndc){sub 2}(bpp)(DMF)]·2DMF (1) and [Cd{sub 3}(hmdb){sub 3}(bpp)]·2DMF·2EtOH (2) (DMF=N,N-Dimethylformamide). Single-crystal X-ray diffraction analyses revealed that compound 1 exhibits a three-dimensional self-penetrating 6-connected framework based on dinuclear cluster second building unit. Compound 2 displays an infinite three-dimensional ‘Lucky Clover’ shape (2,10)-connected network based on the trinuclear cluster and V-shaped organic linkers. The flexible bpp ligand displays different conformations inmore » 1 and 2, which are successfully controlled by size-matching mixed ligands during the self-assembly process. - Graphical abstract: Compound 1 exhibits a 3D self-penetrating 6-connected framework based on dinuclear cluster, and 2 displays an infinite 3D ‘Lucky Clover’ shape (2,10)-connected network based on the trinuclear cluster. The flexible 1,3-bis(4-pyridyl)propane ligand displays different conformations in 1 and 2, which successfully controlled by size-matching mixed ligands during the self-assembly process.« less

Advanced method optimization for volatile aroma profiling of beer using two-dimensional gas chromatography time-of-flight mass spectrometry.

PubMed

Stefanuto, Pierre-Hugues; Perrault, Katelynn A; Dubois, Lena M; L'Homme, Benjamin; Allen, Catherine; Loughnane, Caitriona; Ochiai, Nobuo; Focant, Jean-François

2017-07-21

The complex mixture of volatile organic compounds (VOCs) present in the headspace of Trappist and craft beers was studied to illustrate the efficiency of thermal desorption (TD) comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS) for highlighting subtle differences between highly complex mixtures of VOCs. Headspace solid-phase microextraction (HS-SPME), multiple (and classical) stir bar sorptive extraction (mSBSE), static headspace (SHS), and dynamic headspace (DHS) were compared for the extraction of a set of 21 representative flavor compounds of beer aroma. A Box-Behnken surface response methodology experimental design optimization (DOE) was used for convex hull calculation (Delaunay's triangulation algorithms) of peak dispersion in the chromatographic space. The predicted value of 0.5 for the ratio between the convex hull and the available space was 10% higher than the experimental value, demonstrating the usefulness of the approach to improve optimization of the GC×GC separation. Chemical variations amongst aligned chromatograms were studied by means of Fisher Ratio (FR) determination and F-distribution threshold filtration at different significance levels (α=0.05 and 0.01) and based on z-score normalized area for data reduction. Statistically significant compounds were highlighted following principal component analysis (PCA) and hierarchical cluster analysis (HCA). The dendrogram structure not only provided clear visual information about similarities between products but also permitted direct identification of the chemicals and their relative weight in clustering. The effective coupling of DHS-TD-GC×GC-TOFMS with PCA and HCA was able to highlight the differences and common typical VOC patterns among 24 samples of different Trappist and selected Canadian craft beers. Copyright © 2017 Elsevier B.V. All rights reserved.

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

PubMed Central

Quince, Christopher; de Vargas, Colomban; Dunthorn, Micah

2015-01-01

Previously we presented Swarm v1, a novel and open source amplicon clustering program that produced fine-scale molecular operational taxonomic units (OTUs), free of arbitrary global clustering thresholds and input-order dependency. Swarm v1 worked with an initial phase that used iterative single-linkage with a local clustering threshold (d), followed by a phase that used the internal abundance structures of clusters to break chained OTUs. Here we present Swarm v2, which has two important novel features: (1) a new algorithm for d = 1 that allows the computation time of the program to scale linearly with increasing amounts of data; and (2) the new fastidious option that reduces under-grouping by grafting low abundant OTUs (e.g., singletons and doubletons) onto larger ones. Swarm v2 also directly integrates the clustering and breaking phases, dereplicates sequencing reads with d = 0, outputs OTU representatives in fasta format, and plots individual OTUs as two-dimensional networks. PMID:26713226

Formation and structure of stable aggregates in binary diffusion-limited cluster-cluster aggregation processes

NASA Astrophysics Data System (ADS)

López-López, J. M.; Moncho-Jordá, A.; Schmitt, A.; Hidalgo-Álvarez, R.

2005-09-01

Binary diffusion-limited cluster-cluster aggregation processes are studied as a function of the relative concentration of the two species. Both, short and long time behaviors are investigated by means of three-dimensional off-lattice Brownian Dynamics simulations. At short aggregation times, the validity of the Hogg-Healy-Fuerstenau approximation is shown. At long times, a single large cluster containing all initial particles is found to be formed when the relative concentration of the minority particles lies above a critical value. Below that value, stable aggregates remain in the system. These stable aggregates are composed by a few minority particles that are highly covered by majority ones. Our off-lattice simulations reveal a value of approximately 0.15 for the critical relative concentration. A qualitative explanation scheme for the formation and growth of the stable aggregates is developed. The simulations also explain the phenomenon of monomer discrimination that was observed recently in single cluster light scattering experiments.

Variability in body size and shape of UK offshore workers: A cluster analysis approach.

PubMed

Stewart, Arthur; Ledingham, Robert; Williams, Hector

2017-01-01

Male UK offshore workers have enlarged dimensions compared with UK norms and knowledge of specific sizes and shapes typifying their physiques will assist a range of functions related to health and ergonomics. A representative sample of the UK offshore workforce (n = 588) underwent 3D photonic scanning, from which 19 extracted dimensional measures were used in k-means cluster analysis to characterise physique groups. Of the 11 resulting clusters four somatotype groups were expressed: one cluster was muscular and lean, four had greater muscularity than adiposity, three had equal adiposity and muscularity and three had greater adiposity than muscularity. Some clusters appeared constitutionally similar to others, differing only in absolute size. These cluster centroids represent an evidence-base for future designs in apparel and other applications where body size and proportions affect functional performance. They also constitute phenotypic evidence providing insight into the 'offshore culture' which may underpin the enlarged dimensions of offshore workers. Copyright © 2016 Elsevier Ltd. All rights reserved.

Sliding states of a soft-colloid cluster crystal: Cluster versus single-particle hopping

NASA Astrophysics Data System (ADS)

Rossini, Mirko; Consonni, Lorenzo; Stenco, Andrea; Reatto, Luciano; Manini, Nicola

2018-05-01

We study a two-dimensional model for interacting colloidal particles which displays spontaneous clustering. Within this model we investigate the competition between the pinning to a periodic corrugation potential and a sideways constant pulling force which would promote a sliding state. For a few sample particle densities and amplitudes of the periodic corrugation potential we investigate the depinning from the statically pinned to the dynamically sliding regime. This sliding state exhibits the competition between a dynamics where entire clusters are pulled from a minimum to the next and a dynamics where single colloids or smaller groups leave a cluster and move across the corrugation energy barrier to join the next cluster downstream in the force direction. Both kinds of sliding states can occur either coherently across the entire sample or asynchronously: the two regimes result in different average mobilities. Finite temperature tends to destroy separate sliding regimes, generating a smoother dependence of the mobility on the driving force.

Study on the mapping of dark matter clustering from real space to redshift space

NASA Astrophysics Data System (ADS)

Zheng, Yi; Song, Yong-Seon

2016-08-01

The mapping of dark matter clustering from real space to redshift space introduces the anisotropic property to the measured density power spectrum in redshift space, known as the redshift space distortion effect. The mapping formula is intrinsically non-linear, which is complicated by the higher order polynomials due to indefinite cross correlations between the density and velocity fields, and the Finger-of-God effect due to the randomness of the peculiar velocity field. Whilst the full higher order polynomials remain unknown, the other systematics can be controlled consistently within the same order truncation in the expansion of the mapping formula, as shown in this paper. The systematic due to the unknown non-linear density and velocity fields is removed by separately measuring all terms in the expansion directly using simulations. The uncertainty caused by the velocity randomness is controlled by splitting the FoG term into two pieces, 1) the ``one-point" FoG term being independent of the separation vector between two different points, and 2) the ``correlated" FoG term appearing as an indefinite polynomials which is expanded in the same order as all other perturbative polynomials. Using 100 realizations of simulations, we find that the Gaussian FoG function with only one scale-independent free parameter works quite well, and that our new mapping formulation accurately reproduces the observed 2-dimensional density power spectrum in redshift space at the smallest scales by far, up to k~ 0.2 Mpc-1, considering the resolution of future experiments.

The observation of negative permittivity in stripe and bubble phases

NASA Astrophysics Data System (ADS)

Smet, Jurgen

The physics of itinerant two-dimensional electrons is by and large governed by repulsive Coulomb forces. However, cases exist where the interplay of attractive and repulsive interaction components may instigate spontaneous symmetry lowering and clustering of charges in geometric patterns such as bubbles and stripes, provided these interactions act on different length scales. The existence of these phases in higher Landau levels has so far been concluded from transport behavior. Here, we report surface acoustic wave experiments. They probe the permittivity at small wave vector. This technique offers true directionality, whereas in transport the current distribution is complex and strongly affected by the inhomogeneous density pattern. Outside the charge density wave regime, the measured permittivity is always positive. However, negative permittivity is observed in the bubble phase irrespective of the propagation direction. For the stripe phase the permittivity takes on both positive as well as negative values depending on the propagation direction. This confirms the stripe phase to be a strongly anisotropic medium. The observation of negative permittivity is considered an immediate consequence of the exchange related attractive interaction. It makes charge clustering favorable in higher Landau levels where the repulsive direct Coulomb interaction acts on a longer length scale and is responsible for a negative compressibility of the electronic system. This work has been carried out with B. Friess, K. von Klitzing (MPI-FKF), Y. Peng, F. von Oppen (FU Berlin), B. Rosenow (Uni Leipzig) and V. Umansky (Weizmann Institute of Science).

