[Applying the clustering technique for characterising maintenance outsourcing].

PubMed

Cruz, Antonio M; Usaquén-Perilla, Sandra P; Vanegas-Pabón, Nidia N; Lopera, Carolina

2010-06-01

Using clustering techniques for characterising companies providing health institutions with maintenance services. The study analysed seven pilot areas' equipment inventory (264 medical devices). Clustering techniques were applied using 26 variables. Response time (RT), operation duration (OD), availability and turnaround time (TAT) were amongst the most significant ones. Average biomedical equipment obsolescence value was 0.78. Four service provider clusters were identified: clusters 1 and 3 had better performance, lower TAT, RT and DR values (56 % of the providers coded O, L, C, B, I, S, H, F and G, had 1 to 4 day TAT values: Cluster 0 had medium performance (38 % of providers coded V, M, K, Z, T and Y, having an average 9.79 TAT value). Cluster 2 (6 % - provider J) had low performance, having very a high TAT level (101 days on average). The methodology allowed medical equipment inventory and maintenance service suppliers to be characterised. The cluster technique was effective in identifying the most competitive suppliers.

Constrained spectral clustering under a local proximity structure assumption

NASA Technical Reports Server (NTRS)

Wagstaff, Kiri; Xu, Qianjun; des Jardins, Marie

2005-01-01

This work focuses on incorporating pairwise constraints into a spectral clustering algorithm. A new constrained spectral clustering method is proposed, as well as an active constraint acquisition technique and a heuristic for parameter selection. We demonstrate that our constrained spectral clustering method, CSC, works well when the data exhibits what we term local proximity structure.

RRW: repeated random walks on genome-scale protein networks for local cluster discovery

PubMed Central

Macropol, Kathy; Can, Tolga; Singh, Ambuj K

2009-01-01

Background We propose an efficient and biologically sensitive algorithm based on repeated random walks (RRW) for discovering functional modules, e.g., complexes and pathways, within large-scale protein networks. Compared to existing cluster identification techniques, RRW implicitly makes use of network topology, edge weights, and long range interactions between proteins. Results We apply the proposed technique on a functional network of yeast genes and accurately identify statistically significant clusters of proteins. We validate the biological significance of the results using known complexes in the MIPS complex catalogue database and well-characterized biological processes. We find that 90% of the created clusters have the majority of their catalogued proteins belonging to the same MIPS complex, and about 80% have the majority of their proteins involved in the same biological process. We compare our method to various other clustering techniques, such as the Markov Clustering Algorithm (MCL), and find a significant improvement in the RRW clusters' precision and accuracy values. Conclusion RRW, which is a technique that exploits the topology of the network, is more precise and robust in finding local clusters. In addition, it has the added flexibility of being able to find multi-functional proteins by allowing overlapping clusters. PMID:19740439

Galaxy clusters in local Universe simulations without density constraints: a long uphill struggle

NASA Astrophysics Data System (ADS)

Sorce, Jenny G.

2018-06-01

Galaxy clusters are excellent cosmological probes provided that their formation and evolution within the large scale environment are precisely understood. Therefore studies with simulated galaxy clusters have flourished. However detailed comparisons between simulated and observed clusters and their population - the galaxies - are complicated by the diversity of clusters and their surrounding environment. An original way initiated by Bertschinger as early as 1987, to legitimize the one-to-one comparison exercise down to the details, is to produce simulations constrained to resemble the cluster under study within its large scale environment. Subsequently several methods have emerged to produce simulations that look like the local Universe. This paper highlights one of these methods and its essential steps to get simulations that not only resemble the local Large Scale Structure but also that host the local clusters. It includes a new modeling of the radial peculiar velocity uncertainties to remove the observed correlation between the decreases of the simulated cluster masses and of the amount of data used as constraints with the distance from us. This method has the particularity to use solely radial peculiar velocities as constraints: no additional density constraints are required to get local cluster simulacra. The new resulting simulations host dark matter halos that match the most prominent local clusters such as Coma. Zoom-in simulations of the latter and of a volume larger than the 30h-1 Mpc radius inner sphere become now possible to study local clusters and their effects. Mapping the local Sunyaev-Zel'dovich and Sachs-Wolfe effects can follow.

Moving Object Localization Based on UHF RFID Phase and Laser Clustering

PubMed Central

Fu, Yulu; Wang, Changlong; Liang, Gaoli; Zhang, Hua; Ur Rehman, Shafiq

2018-01-01

RFID (Radio Frequency Identification) offers a way to identify objects without any contact. However, positioning accuracy is limited since RFID neither provides distance nor bearing information about the tag. This paper proposes a new and innovative approach for the localization of moving object using a particle filter by incorporating RFID phase and laser-based clustering from 2d laser range data. First of all, we calculate phase-based velocity of the moving object based on RFID phase difference. Meanwhile, we separate laser range data into different clusters, and compute the distance-based velocity and moving direction of these clusters. We then compute and analyze the similarity between two velocities, and select K clusters having the best similarity score. We predict the particles according to the velocity and moving direction of laser clusters. Finally, we update the weights of the particles based on K clusters and achieve the localization of moving objects. The feasibility of this approach is validated on a Scitos G5 service robot and the results prove that we have successfully achieved a localization accuracy up to 0.25 m. PMID:29522458

Applying Machine Learning to Star Cluster Classification

NASA Astrophysics Data System (ADS)

Fedorenko, Kristina; Grasha, Kathryn; Calzetti, Daniela; Mahadevan, Sridhar

2016-01-01

Catalogs describing populations of star clusters are essential in investigating a range of important issues, from star formation to galaxy evolution. Star cluster catalogs are typically created in a two-step process: in the first step, a catalog of sources is automatically produced; in the second step, each of the extracted sources is visually inspected by 3-to-5 human classifiers and assigned a category. Classification by humans is labor-intensive and time consuming, thus it creates a bottleneck, and substantially slows down progress in star cluster research.We seek to automate the process of labeling star clusters (the second step) through applying supervised machine learning techniques. This will provide a fast, objective, and reproducible classification. Our data is HST (WFC3 and ACS) images of galaxies in the distance range of 3.5-12 Mpc, with a few thousand star clusters already classified by humans as a part of the LEGUS (Legacy ExtraGalactic UV Survey) project. The classification is based on 4 labels (Class 1 - symmetric, compact cluster; Class 2 - concentrated object with some degree of asymmetry; Class 3 - multiple peak system, diffuse; and Class 4 - spurious detection). We start by looking at basic machine learning methods such as decision trees. We then proceed to evaluate performance of more advanced techniques, focusing on convolutional neural networks and other Deep Learning methods. We analyze the results, and suggest several directions for further improvement.

A local framework for calculating coupled cluster singles and doubles excitation energies (LoFEx-CCSD)

DOE PAGES

Baudin, Pablo; Bykov, Dmytro; Liakh, Dmitry I.; ...

2017-02-22

Here, the recently developed Local Framework for calculating Excitation energies (LoFEx) is extended to the coupled cluster singles and doubles (CCSD) model. In the new scheme, a standard CCSD excitation energy calculation is carried out within a reduced excitation orbital space (XOS), which is composed of localised molecular orbitals and natural transition orbitals determined from time-dependent Hartree–Fock theory. The presented algorithm uses a series of reduced second-order approximate coupled cluster singles and doubles (CC2) calculations to optimise the XOS in a black-box manner. This ensures that the requested CCSD excitation energies have been determined to a predefined accuracy compared tomore » a conventional CCSD calculation. We present numerical LoFEx-CCSD results for a set of medium-sized organic molecules, which illustrate the black-box nature of the approach and the computational savings obtained for transitions that are local compared to the size of the molecule. In fact, for such local transitions, the LoFEx-CCSD scheme can be applied to molecular systems where a conventional CCSD implementation is intractable.« less

Localization and orientation of heavy-atom cluster compounds in protein crystals using molecular replacement

PubMed Central

Dahms, Sven O.; Kuester, Miriam; Streb, Carsten; Roth, Christian; Sträter, Norbert; Than, Manuel E.

2013-01-01

Heavy-atom clusters (HA clusters) containing a large number of specifically arranged electron-dense scatterers are especially useful for experimental phase determination of large complex structures, weakly diffracting crystals or structures with large unit cells. Often, the determination of the exact orientation of the HA cluster and hence of the individual heavy-atom positions proves to be the critical step in successful phasing and subsequent structure solution. Here, it is demonstrated that molecular replacement (MR) with either anomalous or isomorphous differences is a useful strategy for the correct placement of HA cluster compounds. The polyoxometallate cluster hexasodium α-metatungstate (HMT) was applied in phasing the structure of death receptor 6. Even though the HA cluster is bound in alternate partially occupied orientations and is located at a special position, its correct localization and orientation could be determined at resolutions as low as 4.9 Å. The broad applicability of this approach was demonstrated for five different derivative crystals that included the compounds tantalum tetradeca­bromide and trisodium phosphotungstate in addition to HMT. The correct placement of the HA cluster depends on the length of the intramolecular vectors chosen for MR, such that both a larger cluster size and the optimal choice of the wavelength used for anomalous data collection strongly affect the outcome. PMID:23385464

Applying local binary patterns in image clustering problems

NASA Astrophysics Data System (ADS)

Skorokhod, Nikolai N.; Elizarov, Alexey I.

2017-11-01

Due to the fact that the cloudiness plays a critical role in the Earth radiative balance, the study of the distribution of different types of clouds and their movements is relevant. The main sources of such information are artificial satellites that provide data in the form of images. The most commonly used method of solving tasks of processing and classification of images of clouds is based on the description of texture features. The use of a set of local binary patterns is proposed to describe the texture image.

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

NASA Astrophysics Data System (ADS)

Sorce, Jenny G.; Tempel, Elmo

2018-06-01

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

Improving local clustering based top-L link prediction methods via asymmetric link clustering information

NASA Astrophysics Data System (ADS)

Wu, Zhihao; Lin, Youfang; Zhao, Yiji; Yan, Hongyan

2018-02-01

Networks can represent a wide range of complex systems, such as social, biological and technological systems. Link prediction is one of the most important problems in network analysis, and has attracted much research interest recently. Many link prediction methods have been proposed to solve this problem with various techniques. We can note that clustering information plays an important role in solving the link prediction problem. In previous literatures, we find node clustering coefficient appears frequently in many link prediction methods. However, node clustering coefficient is limited to describe the role of a common-neighbor in different local networks, because it cannot distinguish different clustering abilities of a node to different node pairs. In this paper, we shift our focus from nodes to links, and propose the concept of asymmetric link clustering (ALC) coefficient. Further, we improve three node clustering based link prediction methods via the concept of ALC. The experimental results demonstrate that ALC-based methods outperform node clustering based methods, especially achieving remarkable improvements on food web, hamster friendship and Internet networks. Besides, comparing with other methods, the performance of ALC-based methods are very stable in both globalized and personalized top-L link prediction tasks.

The Star Cluster System in the Local Group Starburst Galaxy IC 10

NASA Astrophysics Data System (ADS)

Lim, Sungsoon; Lee, Myung Gyoon

2015-05-01

We present a survey of star clusters in the halo of IC 10, a starburst galaxy in the Local Group, based on Subaru R-band images and NOAO Local Group Survey UBVRI images. We find five new star clusters. All of these star clusters are located far from the center of IC 10, while previously known star clusters are mostly located in the main body. Interestingly, the distribution of these star clusters shows an asymmetrical structure elongated along the east and southwest directions. We derive UBVRI photometry of 66 star clusters, including these new star clusters, as well as previously known star clusters. Ages of the star clusters are estimated from a comparison of their UBVRI spectral energy distribution with the simple stellar population models. We find that the star clusters in the halo are all older than 1 Gyr, while those in the main body have various ages, from very young (several Myr) to old (\\gt 1 Gyr). The young clusters (\\lt 10 Myr) are mostly located in the Hα emission regions and are concentrated on a small region at 2\\prime\\prime in the southeast direction from the galaxy center, while the old clusters are distributed in a wider area than the disk. Intermediate-age clusters (∼100 Myr) are found in two groups. One is close to the location of the young clusters and the other is at ∼ 4\\prime\\prime from the location of the young clusters. The latter may be related to past mergers or tidal interaction.

The red-sequence of 72 WINGS local galaxy clusters

NASA Astrophysics Data System (ADS)

Valentinuzzi, T.; Poggianti, B. M.; Fasano, G.; D'Onofrio, M.; Moretti, A.; Ramella, M.; Biviano, A.; Fritz, J.; Varela, J.; Bettoni, D.; Vulcani, B.; Moles, M.; Couch, W. J.; Dressler, A.; Kjærgaard, P.; Omizzolo, A.; Cava, A.

2011-12-01

We study the color - magnitude red sequence and blue fraction of 72 X-ray selected galaxy clusters at z = 0.04-0.07 from the WINGS survey, searching for correlations between the characteristics of the red sequence (RS) and the environment. We consider the slope and scatter of the red sequence, the number ratio of red luminous-to-faint galaxies, the blue fraction, and the fractions of ellipticals, S0s, and spirals that compose the RS. None of these quantities correlate with the cluster velocity dispersion, X-ray luminosity, number of cluster substructures, BCG prevalence over next brightest galaxies, and the spatial concentration of ellipticals. The properties of the RS, instead, depend strongly on local galaxy density. Higher density regions have a smaller RS scatter, a higher luminous-to-faint ratio, a lower blue fraction, and a lower spiral fraction on the RS. Our results clearly illustrate the prominent effect of the local density in setting the epoch when galaxies become passive and join the red sequence, as opposed to the mass of the galaxy host structure.

Performance of small cluster surveys and the clustered LQAS design to estimate local-level vaccination coverage in Mali.

PubMed

Minetti, Andrea; Riera-Montes, Margarita; Nackers, Fabienne; Roederer, Thomas; Koudika, Marie Hortense; Sekkenes, Johanne; Taconet, Aurore; Fermon, Florence; Touré, Albouhary; Grais, Rebecca F; Checchi, Francesco

2012-10-12

Estimation of vaccination coverage at the local level is essential to identify communities that may require additional support. Cluster surveys can be used in resource-poor settings, when population figures are inaccurate. To be feasible, cluster samples need to be small, without losing robustness of results. The clustered LQAS (CLQAS) approach has been proposed as an alternative, as smaller sample sizes are required. We explored (i) the efficiency of cluster surveys of decreasing sample size through bootstrapping analysis and (ii) the performance of CLQAS under three alternative sampling plans to classify local VC, using data from a survey carried out in Mali after mass vaccination against meningococcal meningitis group A. VC estimates provided by a 10 × 15 cluster survey design were reasonably robust. We used them to classify health areas in three categories and guide mop-up activities: i) health areas not requiring supplemental activities; ii) health areas requiring additional vaccination; iii) health areas requiring further evaluation. As sample size decreased (from 10 × 15 to 10 × 3), standard error of VC and ICC estimates were increasingly unstable. Results of CLQAS simulations were not accurate for most health areas, with an overall risk of misclassification greater than 0.25 in one health area out of three. It was greater than 0.50 in one health area out of two under two of the three sampling plans. Small sample cluster surveys (10 × 15) are acceptably robust for classification of VC at local level. We do not recommend the CLQAS method as currently formulated for evaluating vaccination programmes.

Performance of small cluster surveys and the clustered LQAS design to estimate local-level vaccination coverage in Mali

PubMed Central

2012-01-01

Background Estimation of vaccination coverage at the local level is essential to identify communities that may require additional support. Cluster surveys can be used in resource-poor settings, when population figures are inaccurate. To be feasible, cluster samples need to be small, without losing robustness of results. The clustered LQAS (CLQAS) approach has been proposed as an alternative, as smaller sample sizes are required. Methods We explored (i) the efficiency of cluster surveys of decreasing sample size through bootstrapping analysis and (ii) the performance of CLQAS under three alternative sampling plans to classify local VC, using data from a survey carried out in Mali after mass vaccination against meningococcal meningitis group A. Results VC estimates provided by a 10 × 15 cluster survey design were reasonably robust. We used them to classify health areas in three categories and guide mop-up activities: i) health areas not requiring supplemental activities; ii) health areas requiring additional vaccination; iii) health areas requiring further evaluation. As sample size decreased (from 10 × 15 to 10 × 3), standard error of VC and ICC estimates were increasingly unstable. Results of CLQAS simulations were not accurate for most health areas, with an overall risk of misclassification greater than 0.25 in one health area out of three. It was greater than 0.50 in one health area out of two under two of the three sampling plans. Conclusions Small sample cluster surveys (10 × 15) are acceptably robust for classification of VC at local level. We do not recommend the CLQAS method as currently formulated for evaluating vaccination programmes. PMID:23057445

WINGS-SPE. III. Equivalent width measurements, spectral properties, and evolution of local cluster galaxies

NASA Astrophysics Data System (ADS)

Fritz, J.; Poggianti, B. M.; Cava, A.; Moretti, A.; Varela, J.; Bettoni, D.; Couch, W. J.; D'Onofrio D'Onofrio, M.; Dressler, A.; Fasano, G.; Kjærgaard, P.; Marziani, P.; Moles, M.; Omizzolo, A.

2014-06-01

Context. Cluster galaxies are the ideal sites to look at when studying the influence of the environment on the various aspects of the evolution of galaxies, such as the changes in their stellar content and morphological transformations. In the framework of wings, the WIde-field Nearby Galaxy-cluster Survey, we have obtained optical spectra for ~6000 galaxies selected in fields centred on 48 local (0.04 < z < 0.07) X-ray selected clusters to tackle these issues. Aims: By classifying the spectra based on given spectral lines, we investigate the frequency of the various spectral types as a function of both the clusters' properties and the galaxies' characteristics. In this way, using the same classification criteria adopted for studies at higher redshift, we can consistently compare the properties of the local cluster population to those of their more distant counterparts. Methods: We describe a method that we have developed to automatically measure the equivalent width of spectral lines in a robust way, even in spectra with a non optimal signal-to-noise ratio. This way, we can derive a spectral classification reflecting the stellar content, based on the presence and strength of the [Oii] and Hδ lines. Results: After a quality check, we are able to measure 4381 of the ~6000 originally observed spectra in the fields of 48 clusters, of which 2744 are spectroscopically confirmed cluster members. The spectral classification is then analysed as a function of galaxies' luminosity, stellar mass, morphology, local density, and host cluster's global properties and compared to higher redshift samples (MORPHS and EDisCS). The vast majority of galaxies in the local clusters population are passive objects, being also the most luminous and massive. At a magnitude limit of MV < -18, galaxies in a post-starburst phase represent only ~11% of the cluster population, and this fraction is reduced to ~5% at MV < -19.5, which compares to the 18% at the same magnitude limit for high

Spike sorting using locality preserving projection with gap statistics and landmark-based spectral clustering.

PubMed

Nguyen, Thanh; Khosravi, Abbas; Creighton, Douglas; Nahavandi, Saeid

2014-12-30

Understanding neural functions requires knowledge from analysing electrophysiological data. The process of assigning spikes of a multichannel signal into clusters, called spike sorting, is one of the important problems in such analysis. There have been various automated spike sorting techniques with both advantages and disadvantages regarding accuracy and computational costs. Therefore, developing spike sorting methods that are highly accurate and computationally inexpensive is always a challenge in the biomedical engineering practice. An automatic unsupervised spike sorting method is proposed in this paper. The method uses features extracted by the locality preserving projection (LPP) algorithm. These features afterwards serve as inputs for the landmark-based spectral clustering (LSC) method. Gap statistics (GS) is employed to evaluate the number of clusters before the LSC can be performed. The proposed LPP-LSC is highly accurate and computationally inexpensive spike sorting approach. LPP spike features are very discriminative; thereby boost the performance of clustering methods. Furthermore, the LSC method exhibits its efficiency when integrated with the cluster evaluator GS. The proposed method's accuracy is approximately 13% superior to that of the benchmark combination between wavelet transformation and superparamagnetic clustering (WT-SPC). Additionally, LPP-LSC computing time is six times less than that of the WT-SPC. LPP-LSC obviously demonstrates a win-win spike sorting solution meeting both accuracy and computational cost criteria. LPP and LSC are linear algorithms that help reduce computational burden and thus their combination can be applied into real-time spike analysis. Copyright © 2014 Elsevier B.V. All rights reserved.

Accounting for Limited Detection Efficiency and Localization Precision in Cluster Analysis in Single Molecule Localization Microscopy

PubMed Central

Shivanandan, Arun; Unnikrishnan, Jayakrishnan; Radenovic, Aleksandra

2015-01-01

Single Molecule Localization Microscopy techniques like PhotoActivated Localization Microscopy, with their sub-diffraction limit spatial resolution, have been popularly used to characterize the spatial organization of membrane proteins, by means of quantitative cluster analysis. However, such quantitative studies remain challenged by the techniques’ inherent sources of errors such as a limited detection efficiency of less than 60%, due to incomplete photo-conversion, and a limited localization precision in the range of 10 – 30nm, varying across the detected molecules, mainly depending on the number of photons collected from each. We provide analytical methods to estimate the effect of these errors in cluster analysis and to correct for them. These methods, based on the Ripley’s L(r) – r or Pair Correlation Function popularly used by the community, can facilitate potentially breakthrough results in quantitative biology by providing a more accurate and precise quantification of protein spatial organization. PMID:25794150

Detecting cancer clusters in a regional population with local cluster tests and Bayesian smoothing methods: a simulation study

PubMed Central

2013-01-01

Background There is a rising public and political demand for prospective cancer cluster monitoring. But there is little empirical evidence on the performance of established cluster detection tests under conditions of small and heterogeneous sample sizes and varying spatial scales, such as are the case for most existing population-based cancer registries. Therefore this simulation study aims to evaluate different cluster detection methods, implemented in the open soure environment R, in their ability to identify clusters of lung cancer using real-life data from an epidemiological cancer registry in Germany. Methods Risk surfaces were constructed with two different spatial cluster types, representing a relative risk of RR = 2.0 or of RR = 4.0, in relation to the overall background incidence of lung cancer, separately for men and women. Lung cancer cases were sampled from this risk surface as geocodes using an inhomogeneous Poisson process. The realisations of the cancer cases were analysed within small spatial (census tracts, N = 1983) and within aggregated large spatial scales (communities, N = 78). Subsequently, they were submitted to the cluster detection methods. The test accuracy for cluster location was determined in terms of detection rates (DR), false-positive (FP) rates and positive predictive values. The Bayesian smoothing models were evaluated using ROC curves. Results With moderate risk increase (RR = 2.0), local cluster tests showed better DR (for both spatial aggregation scales > 0.90) and lower FP rates (both < 0.05) than the Bayesian smoothing methods. When the cluster RR was raised four-fold, the local cluster tests showed better DR with lower FPs only for the small spatial scale. At a large spatial scale, the Bayesian smoothing methods, especially those implementing a spatial neighbourhood, showed a substantially lower FP rate than the cluster tests. However, the risk increases at this scale were mostly diluted by data

Active learning for semi-supervised clustering based on locally linear propagation reconstruction.

PubMed

Chang, Chin-Chun; Lin, Po-Yi

2015-03-01

The success of semi-supervised clustering relies on the effectiveness of side information. To get effective side information, a new active learner learning pairwise constraints known as must-link and cannot-link constraints is proposed in this paper. Three novel techniques are developed for learning effective pairwise constraints. The first technique is used to identify samples less important to cluster structures. This technique makes use of a kernel version of locally linear embedding for manifold learning. Samples neither important to locally linear propagation reconstructions of other samples nor on flat patches in the learned manifold are regarded as unimportant samples. The second is a novel criterion for query selection. This criterion considers not only the importance of a sample to expanding the space coverage of the learned samples but also the expected number of queries needed to learn the sample. To facilitate semi-supervised clustering, the third technique yields inferred must-links for passing information about flat patches in the learned manifold to semi-supervised clustering algorithms. Experimental results have shown that the learned pairwise constraints can capture the underlying cluster structures and proven the feasibility of the proposed approach. Copyright © 2014 Elsevier Ltd. All rights reserved.

Alignments of the galaxies in and around the Virgo cluster with the local velocity shear

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

Lee, Jounghun; Rey, Soo Chang; Kim, Suk, E-mail: jounghun@astro.snu.ac.kr

2014-08-10

Observational evidence is presented for the alignment between the cosmic sheet and the principal axis of the velocity shear field at the position of the Virgo cluster. The galaxies in and around the Virgo cluster from the Extended Virgo Cluster Catalog that was recently constructed by Kim et al. are used to determine the direction of the local sheet. The peculiar velocity field reconstructed from the Sloan Digital Sky Survey Data Release 7 is analyzed to estimate the local velocity shear tensor at the Virgo center. Showing first that the minor principal axis of the local velocity shear tensor ismore » almost parallel to the direction of the line of sight, we detect a clear signal of alignment between the positions of the Virgo satellites and the intermediate principal axis of the local velocity shear projected onto the plane of the sky. Furthermore, the dwarf satellites are found to appear more strongly aligned than their normal counterparts, which is interpreted as an indication of the following. (1) The normal satellites and the dwarf satellites fall in the Virgo cluster preferentially along the local filament and the local sheet, respectively. (2) The local filament is aligned with the minor principal axis of the local velocity shear while the local sheet is parallel to the plane spanned by the minor and intermediate principal axes. Our result is consistent with the recent numerical claim that the velocity shear is a good tracer of the cosmic web.« less

Removal of impulse noise clusters from color images with local order statistics

NASA Astrophysics Data System (ADS)

Ruchay, Alexey; Kober, Vitaly

2017-09-01

This paper proposes a novel algorithm for restoring images corrupted with clusters of impulse noise. The noise clusters often occur when the probability of impulse noise is very high. The proposed noise removal algorithm consists of detection of bulky impulse noise in three color channels with local order statistics followed by removal of the detected clusters by means of vector median filtering. With the help of computer simulation we show that the proposed algorithm is able to effectively remove clustered impulse noise. The performance of the proposed algorithm is compared in terms of image restoration metrics with that of common successful algorithms.

Evolution of phenotypic clusters through competition and local adaptation along an environmental gradient.

PubMed

Leimar, Olof; Doebeli, Michael; Dieckmann, Ulf

2008-04-01

We have analyzed the evolution of a quantitative trait in populations that are spatially extended along an environmental gradient, with gene flow between nearby locations. In the absence of competition, there is stabilizing selection toward a locally best-adapted trait that changes gradually along the gradient. According to traditional ideas, gradual spatial variation in environmental conditions is expected to lead to gradual variation in the evolved trait. A contrasting possibility is that the trait distribution instead breaks up into discrete clusters. Doebeli and Dieckmann (2003) argued that competition acting locally in trait space and geographical space can promote such clustering. We have investigated this possibility using deterministic population dynamics for asexual populations, analyzing our model numerically and through an analytical approximation. We examined how the evolution of clusters is affected by the shape of competition kernels, by the presence of Allee effects, and by the strength of gene flow along the gradient. For certain parameter ranges clustering was a robust outcome, and for other ranges there was no clustering. Our analysis shows that the shape of competition kernels is important for clustering: the sign structure of the Fourier transform of a competition kernel determines whether the kernel promotes clustering. Also, we found that Allee effects promote clustering, whereas gene flow can have a counteracting influence. In line with earlier findings, we could demonstrate that phenotypic clustering was favored by gradients of intermediate slope.

Spatial and temporal structure of typhoid outbreaks in Washington, D.C., 1906-1909: evaluating local clustering with the Gi* statistic.

PubMed

Hinman, Sarah E; Blackburn, Jason K; Curtis, Andrew

2006-03-27

To better understand the distribution of typhoid outbreaks in Washington, D.C., the U.S. Public Health Service (PHS) conducted four investigations of typhoid fever. These studies included maps of cases reported between 1 May - 31 October 1906 - 1909. These data were entered into a GIS database and analyzed using Ripley's K-function followed by the Gi* statistic in yearly intervals to evaluate spatial clustering, the scale of clustering, and the temporal stability of these clusters. The Ripley's K-function indicated no global spatial autocorrelation. The Gi* statistic indicated clustering of typhoid at multiple scales across the four year time period, refuting the conclusions drawn in all four PHS reports concerning the distribution of cases. While the PHS reports suggested an even distribution of the disease, this study quantified both areas of localized disease clustering, as well as mobile larger regions of clustering. Thus, indicating both highly localized and periodic generalized sources of infection within the city. The methodology applied in this study was useful for evaluating the spatial distribution and annual-level temporal patterns of typhoid outbreaks in Washington, D.C. from 1906 to 1909. While advanced spatial analyses of historical data sets must be interpreted with caution, this study does suggest that there is utility in these types of analyses and that they provide new insights into the urban patterns of typhoid outbreaks during the early part of the twentieth century.

A Socio-spatial Dimension of Local Creative Industry Development in Semarang and Kudus Batik Clusters

NASA Astrophysics Data System (ADS)

Nugroho, P.

2018-02-01

Creative industries existence is inseparable from the underlying social construct which provides sources for creativity and innovation. The working of social capital in a society facilitates information exchange, knowledge transfer and technology acquisition within the industry through social networks. As a result, a socio-spatial divide exists in directing the growth of the creative industries. This paper aims to examine how such a socio-spatial divide contributes to the local creative industry development in Semarang and Kudus batik clusters. Explanatory sequential mixed methods approach covering a quantitative approach followed by a qualitative approach is chosen to understand better the interplay between tangible and intangible variables in the local batik clusters. Surveys on secondary data taken from the government statistics and reports, previous studies, and media exposures are completed in the former approach to identify clustering pattern of the local batik industry and the local embeddedness factors which have shaped the existing business environment. In-depth interviews, content analysis, and field observations are engaged in the latter approach to explore reciprocal relationships between the elements of social capital and the local batik cluster development. The result demonstrates that particular social ties have determined the forms of spatial proximity manifested in forward and backward business linkages. Trust, shared norms, and inherited traditions are the key social capital attributes that lead to such a socio-spatial divide. Therefore, the intermediating roles of the bridging actors are necessary to encouraging cooperation among the participating stakeholders for a better cluster development.

Spatial and temporal structure of typhoid outbreaks in Washington, D.C., 1906–1909: evaluating local clustering with the Gi* statistic

PubMed Central

Hinman, Sarah E; Blackburn, Jason K; Curtis, Andrew

2006-01-01

Background To better understand the distribution of typhoid outbreaks in Washington, D.C., the U.S. Public Health Service (PHS) conducted four investigations of typhoid fever. These studies included maps of cases reported between 1 May – 31 October 1906 – 1909. These data were entered into a GIS database and analyzed using Ripley's K-function followed by the Gi* statistic in yearly intervals to evaluate spatial clustering, the scale of clustering, and the temporal stability of these clusters. Results The Ripley's K-function indicated no global spatial autocorrelation. The Gi* statistic indicated clustering of typhoid at multiple scales across the four year time period, refuting the conclusions drawn in all four PHS reports concerning the distribution of cases. While the PHS reports suggested an even distribution of the disease, this study quantified both areas of localized disease clustering, as well as mobile larger regions of clustering. Thus, indicating both highly localized and periodic generalized sources of infection within the city. Conclusion The methodology applied in this study was useful for evaluating the spatial distribution and annual-level temporal patterns of typhoid outbreaks in Washington, D.C. from 1906 to 1909. While advanced spatial analyses of historical data sets must be interpreted with caution, this study does suggest that there is utility in these types of analyses and that they provide new insights into the urban patterns of typhoid outbreaks during the early part of the twentieth century. PMID:16566830

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.

Geographic information systems and pharmacoepidemiology: using spatial cluster detection to monitor local patterns of prescription opioid abuse.

PubMed

Brownstein, John S; Green, Traci C; Cassidy, Theresa A; Butler, Stephen F

2010-06-01

Understanding the spatial distribution of opioid abuse at the local level may facilitate public health interventions. Using patient-level data from addiction treatment facilities in New Mexico from ASI-MV Connect, we applied geographic information system (GIS) in combination with a spatial scan statistic to generate risk maps of prescription opioid abuse and identify clusters of product- and compound-specific abuse. Prescribed opioid volume data was used to determine whether identified clusters are beyond geographic differences in availability. Data on 24 452 patients residing in New Mexico were collected. Among those patients, 1779 (7.3%) reported abusing any prescription opioid (past 30 days). According to opioid type, 979 patients (4.0%) reported abuse of any hydrocodone, 1007 (4.1%) for any oxycodone, 108 (0.4%) for morphine, 507 (2.1%) for Vicodin or generic equivalent, 390 (1.6%) for OxyContin, and 63 (0.2%) for MS Contin or generic equivalent. Highest rates of abuse were found in the area surrounding Albuquerque with 8.6 patients indicating abuse per 100 interviewed patients. We found clustering of abuse around Albuquerque (P = 0.001; Relative Risk = 1.35, and a radius of 146 km). At the compound level, we found that drug availability was partly responsible for clustering of prescription opioid abuse. After accounting for drug availability, we identified a second foci of Vicodin abuse in the southern rural portion of the state near Las Cruces, NM and El Paso, Texas and bordering Mexico (RR = 2.1; P = 0.001). A better understanding of local risk distribution may have implications for response strategies to future introductions of prescription opioids.

Geographic Informations Systems and Pharmacoepidemiology: Using spatial cluster detection to monitor local patterns of prescription opioid abuse

PubMed Central

Brownstein, John S.; Green, Traci C.; Cassidy, Theresa A.; Butler, Stephen F.

2010-01-01

Purpose Understanding the spatial distribution of opioid abuse at the local level may facilitate public health interventions. Methods Using patient-level data from addiction treatment facilities in New Mexico from ASI-MV® Connect, we applied geographic information system in combination with a spatial scan statistics to generate risk maps of prescription opioid abuse and identify clusters of product- and compound-specific abuse. Prescribed opioid volume data was used to determine whether identified clusters are beyond geographic differences in availability. Results Data on 24,452 patients residing in New Mexico was collected. Among those patients, 1779 (7.3%) reported abusing any prescription opioid (past 30 days). According to opioid type, 979 patients (4.0%) reported abuse of any hydrocodone, 1007 (4.1%) for any oxycodone, 108 (0.4%) for morphine, 507 (2.1%) for Vicodin® or generic equivalent, 390 (1.6%) for OxyContin®, and 63 (0.2%) for MS Contin® or generic equivalent. Highest rates of abuse were found in the area surrounding Albuquerque with 8.6 patients indicating abuse per 100 interviewed patients. We found clustering of abuse around Albuquerque (P=0.001; Relative Risk=1.35 and a radius of 146 km). At the compound level, we found that drug availability was partly responsible for clustering of prescription opioid abuse. After accounting for drug availability, we identified a second foci of Vicodin® abuse in the southern rural portion of the state near Las Cruces, NM and El Paso, Texas and bordering Mexico (RR=2.1; P=0.001). Conclusions A better understanding of local risk distribution may have implications for response strategies to future introductions of prescription opioids. PMID:20535759

The Hyades cluster-supercluster connection - Evidence for a local concentration of dark matter

NASA Technical Reports Server (NTRS)

Casertano, Stefano; Iben, Icko, Jr.; Shiels, Aaron

1993-01-01

Stars that evaporate from the Hyades cluster will remain within a few hundred parsecs of the cluster only if they are dynamically bound to a much more massive entity containing the cluster. A local mass enhancement of at least (5-10) x 10 exp 5 solar masses, with a radius of about 100 pc, can trap stars with an origin related to that of the Hyades cluster and explains the excess of stars with velocities near the Hyades velocity that constitutes the Hyades supercluster. Part of this mass enhancement can be in visible stars, but a substantial fraction is likely to be in the form of dark matter.

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.

Clustering and Installing Satellite Nodes

NASA Astrophysics Data System (ADS)

Lotts, A. P.

This note describes basic clustering and the installation of a MicroVax or VaxStation as a Satellite node of an LAVC (Local Area VaxCluster). It will NOT describe Dual Porting of a MicroVax. It assumes that VMS 4.6 is running on the Boot node, that the LAVC key has been applied and that BOOT_CONFIG has been run as described in the LAVC manual.

No sign (yet) of intergalactic globular clusters in the Local Group

NASA Astrophysics Data System (ADS)

Mackey, A. D.; Beasley, M. A.; Leaman, R.

2016-07-01

We present Gemini Multi-Object Spectrograph (GMOS) imaging of 12 candidate intergalactic globular clusters (IGCs) in the Local Group, identified in a recent survey of the Sloan Digital Sky Survey (SDSS) footprint by di Tullio Zinn & Zinn. Our image quality is sufficiently high, at ˜0.4-0.7 arcsec, that we are able to unambiguously classify all 12 targets as distant galaxies. To reinforce this conclusion we use GMOS images of globular clusters in the M31 halo, taken under very similar conditions, to show that any genuine clusters in the putative IGC sample would be straightforward to distinguish. Based on the stated sensitivity of the di Tullio Zinn & Zinn search algorithm, we conclude that there cannot be a significant number of IGCs with MV ≤ -6 lying unseen in the SDSS area if their properties mirror those of globular clusters in the outskirts of M31 - even a population of 4 would have only a ≈1 per cent chance of non-detection.

Clustering methods for the optimization of atomic cluster structure

NASA Astrophysics Data System (ADS)

Bagattini, Francesco; Schoen, Fabio; Tigli, Luca

2018-04-01

In this paper, we propose a revised global optimization method and apply it to large scale cluster conformation problems. In the 1990s, the so-called clustering methods were considered among the most efficient general purpose global optimization techniques; however, their usage has quickly declined in recent years, mainly due to the inherent difficulties of clustering approaches in large dimensional spaces. Inspired from the machine learning literature, we redesigned clustering methods in order to deal with molecular structures in a reduced feature space. Our aim is to show that by suitably choosing a good set of geometrical features coupled with a very efficient descent method, an effective optimization tool is obtained which is capable of finding, with a very high success rate, all known putative optima for medium size clusters without any prior information, both for Lennard-Jones and Morse potentials. The main result is that, beyond being a reliable approach, the proposed method, based on the idea of starting a computationally expensive deep local search only when it seems worth doing so, is capable of saving a huge amount of searches with respect to an analogous algorithm which does not employ a clustering phase. In this paper, we are not claiming the superiority of the proposed method compared to specific, refined, state-of-the-art procedures, but rather indicating a quite straightforward way to save local searches by means of a clustering scheme working in a reduced variable space, which might prove useful when included in many modern methods.

Cluster detection methods applied to the Upper Cape Cod cancer data.

PubMed

Ozonoff, Al; Webster, Thomas; Vieira, Veronica; Weinberg, Janice; Ozonoff, David; Aschengrau, Ann

2005-09-15

A variety of statistical methods have been suggested to assess the degree and/or the location of spatial clustering of disease cases. However, there is relatively little in the literature devoted to comparison and critique of different methods. Most of the available comparative studies rely on simulated data rather than real data sets. We have chosen three methods currently used for examining spatial disease patterns: the M-statistic of Bonetti and Pagano; the Generalized Additive Model (GAM) method as applied by Webster; and Kulldorff's spatial scan statistic. We apply these statistics to analyze breast cancer data from the Upper Cape Cancer Incidence Study using three different latency assumptions. The three different latency assumptions produced three different spatial patterns of cases and controls. For 20 year latency, all three methods generally concur. However, for 15 year latency and no latency assumptions, the methods produce different results when testing for global clustering. The comparative analyses of real data sets by different statistical methods provides insight into directions for further research. We suggest a research program designed around examining real data sets to guide focused investigation of relevant features using simulated data, for the purpose of understanding how to interpret statistical methods applied to epidemiological data with a spatial component.

Analyzing Patients' Values by Applying Cluster Analysis and LRFM Model in a Pediatric Dental Clinic in Taiwan

PubMed Central

Lin, Shih-Yen; Liu, Chih-Wei

2014-01-01

This study combines cluster analysis and LRFM (length, recency, frequency, and monetary) model in a pediatric dental clinic in Taiwan to analyze patients' values. A two-stage approach by self-organizing maps and K-means method is applied to segment 1,462 patients into twelve clusters. The average values of L, R, and F excluding monetary covered by national health insurance program are computed for each cluster. In addition, customer value matrix is used to analyze customer values of twelve clusters in terms of frequency and monetary. Customer relationship matrix considering length and recency is also applied to classify different types of customers from these twelve clusters. The results show that three clusters can be classified into loyal patients with L, R, and F values greater than the respective average L, R, and F values, while three clusters can be viewed as lost patients without any variable above the average values of L, R, and F. When different types of patients are identified, marketing strategies can be designed to meet different patients' needs. PMID:25045741

Analyzing patients' values by applying cluster analysis and LRFM model in a pediatric dental clinic in Taiwan.

PubMed

Wu, Hsin-Hung; Lin, Shih-Yen; Liu, Chih-Wei

2014-01-01

This study combines cluster analysis and LRFM (length, recency, frequency, and monetary) model in a pediatric dental clinic in Taiwan to analyze patients' values. A two-stage approach by self-organizing maps and K-means method is applied to segment 1,462 patients into twelve clusters. The average values of L, R, and F excluding monetary covered by national health insurance program are computed for each cluster. In addition, customer value matrix is used to analyze customer values of twelve clusters in terms of frequency and monetary. Customer relationship matrix considering length and recency is also applied to classify different types of customers from these twelve clusters. The results show that three clusters can be classified into loyal patients with L, R, and F values greater than the respective average L, R, and F values, while three clusters can be viewed as lost patients without any variable above the average values of L, R, and F. When different types of patients are identified, marketing strategies can be designed to meet different patients' needs.

20 CFR 666.300 - What performance indicators apply to local areas?

Code of Federal Regulations, 2014 CFR

2014-04-01

... 20 Employees' Benefits 4 2014-04-01 2014-04-01 false What performance indicators apply to local areas? 666.300 Section 666.300 Employees' Benefits EMPLOYMENT AND TRAINING ADMINISTRATION, DEPARTMENT OF... Measures of Performance § 666.300 What performance indicators apply to local areas? (a) Each local...

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

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

Sánchez-Conde, Miguel A.; Cannoni, Mirco; Gómez, Mario E.

2011-12-01

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

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

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

Sanchez-Conde, Miguel A.; /KIPAC, Menlo Park /SLAC /IAC, La Laguna /Laguna U., Tenerife; Cannoni, Mirco

2012-06-06

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

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

NASA Astrophysics Data System (ADS)

Sánchez-Conde, Miguel A.; Cannoni, Mirco; Zandanel, Fabio; Gómez, Mario E.; Prada, Francisco

2011-12-01

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

A Technique of Two-Stage Clustering Applied to Environmental and Civil Engineering and Related Methods of Citation Analysis.

ERIC Educational Resources Information Center

Miyamoto, S.; Nakayama, K.

1983-01-01

A method of two-stage clustering of literature based on citation frequency is applied to 5,065 articles from 57 journals in environmental and civil engineering. Results of related methods of citation analysis (hierarchical graph, clustering of journals, multidimensional scaling) applied to same set of articles are compared. Ten references are…

20 CFR 666.300 - What performance indicators apply to local areas?

Code of Federal Regulations, 2010 CFR

2010-04-01

... 20 Employees' Benefits 3 2010-04-01 2010-04-01 false What performance indicators apply to local... Performance § 666.300 What performance indicators apply to local areas? (a) Each local workforce investment area in a State is subject to the same core indicators of performance and the customer satisfaction...

20 CFR 666.300 - What performance indicators apply to local areas?

Code of Federal Regulations, 2011 CFR

2011-04-01

... 20 Employees' Benefits 3 2011-04-01 2011-04-01 false What performance indicators apply to local... Performance § 666.300 What performance indicators apply to local areas? (a) Each local workforce investment area in a State is subject to the same core indicators of performance and the customer satisfaction...

Proximity effect assisted absorption enhancement in thin film with locally clustered nanoholes.

PubMed

Wu, Shaolong; Zhang, Cheng; Li, Xiaofeng; Zhan, Yaohui

2015-03-01

We focus on the light-trapping characteristics of a thin film with locally clustered nanoholes (NHs), considering that the clustering effect is usually encountered in preparing the nanostructures. Our full-wave finite-element simulation indicates that an intentionally introduced clustering effect could be employed for improving the light-trapping performance of the nanostructured thin film. For a 100 nm thick amorphous silicon film, an optimal clustering design with NH diameter of 100 nm is able to double the integrated optical absorption over the solar spectrum, compared to the planar counterpart, as well as show much improved optical performance over that of the nonclustered setup. A further insight into the underlying physics explains the outstanding light-trapping capability in terms of the increased available modes, a stronger power coupling efficiency, a higher fraction of electric field concentrated in absorbable material, and a higher density of photon states.

Using Open Clusters to Trace the Local Milky Way Rotation Curve and Velocity Field

NASA Astrophysics Data System (ADS)

Frinchaboy, Peter M.; Majewski, S. R.

2006-12-01

Establishing the rotation curve of the Milky Way is one of the fundamental contributions needed to understand the Galaxy and its mass distribution. We have undertaken a systematic spectroscopic survey of open star clusters which can serve as tracers of Galactic disk dynamics. We report on our initial sample of 67 clusters for which the Hydra multi-fiber spectrographs on the WIYN and Blanco telescopes have delivered 1-2 km/s radial velocities (RVs) of many dozens of stars in the fields of each cluster, which are used to derive cluster membership and bulk cluster kinematics when combined with Tycho-2 proper motions. The clusters selected for study have a broad spatial distribution in order to be sensitive to the disk velocity field in all Galactic quadrants and across a Galactocentric radius range as much as 3.0 kpc from the solar circle. Through analysis of the cluster sample, we find (1) the rotation velocity of the LSR is 221 (+2,-4) km/s, (2) the local rotation curve is declining with radius having a slope of -9.0 km/s/kpc, (3) we find (using R_0 = 8.5 kpc) the following Galactic parameters: A = 17.0 km/s/kpc and B = -8.9 km/s/kpc, which yields a Galaxy mass within of 1.5 R_0 of M = 0.9 ± 0.2 x 10^11 solar masses and a M/L of 5.9 in solar units. We also explore the distribution of the local velocity field and find evidence for non-circular motion due to the sprial arms.

THE DYNAMICS OF MERGING CLUSTERS: A MONTE CARLO SOLUTION APPLIED TO THE BULLET AND MUSKET BALL CLUSTERS

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

Dawson, William A., E-mail: wadawson@ucdavis.edu

2013-08-01

Merging galaxy clusters have become one of the most important probes of dark matter, providing evidence for dark matter over modified gravity and even constraints on the dark matter self-interaction cross-section. To properly constrain the dark matter cross-section it is necessary to understand the dynamics of the merger, as the inferred cross-section is a function of both the velocity of the collision and the observed time since collision. While the best understanding of merging system dynamics comes from N-body simulations, these are computationally intensive and often explore only a limited volume of the merger phase space allowed by observed parametermore » uncertainty. Simple analytic models exist but the assumptions of these methods invalidate their results near the collision time, plus error propagation of the highly correlated merger parameters is unfeasible. To address these weaknesses I develop a Monte Carlo method to discern the properties of dissociative mergers and propagate the uncertainty of the measured cluster parameters in an accurate and Bayesian manner. I introduce this method, verify it against an existing hydrodynamic N-body simulation, and apply it to two known dissociative mergers: 1ES 0657-558 (Bullet Cluster) and DLSCL J0916.2+2951 (Musket Ball Cluster). I find that this method surpasses existing analytic models-providing accurate (10% level) dynamic parameter and uncertainty estimates throughout the merger history. This, coupled with minimal required a priori information (subcluster mass, redshift, and projected separation) and relatively fast computation ({approx}6 CPU hours), makes this method ideal for large samples of dissociative merging clusters.« less

Psychological Factors Predict Local and Referred Experimental Muscle Pain: A Cluster Analysis in Healthy Adults

PubMed Central

Lee, Jennifer E.; Watson, David; Frey-Law, Laura A.