Fast EEG spike detection via eigenvalue analysis and clustering of spatial amplitude distribution

NASA Astrophysics Data System (ADS)

Fukami, Tadanori; Shimada, Takamasa; Ishikawa, Bunnoshin

2018-06-01

Objective. In the current study, we tested a proposed method for fast spike detection in electroencephalography (EEG). Approach. We performed eigenvalue analysis in two-dimensional space spanned by gradients calculated from two neighboring samples to detect high-amplitude negative peaks. We extracted the spike candidates by imposing restrictions on parameters regarding spike shape and eigenvalues reflecting detection characteristics of individual medical doctors. We subsequently performed clustering, classifying detected peaks by considering the amplitude distribution at 19 scalp electrodes. Clusters with a small number of candidates were excluded. We then defined a score for eliminating spike candidates for which the pattern of detected electrodes differed from the overall pattern in a cluster. Spikes were detected by setting the score threshold. Main results. Based on visual inspection by a psychiatrist experienced in EEG, we evaluated the proposed method using two statistical measures of precision and recall with respect to detection performance. We found that precision and recall exhibited a trade-off relationship. The average recall value was 0.708 in eight subjects with the score threshold that maximized the F-measure, with 58.6  ±  36.2 spikes per subject. Under this condition, the average precision was 0.390, corresponding to a false positive rate 2.09 times higher than the true positive rate. Analysis of the required processing time revealed that, using a general-purpose computer, our method could be used to perform spike detection in 12.1% of the recording time. The process of narrowing down spike candidates based on shape occupied most of the processing time. Significance. Although the average recall value was comparable with that of other studies, the proposed method significantly shortened the processing time.

Mapping disease-related missense mutations in the immunoglobulin-like fold domain of lamin A/C reveals novel genotype-phenotype associations for laminopathies.

PubMed

Scharner, Juergen; Lu, Hui-Chun; Fraternali, Franca; Ellis, Juliet A; Zammit, Peter S

2014-06-01

Mutations in A-type nuclear lamins cause laminopathies. However, genotype-phenotype correlations using the 340 missense mutations within the LMNA gene are unclear: partially due to the limited availability of three-dimensional structure. The immunoglobulin (Ig)-like fold domain has been solved, and using bioinformatics tools (including Polyphen-2, Fold X, Parameter OPtimized Surfaces, and PocketPicker) we characterized 56 missense mutations for position, surface exposure, change in charge and effect on Ig-like fold stability. We find that 21 of the 27 mutations associated with a skeletal muscle phenotype are distributed throughout the Ig-like fold, are nonsurface exposed and predicted to disrupt overall stability of the Ig-like fold domain. Intriguingly, the remaining 6 mutations clustered, had higher surface exposure, and did not affect stability. The majority of 9 lipodystrophy or 10 premature aging syndrome mutations also did not disrupt Ig-like fold domain stability and were surface exposed and clustered in distinct regions that overlap predicted binding pockets. Although buried, the 10 cardiac mutations had no other consistent properties. Finally, most lipodystrophy and premature aging mutations resulted in a -1 net charge change, whereas skeletal muscle mutations caused no consistent net charge changes. Since premature aging, lipodystrophy and the subset of 6 skeletal muscle mutations cluster tightly in distinct, charged regions, they likely affect lamin A/C -protein/DNA/RNA interactions: providing a consistent genotype-phenotype relationship for mutations in this domain. Thus, this subgroup of skeletal muscle laminopathies that we term the 'Skeletal muscle cluster', may have a distinct pathological mechanism. These novel associations refine the ability to predict clinical features caused by certain LMNA missense mutations. © 2013 Wiley Periodicals, Inc.

Chemistry and Processing of Nanostructured Materials

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fox, G A; Baumann, T F; Hope-Weeks, L J

2002-01-18

Nanostructured materials can be formed through the sol-gel polymerization of inorganic or organic monomer systems. For example, a two step polymerization of tetramethoxysilane (TMOS) was developed such that silica aerogels with densities as low as 3 kg/m{sup 3} ({approx} two times the density of air) could be achieved. Organic aerogels based upon resorcinol-formaldehyde and melamine-formaldehyde can also be prepared using the sol-gel process. Materials of this type have received significant attention at LLNL due to their ultrafine cell sizes, continuous porosity, high surface area and low mass density. For both types of aerogels, sol-gel polymerization depends upon the transformation ofmore » these monomers into nanometer-sized clusters followed by cross-linking into a 3-dimensional gel network. While sol-gel chemistry provides the opportunity to synthesize new material compositions, it suffers from the inability to separate the process of cluster formation from gelation. This limitation results in structural deficiencies in the gel that impact the physical properties of the aerogel, xerogel or nanocomposite. In order to control the properties of the resultant gel, one should be able to regulate the formation of the clusters and their subsequent cross-linking. Towards this goal, we are utilizing dendrimer chemistry to separate the cluster formation from the gelation so that new nanostructured materials can be produced. Dendrimers are three-dimensional, highly branched macromolecules that are prepared in such a way that their size, shape and surface functionality are readily controlled. The dendrimers will be used as pre-formed clusters of known size that can be cross-linked to form an ordered gel network.« less

Galactic Doppelgängers: The Chemical Similarity Among Field Stars and Among Stars with a Common Birth Origin

NASA Astrophysics Data System (ADS)

Ness, M.; Rix, H.-W.; Hogg, David W.; Casey, A. R.; Holtzman, J.; Fouesneau, M.; Zasowski, G.; Geisler, D.; Shetrone, M.; Minniti, D.; Frinchaboy, Peter M.; Roman-Lopes, Alexandre

2018-02-01

We explore to what extent stars within Galactic disk open clusters resemble each other in the high-dimensional space of their photospheric element abundances and contrast this with pairs of field stars. Our analysis is based on abundances for 20 elements, homogeneously derived from APOGEE spectra (with carefully quantified uncertainties of typically 0.03 dex). We consider 90 red giant stars in seven open clusters and find that most stars within a cluster have abundances in most elements that are indistinguishable (in a {χ }2-sense) from those of the other members, as expected for stellar birth siblings. An analogous analysis among pairs of > 1000 field stars shows that highly significant abundance differences in the 20 dimensional space can be established for the vast majority of these pairs, and that the APOGEE-based abundance measurements have high discriminating power. However, pairs of field stars whose abundances are indistinguishable even at 0.03 dex precision exist: ∼0.3% of all field star pairs and ∼1.0% of field star pairs at the same (solar) metallicity [Fe/H] = 0 ± 0.02. Most of these pairs are presumably not birth siblings from the same cluster, but rather doppelgängers. Our analysis implies that “chemical tagging” in the strict sense, identifying birth siblings for typical disk stars through their abundance similarity alone, will not work with such data. However, our approach shows that abundances have extremely valuable information for probabilistic chemo-orbital modeling, and combined with velocities, we have identified new cluster members from the field.

Planar-to-Tubular Structural Transition in Boron Clusters: B20 as the Embryo of Single-Walled Boron Nanotubes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Boggavarapu, Kiran; Bulusu, Satya; Zhai, Hua JIN.

Experimental and computational simulations revealed that boron clusters, which favor planar (2D) structures up to 18 atoms, prefer three-dimensional (3D) structures beginning at 20 atoms. Using global optimization methods, we found that the B20 neutral cluster has a double-ring tubular structure with a diameter of 5.2 ?. In the B20- anion, the tubular structure is shown to be isoenergetic to 2D structures, which were observed and confirmed by photoelectron spectroscopy. The 2D to 3D structural transition observed at B20, reminiscent to the ring-to-fullerene transition at C20 in carbon clusters, suggests it may be considered as the embryo of the thinnestmore » single-walled boron nanotubes.« less

Value-based customer grouping from large retail data sets

NASA Astrophysics Data System (ADS)

Strehl, Alexander; Ghosh, Joydeep

2000-04-01

In this paper, we propose OPOSSUM, a novel similarity-based clustering algorithm using constrained, weighted graph- partitioning. Instead of binary presence or absence of products in a market-basket, we use an extended 'revenue per product' measure to better account for management objectives. Typically the number of clusters desired in a database marketing application is only in the teens or less. OPOSSUM proceeds top-down, which is more efficient and takes a small number of steps to attain the desired number of clusters as compared to bottom-up agglomerative clustering approaches. OPOSSUM delivers clusters that are balanced in terms of either customers (samples) or revenue (value). To facilitate data exploration and validation of results we introduce CLUSION, a visualization toolkit for high-dimensional clustering problems. To enable closed loop deployment of the algorithm, OPOSSUM has no user-specified parameters. Thresholding heuristics are avoided and the optimal number of clusters is automatically determined by a search for maximum performance. Results are presented on a real retail industry data-set of several thousand customers and products, to demonstrate the power of the proposed technique.

Stepwise assembly of a semiconducting coordination polymer [Cd8S(SPh)14(DMF)(bpy)]n and its photodegradation of organic dyes.

PubMed

Xu, Chao; Hedin, Niklas; Shi, Hua-Tian; Xin, ZhiFeng; Zhang, Qian-Feng

2015-04-14

Chalcogenolate clusters can be interlinked with organic linkers into semiconducting coordination polymers with photocatalytic properties. Here, discrete clusters of Cd8S(SPh)14(DMF)3 were interlinked with 4,4'-bipyridine into a one dimensional coordination polymer of [Cd8S(SPh)14(DMF)(bpy)]n with helical chains. A stepwise mechanism for the assembly of the coordination polymer in DMF was revealed by an ex situ dynamic light scattering study. The cluster was electrostatically neutral and showed a penta-supertetrahedral structure. During the assembly each cluster was interlinked with two 4,4'-bipyridine molecules, which replaced the two terminal DMF molecules of the clusters. In their solid-state forms, the cluster and the coordination polymer were semiconductors with wide band gaps of 3.08 and 2.80 ev. They photocatalytically degraded rhodamine B and methylene blue in aqueous solutions. The moderate conditions used for the synthesis could allow for further in situ studies of the reaction-assembly of related clusters and coordination polymers.

Do Sexually Oriented Massage Parlors Cluster in Specific Neighborhoods? A Spatial Analysis of Indoor Sex Work in Los Angeles and Orange Counties, California.

PubMed

Chin, John J; Kim, Anna J; Takahashi, Lois; Wiebe, Douglas J

2015-01-01

Social determinants of health may be substantially affected by spatial factors, which together may explain the persistence of health inequities. Clustering of possible sources of negative health and social outcomes points to a spatial focus for future interventions. We analyzed the spatial clustering of sex work businesses in Southern California to examine where and why they cluster. We explored economic and legal factors as possible explanations of clustering. We manually coded data from a website used by paying members to post reviews of female massage parlor workers. We identified clusters of sexually oriented massage parlor businesses using spatial autocorrelation tests. We conducted spatial regression using census tract data to identify predictors of clustering. A total of 889 venues were identified. Clusters of tracts having higher-than-expected numbers of sexually oriented massage parlors ("hot spots") were located outside downtowns. These hot spots were characterized by a higher proportion of adult males, a higher proportion of households below the federal poverty level, and a smaller average household size. Sexually oriented massage parlors in Los Angeles and Orange counties cluster in particular neighborhoods. More research is needed to ascertain the causal factors of such clusters and how interventions can be designed to leverage these spatial factors.

Softly-confined water cluster between freestanding graphene sheets

NASA Astrophysics Data System (ADS)

Agustian, Rifan; Akaishi, Akira; Nakamura, Jun

2018-01-01

Confined water could adopt new forms not seen in the open air, such as a two-dimensional (2D) square ice trapped between two graphene sheets [Algara-Siller et al., Nature 519, 443-445 (2015)]. In this study, in order to investigate how the flexibility of graphene affects the confined structure of water molecules, we employed classical molecular dynamics simulations with Adaptive Intermolecular Reactive Empirical Bond Order (AIREBO) potential to produce a soft-confining property of graphene. We discovered various solid-like structures of water molecules ranging from two-dimensional to three-dimensional structure encapsulated between two freestanding graphene sheets even at room temperature (300K). A small amount of water encapsulation leads to a layered two-dimensional form with triangular structure. On the other hand, large amounts of water molecules take a three-dimensional flying-saucer-like form with the square ice intra-layer structure. There is also a metastable state where both two-dimensional and three-dimensional structures coexist.

Cause and Effect of Feedback: Multiphase Gas in Cluster Cores Heated by AGN Jets

NASA Astrophysics Data System (ADS)

Gaspari, M.; Ruszkowski, M.; Sharma, P.

2012-02-01

Multiwavelength data indicate that the X-ray-emitting plasma in the cores of galaxy clusters is not cooling catastrophically. To a large extent, cooling is offset by heating due to active galactic nuclei (AGNs) via jets. The cool-core clusters, with cooler/denser plasmas, show multiphase gas and signs of some cooling in their cores. These observations suggest that the cool core is locally thermally unstable while maintaining global thermal equilibrium. Using high-resolution, three-dimensional simulations we study the formation of multiphase gas in cluster cores heated by collimated bipolar AGN jets. Our key conclusion is that spatially extended multiphase filaments form only when the instantaneous ratio of the thermal instability and free-fall timescales (t TI/t ff) falls below a critical threshold of ≈10. When this happens, dense cold gas decouples from the hot intracluster medium (ICM) phase and generates inhomogeneous and spatially extended Hα filaments. These cold gas clumps and filaments "rain" down onto the central regions of the core, forming a cold rotating torus and in part feeding the supermassive black hole. Consequently, the self-regulated feedback enhances AGN heating and the core returns to a higher entropy level with t TI/t ff > 10. Eventually, the core reaches quasi-stable global thermal equilibrium, and cold filaments condense out of the hot ICM whenever t TI/t ff <~ 10. This occurs despite the fact that the energy from AGN jets is supplied to the core in a highly anisotropic fashion. The effective spatial redistribution of heat is enabled in part by the turbulent motions in the wake of freely falling cold filaments. Increased AGN activity can locally reverse the cold gas flow, launching cold filamentary gas away from the cluster center. Our criterion for the condensation of spatially extended cold gas is in agreement with observations and previous idealized simulations.

An ensemble framework for clustering protein-protein interaction networks.

PubMed

Asur, Sitaram; Ucar, Duygu; Parthasarathy, Srinivasan

2007-07-01

Protein-Protein Interaction (PPI) networks are believed to be important sources of information related to biological processes and complex metabolic functions of the cell. The presence of biologically relevant functional modules in these networks has been theorized by many researchers. However, the application of traditional clustering algorithms for extracting these modules has not been successful, largely due to the presence of noisy false positive interactions as well as specific topological challenges in the network. In this article, we propose an ensemble clustering framework to address this problem. For base clustering, we introduce two topology-based distance metrics to counteract the effects of noise. We develop a PCA-based consensus clustering technique, designed to reduce the dimensionality of the consensus problem and yield informative clusters. We also develop a soft consensus clustering variant to assign multifaceted proteins to multiple functional groups. We conduct an empirical evaluation of different consensus techniques using topology-based, information theoretic and domain-specific validation metrics and show that our approaches can provide significant benefits over other state-of-the-art approaches. Our analysis of the consensus clusters obtained demonstrates that ensemble clustering can (a) produce improved biologically significant functional groupings; and (b) facilitate soft clustering by discovering multiple functional associations for proteins. Supplementary data are available at Bioinformatics online.

Geometrical, electronic, and magnetic properties of CunFe (n=1-12) clusters: A density functional study

NASA Astrophysics Data System (ADS)

Ling, Wang; Dong, Die; Shi-Jian, Wang; Zheng-Quan, Zhao

2015-01-01

The geometrical, electronic, and magnetic properties of small CunFe (n=1-12) clusters have been investigated by using density functional method B3LYP and LanL2DZ basis set. The structural search reveals that Fe atoms in low-energy CunFe isomers tend to occupy the position with the maximum coordination number. The ground state CunFe clusters possess planar structure for n=2-5 and three-dimensional (3D) structure for n=6-12. The electronic properties of CunFe clusters are analyzed through the averaged binding energy, the second-order energy difference and HOMO-LUMO energy gap. It is found that the magic numbers of stability are 1, 3, 7 and 9 for the ground state CunFe clusters. The energy gap of Fe-encapsulated cage clusters is smaller than that of other configurations. The Cu5Fe and Cu7Fe clusters have a very large energy gap (>2.4 eV). The vertical ionization potential (VIP), electron affinity (EA) and photoelectron spectra are also calculated and simulated theoretically for all the ground-state clusters. The magnetic moment analyses for the ground-state CunFe clusters show that Fe atom can enhance the magnetic moment of the host cluster and carries most of the total magnetic moment.

STAR FORMATION AND SUPERCLUSTER ENVIRONMENT OF 107 NEARBY GALAXY CLUSTERS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cohen, Seth A.; Hickox, Ryan C.; Wegner, Gary A.

We analyze the relationship between star formation (SF), substructure, and supercluster environment in a sample of 107 nearby galaxy clusters using data from the Sloan Digital Sky Survey. Previous works have investigated the relationships between SF and cluster substructure, and cluster substructure and supercluster environment, but definitive conclusions relating all three of these variables has remained elusive. We find an inverse relationship between cluster SF fraction ( f {sub SF}) and supercluster environment density, calculated using the Galaxy luminosity density field at a smoothing length of 8 h {sup −1} Mpc (D8). The slope of f {sub SF} versus D8more » is −0.008 ± 0.002. The f {sub SF} of clusters located in low-density large-scale environments, 0.244 ± 0.011, is higher than for clusters located in high-density supercluster cores, 0.202 ± 0.014. We also divide superclusters, according to their morphology, into filament- and spider-type systems. The inverse relationship between cluster f {sub SF} and large-scale density is dominated by filament- rather than spider-type superclusters. In high-density cores of superclusters, we find a higher f {sub SF} in spider-type superclusters, 0.229 ± 0.016, than in filament-type superclusters, 0.166 ± 0.019. Using principal component analysis, we confirm these results and the direct correlation between cluster substructure and SF. These results indicate that cluster SF is affected by both the dynamical age of the cluster (younger systems exhibit higher amounts of SF); the large-scale density of the supercluster environment (high-density core regions exhibit lower amounts of SF); and supercluster morphology (spider-type superclusters exhibit higher amounts of SF at high densities).« less

EMBEDDED CLUSTERS IN THE LARGE MAGELLANIC CLOUD USING THE VISTA MAGELLANIC CLOUDS SURVEY

DOE Office of Scientific and Technical Information (OSTI.GOV)

Romita, Krista; Lada, Elizabeth; Cioni, Maria-Rosa, E-mail: k.a.romita@ufl.edu, E-mail: elada@ufl.edu, E-mail: mcioni@aip.de

We present initial results of the first large-scale survey of embedded star clusters in molecular clouds in the Large Magellanic Cloud (LMC) using near-infrared imaging from the Visible and Infrared Survey Telescope for Astronomy Magellanic Clouds Survey. We explored a ∼1.65 deg{sup 2} area of the LMC, which contains the well-known star-forming region 30 Doradus as well as ∼14% of the galaxy’s CO clouds, and identified 67 embedded cluster candidates, 45 of which are newly discovered as clusters. We have determined the sizes, luminosities, and masses for these embedded clusters, examined the star formation rates (SFRs) of their corresponding molecularmore » clouds, and made a comparison between the LMC and the Milky Way. Our preliminary results indicate that embedded clusters in the LMC are generally larger, more luminous, and more massive than those in the local Milky Way. We also find that the surface densities of both embedded clusters and molecular clouds is ∼3 times higher than in our local environment, the embedded cluster mass surface density is ∼40 times higher, the SFR is ∼20 times higher, and the star formation efficiency is ∼10 times higher. Despite these differences, the SFRs of the LMC molecular clouds are consistent with the SFR scaling law presented in Lada et al. This consistency indicates that while the conditions of embedded cluster formation may vary between environments, the overall process within molecular clouds may be universal.« less

Off-stoichiometric defect clustering in irradiated oxides

NASA Astrophysics Data System (ADS)

Khalil, Sarah; Allen, Todd; EL-Azab, Anter

2017-04-01

A cluster dynamics model describing the formation of vacancy and interstitial clusters in irradiated oxides has been developed. The model, which tracks the composition of the oxide matrix and the defect clusters, was applied to the early stage formation of voids and dislocation loops in UO2, and the effects of irradiation temperature and dose rate on the evolution of their densities and composition was investigated. The results show that Frenkel defects dominate the nucleation process in irradiated UO2. The results also show that oxygen vacancies drive vacancy clustering while the migration energy of uranium vacancies is a rate-limiting factor for the nucleation and growth of voids. In a stoichiometric UO2 under irradiation, off-stoichiometric vacancy clusters exist with a higher concentration of hyper-stoichiometric clusters. Similarly, off-stoichiometric interstitial clusters form with a higher concentration of hyper-stoichiometric clusters. The UO2 matrix was found to be hyper-stoichiometric due to the accumulation of uranium vacancies.