2012-01-01

Background Recent studies suggest an underlying three- or four-factor structure explains the conceptual overlap and distinctiveness of several negative emotionality and pain-related constructs. However, the validity of these latent factors for predicting pain has not been examined. Methods A cohort of 189 (99F; 90M) healthy volunteers completed eight self-report negative emotionality and pain-related measures (Eysenck Personality Questionnaire-Revised; Positive and Negative Affect Schedule; State-Trait Anxiety Inventory; Pain Catastrophizing Scale; Fear of Pain Questionnaire; Somatosensory Amplification Scale; Anxiety Sensitivity Index; Whiteley Index). Using principal axis factoring, three primary latent factors were extracted: General Distress; Catastrophic Thinking; and Pain-Related Fear. Using these factors, individuals clustered into three subgroups of high, moderate, and low negative emotionality responses. Experimental pain was induced via intramuscular acidic infusion into the anterior tibialis muscle, producing local (infusion site) and/or referred (anterior ankle) pain and hyperalgesia. Results Pain outcomes differed between clusters (multivariate analysis of variance and multinomial regression), with individuals in the highest negative emotionality cluster reporting the greatest local pain (p = 0.05), mechanical hyperalgesia (pressure pain thresholds; p = 0.009) and greater odds (2.21 OR) of experiencing referred pain compared to the lowest negative emotionality cluster. Conclusion Our results provide support for three latent psychological factors explaining the majority of the variance between several pain-related psychological measures, and that individuals in the high negative emotionality subgroup are at increased risk for (1) acute local muscle pain; (2) local hyperalgesia; and (3) referred pain using a standardized nociceptive input. PMID:23165778

Abundances of Local Group Globular Clusters Using High Resolution Integrated Light Spectroscopy

NASA Astrophysics Data System (ADS)

Sakari, Charli; McWilliam, A.; Venn, K.; Shetrone, M. D.; Dotter, A. L.; Mackey, D.

2014-01-01

Abundances and kinematics of extragalactic globular clusters provide valuable clues about galaxy and globular cluster formation in a wide variety of environments. In order to obtain such information about distant, unresolved systems, specific observational techniques are required. An Integrated Light Spectrum (ILS) provides a single spectrum from an entire stellar population, and can therefore be used to determine integrated cluster abundances. This dissertation investigates the accuracy of high resolution ILS analysis methods, using ILS (taken with the Hobby-Eberly Telescope) of globular clusters associated with the Milky Way (47 Tuc, M3, M13, NGC 7006, and M15) and then applies the method to globular clusters in the outer halo of M31 (from the Pan-Andromeda Archaeological Survey, or PAndAS). Results show that: a) as expected, the high resolution method reproduces individual stellar abundances for elements that do not vary within a cluster; b) the presence of multiple populations does affect the abundances of elements that vary within the cluster; c) certain abundance ratios are very sensitive to systematic effects, while others are not; and d) certain abundance ratios (e.g. [Ca/Fe]) can be accurately obtained from unresolved systems. Applications of ILABUNDS to the PAndAS clusters reveal that accretion may have played an important role in the formation of M31's outer halo.

Effects of applied strain on nanoscale self-interstitial cluster formation in BCC iron

NASA Astrophysics Data System (ADS)

Gao, Ning; Setyawan, Wahyu; Kurtz, Richard J.; Wang, Zhiguang

2017-09-01

The effect of applied strains on the configurational evolution of self-interstitial clusters in BCC iron (Fe) is explored with atomistic simulations. A novel cluster configuration is discovered at low temperatures (<600 K), which consists of 〈 110 〉 dumbbells and 〈 111 〉 crowdions in a specific configuration, resulting in an immobile defect. The stability and diffusion of this cluster at higher temperatures is explored. In addition, an anisotropy distribution factor of a particular [ hkl ] interstitial loop within the family of 〈 hkl 〉 loops is calculated as a function of strain. The results show that loop anisotropy is governed by the angle between the stress direction and the orientation of the 〈 111 〉 crowdions in the loop, and directly linked to the stress induced preferred nucleation of self-interstitial atoms.

Effects of applied strain on nanoscale self-interstitial cluster formation in BCC iron

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

Gao, Ning; Setyawan, Wahyu; Kurtz, Richard J.

2017-09-01

The effect of applied strains on the configurational evolution of self-interstitial clusters in BCC iron (Fe) is explored with atomistic simulations. A novel cluster configuration is discovered at low temperatures (<600 K), which consists of <110> dumbbells and <111> crowdions in a specific configuration, resulting in an immobile defect. The stability and diffusion of this cluster at higher temperatures is explored. In addition, an anisotropy distribution factor of a particular [hkl] interstitial loop within the family of loops is calculated as a function of strain. The results show that loop anisotropy is governed by the angle between the stress directionmore » and the orientation of the <111> crowdions in the loop, and directly linked to the stress induced preferred nucleation of self-interstitial atoms.« less

Iterative local Gaussian clustering for expressed genes identification linked to malignancy of human colorectal carcinoma.

PubMed

Wasito, Ito; Hashim, Siti Zaiton M; Sukmaningrum, Sri

2007-12-30

Gene expression profiling plays an important role in the identification of biological and clinical properties of human solid tumors such as colorectal carcinoma. Profiling is required to reveal underlying molecular features for diagnostic and therapeutic purposes. A non-parametric density-estimation-based approach called iterative local Gaussian clustering (ILGC), was used to identify clusters of expressed genes. We used experimental data from a previous study by Muro and others consisting of 1,536 genes in 100 colorectal cancer and 11 normal tissues. In this dataset, the ILGC finds three clusters, two large and one small gene clusters, similar to their results which used Gaussian mixture clustering. The correlation of each cluster of genes and clinical properties of malignancy of human colorectal cancer was analysed for the existence of tumor or normal, the existence of distant metastasis and the existence of lymph node metastasis.

Applying Pose Clustering and MD Simulations To Eliminate False Positives in Molecular Docking.

PubMed

Makeneni, Spandana; Thieker, David F; Woods, Robert J

2018-03-26

In this work, we developed a computational protocol that employs multiple molecular docking experiments, followed by pose clustering, molecular dynamic simulations (10 ns), and energy rescoring to produce reliable 3D models of antibody-carbohydrate complexes. The protocol was applied to 10 antibody-carbohydrate co-complexes and three unliganded (apo) antibodies. Pose clustering significantly reduced the number of potential poses. For each system, 15 or fewer clusters out of 100 initial poses were generated and chosen for further analysis. Molecular dynamics (MD) simulations allowed the docked poses to either converge or disperse, and rescoring increased the likelihood that the best-ranked pose was an acceptable pose. This approach is amenable to automation and can be a valuable aid in determining the structure of antibody-carbohydrate complexes provided there is no major side chain rearrangement or backbone conformational change in the H3 loop of the CDR regions. Further, the basic protocol of docking a small ligand to a known binding site, clustering the results, and performing MD with a suitable force field is applicable to any protein ligand system.

A clustering approach applied to time-lapse ERT interpretation - Case study of Lascaux cave

NASA Astrophysics Data System (ADS)

Xu, Shan; Sirieix, Colette; Riss, Joëlle; Malaurent, Philippe

2017-09-01

The Lascaux cave, located in southwest France, is one of the most important prehistoric cave in the world that shows Paleolithic paintings. This study aims to characterize the structure of the weathered epikarst setting located above the cave using Time-Lapse Electrical Resistivity Tomography (ERT) combined with local hydrogeological and climatic environmental data. Twenty ERT profiles were carried out for two years and helped us to record the seasonal and spatial variations of the electrical resistivity of the hydraulic upstream area of the Lascaux cave. The 20 interpreted resistivity models were merged into a single synthetic model using a multidimensional statistical method (Hierarchical Agglomerative Clustering). The individual blocks from the synthetic model associated with a similar resistivity variability were gathered into 7 clusters. We combined the resistivity temporal variations with climatic and hydrogeological data to propose a geo-electrical model that relates to a conceptual geological model. We provide a geological interpretation for each cluster regarding epikarst features. The superficial clusters (no 1 & 2) are linked to effective rainfall and trees, probably a fractured limestone. Another two clusters (no 6 & 7) are linked to detrital formations (sand and clay respectively). The cluster 3 may correspond to a marly limestone that forms a non-permeable horizon. Finally, the electrical behavior of the last two clusters (no 4 & 5) is correlated with the variation of flow rate; they may be a privileged feed zone of the flow in the cave.

A new Self-Adaptive disPatching System for local clusters

NASA Astrophysics Data System (ADS)

Kan, Bowen; Shi, Jingyan; Lei, Xiaofeng

2015-12-01

The scheduler is one of the most important components of a high performance cluster. This paper introduces a self-adaptive dispatching system (SAPS) based on Torque[1] and Maui[2]. It promotes cluster resource utilization and improves the overall speed of tasks. It provides some extra functions for administrators and users. First of all, in order to allow the scheduling of GPUs, a GPU scheduling module based on Torque and Maui has been developed. Second, SAPS analyses the relationship between the number of queueing jobs and the idle job slots, and then tunes the priority of users’ jobs dynamically. This means more jobs run and fewer job slots are idle. Third, integrating with the monitoring function, SAPS excludes nodes in error states as detected by the monitor, and returns them to the cluster after the nodes have recovered. In addition, SAPS provides a series of function modules including a batch monitoring management module, a comprehensive scheduling accounting module and a real-time alarm module. The aim of SAPS is to enhance the reliability and stability of Torque and Maui. Currently, SAPS has been running stably on a local cluster at IHEP (Institute of High Energy Physics, Chinese Academy of Sciences), with more than 12,000 cpu cores and 50,000 jobs running each day. Monitoring has shown that resource utilization has been improved by more than 26%, and the management work for both administrator and users has been reduced greatly.

Simulations of Fractal Star Cluster Formation. I. New Insights for Measuring Mass Segregation of Star Clusters with Substructure

NASA Astrophysics Data System (ADS)

Yu, Jincheng; Puzia, Thomas H.; Lin, Congping; Zhang, Yiwei

2017-05-01

We compare the existent methods, including the minimum spanning tree based method and the local stellar density based method, in measuring mass segregation of star clusters. We find that the minimum spanning tree method reflects more the compactness, which represents the global spatial distribution of massive stars, while the local stellar density method reflects more the crowdedness, which provides the local gravitational potential information. It is suggested to measure the local and the global mass segregation simultaneously. We also develop a hybrid method that takes both aspects into account. This hybrid method balances the local and the global mass segregation in the sense that the predominant one is either caused by dynamical evolution or purely accidental, especially when such information is unknown a priori. In addition, we test our prescriptions with numerical models and show the impact of binaries in estimating the mass segregation value. As an application, we use these methods on the Orion Nebula Cluster (ONC) observations and the Taurus cluster. We find that the ONC is significantly mass segregated down to the 20th most massive stars. In contrast, the massive stars of the Taurus cluster are sparsely distributed in many different subclusters, showing a low degree of compactness. The massive stars of Taurus are also found to be distributed in the high-density region of the subclusters, showing significant mass segregation at subcluster scales. Meanwhile, we also apply these methods to discuss the possible mechanisms of the dynamical evolution of the simulated substructured star clusters.

Simulations of Fractal Star Cluster Formation. I. New Insights for Measuring Mass Segregation of Star Clusters with Substructure

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

Yu, Jincheng; Puzia, Thomas H.; Lin, Congping

2017-05-10

We compare the existent methods, including the minimum spanning tree based method and the local stellar density based method, in measuring mass segregation of star clusters. We find that the minimum spanning tree method reflects more the compactness, which represents the global spatial distribution of massive stars, while the local stellar density method reflects more the crowdedness, which provides the local gravitational potential information. It is suggested to measure the local and the global mass segregation simultaneously. We also develop a hybrid method that takes both aspects into account. This hybrid method balances the local and the global mass segregationmore » in the sense that the predominant one is either caused by dynamical evolution or purely accidental, especially when such information is unknown a priori. In addition, we test our prescriptions with numerical models and show the impact of binaries in estimating the mass segregation value. As an application, we use these methods on the Orion Nebula Cluster (ONC) observations and the Taurus cluster. We find that the ONC is significantly mass segregated down to the 20th most massive stars. In contrast, the massive stars of the Taurus cluster are sparsely distributed in many different subclusters, showing a low degree of compactness. The massive stars of Taurus are also found to be distributed in the high-density region of the subclusters, showing significant mass segregation at subcluster scales. Meanwhile, we also apply these methods to discuss the possible mechanisms of the dynamical evolution of the simulated substructured star clusters.« less

Meteor tracking via local pattern clustering in spatio-temporal domain

NASA Astrophysics Data System (ADS)

Kukal, Jaromír.; Klimt, Martin; Švihlík, Jan; Fliegel, Karel

2016-09-01

Reliable meteor detection is one of the crucial disciplines in astronomy. A variety of imaging systems is used for meteor path reconstruction. The traditional approach is based on analysis of 2D image sequences obtained from a double station video observation system. Precise localization of meteor path is difficult due to atmospheric turbulence and other factors causing spatio-temporal fluctuations of the image background. The proposed technique performs non-linear preprocessing of image intensity using Box-Cox transform as recommended in our previous work. Both symmetric and asymmetric spatio-temporal differences are designed to be robust in the statistical sense. Resulting local patterns are processed by data whitening technique and obtained vectors are classified via cluster analysis and Self-Organized Map (SOM).

3D reconstruction from non-uniform point clouds via local hierarchical clustering

NASA Astrophysics Data System (ADS)

Yang, Jiaqi; Li, Ruibo; Xiao, Yang; Cao, Zhiguo

2017-07-01

Raw scanned 3D point clouds are usually irregularly distributed due to the essential shortcomings of laser sensors, which therefore poses a great challenge for high-quality 3D surface reconstruction. This paper tackles this problem by proposing a local hierarchical clustering (LHC) method to improve the consistency of point distribution. Specifically, LHC consists of two steps: 1) adaptive octree-based decomposition of 3D space, and 2) hierarchical clustering. The former aims at reducing the computational complexity and the latter transforms the non-uniform point set into uniform one. Experimental results on real-world scanned point clouds validate the effectiveness of our method from both qualitative and quantitative aspects.

Fast clustering using adaptive density peak detection.

PubMed

Wang, Xiao-Feng; Xu, Yifan

2017-12-01

Common limitations of clustering methods include the slow algorithm convergence, the instability of the pre-specification on a number of intrinsic parameters, and the lack of robustness to outliers. A recent clustering approach proposed a fast search algorithm of cluster centers based on their local densities. However, the selection of the key intrinsic parameters in the algorithm was not systematically investigated. It is relatively difficult to estimate the "optimal" parameters since the original definition of the local density in the algorithm is based on a truncated counting measure. In this paper, we propose a clustering procedure with adaptive density peak detection, where the local density is estimated through the nonparametric multivariate kernel estimation. The model parameter is then able to be calculated from the equations with statistical theoretical justification. We also develop an automatic cluster centroid selection method through maximizing an average silhouette index. The advantage and flexibility of the proposed method are demonstrated through simulation studies and the analysis of a few benchmark gene expression data sets. The method only needs to perform in one single step without any iteration and thus is fast and has a great potential to apply on big data analysis. A user-friendly R package ADPclust is developed for public use.

Iterative local Gaussian clustering for expressed genes identification linked to malignancy of human colorectal carcinoma

PubMed Central

Wasito, Ito; Hashim, Siti Zaiton M; Sukmaningrum, Sri

2007-01-01

Gene expression profiling plays an important role in the identification of biological and clinical properties of human solid tumors such as colorectal carcinoma. Profiling is required to reveal underlying molecular features for diagnostic and therapeutic purposes. A non-parametric density-estimation-based approach called iterative local Gaussian clustering (ILGC), was used to identify clusters of expressed genes. We used experimental data from a previous study by Muro and others consisting of 1,536 genes in 100 colorectal cancer and 11 normal tissues. In this dataset, the ILGC finds three clusters, two large and one small gene clusters, similar to their results which used Gaussian mixture clustering. The correlation of each cluster of genes and clinical properties of malignancy of human colorectal cancer was analysed for the existence of tumor or normal, the existence of distant metastasis and the existence of lymph node metastasis. PMID:18305825

Performance of Extended Local Clustering Organization (LCO) for Large Scale Job-Shop Scheduling Problem (JSP)

NASA Astrophysics Data System (ADS)

Konno, Yohko; Suzuki, Keiji

This paper describes an approach to development of a solution algorithm of a general-purpose for large scale problems using “Local Clustering Organization (LCO)” as a new solution for Job-shop scheduling problem (JSP). Using a performance effective large scale scheduling in the study of usual LCO, a solving JSP keep stability induced better solution is examined. In this study for an improvement of a performance of a solution for JSP, processes to a optimization by LCO is examined, and a scheduling solution-structure is extended to a new solution-structure based on machine-division. A solving method introduced into effective local clustering for the solution-structure is proposed as an extended LCO. An extended LCO has an algorithm which improves scheduling evaluation efficiently by clustering of parallel search which extends over plural machines. A result verified by an application of extended LCO on various scale of problems proved to conduce to minimizing make-span and improving on the stable performance.

The Hierarchical Distribution of the Young Stellar Clusters in Six Local Star-forming Galaxies

NASA Astrophysics Data System (ADS)

Grasha, K.; Calzetti, D.; Adamo, A.; Kim, H.; Elmegreen, B. G.; Gouliermis, D. A.; Dale, D. A.; Fumagalli, M.; Grebel, E. K.; Johnson, K. E.; Kahre, L.; Kennicutt, R. C.; Messa, M.; Pellerin, A.; Ryon, J. E.; Smith, L. J.; Shabani, F.; Thilker, D.; Ubeda, L.

2017-05-01

We present a study of the hierarchical clustering of the young stellar clusters in six local (3-15 Mpc) star-forming galaxies using Hubble Space Telescope broadband WFC3/UVIS UV and optical images from the Treasury Program LEGUS (Legacy ExtraGalactic UV Survey). We identified 3685 likely clusters and associations, each visually classified by their morphology, and we use the angular two-point correlation function to study the clustering of these stellar systems. We find that the spatial distribution of the young clusters and associations are clustered with respect to each other, forming large, unbound hierarchical star-forming complexes that are in general very young. The strength of the clustering decreases with increasing age of the star clusters and stellar associations, becoming more homogeneously distributed after ˜40-60 Myr and on scales larger than a few hundred parsecs. In all galaxies, the associations exhibit a global behavior that is distinct and more strongly correlated from compact clusters. Thus, populations of clusters are more evolved than associations in terms of their spatial distribution, traveling significantly from their birth site within a few tens of Myr, whereas associations show evidence of disruption occurring very quickly after their formation. The clustering of the stellar systems resembles that of a turbulent interstellar medium that drives the star formation process, correlating the components in unbound star-forming complexes in a hierarchical manner, dispersing shortly after formation, suggestive of a single, continuous mode of star formation across all galaxies.

Valley and channel networks extraction based on local topographic curvature and k-means clustering of contours

NASA Astrophysics Data System (ADS)

Hooshyar, Milad; Wang, Dingbao; Kim, Seoyoung; Medeiros, Stephen C.; Hagen, Scott C.

2016-10-01

A method for automatic extraction of valley and channel networks from high-resolution digital elevation models (DEMs) is presented. This method utilizes both positive (i.e., convergent topography) and negative (i.e., divergent topography) curvature to delineate the valley network. The valley and ridge skeletons are extracted using the pixels' curvature and the local terrain conditions. The valley network is generated by checking the terrain for the existence of at least one ridge between two intersecting valleys. The transition from unchannelized to channelized sections (i.e., channel head) in each first-order valley tributary is identified independently by categorizing the corresponding contours using an unsupervised approach based on k-means clustering. The method does not require a spatially constant channel initiation threshold (e.g., curvature or contributing area). Moreover, instead of a point attribute (e.g., curvature), the proposed clustering method utilizes the shape of contours, which reflects the entire cross-sectional profile including possible banks. The method was applied to three catchments: Indian Creek and Mid Bailey Run in Ohio and Feather River in California. The accuracy of channel head extraction from the proposed method is comparable to state-of-the-art channel extraction methods.

Spatial clustering and local risk of leprosy in São Paulo, Brazil.

PubMed

Ramos, Antônio Carlos Vieira; Yamamura, Mellina; Arroyo, Luiz Henrique; Popolin, Marcela Paschoal; Chiaravalloti Neto, Francisco; Palha, Pedro Fredemir; Uchoa, Severina Alice da Costa; Pieri, Flávia Meneguetti; Pinto, Ione Carvalho; Fiorati, Regina Célia; Queiroz, Ana Angélica Rêgo de; Belchior, Aylana de Souza; Dos Santos, Danielle Talita; Garcia, Maria Concebida da Cunha; Crispim, Juliane de Almeida; Alves, Luana Seles; Berra, Thaís Zamboni; Arcêncio, Ricardo Alexandre

2017-02-01

Although the detection rate is decreasing, the proportion of new cases with WHO grade 2 disability (G2D) is increasing, creating concern among policy makers and the Brazilian government. This study aimed to identify spatial clustering of leprosy and classify high-risk areas in a major leprosy cluster using the SatScan method. Data were obtained including all leprosy cases diagnosed between January 2006 and December 2013. In addition to the clinical variable, information was also gathered regarding the G2D of the patient at diagnosis and after treatment. The Scan Spatial statistic test, developed by Kulldorff e Nagarwalla, was used to identify spatial clustering and to measure the local risk (Relative Risk-RR) of leprosy. Maps considering these risks and their confidence intervals were constructed. A total of 434 cases were identified, including 188 (43.31%) borderline leprosy and 101 (23.28%) lepromatous leprosy cases. There was a predominance of males, with ages ranging from 15 to 59 years, and 51 patients (11.75%) presented G2D. Two significant spatial clusters and three significant spatial-temporal clusters were also observed. The main spatial cluster (p = 0.000) contained 90 census tracts, a population of approximately 58,438 inhabitants, detection rate of 22.6 cases per 100,000 people and RR of approximately 3.41 (95%CI = 2.721-4.267). Regarding the spatial-temporal clusters, two clusters were observed, with RR ranging between 24.35 (95%CI = 11.133-52.984) and 15.24 (95%CI = 10.114-22.919). These findings could contribute to improvements in policies and programming, aiming for the eradication of leprosy in Brazil. The Spatial Scan statistic test was found to be an interesting resource for health managers and healthcare professionals to map the vulnerability of areas in terms of leprosy transmission risk and areas of underreporting.

Cluster optical coding: from biochips to counterfeit security

NASA Astrophysics Data System (ADS)

Haglmueller, Jakob; Alguel, Yilmaz; Mayer, Christian; Matyushin, Viacheslav; Bauer, Georg; Pittner, Fritz; Leitner, Alfred; Aussenegg, Franz R.; Schalkhammer, Thomas G.

2004-07-01

Spatially tuned resonant nano-clusters allow high local field enhancement when exited by electromagnetic radiation. A number of phenomena had been described and subsequently applied to novel nano- and bionano-devices. Decisive for these types of devices and sensors is the precise nanometric assembly, coupling the local field surrounding a cluster to allow resonance with other elements interacting with this field. In particular, the distance cluster-mirror or cluster-fluorophore gives rise to a variety of enhancement phenomena. High throughput transducers using metal cluster resonance technology are based on surface-enhancement of metal cluster light absorption (SEA). The optical property for the analytical application of metal cluster films is the so-called anomalous absorption. At a well defined nanometric distance of a cluster to a mirror the reflected electromagnetic field has the same phase at the position of the absorbing cluster as the incident fields. This feedback mechanism strongly enhances the effective cluster absorption coefficient. The system is characterised by a narrow reflection minimum. Based on this SEA-phenomenon (licensed to and further developed and optimized by NovemberAG, Germany Erlangen) a number of commercial products have been constructed. Brandsealing(R) uses the patented SEA cluster technology to produce optical codings. Cluster SEA thin film systems show a characteristic color-flip effect and are extremely mechanically and thermally robust. This is the basis for its application as an unique security feature. The specific spectroscopic properties as e.g. narrow band multi-resonance of the cluster layers allow the authentication of the optical code which can be easily achieved with a mobile hand-held reader developed by november AG and Siemens AG. Thus, these features are machine-readable which makes them superior to comparable technologies. Cluster labels are available in two formats: as a label for tamper-proof product packaging, and

The Hierarchical Distribution of the Young Stellar Clusters in Six Local Star-forming Galaxies

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

Grasha, K.; Calzetti, D.; Adamo, A.

We present a study of the hierarchical clustering of the young stellar clusters in six local (3–15 Mpc) star-forming galaxies using Hubble Space Telescope broadband WFC3/UVIS UV and optical images from the Treasury Program LEGUS (Legacy ExtraGalactic UV Survey). We identified 3685 likely clusters and associations, each visually classified by their morphology, and we use the angular two-point correlation function to study the clustering of these stellar systems. We find that the spatial distribution of the young clusters and associations are clustered with respect to each other, forming large, unbound hierarchical star-forming complexes that are in general very young. Themore » strength of the clustering decreases with increasing age of the star clusters and stellar associations, becoming more homogeneously distributed after ∼40–60 Myr and on scales larger than a few hundred parsecs. In all galaxies, the associations exhibit a global behavior that is distinct and more strongly correlated from compact clusters. Thus, populations of clusters are more evolved than associations in terms of their spatial distribution, traveling significantly from their birth site within a few tens of Myr, whereas associations show evidence of disruption occurring very quickly after their formation. The clustering of the stellar systems resembles that of a turbulent interstellar medium that drives the star formation process, correlating the components in unbound star-forming complexes in a hierarchical manner, dispersing shortly after formation, suggestive of a single, continuous mode of star formation across all galaxies.« less

Applying Superresolution Localization-Based Microscopy to Neurons

PubMed Central

ZHONG, HAINING

2016-01-01

Proper brain function requires the precise localization of proteins and signaling molecules on a nanometer scale. The examination of molecular organization at this scale has been difficult in part because it is beyond the reach of conventional, diffraction-limited light microscopy. The recently developed method of superresolution, localization-based fluorescent microscopy (LBM), such as photoactivated localization microscopy (PALM) and stochastic optical reconstruction microscopy (STORM), has demonstrated a resolving power at a 10 nm scale and is poised to become a vital tool in modern neuroscience research. Indeed, LBM has revealed previously unknown cellular architectures and organizational principles in neurons. Here, we discuss the principles of LBM, its current applications in neuroscience, and the challenges that must be met before its full potential is achieved. We also present the unpublished results of our own experiments to establish a sample preparation procedure for applying LBM to study brain tissue. PMID:25648102

Galactic disk dynamical tracers: Open clusters and the local Milky Way rotation curve and velocity field

NASA Astrophysics Data System (ADS)

Frinchaboy, Peter Michael, III

Establishing the rotation curve of the Milky Way is one of the fundamental contributions needed to understand the Galaxy and its mass distribution. We have undertaken a systematic spectroscopic survey of open star clusters which can serve as tracers of Galactic disk dynamics. We report on our initial sample of 67 clusters for which the Hydra multi-fiber spectrographs on the WIYN and Blanco telescopes have delivered ~1-2 km s -1 radial velocities (RVs) of many dozens of stars in the fields of each cluster, which are used to derive cluster membership and bulk cluster kinematics when combined with Tycho-2 proper motions. The clusters selected for study have a broad spatial distribution in order to be sensitive to the disk velocity field in all Galactic quadrants and across a Galactocentric radius range as much as 3.0 kpc from the solar circle. Through analysis of the cluster sample, we find (1) the rotation velocity of the Local Standard of Rest (LSR) is [Special characters omitted.] km s -1 , (2 ) the local rotation curve is declining with radius having a slope of -9.1 km s -1 kpc -1 , (3) we find (using R 0 = 8.5 kpc) the following Galactic parameters: A = 17.0 km s -1 kpc -1 and B = -8.9 km s -1 kpc -1 , which using a flat rotation curve and our determined values for the rotation velocity of the LSR yields a Galaxy mass within 1.5 R 0 of M = 1.4 ± 0.2 × 10 11 [Spe cial characters omitted.] and a M/L of 9 [Special characters omitted.] . We also explore the distribution of the local velocity field and find evidence for non- circular motion due to the spiral arms. Additionally, a number of outer disk ( R gc > 12 kpc) open clusters, including Be29 and Sa1, are studied that have potentially critical leverage on radial, age and metallicity gradients in the outer Galactic disk. We find that the measured kinematics of Sa1 and Be29 are consistent with being associated with the Galactic anticenter stellar structure (GASS; or Monoceros stream), which points to a possible

High and low neurobehavior disinhibition clusters within locales: implications for community efforts to prevent substance use disorder.

PubMed

Ridenour, Ty A; Reynolds, Maureen; Ahlqvist, Ola; Zhai, Zu Wei; Kirisci, Levent; Vanyukov, Michael M; Tarter, Ralph E

2013-05-01

Knowledge of where substance use and other such behavioral problems frequently occur has aided policing, public health, and urban planning strategies to reduce such behaviors. Identifying locales characterized by high childhood neurobehavioral disinhibition (ND), a strong predictor of substance use and consequent disorder (SUD), may likewise improve prevention efforts. The distribution of ND in 10-12-year olds was mapped to metropolitan Pittsburgh, PA, and tested for clustering within locales. The 738 participating families represented the population in terms of economic status, race, and population distribution. ND was measured using indicators of executive cognitive function, emotion regulation, and behavior control. Innovative geospatial analyzes statistically tested clustering of ND within locales while accounting for geographic barriers (large rivers, major highways), parental SUD severity, and neighborhood quality. Clustering of youth with high and low ND occurred in specific locales. Accounting for geographic barriers better delineated where high ND is concentrated, areas which also tended to be characterized by greater parental SUD severity and poorer neighborhood quality. Offering programs that have been demonstrated to improve inhibitory control in locales where youth have high ND on average may reduce youth risk for SUD and other problem behaviors. As demonstrated by the present results, geospatial analysis of youth risk factors, frequently used in community coalition strategies, may be improved with greater statistical and measurement rigor.

Business Clusters: Building on Local Strengths.

ERIC Educational Resources Information Center

Baldwin, Fred D.

2001-01-01

The Northwest Pennsylvania Industrial Resource Center's "wood cluster initiative" illustrates the benefits of rural business clusters. The initiative is turning a loose grouping of timber and forest-product firms into a competitive system by providing technical assistance, helping businesses plan and conduct job training programs,…

Efficient clustering aggregation based on data fragments.

PubMed

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

2012-06-01

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

SAR image change detection using watershed and spectral clustering

NASA Astrophysics Data System (ADS)

Niu, Ruican; Jiao, L. C.; Wang, Guiting; Feng, Jie

2011-12-01

A new method of change detection in SAR images based on spectral clustering is presented in this paper. Spectral clustering is employed to extract change information from a pair images acquired on the same geographical area at different time. Watershed transform is applied to initially segment the big image into non-overlapped local regions, leading to reduce the complexity. Experiments results and system analysis confirm the effectiveness of the proposed algorithm.

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.

Local multiplicity adjustment for the spatial scan statistic using the Gumbel distribution

PubMed Central

Gangnon, Ronald E.

2011-01-01

Summary The spatial scan statistic is an important and widely used tool for cluster detection. It is based on the simultaneous evaluation of the statistical significance of the maximum likelihood ratio test statistic over a large collection of potential clusters. In most cluster detection problems, there is variation in the extent of local multiplicity across the study region. For example, using a fixed maximum geographic radius for clusters, urban areas typically have many overlapping potential clusters, while rural areas have relatively few. The spatial scan statistic does not account for local multiplicity variation. We describe a previously proposed local multiplicity adjustment based on a nested Bonferroni correction and propose a novel adjustment based on a Gumbel distribution approximation to the distribution of a local scan statistic. We compare the performance of all three statistics in terms of power and a novel unbiased cluster detection criterion. These methods are then applied to the well-known New York leukemia dataset and a Wisconsin breast cancer incidence dataset. PMID:21762118

ALMA REVEALS POTENTIAL LOCALIZED DUST ENRICHMENT FROM MASSIVE STAR CLUSTERS IN II Zw 40

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

Consiglio, S. Michelle; Turner, Jean L.; Beck, Sara

2016-12-10

We present subarcsecond images of submillimeter CO and continuum emission from a local galaxy forming massive star clusters: the blue compact dwarf galaxy II Zw 40. At ∼0.″4 resolution (20 pc), the CO(3-2), CO(1-0), 3 mm, and 870 μ m continuum maps illustrate star formation on the scales of individual molecular clouds. Dust contributes about one-third of the 870 μ m continuum emission, with free–free accounting for the rest. On these scales, there is not a good correspondence between gas, dust, and free–free emission. Dust continuum is enhanced toward the star-forming region as compared to the CO emission. We suggestmore » that an unexpectedly low and spatially variable gas-to-dust ratio is the result of rapid and localized dust enrichment of clouds by the massive clusters of the starburst.« less

Automatic categorization of anatomical landmark-local appearances based on diffeomorphic demons and spectral clustering for constructing detector ensembles.

PubMed

Hanaoka, Shouhei; Masutani, Yoshitaka; Nemoto, Mitsutaka; Nomura, Yukihiro; Yoshikawa, Takeharu; Hayashi, Naoto; Ohtomo, Kuni

2012-01-01

A method for categorizing landmark-local appearances extracted from computed tomography (CT) datasets is presented. Anatomical landmarks in the human body inevitably have inter-individual variations that cause difficulty in automatic landmark detection processes. The goal of this study is to categorize subjects (i.e., training datasets) according to local shape variations of such a landmark so that each subgroup has less shape variation and thus the machine learning of each landmark detector is much easier. The similarity between each subject pair is measured based on the non-rigid registration result between them. These similarities are used by the spectral clustering process. After the clustering, all training datasets in each cluster, as well as synthesized intermediate images calculated from all subject-pairs in the cluster, are used to train the corresponding subgroup detector. All of these trained detectors compose a detector ensemble to detect the target landmark. Evaluation with clinical CT datasets showed great improvement in the detection performance.

Basic firefly algorithm for document clustering

NASA Astrophysics Data System (ADS)

Mohammed, Athraa Jasim; Yusof, Yuhanis; Husni, Husniza

2015-12-01

The Document clustering plays significant role in Information Retrieval (IR) where it organizes documents prior to the retrieval process. To date, various clustering algorithms have been proposed and this includes the K-means and Particle Swarm Optimization. Even though these algorithms have been widely applied in many disciplines due to its simplicity, such an approach tends to be trapped in a local minimum during its search for an optimal solution. To address the shortcoming, this paper proposes a Basic Firefly (Basic FA) algorithm to cluster text documents. The algorithm employs the Average Distance to Document Centroid (ADDC) as the objective function of the search. Experiments utilizing the proposed algorithm were conducted on the 20Newsgroups benchmark dataset. Results demonstrate that the Basic FA generates a more robust and compact clusters than the ones produced by K-means and Particle Swarm Optimization (PSO).

Design of double fuzzy clustering-driven context neural networks.

PubMed

Kim, Eun-Hu; Oh, Sung-Kwun; Pedrycz, Witold

2018-08-01

In this study, we introduce a novel category of double fuzzy clustering-driven context neural networks (DFCCNNs). The study is focused on the development of advanced design methodologies for redesigning the structure of conventional fuzzy clustering-based neural networks. The conventional fuzzy clustering-based neural networks typically focus on dividing the input space into several local spaces (implied by clusters). In contrast, the proposed DFCCNNs take into account two distinct local spaces called context and cluster spaces, respectively. Cluster space refers to the local space positioned in the input space whereas context space concerns a local space formed in the output space. Through partitioning the output space into several local spaces, each context space is used as the desired (target) local output to construct local models. To complete this, the proposed network includes a new context layer for reasoning about context space in the output space. In this sense, Fuzzy C-Means (FCM) clustering is useful to form local spaces in both input and output spaces. The first one is used in order to form clusters and train weights positioned between the input and hidden layer, whereas the other one is applied to the output space to form context spaces. The key features of the proposed DFCCNNs can be enumerated as follows: (i) the parameters between the input layer and hidden layer are built through FCM clustering. The connections (weights) are specified as constant terms being in fact the centers of the clusters. The membership functions (represented through the partition matrix) produced by the FCM are used as activation functions located at the hidden layer of the "conventional" neural networks. (ii) Following the hidden layer, a context layer is formed to approximate the context space of the output variable and each node in context layer means individual local model. The outputs of the context layer are specified as a combination of both weights formed as linear

NoSOCS in SDSS - VI. The environmental dependence of AGN in clusters and field in the local Universe

NASA Astrophysics Data System (ADS)

Lopes, P. A. A.; Ribeiro, A. L. B.; Rembold, S. B.

2017-11-01

We investigated the variation in the fraction of optical active galactic nuclei (AGNs) hosts with stellar mass, as well as their local and global environments. Our sample is composed of cluster members and field galaxies at z ≤ 0.1 and we consider only strong AGN. We find a strong variation in the AGN fraction (FAGN) with stellar mass. The field population comprises a higher AGN fraction compared to the global cluster population, especially for objects with log M* > 10.6. Hence, we restricted our analysis to more massive objects. We detected a smooth variation in the FAGN with local stellar mass density for cluster objects, reaching a plateau in the field environment. As a function of cluster-centric distance we verify that FAGN is roughly constant for R > R200, but show a steep decline inwards. We have also verified the dependence of the AGN population on cluster velocity dispersion, finding a constant behaviour for low mass systems (σP ≲ 650-700 km s-1). However, there is a strong decline in FAGN for higher mass clusters (>700 km s-1). When comparing the FAGN in clusters with or without substructure, we only find different results for objects at large radii (R > R200), in the sense that clusters with substructure present some excess in the AGN fraction. Finally, we have found that the phase-space distribution of AGN cluster members is significantly different than other populations. Due to the environmental dependence of FAGN and their phase-space distribution, we interpret AGN to be the result of galaxy interactions, favoured in environments where the relative velocities are low, typical of the field, low mass groups or cluster outskirts.

Clustering Coefficients for Correlation Networks.

PubMed

Masuda, Naoki; Sakaki, Michiko; Ezaki, Takahiro; Watanabe, Takamitsu

2018-01-01

Graph theory is a useful tool for deciphering structural and functional networks of the brain on various spatial and temporal scales. The clustering coefficient quantifies the abundance of connected triangles in a network and is a major descriptive statistics of networks. For example, it finds an application in the assessment of small-worldness of brain networks, which is affected by attentional and cognitive conditions, age, psychiatric disorders and so forth. However, it remains unclear how the clustering coefficient should be measured in a correlation-based network, which is among major representations of brain networks. In the present article, we propose clustering coefficients tailored to correlation matrices. The key idea is to use three-way partial correlation or partial mutual information to measure the strength of the association between the two neighboring nodes of a focal node relative to the amount of pseudo-correlation expected from indirect paths between the nodes. Our method avoids the difficulties of previous applications of clustering coefficient (and other) measures in defining correlational networks, i.e., thresholding on the correlation value, discarding of negative correlation values, the pseudo-correlation problem and full partial correlation matrices whose estimation is computationally difficult. For proof of concept, we apply the proposed clustering coefficient measures to functional magnetic resonance imaging data obtained from healthy participants of various ages and compare them with conventional clustering coefficients. We show that the clustering coefficients decline with the age. The proposed clustering coefficients are more strongly correlated with age than the conventional ones are. We also show that the local variants of the proposed clustering coefficients (i.e., abundance of triangles around a focal node) are useful in characterizing individual nodes. In contrast, the conventional local clustering coefficients were strongly

Clustering Coefficients for Correlation Networks

PubMed Central

Masuda, Naoki; Sakaki, Michiko; Ezaki, Takahiro; Watanabe, Takamitsu

2018-01-01

Graph theory is a useful tool for deciphering structural and functional networks of the brain on various spatial and temporal scales. The clustering coefficient quantifies the abundance of connected triangles in a network and is a major descriptive statistics of networks. For example, it finds an application in the assessment of small-worldness of brain networks, which is affected by attentional and cognitive conditions, age, psychiatric disorders and so forth. However, it remains unclear how the clustering coefficient should be measured in a correlation-based network, which is among major representations of brain networks. In the present article, we propose clustering coefficients tailored to correlation matrices. The key idea is to use three-way partial correlation or partial mutual information to measure the strength of the association between the two neighboring nodes of a focal node relative to the amount of pseudo-correlation expected from indirect paths between the nodes. Our method avoids the difficulties of previous applications of clustering coefficient (and other) measures in defining correlational networks, i.e., thresholding on the correlation value, discarding of negative correlation values, the pseudo-correlation problem and full partial correlation matrices whose estimation is computationally difficult. For proof of concept, we apply the proposed clustering coefficient measures to functional magnetic resonance imaging data obtained from healthy participants of various ages and compare them with conventional clustering coefficients. We show that the clustering coefficients decline with the age. The proposed clustering coefficients are more strongly correlated with age than the conventional ones are. We also show that the local variants of the proposed clustering coefficients (i.e., abundance of triangles around a focal node) are useful in characterizing individual nodes. In contrast, the conventional local clustering coefficients were strongly

The Detection of Clusters with Spatial Heterogeneity

ERIC Educational Resources Information Center

Zhang, Zuoyi

2011-01-01

This thesis consists of two parts. In Chapter 2, we focus on the spatial scan statistics with overdispersion and Chapter 3 is devoted to the randomized permutation test for identifying local patterns of spatial association. The spatial scan statistic has been widely used in spatial disease surveillance and spatial cluster detection. To apply it, a…

Local multiplicity adjustment for the spatial scan statistic using the Gumbel distribution.

PubMed

Gangnon, Ronald E

2012-03-01

The spatial scan statistic is an important and widely used tool for cluster detection. It is based on the simultaneous evaluation of the statistical significance of the maximum likelihood ratio test statistic over a large collection of potential clusters. In most cluster detection problems, there is variation in the extent of local multiplicity across the study region. For example, using a fixed maximum geographic radius for clusters, urban areas typically have many overlapping potential clusters, whereas rural areas have relatively few. The spatial scan statistic does not account for local multiplicity variation. We describe a previously proposed local multiplicity adjustment based on a nested Bonferroni correction and propose a novel adjustment based on a Gumbel distribution approximation to the distribution of a local scan statistic. We compare the performance of all three statistics in terms of power and a novel unbiased cluster detection criterion. These methods are then applied to the well-known New York leukemia dataset and a Wisconsin breast cancer incidence dataset. © 2011, The International Biometric Society.

Multi-cellular natural killer (NK) cell clusters enhance NK cell activation through localizing IL-2 within the cluster

NASA Astrophysics Data System (ADS)

Kim, Miju; Kim, Tae-Jin; Kim, Hye Mi; Doh, Junsang; Lee, Kyung-Mi

2017-01-01

Multi-cellular cluster formation of natural killer (NK) cells occurs during in vivo priming and potentiates their activation to IL-2. However, the precise mechanism underlying this synergy within NK cell clusters remains unclear. We employed lymphocyte-laden microwell technologies to modulate contact-mediated multi-cellular interactions among activating NK cells and to quantitatively assess the molecular events occurring in multi-cellular clusters of NK cells. NK cells in social microwells, which allow cell-to-cell contact, exhibited significantly higher levels of IL-2 receptor (IL-2R) signaling compared with those in lonesome microwells, which prevent intercellular contact. Further, CD25, an IL-2R α chain, and lytic granules of NK cells in social microwells were polarized toward MTOC. Live cell imaging of lytic granules revealed their dynamic and prolonged polarization toward neighboring NK cells without degranulation. These results suggest that IL-2 bound on CD25 of one NK cells triggered IL-2 signaling of neighboring NK cells. These results were further corroborated by findings that CD25-KO NK cells exhibited lower proliferation than WT NK cells, and when mixed with WT NK cells, underwent significantly higher level of proliferation. These data highlights the existence of IL-2 trans-presentation between NK cells in the local microenvironment where the availability of IL-2 is limited.

Correlation Functions Quantify Super-Resolution Images and Estimate Apparent Clustering Due to Over-Counting

PubMed Central

Veatch, Sarah L.; Machta, Benjamin B.; Shelby, Sarah A.; Chiang, Ethan N.; Holowka, David A.; Baird, Barbara A.

2012-01-01

We present an analytical method using correlation functions to quantify clustering in super-resolution fluorescence localization images and electron microscopy images of static surfaces in two dimensions. We use this method to quantify how over-counting of labeled molecules contributes to apparent self-clustering and to calculate the effective lateral resolution of an image. This treatment applies to distributions of proteins and lipids in cell membranes, where there is significant interest in using electron microscopy and super-resolution fluorescence localization techniques to probe membrane heterogeneity. When images are quantified using pair auto-correlation functions, the magnitude of apparent clustering arising from over-counting varies inversely with the surface density of labeled molecules and does not depend on the number of times an average molecule is counted. In contrast, we demonstrate that over-counting does not give rise to apparent co-clustering in double label experiments when pair cross-correlation functions are measured. We apply our analytical method to quantify the distribution of the IgE receptor (FcεRI) on the plasma membranes of chemically fixed RBL-2H3 mast cells from images acquired using stochastic optical reconstruction microscopy (STORM/dSTORM) and scanning electron microscopy (SEM). We find that apparent clustering of FcεRI-bound IgE is dominated by over-counting labels on individual complexes when IgE is directly conjugated to organic fluorophores. We verify this observation by measuring pair cross-correlation functions between two distinguishably labeled pools of IgE-FcεRI on the cell surface using both imaging methods. After correcting for over-counting, we observe weak but significant self-clustering of IgE-FcεRI in fluorescence localization measurements, and no residual self-clustering as detected with SEM. We also apply this method to quantify IgE-FcεRI redistribution after deliberate clustering by crosslinking with two

Fe-S cluster coordination of the chromokinesin KIF4A alters its sub-cellular localization during mitosis.

PubMed

Ben-Shimon, Lilach; Paul, Viktoria D; David-Kadoch, Galit; Volpe, Marina; Stümpfig, Martin; Bill, Eckhard; Mühlenhoff, Ulrich; Lill, Roland; Ben-Aroya, Shay

2018-05-30

Fe-S clusters act as co-factors of proteins with diverse functions, e.g. in DNA repair. Down-regulation of the cytosolic iron-sulfur protein assembly (CIA) machinery promotes genomic instability by the inactivation of multiple DNA repair pathways. Furthermore, CIA deficiencies are associated with so far unexplained mitotic defects. Here, we show that CIA2B and MMS19, constituents of the CIA targeting complex involved in facilitating Fe-S cluster insertion into cytosolic and nuclear target proteins, co-localize with components of the mitotic machinery. Down-regulation of CIA2B and MMS19 impairs the mitotic cycle. We identify the chromokinesin KIF4A as a mitotic component involved in these effects. KIF4A binds a Fe-S cluster in vitro through its conserved cysteine-rich domain. We demonstrate in vivo that this domain is required for the mitosis-related KIF4A localization and for the mitotic defects associated with KIF4A knockout. KIF4A is the first identified mitotic component carrying such a post-translational modification. These findings suggest that the lack of Fe-S clusters in KIF4A upon down-regulation of the CIA targeting complex contributes to the mitotic defects. © 2018. Published by The Company of Biologists Ltd.

Localizing text in scene images by boundary clustering, stroke segmentation, and string fragment classification.

PubMed

Yi, Chucai; Tian, Yingli

2012-09-01

In this paper, we propose a novel framework to extract text regions from scene images with complex backgrounds and multiple text appearances. This framework consists of three main steps: boundary clustering (BC), stroke segmentation, and string fragment classification. In BC, we propose a new bigram-color-uniformity-based method to model both text and attachment surface, and cluster edge pixels based on color pairs and spatial positions into boundary layers. Then, stroke segmentation is performed at each boundary layer by color assignment to extract character candidates. We propose two algorithms to combine the structural analysis of text stroke with color assignment and filter out background interferences. Further, we design a robust string fragment classification based on Gabor-based text features. The features are obtained from feature maps of gradient, stroke distribution, and stroke width. The proposed framework of text localization is evaluated on scene images, born-digital images, broadcast video images, and images of handheld objects captured by blind persons. Experimental results on respective datasets demonstrate that the framework outperforms state-of-the-art localization algorithms.

Cluster-search based monitoring of local earthquakes in SeisComP3

NASA Astrophysics Data System (ADS)

Roessler, D.; Becker, J.; Ellguth, E.; Herrnkind, S.; Weber, B.; Henneberger, R.; Blanck, H.