Experimental and theoretical investigation of three-dimensional nitrogen-doped aluminum clusters AI 8N - and AI 8N

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Leiming; Huang, Wei; Wang, Lai S.

The structure and electronic properties of the Al 8N - and Al 8N clusters were investigated by combined photoelectron spectroscopy and ab initio studies. Congested photoelectron spectra were observed and experimental evidence was obtained for the presence of multiple isomers for Al 8N - Global minimum searches revealed several structures for Al 8N - with close energies. The calculated vertical detachment energies of the two lowest-lying isomers, which are of C 2v and C s symmetry, respectively, were shown to agree well with the experimental data. Unlike the three-dimensional structures of Al 6N - and Al 7N -, in whichmore » the dopant N atom has a high coordination number of 6,the dopant N atom in the two low-lying isomers of Al 8N - has a lower coordination number of 4 and 5, respectively. The competition between the Al–Al and Al–N interactions are shown to determine the global minimum structures of the doped aluminum clusters and results in the structural diversity for both Al 8N - and Al8N. © 2009 American Institute of Physics« less

Noise-free accurate count of microbial colonies by time-lapse shadow image analysis.

PubMed

Ogawa, Hiroyuki; Nasu, Senshi; Takeshige, Motomu; Funabashi, Hisakage; Saito, Mikako; Matsuoka, Hideaki

2012-12-01

Microbial colonies in food matrices could be counted accurately by a novel noise-free method based on time-lapse shadow image analysis. An agar plate containing many clusters of microbial colonies and/or meat fragments was trans-illuminated to project their 2-dimensional (2D) shadow images on a color CCD camera. The 2D shadow images of every cluster distributed within a 3-mm thick agar layer were captured in focus simultaneously by means of a multiple focusing system, and were then converted to 3-dimensional (3D) shadow images. By time-lapse analysis of the 3D shadow images, it was determined whether each cluster comprised single or multiple colonies or a meat fragment. The analytical precision was high enough to be able to distinguish a microbial colony from a meat fragment, to recognize an oval image as two colonies contacting each other, and to detect microbial colonies hidden under a food fragment. The detection of hidden colonies is its outstanding performance in comparison with other systems. The present system attained accuracy for counting fewer than 5 colonies and is therefore of practical importance. Copyright © 2012 Elsevier B.V. All rights reserved.

WIYN OPEN CLUSTER STUDY. LXXI. SPECTROSCOPIC MEMBERSHIP AND ORBITS OF NGC 6791 SUB-SUBGIANTS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Milliman, Katelyn E.; Leiner, Emily; Mathieu, Robert D.

2016-06-01

In an optical color–magnitude diagram, sub-subgiants (SSGs) lie redward of the main sequence and fainter than the base of the red giant branch in a region not easily populated by standard stellar-evolution pathways. In this paper, we present multi-epoch radial velocities for five SSG candidates in the old and metal-rich open cluster NGC 6791 (8 Gyr, [Fe/H] = +0.30). From these data, we are able to make three-dimensional kinematic membership determinations and confirm four SSG candidates as likely cluster members. We also identify three member SSGs as short-period binary systems and present their orbital solutions. These are the first SSGsmore » with known three-dimensional kinematic membership, binary status, and orbital parameters since the two SSGs in M67 studied by Mathieu et al. We also remark on the other properties of these stars including photometric variability, H α emission, and X-ray luminosity. The membership confirmation of these SSGs in NGC 6791 strengthens the case that SSGs are a new class of nonstandard stellar evolution products, and that a physical mechanism must be found that explains the evolutionary paths of these stars.« less

Spatial charge inhomogeneity and defect states in topological Dirac semimetal thin films of Na3Bi

PubMed Central

Edmonds, Mark T.; Collins, James L.; Hellerstedt, Jack; Yudhistira, Indra; Gomes, Lídia C.; Rodrigues, João N. B.; Adam, Shaffique; Fuhrer, Michael S.

2017-01-01

Topological Dirac semimetals (TDSs) are three-dimensional analogs of graphene, with carriers behaving like massless Dirac fermions in three dimensions. In graphene, substrate disorder drives fluctuations in Fermi energy, necessitating construction of heterostructures of graphene and hexagonal boron nitride (h-BN) to minimize the fluctuations. Three-dimensional TDSs obviate the substrate and should show reduced EF fluctuations due to better metallic screening and higher dielectric constants. We map the potential fluctuations in TDS Na3Bi using a scanning tunneling microscope. The rms potential fluctuations are significantly smaller than the thermal energy room temperature (ΔEF,rms = 4 to 6 meV = 40 to 70 K) and comparable to the highest-quality graphene on h-BN. Surface Na vacancies produce a novel resonance close to the Dirac point with surprisingly large spatial extent and provide a unique way to tune the surface density of states in a TDS thin-film material. Sparse defect clusters show bound states whose occupation may be changed by applying a bias to the scanning tunneling microscope tip, offering an opportunity to study a quantum dot connected to a TDS reservoir. PMID:29291249

Adaptation of the three-dimensional wisdom scale (3D-WS) for the Korean cultural context.

PubMed

Kim, Seungyoun; Knight, Bob G

2014-10-23

ABSTRACT Background: Previous research on wisdom has suggested that wisdom is comprised of cognitive, reflective, and affective components and has developed and validated wisdom measures based on samples from Western countries. To apply the measurement to Eastern cultures, the present study revised an existing wisdom scale, the three-dimensional wisdom scale (3D-WS, Ardelt, 2003) for the Korean cultural context. Methods: Participants included 189 Korean heritage adults (age range 19-96) living in Los Angeles. We added a culturally specific factor of wisdom to the 3D-WS: Modesty and Unobtrusiveness (Yang, 2001), which captures an Eastern aspect of wisdom. The structure and psychometrics of the scale were tested. By latent cluster analysis, we determined acculturation subgroups and examined group differences in the means of factors in the revised wisdom scale (3D-WS-K). Results: Three factors, Cognitive Flexibility, Viewpoint Relativism, and Empathic Modesty were found using confirmatory factor analysis. Respondents with high biculturalism were higher on Viewpoint Relativism and lower on Empathic Modesty. Conclusion: This study discovered that a revised wisdom scale had a distinct factor structure and item content in a Korean heritage sample. We also found acculturation influences on the meaning of wisdom.

Evolution of Ge nanoislands on Si(110)-'16 × 2' surface under thermal annealing studied using STM

NASA Astrophysics Data System (ADS)

Gangopadhyay, Subhashis; Yoshimura, Masamichi; Ueda, Kazuyuki

2009-11-01

The initial nucleation of Ge nanoclusters on Si(110) at room temperature (RT), annealing-induced surface roughening and the evolution of three-dimensional Ge nanoislands have been investigated using scanning tunneling microscopy (STM). A few monolayers (ML) of Ge deposited at room temperature lead to the formation of Ge clusters which are homogeneously distributed across the surface. The stripe-like patterns, characteristic of the Si(110)-'16 × 2' surface reconstruction are also retained. Increasing annealing temperatures, however, lead to significant surface diffusion and thus, disruption of the underlying '16 × 2' reconstruction. The annealing-induced removal of the stripe structures (originated from '16 × 2' reconstruction) starts at approximately 300 °C, whereas the terrace structures of Si(110) are thermally stable up to 500 °C. At approximately 650 °C, shallow Ge islands of pyramidal shape with (15,17,1) side facets start to form. Annealing at even higher temperatures enhances Ge island formation. Our findings are explained in terms of partial dewetting of the metastable Ge wetting layer (WL) (formed at room temperature) as well as partial relaxation of lattice strain through three-dimensional (3D) island growth.

Enhanced optical absorbance and fabrication of periodic arrays on nickel surface using nanosecond laser

NASA Astrophysics Data System (ADS)

Fu, Jinxiang; Liang, Hao; Zhang, Jingyuan; Wang, Yibo; Liu, Yannan; Zhang, Zhiyan; Lin, Xuechun

2017-04-01

A hundred-nanosecond pulsed laser was employed to structure the nickel surface. The effects of laser spatial filling interval and laser scanning speed on the optical absorbance capacity and morphologies on the nickel surface were experimentally investigated. The black nickel surface covered with dense micro/nanostructured broccoli-like clusters with strong light trapping capacity ranging from the UV to the near IR was produced at a high laser scanning speed up to v=100 mm/s. The absorbance of the black nickel is as high as 98% in the UV range of 200-400 nm, more than 97% in the visible spectrum, ranging from 400 to 800 nm, and over 90% in the IR between 800 and 2000 nm. In addition, when the nickel surface was irradiated in two-dimensional crossing scans by laser with different processing parameters, self-organized and shape-controllable structures of three-dimensional (3D) periodic arrays can be fabricated. Compared with ultrafast laser systems previously used for such processing, the nanosecond fiber laser used in this work is more cost-effective, compact and allows higher processing rates. This nickel surface structured technique may be applicable in optoelectronics, batteries industry, solar/wave absorbers, and wettability materials.

Dual shell-like magnetic clusters containing Ni(II) and Ln(III) (Ln = La, Pr, and Nd) ions.

PubMed

Kong, Xiang-Jian; Ren, Yan-Ping; Long, La-Sheng; Zheng, Zhiping; Nichol, Gary; Huang, Rong-Bin; Zheng, Lan-Sun

2008-04-07

Dual shell-like nanoscopic magnetic clusters featuring a polynuclear nickel(II) framework encapsulating that of lanthanide ions (Ln = La, Pr, and Nd) were synthesized using Ni(NO3)(2).6H2O, Ln(NO3)(3).6H2O, and iminodiacetic acid (IDA) under hydrothermal conditions. Structurally established by crystallographic studies, these clusters are [La20Ni30(IDA)30(CO3)6(NO3)6(OH)30(H2O)12](CO3)(6).72H2O (1), [Ln20Ni21(C4H5NO4)21(OH)24(C2H2O3)6(C2O4)3(NO3)9(H2O)12](NO3)9.nH2O [C2H2O3 is the alkoxide form of glycolate; Ln = Pr (2), n = 42; Nd (3), n = 50], and {[La4Ni5Na(IDA)5(CO3)(NO3)4(OH)5(H2O)5][CO3].10H2O} infinity (4). Carbonate, oxalate, and glycolate are products of hydrothermal decomposition of IDA. Compositions of these compounds were confirmed by satisfactory elemental analyses. It has been found that the cluster structure is dependent on the identity of the lanthanide ion as well as the starting Ln/Ni/IDA ratio. The cationic cluster of 1 features a core of the Keplerate type with an outer icosidodecahedron of Ni(II) ions encaging a dodecahedral kernel of La(III). Clusters 2 and 3, distinctly different from 1, are isostructural, possessing a core of an outer shell of 21 Ni(II) ions encapsulating an inner shell of 20 Ln(III) ions. Complex 4 is a three-dimensional assembly of cluster building blocks connected by units of Na(NO3)/La(NO3)3; the structure of the building block resembles closely that of 1, with a hydrated La(III) ion internalized in the decanuclear cage being an extra feature. Magnetic studies indicated ferromagnetic interactions in 1, while overall antiferromagnetic interactions were revealed for 2 and 3. The polymeric, three-dimensional cluster network 4 displayed interesting ferrimagnetic interactions.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, Chenyang; Niu, Liangliang; Chen, Nanjun

A grand challenge in material science is to understand the correlation between intrinsic properties and defect dynamics. Radiation tolerant materials are in great demand for safe operation and advancement of nuclear and aerospace systems. Unlike traditional approaches that rely on microstructural and nanoscale features to mitigate radiation damage, this study demonstrates enhancement of radiation tolerance with the suppression of void formation by two orders magnitude at elevated temperatures in equiatomic single-phase concentrated solid solution alloys, and more importantly, reveals its controlling mechanism through a detailed analysis of the depth distribution of defect clusters and an atomistic computer simulation. The enhancedmore » swelling resistance is attributed to the tailored interstitial defect cluster motion in the alloys from a long-range one-dimensional mode to a short-range three-dimensional mode, which leads to enhanced point defect recombination. Finally, the results suggest design criteria for next generation radiation tolerant structural alloys.« less

Charge ordering in two-dimensional ionic liquids

NASA Astrophysics Data System (ADS)

Perera, Aurélien; Urbic, Tomaz

2018-04-01

The structural properties of model two-dimensional (2D) ionic liquids are examined, with a particular focus on the charge ordering process, with the use of computer simulation and integral equation theories. The influence of the logarithmic form of the Coulomb interaction, versus that of a 3D screened interaction form, is analysed. Charge order is found to hold and to be analogous for both interaction models, despite their very different form. The influence of charge ordering in the low density regime is discussed in relation to well known properties of 2D Coulomb fluids, such as the Kosterlitz-Thouless transition and criticality. The present study suggests the existence of a stable thermodynamic labile cluster phase, implying the existence of a liquid-liquid "transition" above the liquid-gas binodal. The liquid-gas and Kosterlitz-Thouless transitions would then take place inside the predicted cluster phase.

Configural approaches to temperament assessment: implications for predicting risk of unintentional injury in children.

PubMed

Berry, Jack W; Schwebel, David C

2009-10-01

This study used two configural approaches to understand how temperament factors (surgency/extraversion, negative affect, and effortful control) might predict child injury risk. In the first approach, clustering procedures were applied to trait dimensions to identify discrete personality prototypes. In the second approach, two- and three-way trait interactions were considered dimensionally in regression models predicting injury outcomes. Injury risk was assessed through four measures: lifetime prevalence of injuries requiring professional medical attention, scores on the Injury Behavior Checklist, and frequency and severity of injuries reported in a 2-week injury diary. In the prototype analysis, three temperament clusters were obtained, which resembled resilient, overcontrolled, and undercontrolled types found in previous research. Undercontrolled children had greater risk of injury than children in the other groups. In the dimensional interaction analyses, an interaction between surgency/extraversion and negative affect tended to predict injury, especially when children lacked capacity for effortful control.

Potential, velocity, and density fields from redshift-distance samples: Application - Cosmography within 6000 kilometers per second

NASA Technical Reports Server (NTRS)

Bertschinger, Edmund; Dekel, Avishai; Faber, Sandra M.; Dressler, Alan; Burstein, David

1990-01-01

A potential flow reconstruction algorithm has been applied to the real universe to reconstruct the three-dimensional potential, velocity, and mass density fields smoothed on large scales. The results are shown as maps of these fields, revealing the three-dimensional structure within 6000 km/s distance from the Local Group. The dominant structure is an extended deep potential well in the Hydra-Centaurus region, stretching across the Galactic plane toward Pavo, broadly confirming the Great Attractor (GA) model of Lynden-Bell et al. (1988). The Local Supercluster appears to be an extended ridge on the near flank of the GA, proceeding through the Virgo Southern Extension to the Virgo and Ursa Major clusters. The Virgo cluster and the Local Group are both falling toward the bottom of the GA potential well with peculiar velocities of 658 + or - 121 km/s and 565 + or - 125 km/s, respectively.

Single-shot three-dimensional reconstruction based on structured light line pattern

NASA Astrophysics Data System (ADS)

Wang, ZhenZhou; Yang, YongMing

2018-07-01

Reconstruction of the object by single-shot is of great importance in many applications, in which the object is moving or its shape is non-rigid and changes irregularly. In this paper, we propose a single-shot structured light 3D imaging technique that calculates the phase map from the distorted line pattern. This technique makes use of the image processing techniques to segment and cluster the projected structured light line pattern from one single captured image. The coordinates of the clustered lines are extracted to form a low-resolution phase matrix which is then transformed to full-resolution phase map by spline interpolation. The 3D shape of the object is computed from the full-resolution phase map and the 2D camera coordinates. Experimental results show that the proposed method was able to reconstruct the three-dimensional shape of the object robustly from one single image.

Enhancing radiation tolerance by controlling defect mobility and migration pathways in multicomponent single-phase alloys

NASA Astrophysics Data System (ADS)

Lu, Chenyang; Niu, Liangliang; Chen, Nanjun; Jin, Ke; Yang, Taini; Xiu, Pengyuan; Zhang, Yanwen; Gao, Fei; Bei, Hongbin; Shi, Shi; He, Mo-Rigen; Robertson, Ian M.; Weber, William J.; Wang, Lumin

2016-12-01

A grand challenge in material science is to understand the correlation between intrinsic properties and defect dynamics. Radiation tolerant materials are in great demand for safe operation and advancement of nuclear and aerospace systems. Unlike traditional approaches that rely on microstructural and nanoscale features to mitigate radiation damage, this study demonstrates enhancement of radiation tolerance with the suppression of void formation by two orders magnitude at elevated temperatures in equiatomic single-phase concentrated solid solution alloys, and more importantly, reveals its controlling mechanism through a detailed analysis of the depth distribution of defect clusters and an atomistic computer simulation. The enhanced swelling resistance is attributed to the tailored interstitial defect cluster motion in the alloys from a long-range one-dimensional mode to a short-range three-dimensional mode, which leads to enhanced point defect recombination. The results suggest design criteria for next generation radiation tolerant structural alloys.

The orbital motion of the quintuplet cluster—a common origin for the arches and quintuplet clusters?

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stolte, A.; Hußmann, B.; Habibi, M.

2014-07-10

We investigate the orbital motion of the Quintuplet cluster near the Galactic center with the aim of constraining formation scenarios of young, massive star clusters in nuclear environments. Three epochs of adaptive optics high-angular resolution imaging with the Keck/NIRC2 and Very Large Telescope/NAOS-CONICA systems were obtained over a time baseline of 5.8 yr, delivering an astrometric accuracy of 0.5-1 mas yr{sup –1}. Proper motions were derived in the cluster reference frame and were used to distinguish cluster members from the majority of the dense field star population toward the inner bulge. Fitting the cluster and field proper motion distributions withmore » two-dimensional (2D) Gaussian models, we derive the orbital motion of the cluster for the first time. The Quintuplet is moving with a 2D velocity of 132 ± 15 km s{sup –1} with respect to the field along the Galactic plane, which yields a three-dimensional orbital velocity of 167 ± 15 km s{sup –1} when combined with the previously known radial velocity. From a sample of 119 stars measured in three epochs, we derive an upper limit to the velocity dispersion of σ{sub 1D} < 10 km s{sup –1} in the core of the Quintuplet cluster. Knowledge of the three velocity components of the Quintuplet allows us to model the cluster orbit in the potential of the inner Galaxy. Under the assumption that the Quintuplet is located in the central 200 pc at the present time, these simulations exclude the possibility that the cluster is moving on a circular orbit. Comparing the Quintuplet's orbit with our earlier measurements of the Arches' orbit, we discuss the possibility that both clusters originated in the same area of the central molecular zone (CMZ). According to the model of Binney et al., two families of stable cloud orbits are located along the major and minor axes of the Galactic bar, named x1 and x2 orbits, respectively. The formation locus of these clusters is consistent with the outermost x2 orbit and might hint at cloud collisions at the transition region between the x1 and x2 orbital families located at the tip of the minor axis of the Galactic bar. The formation of young, massive star clusters in circumnuclear rings is discussed in the framework of the channeling in of dense gas by the bar potential. We conclude that the existence of a large-scale bar plays a major role in supporting ongoing star and cluster formation, not only in nearby spiral galaxies with circumnuclear rings, but also in the Milky Way's CMZ.« less

Low-end mass function of the Quintuplet cluster

NASA Astrophysics Data System (ADS)

Shin, Jihye; Kim, Sungsoo S.