2016-12-01

We present a new cluster-search based SeisComP3 module for locating local and regional earthquakes in real time. Real-time earthquake monitoring systems such as SeisComP3 provide the backbones for earthquake early warning (EEW), tsunami early warning (TEW) and the rapid assessment of natural and induced seismicity. For any earthquake monitoring system fast and accurate event locations are fundamental determining the reliability and the impact of further analysis. SeisComP3 in the OpenSource version includes a two-stage detector for picking P waves and a phase associator for locating earthquakes based on P-wave detections. scanloc is a more advanced earthquake location program developed by gempa GmbH with seamless integration into SeisComP3. scanloc performs advanced cluster search to discriminate earthquakes occurring closely in space and time and makes additional use of S-wave detections. It has proven to provide fast and accurate earthquake locations at local and regional distances where it outperforms the base SeisComP3 tools. We demonstrate the performance of scanloc for monitoring induced seismicity as well as local and regional earthquakes in different tectonic regimes including subduction, spreading and intra-plate regions. In particular we present examples and catalogs from real-time monitoring of earthquake in Northern Chile based on data from the IPOC network by GFZ German Research Centre for Geosciences for the recent years. Depending on epicentral distance and data transmission, earthquake locations are available within a few seconds after origin time when using scanloc. The association of automatic S-wave detections provides a better constraint on focal depth.

Study of atmospheric dynamics and pollution in the coastal area of English Channel using clustering technique

NASA Astrophysics Data System (ADS)

Sokolov, Anton; Dmitriev, Egor; Delbarre, Hervé; Augustin, Patrick; Gengembre, Cyril; Fourmenten, Marc

2016-04-01

The problem of atmospheric contamination by principal air pollutants was considered in the industrialized coastal region of English Channel in Dunkirk influenced by north European metropolitan areas. MESO-NH nested models were used for the simulation of the local atmospheric dynamics and the online calculation of Lagrangian backward trajectories with 15-minute temporal resolution and the horizontal resolution down to 500 m. The one-month mesoscale numerical simulation was coupled with local pollution measurements of volatile organic components, particulate matter, ozone, sulphur dioxide and nitrogen oxides. Principal atmospheric pathways were determined by clustering technique applied to backward trajectories simulated. Six clusters were obtained which describe local atmospheric dynamics, four winds blowing through the English Channel, one coming from the south, and the biggest cluster with small wind speeds. This last cluster includes mostly sea breeze events. The analysis of meteorological data and pollution measurements allows relating the principal atmospheric pathways with local air contamination events. It was shown that contamination events are mostly connected with a channelling of pollution from local sources and low-turbulent states of the local atmosphere.

Kinematic fingerprint of core-collapsed globular clusters

NASA Astrophysics Data System (ADS)

Bianchini, P.; Webb, J. J.; Sills, A.; Vesperini, E.

2018-03-01

Dynamical evolution drives globular clusters towards core collapse, which strongly shapes their internal properties. Diagnostics of core collapse have so far been based on photometry only, namely on the study of the concentration of the density profiles. Here, we present a new method to robustly identify core-collapsed clusters based on the study of their stellar kinematics. We introduce the kinematic concentration parameter, ck, the ratio between the global and local degree of energy equipartition reached by a cluster, and show through extensive direct N-body simulations that clusters approaching core collapse and in the post-core collapse phase are strictly characterized by ck > 1. The kinematic concentration provides a suitable diagnostic to identify core-collapsed clusters, independent from any other previous methods based on photometry. We also explore the effects of incomplete radial and stellar mass coverage on the calculation of ck and find that our method can be applied to state-of-art kinematic data sets.

Clusternomics: Integrative context-dependent clustering for heterogeneous datasets

PubMed Central

Wernisch, Lorenz

2017-01-01

Integrative clustering is used to identify groups of samples by jointly analysing multiple datasets describing the same set of biological samples, such as gene expression, copy number, methylation etc. Most existing algorithms for integrative clustering assume that there is a shared consistent set of clusters across all datasets, and most of the data samples follow this structure. However in practice, the structure across heterogeneous datasets can be more varied, with clusters being joined in some datasets and separated in others. In this paper, we present a probabilistic clustering method to identify groups across datasets that do not share the same cluster structure. The proposed algorithm, Clusternomics, identifies groups of samples that share their global behaviour across heterogeneous datasets. The algorithm models clusters on the level of individual datasets, while also extracting global structure that arises from the local cluster assignments. Clusters on both the local and the global level are modelled using a hierarchical Dirichlet mixture model to identify structure on both levels. We evaluated the model both on simulated and on real-world datasets. The simulated data exemplifies datasets with varying degrees of common structure. In such a setting Clusternomics outperforms existing algorithms for integrative and consensus clustering. In a real-world application, we used the algorithm for cancer subtyping, identifying subtypes of cancer from heterogeneous datasets. We applied the algorithm to TCGA breast cancer dataset, integrating gene expression, miRNA expression, DNA methylation and proteomics. The algorithm extracted clinically meaningful clusters with significantly different survival probabilities. We also evaluated the algorithm on lung and kidney cancer TCGA datasets with high dimensionality, again showing clinically significant results and scalability of the algorithm. PMID:29036190

Clusternomics: Integrative context-dependent clustering for heterogeneous datasets.

PubMed

Gabasova, Evelina; Reid, John; Wernisch, Lorenz

2017-10-01

Integrative clustering is used to identify groups of samples by jointly analysing multiple datasets describing the same set of biological samples, such as gene expression, copy number, methylation etc. Most existing algorithms for integrative clustering assume that there is a shared consistent set of clusters across all datasets, and most of the data samples follow this structure. However in practice, the structure across heterogeneous datasets can be more varied, with clusters being joined in some datasets and separated in others. In this paper, we present a probabilistic clustering method to identify groups across datasets that do not share the same cluster structure. The proposed algorithm, Clusternomics, identifies groups of samples that share their global behaviour across heterogeneous datasets. The algorithm models clusters on the level of individual datasets, while also extracting global structure that arises from the local cluster assignments. Clusters on both the local and the global level are modelled using a hierarchical Dirichlet mixture model to identify structure on both levels. We evaluated the model both on simulated and on real-world datasets. The simulated data exemplifies datasets with varying degrees of common structure. In such a setting Clusternomics outperforms existing algorithms for integrative and consensus clustering. In a real-world application, we used the algorithm for cancer subtyping, identifying subtypes of cancer from heterogeneous datasets. We applied the algorithm to TCGA breast cancer dataset, integrating gene expression, miRNA expression, DNA methylation and proteomics. The algorithm extracted clinically meaningful clusters with significantly different survival probabilities. We also evaluated the algorithm on lung and kidney cancer TCGA datasets with high dimensionality, again showing clinically significant results and scalability of the algorithm.

Galaxy clustering dependence on the [O II] emission line luminosity in the local Universe

NASA Astrophysics Data System (ADS)

Favole, Ginevra; Rodríguez-Torres, Sergio A.; Comparat, Johan; Prada, Francisco; Guo, Hong; Klypin, Anatoly; Montero-Dorta, Antonio D.

2017-11-01

We study the galaxy clustering dependence on the [O II] emission line luminosity in the SDSS DR7 Main galaxy sample at mean redshift z ∼ 0.1. We select volume-limited samples of galaxies with different [O II] luminosity thresholds and measure their projected, monopole and quadrupole two-point correlation functions. We model these observations using the 1 h-1 Gpc MultiDark-Planck cosmological simulation and generate light cones with the SUrvey GenerAtoR algorithm. To interpret our results, we adopt a modified (Sub)Halo Abundance Matching scheme, accounting for the stellar mass incompleteness of the emission line galaxies. The satellite fraction constitutes an extra parameter in this model and allows to optimize the clustering fit on both small and intermediate scales (i.e. rp ≲ 30 h-1 Mpc), with no need of any velocity bias correction. We find that, in the local Universe, the [O II] luminosity correlates with all the clustering statistics explored and with the galaxy bias. This latter quantity correlates more strongly with the SDSS r-band magnitude than [O II] luminosity. In conclusion, we propose a straightforward method to produce reliable clustering models, entirely built on the simulation products, which provides robust predictions of the typical ELG host halo masses and satellite fraction values. The SDSS galaxy data, MultiDark mock catalogues and clustering results are made publicly available.

A natural language processing and geospatial clustering framework for harvesting local place names from geotagged housing advertisements

DOE PAGES

Hu, Yingjie; Mao, Huina; Mckenzie, Grant

2018-04-13

We report that local place names are frequently used by residents living in a geographic region. Such place names may not be recorded in existing gazetteers, due to their vernacular nature, relative insignificance to a gazetteer covering a large area (e.g. the entire world), recent establishment (e.g. the name of a newly-opened shopping center) or other reasons. While not always recorded, local place names play important roles in many applications, from supporting public participation in urban planning to locating victims in disaster response. In this paper, we propose a computational framework for harvesting local place names from geotagged housing advertisements.more » We make use of those advertisements posted on local-oriented websites, such as Craigslist, where local place names are often mentioned. The proposed framework consists of two stages: natural language processing (NLP) and geospatial clustering. The NLP stage examines the textual content of housing advertisements and extracts place name candidates. The geospatial stage focuses on the coordinates associated with the extracted place name candidates and performs multiscale geospatial clustering to filter out the non-place names. We evaluate our framework by comparing its performance with those of six baselines. Finally, we also compare our result with four existing gazetteers to demonstrate the not-yet-recorded local place names discovered by our framework.« less

A natural language processing and geospatial clustering framework for harvesting local place names from geotagged housing advertisements

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

Hu, Yingjie; Mao, Huina; Mckenzie, Grant

We report that local place names are frequently used by residents living in a geographic region. Such place names may not be recorded in existing gazetteers, due to their vernacular nature, relative insignificance to a gazetteer covering a large area (e.g. the entire world), recent establishment (e.g. the name of a newly-opened shopping center) or other reasons. While not always recorded, local place names play important roles in many applications, from supporting public participation in urban planning to locating victims in disaster response. In this paper, we propose a computational framework for harvesting local place names from geotagged housing advertisements.more » We make use of those advertisements posted on local-oriented websites, such as Craigslist, where local place names are often mentioned. The proposed framework consists of two stages: natural language processing (NLP) and geospatial clustering. The NLP stage examines the textual content of housing advertisements and extracts place name candidates. The geospatial stage focuses on the coordinates associated with the extracted place name candidates and performs multiscale geospatial clustering to filter out the non-place names. We evaluate our framework by comparing its performance with those of six baselines. Finally, we also compare our result with four existing gazetteers to demonstrate the not-yet-recorded local place names discovered by our framework.« less

Probing the Properties of AGN Clustering in the Local Universe with Swift-BAT

NASA Astrophysics Data System (ADS)

Powell, M.; Cappelluti, N.; Urry, M.; Koss, M.; Allevato, V.; Ajello, M.

2017-10-01

I present the benchmark measurement of AGN clustering in the local universe with the all-sky Swift-BAT survey. The hard X-ray selection (14-195 keV) allows for the detection of some of the most obscured AGN, providing the largest, most unbiased sample of local AGN to date. We derive for the first time the halo occupation distribution (HOD) of the sample in various bins of black hole mass, accretion rate, and obscuration. In doing so, we characterize the cosmic environment of growing supermassive black holes with unprecedented precision, and determine which black hole parameters depend on environment. We then compare our results to the current evolutionary models of AGN.

Clustered localization of STAT3 during the cell cycle detected by super-resolution fluorescence microscopy

NASA Astrophysics Data System (ADS)

Gao, Jing; Chen, Junling; Cai, Mingjun; Xu, Haijiao; Jiang, Junguang; Tong, Ti; Wang, Hongda

2017-06-01

Signal transducer and activator of transcription 3 (STAT3) plays a key role in various cellular processes such as cell proliferation, differentiation, apoptosis and immune responses. In particular, STAT3 has emerged as a potential molecular target for cancer therapy. The functional role and standard activation mechanism of STAT3 have been well studied, however, the spatial distribution of STAT3 during the cell cycle is poorly known. Therefore, it is indispensable to study STAT3 spatial arrangement and nuclear-cytoplasimic localization at the different phase of cell cycle in cancer cells. By direct stochastic optical reconstruction microscopy imaging, we find that STAT3 forms various number and size of clusters at the different cell-cycle stage, which could not be clearly observed by conventional fluorescent microscopy. STAT3 clusters get more and larger gradually from G1 to G2 phase, during which time transcription and other related activities goes on consistently. The results suggest that there is an intimate relationship between the clustered characteristic of STAT3 and the cell-cycle behavior. Meanwhile, clustering would facilitate STAT3 rapid response to activating signals due to short distances between molecules. Our data might open a new door to develop an antitumor drug for inhibiting STAT3 signaling pathway by destroying its clusters.

Clustering and pasta phases in nuclear density functional theory

DOE PAGES

Schuetrumpf, Bastian; Zhang, Chunli; Nazarewicz, Witold

2017-05-23

Nuclear density functional theory is the tool of choice in describing properties of complex nuclei and intricate phases of bulk nucleonic matter. It is a microscopic approach based on an energy density functional representing the nuclear interaction. An attractive feature of nuclear DFT is that it can be applied to both finite nuclei and pasta phases appearing in the inner crust of neutron stars. While nuclear pasta clusters in a neutron star can be easily characterized through their density distributions, the level of clustering of nucleons in a nucleus can often be difficult to assess. To this end, we usemore » the concept of nucleon localization. We demonstrate that the localization measure provides us with fingerprints of clusters in light and heavy nuclei, including fissioning systems. Furthermore we investigate the rod-like pasta phase using twist-averaged boundary conditions, which enable calculations in finite volumes accessible by state of the art DFT solvers.« less

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

An Analysis of the Optimal Multiobjective Inventory Clustering Decision with Small Quantity and Great Variety Inventory by Applying a DPSO

PubMed Central

Li, Meng-Hua

2014-01-01

When an enterprise has thousands of varieties in its inventory, the use of a single management method could not be a feasible approach. A better way to manage this problem would be to categorise inventory items into several clusters according to inventory decisions and to use different management methods for managing different clusters. The present study applies DPSO (dynamic particle swarm optimisation) to a problem of clustering of inventory items. Without the requirement of prior inventory knowledge, inventory items are automatically clustered into near optimal clustering number. The obtained clustering results should satisfy the inventory objective equation, which consists of different objectives such as total cost, backorder rate, demand relevance, and inventory turnover rate. This study integrates the above four objectives into a multiobjective equation, and inputs the actual inventory items of the enterprise into DPSO. In comparison with other clustering methods, the proposed method can consider different objectives and obtain an overall better solution to obtain better convergence results and inventory decisions. PMID:25197713

An analysis of the optimal multiobjective inventory clustering decision with small quantity and great variety inventory by applying a DPSO.

PubMed

Wang, Shen-Tsu; Li, Meng-Hua

2014-01-01

When an enterprise has thousands of varieties in its inventory, the use of a single management method could not be a feasible approach. A better way to manage this problem would be to categorise inventory items into several clusters according to inventory decisions and to use different management methods for managing different clusters. The present study applies DPSO (dynamic particle swarm optimisation) to a problem of clustering of inventory items. Without the requirement of prior inventory knowledge, inventory items are automatically clustered into near optimal clustering number. The obtained clustering results should satisfy the inventory objective equation, which consists of different objectives such as total cost, backorder rate, demand relevance, and inventory turnover rate. This study integrates the above four objectives into a multiobjective equation, and inputs the actual inventory items of the enterprise into DPSO. In comparison with other clustering methods, the proposed method can consider different objectives and obtain an overall better solution to obtain better convergence results and inventory decisions.

A scoping review of spatial cluster analysis techniques for point-event data.

PubMed

Fritz, Charles E; Schuurman, Nadine; Robertson, Colin; Lear, Scott

2013-05-01

Spatial cluster analysis is a uniquely interdisciplinary endeavour, and so it is important to communicate and disseminate ideas, innovations, best practices and challenges across practitioners, applied epidemiology researchers and spatial statisticians. In this research we conducted a scoping review to systematically search peer-reviewed journal databases for research that has employed spatial cluster analysis methods on individual-level, address location, or x and y coordinate derived data. To illustrate the thematic issues raised by our results, methods were tested using a dataset where known clusters existed. Point pattern methods, spatial clustering and cluster detection tests, and a locally weighted spatial regression model were most commonly used for individual-level, address location data (n = 29). The spatial scan statistic was the most popular method for address location data (n = 19). Six themes were identified relating to the application of spatial cluster analysis methods and subsequent analyses, which we recommend researchers to consider; exploratory analysis, visualization, spatial resolution, aetiology, scale and spatial weights. It is our intention that researchers seeking direction for using spatial cluster analysis methods, consider the caveats and strengths of each approach, but also explore the numerous other methods available for this type of analysis. Applied spatial epidemiology researchers and practitioners should give special consideration to applying multiple tests to a dataset. Future research should focus on developing frameworks for selecting appropriate methods and the corresponding spatial weighting schemes.

A quantitative evaluation of pleural effusion on computed tomography scans using B-spline and local clustering level set.

PubMed

Song, Lei; Gao, Jungang; Wang, Sheng; Hu, Huasi; Guo, Youmin

2017-01-01

Estimation of the pleural effusion's volume is an important clinical issue. The existing methods cannot assess it accurately when there is large volume of liquid in the pleural cavity and/or the patient has some other disease (e.g. pneumonia). In order to help solve this issue, the objective of this study is to develop and test a novel algorithm using B-spline and local clustering level set method jointly, namely BLL. The BLL algorithm was applied to a dataset involving 27 pleural effusions detected on chest CT examination of 18 adult patients with the presence of free pleural effusion. Study results showed that average volumes of pleural effusion computed using the BLL algorithm and assessed manually by the physicians were 586 ml±339 ml and 604±352 ml, respectively. For the same patient, the volume of the pleural effusion, segmented semi-automatically, was 101.8% ±4.6% of that was segmented manually. Dice similarity was found to be 0.917±0.031. The study demonstrated feasibility of applying the new BLL algorithm to accurately measure the volume of pleural effusion.

WordCluster: detecting clusters of DNA words and genomic elements

PubMed Central

2011-01-01

Background Many k-mers (or DNA words) and genomic elements are known to be spatially clustered in the genome. Well established examples are the genes, TFBSs, CpG dinucleotides, microRNA genes and ultra-conserved non-coding regions. Currently, no algorithm exists to find these clusters in a statistically comprehensible way. The detection of clustering often relies on densities and sliding-window approaches or arbitrarily chosen distance thresholds. Results We introduce here an algorithm to detect clusters of DNA words (k-mers), or any other genomic element, based on the distance between consecutive copies and an assigned statistical significance. We implemented the method into a web server connected to a MySQL backend, which also determines the co-localization with gene annotations. We demonstrate the usefulness of this approach by detecting the clusters of CAG/CTG (cytosine contexts that can be methylated in undifferentiated cells), showing that the degree of methylation vary drastically between inside and outside of the clusters. As another example, we used WordCluster to search for statistically significant clusters of olfactory receptor (OR) genes in the human genome. Conclusions WordCluster seems to predict biological meaningful clusters of DNA words (k-mers) and genomic entities. The implementation of the method into a web server is available at http://bioinfo2.ugr.es/wordCluster/wordCluster.php including additional features like the detection of co-localization with gene regions or the annotation enrichment tool for functional analysis of overlapped genes. PMID:21261981

Changes in cluster magnetism and suppression of local superconductivity in amorphous FeCrB alloy irradiated by Ar+ ions

NASA Astrophysics Data System (ADS)

Okunev, V. D.; Samoilenko, Z. A.; Szymczak, H.; Szewczyk, A.; Szymczak, R.; Lewandowski, S. J.; Aleshkevych, P.; Malinowski, A.; Gierłowski, P.; Więckowski, J.; Wolny-Marszałek, M.; Jeżabek, M.; Varyukhin, V. N.; Antoshina, I. A.

2016-02-01

We show that сluster magnetism in ferromagnetic amorphous Fe67Cr18B15 alloy is related to the presence of large, D=150-250 Å, α-(Fe Cr) clusters responsible for basic changes in cluster magnetism, small, D=30-100 Å, α-(Fe, Cr) and Fe3B clusters and subcluster atomic α-(Fe, Cr, B) groupings, D=10-20 Å, in disordered intercluster medium. For initial sample and irradiated one (Φ=1.5×1018 ions/cm2) superconductivity exists in the cluster shells of metallic α-(Fe, Cr) phase where ferromagnetism of iron is counterbalanced by antiferromagnetism of chromium. At Φ=3×1018 ions/cm2, the internal stresses intensify and the process of iron and chromium phase separation, favorable for mesoscopic superconductivity, changes for inverse one promoting more homogeneous distribution of iron and chromium in the clusters as well as gigantic (twice as much) increase in density of the samples. As a result, in the cluster shells ferromagnetism is restored leading to the increase in magnetization of the sample and suppression of local superconductivity. For initial samples, the temperature dependence of resistivity ρ(T) T2 is determined by the electron scattering on quantum defects. In strongly inhomogeneous samples, after irradiation by fluence Φ=1.5×1018 ions/cm2, the transition to a dependence ρ(T) T1/2 is caused by the effects of weak localization. In more homogeneous samples, at Φ=3×1018 ions/cm2, a return to the dependence ρ(T) T2 is observed.

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.

Localization of phonons in mass-disordered alloys: A typical medium dynamical cluster approach

DOE PAGES

Jarrell, Mark; Moreno, Juana; Raja Mondal, Wasim; ...

2017-07-20

The effect of disorder on lattice vibrational modes has been a topic of interest for several decades. In this article, we employ a Green's function based approach, namely, the dynamical cluster approximation (DCA), to investigate phonons in mass-disordered systems. Detailed benchmarks with previous exact calculations are used to validate the method in a wide parameter space. An extension of the method, namely, the typical medium DCA (TMDCA), is used to study Anderson localization of phonons in three dimensions. We show that, for binary isotopic disorder, lighter impurities induce localized modes beyond the bandwidth of the host system, while heavier impuritiesmore » lead to a partial localization of the low-frequency acoustic modes. For a uniform (box) distribution of masses, the physical spectrum is shown to develop long tails comprising mostly localized modes. The mobility edge separating extended and localized modes, obtained through the TMDCA, agrees well with results from the transfer matrix method. A reentrance behavior of the mobility edge with increasing disorder is found that is similar to, but somewhat more pronounced than, the behavior in disordered electronic systems. Our work establishes a computational approach, which recovers the thermodynamic limit, is versatile and computationally inexpensive, to investigate lattice vibrations in disordered lattice systems.« less

Localization of phonons in mass-disordered alloys: A typical medium dynamical cluster approach

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

Jarrell, Mark; Moreno, Juana; Raja Mondal, Wasim

The effect of disorder on lattice vibrational modes has been a topic of interest for several decades. In this article, we employ a Green's function based approach, namely, the dynamical cluster approximation (DCA), to investigate phonons in mass-disordered systems. Detailed benchmarks with previous exact calculations are used to validate the method in a wide parameter space. An extension of the method, namely, the typical medium DCA (TMDCA), is used to study Anderson localization of phonons in three dimensions. We show that, for binary isotopic disorder, lighter impurities induce localized modes beyond the bandwidth of the host system, while heavier impuritiesmore » lead to a partial localization of the low-frequency acoustic modes. For a uniform (box) distribution of masses, the physical spectrum is shown to develop long tails comprising mostly localized modes. The mobility edge separating extended and localized modes, obtained through the TMDCA, agrees well with results from the transfer matrix method. A reentrance behavior of the mobility edge with increasing disorder is found that is similar to, but somewhat more pronounced than, the behavior in disordered electronic systems. Our work establishes a computational approach, which recovers the thermodynamic limit, is versatile and computationally inexpensive, to investigate lattice vibrations in disordered lattice systems.« less

Coherent clusters of inertial particles in homogeneous turbulence

NASA Astrophysics Data System (ADS)

Baker, Lucia; Frankel, Ari; Mani, Ali; Coletti, Filippo

2016-11-01

Clustering of heavy particles in turbulent flows manifests itself in a broad spectrum of physical phenomena, including sediment transport, cloud formation, and spray combustion. However, a clear topological definition of particle cluster has been lacking, limiting our ability to describe their features and dynamics. Here we introduce a definition of coherent cluster based on self-similarity, and apply it to the distribution of heavy particles in direct numerical simulations of homogeneous isotropic turbulence. We consider a range of particle Stokes numbers, with and without the effect of gravity. Clusters show self-similarity at length scales larger than twice the Kolmogorov length, with a specific fractal dimension. In the absence of gravity, clusters demonstrate a tendency to sample regions of the flow where strain is dominant over vorticity, and to align themselves with the local vorticity vector; when gravity is present, the clusters tend to align themselves with gravity, and their fall speed is different from the average settling velocity. This approach yields observations which are consistent with findings obtained from previous studies while opening new avenues for analysis of the topology and evolution of particle clusters in a wealth of applications.

Towards enhancement of performance of K-means clustering using nature-inspired optimization algorithms.

PubMed

Fong, Simon; Deb, Suash; Yang, Xin-She; Zhuang, Yan

2014-01-01

Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative computation to search for global optima while avoiding local optima. The algorithms help speed up the clustering process by converging into a global optimum early with multiple search agents in action. Inspired by nature, some contemporary optimization algorithms which include Ant, Bat, Cuckoo, Firefly, and Wolf search algorithms mimic the swarming behavior allowing them to cooperatively steer towards an optimal objective within a reasonable time. It is known that these so-called nature-inspired optimization algorithms have their own characteristics as well as pros and cons in different applications. When these algorithms are combined with K-means clustering mechanism for the sake of enhancing its clustering quality by avoiding local optima and finding global optima, the new hybrids are anticipated to produce unprecedented performance. In this paper, we report the results of our evaluation experiments on the integration of nature-inspired optimization methods into K-means algorithms. In addition to the standard evaluation metrics in evaluating clustering quality, the extended K-means algorithms that are empowered by nature-inspired optimization methods are applied on image segmentation as a case study of application scenario.

Dark energy and the structure of the Coma cluster of galaxies

NASA Astrophysics Data System (ADS)

Chernin, A. D.; Bisnovatyi-Kogan, G. S.; Teerikorpi, P.; Valtonen, M. J.; Byrd, G. G.; Merafina, M.

2013-05-01

Context. We consider the Coma cluster of galaxies as a gravitationally bound physical system embedded in the perfectly uniform static dark energy background as implied by ΛCDM cosmology. Aims: We ask if the density of dark energy is high enough to affect the structure of a large and rich cluster of galaxies. Methods: We base our work on recent observational data on the Coma cluster, and apply our theory of local dynamical effects of dark energy, including the zero-gravity radius RZG of the local force field as the key parameter. Results: 1) Three masses are defined that characterize the structure of a regular cluster: the matter mass MM, the dark-energy effective mass MDE (<0), and the gravitating mass MG (=MM + MDE). 2) A new matter-density profile is suggested that reproduces the observational data well for the Coma cluster in the radius range from 1.4 Mpc to 14 Mpc and takes the dark energy background into account. 3) Using this profile, we calculate upper limits for the total size of the Coma cluster, R ≤ RZG ≈ 20 Mpc, and its total matter mass, MM ≲ MM(RZG) = 6.2 × 1015 M⊙. Conclusions: The dark energy antigravity affects the structure of the Coma cluster strongly at large radii R ≳ 14 Mpc and should be considered when its total mass is derived.

Unsupervised feature relevance analysis applied to improve ECG heartbeat clustering.

PubMed

Rodríguez-Sotelo, J L; Peluffo-Ordoñez, D; Cuesta-Frau, D; Castellanos-Domínguez, G

2012-10-01

The computer-assisted analysis of biomedical records has become an essential tool in clinical settings. However, current devices provide a growing amount of data that often exceeds the processing capacity of normal computers. As this amount of information rises, new demands for more efficient data extracting methods appear. This paper addresses the task of data mining in physiological records using a feature selection scheme. An unsupervised method based on relevance analysis is described. This scheme uses a least-squares optimization of the input feature matrix in a single iteration. The output of the algorithm is a feature weighting vector. The performance of the method was assessed using a heartbeat clustering test on real ECG records. The quantitative cluster validity measures yielded a correctly classified heartbeat rate of 98.69% (specificity), 85.88% (sensitivity) and 95.04% (general clustering performance), which is even higher than the performance achieved by other similar ECG clustering studies. The number of features was reduced on average from 100 to 18, and the temporal cost was a 43% lower than in previous ECG clustering schemes. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

ClubSub-P: Cluster-Based Subcellular Localization Prediction for Gram-Negative Bacteria and Archaea

PubMed Central

Paramasivam, Nagarajan; Linke, Dirk

2011-01-01

The subcellular localization (SCL) of proteins provides important clues to their function in a cell. In our efforts to predict useful vaccine targets against Gram-negative bacteria, we noticed that misannotated start codons frequently lead to wrongly assigned SCLs. This and other problems in SCL prediction, such as the relatively high false-positive and false-negative rates of some tools, can be avoided by applying multiple prediction tools to groups of homologous proteins. Here we present ClubSub-P, an online database that combines existing SCL prediction tools into a consensus pipeline from more than 600 proteomes of fully sequenced microorganisms. On top of the consensus prediction at the level of single sequences, the tool uses clusters of homologous proteins from Gram-negative bacteria and from Archaea to eliminate false-positive and false-negative predictions. ClubSub-P can assign the SCL of proteins from Gram-negative bacteria and Archaea with high precision. The database is searchable, and can easily be expanded using either new bacterial genomes or new prediction tools as they become available. This will further improve the performance of the SCL prediction, as well as the detection of misannotated start codons and other annotation errors. ClubSub-P is available online at http://toolkit.tuebingen.mpg.de/clubsubp/ PMID:22073040

The JCMT Gould Belt Survey: Dense Core Clusters in Orion B

NASA Astrophysics Data System (ADS)

Kirk, H.; Johnstone, D.; Di Francesco, J.; Lane, J.; Buckle, J.; Berry, D. S.; Broekhoven-Fiene, H.; Currie, M. J.; Fich, M.; Hatchell, J.; Jenness, T.; Mottram, J. C.; Nutter, D.; Pattle, K.; Pineda, J. E.; Quinn, C.; Salji, C.; Tisi, S.; Hogerheijde, M. R.; Ward-Thompson, D.; The JCMT Gould Belt Survey Team

2016-04-01

The James Clerk Maxwell Telescope Gould Belt Legacy Survey obtained SCUBA-2 observations of dense cores within three sub-regions of Orion B: LDN 1622, NGC 2023/2024, and NGC 2068/2071, all of which contain clusters of cores. We present an analysis of the clustering properties of these cores, including the two-point correlation function and Cartwright’s Q parameter. We identify individual clusters of dense cores across all three regions using a minimal spanning tree technique, and find that in each cluster, the most massive cores tend to be centrally located. We also apply the independent M-Σ technique and find a strong correlation between core mass and the local surface density of cores. These two lines of evidence jointly suggest that some amount of mass segregation in clusters has happened already at the dense core stage.

Stroke localization and classification using microwave tomography with k-means clustering and support vector machine.

PubMed

Guo, Lei; Abbosh, Amin

2018-05-01

For any chance for stroke patients to survive, the stroke type should be classified to enable giving medication within a few hours of the onset of symptoms. In this paper, a microwave-based stroke localization and classification framework is proposed. It is based on microwave tomography, k-means clustering, and a support vector machine (SVM) method. The dielectric profile of the brain is first calculated using the Born iterative method, whereas the amplitude of the dielectric profile is then taken as the input to k-means clustering. The cluster is selected as the feature vector for constructing and testing the SVM. A database of MRI-derived realistic head phantoms at different signal-to-noise ratios is used in the classification procedure. The performance of the proposed framework is evaluated using the receiver operating characteristic (ROC) curve. The results based on a two-dimensional framework show that 88% classification accuracy, with a sensitivity of 91% and a specificity of 87%, can be achieved. Bioelectromagnetics. 39:312-324, 2018. © 2018 Wiley Periodicals, Inc. © 2018 Wiley Periodicals, Inc.

Enhancement of local surface plasmon resonance (LSPR) effect by biocompatible metal clustering based on ZnO nanorods in Raman measurements.

PubMed

Lee, Sanghwa; Lee, Seung Ho; Paulson, Bjorn; Lee, Jae-Chul; Kim, Jun Ki

2018-06-20

The development of size-selective and non-destructive detection techniques for nanosized biomarkers has many reasons, including the study of living cells and diagnostic applications. We present an approach for Raman signal enhancement on biocompatible sensing chips based on surface enhancement Raman spectroscopy (SERS). A sensing chip was fabricated by forming a ZnO-based nanorod structure so that the Raman enhancement occurred at a gap of several tens to several hundred nanometers. The effect of coffee-ring formation was eliminated by introducing the porous ZnO nanorods for the bio-liquid sample. A peculiarity of this approach is that the gold sputtered on the ZnO nanorods initially grows at their heads forming clusters, as confirmed by secondary electron microscopy. This clustering was verified by finite element analysis to be the main factor for enhancement of local surface plasmon resonance (LSPR). This clustering property and the ability to adjust the size of the nanorods enabled the signal acquisition points to be refined using confocal based Raman spectroscopy, which could be applied directly to the sensor chip based on the optimization process in this experiment. It was demonstrated by using common cancer cell lines that cell growth was high on these gold-clad ZnO nanorod-based surface-enhanced Raman substrates. The porosity of the sensing chip, the improved structure for signal enhancement, and the cell assay make these gold-coated ZnO nanorods substrates promising biosensing chips with excellent potential for detecting nanometric biomarkers secreted by cells. Copyright © 2018 Elsevier B.V. All rights reserved.

Profiling Local Optima in K-Means Clustering: Developing a Diagnostic Technique

ERIC Educational Resources Information Center

Steinley, Douglas

2006-01-01

Using the cluster generation procedure proposed by D. Steinley and R. Henson (2005), the author investigated the performance of K-means clustering under the following scenarios: (a) different probabilities of cluster overlap; (b) different types of cluster overlap; (c) varying samples sizes, clusters, and dimensions; (d) different multivariate…

Brain vascular image segmentation based on fuzzy local information C-means clustering

NASA Astrophysics Data System (ADS)

Hu, Chaoen; Liu, Xia; Liang, Xiao; Hui, Hui; Yang, Xin; Tian, Jie

2017-02-01

Light sheet fluorescence microscopy (LSFM) is a powerful optical resolution fluorescence microscopy technique which enables to observe the mouse brain vascular network in cellular resolution. However, micro-vessel structures are intensity inhomogeneity in LSFM images, which make an inconvenience for extracting line structures. In this work, we developed a vascular image segmentation method by enhancing vessel details which should be useful for estimating statistics like micro-vessel density. Since the eigenvalues of hessian matrix and its sign describes different geometric structure in images, which enable to construct vascular similarity function and enhance line signals, the main idea of our method is to cluster the pixel values of the enhanced image. Our method contained three steps: 1) calculate the multiscale gradients and the differences between eigenvalues of Hessian matrix. 2) In order to generate the enhanced microvessels structures, a feed forward neural network was trained by 2.26 million pixels for dealing with the correlations between multi-scale gradients and the differences between eigenvalues. 3) The fuzzy local information c-means clustering (FLICM) was used to cluster the pixel values in enhance line signals. To verify the feasibility and effectiveness of this method, mouse brain vascular images have been acquired by a commercial light-sheet microscope in our lab. The experiment of the segmentation method showed that dice similarity coefficient can reach up to 85%. The results illustrated that our approach extracting line structures of blood vessels dramatically improves the vascular image and enable to accurately extract blood vessels in LSFM images.

Does objective cluster analysis serve as a useful precursor to seasonal precipitation prediction at local scale? Application to western Ethiopia

NASA Astrophysics Data System (ADS)

Zhang, Ying; Moges, Semu; Block, Paul

2018-01-01

Prediction of seasonal precipitation can provide actionable information to guide management of various sectoral activities. For instance, it is often translated into hydrological forecasts for better water resources management. However, many studies assume homogeneity in precipitation across an entire study region, which may prove ineffective for operational and local-level decisions, particularly for locations with high spatial variability. This study proposes advancing local-level seasonal precipitation predictions by first conditioning on regional-level predictions, as defined through objective cluster analysis, for western Ethiopia. To our knowledge, this is the first study predicting seasonal precipitation at high resolution in this region, where lives and livelihoods are vulnerable to precipitation variability given the high reliance on rain-fed agriculture and limited water resources infrastructure. The combination of objective cluster analysis, spatially high-resolution prediction of seasonal precipitation, and a modeling structure spanning statistical and dynamical approaches makes clear advances in prediction skill and resolution, as compared with previous studies. The statistical model improves versus the non-clustered case or dynamical models for a number of specific clusters in northwestern Ethiopia, with clusters having regional average correlation and ranked probability skill score (RPSS) values of up to 0.5 and 33 %, respectively. The general skill (after bias correction) of the two best-performing dynamical models over the entire study region is superior to that of the statistical models, although the dynamical models issue predictions at a lower resolution and the raw predictions require bias correction to guarantee comparable skills.

Beyond Low-Rank Representations: Orthogonal clustering basis reconstruction with optimized graph structure for multi-view spectral clustering.

PubMed

Wang, Yang; Wu, Lin

2018-07-01

Low-Rank Representation (LRR) is arguably one of the most powerful paradigms for Multi-view spectral clustering, which elegantly encodes the multi-view local graph/manifold structures into an intrinsic low-rank self-expressive data similarity embedded in high-dimensional space, to yield a better graph partition than their single-view counterparts. In this paper we revisit it with a fundamentally different perspective by discovering LRR as essentially a latent clustered orthogonal projection based representation winged with an optimized local graph structure for spectral clustering; each column of the representation is fundamentally a cluster basis orthogonal to others to indicate its members, which intuitively projects the view-specific feature representation to be the one spanned by all orthogonal basis to characterize the cluster structures. Upon this finding, we propose our technique with the following: (1) We decompose LRR into latent clustered orthogonal representation via low-rank matrix factorization, to encode the more flexible cluster structures than LRR over primal data objects; (2) We convert the problem of LRR into that of simultaneously learning orthogonal clustered representation and optimized local graph structure for each view; (3) The learned orthogonal clustered representations and local graph structures enjoy the same magnitude for multi-view, so that the ideal multi-view consensus can be readily achieved. The experiments over multi-view datasets validate its superiority, especially over recent state-of-the-art LRR models. Copyright © 2018 Elsevier Ltd. All rights reserved.

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. 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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.

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; 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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; 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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.

Ensemble Clustering Classification compete SVM and One-Class classifiers applied on plant microRNAs Data.

PubMed

Yousef, Malik; Khalifa, Waleed; AbedAllah, Loai

2016-12-22

The performance of many learning and data mining algorithms depends critically on suitable metrics to assess efficiency over the input space. Learning a suitable metric from examples may, therefore, be the key to successful application of these algorithms. We have demonstrated that the k-nearest neighbor (kNN) classification can be significantly improved by learning a distance metric from labeled examples. The clustering ensemble is used to define the distance between points in respect to how they co-cluster. This distance is then used within the framework of the kNN algorithm to define a classifier named ensemble clustering kNN classifier (EC-kNN). In many instances in our experiments we achieved highest accuracy while SVM failed to perform as well. In this study, we compare the performance of a two-class classifier using EC-kNN with different one-class and two-class classifiers. The comparison was applied to seven different plant microRNA species considering eight feature selection methods. In this study, the averaged results show that ECkNN outperforms all other methods employed here and previously published results for the same data. In conclusion, this study shows that the chosen classifier shows high performance when the distance metric is carefully chosen.

Towards Enhancement of Performance of K-Means Clustering Using Nature-Inspired Optimization Algorithms

PubMed Central

Deb, Suash; Yang, Xin-She

2014-01-01

Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative computation to search for global optima while avoiding local optima. The algorithms help speed up the clustering process by converging into a global optimum early with multiple search agents in action. Inspired by nature, some contemporary optimization algorithms which include Ant, Bat, Cuckoo, Firefly, and Wolf search algorithms mimic the swarming behavior allowing them to cooperatively steer towards an optimal objective within a reasonable time. It is known that these so-called nature-inspired optimization algorithms have their own characteristics as well as pros and cons in different applications. When these algorithms are combined with K-means clustering mechanism for the sake of enhancing its clustering quality by avoiding local optima and finding global optima, the new hybrids are anticipated to produce unprecedented performance. In this paper, we report the results of our evaluation experiments on the integration of nature-inspired optimization methods into K-means algorithms. In addition to the standard evaluation metrics in evaluating clustering quality, the extended K-means algorithms that are empowered by nature-inspired optimization methods are applied on image segmentation as a case study of application scenario. PMID:25202730

Whole-brain structural topology in adult attention-deficit/hyperactivity disorder: Preserved global - disturbed local network organization.

PubMed

Sidlauskaite, Justina; Caeyenberghs, Karen; Sonuga-Barke, Edmund; Roeyers, Herbert; Wiersema, Jan R

2015-01-01

Prior studies demonstrate altered organization of functional brain networks in attention-deficit/hyperactivity disorder (ADHD). However, the structural underpinnings of these functional disturbances are poorly understood. In the current study, we applied a graph-theoretic approach to whole-brain diffusion magnetic resonance imaging data to investigate the organization of structural brain networks in adults with ADHD and unaffected controls using deterministic fiber tractography. Groups did not differ in terms of global network metrics - small-worldness, global efficiency and clustering coefficient. However, there were widespread ADHD-related effects at the nodal level in relation to local efficiency and clustering. The affected nodes included superior occipital, supramarginal, superior temporal, inferior parietal, angular and inferior frontal gyri, as well as putamen, thalamus and posterior cerebellum. Lower local efficiency of left superior temporal and supramarginal gyri was associated with higher ADHD symptom scores. Also greater local clustering of right putamen and lower local clustering of left supramarginal gyrus correlated with ADHD symptom severity. Overall, the findings indicate preserved global but altered local network organization in adult ADHD implicating regions underpinning putative ADHD-related neuropsychological deficits.

ABCluster: the artificial bee colony algorithm for cluster global optimization.

PubMed

Zhang, Jun; Dolg, Michael

2015-10-07

Global optimization of cluster geometries is of fundamental importance in chemistry and an interesting problem in applied mathematics. In this work, we introduce a relatively new swarm intelligence algorithm, i.e. the artificial bee colony (ABC) algorithm proposed in 2005, to this field. It is inspired by the foraging behavior of a bee colony, and only three parameters are needed to control it. We applied it to several potential functions of quite different nature, i.e., the Coulomb-Born-Mayer, Lennard-Jones, Morse, Z and Gupta potentials. The benchmarks reveal that for long-ranged potentials the ABC algorithm is very efficient in locating the global minimum, while for short-ranged ones it is sometimes trapped into a local minimum funnel on a potential energy surface of large clusters. We have released an efficient, user-friendly, and free program "ABCluster" to realize the ABC algorithm. It is a black-box program for non-experts as well as experts and might become a useful tool for chemists to study clusters.

Scientific Cluster Deployment and Recovery - Using puppet to simplify cluster management

NASA Astrophysics Data System (ADS)

Hendrix, Val; Benjamin, Doug; Yao, Yushu

2012-12-01

Deployment, maintenance and recovery of a scientific cluster, which has complex, specialized services, can be a time consuming task requiring the assistance of Linux system administrators, network engineers as well as domain experts. Universities and small institutions that have a part-time FTE with limited time for and knowledge of the administration of such clusters can be strained by such maintenance tasks. This current work is the result of an effort to maintain a data analysis cluster (DAC) with minimal effort by a local system administrator. The realized benefit is the scientist, who is the local system administrator, is able to focus on the data analysis instead of the intricacies of managing a cluster. Our work provides a cluster deployment and recovery process (CDRP) based on the puppet configuration engine allowing a part-time FTE to easily deploy and recover entire clusters with minimal effort. Puppet is a configuration management system (CMS) used widely in computing centers for the automatic management of resources. Domain experts use Puppet's declarative language to define reusable modules for service configuration and deployment. Our CDRP has three actors: domain experts, a cluster designer and a cluster manager. The domain experts first write the puppet modules for the cluster services. A cluster designer would then define a cluster. This includes the creation of cluster roles, mapping the services to those roles and determining the relationships between the services. Finally, a cluster manager would acquire the resources (machines, networking), enter the cluster input parameters (hostnames, IP addresses) and automatically generate deployment scripts used by puppet to configure it to act as a designated role. In the event of a machine failure, the originally generated deployment scripts along with puppet can be used to easily reconfigure a new machine. The cluster definition produced in our CDRP is an integral part of automating cluster deployment

HUBBLE SPACE TELESCOPE SNAPSHOT SEARCH FOR PLANETARY NEBULAE IN GLOBULAR CLUSTERS OF THE LOCAL GROUP

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

Bond, Howard E., E-mail: heb11@psu.edu

2015-04-15

Single stars in ancient globular clusters (GCs) are believed incapable of producing planetary nebulae (PNs), because their post-asymptotic-giant-branch evolutionary timescales are slower than the dissipation timescales for PNs. Nevertheless, four PNs are known in Galactic GCs. Their existence likely requires more exotic evolutionary channels, including stellar mergers and common-envelope binary interactions. I carried out a snapshot imaging search with the Hubble Space Telescope (HST) for PNs in bright Local Group GCs outside the Milky Way. I used a filter covering the 5007 Å nebular emission line of [O iii], and another one in the nearby continuum, to image 66 GCs.more » Inclusion of archival HST frames brought the total number of extragalactic GCs imaged at 5007 Å to 75, whose total luminosity slightly exceeds that of the entire Galactic GC system. I found no convincing PNs in these clusters, aside from one PN in a young M31 cluster misclassified as a GC, and two PNs at such large angular separations from an M31 GC that membership is doubtful. In a ground-based spectroscopic survey of 274 old GCs in M31, Jacoby et al. found three candidate PNs. My HST images of one of them suggest that the [O iii] emission actually arises from ambient interstellar medium rather than a PN; for the other two candidates, there are broadband archival UV HST images that show bright, blue point sources that are probably the PNs. In a literature search, I also identified five further PN candidates lying near old GCs in M31, for which follow-up observations are necessary to confirm their membership. The rates of incidence of PNs are similar, and small but nonzero, throughout the GCs of the Local Group.« less

Analysis of Network Clustering Algorithms and Cluster Quality Metrics at Scale

PubMed Central

Kobourov, Stephen; Gallant, Mike; Börner, Katy

2016-01-01

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

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

Star cluster formation in cosmological simulations. I. Properties of young clusters

DOE PAGES

Li, Hui; Gnedin, Oleg Y.; Gnedin, Nickolay Y.; ...

2017-01-03

We present a new implementation of star formation in cosmological simulations by considering star clusters as a unit of star formation. Cluster particles grow in mass over several million years at the rate determined by local gas properties, with high time resolution. The particle growth is terminated by its own energy and momentum feedback on the interstellar medium. We test this implementation for Milky Way-sized galaxies at high redshift by comparing the properties of model clusters with observations of young star clusters. We find that the cluster initial mass function is best described by a Schechter function rather than a single power law. In agreement with observations, at low masses the logarithmic slope ismore » $$\\alpha \\approx 1.8\\mbox{–}2$$, while the cutoff at high mass scales with the star formation rate (SFR). A related trend is a positive correlation between the surface density of the SFR and fraction of stars contained in massive clusters. Both trends indicate that the formation of massive star clusters is preferred during bursts of star formation. These bursts are often associated with major-merger events. We also find that the median timescale for cluster formation ranges from 0.5 to 4 Myr and decreases systematically with increasing star formation efficiency. Local variations in the gas density and cluster accretion rate naturally lead to the scatter of the overall formation efficiency by an order of magnitude, even when the instantaneous efficiency is kept constant. As a result, comparison of the formation timescale with the observed age spread of young star clusters provides an additional important constraint on the modeling of star formation and feedback schemes.« less

Star cluster formation in cosmological simulations. I. Properties of young clusters

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

Li, Hui; Gnedin, Oleg Y.; Gnedin, Nickolay Y.