2016-08-01

The Quintuplet and Arches clusters, which were formed in the harsh environment of the Galactic Centre (GC) a few million years ago, have been excellent targets for studying the effects of a star-forming environment on the initial mass function (IMF). In order to estimate the shape of the low-end IMF of the Arches cluster, Shin & Kim devised a novel photometric method that utilizes pixel intensity histograms (PIHs) of the observed images. Here, we apply the PIH method to the Quintuplet cluster and estimate the shape of its low-end IMF below the magnitude of completeness limit as set by conventional photometry. We found that the low-end IMF of the Quintuplet is consistent with that found for the Arches cluster-Kroupa MF, with a significant number of low-mass stars below 1 M⊙. We conclude that the most likely IMFs of the Quintuplet and the Arches clusters are not too different from the IMFs found in the Galactic disc. We also find that the observed PIHs and stellar number density profiles of both clusters are best reproduced when the clusters are assumed to be at three-dimensional distances of approximately 100 pc from the GC.

Thematic clustering of text documents using an EM-based approach

PubMed Central

2012-01-01

Clustering textual contents is an important step in mining useful information on the web or other text-based resources. The common task in text clustering is to handle text in a multi-dimensional space, and to partition documents into groups, where each group contains documents that are similar to each other. However, this strategy lacks a comprehensive view for humans in general since it cannot explain the main subject of each cluster. Utilizing semantic information can solve this problem, but it needs a well-defined ontology or pre-labeled gold standard set. In this paper, we present a thematic clustering algorithm for text documents. Given text, subject terms are extracted and used for clustering documents in a probabilistic framework. An EM approach is used to ensure documents are assigned to correct subjects, hence it converges to a locally optimal solution. The proposed method is distinctive because its results are sufficiently explanatory for human understanding as well as efficient for clustering performance. The experimental results show that the proposed method provides a competitive performance compared to other state-of-the-art approaches. We also show that the extracted themes from the MEDLINE® dataset represent the subjects of clusters reasonably well. PMID:23046528

Plasma flow around and charge distribution of a dust cluster in a rf discharge

NASA Astrophysics Data System (ADS)

Schleede, J.; Lewerentz, L.; Bronold, F. X.; Schneider, R.; Fehske, H.

2018-04-01

We employ a particle-in-cell Monte Carlo collision/particle-particle particle-mesh simulation to study the plasma flow around and the charge distribution of a three-dimensional dust cluster in the sheath of a low-pressure rf argon discharge. The geometry of the cluster and its position in the sheath are fixed to the experimental values, prohibiting a mechanical response of the cluster. Electrically, however, the cluster and the plasma environment, mimicking also the experimental situation, are coupled self-consistently. We find a broad distribution of the charges collected by the grains. The ion flux shows on the scale of the Debye length strong focusing and shadowing inside and outside the cluster due to the attraction of the ions to the negatively charged grains, whereas the electron flux is characterized on this scale only by a weak spatial modulation of its magnitude depending on the rf phase. On the scale of the individual dust potentials, however, the electron flux deviates in the vicinity of the cluster strongly from the laminar flow associated with the plasma sheath. It develops convection patterns to compensate for the depletion of electrons inside the dust cluster.

Asteroid families - An initial search

NASA Technical Reports Server (NTRS)

Williams, James G.

1992-01-01

A stereo examination was conducted for clusters in three-dimensional proper element space within a sample of both numbered and faint Palomar-Leiden Survey (PLS) asteroids. The clusters were then objectively filtered for small Poisson probability of chance occurrence; 104 were accepted as families with 4- to 12-member populations, and are interpreted as impact-generated. Structure is common in the well-populated families: the better-sampled families are accordingly discussed in terms of their geometry and taxonomy. Some families are very rich in faint PLS members.

Dynamic competitive probabilistic principal components analysis.

PubMed

López-Rubio, Ezequiel; Ortiz-DE-Lazcano-Lobato, Juan Miguel

2009-04-01

We present a new neural model which extends the classical competitive learning (CL) by performing a Probabilistic Principal Components Analysis (PPCA) at each neuron. The model also has the ability to learn the number of basis vectors required to represent the principal directions of each cluster, so it overcomes a drawback of most local PCA models, where the dimensionality of a cluster must be fixed a priori. Experimental results are presented to show the performance of the network with multispectral image data.

Initiation of pharmacotherapy for post-traumatic stress disorder among veterans from Iraq and Afghanistan: a dimensional, symptom cluster approach

PubMed Central

Rosenheck, Robert; Mohamed, Somaia; Pietrzak, Robert; Hoff, Rani

2016-01-01

Background The pharmacological treatment of post-traumatic stress disorder (PTSD) is extremely challenging, as no specific agent has been developed exclusively to treat this disorder. Thus, there are growing concerns among the public, providers and consumers associated with its use as the efficacy of some agents is still in question. Aims We applied a dimensional and symptom cluster-based approach to better understand how the heterogeneous phenotypic presentation of PTSD may relate to the initiation of pharmacotherapy for PTSD initial episode. Method US veterans who served in the conflicts in Iraq and Afghanistan and received an initial PTSD diagnosis at the US Veterans Health Administration between 2008 and 2011 were included in this study. Veterans were followed for 365 days from initial PTSD diagnosis to identify initiation for antidepressants, anxiolytics/sedatives/hypnotics, antipsychotics and prazosin. Multivariable analyses were used to assess the relationship between the severity of unique PTSD symptom clusters and receiving prescriptions from each medication class, as well as the time from diagnosis to first prescription. Results Increased severity of emotional numbing symptoms was independently associated with the prescription of antidepressants, and they were prescribed after a substantially shorter period of time than other medications. Anxiolytics/sedatives/hypnotics prescription was associated with heightened re-experiencing symptoms and sleep difficulties. Antipsychotics were associated with elevated re-experiencing and numbing symptoms and prazosin with reported nightmares. Conclusions Prescribing practices for military-related PTSD appear to follow US VA/DoD clinical guidelines. Results of this study suggest that a novel dimensional and symptom cluster-based approach to classifying the phenotypic presentation of military-related PTSD symptoms may help inform prescribing patterns for PTSD. Declaration of interest None. Copyright and usage © The Royal College of Psychiatrists 2016. This is an open access article distributed under the terms of the Creative Commons Non-Commercial, No Derivatives (CC BY-NC-ND) license. PMID:27703791

Modeling clustered activity increase in amyloid-beta positron emission tomographic images with statistical descriptors.

PubMed

Shokouhi, Sepideh; Rogers, Baxter P; Kang, Hakmook; Ding, Zhaohua; Claassen, Daniel O; Mckay, John W; Riddle, William R

2015-01-01

Amyloid-beta (Aβ) imaging with positron emission tomography (PET) holds promise for detecting the presence of Aβ plaques in the cortical gray matter. Many image analyses focus on regional average measurements of tracer activity distribution; however, considerable additional information is available in the images. Metrics that describe the statistical properties of images, such as the two-point correlation function (S2), have found wide applications in astronomy and materials science. S2 provides a detailed characterization of spatial patterns in images typically referred to as clustering or flocculence. The objective of this study was to translate the two-point correlation method into Aβ-PET of the human brain using 11C-Pittsburgh compound B (11C-PiB) to characterize longitudinal changes in the tracer distribution that may reflect changes in Aβ plaque accumulation. We modified the conventional S2 metric, which is primarily used for binary images and formulated a weighted two-point correlation function (wS2) to describe nonbinary, real-valued PET images with a single statistical function. Using serial 11C-PiB scans, we calculated wS2 functions from two-dimensional PET images of different cortical regions as well as three-dimensional data from the whole brain. The area under the wS2 functions was calculated and compared with the mean/median of the standardized uptake value ratio (SUVR). For three-dimensional data, we compared the area under the wS2 curves with the subjects' cerebrospinal fluid measures. Overall, the longitudinal changes in wS2 correlated with the increase in mean SUVR but showed lower variance. The whole brain results showed a higher inverse correlation between the cerebrospinal Aβ and wS2 than between the cerebrospinal Aβ and SUVR mean/median. We did not observe any confounding of wS2 by region size or injected dose. The wS2 detects subtle changes and provides additional information about the binding characteristics of radiotracers and Aβ accumulation that are difficult to verify with mean SUVR alone.

TripAdvisor^{N-D}: A Tourism-Inspired High-Dimensional Space Exploration Framework with Overview and Detail.

PubMed

Nam, Julia EunJu; Mueller, Klaus

2013-02-01

Gaining a true appreciation of high-dimensional space remains difficult since all of the existing high-dimensional space exploration techniques serialize the space travel in some way. This is not so foreign to us since we, when traveling, also experience the world in a serial fashion. But we typically have access to a map to help with positioning, orientation, navigation, and trip planning. Here, we propose a multivariate data exploration tool that compares high-dimensional space navigation with a sightseeing trip. It decomposes this activity into five major tasks: 1) Identify the sights: use a map to identify the sights of interest and their location; 2) Plan the trip: connect the sights of interest along a specifyable path; 3) Go on the trip: travel along the route; 4) Hop off the bus: experience the location, look around, zoom into detail; and 5) Orient and localize: regain bearings in the map. We describe intuitive and interactive tools for all of these tasks, both global navigation within the map and local exploration of the data distributions. For the latter, we describe a polygonal touchpad interface which enables users to smoothly tilt the projection plane in high-dimensional space to produce multivariate scatterplots that best convey the data relationships under investigation. Motion parallax and illustrative motion trails aid in the perception of these transient patterns. We describe the use of our system within two applications: 1) the exploratory discovery of data configurations that best fit a personal preference in the presence of tradeoffs and 2) interactive cluster analysis via cluster sculpting in N-D.

Properties of iron sulfide, hydrosulfide, and mixed sulfide/hydrosulfide cluster anions through photoelectron spectroscopy and density functional theory calculations.

PubMed

Yin, Shi; Bernstein, Elliot R

2016-10-21

A new magnetic-bottle time-of-flight photoelectron spectroscopy (PES) apparatus is constructed in our laboratory. The PES spectra of iron sulfide, hydrosulfide, and mixed sulfide/hydrosulfide [FeS m (SH) n - ; m, n = 0-3, 0 < (m + n) ≤ 3] cluster anions, obtained at 2.331 eV (532 nm) and 3.492 eV (355 nm) photon energies, are reported. The electronic structure and bonding properties of these clusters are additionally investigated at different levels of density functional theory. The most probable structures and ground state spin multiplicity for these cluster anions are tentatively assigned by comparing their theoretical first vertical detachment energies (VDEs) with their respective experiment values. The behavior of S and (SH) as ligands in these iron sulfide, hydrosulfide, and mixed sulfide/hydrosulfide cluster anions is investigated and compared. The experimental first VDEs for Fe(SH) 1-3 - cluster anions are lower than those found for their respective FeS 1-3 - cluster anions. The experimental first VDEs for FeS 1-3 - clusters are observed to increase for the first two S atoms bound to Fe - ; however, due to the formation of an S-S bond for the FeS 3 - cluster, its first VDE is found to be ∼0.41 eV lower than the first VDE for the FeS 2 - cluster. The first VDEs of Fe(SH) 1-3 - cluster anions are observed to increase with the increasing numbers of SH groups. The calculated partial charges of the Fe atom for ground state FeS 1-3 - and Fe(SH) 1-3 - clusters are apparently related to and correlated with their determined first VDEs. The higher first VDE is correlated with a higher, more positive partial charge for the Fe atom of these cluster anions. Iron sulfide/hydrosulfide mixed cluster anions are also explored in this work: the first VDE for FeS(SH) - is lower than that for FeS 2 - , but higher than that for Fe(SH) 2 - ; the first VDEs for FeS 2 (SH) - and FeS(SH) 2 - are close to that for FeS 3 - , but higher than that for Fe(SH) 3 - . The first VDEs of general iron sulfide, hydrosulfide, and mixed sulfide/hydrosulfide clusters [FeS m (SH) n - ; m, n = 0-3, 0 < (m + n) ≤ 3] are dependent on three properties of these anions: 1. the partial charge on the Fe atom, 2. disulfide bond formation (S-S) in the cluster, and 3. the number of hydrosulfide ligands in the cluster. The higher the partial charge on the Fe atom of these clusters, the larger the first VDE; however, cluster S-S bonding and more (SH) ligands in the cluster lower the cluster anion first VDE.

Properties of iron sulfide, hydrosulfide, and mixed sulfide/hydrosulfide cluster anions through photoelectron spectroscopy and density functional theory calculations

NASA Astrophysics Data System (ADS)

Yin, Shi; Bernstein, Elliot R.

2016-10-01

A new magnetic-bottle time-of-flight photoelectron spectroscopy (PES) apparatus is constructed in our laboratory. The PES spectra of iron sulfide, hydrosulfide, and mixed sulfide/hydrosulfide [FeSm(SH)n-; m, n = 0-3, 0 < (m + n) ≤ 3] cluster anions, obtained at 2.331 eV (532 nm) and 3.492 eV (355 nm) photon energies, are reported. The electronic structure and bonding properties of these clusters are additionally investigated at different levels of density functional theory. The most probable structures and ground state spin multiplicity for these cluster anions are tentatively assigned by comparing their theoretical first vertical detachment energies (VDEs) with their respective experiment values. The behavior of S and (SH) as ligands in these iron sulfide, hydrosulfide, and mixed sulfide/hydrosulfide cluster anions is investigated and compared. The experimental first VDEs for Fe(SH)1-3- cluster anions are lower than those found for their respective FeS1-3- cluster anions. The experimental first VDEs for FeS1-3- clusters are observed to increase for the first two S atoms bound to Fe-; however, due to the formation of an S-S bond for the FeS3- cluster, its first VDE is found to be ˜0.41 eV lower than the first VDE for the FeS2- cluster. The first VDEs of Fe(SH)1-3- cluster anions are observed to increase with the increasing numbers of SH groups. The calculated partial charges of the Fe atom for ground state FeS1-3- and Fe(SH)1-3- clusters are apparently related to and correlated with their determined first VDEs. The higher first VDE is correlated with a higher, more positive partial charge for the Fe atom of these cluster anions. Iron sulfide/hydrosulfide mixed cluster anions are also explored in this work: the first VDE for FeS(SH)- is lower than that for FeS2-, but higher than that for Fe(SH)2-; the first VDEs for FeS2(SH)- and FeS(SH)2- are close to that for FeS3-, but higher than that for Fe(SH)3-. The first VDEs of general iron sulfide, hydrosulfide, and mixed sulfide/hydrosulfide clusters [FeSm(SH)n-; m, n = 0-3, 0 < (m + n) ≤ 3] are dependent on three properties of these anions: 1. the partial charge on the Fe atom, 2. disulfide bond formation (S-S) in the cluster, and 3. the number of hydrosulfide ligands in the cluster. The higher the partial charge on the Fe atom of these clusters, the larger the first VDE; however, cluster S-S bonding and more (SH) ligands in the cluster lower the cluster anion first VDE.

Visualization of Sources in the Universe

NASA Astrophysics Data System (ADS)

Kafatos, M.; Cebral, J. R.

1993-12-01

We have begun to develop a series of visualization tools of importance to the display of astronomical data and have applied these to the visualization of cosmological sources in the recently formed Institute for Computational Sciences and Informatics at GMU. One can use a three-dimensional perspective plot of the density surface for three dimensional data and in this case the iso-level contours are three- dimensional surfaces. Sophisticated rendering algorithms combined with multiple source lighting allow us to look carefully at such density contours and to see fine structure on the surface of the density contours. Stereoscopic and transparent rendering can give an even more sophisticated approach with multi-layered surfaces providing information at different levels. We have applied these methods to looking at density surfaces of 3-D data such as 100 clusters of galaxies and 2500 galaxies in the CfA redshift survey. Our plots presented are based on three variables, right ascension, declination and redshift. We have also obtained density structures in 2-D for the distribution of gamma-ray bursts (where distances are unknown) and the distribution of a variety of sources such as clusters of galaxies. Our techniques allow for correlations to be done visually.

A Distributed GPU-Based Framework for Real-Time 3D Volume Rendering of Large Astronomical Data Cubes

NASA Astrophysics Data System (ADS)

Hassan, A. H.; Fluke, C. J.; Barnes, D. G.

2012-05-01

We present a framework to volume-render three-dimensional data cubes interactively using distributed ray-casting and volume-bricking over a cluster of workstations powered by one or more graphics processing units (GPUs) and a multi-core central processing unit (CPU). The main design target for this framework is to provide an in-core visualization solution able to provide three-dimensional interactive views of terabyte-sized data cubes. We tested the presented framework using a computing cluster comprising 64 nodes with a total of 128GPUs. The framework proved to be scalable to render a 204GB data cube with an average of 30 frames per second. Our performance analyses also compare the use of NVIDIA Tesla 1060 and 2050GPU architectures and the effect of increasing the visualization output resolution on the rendering performance. Although our initial focus, as shown in the examples presented in this work, is volume rendering of spectral data cubes from radio astronomy, we contend that our approach has applicability to other disciplines where close to real-time volume rendering of terabyte-order three-dimensional data sets is a requirement.

Shape component analysis: structure-preserving dimension reduction on biological shape spaces.

PubMed

Lee, Hao-Chih; Liao, Tao; Zhang, Yongjie Jessica; Yang, Ge

2016-03-01

Quantitative shape analysis is required by a wide range of biological studies across diverse scales, ranging from molecules to cells and organisms. In particular, high-throughput and systems-level studies of biological structures and functions have started to produce large volumes of complex high-dimensional shape data. Analysis and understanding of high-dimensional biological shape data require dimension-reduction techniques. We have developed a technique for non-linear dimension reduction of 2D and 3D biological shape representations on their Riemannian spaces. A key feature of this technique is that it preserves distances between different shapes in an embedded low-dimensional shape space. We demonstrate an application of this technique by combining it with non-linear mean-shift clustering on the Riemannian spaces for unsupervised clustering of shapes of cellular organelles and proteins. Source code and data for reproducing results of this article are freely available at https://github.com/ccdlcmu/shape_component_analysis_Matlab The implementation was made in MATLAB and supported on MS Windows, Linux and Mac OS. geyang@andrew.cmu.edu. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

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

NASA Astrophysics Data System (ADS)

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

2017-01-01

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

Magnetic quantum tunneling: key insights from multi-dimensional high-field EPR.

PubMed

Lawrence, J; Yang, E-C; Hendrickson, D N; Hill, S

2009-08-21

Multi-dimensional high-field/frequency electron paramagnetic resonance (HFEPR) spectroscopy is performed on single-crystals of the high-symmetry spin S = 4 tetranuclear single-molecule magnet (SMM) [Ni(hmp)(dmb)Cl](4), where hmp(-) is the anion of 2-hydroxymethylpyridine and dmb is 3,3-dimethyl-1-butanol. Measurements performed as a function of the applied magnetic field strength and its orientation within the hard-plane reveal the four-fold behavior associated with the fourth order transverse zero-field splitting (ZFS) interaction, (1/2)B(S + S), within the framework of a rigid spin approximation (with S = 4). This ZFS interaction mixes the m(s) = +/-4 ground states in second order of perturbation, generating a sizeable (12 MHz) tunnel splitting, which explains the fast magnetic quantum tunneling in this SMM. Meanwhile, multi-frequency measurements performed with the field parallel to the easy-axis reveal HFEPR transitions associated with excited spin multiplets (S < 4). Analysis of the temperature dependence of the intensities of these transitions enables determination of the isotropic Heisenberg exchange constant, J = -6.0 cm(-1), which couples the four spin s = 1 Ni(II) ions within the cluster, as well as a characterization of the ZFS within excited states. The combined experimental studies support recent work indicating that the fourth order anisotropy associated with the S = 4 state originates from second order ZFS interactions associated with the individual Ni(II) centers, but only as a result of higher-order processes that occur via S-mixing between the ground state and higher-lying (S < 4) spin multiplets. We argue that this S-mixing plays an important role in the low-temperature quantum dynamics associated with many other well known SMMs.