We present a new implementation of star formation in cosmological simulations by considering star clusters as a unit of star formation. Cluster particles grow in mass over several million years at the rate determined by local gas properties, with high time resolution. The particle growth is terminated by its own energy and momentum feedback on the interstellar medium. We test this implementation for Milky Way-sized galaxies at high redshift by comparing the properties of model clusters with observations of young star clusters. We find that the cluster initial mass function is best described by a Schechter function rather than a single power law. In agreement with observations, at low masses the logarithmic slope ismore » $$\\alpha \\approx 1.8\\mbox{–}2$$, while the cutoff at high mass scales with the star formation rate (SFR). A related trend is a positive correlation between the surface density of the SFR and fraction of stars contained in massive clusters. Both trends indicate that the formation of massive star clusters is preferred during bursts of star formation. These bursts are often associated with major-merger events. We also find that the median timescale for cluster formation ranges from 0.5 to 4 Myr and decreases systematically with increasing star formation efficiency. Local variations in the gas density and cluster accretion rate naturally lead to the scatter of the overall formation efficiency by an order of magnitude, even when the instantaneous efficiency is kept constant. As a result, comparison of the formation timescale with the observed age spread of young star clusters provides an additional important constraint on the modeling of star formation and feedback schemes.« less

Star Cluster Formation in Cosmological Simulations. I. Properties of Young Clusters

NASA Astrophysics Data System (ADS)

Li, Hui; Gnedin, Oleg Y.; Gnedin, Nickolay Y.; Meng, Xi; Semenov, Vadim A.; Kravtsov, Andrey V.

2017-01-01

We present a new implementation of star formation in cosmological simulations by considering star clusters as a unit of star formation. Cluster particles grow in mass over several million years at the rate determined by local gas properties, with high time resolution. The particle growth is terminated by its own energy and momentum feedback on the interstellar medium. We test this implementation for Milky Way-sized galaxies at high redshift by comparing the properties of model clusters with observations of young star clusters. We find that the cluster initial mass function is best described by a Schechter function rather than a single power law. In agreement with observations, at low masses the logarithmic slope is α ≈ 1.8{--}2, while the cutoff at high mass scales with the star formation rate (SFR). A related trend is a positive correlation between the surface density of the SFR and fraction of stars contained in massive clusters. Both trends indicate that the formation of massive star clusters is preferred during bursts of star formation. These bursts are often associated with major-merger events. We also find that the median timescale for cluster formation ranges from 0.5 to 4 Myr and decreases systematically with increasing star formation efficiency. Local variations in the gas density and cluster accretion rate naturally lead to the scatter of the overall formation efficiency by an order of magnitude, even when the instantaneous efficiency is kept constant. Comparison of the formation timescale with the observed age spread of young star clusters provides an additional important constraint on the modeling of star formation and feedback schemes.

Structure based alignment and clustering of proteins (STRALCP)

DOEpatents

Zemla, Adam T.; Zhou, Carol E.; Smith, Jason R.; Lam, Marisa W.

2013-06-18

Disclosed are computational methods of clustering a set of protein structures based on local and pair-wise global similarity values. Pair-wise local and global similarity values are generated based on pair-wise structural alignments for each protein in the set of protein structures. Initially, the protein structures are clustered based on pair-wise local similarity values. The protein structures are then clustered based on pair-wise global similarity values. For each given cluster both a representative structure and spans of conserved residues are identified. The representative protein structure is used to assign newly-solved protein structures to a group. The spans are used to characterize conservation and assign a "structural footprint" to the cluster.

Traveling-cluster approximation for uncorrelated amorphous systems

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

Sen, A.K.; Mills, R.; Kaplan, T.

1984-11-15

We have developed a formalism for including cluster effects in the one-electron Green's function for a positionally disordered (liquid or amorphous) system without any correlation among the scattering sites. This method is an extension of the technique known as the traveling-cluster approximation (TCA) originally obtained and applied to a substitutional alloy by Mills and Ratanavararaksa. We have also proved the appropriate fixed-point theorem, which guarantees, for a bounded local potential, that the self-consistent equations always converge upon iteration to a unique, Herglotz solution. To our knowledge, this is the only analytic theory for considering cluster effects. Furthermore, we have performedmore » some computer calculations in the pair TCA, for the model case of delta-function potentials on a one-dimensional random chain. These results have been compared with ''exact calculations'' (which, in principle, take into account all cluster effects) and with the coherent-potential approximation (CPA), which is the single-site TCA. The density of states for the pair TCA clearly shows some improvement over the CPA and yet, apparently, the pair approximation distorts some of the features of the exact results.« less

Applying Best Practices to Florida Local Government Retrofit Programs

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

McIlvaine, J.; Sutherland, K.

In some communities, local government and non-profit entities have funds to purchase and renovate distressed, foreclosed homes for resale in the affordable housing market. Numerous opportunities to improve whole house energy efficiency are inherent in these comprehensive renovations. BA-PIRC worked together in a multi-year field study making recommendations in individual homes, meanwhile compiling improvement costs, projected energy savings, practical challenges, and labor force factors surrounding common energy-related renovation measures. The field study, Phase 1 of this research, resulted in a set of best practices appropriate to the current labor pool and market conditions in central Florida to achieve projected annualmore » energy savings of 15-30% and higher. This report describes Phase 2 of the work where researchers worked with a local government partner to implement and refine the 'current best practices.' A simulation study was conducted to characterize savings potential under three sets of conditions representing varying replacement needs for energy-related equipment and envelope components. The three scenarios apply readily to the general remodeling industry as for renovation of foreclosed homes for the affordable housing market. Our new local government partner, the City of Melbourne, implemented the best practices in a community-scale renovation program that included ten homes in 2012.« less

Applying Best Practices to Florida Local Government Retrofit Programs

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

McIlvaine, J.; Sutherland, K.

In some communities, local government and non-profit entities have funds to purchase and renovate distressed, foreclosed homes for resale in the affordable housing market. Numerous opportunities to improve whole house energy efficiency are inherent in these comprehensive renovations. BA-PIRC worked together in a multiyear field study making recommendations in individual homes, meanwhile compiling improvement costs, projected energy savings, practical challenges, and labor force factors surrounding common energy-related renovation measures. The field study, Phase 1 of this research, resulted in a set of best practices appropriate to the current labor pool and market conditions in central Florida to achieve projected annualmore » energy savings of 15%-30% and higher. This report describes Phase 2 of the work where researchers worked with a local government partner to implement and refine the "current best practices". A simulation study was conducted to characterize savings potential under three sets of conditions representing varying replacement needs for energy-related equipment and envelope components. The three scenarios apply readily to the general remodeling industry as for renovation of foreclosed homes for the affordable housing market. The new local government partner, the City of Melbourne, implemented the best practices in a community-scale renovation program that included ten homes in 2012.« less

Macula segmentation and fovea localization employing image processing and heuristic based clustering for automated retinal screening.

PubMed

R, GeethaRamani; Balasubramanian, Lakshmi

2018-07-01

Macula segmentation and fovea localization is one of the primary tasks in retinal analysis as they are responsible for detailed vision. Existing approaches required segmentation of retinal structures viz. optic disc and blood vessels for this purpose. This work avoids knowledge of other retinal structures and attempts data mining techniques to segment macula. Unsupervised clustering algorithm is exploited for this purpose. Selection of initial cluster centres has a great impact on performance of clustering algorithms. A heuristic based clustering in which initial centres are selected based on measures defining statistical distribution of data is incorporated in the proposed methodology. The initial phase of proposed framework includes image cropping, green channel extraction, contrast enhancement and application of mathematical closing. Then, the pre-processed image is subjected to heuristic based clustering yielding a binary map. The binary image is post-processed to eliminate unwanted components. Finally, the component which possessed the minimum intensity is finalized as macula and its centre constitutes the fovea. The proposed approach outperforms existing works by reporting that 100%,of HRF, 100% of DRIVE, 96.92% of DIARETDB0, 97.75% of DIARETDB1, 98.81% of HEI-MED, 90% of STARE and 99.33% of MESSIDOR images satisfy the 1R criterion, a standard adopted for evaluating performance of macula and fovea identification. The proposed system thus helps the ophthalmologists in identifying the macula thereby facilitating to identify if any abnormality is present within the macula region. Copyright © 2018 Elsevier B.V. All rights reserved.

Uncertainties in the cluster-cluster correlation function

NASA Astrophysics Data System (ADS)

Ling, E. N.; Frenk, C. S.; Barrow, J. D.

1986-12-01

The bootstrap resampling technique is applied to estimate sampling errors and significance levels of the two-point correlation functions determined for a subset of the CfA redshift survey of galaxies and a redshift sample of 104 Abell clusters. The angular correlation function for a sample of 1664 Abell clusters is also calculated. The standard errors in xi(r) for the Abell data are found to be considerably larger than quoted 'Poisson errors'. The best estimate for the ratio of the correlation length of Abell clusters (richness class R greater than or equal to 1, distance class D less than or equal to 4) to that of CfA galaxies is 4.2 + 1.4 or - 1.0 (68 percentile error). The enhancement of cluster clustering over galaxy clustering is statistically significant in the presence of resampling errors. The uncertainties found do not include the effects of possible systematic biases in the galaxy and cluster catalogs and could be regarded as lower bounds on the true uncertainty range.

Supporting breastfeeding In Local Communities (SILC) in Victoria, Australia: a cluster randomised controlled trial

PubMed Central

McLachlan, Helen L; Forster, Della A; Amir, Lisa H; Cullinane, Meabh; Shafiei, Touran; Watson, Lyndsey F; Ridgway, Lael; Cramer, Rhian L; Small, Rhonda

2016-01-01

Objectives Breastfeeding has significant health benefits for mothers and infants. Despite recommendations from the WHO, by 6 months of age 40% of Australian infants are receiving no breast milk. Increased early postpartum breastfeeding support may improve breastfeeding maintenance. 2 community-based interventions to increase breastfeeding duration in local government areas (LGAs) in Victoria, Australia, were implemented and evaluated. Design 3-arm cluster randomised trial. Setting LGAs in Victoria, Australia. Participants LGAs across Victoria with breastfeeding initiation rates below the state average and > 450 births/year were eligible for inclusion. The LGA was the unit of randomisation, and maternal and child health centres in the LGAs comprised the clusters. Interventions Early home-based breastfeeding support by a maternal and child health nurse (home visit, HV) with or without access to a community-based breastfeeding drop-in centre (HV+drop-in). Main outcome measures The proportion of infants receiving ‘any’ breast milk at 3, 4 and 6 months (women's self-report). Findings 4 LGAs were randomised to the comparison arm and provided usual care (n=41 clusters; n=2414 women); 3 to HV (n=32 clusters; n=2281 women); and 3 to HV+drop-in (n=26 clusters; 2344 women). There was no difference in breastfeeding at 4 months in either HV (adjusted OR 1.04; 95% CI 0.84 to 1.29) or HV+drop-in (adjusted OR 0.92; 95% CI 0.78 to 1.08) compared with the comparison arm, no difference at 3 or 6 months, nor in any LGA in breastfeeding before and after the intervention. Some issues were experienced with intervention protocol fidelity. Conclusions Early home-based and community-based support proved difficult to implement. Interventions to increase breastfeeding in complex community settings require sufficient time and partnership building for successful implementation. We cannot conclude that additional community-based support is ineffective in improving breastfeeding

Nearest clusters based partial least squares discriminant analysis for the classification of spectral data.

PubMed

Song, Weiran; Wang, Hui; Maguire, Paul; Nibouche, Omar

2018-06-07

Partial Least Squares Discriminant Analysis (PLS-DA) is one of the most effective multivariate analysis methods for spectral data analysis, which extracts latent variables and uses them to predict responses. In particular, it is an effective method for handling high-dimensional and collinear spectral data. However, PLS-DA does not explicitly address data multimodality, i.e., within-class multimodal distribution of data. In this paper, we present a novel method termed nearest clusters based PLS-DA (NCPLS-DA) for addressing the multimodality and nonlinearity issues explicitly and improving the performance of PLS-DA on spectral data classification. The new method applies hierarchical clustering to divide samples into clusters and calculates the corresponding centre of every cluster. For a given query point, only clusters whose centres are nearest to such a query point are used for PLS-DA. Such a method can provide a simple and effective tool for separating multimodal and nonlinear classes into clusters which are locally linear and unimodal. Experimental results on 17 datasets, including 12 UCI and 5 spectral datasets, show that NCPLS-DA can outperform 4 baseline methods, namely, PLS-DA, kernel PLS-DA, local PLS-DA and k-NN, achieving the highest classification accuracy most of the time. Copyright © 2018 Elsevier B.V. All rights reserved.

Coagulation-fragmentation for a finite number of particles and application to telomere clustering in the yeast nucleus

NASA Astrophysics Data System (ADS)

Hozé, Nathanaël; Holcman, David

2012-01-01

We develop a coagulation-fragmentation model to study a system composed of a small number of stochastic objects moving in a confined domain, that can aggregate upon binding to form local clusters of arbitrary sizes. A cluster can also dissociate into two subclusters with a uniform probability. To study the statistics of clusters, we combine a Markov chain analysis with a partition number approach. Interestingly, we obtain explicit formulas for the size and the number of clusters in terms of hypergeometric functions. Finally, we apply our analysis to study the statistical physics of telomeres (ends of chromosomes) clustering in the yeast nucleus and show that the diffusion-coagulation-fragmentation process can predict the organization of telomeres.

Applying a 2D based CAD scheme for detecting micro-calcification clusters using digital breast tomosynthesis images: an assessment

NASA Astrophysics Data System (ADS)

Park, Sang Cheol; Zheng, Bin; Wang, Xiao-Hui; Gur, David

2008-03-01

Digital breast tomosynthesis (DBT) has emerged as a promising imaging modality for screening mammography. However, visually detecting micro-calcification clusters depicted on DBT images is a difficult task. Computer-aided detection (CAD) schemes for detecting micro-calcification clusters depicted on mammograms can achieve high performance and the use of CAD results can assist radiologists in detecting subtle micro-calcification clusters. In this study, we compared the performance of an available 2D based CAD scheme with one that includes a new grouping and scoring method when applied to both projection and reconstructed DBT images. We selected a dataset involving 96 DBT examinations acquired on 45 women. Each DBT image set included 11 low dose projection images and a varying number of reconstructed image slices ranging from 18 to 87. In this dataset 20 true-positive micro-calcification clusters were visually detected on the projection images and 40 were visually detected on the reconstructed images, respectively. We first applied the CAD scheme that was previously developed in our laboratory to the DBT dataset. We then tested a new grouping method that defines an independent cluster by grouping the same cluster detected on different projection or reconstructed images. We then compared four scoring methods to assess the CAD performance. The maximum sensitivity level observed for the different grouping and scoring methods were 70% and 88% for the projection and reconstructed images with a maximum false-positive rate of 4.0 and 15.9 per examination, respectively. This preliminary study demonstrates that (1) among the maximum, the minimum or the average CAD generated scores, using the maximum score of the grouped cluster regions achieved the highest performance level, (2) the histogram based scoring method is reasonably effective in reducing false-positive detections on the projection images but the overall CAD sensitivity is lower due to lower signal-to-noise ratio

Spatial cluster analysis of nanoscopically mapped serotonin receptors for classification of fixed brain tissue

NASA Astrophysics Data System (ADS)

Sams, Michael; Silye, Rene; Göhring, Janett; Muresan, Leila; Schilcher, Kurt; Jacak, Jaroslaw

2014-01-01

We present a cluster spatial analysis method using nanoscopic dSTORM images to determine changes in protein cluster distributions within brain tissue. Such methods are suitable to investigate human brain tissue and will help to achieve a deeper understanding of brain disease along with aiding drug development. Human brain tissue samples are usually treated postmortem via standard fixation protocols, which are established in clinical laboratories. Therefore, our localization microscopy-based method was adapted to characterize protein density and protein cluster localization in samples fixed using different protocols followed by common fluorescent immunohistochemistry techniques. The localization microscopy allows nanoscopic mapping of serotonin 5-HT1A receptor groups within a two-dimensional image of a brain tissue slice. These nanoscopically mapped proteins can be confined to clusters by applying the proposed statistical spatial analysis. Selected features of such clusters were subsequently used to characterize and classify the tissue. Samples were obtained from different types of patients, fixed with different preparation methods, and finally stored in a human tissue bank. To verify the proposed method, samples of a cryopreserved healthy brain have been compared with epitope-retrieved and paraffin-fixed tissues. Furthermore, samples of healthy brain tissues were compared with data obtained from patients suffering from mental illnesses (e.g., major depressive disorder). Our work demonstrates the applicability of localization microscopy and image analysis methods for comparison and classification of human brain tissues at a nanoscopic level. Furthermore, the presented workflow marks a unique technological advance in the characterization of protein distributions in brain tissue sections.

Clique-Based Neural Associative Memories with Local Coding and Precoding.

PubMed

Mofrad, Asieh Abolpour; Parker, Matthew G; Ferdosi, Zahra; Tadayon, Mohammad H

2016-08-01

Techniques from coding theory are able to improve the efficiency of neuroinspired and neural associative memories by forcing some construction and constraints on the network. In this letter, the approach is to embed coding techniques into neural associative memory in order to increase their performance in the presence of partial erasures. The motivation comes from recent work by Gripon, Berrou, and coauthors, which revisited Willshaw networks and presented a neural network with interacting neurons that partitioned into clusters. The model introduced stores patterns as small-size cliques that can be retrieved in spite of partial error. We focus on improving the success of retrieval by applying two techniques: doing a local coding in each cluster and then applying a precoding step. We use a slightly different decoding scheme, which is appropriate for partial erasures and converges faster. Although the ideas of local coding and precoding are not new, the way we apply them is different. Simulations show an increase in the pattern retrieval capacity for both techniques. Moreover, we use self-dual additive codes over field [Formula: see text], which have very interesting properties and a simple-graph representation.

Enhancing local health department disaster response capacity with rapid community needs assessments: validation of a computerized program for binary attribute cluster sampling.

PubMed

Groenewold, Matthew R

2006-01-01

Local health departments are among the first agencies to respond to disasters or other mass emergencies. However, they often lack the ability to handle large-scale events. Plans including locally developed and deployed tools may enhance local response. Simplified cluster sampling methods can be useful in assessing community needs after a sudden-onset, short duration event. Using an adaptation of the methodology used by the World Health Organization Expanded Programme on Immunization (EPI), a Microsoft Access-based application for two-stage cluster sampling of residential addresses in Louisville/Jefferson County Metro, Kentucky was developed. The sampling frame was derived from geographically referenced data on residential addresses and political districts available through the Louisville/Jefferson County Information Consortium (LOJIC). The program randomly selected 30 clusters, defined as election precincts, from within the area of interest, and then, randomly selected 10 residential addresses from each cluster. The program, called the Rapid Assessment Tools Package (RATP), was tested in terms of accuracy and precision using data on a dichotomous characteristic of residential addresses available from the local tax assessor database. A series of 30 samples were produced and analyzed with respect to their precision and accuracy in estimating the prevalence of the study attribute. Point estimates with 95% confidence intervals were calculated by determining the proportion of the study attribute values in each of the samples and compared with the population proportion. To estimate the design effect, corresponding simple random samples of 300 addresses were taken after each of the 30 cluster samples. The sample proportion fell within +/-10 absolute percentage points of the true proportion in 80% of the samples. In 93.3% of the samples, the point estimate fell within +/-12.5%, and 96.7% fell within +/-15%. All of the point estimates fell within +/-20% of the true

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

PubMed

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

2016-01-01

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

Detecting Anomalies from End-to-End Internet Performance Measurements (PingER) Using Cluster Based Local Outlier Factor

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

Ali, Saqib; Wang, Guojun; Cottrell, Roger Leslie

PingER (Ping End-to-End Reporting) is a worldwide end-to-end Internet performance measurement framework. It was developed by the SLAC National Accelerator Laboratory, Stanford, USA and running from the last 20 years. It has more than 700 monitoring agents and remote sites which monitor the performance of Internet links around 170 countries of the world. At present, the size of the compressed PingER data set is about 60 GB comprising of 100,000 flat files. The data is publicly available for valuable Internet performance analyses. However, the data sets suffer from missing values and anomalies due to congestion, bottleneck links, queuing overflow, networkmore » software misconfiguration, hardware failure, cable cuts, and social upheavals. Therefore, the objective of this paper is to detect such performance drops or spikes labeled as anomalies or outliers for the PingER data set. In the proposed approach, the raw text files of the data set are transformed into a PingER dimensional model. The missing values are imputed using the k-NN algorithm. The data is partitioned into similar instances using the k-means clustering algorithm. Afterward, clustering is integrated with the Local Outlier Factor (LOF) using the Cluster Based Local Outlier Factor (CBLOF) algorithm to detect the anomalies or outliers from the PingER data. Lastly, anomalies are further analyzed to identify the time frame and location of the hosts generating the major percentage of the anomalies in the PingER data set ranging from 1998 to 2016.« less

Detecting Anomalies from End-to-End Internet Performance Measurements (PingER) Using Cluster Based Local Outlier Factor

DOE PAGES

Ali, Saqib; Wang, Guojun; Cottrell, Roger Leslie; ...

2018-05-28

PingER (Ping End-to-End Reporting) is a worldwide end-to-end Internet performance measurement framework. It was developed by the SLAC National Accelerator Laboratory, Stanford, USA and running from the last 20 years. It has more than 700 monitoring agents and remote sites which monitor the performance of Internet links around 170 countries of the world. At present, the size of the compressed PingER data set is about 60 GB comprising of 100,000 flat files. The data is publicly available for valuable Internet performance analyses. However, the data sets suffer from missing values and anomalies due to congestion, bottleneck links, queuing overflow, networkmore » software misconfiguration, hardware failure, cable cuts, and social upheavals. Therefore, the objective of this paper is to detect such performance drops or spikes labeled as anomalies or outliers for the PingER data set. In the proposed approach, the raw text files of the data set are transformed into a PingER dimensional model. The missing values are imputed using the k-NN algorithm. The data is partitioned into similar instances using the k-means clustering algorithm. Afterward, clustering is integrated with the Local Outlier Factor (LOF) using the Cluster Based Local Outlier Factor (CBLOF) algorithm to detect the anomalies or outliers from the PingER data. Lastly, anomalies are further analyzed to identify the time frame and location of the hosts generating the major percentage of the anomalies in the PingER data set ranging from 1998 to 2016.« less

Searching remote homology with spectral clustering with symmetry in neighborhood cluster kernels.

PubMed

Maulik, Ujjwal; Sarkar, Anasua

2013-01-01

Remote homology detection among proteins utilizing only the unlabelled sequences is a central problem in comparative genomics. The existing cluster kernel methods based on neighborhoods and profiles and the Markov clustering algorithms are currently the most popular methods for protein family recognition. The deviation from random walks with inflation or dependency on hard threshold in similarity measure in those methods requires an enhancement for homology detection among multi-domain proteins. We propose to combine spectral clustering with neighborhood kernels in Markov similarity for enhancing sensitivity in detecting homology independent of "recent" paralogs. The spectral clustering approach with new combined local alignment kernels more effectively exploits the unsupervised protein sequences globally reducing inter-cluster walks. When combined with the corrections based on modified symmetry based proximity norm deemphasizing outliers, the technique proposed in this article outperforms other state-of-the-art cluster kernels among all twelve implemented kernels. The comparison with the state-of-the-art string and mismatch kernels also show the superior performance scores provided by the proposed kernels. Similar performance improvement also is found over an existing large dataset. Therefore the proposed spectral clustering framework over combined local alignment kernels with modified symmetry based correction achieves superior performance for unsupervised remote homolog detection even in multi-domain and promiscuous domain proteins from Genolevures database families with better biological relevance. Source code available upon request. sarkar@labri.fr.

Cluster Differences Scaling with a Within-Clusters Loss Component and a Fuzzy Successive Approximation Strategy To Avoid Local Minima.

ERIC Educational Resources Information Center

Heiser, Willem J.; And Others

1997-01-01

The least squares loss function of cluster differences scaling, originally defined only on residuals of pairs allocated to different clusters, is extended with a loss component for pairs allocated to the same cluster. Findings show that this makes the method equivalent to multidimensional scaling with cluster constraints on the coordinates. (SLD)

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

Inflation data clustering of some cities in Indonesia

NASA Astrophysics Data System (ADS)

Setiawan, Adi; Susanto, Bambang; Mahatma, Tundjung

2017-06-01

In this paper, it is presented how to cluster inflation data of cities in Indonesia by using k-means cluster method and fuzzy c-means method. The data that are used is limited to the monthly inflation data from 15 cities across Indonesia which have highest weight of donations and is supplemented with 5 cities used in the calculation of inflation in Indonesia. When they are applied into two clusters with k = 2 for k-means cluster method and c = 2, w = 1.25 for fuzzy c-means cluster method, Ambon, Manado and Jayapura tend to become one cluster (high inflation) meanwhile other cities tend to become members of other cluster (low inflation). However, if they are applied into two clusters with c=2, w=1.5, Surabaya, Medan, Makasar, Samarinda, Makasar, Manado, Ambon dan Jayapura tend to become one cluster (high inflation) meanwhile other cities tend to become members of other cluster (low inflation). Furthermore, when we use two clusters with k=3 for k-means cluster method and c=3, w = 1.25 for fuzzy c-means cluster method, Ambon tends to become member of first cluster (high inflation), Manado and Jayapura tend to become member of second cluster (moderate inflation), other cities tend to become members of third cluster (low inflation). If it is applied c=3, w = 1.5, Ambon, Manado and Jayapura tend to become member of first cluster (high inflation), Surabaya, Bandung, Medan, Makasar, Banyuwangi, Denpasar, Samarinda dan Mataram tend to become members of second cluster (moderate inflation), meanwhile other cities tend to become members of third cluster (low inflation). Similarly, interpretation can be made to the results of applying 5 clusters.

The HectoMAP Cluster Survey. II. X-Ray Clusters

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

Sohn, Jubee; Chon, Gayoung; Bohringer, Hans

Here, we apply a friends-of-friends algorithm to the HectoMAP redshift survey and cross-identify associated X-ray emission in the ROSAT All-Sky Survey data (RASS). The resulting flux-limited catalog of X-ray cluster surveys is complete to a limiting flux of ~3 × 10 –13 erg s –1 cm –2 and includes 15 clusters (7 newly discovered) with redshifts z ≤ 0.4. HectoMAP is a dense survey (~1200 galaxies deg –2) that provides ~50 members (median) in each X-ray cluster. We provide redshifts for the 1036 cluster members. Subaru/Hyper Suprime-Cam imaging covers three of the X-ray systems and confirms that they are impressivemore » clusters. The HectoMAP X-ray clusters have an L X–σ cl scaling relation similar to that of known massive X-ray clusters. The HectoMAP X-ray cluster sample predicts ~12,000 ± 3000 detectable X-ray clusters in RASS to the limiting flux, comparable with previous estimates.« less

The HectoMAP Cluster Survey. II. X-Ray Clusters

DOE PAGES

Sohn, Jubee; Chon, Gayoung; Bohringer, Hans; ...

2018-03-10

Here, we apply a friends-of-friends algorithm to the HectoMAP redshift survey and cross-identify associated X-ray emission in the ROSAT All-Sky Survey data (RASS). The resulting flux-limited catalog of X-ray cluster surveys is complete to a limiting flux of ~3 × 10 –13 erg s –1 cm –2 and includes 15 clusters (7 newly discovered) with redshifts z ≤ 0.4. HectoMAP is a dense survey (~1200 galaxies deg –2) that provides ~50 members (median) in each X-ray cluster. We provide redshifts for the 1036 cluster members. Subaru/Hyper Suprime-Cam imaging covers three of the X-ray systems and confirms that they are impressivemore » clusters. The HectoMAP X-ray clusters have an L X–σ cl scaling relation similar to that of known massive X-ray clusters. The HectoMAP X-ray cluster sample predicts ~12,000 ± 3000 detectable X-ray clusters in RASS to the limiting flux, comparable with previous estimates.« less

Vertebra identification using template matching modelmp and K-means clustering.

PubMed

Larhmam, Mohamed Amine; Benjelloun, Mohammed; Mahmoudi, Saïd

2014-03-01

Accurate vertebra detection and segmentation are essential steps for automating the diagnosis of spinal disorders. This study is dedicated to vertebra alignment measurement, the first step in a computer-aided diagnosis tool for cervical spine trauma. Automated vertebral segment alignment determination is a challenging task due to low contrast imaging and noise. A software tool for segmenting vertebrae and detecting subluxations has clinical significance. A robust method was developed and tested for cervical vertebra identification and segmentation that extracts parameters used for vertebra alignment measurement. Our contribution involves a novel combination of a template matching method and an unsupervised clustering algorithm. In this method, we build a geometric vertebra mean model. To achieve vertebra detection, manual selection of the region of interest is performed initially on the input image. Subsequent preprocessing is done to enhance image contrast and detect edges. Candidate vertebra localization is then carried out by using a modified generalized Hough transform (GHT). Next, an adapted cost function is used to compute local voted centers and filter boundary data. Thereafter, a K-means clustering algorithm is applied to obtain clusters distribution corresponding to the targeted vertebrae. These clusters are combined with the vote parameters to detect vertebra centers. Rigid segmentation is then carried out by using GHT parameters. Finally, cervical spine curves are extracted to measure vertebra alignment. The proposed approach was successfully applied to a set of 66 high-resolution X-ray images. Robust detection was achieved in 97.5 % of the 330 tested cervical vertebrae. An automated vertebral identification method was developed and demonstrated to be robust to noise and occlusion. This work presents a first step toward an automated computer-aided diagnosis system for cervical spine trauma detection.

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

PubMed

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

2012-10-01

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

Emergy-based comparative analysis on industrial clusters: economic and technological development zone of Shenyang area, China.

PubMed

Liu, Zhe; Geng, Yong; Zhang, Pan; Dong, Huijuan; Liu, Zuoxi

2014-09-01

In China, local governments of many areas prefer to give priority to the development of heavy industrial clusters in pursuit of high value of gross domestic production (GDP) growth to get political achievements, which usually results in higher costs from ecological degradation and environmental pollution. Therefore, effective methods and reasonable evaluation system are urgently needed to evaluate the overall efficiency of industrial clusters. Emergy methods links economic and ecological systems together, which can evaluate the contribution of ecological products and services as well as the load placed on environmental systems. This method has been successfully applied in many case studies of ecosystem but seldom in industrial clusters. This study applied the methodology of emergy analysis to perform the efficiency of industrial clusters through a series of emergy-based indices as well as the proposed indicators. A case study of Shenyang Economic Technological Development Area (SETDA) was investigated to show the emergy method's practical potential to evaluate industrial clusters to inform environmental policy making. The results of our study showed that the industrial cluster of electric equipment and electronic manufacturing produced the most economic value and had the highest efficiency of energy utilization among the four industrial clusters. However, the sustainability index of the industrial cluster of food and beverage processing was better than the other industrial clusters.

Spatial Clustering and Local Risk Factors of Chronic Obstructive Pulmonary Disease (COPD).

PubMed

Chan, Ta-Chien; Wang, Hsuan-Wen; Tseng, Tzu-Jung; Chiang, Po-Huang

2015-12-10

Chronic obstructive pulmonary disease (COPD) mortality has been steadily increasing in Taiwan since 2009. In order to understand where the hotspot areas are and what the local risk factors are, we integrated an ecological and a case-control study. We used a two-stage approach to identify hotspots and explore the possible risk factors for developing COPD. The first stage used the annual township COPD mortality from 2000 to 2012 and applied the retrospective space-time scan statistic to calculate the local relative risks in each township. In the second stage, we conducted a case-control study, recruiting 200 patients from one local hospital within the one identified hotspot area located in southern Taiwan. Logistic regression was applied for analyzing the personal risk factors of COPD. The univariate analyses showed that higher percentages of aborigines, patients with tuberculosis (TB) history, and those with smoking history had COPD (p < 0.05). After controlling for demographic variables, aboriginal status (adjusted odds ratios (AORs): 3.01, 95% CI: 1.52-5.93) and smoking history (AORs: 2.64, 95% CI: 1.46-4.76) were still the two significant risk factors. This two-stage approach might be beneficial to examine and cross-validate the findings from an aggregate to an individual scale, and can be easily extended to other chronic diseases.

Local induction of acetylcholine receptor clustering in myotube cultures using microfluidic application of agrin.

PubMed

Tourovskaia, Anna; Kosar, T Fettah; Folch, Albert

2006-03-15

During neuromuscular synaptogenesis, the exchange of spatially localized signals between nerve and muscle initiates the coordinated focal accumulation of the acetylcholine (ACh) release machinery and the ACh receptors (AChRs). One of the key first steps is the release of the proteoglycan agrin focalized at the axon tip, which induces the clustering of AChRs on the postsynaptic membrane at the neuromuscular junction. The lack of a suitable method for focal application of agrin in myotube cultures has limited the majority of in vitro studies to the application of agrin baths. We used a microfluidic device and surface microengineering to focally stimulate muscle cells with agrin at a small portion of their membrane and at a time and position chosen by the user. The device is used to verify the hypothesis that focal application of agrin to the muscle cell membrane induces local aggregation of AChRs in differentiated C2C12 myotubes.

Hubble Space Telescope Snapshot Search for Planetary Nebulae in Globular Clusters of the Local Group

NASA Astrophysics Data System (ADS)

Bond, Howard E.

2015-04-01

Single stars in ancient globular clusters (GCs) are believed incapable of producing planetary nebulae (PNs), because their post-asymptotic-giant-branch evolutionary timescales are slower than the dissipation timescales for PNs. Nevertheless, four PNs are known in Galactic GCs. Their existence likely requires more exotic evolutionary channels, including stellar mergers and common-envelope binary interactions. I carried out a snapshot imaging search with the Hubble Space Telescope (HST) for PNs in bright Local Group GCs outside the Milky Way. I used a filter covering the 5007 Å nebular emission line of [O iii], and another one in the nearby continuum, to image 66 GCs. Inclusion of archival HST frames brought the total number of extragalactic GCs imaged at 5007 Å to 75, whose total luminosity slightly exceeds that of the entire Galactic GC system. I found no convincing PNs in these clusters, aside from one PN in a young M31 cluster misclassified as a GC, and two PNs at such large angular separations from an M31 GC that membership is doubtful. In a ground-based spectroscopic survey of 274 old GCs in M31, Jacoby et al. found three candidate PNs. My HST images of one of them suggest that the [O iii] emission actually arises from ambient interstellar medium rather than a PN; for the other two candidates, there are broadband archival UV HST images that show bright, blue point sources that are probably the PNs. In a literature search, I also identified five further PN candidates lying near old GCs in M31, for which follow-up observations are necessary to confirm their membership. The rates of incidence of PNs are similar, and small but nonzero, throughout the GCs of the Local Group. Based on observations with the NASA/ESA Hubble Space Telescope obtained at the Space Telescope Science Institute, and from the data archive at STScI, which are operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS5-26555.

Detection of Functional Change Using Cluster Trend Analysis in Glaucoma.

PubMed

Gardiner, Stuart K; Mansberger, Steven L; Demirel, Shaban

2017-05-01

Global analyses using mean deviation (MD) assess visual field progression, but can miss localized changes. Pointwise analyses are more sensitive to localized progression, but more variable so require confirmation. This study assessed whether cluster trend analysis, averaging information across subsets of locations, could improve progression detection. A total of 133 test-retest eyes were tested 7 to 10 times. Rates of change and P values were calculated for possible re-orderings of these series to generate global analysis ("MD worsening faster than x dB/y with P < y"), pointwise and cluster analyses ("n locations [or clusters] worsening faster than x dB/y with P < y") with specificity exactly 95%. These criteria were applied to 505 eyes tested over a mean of 10.5 years, to find how soon each detected "deterioration," and compared using survival models. This was repeated including two subsequent visual fields to determine whether "deterioration" was confirmed. The best global criterion detected deterioration in 25% of eyes in 5.0 years (95% confidence interval [CI], 4.7-5.3 years), compared with 4.8 years (95% CI, 4.2-5.1) for the best cluster analysis criterion, and 4.1 years (95% CI, 4.0-4.5) for the best pointwise criterion. However, for pointwise analysis, only 38% of these changes were confirmed, compared with 61% for clusters and 76% for MD. The time until 25% of eyes showed subsequently confirmed deterioration was 6.3 years (95% CI, 6.0-7.2) for global, 6.3 years (95% CI, 6.0-7.0) for pointwise, and 6.0 years (95% CI, 5.3-6.6) for cluster analyses. Although the specificity is still suboptimal, cluster trend analysis detects subsequently confirmed deterioration sooner than either global or pointwise analyses.

Spatial cluster analysis of human cases of Crimean Congo hemorrhagic fever reported in Pakistan.

PubMed

Abbas, Tariq; Younus, Muhammad; Muhammad, Sayyad Aun

2015-01-01

Crimean Congo hemorrhagic fever (CCHF) is a tick-borne viral zoonotic disease that has been reported in almost all geographic regions in Pakistan. The aim of this study was to identify spatial clusters of human cases of CCHF reported in country. Kulldorff's spatial scan statisitc, Anselin's Local Moran's I and Getis Ord Gi* tests were applied on data (i.e. number of laboratory confirmed cases reported from each district during year 2013). The analyses revealed a large multi-district cluster of high CCHF incidence in the uplands of Balochistan province near it border with Afghanistan. The cluster comprised the following districts: Qilla Abdullah; Qilla Saifullah; Loralai, Quetta, Sibi, Chagai, and Mastung. Another cluster was detected in Punjab and included Rawalpindi district and a part of Islamabad. We provide empirical evidence of spatial clustering of human CCHF cases in the country. The districts in the clusters should be given priority in surveillance, control programs, and further research.

Defining objective clusters for rabies virus sequences using affinity propagation clustering

PubMed Central

Fischer, Susanne; Freuling, Conrad M.; Pfaff, Florian; Bodenhofer, Ulrich; Höper, Dirk; Fischer, Mareike; Marston, Denise A.; Fooks, Anthony R.; Mettenleiter, Thomas C.; Conraths, Franz J.; Homeier-Bachmann, Timo

2018-01-01

Rabies is caused by lyssaviruses, and is one of the oldest known zoonoses. In recent years, more than 21,000 nucleotide sequences of rabies viruses (RABV), from the prototype species rabies lyssavirus, have been deposited in public databases. Subsequent phylogenetic analyses in combination with metadata suggest geographic distributions of RABV. However, these analyses somewhat experience technical difficulties in defining verifiable criteria for cluster allocations in phylogenetic trees inviting for a more rational approach. Therefore, we applied a relatively new mathematical clustering algorythm named ‘affinity propagation clustering’ (AP) to propose a standardized sub-species classification utilizing full-genome RABV sequences. Because AP has the advantage that it is computationally fast and works for any meaningful measure of similarity between data samples, it has previously been applied successfully in bioinformatics, for analysis of microarray and gene expression data, however, cluster analysis of sequences is still in its infancy. Existing (516) and original (46) full genome RABV sequences were used to demonstrate the application of AP for RABV clustering. On a global scale, AP proposed four clusters, i.e. New World cluster, Arctic/Arctic-like, Cosmopolitan, and Asian as previously assigned by phylogenetic studies. By combining AP with established phylogenetic analyses, it is possible to resolve phylogenetic relationships between verifiably determined clusters and sequences. This workflow will be useful in confirming cluster distributions in a uniform transparent manner, not only for RABV, but also for other comparative sequence analyses. PMID:29357361

Protoplanetary disc truncation mechanisms in stellar clusters: comparing external photoevaporation and tidal encounters

NASA Astrophysics Data System (ADS)

Winter, A. J.; Clarke, C. J.; Rosotti, G.; Ih, J.; Facchini, S.; Haworth, T. J.

2018-04-01

Most stars form and spend their early life in regions of enhanced stellar density. Therefore the evolution of protoplanetary discs (PPDs) hosted by such stars are subject to the influence of other members of the cluster. Physically, PPDs might be truncated either by photoevaporation due to ultraviolet flux from massive stars, or tidal truncation due to close stellar encounters. Here we aim to compare the two effects in real cluster environments. In this vein we first review the properties of well studied stellar clusters with a focus on stellar number density, which largely dictates the degree of tidal truncation, and far ultraviolet (FUV) flux, which is indicative of the rate of external photoevaporation. We then review the theoretical PPD truncation radius due to an arbitrary encounter, additionally taking into account the role of eccentric encounters that play a role in hot clusters with a 1D velocity dispersion σv ≳ 2 km/s. Our treatment is then applied statistically to varying local environments to establish a canonical threshold for the local stellar density (nc ≳ 104 pc-3) for which encounters can play a significant role in shaping the distribution of PPD radii over a timescale ˜3 Myr. By combining theoretical mass loss rates due to FUV flux with viscous spreading in a PPD we establish a similar threshold for which a massive disc is completely destroyed by external photoevaporation. Comparing these thresholds in local clusters we find that if either mechanism has a significant impact on the PPD population then photoevaporation is always the dominating influence.

Searching Remote Homology with Spectral Clustering with Symmetry in Neighborhood Cluster Kernels

PubMed Central

Maulik, Ujjwal; Sarkar, Anasua

2013-01-01

Remote homology detection among proteins utilizing only the unlabelled sequences is a central problem in comparative genomics. The existing cluster kernel methods based on neighborhoods and profiles and the Markov clustering algorithms are currently the most popular methods for protein family recognition. The deviation from random walks with inflation or dependency on hard threshold in similarity measure in those methods requires an enhancement for homology detection among multi-domain proteins. We propose to combine spectral clustering with neighborhood kernels in Markov similarity for enhancing sensitivity in detecting homology independent of “recent” paralogs. The spectral clustering approach with new combined local alignment kernels more effectively exploits the unsupervised protein sequences globally reducing inter-cluster walks. When combined with the corrections based on modified symmetry based proximity norm deemphasizing outliers, the technique proposed in this article outperforms other state-of-the-art cluster kernels among all twelve implemented kernels. The comparison with the state-of-the-art string and mismatch kernels also show the superior performance scores provided by the proposed kernels. Similar performance improvement also is found over an existing large dataset. Therefore the proposed spectral clustering framework over combined local alignment kernels with modified symmetry based correction achieves superior performance for unsupervised remote homolog detection even in multi-domain and promiscuous domain proteins from Genolevures database families with better biological relevance. Source code available upon request. Contact: sarkar@labri.fr. PMID:23457439

Local resonances in STM manipulation of chlorobenzene on Si(111)-7×7: performance of different cluster models and density functionals

NASA Astrophysics Data System (ADS)

Utecht, Manuel; Klamroth, Tillmann

2018-07-01

Hot localised charge carriers on the Si(111)-7×7 surface are modelled by small charged clusters. Such resonances induce non-local desorption, i.e. more than 10 nm away from the injection site, of chlorobenzene in scanning tunnelling microscope experiments. We used such a cluster model to characterise resonance localisation and vibrational activation for positive and negative resonances recently. In this work, we investigate to which extent the model depends on details of the used cluster or quantum chemistry methods and try to identify the smallest possible cluster suitable for a description of the neutral surface and the ion resonances. Furthermore, a detailed analysis for different chemisorption orientations is performed. While some properties, as estimates of the resonance energy or absolute values for atomic changes, show such a dependency, the main findings are very robust with respect to changes in the model and/or the chemisorption geometry.

Topological Aspects of Infinite Metal Clusters and Superconductors.

DTIC Science & Technology

1987-08-11

tetrahedra. T chemical bonding topologies qf discrete octahedral metal clusters can be either edge- localized (e.g., MoX8L 6 4 derivatives), face- localized ...constructed from edge- localized metal polyhedra such as the Mo 6 octahedra in the ternary molybdenum chalcogenides (Chevrel phases) and Rh4 tetrahedra in the...chemical bonding topologies of discrete octahedral metal clusters can be either e4ge- localized (e.g., No X L 4+ derivatives), face- localized (e.g

Cluster dynamics and cluster size distributions in systems of self-propelled particles

NASA Astrophysics Data System (ADS)

Peruani, F.; Schimansky-Geier, L.; Bär, M.

2010-12-01

Systems of self-propelled particles (SPP) interacting by a velocity alignment mechanism in the presence of noise exhibit rich clustering dynamics. Often, clusters are responsible for the distribution of (local) information in these systems. Here, we investigate the properties of individual clusters in SPP systems, in particular the asymmetric spreading behavior of clusters with respect to their direction of motion. In addition, we formulate a Smoluchowski-type kinetic model to describe the evolution of the cluster size distribution (CSD). This model predicts the emergence of steady-state CSDs in SPP systems. We test our theoretical predictions in simulations of SPP with nematic interactions and find that our simple kinetic model reproduces qualitatively the transition to aggregation observed in simulations.

Extending Beowulf Clusters

USGS Publications Warehouse

Steinwand, Daniel R.; Maddox, Brian; Beckmann, Tim; Hamer, George

2003-01-01

Beowulf clusters can provide a cost-effective way to compute numerical models and process large amounts of remote sensing image data. Usually a Beowulf cluster is designed to accomplish a specific set of processing goals, and processing is very efficient when the problem remains inside the constraints of the original design. There are cases, however, when one might wish to compute a problem that is beyond the capacity of the local Beowulf system. In these cases, spreading the problem to multiple clusters or to other machines on the network may provide a cost-effective solution.

Composition Formulas of Inorganic Compounds in Terms of Cluster Plus Glue Atom Model.

PubMed

Ma, Yanping; Dong, Dandan; Wu, Aimin; Dong, Chuang

2018-01-16

The present paper attempts to identify the molecule-like structural units in inorganic compounds, by applying the so-called "cluster plus glue atom model". This model, originating from metallic glasses and quasi-crystals, describes any structure in terms of a nearest-neighbor cluster and a few outer-shell glue atoms, expressed in the cluster formula [cluster](glue atoms). Similar to the case for normal molecules where the charge transfer occurs within the molecule to meet the commonly known octet electron rule, the octet state is reached after matching the nearest-neighbor cluster with certain outer-shell glue atoms. These kinds of structural units contain information on local atomic configuration, chemical composition, and electron numbers, just as for normal molecules. It is shown that the formulas of typical inorganic compounds, such as fluorides, oxides, and nitrides, satisfy a similar octet electron rule, with the total number of valence electrons per unit formula being multiples of eight.

Ages of Extragalactic Intermediate-Age Star Clusters

NASA Technical Reports Server (NTRS)

Flower, P. J.

1983-01-01

A dating technique for faint, distant star clusters observable in the local group of galaxies with the space telescope is discussed. Color-magnitude diagrams of Magellanic Cloud clusters are mentioned along with the metallicity of star clusters.

Estimating the concrete compressive strength using hard clustering and fuzzy clustering based regression techniques.

PubMed

Nagwani, Naresh Kumar; Deo, Shirish V

2014-01-01

Understanding of the compressive strength of concrete is important for activities like construction arrangement, prestressing operations, and proportioning new mixtures and for the quality assurance. Regression techniques are most widely used for prediction tasks where relationship between the independent variables and dependent (prediction) variable is identified. The accuracy of the regression techniques for prediction can be improved if clustering can be used along with regression. Clustering along with regression will ensure the more accurate curve fitting between the dependent and independent variables. In this work cluster regression technique is applied for estimating the compressive strength of the concrete and a novel state of the art is proposed for predicting the concrete compressive strength. The objective of this work is to demonstrate that clustering along with regression ensures less prediction errors for estimating the concrete compressive strength. The proposed technique consists of two major stages: in the first stage, clustering is used to group the similar characteristics concrete data and then in the second stage regression techniques are applied over these clusters (groups) to predict the compressive strength from individual clusters. It is found from experiments that clustering along with regression techniques gives minimum errors for predicting compressive strength of concrete; also fuzzy clustering algorithm C-means performs better than K-means algorithm.

Estimating the Concrete Compressive Strength Using Hard Clustering and Fuzzy Clustering Based Regression Techniques

PubMed Central

Nagwani, Naresh Kumar; Deo, Shirish V.