Clusters of midlife women by physical activity and their racial/ethnic differences.

PubMed

Im, Eun-Ok; Ko, Young; Chee, Eunice; Chee, Wonshik; Mao, Jun James

2017-04-01

The purpose of this study was to identify clusters of midlife women by physical activity and to determine racial/ethnic differences in physical activities in each cluster. This was a secondary analysis of the data from 542 women (157 non-Hispanic [NH] Whites, 127 Hispanics, 135 NH African Americans, and 123 NH Asian) in a larger Internet study on midlife women's attitudes toward physical activity. The instruments included the Barriers to Health Activities Scale, the Physical Activity Assessment Inventory, the Questions on Attitudes toward Physical Activity, Subjective Norm, Perceived Behavioral Control, and Behavioral Intention, and the Kaiser Physical Activity Survey. The data were analyzed using hierarchical cluster analyses, analysis of variance, and multinominal logistic analyses. A three-cluster solution was adopted: cluster 1 (high active living and sports/exercise activity group; 48%), cluster 2 (high household/caregiving and occupational activity group; 27%), and cluster 3 (low active living and sports/exercise activity group; 26%). There were significant racial/ethnic differences in occupational activities of clusters 1 and 3 (all P < 0.01). Compared with cluster 1, cluster 2 tended to have lower family income, less access to health care, higher unemployment, higher perceived barriers scores, and lower social influences scores (all P < 0.01). Compared with cluster 1, cluster 3 tended to have greater obesity, less access to health care, higher perceived barriers scores, more negative attitudes toward physical activity, and lower self-efficacy scores (all P < 0.01). Midlife women's unique patterns of physical activity and their associated factors need to be considered in future intervention development.

Clusters of Midlife Women by Physical Activity and Their Racial/Ethnic Differences

PubMed Central

Im, Eun-Ok; Ko, Young; Chee, Eunice; Chee, Wonshik; Mao, Jun James

2016-01-01

Objective The purpose of this study was to identify clusters of midlife women by physical activity and to determine racial/ethnic differences in physical activities in each cluster. Methods This was a secondary analysis of the data from 542 women (157 Non-Hispanic [NH] Whites, 127 Hispanics, 135 NH African Americans, and 123 NH Asian) in a larger Internet study on midlife women’s attitudes toward physical activity. The instruments included the Barriers to Health Activities Scale, the Physical Activity Assessment Inventory, the Questions on Attitudes toward Physical Activity, Subjective Norm, Perceived Behavioral Control, and Behavioral Intention, and the Kaiser Physical Activity Survey. The data were analyzed using hierarchical cluster analyses, ANOVA, and multinominal logistic analyses. Results A three cluster solution was adopted: Cluster 1 (high active living and sports/exercise activity group; 48%), Cluster 2 (high household/caregiving and occupational activity group; 27%), and Cluster 3 (low active living and sports/exercise activity group; 26%). There were significant racial/ethnic differences in occupational activities of Clusters 1 and 3 (all p<.01). Compared with Cluster 1, Cluster 2 tended to have lower family income, less access to health care, higher unemployment, higher perceived barriers scores, and lower social influences scores (all p<.01). Compared with Cluster 1, Cluster 3 tended to have greater obesity, less access to health care, higher perceived barriers scores, more negative attutides toward physical activity, and lower self-efficacy scores (all p<.01). Conclusions Midlife women’s unique patterns of physical activity and their associated factors need to be considered in future intervention development. PMID:27846052

Structures, stability and electronic properties of bimetallic Cun-1Sc and Cun-2Sc2 (n = 2-7) clusters

NASA Astrophysics Data System (ADS)

Li, Zhi; Zhao, Zhen; Zhou, Zhonghao; Wang, Qi

2018-02-01

To investigate the interface between the main phases of Cu-Sc alloys, the structures, stability and electronic properties of bimetallic Cun-1Sc and Cun-2Sc2 (n = 2-7) clusters are systematically calculated by the GGA-PW91 functional. The results reveal that the structures of Cun-1Sc and Cun-2Sc2 (n = 2-7) clusters inherited those of pure Cun (n = 2-7) clusters and they maintained higher symmetry. Cu5Sc cluster possesses more stable than its neighbors while Cu2Sc2 cluster is less stable than its neighbors by binding energy. Cu5Sc cluster possesses the highest kinetic stability of Cun-1Sc clusters and CuSc2, Cu3Sc2 and Cu5Sc2 clusters possess higher kinetic stability than their neighbors by HOMO-LUMO gap. NBO analysis reveals that Cu-Sc atoms have less pd orbital hybridization in the Sc doping Cun (n = 2-7) clusters.

Cosmic mass spectrometer

NASA Astrophysics Data System (ADS)

Anchordoqui, Luis A.; Barger, Vernon; Weiler, Thomas J.

2018-03-01

We argue that if ultrahigh-energy (E ≳1010GeV) cosmic rays are heavy nuclei (as indicated by existing data), then the pointing of cosmic rays to their nearest extragalactic sources is expected for 1010.6 ≲ E /GeV ≲1011. This is because for a nucleus of charge Ze and baryon number A, the bending of the cosmic ray decreases as Z / E with rising energy, so that pointing to nearby sources becomes possible in this particular energy range. In addition, the maximum energy of acceleration capability of the sources grows linearly in Z, while the energy loss per distance traveled decreases with increasing A. Each of these two points tend to favor heavy nuclei at the highest energies. The traditional bi-dimensional analyses, which simultaneously reproduce Auger data on the spectrum and nuclear composition, may not be capable of incorporating the relative importance of all these phenomena. In this paper we propose a multi-dimensional reconstruction of the individual emission spectra (in E, direction, and cross-correlation with nearby putative sources) to study the hypothesis that primaries are heavy nuclei subject to GZK photo-disintegration, and to determine the nature of the extragalactic sources. More specifically, we propose to combine information on nuclear composition and arrival direction to associate a potential clustering of events with a 3-dimensional position in the sky. Actually, both the source distance and maximum emission energy can be obtained through a multi-parameter likelihood analysis to accommodate the observed nuclear composition of each individual event in the cluster. We show that one can track the level of GZK interactions on an statistical basis by comparing the maximum energy at the source of each cluster. We also show that nucleus-emitting-sources exhibit a cepa stratis structure on Earth which could be pealed off by future space-missions, such as POEMMA. Finally, we demonstrate that metal-rich starburst galaxies are highly-plausible candidate sources, and we use them as an explicit example of our proposed multi-dimensional analysis.

Metal Adatoms and Clusters on Ultrathin Zirconia Films

PubMed Central

2016-01-01

Nucleation and growth of transition metals on zirconia has been studied by scanning tunneling microscopy (STM) and density functional theory (DFT) calculations. Since STM requires electrical conductivity, ultrathin ZrO2 films grown by oxidation of Pt3Zr(0001) and Pd3Zr(0001) were used as model systems. DFT studies were performed for single metal adatoms on supported ZrO2 films as well as the (1̅11) surface of monoclinic ZrO2. STM shows decreasing cluster size, indicative of increasing metal–oxide interaction, in the sequence Ag < Pd ≈ Au < Ni ≈ Fe. Ag and Pd nucleate mostly at steps and domain boundaries of ZrO2/Pt3Zr(0001) and form three-dimensional clusters. Deposition of low coverages of Ni and Fe at room temperature leads to a high density of few-atom clusters on the oxide terraces. Weak bonding of Ag to the oxide is demonstrated by removing Ag clusters with the STM tip. DFT calculations for single adatoms show that the metal–oxide interaction strength increases in the sequence Ag < Au < Pd < Ni on monoclinic ZrO2, and Ag ≈ Au < Pd < Ni on the supported ultrathin ZrO2 film. With the exception of Au, metal nucleation and growth on ultrathin zirconia films follow the usual rules: More reactive (more electropositive) metals result in a higher cluster density and wet the surface more strongly than more noble metals. These bind mainly to the oxygen anions of the oxide. Au is an exception because it can bind strongly to the Zr cations. Au diffusion may be impeded by changing its charge state between −1 and +1. We discuss differences between the supported ultrathin zirconia films and the surfaces of bulk ZrO2, such as the possibility of charge transfer to the substrate of the films. Due to their large in-plane lattice constant and the variety of adsorption sites, ZrO2{111} surfaces are more reactive than many other oxygen-terminated oxide surfaces. PMID:27213024

Infinite number of solvable generalizations of XY-chain, with cluster state, and with central charge c = m/2

NASA Astrophysics Data System (ADS)

Minami, Kazuhiko

2017-12-01

An infinite number of spin chains are solved and it is derived that the ground-state phase transitions belong to the universality classes with central charge c = m / 2, where m is an integer. The models are diagonalized by automatically obtained transformations, many of which are different from the Jordan-Wigner transformation. The free energies, correlation functions, string order parameters, exponents, central charges, and the phase diagram are obtained. Most of the examples consist of the stabilizers of the cluster state. A unified structure of the one-dimensional XY and cluster-type spin chains is revealed, and other series of solvable models can be obtained through this formula.

Characterization of Acousto-Electric Cluster and Array Levitation and its Application to Evaporation

NASA Technical Reports Server (NTRS)

Robert E. Apfel; Zheng, Yibing

2000-01-01

An acousto-electric levitator has been developed to study the behavior of liquid drop and solid particle clusters and arrays. Unlike an ordinary acoustic levitator that uses only a standing acoustic wave to levitate a single drop or particle, this device uses an extra electric static field and the acoustic field simultaneously to generate and levitate charged drops in two-dimensional arrays in air without any contact to a solid surface. This cluster and array generation (CAG) instrument enables us to steadily position drops and arrays to study the behavior of multiple drop and particle systems such as spray and aerosol systems relevant to the energy, environmental, and material sciences.