2014-01-01

Understanding of the compressive strength of concrete is important for activities like construction arrangement, prestressing operations, and proportioning new mixtures and for the quality assurance. Regression techniques are most widely used for prediction tasks where relationship between the independent variables and dependent (prediction) variable is identified. The accuracy of the regression techniques for prediction can be improved if clustering can be used along with regression. Clustering along with regression will ensure the more accurate curve fitting between the dependent and independent variables. In this work cluster regression technique is applied for estimating the compressive strength of the concrete and a novel state of the art is proposed for predicting the concrete compressive strength. The objective of this work is to demonstrate that clustering along with regression ensures less prediction errors for estimating the concrete compressive strength. The proposed technique consists of two major stages: in the first stage, clustering is used to group the similar characteristics concrete data and then in the second stage regression techniques are applied over these clusters (groups) to predict the compressive strength from individual clusters. It is found from experiments that clustering along with regression techniques gives minimum errors for predicting compressive strength of concrete; also fuzzy clustering algorithm C-means performs better than K-means algorithm. PMID:25374939

Ensemble Clustering Classification Applied to Competing SVM and One-Class Classifiers Exemplified by Plant MicroRNAs Data.

PubMed

Yousef, Malik; Khalifa, Waleed; AbdAllah, Loai

2016-12-01

The performance of many learning and data mining algorithms depends critically on suitable metrics to assess efficiency over the input space. Learning a suitable metric from examples may, therefore, be the key to successful application of these algorithms. We have demonstrated that the k-nearest neighbor (kNN) classification can be significantly improved by learning a distance metric from labeled examples. The clustering ensemble is used to define the distance between points in respect to how they co-cluster. This distance is then used within the framework of the kNN algorithm to define a classifier named ensemble clustering kNN classifier (EC-kNN). In many instances in our experiments we achieved highest accuracy while SVM failed to perform as well. In this study, we compare the performance of a two-class classifier using EC-kNN with different one-class and two-class classifiers. The comparison was applied to seven different plant microRNA species considering eight feature selection methods. In this study, the averaged results show that EC-kNN outperforms all other methods employed here and previously published results for the same data. In conclusion, this study shows that the chosen classifier shows high performance when the distance metric is carefully chosen.

Density-functional theory applied to d- and f-electron systems

NASA Astrophysics Data System (ADS)

Wu, Xueyuan

Density functional theory (DFT) has been applied to study the electronic and geometric structures of prototype d- and f-electron systems. For the d-electron system, all electron DFT with gradient corrections to the exchange and correlation functionals has been used to investigate the properties of small neutral and cationic vanadium clusters. Results are in good agreement with available experimental and other theoretical data. For the f-electron system, a hybrid DFT, namely, B3LYP (Becke's 3-parameter hybrid functional using the correlation functional of Lee, Yang and Parr) with relativistic effective core potentials and cluster models has been applied to investigate the nature of chemical bonding of both the bulk and the surfaces of plutonium monoxide and dioxide. Using periodic models, the electronic and geometric structures of PuO2 and its (110) surface, as well as water adsorption on this surface have also been investigated using DFT in both local density approximation (LDA) and generalized gradient approximation (GGA) formalisms.

20 CFR 666.310 - What levels of performance apply to the indicators of performance in local areas?

Code of Federal Regulations, 2011 CFR

2011-04-01

... indicators of performance in local areas? 666.310 Section 666.310 Employees' Benefits EMPLOYMENT AND TRAINING... Local Measures of Performance § 666.310 What levels of performance apply to the indicators of... Governor and reach agreement on the local levels of performance for each indicator identified under § 666...

20 CFR 666.310 - What levels of performance apply to the indicators of performance in local areas?

Code of Federal Regulations, 2010 CFR

2010-04-01

... indicators of performance in local areas? 666.310 Section 666.310 Employees' Benefits EMPLOYMENT AND TRAINING... Local Measures of Performance § 666.310 What levels of performance apply to the indicators of... Governor and reach agreement on the local levels of performance for each indicator identified under § 666...

Experimental and theoretical study of the microsolvation of sodium atoms in methanol clusters: differences and similarities to sodium-water and sodium-ammonia.

PubMed

Dauster, Ingo; Suhm, Martin A; Buck, Udo; Zeuch, Thomas

2008-01-07

Methanol clusters are generated in a continuous He-seeded supersonic expansion and doped with sodium atoms in a pick-up cell. By this method, clusters of the type Na(CH(3)OH)(n) are formed and subsequently photoionized by applying a tunable dye-laser system. The microsolvation process of the Na 3s electron is studied by determining the ionization potentials (IPs) of these clusters size-selectively for n = 2-40. A decrease is found from n = 2 to 6 and a constant value of 3.19 +/- 0.07 eV for n = 6-40. The experimentally-determined ionization potentials are compared with ionization potentials derived from quantum-chemical calculations, assuming limiting vertical and adiabatic processes. In the first case, energy differences are calculated between the neutral and the ionized cationic clusters of the same geometry. In the second case, the ionized clusters are used in their optimized relaxed geometry. These energy differences and relative stabilities of isomeric clusters vary significantly with the applied quantum-chemical method (B3LYP or MP2). The comparison with the experiment for n = 2-7 reveals strong variations of the ionization potential with the cluster structure indicating that structural diversity and non-vertical pathways give significant signal contributions at the threshold. Based on these findings, a possible explanation for the remarkable difference in IP evolutions of methanol or water and ammonia is presented: for methanol and water a rather localized surface or semi-internal Na 3s electron is excited to either high Rydberg or more localized states below the vertical ionization threshold. This excitation is followed by a local structural relaxation that couples to an autoionization process. For small clusters with n < 6 for methanol and n < 4 for water the addition of solvent molecules leads to larger solvent-metal-ion interaction energies, which consequently lead to lower ionization thresholds. For n = 6 (methanol) and n = 4 (water) this effect comes

Finding reproducible cluster partitions for the k-means algorithm

PubMed Central

2013-01-01

K-means clustering is widely used for exploratory data analysis. While its dependence on initialisation is well-known, it is common practice to assume that the partition with lowest sum-of-squares (SSQ) total i.e. within cluster variance, is both reproducible under repeated initialisations and also the closest that k-means can provide to true structure, when applied to synthetic data. We show that this is generally the case for small numbers of clusters, but for values of k that are still of theoretical and practical interest, similar values of SSQ can correspond to markedly different cluster partitions. This paper extends stability measures previously presented in the context of finding optimal values of cluster number, into a component of a 2-d map of the local minima found by the k-means algorithm, from which not only can values of k be identified for further analysis but, more importantly, it is made clear whether the best SSQ is a suitable solution or whether obtaining a consistently good partition requires further application of the stability index. The proposed method is illustrated by application to five synthetic datasets replicating a real world breast cancer dataset with varying data density, and a large bioinformatics dataset. PMID:23369085

Finding reproducible cluster partitions for the k-means algorithm.

PubMed

Lisboa, Paulo J G; Etchells, Terence A; Jarman, Ian H; Chambers, Simon J

2013-01-01

K-means clustering is widely used for exploratory data analysis. While its dependence on initialisation is well-known, it is common practice to assume that the partition with lowest sum-of-squares (SSQ) total i.e. within cluster variance, is both reproducible under repeated initialisations and also the closest that k-means can provide to true structure, when applied to synthetic data. We show that this is generally the case for small numbers of clusters, but for values of k that are still of theoretical and practical interest, similar values of SSQ can correspond to markedly different cluster partitions. This paper extends stability measures previously presented in the context of finding optimal values of cluster number, into a component of a 2-d map of the local minima found by the k-means algorithm, from which not only can values of k be identified for further analysis but, more importantly, it is made clear whether the best SSQ is a suitable solution or whether obtaining a consistently good partition requires further application of the stability index. The proposed method is illustrated by application to five synthetic datasets replicating a real world breast cancer dataset with varying data density, and a large bioinformatics dataset.

Structure and vibrational spectra of low-energy silicon clusters

NASA Astrophysics Data System (ADS)

Sieck, A.; Porezag, D.; Frauenheim, Th.; Pederson, M. R.; Jackson, K.

1997-12-01

We have identified low-energy structures of silicon clusters with 9 to 14 atoms using a nonorthogonal tight-binding method (TB) based on density-functional theory (DF). We have further investigated the resulting structures with an accurate all-electron first-principles technique. The results for cohesive energies, cluster geometries, and highest occupied to lowest unoccupied molecular orbital (HOMO-LUMO) gaps show an overall good agreement between DF-TB and self-consistent-field (SCF) DF theory. For Si9 and Si14, we have found equilibrium structures, whereas for Si11, Si12, and Si13, we present clusters with energies close to that of the corresponding ground-state structure recently proposed in the literature. The bonding scheme of clusters in this size range is different from the bulk tetrahedral symmetry. The most stable structures, characterized by low energies and large HOMO-LUMO gaps, have similar common subunits. To aid in their experimental identification, we have computed the full vibrational spectra of the structures, along with the Raman activities, IR intensities, and static polarizabilities, using SCF-DF theory within the local-density approximation (LDA). This method has already been successfully applied to the determination of Raman and IR spectra of silicon clusters with 3-8, 10, 13, 20, and 21 atoms.

The molecular epidemiology of HIV-1 in the Comunidad Valenciana (Spain): analysis of transmission clusters.

PubMed

Patiño-Galindo, Juan Ángel; Torres-Puente, Manoli; Bracho, María Alma; Alastrué, Ignacio; Juan, Amparo; Navarro, David; Galindo, María José; Ocete, Dolores; Ortega, Enrique; Gimeno, Concepción; Belda, Josefina; Domínguez, Victoria; Moreno, Rosario; González-Candelas, Fernando

2017-09-14

HIV infections are still a very serious concern for public heath worldwide. We have applied molecular evolution methods to study the HIV-1 epidemics in the Comunidad Valenciana (CV, Spain) from a public health surveillance perspective. For this, we analysed 1804 HIV-1 sequences comprising protease and reverse transcriptase (PR/RT) coding regions, sampled between 2004 and 2014. These sequences were subtyped and subjected to phylogenetic analyses in order to detect transmission clusters. In addition, univariate and multinomial comparisons were performed to detect epidemiological differences between HIV-1 subtypes, and risk groups. The HIV epidemic in the CV is dominated by subtype B infections among local men who have sex with men (MSM). 270 transmission clusters were identified (>57% of the dataset), 12 of which included ≥10 patients; 11 of subtype B (9 affecting MSMs) and one (n = 21) of CRF14, affecting predominately intravenous drug users (IDUs). Dated phylogenies revealed these large clusters to have originated from the mid-80s to the early 00 s. Subtype B is more likely to form transmission clusters than non-B variants and MSMs to cluster than other risk groups. Multinomial analyses revealed an association between non-B variants, which are not established in the local population yet, and different foreign groups.

Hausdorff clustering

NASA Astrophysics Data System (ADS)

Basalto, Nicolas; Bellotti, Roberto; de Carlo, Francesco; Facchi, Paolo; Pantaleo, Ester; Pascazio, Saverio

2008-10-01

A clustering algorithm based on the Hausdorff distance is analyzed and compared to the single, complete, and average linkage algorithms. The four clustering procedures are applied to a toy example and to the time series of financial data. The dendrograms are scrutinized and their features compared. The Hausdorff linkage relies on firm mathematical grounds and turns out to be very effective when one has to discriminate among complex structures.

Locally applied leptin induces regional aortic wall degeneration preceding aneurysm formation in apolipoprotein E-deficient mice.

PubMed

Tao, Ming; Yu, Peng; Nguyen, Binh T; Mizrahi, Boaz; Savion, Naphtali; Kolodgie, Frank D; Virmani, Renu; Hao, Shuai; Ozaki, C Keith; Schneiderman, Jacob

2013-02-01

Leptin promotes atherosclerosis and vessel wall remodeling. As abdominal aortic aneurysm (AAA) formation involves tissue remodeling, we hypothesized that local leptin synthesis initiates and promotes this process. Human surgical AAA walls were analyzed for antigen and mRNA levels of leptin and leptin receptor, as well as mRNA for matrix metalloproteinases (MMP)-9 and MMP-12. Leptin and leptin receptor antigen were evident in all AAAs, and leptin, MMP-9, and MMP-12 mRNA was increased relative to age-matched nondilated controls. To simulate in vivo local leptin synthesis, ApoE(-/-) mice were subjected to a paravisceral periaortic application of low-dose leptin. Leptin-treated aortas exhibited decreased transforming growth factor-β and increased MMP-9 mRNA levels 5 days after surgery, and leptin receptor mRNA was upregulated by day 28. Serial ultrasonography demonstrated accelerated regional aortic diameter growth after 28 days, correlating with local medial degeneration, increased MMP-9, MMP-12, and periadventitial macrophage clustering. Furthermore, the combination of local periaortic leptin and systemic angiotensin II administration augmented medial MMP-9 synthesis and aortic aneurysm size. Leptin is locally synthesized in human AAA wall. Paravisceral aortic leptin in ApoE(-/-) mice induces local medial degeneration and augments angiotensin II-induced AAA, thus suggesting novel mechanistic links between leptin and AAA formation.

Locally Applied Leptin Induces Regional Aortic Wall Degeneration Preceding Aneurysm Formation in ApoE Deficient Mice

PubMed Central

Tao, Ming; Yu, Peng; Nguyen, Binh T.; Mizrahi, Boaz; Savion, Naphtali; Kolodgie, Frank D.; Virmani, Renu; Hao, Shuai; Ozaki, C. Keith; Schneiderman, Jacob

2013-01-01

Objective Leptin promotes atherosclerosis and vessel wall remodeling. As abdominal aorta aneurysm (AAA) formation involves tissue remodeling, we hypothesized that local leptin synthesis initiates and promotes this process. Methods and Results Human surgical AAA walls were analyzed for antigen and mRNA levels of leptin and leptin receptor (ObR), as well as mRNA for matrix metalloproteinases (MMP)-9, and MMP-12. Leptin and ObR antigen were evident in all AAAs, and, leptin, MMP-9, and MMP-12 mRNA was increased relative to age-matched non-dilated controls. To simulate in vivo local leptin synthesis, ApoE-/- mice were subjected to a para-visceral peri-aortic application of low-dose leptin. Leptin-treated aortas exhibited decreased TGFβ and increased MMP-9 mRNA levels 5 days after surgery, and ObR mRNA was up-regulated by day 28. Serial ultrasonography demonstrated accelerated regional aortic diameter growth after 28 days, correlating with local medial degeneration, increased MMP-9, MMP-12 and peri-adventitial macrophage clustering. Furthermore, the combination of local peri-aortic leptin and systemic angiotensin II administration augmented medial MMP-9 synthesis and aortic aneurysm size. Conclusions Leptin is locally synthesized in human AAA wall. Para-visceral aortic leptin in ApoE-/- mice induces local medial degeneration, and augments angiotensin II-induced AAA, thus suggesting novel mechanistic links between leptin and AAA formation. PMID:23220275

Reproducing the local and global morphological segregation between S and S0 galaxies in rich clusters by simple ram-pressure stripping

NASA Astrophysics Data System (ADS)

Solanes, Jose M.; Salvador-Sole, Eduardo

1992-08-01

We calculate the morphological segregation in rich galaxy clusters expected to arise from the possible evolution of S galaxies into S0 galaxies via the gas removal of their disks by ram-pressure stripping. The calculation is run on Monte Carlo simulations by following individual S galaxies in the potential well of a spherical anisotropic cluster making use of Gunn and Gott's (1972) stripping condition. The results are compared with both Dressler's (1980) local type/density relation and a global population-richness correlation inferred from real data in the present work. We find that, contrary to a rather extended opinion, this evolution scheme reproduces very well the observed morphological segregation between S and S0 galaxies in rich clusters provided that the initial populations are close to those i dense loose groups.

Localization Microscopy Analyses of MRE11 Clusters in 3D-Conserved Cell Nuclei of Different Cell Lines.

PubMed

Eryilmaz, Marion; Schmitt, Eberhard; Krufczik, Matthias; Theda, Franziska; Lee, Jin-Ho; Cremer, Christoph; Bestvater, Felix; Schaufler, Wladimir; Hausmann, Michael; Hildenbrand, Georg

2018-01-22

In radiation biophysics, it is a subject of nowadays research to investigate DNA strand break repair in detail after damage induction by ionizing radiation. It is a subject of debate as to what makes up the cell's decision to use a certain repair pathway and how the repair machinery recruited in repair foci is spatially and temporarily organized. Single-molecule localization microscopy (SMLM) allows super-resolution analysis by precise localization of single fluorescent molecule tags, resulting in nuclear structure analysis with a spatial resolution in the 10 nm regime. Here, we used SMLM to study MRE11 foci. MRE11 is one of three proteins involved in the MRN-complex (MRE11-RAD50-NBS1 complex), a prominent DNA strand resection and broken end bridging component involved in homologous recombination repair (HRR) and alternative non-homologous end joining (a-NHEJ). We analyzed the spatial arrangements of antibody-labelled MRE11 proteins in the nuclei of a breast cancer and a skin fibroblast cell line along a time-course of repair (up to 48 h) after irradiation with a dose of 2 Gy. Different kinetics for cluster formation and relaxation were determined. Changes in the internal nano-scaled structure of the clusters were quantified and compared between the two cell types. The results indicate a cell type-dependent DNA damage response concerning MRE11 recruitment and cluster formation. The MRE11 data were compared to H2AX phosphorylation detected by γH2AX molecule distribution. These data suggested modulations of MRE11 signal frequencies that were not directly correlated to DNA damage induction. The application of SMLM in radiation biophysics offers new possibilities to investigate spatial foci organization after DNA damaging and during subsequent repair.

Localization Microscopy Analyses of MRE11 Clusters in 3D-Conserved Cell Nuclei of Different Cell Lines

PubMed Central

Eryilmaz, Marion; Schmitt, Eberhard; Krufczik, Matthias; Theda, Franziska; Lee, Jin-Ho; Cremer, Christoph; Bestvater, Felix; Schaufler, Wladimir; Hildenbrand, Georg

2018-01-01

In radiation biophysics, it is a subject of nowadays research to investigate DNA strand break repair in detail after damage induction by ionizing radiation. It is a subject of debate as to what makes up the cell’s decision to use a certain repair pathway and how the repair machinery recruited in repair foci is spatially and temporarily organized. Single-molecule localization microscopy (SMLM) allows super-resolution analysis by precise localization of single fluorescent molecule tags, resulting in nuclear structure analysis with a spatial resolution in the 10 nm regime. Here, we used SMLM to study MRE11 foci. MRE11 is one of three proteins involved in the MRN-complex (MRE11-RAD50-NBS1 complex), a prominent DNA strand resection and broken end bridging component involved in homologous recombination repair (HRR) and alternative non-homologous end joining (a-NHEJ). We analyzed the spatial arrangements of antibody-labelled MRE11 proteins in the nuclei of a breast cancer and a skin fibroblast cell line along a time-course of repair (up to 48 h) after irradiation with a dose of 2 Gy. Different kinetics for cluster formation and relaxation were determined. Changes in the internal nano-scaled structure of the clusters were quantified and compared between the two cell types. The results indicate a cell type-dependent DNA damage response concerning MRE11 recruitment and cluster formation. The MRE11 data were compared to H2AX phosphorylation detected by γH2AX molecule distribution. These data suggested modulations of MRE11 signal frequencies that were not directly correlated to DNA damage induction. The application of SMLM in radiation biophysics offers new possibilities to investigate spatial foci organization after DNA damaging and during subsequent repair. PMID:29361783

28 CFR 65.85 - Procedures for State or local governments applying for funding.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 28 Judicial Administration 2 2012-07-01 2012-07-01 false Procedures for State or local governments applying for funding. 65.85 Section 65.85 Judicial Administration DEPARTMENT OF JUSTICE (CONTINUED) EMERGENCY FEDERAL LAW ENFORCEMENT ASSISTANCE Immigration Emergency Fund § 65.85 Procedures for State or...

28 CFR 65.85 - Procedures for State or local governments applying for funding.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 28 Judicial Administration 2 2014-07-01 2014-07-01 false Procedures for State or local governments applying for funding. 65.85 Section 65.85 Judicial Administration DEPARTMENT OF JUSTICE (CONTINUED) EMERGENCY FEDERAL LAW ENFORCEMENT ASSISTANCE Immigration Emergency Fund § 65.85 Procedures for State or...

28 CFR 65.85 - Procedures for State or local governments applying for funding.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 28 Judicial Administration 2 2010-07-01 2010-07-01 false Procedures for State or local governments applying for funding. 65.85 Section 65.85 Judicial Administration DEPARTMENT OF JUSTICE (CONTINUED) EMERGENCY FEDERAL LAW ENFORCEMENT ASSISTANCE Immigration Emergency Fund § 65.85 Procedures for State or...

28 CFR 65.85 - Procedures for State or local governments applying for funding.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 28 Judicial Administration 2 2011-07-01 2011-07-01 false Procedures for State or local governments applying for funding. 65.85 Section 65.85 Judicial Administration DEPARTMENT OF JUSTICE (CONTINUED) EMERGENCY FEDERAL LAW ENFORCEMENT ASSISTANCE Immigration Emergency Fund § 65.85 Procedures for State or...

28 CFR 65.85 - Procedures for State or local governments applying for funding.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 28 Judicial Administration 2 2013-07-01 2013-07-01 false Procedures for State or local governments applying for funding. 65.85 Section 65.85 Judicial Administration DEPARTMENT OF JUSTICE (CONTINUED) EMERGENCY FEDERAL LAW ENFORCEMENT ASSISTANCE Immigration Emergency Fund § 65.85 Procedures for State or...

Does clinical equipoise apply to cluster randomized trials in health research?

PubMed Central

2011-01-01

This article is part of a series of papers examining ethical issues in cluster randomized trials (CRTs) in health research. In the introductory paper in this series, Weijer and colleagues set out six areas of inquiry that must be addressed if the cluster trial is to be set on a firm ethical foundation. This paper addresses the third of the questions posed, namely, does clinical equipoise apply to CRTs in health research? The ethical principle of beneficence is the moral obligation not to harm needlessly and, when possible, to promote the welfare of research subjects. Two related ethical problems have been discussed in the CRT literature. First, are control groups that receive only usual care unduly disadvantaged? Second, when accumulating data suggests the superiority of one intervention in a trial, is there an ethical obligation to act? In individually randomized trials involving patients, similar questions are addressed by the concept of clinical equipoise, that is, the ethical requirement that, at the start of a trial, there be a state of honest, professional disagreement in the community of expert practitioners as to the preferred treatment. Since CRTs may not involve physician-researchers and patient-subjects, the applicability of clinical equipoise to CRTs is uncertain. Here we argue that clinical equipoise may be usefully grounded in a trust relationship between the state and research subjects, and, as a result, clinical equipoise is applicable to CRTs. Clinical equipoise is used to argue that control groups receiving only usual care are not disadvantaged so long as the evidence supporting the experimental and control interventions is such that experts would disagree as to which is preferred. Further, while data accumulating during the course of a CRT may favor one intervention over another, clinical equipoise supports continuing the trial until the results are likely to be broadly convincing, often coinciding with the planned completion of the trial

Case-control geographic clustering for residential histories accounting for risk factors and covariates.

PubMed

Jacquez, Geoffrey M; Meliker, Jaymie R; Avruskin, Gillian A; Goovaerts, Pierre; Kaufmann, Andy; Wilson, Mark L; Nriagu, Jerome

2006-08-03

Methods for analyzing space-time variation in risk in case-control studies typically ignore residential mobility. We develop an approach for analyzing case-control data for mobile individuals and apply it to study bladder cancer in 11 counties in southeastern Michigan. At this time data collection is incomplete and no inferences should be drawn - we analyze these data to demonstrate the novel methods. Global, local and focused clustering of residential histories for 219 cases and 437 controls is quantified using time-dependent nearest neighbor relationships. Business address histories for 268 industries that release known or suspected bladder cancer carcinogens are analyzed. A logistic model accounting for smoking, gender, age, race and education specifies the probability of being a case, and is incorporated into the cluster randomization procedures. Sensitivity of clustering to definition of the proximity metric is assessed for 1 to 75 k nearest neighbors. Global clustering is partly explained by the covariates but remains statistically significant at 12 of the 14 levels of k considered. After accounting for the covariates 26 Local clusters are found in Lapeer, Ingham, Oakland and Jackson counties, with the clusters in Ingham and Oakland counties appearing in 1950 and persisting to the present. Statistically significant focused clusters are found about the business address histories of 22 industries located in Oakland (19 clusters), Ingham (2) and Jackson (1) counties. Clusters in central and southeastern Oakland County appear in the 1930's and persist to the present day. These methods provide a systematic approach for evaluating a series of increasingly realistic alternative hypotheses regarding the sources of excess risk. So long as selection of cases and controls is population-based and not geographically biased, these tools can provide insights into geographic risk factors that were not specifically assessed in the case-control study design.

Case-control geographic clustering for residential histories accounting for risk factors and covariates

PubMed Central

2006-01-01

Background Methods for analyzing space-time variation in risk in case-control studies typically ignore residential mobility. We develop an approach for analyzing case-control data for mobile individuals and apply it to study bladder cancer in 11 counties in southeastern Michigan. At this time data collection is incomplete and no inferences should be drawn – we analyze these data to demonstrate the novel methods. Global, local and focused clustering of residential histories for 219 cases and 437 controls is quantified using time-dependent nearest neighbor relationships. Business address histories for 268 industries that release known or suspected bladder cancer carcinogens are analyzed. A logistic model accounting for smoking, gender, age, race and education specifies the probability of being a case, and is incorporated into the cluster randomization procedures. Sensitivity of clustering to definition of the proximity metric is assessed for 1 to 75 k nearest neighbors. Results Global clustering is partly explained by the covariates but remains statistically significant at 12 of the 14 levels of k considered. After accounting for the covariates 26 Local clusters are found in Lapeer, Ingham, Oakland and Jackson counties, with the clusters in Ingham and Oakland counties appearing in 1950 and persisting to the present. Statistically significant focused clusters are found about the business address histories of 22 industries located in Oakland (19 clusters), Ingham (2) and Jackson (1) counties. Clusters in central and southeastern Oakland County appear in the 1930's and persist to the present day. Conclusion These methods provide a systematic approach for evaluating a series of increasingly realistic alternative hypotheses regarding the sources of excess risk. So long as selection of cases and controls is population-based and not geographically biased, these tools can provide insights into geographic risk factors that were not specifically assessed in the case

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

NASA Astrophysics Data System (ADS)

Closser, Kristina Danielle

superpositions of atomic states with surface states appearing close to the atomic excitation energies and interior states being blue shifted by up to ≈2 eV. The dynamics resulting from excitation of He_7 were subsequently explored using ab initio molecular dynamics (AIMD). These simulations were performed with classical adiabatic dynamics coupled to a new state-following algorithm on CIS potential energy surfaces. Most clusters were found to completely dissociate and resulted in a single excited atomic state (90%), however, some trajectories formed bound, He*2 (3%), and a few yielded excited trimers (<0.5%). Comparisons were made with available experimental information on much larger clusters. Various applications of this state following algorithm are also presented. In addition to AIMD, these include excited-state geometry optimization and minimal energy path finding via the growing string method. When using state following we demonstrate that more physical results can be obtained with AIMD calculations. Also, the optimized geometries of three excited states of cytosine, two of which were not found without state following, and the minimal energy path between the lowest two singlet excited states of protonated formaldimine are offered as example applications. Finally, to address large clusters, a local variation of CIS was developed. This method exploits the properties of absolutely localized molecular orbitals (ALMOs) to limit the total number of excitations to scaling only linearly with cluster size, which results in formal scaling with the third power of the system size. The derivation of the equations and design of the algorithm are discussed in detail, and computational timings as well as a pilot application to the size dependence of the helium cluster spectrum are presented.

Clustering and flow around a sphere moving into a grain cloud.

PubMed

Seguin, A; Lefebvre-Lepot, A; Faure, S; Gondret, P

2016-06-01

A bidimensional simulation of a sphere moving at constant velocity into a cloud of smaller spherical grains far from any boundaries and without gravity is presented with a non-smooth contact dynamics method. A dense granular "cluster" zone builds progressively around the moving sphere until a stationary regime appears with a constant upstream cluster size. The key point is that the upstream cluster size increases with the initial solid fraction [Formula: see text] but the cluster packing fraction takes an about constant value independent of [Formula: see text]. Although the upstream cluster size around the moving sphere diverges when [Formula: see text] approaches a critical value, the drag force exerted by the grains on the sphere does not. The detailed analysis of the local strain rate and local stress fields made in the non-parallel granular flow inside the cluster allows us to extract the local invariants of the two tensors: dilation rate, shear rate, pressure and shear stress. Despite different spatial variations of these invariants, the local friction coefficient μ appears to depend only on the local inertial number I as well as the local solid fraction, which means that a local rheology does exist in the present non-parallel flow. The key point is that the spatial variations of I inside the cluster do not depend on the sphere velocity and explore only a small range around the value one.

WaterWorld, a spatial hydrological model applied at scales from local to global: key challenges to local application

NASA Astrophysics Data System (ADS)

Burke, Sophia; Mulligan, Mark

2017-04-01

WaterWorld is a widely used spatial hydrological policy support system. The last user census indicates regular use by 1029 institutions across 141 countries. A key feature of WaterWorld since 2001 is that it comes pre-loaded with all of the required data for simulation anywhere in the world at a 1km or 1 ha resolution. This means that it can be easily used, without specialist technical ability, to examine baseline hydrology and the impacts of scenarios for change or management interventions to support policy formulation, hence its labelling as a policy support system. WaterWorld is parameterised by an extensive global gridded database of more than 600 variables, developed from many sources, since 1998, the so-called simTerra database. All of these data are available globally at 1km resolution and some variables (terrain, land cover, urban areas, water bodies) are available globally at 1ha resolution. If users have access to better data than is pre-loaded, they can upload their own data. WaterWorld is generally applied at the national or basin scale at 1km resolution, or locally (for areas of <10,000km2) at 1ha resolution, though continental (1km resolution) and global (10km resolution) applications are possible so it is a model with local to global applications. WaterWorld requires some 140 maps to run including monthly climate data, land cover and use, terrain, population, water bodies and more. Whilst publically-available terrain and land cover data are now well developed for local scale application, climate and land use data remain a challenge, with most global products being available at 1km or 10km resolution or worse, which is rather coarse for local application. As part of the EartH2Observe project we have used WFDEI (WATCH Forcing Data methodology applied to ERA-Interim data) at 1km resolution to provide an alternative input to WaterWorld's preloaded climate data. Here we examine the impacts of that on key hydrological outputs: water balance, water quality

A proximity-based graph clustering method for the identification and application of transcription factor clusters.

PubMed

Spadafore, Maxwell; Najarian, Kayvan; Boyle, Alan P

2017-11-29

Transcription factors (TFs) form a complex regulatory network within the cell that is crucial to cell functioning and human health. While methods to establish where a TF binds to DNA are well established, these methods provide no information describing how TFs interact with one another when they do bind. TFs tend to bind the genome in clusters, and current methods to identify these clusters are either limited in scope, unable to detect relationships beyond motif similarity, or not applied to TF-TF interactions. Here, we present a proximity-based graph clustering approach to identify TF clusters using either ChIP-seq or motif search data. We use TF co-occurrence to construct a filtered, normalized adjacency matrix and use the Markov Clustering Algorithm to partition the graph while maintaining TF-cluster and cluster-cluster interactions. We then apply our graph structure beyond clustering, using it to increase the accuracy of motif-based TFBS searching for an example TF. We show that our method produces small, manageable clusters that encapsulate many known, experimentally validated transcription factor interactions and that our method is capable of capturing interactions that motif similarity methods might miss. Our graph structure is able to significantly increase the accuracy of motif TFBS searching, demonstrating that the TF-TF connections within the graph correlate with biological TF-TF interactions. The interactions identified by our method correspond to biological reality and allow for fast exploration of TF clustering and regulatory dynamics.

Semi-supervised clustering methods.

PubMed

Bair, Eric

2013-01-01

Cluster analysis methods seek to partition a data set into homogeneous subgroups. It is useful in a wide variety of applications, including document processing and modern genetics. Conventional clustering methods are unsupervised, meaning that there is no outcome variable nor is anything known about the relationship between the observations in the data set. In many situations, however, information about the clusters is available in addition to the values of the features. For example, the cluster labels of some observations may be known, or certain observations may be known to belong to the same cluster. In other cases, one may wish to identify clusters that are associated with a particular outcome variable. This review describes several clustering algorithms (known as "semi-supervised clustering" methods) that can be applied in these situations. The majority of these methods are modifications of the popular k-means clustering method, and several of them will be described in detail. A brief description of some other semi-supervised clustering algorithms is also provided.

The anterior hypothalamus in cluster headache.

PubMed

Arkink, Enrico B; Schmitz, Nicole; Schoonman, Guus G; van Vliet, Jorine A; Haan, Joost; van Buchem, Mark A; Ferrari, Michel D; Kruit, Mark C

2017-10-01

Objective To evaluate the presence, localization, and specificity of structural hypothalamic and whole brain changes in cluster headache and chronic paroxysmal hemicrania (CPH). Methods We compared T1-weighted magnetic resonance images of subjects with cluster headache (episodic n = 24; chronic n = 23; probable n = 14), CPH ( n = 9), migraine (with aura n = 14; without aura n = 19), and no headache ( n = 48). We applied whole brain voxel-based morphometry (VBM) using two complementary methods to analyze structural changes in the hypothalamus: region-of-interest analyses in whole brain VBM, and manual segmentation of the hypothalamus to calculate volumes. We used both conservative VBM thresholds, correcting for multiple comparisons, and less conservative thresholds for exploratory purposes. Results Using region-of-interest VBM analyses mirrored to the headache side, we found enlargement ( p < 0.05, small volume correction) in the anterior hypothalamic gray matter in subjects with chronic cluster headache compared to controls, and in all participants with episodic or chronic cluster headache taken together compared to migraineurs. After manual segmentation, hypothalamic volume (mean±SD) was larger ( p < 0.05) both in subjects with episodic (1.89 ± 0.18 ml) and chronic (1.87 ± 0.21 ml) cluster headache compared to controls (1.72 ± 0.15 ml) and migraineurs (1.68 ± 0.19 ml). Similar but non-significant trends were observed for participants with probable cluster headache (1.82 ± 0.19 ml; p = 0.07) and CPH (1.79 ± 0.20 ml; p = 0.15). Increased hypothalamic volume was primarily explained by bilateral enlargement of the anterior hypothalamus. Exploratory whole brain VBM analyses showed widespread changes in pain-modulating areas in all subjects with headache. Interpretation The anterior hypothalamus is enlarged in episodic and chronic cluster headache and possibly also in probable cluster

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

Local regression type methods applied to the study of geophysics and high frequency financial data

NASA Astrophysics Data System (ADS)

Mariani, M. C.; Basu, K.

2014-09-01

In this work we applied locally weighted scatterplot smoothing techniques (Lowess/Loess) to Geophysical and high frequency financial data. We first analyze and apply this technique to the California earthquake geological data. A spatial analysis was performed to show that the estimation of the earthquake magnitude at a fixed location is very accurate up to the relative error of 0.01%. We also applied the same method to a high frequency data set arising in the financial sector and obtained similar satisfactory results. The application of this approach to the two different data sets demonstrates that the overall method is accurate and efficient, and the Lowess approach is much more desirable than the Loess method. The previous works studied the time series analysis; in this paper our local regression models perform a spatial analysis for the geophysics data providing different information. For the high frequency data, our models estimate the curve of best fit where data are dependent on time.

Locally adaptive decision in detection of clustered microcalcifications in mammograms.

PubMed

Sainz de Cea, María V; Nishikawa, Robert M; Yang, Yongyi

2018-02-15

In computer-aided detection or diagnosis of clustered microcalcifications (MCs) in mammograms, the performance often suffers from not only the presence of false positives (FPs) among the detected individual MCs but also large variability in detection accuracy among different cases. To address this issue, we investigate a locally adaptive decision scheme in MC detection by exploiting the noise characteristics in a lesion area. Instead of developing a new MC detector, we propose a decision scheme on how to best decide whether a detected object is an MC or not in the detector output. We formulate the individual MCs as statistical outliers compared to the many noisy detections in a lesion area so as to account for the local image characteristics. To identify the MCs, we first consider a parametric method for outlier detection, the Mahalanobis distance detector, which is based on a multi-dimensional Gaussian distribution on the noisy detections. We also consider a non-parametric method which is based on a stochastic neighbor graph model of the detected objects. We demonstrated the proposed decision approach with two existing MC detectors on a set of 188 full-field digital mammograms (95 cases). The results, evaluated using free response operating characteristic (FROC) analysis, showed a significant improvement in detection accuracy by the proposed outlier decision approach over traditional thresholding (the partial area under the FROC curve increased from 3.95 to 4.25, p-value  <10 -4 ). There was also a reduction in case-to-case variability in detected FPs at a given sensitivity level. The proposed adaptive decision approach could not only reduce the number of FPs in detected MCs but also improve case-to-case consistency in detection.

Locally adaptive decision in detection of clustered microcalcifications in mammograms

NASA Astrophysics Data System (ADS)

Sainz de Cea, María V.; Nishikawa, Robert M.; Yang, Yongyi

2018-02-01

In computer-aided detection or diagnosis of clustered microcalcifications (MCs) in mammograms, the performance often suffers from not only the presence of false positives (FPs) among the detected individual MCs but also large variability in detection accuracy among different cases. To address this issue, we investigate a locally adaptive decision scheme in MC detection by exploiting the noise characteristics in a lesion area. Instead of developing a new MC detector, we propose a decision scheme on how to best decide whether a detected object is an MC or not in the detector output. We formulate the individual MCs as statistical outliers compared to the many noisy detections in a lesion area so as to account for the local image characteristics. To identify the MCs, we first consider a parametric method for outlier detection, the Mahalanobis distance detector, which is based on a multi-dimensional Gaussian distribution on the noisy detections. We also consider a non-parametric method which is based on a stochastic neighbor graph model of the detected objects. We demonstrated the proposed decision approach with two existing MC detectors on a set of 188 full-field digital mammograms (95 cases). The results, evaluated using free response operating characteristic (FROC) analysis, showed a significant improvement in detection accuracy by the proposed outlier decision approach over traditional thresholding (the partial area under the FROC curve increased from 3.95 to 4.25, p-value  <10-4). There was also a reduction in case-to-case variability in detected FPs at a given sensitivity level. The proposed adaptive decision approach could not only reduce the number of FPs in detected MCs but also improve case-to-case consistency in detection.

Spin-state transition in LaCoO3 by variational cluster approximation

NASA Astrophysics Data System (ADS)

Eder, R.

2010-01-01

The variational cluster approximation (VCA) is applied to the calculation of thermodynamical quantities and single-particle spectra of LaCoO3 . Trial self-energies and the numerical value of the Luttinger-Ward functional are obtained by exact diagonalization of a CoO6 cluster. The VCA correctly predicts LaCoO3 as a paramagnetic insulator, and a gradual and relatively smooth increase in the occupation of high-spin Co3+ ions causes the temperature dependence of entropy and magnetic susceptibility. The single-particle spectral function agrees well with experiment; the experimentally observed temperature dependence of photoelectron spectra is reproduced satisfactorily. Remaining discrepancies with experiment highlight the importance of spin-orbit coupling and local lattice relaxation.

*K-means and cluster models for cancer signatures.

PubMed

Kakushadze, Zura; Yu, Willie

2017-09-01

We present *K-means clustering algorithm and source code by expanding statistical clustering methods applied in https://ssrn.com/abstract=2802753 to quantitative finance. *K-means is statistically deterministic without specifying initial centers, etc. We apply *K-means to extracting cancer signatures from genome data without using nonnegative matrix factorization (NMF). *K-means' computational cost is a fraction of NMF's. Using 1389 published samples for 14 cancer types, we find that 3 cancers (liver cancer, lung cancer and renal cell carcinoma) stand out and do not have cluster-like structures. Two clusters have especially high within-cluster correlations with 11 other cancers indicating common underlying structures. Our approach opens a novel avenue for studying such structures. *K-means is universal and can be applied in other fields. We discuss some potential applications in quantitative finance.

Localization Strategies in WSNs as applied to Landslide Monitoring (Invited)

NASA Astrophysics Data System (ADS)

Massa, A.; Robol, F.; Polo, A.; Giarola, E.; Viani, F.

2013-12-01

In the last years, heterogeneous integrated smart systems based on wireless sensor network (WSN) technology have been developed at the ELEDIA Research Center of the University of Trento [1]. One of the key features of WSNs as applied to distributed monitoring is that, while the capabilities of each single sensor node is limited, the implementation of cooperative schemes throughout the whole network enables the solution of even complex tasks, as the landslide monitoring. The capability of localizing targets respect to the position of the sensor nodes turns out to be fundamental in those application fields where relative movements arise. The main properties like the target typology, the movement characteristics, and the required localization resolution are different changing the reference scenario. However, the common key issue is still the localization of moving targets within the area covered by the sensor network. Many experiences were preparatory for the challenging activities in the field of landslide monitoring where the basic idea is mostly that of detecting slight soil movements. Among them, some examples of WSN-based systems experimentally applied to the localization of people [2] and wildlife [3] have been proposed. More recently, the WSN backbone as well as the investigated sensing technologies have been customized for monitoring superficial movements of the soil. The relative positions of wireless sensor nodes deployed where high probability of landslide exists is carefully monitored to forecast dangerous events. Multiple sensors like ultrasound, laser, high precision GPS, for the precise measurement of relative distances between the nodes of the network and the absolute positions respect to reference targets have been integrated in a prototype system. The millimeter accuracy in the position estimation enables the detection of small soil modifications and to infer the superficial evolution profile of the landslide. This information locally acquired also

Graph partitions and cluster synchronization in networks of oscillators

PubMed Central

Schaub, Michael T.; O’Clery, Neave; Billeh, Yazan N.; Delvenne, Jean-Charles; Lambiotte, Renaud; Barahona, Mauricio

2017-01-01

Synchronization over networks depends strongly on the structure of the coupling between the oscillators. When the coupling presents certain regularities, the dynamics can be coarse-grained into clusters by means of External Equitable Partitions of the network graph and their associated quotient graphs. We exploit this graph-theoretical concept to study the phenomenon of cluster synchronization, in which different groups of nodes converge to distinct behaviors. We derive conditions and properties of networks in which such clustered behavior emerges, and show that the ensuing dynamics is the result of the localization of the eigenvectors of the associated graph Laplacians linked to the existence of invariant subspaces. The framework is applied to both linear and non-linear models, first for the standard case of networks with positive edges, before being generalized to the case of signed networks with both positive and negative interactions. We illustrate our results with examples of both signed and unsigned graphs for consensus dynamics and for partial synchronization of oscillator networks under the master stability function as well as Kuramoto oscillators. PMID:27781454

Smoothing metallic glasses without introducing crystallization by gas cluster ion beam

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

Shao, Lin; Chen, Di; Myers, Michael

2013-03-11

We show that 30 keV Ar cluster ion bombardment of Ni{sub 52.5}Nb{sub 10}Zr{sub 15}Ti{sub 15}Pt{sub 7.5} metallic glass (MG) can remove surface mountain-like features and reduce the root mean square surface roughness from 12 nm to 0.7 nm. X-ray diffraction analysis reveals no crystallization after cluster ion irradiation. Molecular dynamics simulations show that, although damage cascades lead to local melting, the subsequent quenching rate is a few orders of magnitude higher than the critical cooling rate for MG formation, thus the melted zone retains its amorphous nature down to room temperature. These findings can be applied to obtain ultra-smooth MGsmore » without introducing crystallization.« less

Epidemiological study of phylogenetic transmission clusters in a local HIV-1 epidemic reveals distinct differences between subtype B and non-B infections.

PubMed

Chalmet, Kristen; Staelens, Delfien; Blot, Stijn; Dinakis, Sylvie; Pelgrom, Jolanda; Plum, Jean; Vogelaers, Dirk; Vandekerckhove, Linos; Verhofstede, Chris

2010-09-07

The number of HIV-1 infected individuals in the Western world continues to rise. More in-depth understanding of regional HIV-1 epidemics is necessary for the optimal design and adequate use of future prevention strategies. The use of a combination of phylogenetic analysis of HIV sequences, with data on patients' demographics, infection route, clinical information and laboratory results, will allow a better characterization of individuals responsible for local transmission. Baseline HIV-1 pol sequences, obtained through routine drug-resistance testing, from 506 patients, newly diagnosed between 2001 and 2009, were used to construct phylogenetic trees and identify transmission-clusters. Patients' demographics, laboratory and clinical data, were retrieved anonymously. Statistical analysis was performed to identify subtype-specific and transmission-cluster-specific characteristics. Multivariate analysis showed significant differences between the 59.7% of individuals with subtype B infection and the 40.3% non-B infected individuals, with regard to route of transmission, origin, infection with Chlamydia (p = 0.01) and infection with Hepatitis C virus (p = 0.017). More and larger transmission-clusters were identified among the subtype B infections (p < 0.001). Overall, in multivariate analysis, clustering was significantly associated with Caucasian origin, infection through homosexual contact and younger age (all p < 0.001). Bivariate analysis additionally showed a correlation between clustering and syphilis (p < 0.001), higher CD4 counts (p = 0.002), Chlamydia infection (p = 0.013) and primary HIV (p = 0.017). Combination of phylogenetics with demographic information, laboratory and clinical data, revealed that HIV-1 subtype B infected Caucasian men-who-have-sex-with-men with high prevalence of sexually transmitted diseases, account for the majority of local HIV-transmissions. This finding elucidates observed epidemiological trends through molecular analysis, and

Local Matrix-Cluster Interactions In La1-x SrxCoO3.

NASA Astrophysics Data System (ADS)

Giblin, Sean; Terry, Ian; Boothroyd, Andrew; Prabhakaran, Dharmalingiam; Wu, Jing; Leighton, Chris

2006-03-01

Magneto-electronic phase separation plays an integral part in many recent advances in the understanding of correlated electron systems. We have studied the magnetically phase separated material La1-x SrxCoO3 and the parent compound LaCoO3, using muon spectroscopy and magnetic susceptibility measurements. The muon as a local magnetic probe is sensitive to the magnetic field distribution in LaCoO3 in the LS state, which is a direct consequence of magnetic excitons. We believe that these excitons are interacting with the Co ions undergoing the known thermally induced spin transition. By directly comparing the results of the parent compound with La1-x SrxCoO3 we can observe the hole-rich ferromagnetic clusters interacting with the neighboring hole poor matrix for low x. This mechanism, detected here for the first time, may play an important role in the rich electrical and magnetic properties of La1-x SrxCoO3.

Investigating Faculty Familiarity with Assessment Terminology by Applying Cluster Analysis to Interpret Survey Data

ERIC Educational Resources Information Center

Raker, Jeffrey R.; Holme, Thomas A.

2014-01-01

A cluster analysis was conducted with a set of survey data on chemistry faculty familiarity with 13 assessment terms. Cluster groupings suggest a high, middle, and low overall familiarity with the terminology and an independent high and low familiarity with terms related to fundamental statistics. The six resultant clusters were found to be…

GC-MS analyses and chemometric processing to discriminate the local and long-distance sources of PAHs associated to atmospheric PM2.5.

PubMed

Masiol, Mauro; Centanni, Elena; Squizzato, Stefania; Hofer, Angelika; Pecorari, Eliana; Rampazzo, Giancarlo; Pavoni, Bruno

2012-09-01

This study presents a procedure to differentiate the local and remote sources of particulate-bound polycyclic aromatic hydrocarbons (PAHs). Data were collected during an extended PM(2.5) sampling campaign (2009-2010) carried out for 1 year in Venice-Mestre, Italy, at three stations with different emissive scenarios: urban, industrial, and semirural background. Diagnostic ratios and factor analysis were initially applied to point out the most probable sources. In a second step, the areal distribution of the identified sources was studied by applying the discriminant analysis on factor scores. Third, samples collected in days with similar atmospheric circulation patterns were grouped using a cluster analysis on wind data. Local contributions to PM(2.5) and PAHs were then assessed by interpreting cluster results with chemical data. Results evidenced that significantly lower levels of PM(2.5) and PAHs were found when faster winds changed air masses, whereas in presence of scarce ventilation, locally emitted pollutants were trapped and concentrations increased. This way, an estimation of pollutant loads due to local sources can be derived from data collected in days with similar wind patterns. Long-range contributions were detected by a cluster analysis on the air mass back-trajectories. Results revealed that PM(2.5) concentrations were relatively high when air masses had passed over the Po Valley. However, external sources do not significantly contribute to the PAHs load. The proposed procedure can be applied to other environments with minor modifications, and the obtained information can be useful to design local and national air pollution control strategies.

Locating sources within a dense sensor array using graph clustering

NASA Astrophysics Data System (ADS)

Gerstoft, P.; Riahi, N.

2017-12-01

We develop a model-free technique to identify weak sources within dense sensor arrays using graph clustering. No knowledge about the propagation medium is needed except that signal strengths decay to insignificant levels within a scale that is shorter than the aperture. We then reinterpret the spatial coherence matrix of a wave field as a matrix whose support is a connectivity matrix of a graph with sensors as vertices. In a dense network, well-separated sources induce clusters in this graph. The geographic spread of these clusters can serve to localize the sources. The support of the covariance matrix is estimated from limited-time data using a hypothesis test with a robust phase-only coherence test statistic combined with a physical distance criterion. The latter criterion ensures graph sparsity and thus prevents clusters from forming by chance. We verify the approach and quantify its reliability on a simulated dataset. The method is then applied to data from a dense 5200 element geophone array that blanketed of the city of Long Beach (CA). The analysis exposes a helicopter traversing the array and oil production facilities.

Spiral Arm Morphology in Cluster Environment

NASA Astrophysics Data System (ADS)

Choi, Isaac Yeoun-Gyu; Ann, Hong Bae

2011-10-01

We examine the dependence of the morphology of spiral galaxies on the environment using the KIAS Value Added Galaxy Catalog (VAGC) which is derived from the Sloan Digital Sky Survey (SDSS) DR7. Our goal is to understand whether the local environment or global conditions dominate in determining the morphology of spiral galaxies. For the analysis, we conduct a morphological classification of galaxies in 20 X-ray selected Abell clusters up to z˜0.06, using SDSS color images and the X-ray data from the Northern ROSAT All-Sky (NORAS) catalog. We analyze the distribution of arm classes along the clustercentric radius as well as that of Hubble types. To segregate the effect of local environment from the global environment, we compare the morphological distribution of galaxies in two X-lay luminosity groups, the low-Lx clusters (Lx < 0.15×1044erg/s) and high-Lx clusters (Lx > 1.8×1044erg/s). We find that the morphology-clustercentric relation prevails in the cluster envirnment although there is a brake near the cluster virial radius. The grand design arms comprise about 40% of the cluster spiral galaxies with a weak morphology-clustercentric radius relation for the arm classes, in the sense that flocculent galaxies tend to increase outward, regardless of the X-ray luminosity. From the cumulative radial distribution of cluster galaxies, we found that the low-Lx clusters are fully virialized while the high-Lx clusters are not.

The distribution of saturated clusters in wetted granular materials

NASA Astrophysics Data System (ADS)

Li, Shuoqi; Hanaor, Dorian; Gan, Yixiang

2017-06-01

The hydro-mechanical behaviour of partially saturated granular materials is greatly influenced by the spatial and temporal distribution of liquid within the media. The aim of this paper is to characterise the distribution of saturated clusters in granular materials using an optical imaging method under different water drainage conditions. A saturated cluster is formed when a liquid phase fully occupies the pore space between solid grains in a localized region. The samples considered here were prepared by vibrating mono-sized glass beads to form closely packed assemblies in a rectangular container. A range of drainage conditions were applied to the specimen by tilting the container and employing different flow rates, and the liquid pressure was recorded at different positions in the experimental cell. The formation of saturated clusters during the liquid withdrawal processes is governed by three competing mechanisms arising from viscous, capillary, and gravitational forces. When the flow rate is sufficiently large and the gravity component is sufficiently small, the viscous force tends to destabilize the liquid front leading to the formation of narrow fingers of saturated material. As the water channels along these liquid fingers break, saturated clusters are formed inside the specimen. Subsequently, a spatial and temporal distribution of saturated clusters can be observed. We investigated the resulting saturated cluster distribution as a function of flow rate and gravity to achieve a fundamental understanding of the formation and evolution of such clusters in partially saturated granular materials. This study serves as a bridge between pore-scale behavior and the overall hydro-mechanical characteristics in partially saturated soils.

Applying the Anderson-Darling test to suicide clusters: evidence of contagion at U. S. universities?

PubMed

MacKenzie, Donald W

2013-01-01

Suicide clusters at Cornell University and the Massachusetts Institute of Technology (MIT) prompted popular and expert speculation of suicide contagion. However, some clustering is to be expected in any random process. This work tested whether suicide clusters at these two universities differed significantly from those expected under a homogeneous Poisson process, in which suicides occur randomly and independently of one another. Suicide dates were collected for MIT and Cornell for 1990-2012. The Anderson-Darling statistic was used to test the goodness-of-fit of the intervals between suicides to distribution expected under the Poisson process. Suicides at MIT were consistent with the homogeneous Poisson process, while those at Cornell showed clustering inconsistent with such a process (p = .05). The Anderson-Darling test provides a statistically powerful means to identify suicide clustering in small samples. Practitioners can use this method to test for clustering in relevant communities. The difference in clustering behavior between the two institutions suggests that more institutions should be studied to determine the prevalence of suicide clustering in universities and its causes.

k-filtering applied to Cluster density measurements in the Solar Wind: Early findings

NASA Astrophysics Data System (ADS)

Jeska, Lauren; Roberts, Owen; Li, Xing

2014-05-01

Studies of solar wind turbulence indicate that a large proportion of the energy is Alfvénic (incompressible) at inertial scales. The properties of the turbulence found in the dissipation range are still under debate ~ while it is widely believed that kinetic Alfvén waves form the dominant component, the constituents of the remaining compressible turbulence are disputed. Using k-filtering, the power can be measured without assuming the validity of Taylor's hypothesis, and its distribution in (ω, k)-space can be determined to assist the identification of weak turbulence components. This technique is applied to Cluster electron density measurements and compared to the power in |B(t)|. As the direct electron density measurements from the WHISPER instrument have a low cadency of only 2.2s, proxy data derived from the spacecraft potential, measured every 0.2s by the EFW instrument, are used to extend this study to ion scales.

Cluster Correspondence Analysis.

PubMed

van de Velden, M; D'Enza, A Iodice; Palumbo, F

2017-03-01

A method is proposed that combines dimension reduction and cluster analysis for categorical data by simultaneously assigning individuals to clusters and optimal scaling values to categories in such a way that a single between variance maximization objective is achieved. In a unified framework, a brief review of alternative methods is provided and we show that the proposed method is equivalent to GROUPALS applied to categorical data. Performance of the methods is appraised by means of a simulation study. The results of the joint dimension reduction and clustering methods are compared with the so-called tandem approach, a sequential analysis of dimension reduction followed by cluster analysis. The tandem approach is conjectured to perform worse when variables are added that are unrelated to the cluster structure. Our simulation study confirms this conjecture. Moreover, the results of the simulation study indicate that the proposed method also consistently outperforms alternative joint dimension reduction and clustering methods.

Substructures in Clusters of Galaxies

NASA Astrophysics Data System (ADS)

Lehodey, Brigitte Tome

2000-01-01

This dissertation presents two methods for the detection of substructures in clusters of galaxies and the results of their application to a group of four clusters. In chapters 2 and 3, we remember the main properties of clusters of galaxies and give the definition of substructures. We also try to show why the study of substructures in clusters of galaxies is so important for Cosmology. Chapters 4 and 5 describe these two methods, the first one, the adaptive Kernel, is applied to the study of the spatial and kinematical distribution of the cluster galaxies. The second one, the MVM (Multiscale Vision Model), is applied to analyse the cluster diffuse X-ray emission, i.e., the intracluster gas distribution. At the end of these two chapters, we also present the results of the application of these methods to our sample of clusters. In chapter 6, we draw the conclusions from the comparison of the results we obtain with each method. In the last chapter, we present the main conclusions of this work trying to point out possible developments. We close with two appendices in which we detail some questions raised in this work not directly linked to the problem of substructures detection.

Evidence of toroidally localized turbulence with applied 3D fields in the DIII-D tokamak

DOE PAGES

Wilcox, R. S.; Shafer, M. W.; Ferraro, N. M.; ...

2016-09-21

New evidence indicates that there is significant 3D variation in density fluctuations near the boundary of weakly 3D tokamak plasmas when resonant magnetic perturbations are applied to suppress transient edge instabilities. The increase in fluctuations is concomitant with an increase in the measured density gradient, suggesting that this toroidally localized gradient increase could be a mechanism for turbulence destabilization in localized flux tubes. Two-fluid magnetohydrodynamic simulations find that, although changes to the magnetic field topology are small, there is a significant 3D variation of the density gradient within the flux surfaces that is extended along field lines. This modeling agreesmore » qualitatively with the measurements. The observed gradient and fluctuation asymmetries are proposed as a mechanism by which global profile gradients in the pedestal could be relaxed due to a local change in the 3D equilibrium. In conclusion, these processes may play an important role in pedestal and scrape-off layer transport in ITER and other future tokamak devices with small applied 3D fields.« less

A Global Model for Circumgalactic and Cluster-core Precipitation

NASA Astrophysics Data System (ADS)

Voit, G. Mark; Meece, Greg; Li, Yuan; O'Shea, Brian W.; Bryan, Greg L.; Donahue, Megan

2017-08-01

We provide an analytic framework for interpreting observations of multiphase circumgalactic gas that is heavily informed by recent numerical simulations of thermal instability and precipitation in cool-core galaxy clusters. We start by considering the local conditions required for the formation of multiphase gas via two different modes: (1) uplift of ambient gas by galactic outflows, and (2) condensation in a stratified stationary medium in which thermal balance is explicitly maintained. Analytic exploration of these two modes provides insights into the relationships between the local ratio of the cooling and freefall timescales (I.e., {t}{cool}/{t}{ff}), the large-scale gradient of specific entropy, and the development of precipitation and multiphase media in circumgalactic gas. We then use these analytic findings to interpret recent simulations of circumgalactic gas in which global thermal balance is maintained. We show that long-lasting configurations of gas with 5≲ \\min ({t}{cool}/{t}{ff})≲ 20 and radial entropy profiles similar to observations of cool cores in galaxy clusters are a natural outcome of precipitation-regulated feedback. We conclude with some observational predictions that follow from these models. This work focuses primarily on precipitation and AGN feedback in galaxy-cluster cores, because that is where the observations of multiphase gas around galaxies are most complete. However, many of the physical principles that govern condensation in those environments apply to circumgalactic gas around galaxies of all masses.

SAR image segmentation using skeleton-based fuzzy clustering

NASA Astrophysics Data System (ADS)

Cao, Yun Yi; Chen, Yan Qiu

2003-06-01

SAR image segmentation can be converted to a clustering problem in which pixels or small patches are grouped together based on local feature information. In this paper, we present a novel framework for segmentation. The segmentation goal is achieved by unsupervised clustering upon characteristic descriptors extracted from local patches. The mixture model of characteristic descriptor, which combines intensity and texture feature, is investigated. The unsupervised algorithm is derived from the recently proposed Skeleton-Based Data Labeling method. Skeletons are constructed as prototypes of clusters to represent arbitrary latent structures in image data. Segmentation using Skeleton-Based Fuzzy Clustering is able to detect the types of surfaces appeared in SAR images automatically without any user input.

Magnetic properties of Co-doped Nb clusters

NASA Astrophysics Data System (ADS)

Diaz-Bachs, A.; Peters, L.; Logemann, R.; Chernyy, V.; Bakker, J. M.; Katsnelson, M. I.; Kirilyuk, A.

2018-04-01

Magnetic deflection experiments on isolated Co-doped Nb clusters demonstrate a strong size dependence of magnetic properties, with large magnetic moments in certain cluster sizes and fully nonmagnetic behavior of others. There are in principle two explanations for this behavior. Either the local moment at the Co site is absent or it is screened by the delocalized electrons of the cluster, i.e., the Kondo effect. In order to reveal the physical origin, first, we established the ground state geometry of the clusters by experimentally obtaining their vibrational spectra and comparing them with a density functional theory study. Then, we performed an analysis based on the Anderson impurity model. It appears that the nonmagnetic clusters are due to the absence of the local Co moment and not due to the Kondo effect. In addition, the magnetic behavior of the clusters can be understood from an inspection of their electronic structure. Here magnetism is favored when the effective hybridization around the chemical potential is small, while the absence of magnetism is signaled by a large effective hybridization around the chemical potential.

Photoionization cross section by Stieltjes imaging applied to coupled cluster Lanczos pseudo-spectra

NASA Astrophysics Data System (ADS)

Cukras, Janusz; Coriani, Sonia; Decleva, Piero; Christiansen, Ove; Norman, Patrick

2013-09-01

A recently implemented asymmetric Lanczos algorithm for computing (complex) linear response functions within the coupled cluster singles (CCS), coupled cluster singles and iterative approximate doubles (CC2), and coupled cluster singles and doubles (CCSD) is coupled to a Stieltjes imaging technique in order to describe the photoionization cross section of atoms and molecules, in the spirit of a similar procedure recently proposed by Averbukh and co-workers within the Algebraic Diagrammatic Construction approach. Pilot results are reported for the atoms He, Ne, and Ar and for the molecules H2, H2O, NH3, HF, CO, and CO2.

Photoionization cross section by Stieltjes imaging applied to coupled cluster Lanczos pseudo-spectra.

PubMed

Cukras, Janusz; Coriani, Sonia; Decleva, Piero; Christiansen, Ove; Norman, Patrick

2013-09-07

A recently implemented asymmetric Lanczos algorithm for computing (complex) linear response functions within the coupled cluster singles (CCS), coupled cluster singles and iterative approximate doubles (CC2), and coupled cluster singles and doubles (CCSD) is coupled to a Stieltjes imaging technique in order to describe the photoionization cross section of atoms and molecules, in the spirit of a similar procedure recently proposed by Averbukh and co-workers within the Algebraic Diagrammatic Construction approach. Pilot results are reported for the atoms He, Ne, and Ar and for the molecules H2, H2O, NH3, HF, CO, and CO2.

A Smartphone Indoor Localization Algorithm Based on WLAN Location Fingerprinting with Feature Extraction and Clustering.

PubMed

Luo, Junhai; Fu, Liang

2017-06-09

With the development of communication technology, the demand for location-based services is growing rapidly. This paper presents an algorithm for indoor localization based on Received Signal Strength (RSS), which is collected from Access Points (APs). The proposed localization algorithm contains the offline information acquisition phase and online positioning phase. Firstly, the AP selection algorithm is reviewed and improved based on the stability of signals to remove useless AP; secondly, Kernel Principal Component Analysis (KPCA) is analyzed and used to remove the data redundancy and maintain useful characteristics for nonlinear feature extraction; thirdly, the Affinity Propagation Clustering (APC) algorithm utilizes RSS values to classify data samples and narrow the positioning range. In the online positioning phase, the classified data will be matched with the testing data to determine the position area, and the Maximum Likelihood (ML) estimate will be employed for precise positioning. Eventually, the proposed algorithm is implemented in a real-world environment for performance evaluation. Experimental results demonstrate that the proposed algorithm improves the accuracy and computational complexity.

Semi-supervised clustering methods

PubMed Central

Bair, Eric

2013-01-01

Cluster analysis methods seek to partition a data set into homogeneous subgroups. It is useful in a wide variety of applications, including document processing and modern genetics. Conventional clustering methods are unsupervised, meaning that there is no outcome variable nor is anything known about the relationship between the observations in the data set. In many situations, however, information about the clusters is available in addition to the values of the features. For example, the cluster labels of some observations may be known, or certain observations may be known to belong to the same cluster. In other cases, one may wish to identify clusters that are associated with a particular outcome variable. This review describes several clustering algorithms (known as “semi-supervised clustering” methods) that can be applied in these situations. The majority of these methods are modifications of the popular k-means clustering method, and several of them will be described in detail. A brief description of some other semi-supervised clustering algorithms is also provided. PMID:24729830

Open star clusters and Galactic structure

NASA Astrophysics Data System (ADS)

Joshi, Yogesh C.

2018-04-01

In order to understand the Galactic structure, we perform a statistical analysis of the distribution of various cluster parameters based on an almost complete sample of Galactic open clusters yet available. The geometrical and physical characteristics of a large number of open clusters given in the MWSC catalogue are used to study the spatial distribution of clusters in the Galaxy and determine the scale height, solar offset, local mass density and distribution of reddening material in the solar neighbourhood. We also explored the mass-radius and mass-age relations in the Galactic open star clusters. We find that the estimated parameters of the Galactic disk are largely influenced by the choice of cluster sample.

A self-consistent density based embedding scheme applied to the adsorption of CO on Pd(111)

NASA Astrophysics Data System (ADS)

Lahav, D.; Klüner, T.

2007-06-01

We derive a variant of a density based embedded cluster approach as an improvement to a recently proposed embedding theory for metallic substrates (Govind et al 1999 J. Chem. Phys. 110 7677; Klüner et al 2001 Phys. Rev. Lett. 86 5954). In this scheme, a local region in space is represented by a small cluster which is treated by accurate quantum chemical methodology. The interaction of the cluster with the infinite solid is taken into account by an effective one-electron embedding operator representing the surrounding region. We propose a self-consistent embedding scheme which resolves intrinsic problems of the former theory, in particular a violation of strict density conservation. The proposed scheme is applied to the well-known benchmark system CO/Pd(111).

Photometric redshifts as a tool for studying the Coma cluster galaxy populations

NASA Astrophysics Data System (ADS)

Adami, C.; Ilbert, O.; Pelló, R.; Cuillandre, J. C.; Durret, F.; Mazure, A.; Picat, J. P.; Ulmer, M. P.

2008-12-01

Aims: We apply photometric redshift techniques to an investigation of the Coma cluster galaxy luminosity function (GLF) at faint magnitudes, in particular in the u* band where basically no studies are presently available at these magnitudes. Methods: Cluster members were selected based on probability distribution function from photometric redshift calculations applied to deep u^*, B, V, R, I images covering a region of almost 1 deg2 (completeness limit R ~ 24). In the area covered only by the u* image, the GLF was also derived after a statistical background subtraction. Results: Global and local GLFs in the B, V, R, and I bands obtained with photometric redshift selection are consistent with our previous results based on a statistical background subtraction. The GLF in the u* band shows an increase in the faint end slope towards the outer regions of the cluster. The analysis of the multicolor type spatial distribution reveals that late type galaxies are distributed in clumps in the cluster outskirts, where X-ray substructures are also detected and where the GLF in the u* band is steeper. Conclusions: We can reproduce the GLFs computed with classical statistical subtraction methods by applying a photometric redshift technique. The u* GLF slope is steeper in the cluster outskirts, varying from α ~ -1 in the cluster center to α ~ -2 in the cluster periphery. The concentrations of faint late type galaxies in the cluster outskirts could explain these very steep slopes, assuming a short burst of star formation in these galaxies when entering the cluster. Based on observations obtained with MegaPrime/MegaCam, a joint project of CFHT and CEA/DAPNIA, at the Canada-France-Hawaii Telescope (CFHT) which is operated by the National Research Council (NRC) of Canada, the Institut National des Sciences de l'Univers of the Centre National de la Recherche Scientifique (CNRS) of France, and the University of Hawaii. This work is also partly based on data products produced at

Predicting item popularity: Analysing local clustering behaviour of users

NASA Astrophysics Data System (ADS)

Liebig, Jessica; Rao, Asha

2016-01-01

Predicting the popularity of items in rating networks is an interesting but challenging problem. This is especially so when an item has first appeared and has received very few ratings. In this paper, we propose a novel approach to predicting the future popularity of new items in rating networks, defining a new bipartite clustering coefficient to predict the popularity of movies and stories in the MovieLens and Digg networks respectively. We show that the clustering behaviour of the first user who rates a new item gives insight into the future popularity of that item. Our method predicts, with a success rate of over 65% for the MovieLens network and over 50% for the Digg network, the future popularity of an item. This is a major improvement on current results.

Local and regional components of aerosol in a heavily trafficked street canyon in central London derived from PMF and cluster analysis of single-particle ATOFMS spectra.

PubMed

Giorio, Chiara; Tapparo, Andrea; Dall'Osto, Manuel; Beddows, David C S; Esser-Gietl, Johanna K; Healy, Robert M; Harrison, Roy M

2015-03-17

Positive matrix factorization (PMF) has been applied to single particle ATOFMS spectra collected on a six lane heavily trafficked road in central London (Marylebone Road), which well represents an urban street canyon. PMF analysis successfully extracted 11 factors from mass spectra of about 700,000 particles as a complement to information on particle types (from K-means cluster analysis). The factors were associated with specific sources and represent the contribution of different traffic related components (i.e., lubricating oils, fresh elemental carbon, organonitrogen and aromatic compounds), secondary aerosol locally produced (i.e., nitrate, oxidized organic aerosol and oxidized organonitrogen compounds), urban background together with regional transport (aged elemental carbon and ammonium) and fresh sea spray. An important result from this study is the evidence that rapid chemical processes occur in the street canyon with production of secondary particles from road traffic emissions. These locally generated particles, together with aging processes, dramatically affected aerosol composition producing internally mixed particles. These processes may become important with stagnant air conditions and in countries where gasoline vehicles are predominant and need to be considered when quantifying the impact of traffic emissions.

Information Theory and Voting Based Consensus Clustering for Combining Multiple Clusterings of Chemical Structures.

PubMed

Saeed, Faisal; Salim, Naomie; Abdo, Ammar

2013-07-01

Many consensus clustering methods have been applied in different areas such as pattern recognition, machine learning, information theory and bioinformatics. However, few methods have been used for chemical compounds clustering. In this paper, an information theory and voting based algorithm (Adaptive Cumulative Voting-based Aggregation Algorithm A-CVAA) was examined for combining multiple clusterings of chemical structures. The effectiveness of clusterings was evaluated based on the ability of the clustering method to separate active from inactive molecules in each cluster, and the results were compared with Ward's method. The chemical dataset MDL Drug Data Report (MDDR) and the Maximum Unbiased Validation (MUV) dataset were used. Experiments suggest that the adaptive cumulative voting-based consensus method can improve the effectiveness of combining multiple clusterings of chemical structures. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Polynomial Similarity Transformation Theory: A smooth interpolation between coupled cluster doubles and projected BCS applied to the reduced BCS Hamiltonian

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

Degroote, M.; Henderson, T. M.; Zhao, J.

We present a similarity transformation theory based on a polynomial form of a particle-hole pair excitation operator. In the weakly correlated limit, this polynomial becomes an exponential, leading to coupled cluster doubles. In the opposite strongly correlated limit, the polynomial becomes an extended Bessel expansion and yields the projected BCS wavefunction. In between, we interpolate using a single parameter. The e ective Hamiltonian is non-hermitian and this Polynomial Similarity Transformation Theory follows the philosophy of traditional coupled cluster, left projecting the transformed Hamiltonian onto subspaces of the Hilbert space in which the wave function variance is forced to be zero.more » Similarly, the interpolation parameter is obtained through minimizing the next residual in the projective hierarchy. We rationalize and demonstrate how and why coupled cluster doubles is ill suited to the strongly correlated limit whereas the Bessel expansion remains well behaved. The model provides accurate wave functions with energy errors that in its best variant are smaller than 1% across all interaction stengths. The numerical cost is polynomial in system size and the theory can be straightforwardly applied to any realistic Hamiltonian.« less

Improved Cluster Method Applied to the InSAR data of the 2007 Piton de la Fournaise eruption

NASA Astrophysics Data System (ADS)

Cayol, V.; Augier, A.; Froger, J. L.; Menassian, S.

2016-12-01

Interpretation of surface displacement induced by reservoirs, whether magmatic, hydrothermal or gaseous, can be done at reduced numerical cost and with little a priori knowledge using cluster methods, where reservoirs are represented by point sources embedded in an elastic half-space. Most of the time, the solution representing the best trade-off between the data fit and the model smoothness (L-curve criterion) is chosen. This study relies on synthetic tests to improve cluster methods in several ways. Firstly, to solve problems involving steep topographies, we construct unit sources numerically. Secondly, we show that the L-curve criterion leads to several plausible solutions where the most realistic are not necessarily the best fitting. We determine that the cross-validation method, with data geographically grouped, is a more reliable way to determine the solution. Thirdly, we propose a new method, based on source ranking according to their contribution and minimization of the Akaike information criteria, to retrieve reservoirs' geometry more accurately and to better reflect information contained in the data. We show that the solution is robust in the presence of correlated noise and that reservoir complexity that can be retrieved decreases with increasing noise. We also show that it is inappropriate to use cluster methods for pressurized fractures. Finally, the method is applied to the summit deflation recorded by InSAR after the caldera collapse which occurred at Piton de la Fournaise in April 2007. Comparison with other data indicate that the deflation is probably related to poro-elastic compaction and fluid flow subsequent to the crater collapse.

New Techniques for Ancient Proteins: Direct Coupling Analysis Applied on Proteins Involved in Iron Sulfur Cluster Biogenesis

PubMed Central

Fantini, Marco; Malinverni, Duccio; De Los Rios, Paolo; Pastore, Annalisa

2017-01-01

Direct coupling analysis (DCA) is a powerful statistical inference tool used to study protein evolution. It was introduced to predict protein folds and protein-protein interactions, and has also been applied to the prediction of entire interactomes. Here, we have used it to analyze three proteins of the iron-sulfur biogenesis machine, an essential metabolic pathway conserved in all organisms. We show that DCA can correctly reproduce structural features of the CyaY/frataxin family (a protein involved in the human disease Friedreich's ataxia) despite being based on the relatively small number of sequences allowed by its genomic distribution. This result gives us confidence in the method. Its application to the iron-sulfur cluster scaffold protein IscU, which has been suggested to function both as an ordered and a disordered form, allows us to distinguish evolutionary traces of the structured species, suggesting that, if present in the cell, the disordered form has not left evolutionary imprinting. We observe instead, for the first time, direct indications of how the protein can dimerize head-to-head and bind 4Fe4S clusters. Analysis of the alternative scaffold protein IscA provides strong support to a coordination of the cluster by a dimeric form rather than a tetramer, as previously suggested. Our analysis also suggests the presence in solution of a mixture of monomeric and dimeric species, and guides us to the prevalent one. Finally, we used DCA to analyze interactions between some of these proteins, and discuss the potentials and limitations of the method. PMID:28664160

Spatial heterogeneity of type I error for local cluster detection tests

PubMed Central

2014-01-01

Background Just as power, type I error of cluster detection tests (CDTs) should be spatially assessed. Indeed, CDTs’ type I error and power have both a spatial component as CDTs both detect and locate clusters. In the case of type I error, the spatial distribution of wrongly detected clusters (WDCs) can be particularly affected by edge effect. This simulation study aims to describe the spatial distribution of WDCs and to confirm and quantify the presence of edge effect. Methods A simulation of 40 000 datasets has been performed under the null hypothesis of risk homogeneity. The simulation design used realistic parameters from survey data on birth defects, and in particular, two baseline risks. The simulated datasets were analyzed using the Kulldorff’s spatial scan as a commonly used test whose behavior is otherwise well known. To describe the spatial distribution of type I error, we defined the participation rate for each spatial unit of the region. We used this indicator in a new statistical test proposed to confirm, as well as quantify, the edge effect. Results The predefined type I error of 5% was respected for both baseline risks. Results showed strong edge effect in participation rates, with a descending gradient from center to edge, and WDCs more often centrally situated. Conclusions In routine analysis of real data, clusters on the edge of the region should be carefully considered as they rarely occur when there is no cluster. Further work is needed to combine results from power studies with this work in order to optimize CDTs performance. PMID:24885343

A cluster expansion model for predicting activation barrier of atomic processes

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

Rehman, Tafizur; Jaipal, M.; Chatterjee, Abhijit, E-mail: achatter@iitk.ac.in

2013-06-15

We introduce a procedure based on cluster expansion models for predicting the activation barrier of atomic processes encountered while studying the dynamics of a material system using the kinetic Monte Carlo (KMC) method. Starting with an interatomic potential description, a mathematical derivation is presented to show that the local environment dependence of the activation barrier can be captured using cluster interaction models. Next, we develop a systematic procedure for training the cluster interaction model on-the-fly, which involves: (i) obtaining activation barriers for handful local environments using nudged elastic band (NEB) calculations, (ii) identifying the local environment by analyzing the NEBmore » results, and (iii) estimating the cluster interaction model parameters from the activation barrier data. Once a cluster expansion model has been trained, it is used to predict activation barriers without requiring any additional NEB calculations. Numerical studies are performed to validate the cluster expansion model by studying hop processes in Ag/Ag(100). We show that the use of cluster expansion model with KMC enables efficient generation of an accurate process rate catalog.« less

Galaxy cluster luminosities and colours, and their dependence on cluster mass and merger state

NASA Astrophysics Data System (ADS)

Mulroy, Sarah L.; McGee, Sean L.; Gillman, Steven; Smith, Graham P.; Haines, Chris P.; Démoclès, Jessica; Okabe, Nobuhiro; Egami, Eiichi

2017-12-01

We study a sample of 19 galaxy clusters in the redshift range 0.15 < z < 0.30 with highly complete spectroscopic membership catalogues (to K < K*(z) + 1.5) from the Arizona Cluster Redshift Survey, individual weak-lensing masses and near-infrared data from the Local Cluster Substructure Survey, and optical photometry from the Sloan Digital Sky Survey. We fit the scaling relations between total cluster luminosity in each of six bandpasses (grizJK) and cluster mass, finding cluster luminosity to be a promising mass proxy with low intrinsic scatter σln L|M of only ∼10-20 per cent for all relations. At fixed overdensity radius, the intercept increases with wavelength, consistent with an old stellar population. The scatter and slope are consistent across all wavelengths, suggesting that cluster colour is not a function of mass. Comparing colour with indicators of the level of disturbance in the cluster, we find a narrower variety in the cluster colours of 'disturbed' clusters than of 'undisturbed' clusters. This trend is more pronounced with indicators sensitive to the initial stages of a cluster merger, e.g. the Dressler Schectman statistic. We interpret this as possible evidence that the total cluster star formation rate is 'standardized' in mergers, perhaps through a process such as a system-wide shock in the intracluster medium.

Subspace K-means clustering.

PubMed

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

2013-12-01

To achieve an insightful clustering of multivariate data, we propose subspace K-means. Its central idea is to model the centroids and cluster residuals in reduced spaces, which allows for dealing with a wide range of cluster types and yields rich interpretations of the clusters. We review the existing related clustering methods, including deterministic, stochastic, and unsupervised learning approaches. To evaluate subspace K-means, we performed a comparative simulation study, in which we manipulated the overlap of subspaces, the between-cluster variance, and the error variance. The study shows that the subspace K-means algorithm is sensitive to local minima but that the problem can be reasonably dealt with by using partitions of various cluster procedures as a starting point for the algorithm. Subspace K-means performs very well in recovering the true clustering across all conditions considered and appears to be superior to its competitor methods: K-means, reduced K-means, factorial K-means, mixtures of factor analyzers (MFA), and MCLUST. The best competitor method, MFA, showed a performance similar to that of subspace K-means in easy conditions but deteriorated in more difficult ones. Using data from a study on parental behavior, we show that subspace K-means analysis provides a rich insight into the cluster characteristics, in terms of both the relative positions of the clusters (via the centroids) and the shape of the clusters (via the within-cluster residuals).

Identifying and ranking influential spreaders in complex networks by combining a local-degree sum and the clustering coefficient

NASA Astrophysics Data System (ADS)

Li, Mengtian; Zhang, Ruisheng; Hu, Rongjing; Yang, Fan; Yao, Yabing; Yuan, Yongna

2018-03-01

Identifying influential spreaders is a crucial problem that can help authorities to control the spreading process in complex networks. Based on the classical degree centrality (DC), several improved measures have been presented. However, these measures cannot rank spreaders accurately. In this paper, we first calculate the sum of the degrees of the nearest neighbors of a given node, and based on the calculated sum, a novel centrality named clustered local-degree (CLD) is proposed, which combines the sum and the clustering coefficients of nodes to rank spreaders. By assuming that the spreading process in networks follows the susceptible-infectious-recovered (SIR) model, we perform extensive simulations on a series of real networks to compare the performances between the CLD centrality and other six measures. The results show that the CLD centrality has a competitive performance in distinguishing the spreading ability of nodes, and exposes the best performance to identify influential spreaders accurately.

Nonlocalized clustering: a new concept in nuclear cluster structure physics.

PubMed

Zhou, Bo; Funaki, Y; Horiuchi, H; Ren, Zhongzhou; Röpke, G; Schuck, P; Tohsaki, A; Xu, Chang; Yamada, T

2013-06-28

We investigate the α+^{16}O cluster structure in the inversion-doublet band (Kπ=0(1)±}) states of 20Ne with an angular-momentum-projected version of the Tohsaki-Horiuchi-Schuck-Röpke (THSR) wave function, which was successful "in its original form" for the description of, e.g., the famous Hoyle state. In contrast with the traditional view on clusters as localized objects, especially in inversion doublets, we find that these single THSR wave functions, which are based on the concept of nonlocalized clustering, can well describe the Kπ=0(1)- band and the Kπ=0(1)+ band. For instance, they have 99.98% and 99.87% squared overlaps for 1- and 3- states (99.29%, 98.79%, and 97.75% for 0+, 2+, and 4+ states), respectively, with the corresponding exact solution of the α+16O resonating group method. These astounding results shed a completely new light on the physics of low energy nuclear cluster states in nuclei: The clusters are nonlocalized and move around in the whole nuclear volume, only avoiding mutual overlap due to the Pauli blocking effect.

Addressing a community's cancer cluster concerns

PubMed Central

Gavin, AT; Catney, D

2006-01-01

The felling of a telecommunications mast highlighted a community's concern regarding an alleged cancer cluster of eleven cases in a small rural area of Northern Ireland. At the request of the Local District Council, the Northern Ireland Cancer Registry (NICR) undertook an investigation. After extensive searching and contact with the community, only 6 of the alleged cases could be identified. Of these six, two did not have cancer and one had a non-malignant tumour. In addition to the three confirmed cancer cases, a search of the NICR database identified a further 17 cancers of mixed types in keeping with the population pattern of cancers. Standardised incidence and mortality rates were within, or lower than, the expected level. The results were presented to the local community at an open meeting. Despite extensive media interest when the issue of the alleged cluster was first raised, the negative findings received only local media attention. This study illustrates the value of an accurate population cancer registry in addressing cancer cluster concerns. PMID:16964811

CytoCluster: A Cytoscape Plugin for Cluster Analysis and Visualization of Biological Networks.

PubMed

Li, Min; Li, Dongyan; Tang, Yu; Wu, Fangxiang; Wang, Jianxin

2017-08-31

Nowadays, cluster analysis of biological networks has become one of the most important approaches to identifying functional modules as well as predicting protein complexes and network biomarkers. Furthermore, the visualization of clustering results is crucial to display the structure of biological networks. Here we present CytoCluster, a cytoscape plugin integrating six clustering algorithms, HC-PIN (Hierarchical Clustering algorithm in Protein Interaction Networks), OH-PIN (identifying Overlapping and Hierarchical modules in Protein Interaction Networks), IPCA (Identifying Protein Complex Algorithm), ClusterONE (Clustering with Overlapping Neighborhood Expansion), DCU (Detecting Complexes based on Uncertain graph model), IPC-MCE (Identifying Protein Complexes based on Maximal Complex Extension), and BinGO (the Biological networks Gene Ontology) function. Users can select different clustering algorithms according to their requirements. The main function of these six clustering algorithms is to detect protein complexes or functional modules. In addition, BinGO is used to determine which Gene Ontology (GO) categories are statistically overrepresented in a set of genes or a subgraph of a biological network. CytoCluster can be easily expanded, so that more clustering algorithms and functions can be added to this plugin. Since it was created in July 2013, CytoCluster has been downloaded more than 9700 times in the Cytoscape App store and has already been applied to the analysis of different biological networks. CytoCluster is available from http://apps.cytoscape.org/apps/cytocluster.

Localized diabatization applied to excitons in molecular crystals

NASA Astrophysics Data System (ADS)

Jin, Zuxin; Subotnik, Joseph E.

2017-06-01

Traditional ab initio electronic structure calculations of periodic systems yield delocalized eigenstates that should be understood as adiabatic states. For example, excitons are bands of extended states which superimpose localized excitations on every lattice site. However, in general, in order to study the effects of nuclear motion on exciton transport, it is standard to work with a localized description of excitons, especially in a hopping regime; even in a band regime, a localized description can be helpful. To extract localized excitons from a band requires essentially a diabatization procedure. In this paper, three distinct methods are proposed for such localized diabatization: (i) a simple projection method, (ii) a more general Pipek-Mezey localization scheme, and (iii) a variant of Boys diabatization. Approaches (i) and (ii) require localized, single-particle Wannier orbitals, while approach (iii) has no such dependence. These methods should be very useful for studying energy transfer through solids with ab initio calculations.

Photodetachment and UV-Vis spectral properties of Cl2rad -·nHO clusters: Extrapolation to bulk

NASA Astrophysics Data System (ADS)

Pathak, A. K.; Mukherjee, T.; Maity, D. K.

2008-03-01

Vertical detachment energy (VDE) and UV-Vis spectra of Cl2rad -·nHO clusters ( n = 1-11) are reported based on first principle electronic structure calculations. VDE of the hydrated clusters are calculated following second order Moller-Plesset perturbation (MP2) as well as coupled cluster theory with 6-311++G(d,p) set of basis function. The excess electron in these hydrated clusters is mainly localized over the solute Cl atoms. A linear relationship is obtained for VDE vs. ( n + 2.6) -1/3 and bulk VDE of Cl2rad - aqueous solution is calculated as 10.61 eV at CCSD(T) level of theory. UV-Vis spectra of these hydrated clusters are calculated applying CI with single electron (CIS) excitation procedure. Simulated UV-Vis spectra of Cl2rad -·10HO cluster is noted to be in excellent agreement with the reported spectra of Cl2rad - (aq) system, λmax for Cl2rad -·11HO system is calculated to be red shifted though.

A Constrained-Clustering Approach to the Analysis of Remote Sensing Data.

DTIC Science & Technology

1983-01-01

One old and two new clustering methods were applied to the constrained-clustering problem of separating different agricultural fields based on multispectral remote sensing satellite data. (Constrained-clustering involves double clustering in multispectral measurement similarity and geographical location.) The results of applying the three methods are provided along with a discussion of their relative strengths and weaknesses and a detailed description of their algorithms.

A comparison of heuristic and model-based clustering methods for dietary pattern analysis.

PubMed

Greve, Benjamin; Pigeot, Iris; Huybrechts, Inge; Pala, Valeria; Börnhorst, Claudia

2016-02-01

Cluster analysis is widely applied to identify dietary patterns. A new method based on Gaussian mixture models (GMM) seems to be more flexible compared with the commonly applied k-means and Ward's method. In the present paper, these clustering approaches are compared to find the most appropriate one for clustering dietary data. The clustering methods were applied to simulated data sets with different cluster structures to compare their performance knowing the true cluster membership of observations. Furthermore, the three methods were applied to FFQ data assessed in 1791 children participating in the IDEFICS (Identification and Prevention of Dietary- and Lifestyle-Induced Health Effects in Children and Infants) Study to explore their performance in practice. The GMM outperformed the other methods in the simulation study in 72 % up to 100 % of cases, depending on the simulated cluster structure. Comparing the computationally less complex k-means and Ward's methods, the performance of k-means was better in 64-100 % of cases. Applied to real data, all methods identified three similar dietary patterns which may be roughly characterized as a 'non-processed' cluster with a high consumption of fruits, vegetables and wholemeal bread, a 'balanced' cluster with only slight preferences of single foods and a 'junk food' cluster. The simulation study suggests that clustering via GMM should be preferred due to its higher flexibility regarding cluster volume, shape and orientation. The k-means seems to be a good alternative, being easier to use while giving similar results when applied to real data.

Study of multiband disordered systems using the typical medium dynamical cluster approximation

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

Zhang, Yi; Terletska, Hanna; Moore, C.

We generalize the typical medium dynamical cluster approximation to multiband disordered systems. Using our extended formalism, we perform a systematic study of the nonlocal correlation effects induced by disorder on the density of states and the mobility edge of the three-dimensional two-band Anderson model. We include interband and intraband hopping and an intraband disorder potential. Our results are consistent with those obtained by the transfer matrix and the kernel polynomial methods. We also apply the method to K xFe 2-ySe 2 with Fe vacancies. Despite the strong vacancy disorder and anisotropy, we find the material is not an Anderson insulator.more » Moreover our results demonstrate the application of the typical medium dynamical cluster approximation method to study Anderson localization in real materials.« less

Study of multiband disordered systems using the typical medium dynamical cluster approximation

DOE PAGES

Zhang, Yi; Terletska, Hanna; Moore, C.; ...

2015-11-06

We generalize the typical medium dynamical cluster approximation to multiband disordered systems. Using our extended formalism, we perform a systematic study of the nonlocal correlation effects induced by disorder on the density of states and the mobility edge of the three-dimensional two-band Anderson model. We include interband and intraband hopping and an intraband disorder potential. Our results are consistent with those obtained by the transfer matrix and the kernel polynomial methods. We also apply the method to K xFe 2-ySe 2 with Fe vacancies. Despite the strong vacancy disorder and anisotropy, we find the material is not an Anderson insulator.more » Moreover our results demonstrate the application of the typical medium dynamical cluster approximation method to study Anderson localization in real materials.« less

Performance of cancer cluster Q-statistics for case-control residential histories

PubMed Central

Sloan, Chantel D.; Jacquez, Geoffrey M.; Gallagher, Carolyn M.; Ward, Mary H.; Raaschou-Nielsen, Ole; Nordsborg, Rikke Baastrup; Meliker, Jaymie R.

2012-01-01

Few investigations of health event clustering have evaluated residential mobility, though causative exposures for chronic diseases such as cancer often occur long before diagnosis. Recently developed Q-statistics incorporate human mobility into disease cluster investigations by quantifying space- and time-dependent nearest neighbor relationships. Using residential histories from two cancer case-control studies, we created simulated clusters to examine Q-statistic performance. Results suggest the intersection of cases with significant clustering over their life course, Qi, with cases who are constituents of significant local clusters at given times, Qit, yielded the best performance, which improved with increasing cluster size. Upon comparison, a larger proportion of true positives were detected with Kulldorf’s spatial scan method if the time of clustering was provided. We recommend using Q-statistics to identify when and where clustering may have occurred, followed by the scan method to localize the candidate clusters. Future work should investigate the generalizability of these findings. PMID:23149326

Mapping the dynamics of force transduction at cell–cell junctions of epithelial clusters

PubMed Central

Ng, Mei Rosa; Besser, Achim; Brugge, Joan S; Danuser, Gaudenz

2014-01-01

Force transduction at cell-cell adhesions regulates tissue development, maintenance and adaptation. We developed computational and experimental approaches to quantify, with both sub-cellular and multi-cellular resolution, the dynamics of force transmission in cell clusters. Applying this technology to spontaneously-forming adherent epithelial cell clusters, we found that basal force fluctuations were coupled to E-cadherin localization at the level of individual cell-cell junctions. At the multi-cellular scale, cell-cell force exchange depended on the cell position within a cluster, and was adaptive to reconfigurations due to cell divisions or positional rearrangements. Importantly, force transmission through a cell required coordinated modulation of cell-matrix adhesion and actomyosin contractility in the cell and its neighbors. These data provide insights into mechanisms that could control mechanical stress homeostasis in dynamic epithelial tissues, and highlight our methods as a resource for the study of mechanotransduction in cell-cell adhesions. DOI: http://dx.doi.org/10.7554/eLife.03282.001 PMID:25479385

Wavelength Dependent Luminosity Functions for Super Star Clusters

NASA Astrophysics Data System (ADS)

Garmany, Catharine

1997-07-01

Starburst galaxies, considered to exhibit enhanced star formation on a galaxy-wide scale, have now been found with HST to contain very intense knots of star formation, referred to as ``super star clusters'', or SSCs. A steepening of the luminosity function with increasing wavelength for young burst populations, such as SSCs, has recently been predicted by Hogg & Phinney {1997}. This prediction, not previously addressed in the literature, is straightforward to test with multi- wavelength photometry. Using the colors of the SSCs in a galaxy in combination with the difference in slopes of the luminosity functions derived from different wavelength bands and applying population synthesis models, we can also constrain the high mass stellar initial mass function {IMF}. Recent work has suggested that the slope of the IMF is roughly constant in a variety of local environments, from galactic OB associations to the closest analog of a super star cluster, R136 in the LMC. This investigation will allow us to compare the IMFs in the extreme environments of SSCs in starburst galaxies to IMFs found locally in the Galaxy, LMC, and SMC. Archival imaging data in both the UV and optical bands is available for about 10 young starburst systems. These data will allow us to test the predictions of Hogg & Phinney, as well as constrain the IMF for environments not found in the nearby universe.

Cluster structures influenced by interaction with a surface.

PubMed

Witt, Christopher; Dieterich, Johannes M; Hartke, Bernd

2018-05-30

Clusters on surfaces are vitally important for nanotechnological applications. Clearly, cluster-surface interactions heavily influence the preferred cluster structures, compared to clusters in vacuum. Nevertheless, systematic explorations and an in-depth understanding of these interactions and how they determine the cluster structures are still lacking. Here we present an extension of our well-established non-deterministic global optimization package OGOLEM from isolated clusters to clusters on surfaces. Applying this approach to intentionally simple Lennard-Jones test systems, we produce a first systematic exploration that relates changes in cluster-surface interactions to resulting changes in adsorbed cluster structures.

Goal oriented soil mapping: applying modern methods supported by local knowledge: A review

NASA Astrophysics Data System (ADS)

Pereira, Paulo; Brevik, Eric; Oliva, Marc; Estebaranz, Ferran; Depellegrin, Daniel; Novara, Agata; Cerda, Artemi; Menshov, Oleksandr

2017-04-01

In the recent years the amount of soil data available increased importantly. This facilitated the production of better and accurate maps, important for sustainable land management (Pereira et al., 2017). Despite these advances, the human knowledge is extremely important to understand the natural characteristics of the landscape. The knowledge accumulated and transmitted generation after generation is priceless, and should be considered as a valuable data source for soil mapping and modelling. The local knowledge and wisdom can complement the new advances in soil analysis. In addition, farmers are the most interested in the participation and incorporation of their knowledge in the models, since they are the end-users of the study that soil scientists produce. Integration of local community's vision and understanding about nature is assumed to be an important step to the implementation of decision maker's policies. Despite this, many challenges appear regarding the integration of local and scientific knowledge, since in some cases there is no spatial correlation between folk and scientific classifications, which may be attributed to the different cultural variables that influence local soil classification. The objective of this work is to review how modern soil methods incorporated local knowledge in their models. References Pereira, P., Brevik, E., Oliva, M., Estebaranz, F., Depellegrin, D., Novara, A., Cerda, A., Menshov, O. (2017) Goal Oriented soil mapping: applying modern methods supported by local knowledge. In: Pereira, P., Brevik, E., Munoz-Rojas, M., Miller, B. (Eds.) Soil mapping and process modelling for sustainable land use management (Elsevier Publishing House) ISBN: 9780128052006

An efficient and near linear scaling pair natural orbital based local coupled cluster method.

PubMed

Riplinger, Christoph; Neese, Frank

2013-01-21

In previous publications, it was shown that an efficient local coupled cluster method with single- and double excitations can be based on the concept of pair natural orbitals (PNOs) [F. Neese, A. Hansen, and D. G. Liakos, J. Chem. Phys. 131, 064103 (2009)]. The resulting local pair natural orbital-coupled-cluster single double (LPNO-CCSD) method has since been proven to be highly reliable and efficient. For large molecules, the number of amplitudes to be determined is reduced by a factor of 10(5)-10(6) relative to a canonical CCSD calculation on the same system with the same basis set. In the original method, the PNOs were expanded in the set of canonical virtual orbitals and single excitations were not truncated. This led to a number of fifth order scaling steps that eventually rendered the method computationally expensive for large molecules (e.g., >100 atoms). In the present work, these limitations are overcome by a complete redesign of the LPNO-CCSD method. The new method is based on the combination of the concepts of PNOs and projected atomic orbitals (PAOs). Thus, each PNO is expanded in a set of PAOs that in turn belong to a given electron pair specific domain. In this way, it is possible to fully exploit locality while maintaining the extremely high compactness of the original LPNO-CCSD wavefunction. No terms are dropped from the CCSD equations and domains are chosen conservatively. The correlation energy loss due to the domains remains below <0.05%, which implies typically 15-20 but occasionally up to 30 atoms per domain on average. The new method has been given the acronym DLPNO-CCSD ("domain based LPNO-CCSD"). The method is nearly linear scaling with respect to system size. The original LPNO-CCSD method had three adjustable truncation thresholds that were chosen conservatively and do not need to be changed for actual applications. In the present treatment, no additional truncation parameters have been introduced. Any additional truncation is performed on

Advanced dynamic statistical parametric mapping with MEG in localizing epileptogenicity of the bottom of sulcus dysplasia.

PubMed

Nakajima, Midori; Wong, Simeon; Widjaja, Elysa; Baba, Shiro; Okanishi, Tohru; Takada, Lynne; Sato, Yosuke; Iwata, Hiroki; Sogabe, Maya; Morooka, Hikaru; Whitney, Robyn; Ueda, Yuki; Ito, Tomoshiro; Yagyu, Kazuyori; Ochi, Ayako; Carter Snead, O; Rutka, James T; Drake, James M; Doesburg, Sam; Takeuchi, Fumiya; Shiraishi, Hideaki; Otsubo, Hiroshi

2018-06-01

To investigate whether advanced dynamic statistical parametric mapping (AdSPM) using magnetoencephalography (MEG) can better localize focal cortical dysplasia at bottom of sulcus (FCDB). We analyzed 15 children with diagnosis of FCDB in surgical specimen and 3 T MRI by using MEG. Using AdSPM, we analyzed a ±50 ms epoch relative to each single moving dipole (SMD) and applied summation technique to estimate the source activity. The most active area in AdSPM was defined as the location of AdSPM spike source. We compared spatial congruence between MRI-visible FCDB and (1) dipole cluster in SMD method; and (2) AdSPM spike source. AdSPM localized FCDB in 12 (80%) of 15 children whereas dipole cluster localized six (40%). AdSPM spike source was concordant within seizure onset zone in nine (82%) of 11 children with intracranial video EEG. Eleven children with resective surgery achieved seizure freedom with follow-up period of 1.9 ± 1.5 years. Ten (91%) of them had an AdSPM spike source in the resection area. AdSPM can noninvasively and neurophysiologically localize epileptogenic FCDB, whether it overlaps with the dipole cluster or not. This is the first study to localize epileptogenic FCDB using MEG. Copyright © 2018 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

[Cluster-immunotherapy in seasonal allergic rhinitis: safety aspects of induction therapy with depot allergoids (Purethal)].

PubMed

Hansen, I; Hörmann, K; Stuck, B A; Schneider-Gêne, S; Mösges, R; Klimek, L

2003-08-01

Specific immunotherapy (SIT) represents the only specific treatment that can be offered to allergic patients apart from allergen avoidance. SIT has been widely used in pollen allergic rhinitis. Clinical efficacy has been demonstrated in several controlled clinical trials and depends on the specific allergen the individual patient is sensitive to, the quality and total amount of allergen applied, and the SIT schedule. In classic SIT, gradually increasing dosages of the allergen extract are injected subcutaneously. Several dosage schedules for subcutaneous SIT can be applied. In Cluster-SIT, 2 - 3 injections per day of treatment are given once a week during induction treatment. In this study, we investigated 64 patients (33 female, 31 male) from 18 to 54 years (26.9 +/- 5.1 years) in terms of side-effects of Cluster-SIT during induction treatment. The total amount of enlarged local reactions (> grade 1) was n = 77 or 15.2 % of all injections. Of these, 68 (88 %) were classified as immediate reactions, 8 (11 %) were late phase reactions and 1 (1 %) was immediate as well as late phase reaction. Of all enlarged local reactions, 48 (62 %) were grade 1 reactions, 13 (17 %) were grade 2 reactions, 13 (17 %) were grade 3 reactions and 1 (1 %) was a grade 4 reaction. The total amount of systemic reactions was n = 22 or 4.3 % of all injections. Of these, 19 (86 %) were classified as immediate reactions, 3 (14 %) were delayed reactions. Of all systemic reactions, 18 (82 %) were grade 1 reactions and 4 (18 %) grade 2 reactions. Grade 3 or grade 4 reactions did not occur. There were no differences in gender or age regarding the occurrence of side effects (all p > 0.05). Frequency and severity of adverse side effects in Cluster-SIT correspond to those in other dosage schedules. On behalf of security aspects, Cluster-SIT could become an interesting alternative dosage schedule for dose increase during SIT. Furthermore, in Cluster-SIT with allergoids, induction treatment can be

Cluster compression algorithm: A joint clustering/data compression concept

NASA Technical Reports Server (NTRS)

Hilbert, E. E.

1977-01-01

The Cluster Compression Algorithm (CCA), which was developed to reduce costs associated with transmitting, storing, distributing, and interpreting LANDSAT multispectral image data is described. The CCA is a preprocessing algorithm that uses feature extraction and data compression to more efficiently represent the information in the image data. The format of the preprocessed data enables simply a look-up table decoding and direct use of the extracted features to reduce user computation for either image reconstruction, or computer interpretation of the image data. Basically, the CCA uses spatially local clustering to extract features from the image data to describe spectral characteristics of the data set. In addition, the features may be used to form a sequence of scalar numbers that define each picture element in terms of the cluster features. This sequence, called the feature map, is then efficiently represented by using source encoding concepts. Various forms of the CCA are defined and experimental results are presented to show trade-offs and characteristics of the various implementations. Examples are provided that demonstrate the application of the cluster compression concept to multi-spectral images from LANDSAT and other sources.

HUBBLE SPIES GLOBULAR CLUSTER IN NEIGHBORING GALAXY

NASA Technical Reports Server (NTRS)

2002-01-01

Hubble Space Telescope has captured a view of a globular cluster called G1, a large, bright ball of light in the center of the photograph consisting of at least 300,000 old stars. G1, also known as Mayall II, orbits the Andromeda galaxy (M31), the nearest major spiral galaxy to our Milky Way. Located 130,000 light-years from Andromeda's nucleus, G1 is the brightest globular cluster in the Local Group of galaxies. The Local Group consists of about 20 nearby galaxies, including the Milky Way. The crisp image is comparable to ground-based telescope views of similar clusters orbiting the Milky Way. The Andromeda cluster, however, is nearly 100 times farther away. A glimpse into the cluster's finer details allow astronomers to see its fainter helium-burning stars whose temperatures and brightnesses show that this cluster in Andromeda and the oldest Milky Way clusters have approximately the same age. These clusters probably were formed shortly after the beginning of the universe, providing astronomers with a record of the earliest era of galaxy formation. During the next two years, astronomers will use Hubble to study about 20 more globular clusters in Andromeda. The color picture was assembled from separate images taken in visible and near-infrared wavelengths taken in July of 1994. CREDIT: Michael Rich, Kenneth Mighell, and James D. Neill (Columbia University), and Wendy Freedman (Carnegie Observatories), and NASA Image files in GIF and JPEG format and captions may be accessed on Internet via anonymous ftp from oposite.stsci.edu in /pubinfo.

Localized diabatization applied to excitons in molecular crystals

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

Jin, Zuxin; Subotnik, Joseph E.

Traditional ab initio electronic structure calculations of periodic systems yield delocalized eigenstates that should be understood as adiabatic states. For example, excitons are bands of extended states which superimpose localized excitations on every lattice site. However, in general, in order to study the effects of nuclear motion on exciton transport, it is standard to work with a localized description of excitons, especially in a hopping regime; even in a band regime, a localized description can be helpful. To extract localized excitons from a band requires essentially a diabatization procedure. In this paper, three distinct methods are proposed for such localizedmore » diabatization: (i) a simple projection method, (ii) a more general Pipek-Mezey localization scheme, and (iii) a variant of Boys diabatization. Approaches (i) and (ii) require localized, single-particle Wannier orbitals, while approach (iii) has no such dependence. Lastly, these methods should be very useful for studying energy transfer through solids with ab initio calculations.« less

Localized diabatization applied to excitons in molecular crystals

DOE PAGES

Jin, Zuxin; Subotnik, Joseph E.

2017-06-28

Traditional ab initio electronic structure calculations of periodic systems yield delocalized eigenstates that should be understood as adiabatic states. For example, excitons are bands of extended states which superimpose localized excitations on every lattice site. However, in general, in order to study the effects of nuclear motion on exciton transport, it is standard to work with a localized description of excitons, especially in a hopping regime; even in a band regime, a localized description can be helpful. To extract localized excitons from a band requires essentially a diabatization procedure. In this paper, three distinct methods are proposed for such localizedmore » diabatization: (i) a simple projection method, (ii) a more general Pipek-Mezey localization scheme, and (iii) a variant of Boys diabatization. Approaches (i) and (ii) require localized, single-particle Wannier orbitals, while approach (iii) has no such dependence. Lastly, these methods should be very useful for studying energy transfer through solids with ab initio calculations.« less

CytoCluster: A Cytoscape Plugin for Cluster Analysis and Visualization of Biological Networks

PubMed Central

Li, Min; Li, Dongyan; Tang, Yu; Wang, Jianxin

2017-01-01

Nowadays, cluster analysis of biological networks has become one of the most important approaches to identifying functional modules as well as predicting protein complexes and network biomarkers. Furthermore, the visualization of clustering results is crucial to display the structure of biological networks. Here we present CytoCluster, a cytoscape plugin integrating six clustering algorithms, HC-PIN (Hierarchical Clustering algorithm in Protein Interaction Networks), OH-PIN (identifying Overlapping and Hierarchical modules in Protein Interaction Networks), IPCA (Identifying Protein Complex Algorithm), ClusterONE (Clustering with Overlapping Neighborhood Expansion), DCU (Detecting Complexes based on Uncertain graph model), IPC-MCE (Identifying Protein Complexes based on Maximal Complex Extension), and BinGO (the Biological networks Gene Ontology) function. Users can select different clustering algorithms according to their requirements. The main function of these six clustering algorithms is to detect protein complexes or functional modules. In addition, BinGO is used to determine which Gene Ontology (GO) categories are statistically overrepresented in a set of genes or a subgraph of a biological network. CytoCluster can be easily expanded, so that more clustering algorithms and functions can be added to this plugin. Since it was created in July 2013, CytoCluster has been downloaded more than 9700 times in the Cytoscape App store and has already been applied to the analysis of different biological networks. CytoCluster is available from http://apps.cytoscape.org/apps/cytocluster. PMID:28858211

Westerlund 1: monolithic formation of a starburst cluster

NASA Astrophysics Data System (ADS)

Negueruela, Ignacio; Clark, J. Simon; Ritchie, Ben W.; Goodwin, Simon P.

2017-03-01

Westerlund 1 is in all likelihood the most massive young cluster in the Milky Way, with a mass on the order of 105 M ⊙. To determine its bulk properties we have made multi-epoch radial velocity measurements for a substantial fraction of its OB stars and evolved supergiants and obtained multi-object spectroscopy of candidate cluster members in its locale. The results of these two studies show that Westerlund 1 is apparently subvirial and appears completely isolated, with hardly any massive star in its vicinity that could be associated with it in terms of distance modulus or radial velocity. The cluster halo does not extend much further than five parsec away from the centre. All these properties are very unusual among starburst clusters in the Local Universe, which tend to form in the context of large star-forming regions.

Conformational Clusters of Phosphorylated Tyrosine.

PubMed

Abdelrasoul, Maha; Ponniah, Komala; Mao, Alice; Warden, Meghan S; Elhefnawy, Wessam; Li, Yaohang; Pascal, Steven M

2017-12-06

Tyrosine phosphorylation plays an important role in many cellular and intercellular processes including signal transduction, subcellular localization, and regulation of enzymatic activity. In 1999, Blom et al., using the limited number of protein data bank (PDB) structures available at that time, reported that the side chain structures of phosphorylated tyrosine (pY) are partitioned into two conserved conformational clusters ( Blom, N.; Gammeltoft, S.; Brunak, S. J. Mol. Biol. 1999 , 294 , 1351 - 1362 ). We have used the spectral clustering algorithm to cluster the increasingly growing number of protein structures with pY sites, and have found that the pY residues cluster into three distinct side chain conformations. Two of these pY conformational clusters associate strongly with a narrow range of tyrosine backbone conformation. The novel cluster also highly correlates with the identity of the n + 1 residue, and is strongly associated with a sequential pYpY conformation which places two adjacent pY side chains in a specific relative orientation. Further analysis shows that the three pY clusters are associated with distinct distributions of cognate protein kinases.

Relaxation and collective excitations of cluster nano-plasmas

NASA Astrophysics Data System (ADS)

Reinholz, Heidi; Röpke, Gerd; Broda, Ingrid; Morozov, Igor; Bystryi, Roman; Lavrinenko, Yaroslav

2018-01-01

Nano-plasmas produced, for example, in clusters after short-pulse laser irradiation, can show collective excitations, as derived from the time evolution of fluctuations in thermodynamic equilibrium. Molecular dynamical simulations are performed for various cluster sizes. New data are obtained for the minimum value of the stationary cluster charge. The bi-local autocorrelation function gives the spatial structure of the eigenmodes, for which energy eigenvalues are obtained. By varying the cluster size, starting from a few-particle cluster, the emergence of macroscopic properties such as collective excitations is shown.

MicroRNA-Target Network Inference and Local Network Enrichment Analysis Identify Two microRNA Clusters with Distinct Functions in Head and Neck Squamous Cell Carcinoma

PubMed Central

Sass, Steffen; Pitea, Adriana; Unger, Kristian; Hess, Julia; Mueller, Nikola S.; Theis, Fabian J.

2015-01-01

MicroRNAs represent ~22 nt long endogenous small RNA molecules that have been experimentally shown to regulate gene expression post-transcriptionally. One main interest in miRNA research is the investigation of their functional roles, which can typically be accomplished by identification of mi-/mRNA interactions and functional annotation of target gene sets. We here present a novel method “miRlastic”, which infers miRNA-target interactions using transcriptomic data as well as prior knowledge and performs functional annotation of target genes by exploiting the local structure of the inferred network. For the network inference, we applied linear regression modeling with elastic net regularization on matched microRNA and messenger RNA expression profiling data to perform feature selection on prior knowledge from sequence-based target prediction resources. The novelty of miRlastic inference originates in predicting data-driven intra-transcriptome regulatory relationships through feature selection. With synthetic data, we showed that miRlastic outperformed commonly used methods and was suitable even for low sample sizes. To gain insight into the functional role of miRNAs and to determine joint functional properties of miRNA clusters, we introduced a local enrichment analysis procedure. The principle of this procedure lies in identifying regions of high functional similarity by evaluating the shortest paths between genes in the network. We can finally assign functional roles to the miRNAs by taking their regulatory relationships into account. We thoroughly evaluated miRlastic on a cohort of head and neck cancer (HNSCC) patients provided by The Cancer Genome Atlas. We inferred an mi-/mRNA regulatory network for human papilloma virus (HPV)-associated miRNAs in HNSCC. The resulting network best enriched for experimentally validated miRNA-target interaction, when compared to common methods. Finally, the local enrichment step identified two functional clusters of mi

MicroRNA-Target Network Inference and Local Network Enrichment Analysis Identify Two microRNA Clusters with Distinct Functions in Head and Neck Squamous Cell Carcinoma.

PubMed

Sass, Steffen; Pitea, Adriana; Unger, Kristian; Hess, Julia; Mueller, Nikola S; Theis, Fabian J

2015-12-18

MicroRNAs represent ~22 nt long endogenous small RNA molecules that have been experimentally shown to regulate gene expression post-transcriptionally. One main interest in miRNA research is the investigation of their functional roles, which can typically be accomplished by identification of mi-/mRNA interactions and functional annotation of target gene sets. We here present a novel method "miRlastic", which infers miRNA-target interactions using transcriptomic data as well as prior knowledge and performs functional annotation of target genes by exploiting the local structure of the inferred network. For the network inference, we applied linear regression modeling with elastic net regularization on matched microRNA and messenger RNA expression profiling data to perform feature selection on prior knowledge from sequence-based target prediction resources. The novelty of miRlastic inference originates in predicting data-driven intra-transcriptome regulatory relationships through feature selection. With synthetic data, we showed that miRlastic outperformed commonly used methods and was suitable even for low sample sizes. To gain insight into the functional role of miRNAs and to determine joint functional properties of miRNA clusters, we introduced a local enrichment analysis procedure. The principle of this procedure lies in identifying regions of high functional similarity by evaluating the shortest paths between genes in the network. We can finally assign functional roles to the miRNAs by taking their regulatory relationships into account. We thoroughly evaluated miRlastic on a cohort of head and neck cancer (HNSCC) patients provided by The Cancer Genome Atlas. We inferred an mi-/mRNA regulatory network for human papilloma virus (HPV)-associated miRNAs in HNSCC. The resulting network best enriched for experimentally validated miRNA-target interaction, when compared to common methods. Finally, the local enrichment step identified two functional clusters of miRNAs that

Anharmonic effects in the quantum cluster equilibrium method

NASA Astrophysics Data System (ADS)

von Domaros, Michael; Perlt, Eva

2017-03-01

The well-established quantum cluster equilibrium (QCE) model provides a statistical thermodynamic framework to apply high-level ab initio calculations of finite cluster structures to macroscopic liquid phases using the partition function. So far, the harmonic approximation has been applied throughout the calculations. In this article, we apply an important correction in the evaluation of the one-particle partition function and account for anharmonicity. Therefore, we implemented an analytical approximation to the Morse partition function and the derivatives of its logarithm with respect to temperature, which are required for the evaluation of thermodynamic quantities. This anharmonic QCE approach has been applied to liquid hydrogen chloride and cluster distributions, and the molar volume, the volumetric thermal expansion coefficient, and the isobaric heat capacity have been calculated. An improved description for all properties is observed if anharmonic effects are considered.

Dynamic evolution of nearby galaxy clusters

NASA Astrophysics Data System (ADS)

Biernacka, M.; Flin, P.

2011-06-01

A study of the evolution of 377 rich ACO clusters with redshift z<0.2 is presented. The data concerning galaxies in the investigated clusters were obtained using FOCAS packages applied to Digital Sky Survey I. The 377 galaxy clusters constitute a statistically uniform sample to which visual galaxy/star reclassifications were applied. Cluster shape within 2.0 h-1 Mpc from the adopted cluster centre (the mean and the median of all galaxy coordinates, the position of the brightest and of the third brightest galaxy in the cluster) was determined through its ellipticity calculated using two methods: the covariance ellipse method (hereafter CEM) and the method based on Minkowski functionals (hereafter MFM). We investigated ellipticity dependence on the radius of circular annuli, in which ellipticity was calculated. This was realized by varying the radius from 0.5 to 2 Mpc in steps of 0.25 Mpc. By performing Monte Carlo simulations, we generated clusters to which the two ellipticity methods were applied. We found that the covariance ellipse method works better than the method based on Minkowski functionals. We also found that ellipticity distributions are different for different methods used. Using the ellipticity-redshift relation, we investigated the possibility of cluster evolution in the low-redshift Universe. The correlation of cluster ellipticities with redshifts is undoubtly an indicator of structural evolution. Using the t-Student statistics, we found a statistically significant correlation between ellipticity and redshift at the significance level of α = 0.95. In one of the two shape determination methods we found that ellipticity grew with redshift, while the other method gave opposite results. Monte Carlo simulations showed that only ellipticities calculated at the distance of 1.5 Mpc from cluster centre in the Minkowski functional method are robust enough to be taken into account, but for that radius we did not find any relation between e and z. Since CEM

Estimating the Impacts of Local Policy Innovation: The Synthetic Control Method Applied to Tropical Deforestation

PubMed Central

Sills, Erin O.; Herrera, Diego; Kirkpatrick, A. Justin; Brandão, Amintas; Dickson, Rebecca; Hall, Simon; Pattanayak, Subhrendu; Shoch, David; Vedoveto, Mariana; Young, Luisa; Pfaff, Alexander

2015-01-01

Quasi-experimental methods increasingly are used to evaluate the impacts of conservation interventions by generating credible estimates of counterfactual baselines. These methods generally require large samples for statistical comparisons, presenting a challenge for evaluating innovative policies implemented within a few pioneering jurisdictions. Single jurisdictions often are studied using comparative methods, which rely on analysts’ selection of best case comparisons. The synthetic control method (SCM) offers one systematic and transparent way to select cases for comparison, from a sizeable pool, by focusing upon similarity in outcomes before the intervention. We explain SCM, then apply it to one local initiative to limit deforestation in the Brazilian Amazon. The municipality of Paragominas launched a multi-pronged local initiative in 2008 to maintain low deforestation while restoring economic production. This was a response to having been placed, due to high deforestation, on a federal “blacklist” that increased enforcement of forest regulations and restricted access to credit and output markets. The local initiative included mapping and monitoring of rural land plus promotion of economic alternatives compatible with low deforestation. The key motivation for the program may have been to reduce the costs of blacklisting. However its stated purpose was to limit deforestation, and thus we apply SCM to estimate what deforestation would have been in a (counterfactual) scenario of no local initiative. We obtain a plausible estimate, in that deforestation patterns before the intervention were similar in Paragominas and the synthetic control, which suggests that after several years, the initiative did lower deforestation (significantly below the synthetic control in 2012). This demonstrates that SCM can yield helpful land-use counterfactuals for single units, with opportunities to integrate local and expert knowledge and to test innovations and permutations on

Estimating the Impacts of Local Policy Innovation: The Synthetic Control Method Applied to Tropical Deforestation.

PubMed

Sills, Erin O; Herrera, Diego; Kirkpatrick, A Justin; Brandão, Amintas; Dickson, Rebecca; Hall, Simon; Pattanayak, Subhrendu; Shoch, David; Vedoveto, Mariana; Young, Luisa; Pfaff, Alexander

2015-01-01

Quasi-experimental methods increasingly are used to evaluate the impacts of conservation interventions by generating credible estimates of counterfactual baselines. These methods generally require large samples for statistical comparisons, presenting a challenge for evaluating innovative policies implemented within a few pioneering jurisdictions. Single jurisdictions often are studied using comparative methods, which rely on analysts' selection of best case comparisons. The synthetic control method (SCM) offers one systematic and transparent way to select cases for comparison, from a sizeable pool, by focusing upon similarity in outcomes before the intervention. We explain SCM, then apply it to one local initiative to limit deforestation in the Brazilian Amazon. The municipality of Paragominas launched a multi-pronged local initiative in 2008 to maintain low deforestation while restoring economic production. This was a response to having been placed, due to high deforestation, on a federal "blacklist" that increased enforcement of forest regulations and restricted access to credit and output markets. The local initiative included mapping and monitoring of rural land plus promotion of economic alternatives compatible with low deforestation. The key motivation for the program may have been to reduce the costs of blacklisting. However its stated purpose was to limit deforestation, and thus we apply SCM to estimate what deforestation would have been in a (counterfactual) scenario of no local initiative. We obtain a plausible estimate, in that deforestation patterns before the intervention were similar in Paragominas and the synthetic control, which suggests that after several years, the initiative did lower deforestation (significantly below the synthetic control in 2012). This demonstrates that SCM can yield helpful land-use counterfactuals for single units, with opportunities to integrate local and expert knowledge and to test innovations and permutations on policies

Spectroscopic Study of Local Interactions of Platinum in Small [CexOy]Ptx' - Clusters

NASA Astrophysics Data System (ADS)

Ray, Manisha; Kafader, Jared O.; Chick Jarrold, Caroline

2016-06-01

Cerium oxide is a good ionic conductor, and the conductivity can be enhanced with oxygen vacancies and doping. This conductivity may play an important role in the enhancement of noble or coinage metal toward the water-gas shift reaction when supported by cerium oxide. The ceria-supported platinum catalyst in particular has received much attention because of higher activity at lower temperatures (LT) compared to the most common commercial LT-WGS catalyst. We have used a combination of anion photoelectron spectroscopy and density functional theory calculations to study the interesting molecular and electronic structures and properties of cluster models of ceria-supported platinum. [CexOy]Ptx' - (x,x'=1,2 ; y≤2x') clusters exhibit evidence of ionic bonding possible because of the high electron affinity of Pt and the low ionization potential of cerium oxide clusters. In addition, Pt- is a common daughter ion resulting from photodissociation of [CexOy]Ptx' - clusters. Finally, several of the anion and neutral clusters have profoundly different structures. These features may play a role in the enhancement of catalytic activity toward the water-gas shift reaction.

Changing cluster composition in cluster randomised controlled trials: design and analysis considerations

PubMed Central

2014-01-01

Background There are many methodological challenges in the conduct and analysis of cluster randomised controlled trials, but one that has received little attention is that of post-randomisation changes to cluster composition. To illustrate this, we focus on the issue of cluster merging, considering the impact on the design, analysis and interpretation of trial outcomes. Methods We explored the effects of merging clusters on study power using standard methods of power calculation. We assessed the potential impacts on study findings of both homogeneous cluster merges (involving clusters randomised to the same arm of a trial) and heterogeneous merges (involving clusters randomised to different arms of a trial) by simulation. To determine the impact on bias and precision of treatment effect estimates, we applied standard methods of analysis to different populations under analysis. Results Cluster merging produced a systematic reduction in study power. This effect depended on the number of merges and was most pronounced when variability in cluster size was at its greatest. Simulations demonstrate that the impact on analysis was minimal when cluster merges were homogeneous, with impact on study power being balanced by a change in observed intracluster correlation coefficient (ICC). We found a decrease in study power when cluster merges were heterogeneous, and the estimate of treatment effect was attenuated. Conclusions Examples of cluster merges found in previously published reports of cluster randomised trials were typically homogeneous rather than heterogeneous. Simulations demonstrated that trial findings in such cases would be unbiased. However, simulations also showed that any heterogeneous cluster merges would introduce bias that would be hard to quantify, as well as having negative impacts on the precision of estimates obtained. Further methodological development is warranted to better determine how to analyse such trials appropriately. Interim recommendations

Coordinate-Based Clustering Method for Indoor Fingerprinting Localization in Dense Cluttered Environments.

PubMed

Liu, Wen; Fu, Xiao; Deng, Zhongliang

2016-12-02

Indoor positioning technologies has boomed recently because of the growing commercial interest in indoor location-based service (ILBS). Due to the absence of satellite signal in Global Navigation Satellite System (GNSS), various technologies have been proposed for indoor applications. Among them, Wi-Fi fingerprinting has been attracting much interest from researchers because of its pervasive deployment, flexibility and robustness to dense cluttered indoor environments. One challenge, however, is the deployment of Access Points (AP), which would bring a significant influence on the system positioning accuracy. This paper concentrates on WLAN based fingerprinting indoor location by analyzing the AP deployment influence, and studying the advantages of coordinate-based clustering compared to traditional RSS-based clustering. A coordinate-based clustering method for indoor fingerprinting location, named Smallest-Enclosing-Circle-based (SEC), is then proposed aiming at reducing the positioning error lying in the AP deployment and improving robustness to dense cluttered environments. All measurements are conducted in indoor public areas, such as the National Center For the Performing Arts (as Test-bed 1) and the XiDan Joy City (Floors 1 and 2, as Test-bed 2), and results show that SEC clustering algorithm can improve system positioning accuracy by about 32.7% for Test-bed 1, 71.7% for Test-bed 2 Floor 1 and 73.7% for Test-bed 2 Floor 2 compared with traditional RSS-based clustering algorithms such as K-means.

Coordinate-Based Clustering Method for Indoor Fingerprinting Localization in Dense Cluttered Environments

PubMed Central

Liu, Wen; Fu, Xiao; Deng, Zhongliang

2016-01-01

Indoor positioning technologies has boomed recently because of the growing commercial interest in indoor location-based service (ILBS). Due to the absence of satellite signal in Global Navigation Satellite System (GNSS), various technologies have been proposed for indoor applications. Among them, Wi-Fi fingerprinting has been attracting much interest from researchers because of its pervasive deployment, flexibility and robustness to dense cluttered indoor environments. One challenge, however, is the deployment of Access Points (AP), which would bring a significant influence on the system positioning accuracy. This paper concentrates on WLAN based fingerprinting indoor location by analyzing the AP deployment influence, and studying the advantages of coordinate-based clustering compared to traditional RSS-based clustering. A coordinate-based clustering method for indoor fingerprinting location, named Smallest-Enclosing-Circle-based (SEC), is then proposed aiming at reducing the positioning error lying in the AP deployment and improving robustness to dense cluttered environments. All measurements are conducted in indoor public areas, such as the National Center For the Performing Arts (as Test-bed 1) and the XiDan Joy City (Floors 1 and 2, as Test-bed 2), and results show that SEC clustering algorithm can improve system positioning accuracy by about 32.7% for Test-bed 1, 71.7% for Test-bed 2 Floor 1 and 73.7% for Test-bed 2 Floor 2 compared with traditional RSS-based clustering algorithms such as K-means. PMID:27918454

Cluster and constraint analysis in tetrahedron packings

NASA Astrophysics Data System (ADS)

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

2015-04-01

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

Accurate Construction of Photoactivated Localization Microscopy (PALM) Images for Quantitative Measurements

PubMed Central

Coltharp, Carla; Kessler, Rene P.; Xiao, Jie

2012-01-01

Localization-based superresolution microscopy techniques such as Photoactivated Localization Microscopy (PALM) and Stochastic Optical Reconstruction Microscopy (STORM) have allowed investigations of cellular structures with unprecedented optical resolutions. One major obstacle to interpreting superresolution images, however, is the overcounting of molecule numbers caused by fluorophore photoblinking. Using both experimental and simulated images, we determined the effects of photoblinking on the accurate reconstruction of superresolution images and on quantitative measurements of structural dimension and molecule density made from those images. We found that structural dimension and relative density measurements can be made reliably from images that contain photoblinking-related overcounting, but accurate absolute density measurements, and consequently faithful representations of molecule counts and positions in cellular structures, require the application of a clustering algorithm to group localizations that originate from the same molecule. We analyzed how applying a simple algorithm with different clustering thresholds (tThresh and dThresh) affects the accuracy of reconstructed images, and developed an easy method to select optimal thresholds. We also identified an empirical criterion to evaluate whether an imaging condition is appropriate for accurate superresolution image reconstruction with the clustering algorithm. Both the threshold selection method and imaging condition criterion are easy to implement within existing PALM clustering algorithms and experimental conditions. The main advantage of our method is that it generates a superresolution image and molecule position list that faithfully represents molecule counts and positions within a cellular structure, rather than only summarizing structural properties into ensemble parameters. This feature makes it particularly useful for cellular structures of heterogeneous densities and irregular geometries, and

Deconvoluting simulated metagenomes: the performance of hard- and soft- clustering algorithms applied to metagenomic chromosome conformation capture (3C)

PubMed Central

DeMaere, Matthew Z.

2016-01-01

Background Chromosome conformation capture, coupled with high throughput DNA sequencing in protocols like Hi-C and 3C-seq, has been proposed as a viable means of generating data to resolve the genomes of microorganisms living in naturally occuring environments. Metagenomic Hi-C and 3C-seq datasets have begun to emerge, but the feasibility of resolving genomes when closely related organisms (strain-level diversity) are present in the sample has not yet been systematically characterised. Methods We developed a computational simulation pipeline for metagenomic 3C and Hi-C sequencing to evaluate the accuracy of genomic reconstructions at, above, and below an operationally defined species boundary. We simulated datasets and measured accuracy over a wide range of parameters. Five clustering algorithms were evaluated (2 hard, 3 soft) using an adaptation of the extended B-cubed validation measure. Results When all genomes in a sample are below 95% sequence identity, all of the tested clustering algorithms performed well. When sequence data contains genomes above 95% identity (our operational definition of strain-level diversity), a naive soft-clustering extension of the Louvain method achieves the highest performance. Discussion Previously, only hard-clustering algorithms have been applied to metagenomic 3C and Hi-C data, yet none of these perform well when strain-level diversity exists in a metagenomic sample. Our simple extension of the Louvain method performed the best in these scenarios, however, accuracy remained well below the levels observed for samples without strain-level diversity. Strain resolution is also highly dependent on the amount of available 3C sequence data, suggesting that depth of sequencing must be carefully considered during experimental design. Finally, there appears to be great scope to improve the accuracy of strain resolution through further algorithm development. PMID:27843713

Cluster-cluster clustering

NASA Technical Reports Server (NTRS)

Barnes, J.; Dekel, A.; Efstathiou, G.; Frenk, C. S.

1985-01-01

The cluster correlation function xi sub c(r) is compared with the particle correlation function, xi(r) in cosmological N-body simulations with a wide range of initial conditions. The experiments include scale-free initial conditions, pancake models with a coherence length in the initial density field, and hybrid models. Three N-body techniques and two cluster-finding algorithms are used. In scale-free models with white noise initial conditions, xi sub c and xi are essentially identical. In scale-free models with more power on large scales, it is found that the amplitude of xi sub c increases with cluster richness; in this case the clusters give a biased estimate of the particle correlations. In the pancake and hybrid models (with n = 0 or 1), xi sub c is steeper than xi, but the cluster correlation length exceeds that of the points by less than a factor of 2, independent of cluster richness. Thus the high amplitude of xi sub c found in studies of rich clusters of galaxies is inconsistent with white noise and pancake models and may indicate a primordial fluctuation spectrum with substantial power on large scales.

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

Segmentation by fusion of histogram-based k-means clusters in different color spaces.

PubMed

Mignotte, Max

2008-05-01

This paper presents a new, simple, and efficient segmentation approach, based on a fusion procedure which aims at combining several segmentation maps associated to simpler partition models in order to finally get a more reliable and accurate segmentation result. The different label fields to be fused in our application are given by the same and simple (K-means based) clustering technique on an input image expressed in different color spaces. Our fusion strategy aims at combining these segmentation maps with a final clustering procedure using as input features, the local histogram of the class labels, previously estimated and associated to each site and for all these initial partitions. This fusion framework remains simple to implement, fast, general enough to be applied to various computer vision applications (e.g., motion detection and segmentation), and has been successfully applied on the Berkeley image database. The experiments herein reported in this paper illustrate the potential of this approach compared to the state-of-the-art segmentation methods recently proposed in the literature.

An incremental DPMM-based method for trajectory clustering, modeling, and retrieval.

PubMed

Hu, Weiming; Li, Xi; Tian, Guodong; Maybank, Stephen; Zhang, Zhongfei

2013-05-01

Trajectory analysis is the basis for many applications, such as indexing of motion events in videos, activity recognition, and surveillance. In this paper, the Dirichlet process mixture model (DPMM) is applied to trajectory clustering, modeling, and retrieval. We propose an incremental version of a DPMM-based clustering algorithm and apply it to cluster trajectories. An appropriate number of trajectory clusters is determined automatically. When trajectories belonging to new clusters arrive, the new clusters can be identified online and added to the model without any retraining using the previous data. A time-sensitive Dirichlet process mixture model (tDPMM) is applied to each trajectory cluster for learning the trajectory pattern which represents the time-series characteristics of the trajectories in the cluster. Then, a parameterized index is constructed for each cluster. A novel likelihood estimation algorithm for the tDPMM is proposed, and a trajectory-based video retrieval model is developed. The tDPMM-based probabilistic matching method and the DPMM-based model growing method are combined to make the retrieval model scalable and adaptable. Experimental comparisons with state-of-the-art algorithms demonstrate the effectiveness of our algorithm.

Cluster Sampling Bias in Government-Sponsored Evaluations: A Correlational Study of Employment and Welfare Pilots in England.

PubMed

Vaganay, Arnaud

2016-01-01

For pilot or experimental employment programme results to apply beyond their test bed, researchers must select 'clusters' (i.e. the job centres delivering the new intervention) that are reasonably representative of the whole territory. More specifically, this requirement must account for conditions that could artificially inflate the effect of a programme, such as the fluidity of the local labour market or the performance of the local job centre. Failure to achieve representativeness results in Cluster Sampling Bias (CSB). This paper makes three contributions to the literature. Theoretically, it approaches the notion of CSB as a human behaviour. It offers a comprehensive theory, whereby researchers with limited resources and conflicting priorities tend to oversample 'effect-enhancing' clusters when piloting a new intervention. Methodologically, it advocates for a 'narrow and deep' scope, as opposed to the 'wide and shallow' scope, which has prevailed so far. The PILOT-2 dataset was developed to test this idea. Empirically, it provides evidence on the prevalence of CSB. In conditions similar to the PILOT-2 case study, investigators (1) do not sample clusters with a view to maximise generalisability; (2) do not oversample 'effect-enhancing' clusters; (3) consistently oversample some clusters, including those with higher-than-average client caseloads; and (4) report their sampling decisions in an inconsistent and generally poor manner. In conclusion, although CSB is prevalent, it is still unclear whether it is intentional and meant to mislead stakeholders about the expected effect of the intervention or due to higher-level constraints or other considerations.

Clustering analysis strategies for electron energy loss spectroscopy (EELS).

PubMed

Torruella, Pau; Estrader, Marta; López-Ortega, Alberto; Baró, Maria Dolors; Varela, Maria; Peiró, Francesca; Estradé, Sònia

2018-02-01

In this work, the use of cluster analysis algorithms, widely applied in the field of big data, is proposed to explore and analyze electron energy loss spectroscopy (EELS) data sets. Three different data clustering approaches have been tested both with simulated and experimental data from Fe 3 O 4 /Mn 3 O 4 core/shell nanoparticles. The first method consists on applying data clustering directly to the acquired spectra. A second approach is to analyze spectral variance with principal component analysis (PCA) within a given data cluster. Lastly, data clustering on PCA score maps is discussed. The advantages and requirements of each approach are studied. Results demonstrate how clustering is able to recover compositional and oxidation state information from EELS data with minimal user input, giving great prospects for its usage in EEL spectroscopy. Copyright © 2017 Elsevier B.V. All rights reserved.

EEG and MEG source localization using recursively applied (RAP) MUSIC

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

Mosher, J.C.; Leahy, R.M.

1996-12-31

The multiple signal characterization (MUSIC) algorithm locates multiple asynchronous dipolar sources from electroencephalography (EEG) and magnetoencephalography (MEG) data. A signal subspace is estimated from the data, then the algorithm scans a single dipole model through a three-dimensional head volume and computes projections onto this subspace. To locate the sources, the user must search the head volume for local peaks in the projection metric. Here we describe a novel extension of this approach which we refer to as RAP (Recursively APplied) MUSIC. This new procedure automatically extracts the locations of the sources through a recursive use of subspace projections, which usesmore » the metric of principal correlations as a multidimensional form of correlation analysis between the model subspace and the data subspace. The dipolar orientations, a form of `diverse polarization,` are easily extracted using the associated principal vectors.« less

Nucleon localization and fragment formation in nuclear fission

DOE PAGES

Zhang, C. L.; Schuetrumpf, B.; Nazarewicz, W.

2016-12-27

An electron localization measure was originally introduced to characterize chemical bond structures in molecules. Recently, a nucleon localization based on Hartree-Fock densities has been introduced to investigate α-cluster structures in light nuclei. Compared to the local nucleonic densities, the nucleon localization function has been shown to be an excellent indicator of shell effects and cluster correlations. In this work, using the spatial nucleon localization measure, we investigated the emergence of fragments in fissioning heavy nuclei using the self-consistent energy density functional method with a quantified energy density functional optimized for fission studies. We studied the particle densities and spatial nucleonmore » localization distributions along the fission pathways of 264Fm, 232Th, and 240Pu. We demonstrated that the fission fragments were formed fairly early in the evolution, well before scission. To illustrate the usefulness of the localization measure, we showed how the hyperdeformed state of 232Th could be understood in terms of a quasimolecular state made of 132Sn and 100Zr fragments. Compared to nucleonic distributions, the nucleon localization function more effectively quantifies nucleonic clustering: its characteristic oscillating pattern, traced back to shell effects, is a clear fingerprint of cluster/fragment configurations. This is of particular interest for studies of fragment formation and fragment identification in fissioning nuclei.« less

New detections of embedded clusters in the Galactic halo

NASA Astrophysics Data System (ADS)

Camargo, D.; Bica, E.; Bonatto, C.

2016-09-01

Context. Until recently it was thought that high Galactic latitude clouds were a non-star-forming ensemble. However, in a previous study we reported the discovery of two embedded clusters (ECs) far away from the Galactic plane (~ 5 kpc). In our recent star cluster catalogue we provided additional high and intermediate latitude cluster candidates. Aims: This work aims to clarify whether our previous detection of star clusters far away from the disc represents just an episodic event or whether star cluster formation is currently a systematic phenomenon in the Galactic halo. We analyse the nature of four clusters found in our recent catalogue and report the discovery of three new ECs each with an unusually high latitude and distance from the Galactic disc midplane. Methods: The analysis is based on 2MASS and WISE colour-magnitude diagrams (CMDs), and stellar radial density profiles (RDPs). The CMDs are built by applying a field-star decontamination procedure, which uncovers the cluster's intrinsic CMD morphology. Results: All of these clusters are younger than 5 Myr. The high-latitude ECs C 932, C 934, and C 939 appear to be related to a cloud complex about 5 kpc below the Galactic disc, under the Local arm. The other clusters are above the disc, C 1074 and C 1100 with a vertical distance of ~3 kpc, C 1099 with ~ 2 kpc, and C 1101 with ~1.8 kpc. Conclusions: According to the derived parameters ECs located below and above the disc occur, which gives evidence of widespread star cluster formation throughout the Galactic halo. This study therefore represents a paradigm shift, by demonstrating that a sterile halo must now be understood as a host for ongoing star formation. The origin and fate of these ECs remain open. There are two possibilities for their origin, Galactic fountains or infall. The discovery of ECs far from the disc suggests that the Galactic halo is more actively forming stars than previously thought. Furthermore, since most ECs do not survive the infant

Oxygen Vacancy Linear Clustering in a Perovskite Oxide

DOE PAGES

Eom, Kitae; Choi, Euiyoung; Choi, Minsu; ...

2017-07-14

Oxygen vacancies have been implicitly assumed isolated ones, and understanding oxide materials possibly containing oxygen vacancies remains elusive within the scheme of the isolated vacancies, although the oxygen vacancies have been playing a decisive role in oxide materials. We report the presence of oxygen vacancy linear clusters and their orientation along a specific crystallographic direction in SrTiO 3, a representative of a perovskite oxide. The presence of the linear clusters and associated electron localization was revealed by an electronic structure represented in the increase in the Ti 2+ valence state or corresponding Ti 3d 2 electronic configuration along with divacancymore » cluster model analysis and transport measurement. The orientation of the linear clusters along the [001] direction in perovskite SrTiO 3 was verified by further X-ray diffuse scattering analysis. And because SrTiO 3 is an archetypical perovskite oxide, the vacancy linear clustering with the specific aligned direction and electron localization can be extended to a wide variety of the perovskite oxides.« less

Oxygen Vacancy Linear Clustering in a Perovskite Oxide

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

Eom, Kitae; Choi, Euiyoung; Choi, Minsu

Oxygen vacancies have been implicitly assumed isolated ones, and understanding oxide materials possibly containing oxygen vacancies remains elusive within the scheme of the isolated vacancies, although the oxygen vacancies have been playing a decisive role in oxide materials. We report the presence of oxygen vacancy linear clusters and their orientation along a specific crystallographic direction in SrTiO 3, a representative of a perovskite oxide. The presence of the linear clusters and associated electron localization was revealed by an electronic structure represented in the increase in the Ti 2+ valence state or corresponding Ti 3d 2 electronic configuration along with divacancymore » cluster model analysis and transport measurement. The orientation of the linear clusters along the [001] direction in perovskite SrTiO 3 was verified by further X-ray diffuse scattering analysis. And because SrTiO 3 is an archetypical perovskite oxide, the vacancy linear clustering with the specific aligned direction and electron localization can be extended to a wide variety of the perovskite oxides.« 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

Prognostic Subcellular Notch2, Notch3 and Jagged1 Localization Patterns in Early Triple-negative Breast Cancer.

PubMed

Strati, Titika-Marina; Kotoula, Vassiliki; Kostopoulos, Ioannis; Manousou, Kyriaki; Papadimitriou, Christos; Lazaridis, Georgios; Lakis, Sotiris; Pentheroudakis, George; Pectasides, Dimitrios; Pazarli, Elissavet; Christodoulou, Christos; Razis, Evangelia; Pavlakis, Kitty; Magkou, Christina; Chrisafi, Sofia; Aravantinos, Gerasimos; Bafaloukos, Dimitrios; Papakostas, Pavlos; Gogas, Helen; Kalogeras, Konstantine T; Fountzilas, George

2017-05-01

The Notch pathway has been implicated in triple-negative breast cancer (TNBC). Herein, we studied the subcellular localization of the less investigated Notch2 and Notch3 and that of the Jagged1 (Jag1) ligand in patients with operable TNBC. We applied immunohistochemistry for Notch2, Notch3 and Jag1 in 333 tumors from TNBC patients treated with adjuvant anthracycline-based chemotherapy. We evaluated cytoplasmic (c), membranous (m) and nuclear (n) protein localization. c-Notch2 (35% positive tumors), c-Notch3 (63%), c-Jag1 (43%), m-Notch3 (23%) and n-Jag1 (17%) were analyzed individually and by using hierarchical clustering for prognostic evaluation. Upon multivariate analysis, compared to high m-Notch3 in the absence of n-Jag1 (cluster 4), all other marker combinations (clusters 1, 2, 3) conferred significantly higher risk for relapse (p<0.05). Specific Notch3 and Jag1 subcellular localization patterns may provide clues for the behavior of the tumors and potentially for Jag1 targeting in TNBC patients. Copyright© 2017, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

Galaxy clusters in the SDSS Stripe 82 based on photometric redshifts

DOE PAGES

Durret, F.; Adami, C.; Bertin, E.; ...

2015-06-10

Based on a recent photometric redshift galaxy catalogue, we have searched for galaxy clusters in the Stripe ~82 region of the Sloan Digital Sky Survey by applying the Adami & MAzure Cluster FInder (AMACFI). Extensive tests were made to fine-tune the AMACFI parameters and make the cluster detection as reliable as possible. The same method was applied to the Millennium simulation to estimate our detection efficiency and the approximate masses of the detected clusters. Considering all the cluster galaxies (i.e. within a 1 Mpc radius of the cluster to which they belong and with a photoz differing by less thanmore » 0.05 from that of the cluster), we stacked clusters in various redshift bins to derive colour-magnitude diagrams and galaxy luminosity functions (GLFs). For each galaxy with absolute magnitude brighter than -19.0 in the r band, we computed the disk and spheroid components by applying SExtractor, and by stacking clusters we determined how the disk-to-spheroid flux ratio varies with cluster redshift and mass. We also detected 3663 clusters in the redshift range 0.1513 and a few 10 14 solar masses. Furthermore, by stacking the cluster galaxies in various redshift bins, we find a clear red sequence in the (g'-r') versus r' colour-magnitude diagrams, and the GLFs are typical of clusters, though with a possible contamination from field galaxies. The morphological analysis of the cluster galaxies shows that the fraction of late-type to early-type galaxies shows an increase with redshift (particularly in high mass clusters) and a decrease with detection level, i.e. cluster mass. From the properties of the cluster galaxies, the majority of the candidate clusters detected here seem to be real clusters with typical cluster properties.« less

Galaxy clusters in the SDSS Stripe 82 based on photometric redshifts

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

Durret, F.; Adami, C.; Bertin, E.

Based on a recent photometric redshift galaxy catalogue, we have searched for galaxy clusters in the Stripe ~82 region of the Sloan Digital Sky Survey by applying the Adami & MAzure Cluster FInder (AMACFI). Extensive tests were made to fine-tune the AMACFI parameters and make the cluster detection as reliable as possible. The same method was applied to the Millennium simulation to estimate our detection efficiency and the approximate masses of the detected clusters. Considering all the cluster galaxies (i.e. within a 1 Mpc radius of the cluster to which they belong and with a photoz differing by less thanmore » 0.05 from that of the cluster), we stacked clusters in various redshift bins to derive colour-magnitude diagrams and galaxy luminosity functions (GLFs). For each galaxy with absolute magnitude brighter than -19.0 in the r band, we computed the disk and spheroid components by applying SExtractor, and by stacking clusters we determined how the disk-to-spheroid flux ratio varies with cluster redshift and mass. We also detected 3663 clusters in the redshift range 0.1513 and a few 10 14 solar masses. Furthermore, by stacking the cluster galaxies in various redshift bins, we find a clear red sequence in the (g'-r') versus r' colour-magnitude diagrams, and the GLFs are typical of clusters, though with a possible contamination from field galaxies. The morphological analysis of the cluster galaxies shows that the fraction of late-type to early-type galaxies shows an increase with redshift (particularly in high mass clusters) and a decrease with detection level, i.e. cluster mass. From the properties of the cluster galaxies, the majority of the candidate clusters detected here seem to be real clusters with typical cluster properties.« less

The structure of clusters of galaxies

NASA Astrophysics Data System (ADS)

Fox, David Charles

When infalling gas is accreted onto a cluster of galaxies, its kinetic energy is converted to thermal energy in a shock, heating the ions. Using a self-similar spherical model, we calculate the collisional heating of the electrons by the ions, and predict the electron and ion temperature profiles. While there are significant differences between the two, they occur at radii larger than currently observable, and too large to explain observed X-ray temperature declines in clusters. Numerical simulations by Navarro, Frenk, & White (1996) predict a universal dark matter density profile. We calculate the expected number of multiply-imaged background galaxies in the Hubble Deep Field due to foreground groups and clusters with this profile. Such groups are up to 1000 times less efficient at lensing than the standard singular isothermal spheres. However, with either profile, the expected number of galaxies lensed by groups in the Hubble Deep Field is at most one, consistent with the lack of clearly identified group lenses. X-ray and Sunyaev-Zel'dovich (SZ) effect observations can be combined to determine the distance to clusters of galaxies, provided the clusters are spherical. When applied to an aspherical cluster, this method gives an incorrect distance. We demonstrate a method for inferring the three-dimensional shape of a cluster and its correct distance from X-ray, SZ effect, and weak gravitational lensing observations, under the assumption of hydrostatic equilibrium. We apply this method to simple, analytic models of clusters, and to a numerically simulated cluster. Using artificial observations based on current X-ray and SZ effect instruments, we recover the true distance without detectable bias and with uncertainties of 4 percent.

A Spatial Division Clustering Method and Low Dimensional Feature Extraction Technique Based Indoor Positioning System

PubMed Central

Mo, Yun; Zhang, Zhongzhao; Meng, Weixiao; Ma, Lin; Wang, Yao

2014-01-01

Indoor positioning systems based on the fingerprint method are widely used due to the large number of existing devices with a wide range of coverage. However, extensive positioning regions with a massive fingerprint database may cause high computational complexity and error margins, therefore clustering methods are widely applied as a solution. However, traditional clustering methods in positioning systems can only measure the similarity of the Received Signal Strength without being concerned with the continuity of physical coordinates. Besides, outage of access points could result in asymmetric matching problems which severely affect the fine positioning procedure. To solve these issues, in this paper we propose a positioning system based on the Spatial Division Clustering (SDC) method for clustering the fingerprint dataset subject to physical distance constraints. With the Genetic Algorithm and Support Vector Machine techniques, SDC can achieve higher coarse positioning accuracy than traditional clustering algorithms. In terms of fine localization, based on the Kernel Principal Component Analysis method, the proposed positioning system outperforms its counterparts based on other feature extraction methods in low dimensionality. Apart from balancing online matching computational burden, the new positioning system exhibits advantageous performance on radio map clustering, and also shows better robustness and adaptability in the asymmetric matching problem aspect. PMID:24451470

Probabilistic cluster labeling of imagery data

NASA Technical Reports Server (NTRS)

Chittineni, C. B. (Principal Investigator)

1980-01-01

The problem of obtaining the probabilities of class labels for the clusters using spectral and spatial information from a given set of labeled patterns and their neighbors is considered. A relationship is developed between class and clusters conditional densities in terms of probabilities of class labels for the clusters. Expressions are presented for updating the a posteriori probabilities of the classes of a pixel using information from its local neighborhood. Fixed-point iteration schemes are developed for obtaining the optimal probabilities of class labels for the clusters. These schemes utilize spatial information and also the probabilities of label imperfections. Experimental results from the processing of remotely sensed multispectral scanner imagery data are presented.

Swarm: robust and fast clustering method for amplicon-based studies.

PubMed

Mahé, Frédéric; Rognes, Torbjørn; Quince, Christopher; de Vargas, Colomban; Dunthorn, Micah

2014-01-01

Popular de novo amplicon clustering methods suffer from two fundamental flaws: arbitrary global clustering thresholds, and input-order dependency induced by centroid selection. Swarm was developed to address these issues by first clustering nearly identical amplicons iteratively using a local threshold, and then by using clusters' internal structure and amplicon abundances to refine its results. This fast, scalable, and input-order independent approach reduces the influence of clustering parameters and produces robust operational taxonomic units.

Alternative Parameterizations for Cluster Editing

NASA Astrophysics Data System (ADS)

Komusiewicz, Christian; Uhlmann, Johannes

Given an undirected graph G and a nonnegative integer k, the NP-hard Cluster Editing problem asks whether G can be transformed into a disjoint union of cliques by applying at most k edge modifications. In the field of parameterized algorithmics, Cluster Editing has almost exclusively been studied parameterized by the solution size k. Contrastingly, in many real-world instances it can be observed that the parameter k is not really small. This observation motivates our investigation of parameterizations of Cluster Editing different from the solution size k. Our results are as follows. Cluster Editing is fixed-parameter tractable with respect to the parameter "size of a minimum cluster vertex deletion set of G", a typically much smaller parameter than k. Cluster Editing remains NP-hard on graphs with maximum degree six. A restricted but practically relevant version of Cluster Editing is fixed-parameter tractable with respect to the combined parameter "number of clusters in the target graph" and "maximum number of modified edges incident to any vertex in G". Many of our results also transfer to the NP-hard Cluster Deletion problem, where only edge deletions are allowed.

Size-guided multi-seed heuristic method for geometry optimization of clusters: Application to benzene clusters.

PubMed

Takeuchi, Hiroshi

2018-05-08

Since searching for the global minimum on the potential energy surface of a cluster is very difficult, many geometry optimization methods have been proposed, in which initial geometries are randomly generated and subsequently improved with different algorithms. In this study, a size-guided multi-seed heuristic method is developed and applied to benzene clusters. It produces initial configurations of the cluster with n molecules from the lowest-energy configurations of the cluster with n - 1 molecules (seeds). The initial geometries are further optimized with the geometrical perturbations previously used for molecular clusters. These steps are repeated until the size n satisfies a predefined one. The method locates putative global minima of benzene clusters with up to 65 molecules. The performance of the method is discussed using the computational cost, rates to locate the global minima, and energies of initial geometries. © 2018 Wiley Periodicals, Inc. © 2018 Wiley Periodicals, Inc.

Hole localization in Fe2O3 from density functional theory and wave-function-based methods

NASA Astrophysics Data System (ADS)

Ansari, Narjes; Ulman, Kanchan; Camellone, Matteo Farnesi; Seriani, Nicola; Gebauer, Ralph; Piccinin, Simone

2017-08-01

Hematite (α -Fe2O3 ) is a promising photocatalyst material for water splitting, where photoinduced holes lead to the oxidation of water and the release of molecular oxygen. In this work, we investigate the properties of holes in hematite using density functional theory (DFT) calculations with hybrid functionals. We find that holes form small polarons and, depending on the fraction of exact exchange included in the PBE0 functional, the site where the holes localize changes from Fe to O. We find this result to be independent of the size and structure of the system: small Fe2O3 clusters with tetrahedral coordination, larger clusters with octahedral coordination, Fe2O3 (001) surfaces in contact with water, and bulk Fe2O3 display a very similar behavior in terms of hole localization as a function of the fraction of exact exchange. We then use wave-function-based methods such as coupled cluster with single and double excitations and Møller-Plesset second-order perturbation theory applied on a cluster model of Fe2O3 to shed light on which of the two solutions is correct. We find that these high-level quantum chemistry methods suggest holes in hematite are localized on oxygen atoms. We also explore the use of the DFT +U approach as a computationally convenient way to overcome the known limitations of generalized gradient approximation functionals and recover a gap in line with experiments and hole localization on oxygen in agreement with quantum chemistry methods.

Dynamical Mass Measurements of Contaminated Galaxy Clusters Using Machine Learning

NASA Astrophysics Data System (ADS)

Ntampaka, M.; Trac, H.; Sutherland, D. J.; Fromenteau, S.; Póczos, B.; Schneider, J.

2016-11-01

We study dynamical mass measurements of galaxy clusters contaminated by interlopers and show that a modern machine learning algorithm can predict masses by better than a factor of two compared to a standard scaling relation approach. We create two mock catalogs from Multidark’s publicly available N-body MDPL1 simulation, one with perfect galaxy cluster membership information and the other where a simple cylindrical cut around the cluster center allows interlopers to contaminate the clusters. In the standard approach, we use a power-law scaling relation to infer cluster mass from galaxy line-of-sight (LOS) velocity dispersion. Assuming perfect membership knowledge, this unrealistic case produces a wide fractional mass error distribution, with a width of {{Δ }}ε ≈ 0.87. Interlopers introduce additional scatter, significantly widening the error distribution further ({{Δ }}ε ≈ 2.13). We employ the support distribution machine (SDM) class of algorithms to learn from distributions of data to predict single values. Applied to distributions of galaxy observables such as LOS velocity and projected distance from the cluster center, SDM yields better than a factor-of-two improvement ({{Δ }}ε ≈ 0.67) for the contaminated case. Remarkably, SDM applied to contaminated clusters is better able to recover masses than even the scaling relation approach applied to uncontaminated clusters. We show that the SDM method more accurately reproduces the cluster mass function, making it a valuable tool for employing cluster observations to evaluate cosmological models.

X ray studies of the Hyades cluster

NASA Technical Reports Server (NTRS)

Stern, Robert A.

1993-01-01

The Hyades cluster occupies a unique position in both the history of astronomy and at the frontiers of contemporary astronomical research. At a distance of only 45 pc, the Hyades is the nearest star cluster in the Galaxy which is localized in the sky: the UMa cluster, which is closer, but much sparser, essentially surrounds the Solar neighborhood. The Hyades is the prototype cluster for distance determination using the 'moving-cluster' method, and thus serves to define the zero-age main sequence from which the cosmic distance scale is essentially bootstrapped. The Hyades age (0.6-0.7 Gyr), nearly 8 times younger than the Sun, guarantees the Hyades critical importance to studies of stellar evolution. The results of a complete survey of the Hyades cluster using the ROSAT All Sky Survey (RASS) are reported.

Locally applied simvastatin improves fracture healing at late period in osteoporotic rat

NASA Astrophysics Data System (ADS)

Tian, Faming; Zhang, Liu; Kang, Yuchuan; Zhang, Junshan; Ao, Jiao; Yang, Fang

effect of simvastatin locally applied from a bioactive polymer coating of implants on osteoporotic fracture healing at late period. Methods:Femur fracture model was established on normal or osteotoporotic mature female SD rats, intramedullary stabilization was achieved with uncoated titanium Kirschnerwires in normal rats(group A),with polymer-only coated vs. polymer plus simvastatin coated titanium Kirschner wires in osteoporotic rats(group B and C, respectively).Femurs were harvested after 12 weeks, and underwent radiographic and histologic analysis, as well as immunohistochemical evaluation for BMP-2 expression. Results:Radiographic results demonstrated progressed callus in the simvastatin-treated groups compared to the uncoated group.The histologic analysis revealed a significantly processed callus with irregular-shaped newly formed bone trabeculae in simvastatin-treated group. Immunohistochemical evaluation showed markedly higher expression levels of B:MP-2 in simvastatin-treated group.Conclusions: The present study revealed a improved fracture healing under local application of simvastatin in osteoporotic rat,which might partially from upregulation of the B:MP-2 expression at fractured site.

Influence of Aromatic Molecules on the Structure and Spectroscopy of Water Clusters

NASA Astrophysics Data System (ADS)

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

2016-06-01

Isomer-specific resonant ion-dip infrared spectra are presented for benzene-(water)_n, 1-2-diphenoxyethane-(water)_n, and tricyclophane-(water)_n clusters. The IR spectra are modeled with a local mode Hamiltonian that was originally formulated for the analysis of benzene-(water)_n clusters with up to seven waters. The model accounts for stretch-bend Fermi coupling, which can complicate the IR spectra in the 3150-3300 cm-1 region. When the water clusters interact with each of the solutes, the hydrogen bond lengths between the water molecules change in a characteristic way, reflecting the strength of the solute-water interaction. These structural effects are also reflected spectroscopically in the shifts of the local mode OH stretch frequencies. When diphenoxyethane is the solute, the water clusters distort more significantly than when bound to benzene. Tricyclophane's structure provides an aromatic-rich binding pocket for the water clusters. The local mode model is used to extract Hamiltonians for individual water molecules. These monomer Hamiltonians divide into groups based on their local H-bonding architecture, allowing for further classification of the wide variety of water environments encountered in this study.

Clustering of Variables for Mixed Data

NASA Astrophysics Data System (ADS)

Saracco, J.; Chavent, M.

2016-05-01

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

First Description of a Cluster of Acute/Subacute Paracoccidioidomycosis Cases and Its Association with a Climatic Anomaly

PubMed Central

Silva, Maria Elisa Siqueira; Bagagli, Eduardo; Marques, Silvio Alencar; Mendes, Rinaldo Poncio

2010-01-01

Background Identifying clusters of acute paracoccidioidomycosis cases could potentially help in identifying the environmental factors that influence the incidence of this mycosis. However, unlike other endemic mycoses, there are no published reports of clusters of paracoccidioidomycosis. Methodology/Principal Findings A retrospective cluster detection test was applied to verify if an excess of acute form (AF) paracoccidioidomycosis cases in time and/or space occurred in Botucatu, an endemic area in São Paulo State. The scan-test SaTScan v7.0.3 was set to find clusters for the maximum temporal period of 1 year. The temporal test indicated a significant cluster in 1985 (P<0.005). This cluster comprised 10 cases, although 2.19 were expected for this year in this area. Age and clinical presentation of these cases were typical of AF paracccidioidomycosis. The space-time test confirmed the temporal cluster in 1985 and showed the localities where the risk was higher in that year. The cluster suggests that some particularities took place in the antecedent years in those localities. Analysis of climate variables showed that soil water storage was atypically high in 1982/83 (∼2.11/2.5 SD above mean), and the absolute air humidity in 1984, the year preceding the cluster, was much higher than normal (∼1.6 SD above mean), conditions that may have favored, respectively, antecedent fungal growth in the soil and conidia liberation in 1984, the probable year of exposure. These climatic anomalies in this area was due to the 1982/83 El Niño event, the strongest in the last 50 years. Conclusions/Significance We describe the first cluster of AF paracoccidioidomycosis, which was potentially linked to a climatic anomaly caused by the 1982/83 El Niño Southern Oscillation. This finding is important because it may help to clarify the conditions that favor Paracoccidioides brasiliensis survival and growth in the environment and that enhance human exposure, thus allowing the

Global detection approach for clustered microcalcifications in mammograms using a deep learning network.

PubMed

Wang, Juan; Nishikawa, Robert M; Yang, Yongyi

2017-04-01

In computerized detection of clustered microcalcifications (MCs) from mammograms, the traditional approach is to apply a pattern detector to locate the presence of individual MCs, which are subsequently grouped into clusters. Such an approach is often susceptible to the occurrence of false positives (FPs) caused by local image patterns that resemble MCs. We investigate the feasibility of a direct detection approach to determining whether an image region contains clustered MCs or not. Toward this goal, we develop a deep convolutional neural network (CNN) as the classifier model to which the input consists of a large image window ([Formula: see text] in size). The multiple layers in the CNN classifier are trained to automatically extract image features relevant to MCs at different spatial scales. In the experiments, we demonstrated this approach on a dataset consisting of both screen-film mammograms and full-field digital mammograms. We evaluated the detection performance both on classifying image regions of clustered MCs using a receiver operating characteristic (ROC) analysis and on detecting clustered MCs from full mammograms by a free-response receiver operating characteristic analysis. For comparison, we also considered a recently developed MC detector with FP suppression. In classifying image regions of clustered MCs, the CNN classifier achieved 0.971 in the area under the ROC curve, compared to 0.944 for the MC detector. In detecting clustered MCs from full mammograms, at 90% sensitivity, the CNN classifier obtained an FP rate of 0.69 clusters/image, compared to 1.17 clusters/image by the MC detector. These results indicate that using global image features can be more effective in discriminating clustered MCs from FPs caused by various sources, such as linear structures, thereby providing a more accurate detection of clustered MCs on mammograms.

Gas Dynamics in Galaxy Clusters

NASA Astrophysics Data System (ADS)

McCourt, Michael Kingsley, Jr.

develop significant anisotropies with respect to the local magnetic field. This interesting regime is one of the frontiers in theoretical studies of fluid dynamics. Unlike other astrophysical environments of similar collisionality (e. g. accretion disk coronae), galaxy clusters are optically thin and subtend large angles on the sky. Thus, they are easily observed in the x-ray (to constrain thermal processes) and in the radio (to constrain non-thermal processes) and provide a wonderful environment to develop our understanding of dilute plasmas. This thesis studies the dynamics of the hot gas in galaxy clusters, which touches on all three of the above topics. Chapter 2 shows that galaxy clusters are likely to be unstable to a new, vigorous form of convection. As a dynamical process which involves thermodynamic and magnetic properties of the gas, this convection bears directly on our understanding of the physics of dilute plas- mas. Furthermore, by moving metals and thermal energy through the cluster, convection may change the cooling rate of the gas and thus significantly impact the process of galaxy formation. Cluster convection also impacts the use of clusters as cosmological probes. Convection may drive turbulence in clusters with mean Mach numbers of order-unity. This changes the force balance in clusters, decreasing the thermal energy of a cluster of a given mass. Current methods for using clusters to constrain dark energy rely on observational probes of the thermal energy as a proxy for total mass. The accuracy of these methods depends on how vigorous cluster convection is. Chapter 3 studies thermal instability in galaxy clusters. I argue that clusters are all likely to be thermally unstable, but that this instability only grows to large amplitude in a subset of systems. Later studies have applied this result to galaxy formation in clusters and shown that one can reproduce some features of the well-known non-self-similarity at the high mass end of the galaxy

Country clustering applied to the water & sanitation sector: a new tool with potential applications in research & policy

PubMed Central

Onda, Kyle; Crocker, Jonny; Kayser, Georgia Lyn; Bartram, Jamie

2013-01-01

The fields of global health and international development commonly cluster countries by geography and income to target resources and describe progress. For any given sector of interest, a range of relevant indicators can serve as a more appropriate basis for classification. We create a new typology of country clusters specific to the water and sanitation (WatSan) sector based on similarities across multiple WatSan-related indicators. After a literature review and consultation with experts in the WatSan sector, nine indicators were selected. Indicator selection was based on relevance to and suggested influence on national water and sanitation service delivery, and to maximize data availability across as many countries as possible. A hierarchical clustering method and a gap statistic analysis were used to group countries into a natural number of relevant clusters. Two stages of clustering resulted in five clusters, representing 156 countries or 6.75 billion people. The five clusters were not well explained by income or geography, and were unique from existing country clusters used in international development. Analysis of these five clusters revealed that they were more compact and well separated than United Nations and World Bank country clusters. This analysis and resulting country typology suggest that previous geography- or income-based country groupings can be improved upon for applications in the WatSan sector by utilizing globally available WatSan-related indicators. Potential applications include guiding and discussing research, informing policy, improving resource targeting, describing sector progress, and identifying critical knowledge gaps in the WatSan sector. PMID:24054545

Mean-cluster approach indicates cell sorting time scales are determined by collective dynamics

NASA Astrophysics Data System (ADS)

Beatrici, Carine P.; de Almeida, Rita M. C.; Brunnet, Leonardo G.

2017-03-01

Cell migration is essential to cell segregation, playing a central role in tissue formation, wound healing, and tumor evolution. Considering random mixtures of two cell types, it is still not clear which cell characteristics define clustering time scales. The mass of diffusing clusters merging with one another is expected to grow as td /d +2 when the diffusion constant scales with the inverse of the cluster mass. Cell segregation experiments deviate from that behavior. Explanations for that could arise from specific microscopic mechanisms or from collective effects, typical of active matter. Here we consider a power law connecting diffusion constant and cluster mass to propose an analytic approach to model cell segregation where we explicitly take into account finite-size corrections. The results are compared with active matter model simulations and experiments available in the literature. To investigate the role played by different mechanisms we considered different hypotheses describing cell-cell interaction: differential adhesion hypothesis and different velocities hypothesis. We find that the simulations yield normal diffusion for long time intervals. Analytic and simulation results show that (i) cluster evolution clearly tends to a scaling regime, disrupted only at finite-size limits; (ii) cluster diffusion is greatly enhanced by cell collective behavior, such that for high enough tendency to follow the neighbors, cluster diffusion may become independent of cluster size; (iii) the scaling exponent for cluster growth depends only on the mass-diffusion relation, not on the detailed local segregation mechanism. These results apply for active matter systems in general and, in particular, the mechanisms found underlying the increase in cell sorting speed certainly have deep implications in biological evolution as a selection mechanism.

Characterization of an F-center in an alkali halide cluster

NASA Astrophysics Data System (ADS)

Bader, R. F. W.; Platts, J. A.

1997-11-01

The removal of a fluorine atom from its central position in a cubiclike Li14F13+ cluster creates an F-center vacancy that may or may not be occupied by the remaining odd electron. The topology exhibited by the electron density in Li14F12+, the F-center cluster, enables one to make a clear distinction between the two possible forms that the odd electron can assume. If it possesses a separate identity, then a local maximum in the electron density will be found within the vacancy and the F-center will behave quantum mechanically as an open system, bounded by a surface of local zero flux in the gradient vector field of the electron density. If, however, the density of the odd electron is primarily delocalized onto the neighboring ions, then a cage critical point, a local minimum in the density, will be found at the center of the vacancy. Without an associated local maximum, the vacancy has no boundary and is undefined. Self-consistent field (SCF) calculations with geometry optimization of the Li14F13+ cluster and of the doublet state of Li14F12+ show that the creation of the central vacancy has only a minor effect upon the geometry of the cluster, the result of a local maximum in the electron density being formed within the vacancy. Thus the F-center is the physical manifestation of a non-nuclear attractor in the electron density. It is consequently a proper open system with a definable set of properties, the most characteristic being its low kinetic energy per electron. In addition to determining the properties of the F-center, the effect of its formation on the energies, volumes, populations, both electron and spin, and electron localizations of the ions in the cluster are determined.

EXPLORING FUNCTIONAL CONNECTIVITY IN FMRI VIA CLUSTERING.

PubMed

Venkataraman, Archana; Van Dijk, Koene R A; Buckner, Randy L; Golland, Polina

2009-04-01

In this paper we investigate the use of data driven clustering methods for functional connectivity analysis in fMRI. In particular, we consider the K-Means and Spectral Clustering algorithms as alternatives to the commonly used Seed-Based Analysis. To enable clustering of the entire brain volume, we use the Nyström Method to approximate the necessary spectral decompositions. We apply K-Means, Spectral Clustering and Seed-Based Analysis to resting-state fMRI data collected from 45 healthy young adults. Without placing any a priori constraints, both clustering methods yield partitions that are associated with brain systems previously identified via Seed-Based Analysis. Our empirical results suggest that clustering provides a valuable tool for functional connectivity analysis.

The X-Ray Luminosity-Mass Relation for Local Clusters of Galaxies

NASA Astrophysics Data System (ADS)

Stanek, Rebecca; Evrard, A.; Boehringer, H.; Schuecker, P.; Nord, B.

2006-12-01

My thesis is centered on investigating scaling relations of galaxy clusters. Focusing on the relationship between soft X-ray luminosity and mass (L-M) for low-redshift clusters of galaxies, I have determined the mean parameters to 5%, and calculated a formal measure of the scatter in the L-M relation. I model the L-M relation with a conditional probability function including a mean power-law scaling relation, L Mpρsc(z), and log-normal scatter in mass at fixed luminosity, σlnM. Convolving with the halo mass function, I compute expected counts in redshift and flux that, after appropriate survey effects are included, are compared to REFLEX survey data. Combining the likelihood analysis with the measured variance in L-T relation from HIFLUGCS, I obtain fit parameters p=1.59+/-0.05, lnL15,0=1.34+/-0.09, and σlnM=0.37+/-0.05 for self-similar redshift evolution (s = 7/6) in a concordance (Ωm=0.3, ΩΛ=0.7, σ8=0.9) universe. I find a substantially (factor 2) dimmer intercept and slightly steeper slope than the values published using hydrostatic mass estimates of the HIFLUGCS sample and show that a Malmquist bias of the X-ray flux-limited sample accounts for this effect. I accommodate the new WMAP constraints with a compromise model with Ωm=0.24, σ8=0.85, and somewhat lower scatter σlnM=0.25. I will also present work in progress from galaxy cluster population statistics in the Millennium Simulation with Gas (MSG), specifically focusing on the scatter and covariance between cluster properties at a fixed epoch.

The spatial clustering of obesity: does the built environment matter?

PubMed

Huang, R; Moudon, A V; Cook, A J; Drewnowski, A

2015-12-01

Obesity rates in the USA show distinct geographical patterns. The present study used spatial cluster detection methods and individual-level data to locate obesity clusters and to analyse them in relation to the neighbourhood built environment. The 2008-2009 Seattle Obesity Study provided data on the self-reported height, weight, and sociodemographic characteristics of 1602 King County adults. Home addresses were geocoded. Clusters of high or low body mass index were identified using Anselin's Local Moran's I and a spatial scan statistic with regression models that searched for unmeasured neighbourhood-level factors from residuals, adjusting for measured individual-level covariates. Spatially continuous values of objectively measured features of the local neighbourhood built environment (SmartMaps) were constructed for seven variables obtained from tax rolls and commercial databases. Both the Local Moran's I and a spatial scan statistic identified similar spatial concentrations of obesity. High and low obesity clusters were attenuated after adjusting for age, gender, race, education and income, and they disappeared once neighbourhood residential property values and residential density were included in the model. Using individual-level data to detect obesity clusters with two cluster detection methods, the present study showed that the spatial concentration of obesity was wholly explained by neighbourhood composition and socioeconomic characteristics. These characteristics may serve to more precisely locate obesity prevention and intervention programmes. © 2014 The British Dietetic Association Ltd.

Link prediction with node clustering coefficient

NASA Astrophysics Data System (ADS)

Wu, Zhihao; Lin, Youfang; Wang, Jing; Gregory, Steve

2016-06-01

Predicting missing links in incomplete complex networks efficiently and accurately is still a challenging problem. The recently proposed Cannistrai-Alanis-Ravai (CAR) index shows the power of local link/triangle information in improving link-prediction accuracy. Inspired by the idea of employing local link/triangle information, we propose a new similarity index with more local structure information. In our method, local link/triangle structure information can be conveyed by clustering coefficient of common-neighbors directly. The reason why clustering coefficient has good effectiveness in estimating the contribution of a common-neighbor is that it employs links existing between neighbors of a common-neighbor and these links have the same structural position with the candidate link to this common-neighbor. In our experiments, three estimators: precision, AUP and AUC are used to evaluate the accuracy of link prediction algorithms. Experimental results on ten tested networks drawn from various fields show that our new index is more effective in predicting missing links than CAR index, especially for networks with low correlation between number of common-neighbors and number of links between common-neighbors.

α clustering with a hollow structure: Geometrical structure of α clusters from platonic solids to fullerene shape

NASA Astrophysics Data System (ADS)

Tohsaki, Akihiro; Itagaki, Naoyuki

2018-01-01

We study α -cluster structure based on the geometric configurations with a microscopic framework, which takes full account of the Pauli principle, and which also employs an effective internucleon force including finite-range three-body terms suitable for microscopic α -cluster models. Here, special attention is focused upon the α clustering with a hollow structure; all the α clusters are put on the surface of a sphere. All the platonic solids (five regular polyhedra) and the fullerene-shaped polyhedron coming from icosahedral structure are considered. Furthermore, two configurations with dual polyhedra, hexahedron-octahedron and dodecahedron-icosahedron, are also scrutinized. When approaching each other from large distances with these symmetries, α clusters create certain local energy pockets. As a consequence, we insist on the possible existence of α clustering with a geometric shape and hollow structure, which is favored from Coulomb energy point of view. Especially, two configurations, that is, dual polyhedra of dodecahedron-icosahedron and fullerene, have a prominent hollow structure compared with the other six configurations.

Accurate prediction of secondary metabolite gene clusters in filamentous fungi.

PubMed

Andersen, Mikael R; Nielsen, Jakob B; Klitgaard, Andreas; Petersen, Lene M; Zachariasen, Mia; Hansen, Tilde J; Blicher, Lene H; Gotfredsen, Charlotte H; Larsen, Thomas O; Nielsen, Kristian F; Mortensen, Uffe H

2013-01-02

Biosynthetic pathways of secondary metabolites from fungi are currently subject to an intense effort to elucidate the genetic basis for these compounds due to their large potential within pharmaceutics and synthetic biochemistry. The preferred method is methodical gene deletions to identify supporting enzymes for key synthases one cluster at a time. In this study, we design and apply a DNA expression array for Aspergillus nidulans in combination with legacy data to form a comprehensive gene expression compendium. We apply a guilt-by-association-based analysis to predict the extent of the biosynthetic clusters for the 58 synthases active in our set of experimental conditions. A comparison with legacy data shows the method to be accurate in 13 of 16 known clusters and nearly accurate for the remaining 3 clusters. Furthermore, we apply a data clustering approach, which identifies cross-chemistry between physically separate gene clusters (superclusters), and validate this both with legacy data and experimentally by prediction and verification of a supercluster consisting of the synthase AN1242 and the prenyltransferase AN11080, as well as identification of the product compound nidulanin A. We have used A. nidulans for our method development and validation due to the wealth of available biochemical data, but the method can be applied to any fungus with a sequenced and assembled genome, thus supporting further secondary metabolite pathway elucidation in the fungal kingdom.

A machine learning approach for ranking clusters of docked protein‐protein complexes by pairwise cluster comparison

PubMed Central

Pfeiffenberger, Erik; Chaleil, Raphael A.G.; Moal, Iain H.

2017-01-01

ABSTRACT Reliable identification of near‐native poses of docked protein–protein complexes is still an unsolved problem. The intrinsic heterogeneity of protein–protein interactions is challenging for traditional biophysical or knowledge based potentials and the identification of many false positive binding sites is not unusual. Often, ranking protocols are based on initial clustering of docked poses followed by the application of an energy function to rank each cluster according to its lowest energy member. Here, we present an approach of cluster ranking based not only on one molecular descriptor (e.g., an energy function) but also employing a large number of descriptors that are integrated in a machine learning model, whereby, an extremely randomized tree classifier based on 109 molecular descriptors is trained. The protocol is based on first locally enriching clusters with additional poses, the clusters are then characterized using features describing the distribution of molecular descriptors within the cluster, which are combined into a pairwise cluster comparison model to discriminate near‐native from incorrect clusters. The results show that our approach is able to identify clusters containing near‐native protein–protein complexes. In addition, we present an analysis of the descriptors with respect to their power to discriminate near native from incorrect clusters and how data transformations and recursive feature elimination can improve the ranking performance. Proteins 2017; 85:528–543. © 2016 Wiley Periodicals, Inc. PMID:27935158

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

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

Regression analysis of clustered failure time data with informative cluster size under the additive transformation models.

PubMed

Chen, Ling; Feng, Yanqin; Sun, Jianguo

2017-10-01

This paper discusses regression analysis of clustered failure time data, which occur when the failure times of interest are collected from clusters. In particular, we consider the situation where the correlated failure times of interest may be related to cluster sizes. For inference, we present two estimation procedures, the weighted estimating equation-based method and the within-cluster resampling-based method, when the correlated failure times of interest arise from a class of additive transformation models. The former makes use of the inverse of cluster sizes as weights in the estimating equations, while the latter can be easily implemented by using the existing software packages for right-censored failure time data. An extensive simulation study is conducted and indicates that the proposed approaches work well in both the situations with and without informative cluster size. They are applied to a dental study that motivated this study.

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.

An algorithm for spatial heirarchy clustering

NASA Technical Reports Server (NTRS)

Dejesusparada, N. (Principal Investigator); Velasco, F. R. D.

1981-01-01

A method for utilizing both spectral and spatial redundancy in compacting and preclassifying images is presented. In multispectral satellite images, a high correlation exists between neighboring image points which tend to occupy dense and restricted regions of the feature space. The image is divided into windows of the same size where the clustering is made. The classes obtained in several neighboring windows are clustered, and then again successively clustered until only one region corresponding to the whole image is obtained. By employing this algorithm only a few points are considered in each clustering, thus reducing computational effort. The method is illustrated as applied to LANDSAT images.

Semantic Clustering of Search Engine Results

PubMed Central

Soliman, Sara Saad; El-Sayed, Maged F.; Hassan, Yasser F.

2015-01-01

This paper presents a novel approach for search engine results clustering that relies on the semantics of the retrieved documents rather than the terms in those documents. The proposed approach takes into consideration both lexical and semantics similarities among documents and applies activation spreading technique in order to generate semantically meaningful clusters. This approach allows documents that are semantically similar to be clustered together rather than clustering documents based on similar terms. A prototype is implemented and several experiments are conducted to test the prospered solution. The result of the experiment confirmed that the proposed solution achieves remarkable results in terms of precision. PMID:26933673

Spatial clustering and halo occupation distribution modelling of local AGN via cross-correlation measurements with 2MASS galaxies

NASA Astrophysics Data System (ADS)

Krumpe, Mirko; Miyaji, Takamitsu; Coil, Alison L.; Aceves, Hector

2018-02-01

We present the clustering properties and halo occupation distribution (HOD) modelling of very low redshift, hard X-ray-detected active galactic nuclei (AGN) using cross-correlation function measurements with Two-Micron All Sky Survey galaxies. Spanning a redshift range of 0.007 < z < 0.037, with a median z = 0.024, we present a precise AGN clustering study of the most local AGN in the Universe. The AGN sample is drawn from the SWIFT/BAT 70-month and INTEGRAL/IBIS eight year all-sky X-ray surveys and contains both type I and type II AGN. We find a large-scale bias for the full AGN sample of b=1.04^{+0.10}_{-0.11}, which corresponds to a typical host dark matter halo mass of M_h^typ=12.84^{+0.22}_{-0.30} h^{-1} M_{⊙}. When split into low and high X-ray luminosity and type I and type II AGN subsamples, we detect no statistically significant differences in the large-scale bias parameters. However, there are differences in the small-scale clustering, which are reflected in the full HOD model results. We find that low and high X-ray luminosity AGN, as well as type I and type II AGN, occupy dark matter haloes differently, with 3.4σ and 4.0σ differences in their mean halo masses, respectively, when split by luminosity and type. The latter finding contradicts a simple orientation-based AGN unification model. As a by-product of our cross-correlation approach, we also present the first HOD model of 2MASS galaxies.

Clusters of Galaxies

NASA Astrophysics Data System (ADS)

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

1981-09-01

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

SOTXTSTREAM: Density-based self-organizing clustering of text streams.

PubMed

Bryant, Avory C; Cios, Krzysztof J

2017-01-01

A streaming data clustering algorithm is presented building upon the density-based self-organizing stream clustering algorithm SOSTREAM. Many density-based clustering algorithms are limited by their inability to identify clusters with heterogeneous density. SOSTREAM addresses this limitation through the use of local (nearest neighbor-based) density determinations. Additionally, many stream clustering algorithms use a two-phase clustering approach. In the first phase, a micro-clustering solution is maintained online, while in the second phase, the micro-clustering solution is clustered offline to produce a macro solution. By performing self-organization techniques on micro-clusters in the online phase, SOSTREAM is able to maintain a macro clustering solution in a single phase. Leveraging concepts from SOSTREAM, a new density-based self-organizing text stream clustering algorithm, SOTXTSTREAM, is presented that addresses several shortcomings of SOSTREAM. Gains in clustering performance of this new algorithm are demonstrated on several real-world text stream datasets.

Multi-mode clustering model for hierarchical wireless sensor networks

NASA Astrophysics Data System (ADS)

Hu, Xiangdong; Li, Yongfu; Xu, Huifen

2017-03-01

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

A partial list of southern clusters of galaxies

NASA Technical Reports Server (NTRS)

Quintana, H.; White, R. A.

1990-01-01

An inspection of 34 SRC/ESO J southern sky fields is the basis of the present list of clusters of galaxies and their approximate classifications in terms of cluster concentration, defined independently of richness and shape-symmetry. Where possible, an estimate of the cluster morphological population is provided. The Bautz-Morgan classification was applied using a strict comparison with clusters on the Palomar Sky Survey. Magnitudes were estimated on the basis of galaxies with photoelectric or photographic magnitudes.

Applying Best Practices to Florida Local Government Retrofit Programs, Central Florida (Fact Sheet)

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

None, None

In some communities, local government and non-profit entities have funds to purchase and renovate distressed, foreclosed homes for resale in the affordable housing market. Numerous opportunities to improve whole house energy efficiency are inherent in these comprehensive renovations. BA-PIRC worked together in a multi-year field study making recommendations in individual homes, meanwhile compiling improvement costs, projected energy savings, practical challenges, and labor force factors surrounding common energy-related renovation measures. The field study, Phase 1 of this research, resulted in a set of best practices appropriate to the current labor pool and market conditions in central Florida to achieve projected annualmore » energy savings of 15-30% and higher. This report describes Phase 2 of the work where researchers worked with a local government partner to implement and refine the "current best practices". A simulation study was conducted to characterize savings potential under three sets of conditions representing varying replacement needs for energy-related equipment and envelope components. The three scenarios apply readily to the general remodeling industry as for renovation of foreclosed homes for the affordable housing market. Our new local government partner, the City of Melbourne, implemented the best practices in a community-scale renovation program that included ten homes in 2012.« less

Water clusters in amorphous pharmaceuticals.

PubMed

Authelin, Jean-Rene; MacKenzie, Alan P; Rasmussen, Don H; Shalaev, Evgenyi Y

2014-09-01

Amorphous materials, although lacking the long-range translational and rotational order of crystalline and liquid crystalline materials, possess certain local (short-range) structure. This paper reviews the distribution of one particular component present in all amorphous pharmaceuticals, that is, water. Based on the current understanding of the structure of water, water molecules can exist in either unclustered form or as aggregates (clusters) of different sizes and geometries. Water clusters are reported in a range of amorphous systems including carbohydrates and their aqueous solutions, synthetic polymers, and proteins. Evidence of water clustering is obtained by various methods that include neutron and X-ray scattering, molecular dynamics simulation, water sorption isotherm, concentration dependence of the calorimetric Tg , dielectric relaxation, and nuclear magnetic resonance. A review of the published data suggests that clustering depends on water concentration, with unclustered water molecules existing at low water contents, whereas clusters form at intermediate water contents. The transition from water clusters to unclustered water molecules can be expected to change water dependence of pharmaceutical properties, such as rates of degradation. We conclude that a mechanistic understanding of the impact of water on the stability of amorphous pharmaceuticals would require systematic studies of water distribution and clustering, while such investigations are lacking. © 2014 Wiley Periodicals, Inc. and the American Pharmacists Association.

Quantum annealing for combinatorial clustering

NASA Astrophysics Data System (ADS)

Kumar, Vaibhaw; Bass, Gideon; Tomlin, Casey; Dulny, Joseph

2018-02-01

Clustering is a powerful machine learning technique that groups "similar" data points based on their characteristics. Many clustering algorithms work by approximating the minimization of an objective function, namely the sum of within-the-cluster distances between points. The straightforward approach involves examining all the possible assignments of points to each of the clusters. This approach guarantees the solution will be a global minimum; however, the number of possible assignments scales quickly with the number of data points and becomes computationally intractable even for very small datasets. In order to circumvent this issue, cost function minima are found using popular local search-based heuristic approaches such as k-means and hierarchical clustering. Due to their greedy nature, such techniques do not guarantee that a global minimum will be found and can lead to sub-optimal clustering assignments. Other classes of global search-based techniques, such as simulated annealing, tabu search, and genetic algorithms, may offer better quality results but can be too time-consuming to implement. In this work, we describe how quantum annealing can be used to carry out clustering. We map the clustering objective to a quadratic binary optimization problem and discuss two clustering algorithms which are then implemented on commercially available quantum annealing hardware, as well as on a purely classical solver "qbsolv." The first algorithm assigns N data points to K clusters, and the second one can be used to perform binary clustering in a hierarchical manner. We present our results in the form of benchmarks against well-known k-means clustering and discuss the advantages and disadvantages of the proposed techniques.

Deep spectroscopy of nearby galaxy clusters - II. The Hercules cluster

NASA Astrophysics Data System (ADS)

Agulli, I.; Aguerri, J. A. L.; Diaferio, A.; Dominguez Palmero, L.; Sánchez-Janssen, R.

2017-06-01

We carried out the deep spectroscopic observations of the nearby cluster A 2151 with AF2/WYFFOS@WHT. The caustic technique enables us to identify 360 members brighter than Mr = -16 and within 1.3R200. We separated the members into subsamples according to photometrical and dynamical properties such as colour, local environment and infall time. The completeness of the catalogue and our large sample allow us to analyse the velocity dispersion and the luminosity functions (LFs) of the identified populations. We found evidence of a cluster still in its collapsing phase. The LF of the red population of A 2151 shows a deficit of dwarf red galaxies. Moreover, the normalized LFs of the red and blue populations of A 2151 are comparable to the red and blue LFs of the field, even if the blue galaxies start dominating 1 mag fainter and the red LF is well represented by a single Schechter function rather than a double Schechter function. We discuss how the evolution of cluster galaxies depends on their mass: bright and intermediate galaxies are mainly affected by dynamical friction and internal/mass quenching, while the evolution of dwarfs is driven by environmental processes that need time and a hostile cluster environment to remove the gas reservoirs and halt the star formation.

BlastNeuron for Automated Comparison, Retrieval and Clustering of 3D Neuron Morphologies.

PubMed

Wan, Yinan; Long, Fuhui; Qu, Lei; Xiao, Hang; Hawrylycz, Michael; Myers, Eugene W; Peng, Hanchuan

2015-10-01

Characterizing the identity and types of neurons in the brain, as well as their associated function, requires a means of quantifying and comparing 3D neuron morphology. Presently, neuron comparison methods are based on statistics from neuronal morphology such as size and number of branches, which are not fully suitable for detecting local similarities and differences in the detailed structure. We developed BlastNeuron to compare neurons in terms of their global appearance, detailed arborization patterns, and topological similarity. BlastNeuron first compares and clusters 3D neuron reconstructions based on global morphology features and moment invariants, independent of their orientations, sizes, level of reconstruction and other variations. Subsequently, BlastNeuron performs local alignment between any pair of retrieved neurons via a tree-topology driven dynamic programming method. A 3D correspondence map can thus be generated at the resolution of single reconstruction nodes. We applied BlastNeuron to three datasets: (1) 10,000+ neuron reconstructions from a public morphology database, (2) 681 newly and manually reconstructed neurons, and (3) neurons reconstructions produced using several independent reconstruction methods. Our approach was able to accurately and efficiently retrieve morphologically and functionally similar neuron structures from large morphology database, identify the local common structures, and find clusters of neurons that share similarities in both morphology and molecular profiles.

Textural defect detect using a revised ant colony clustering algorithm

NASA Astrophysics Data System (ADS)

Zou, Chao; Xiao, Li; Wang, Bingwen

2007-11-01

We propose a totally novel method based on a revised ant colony clustering algorithm (ACCA) to explore the topic of textural defect detection. In this algorithm, our efforts are mainly made on the definition of local irregularity measurement and the implementation of the revised ACCA. The local irregular measurement defined evaluates the local textural inconsistency of each pixel against their mini-environment. In our revised ACCA, the behaviors of each ant are divided into two steps: release pheromone and act. The quantity of pheromone released is proportional to the irregularity measurement; the actions of the ants to act next are chosen independently of each other in a stochastic way according to some evaluated heuristic knowledge. The independency of ants implies the inherent parallel computation architecture of this algorithm. We apply the proposed method in some typical textural images with defects. From the series of pheromone distribution map (PDM), it can be clearly seen that the pheromone distribution approaches the textual defects gradually. By some post-processing, the final distribution of pheromone can demonstrate the shape and area of the defects well.

Cluster Sampling Bias in Government-Sponsored Evaluations: A Correlational Study of Employment and Welfare Pilots in England

PubMed Central

2016-01-01

For pilot or experimental employment programme results to apply beyond their test bed, researchers must select ‘clusters’ (i.e. the job centres delivering the new intervention) that are reasonably representative of the whole territory. More specifically, this requirement must account for conditions that could artificially inflate the effect of a programme, such as the fluidity of the local labour market or the performance of the local job centre. Failure to achieve representativeness results in Cluster Sampling Bias (CSB). This paper makes three contributions to the literature. Theoretically, it approaches the notion of CSB as a human behaviour. It offers a comprehensive theory, whereby researchers with limited resources and conflicting priorities tend to oversample ‘effect-enhancing’ clusters when piloting a new intervention. Methodologically, it advocates for a ‘narrow and deep’ scope, as opposed to the ‘wide and shallow’ scope, which has prevailed so far. The PILOT-2 dataset was developed to test this idea. Empirically, it provides evidence on the prevalence of CSB. In conditions similar to the PILOT-2 case study, investigators (1) do not sample clusters with a view to maximise generalisability; (2) do not oversample ‘effect-enhancing’ clusters; (3) consistently oversample some clusters, including those with higher-than-average client caseloads; and (4) report their sampling decisions in an inconsistent and generally poor manner. In conclusion, although CSB is prevalent, it is still unclear whether it is intentional and meant to mislead stakeholders about the expected effect of the intervention or due to higher-level constraints or other considerations. PMID:27504823

Clustering of local group distances: publication bias or correlated measurements? I. The large Magellanic cloud

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

De Grijs, Richard; Wicker, James E.; Bono, Giuseppe

2014-05-01

The distance to the Large Magellanic Cloud (LMC) represents a key local rung of the extragalactic distance ladder yet the galaxy's distance modulus has long been an issue of contention, in particular in view of claims that most newly determined distance moduli cluster tightly—and with a small spread—around the 'canonical' distance modulus, (m – M){sub 0} = 18.50 mag. We compiled 233 separate LMC distance determinations published between 1990 and 2013. Our analysis of the individual distance moduli, as well as of their two-year means and standard deviations resulting from this largest data set of LMC distance moduli available tomore » date, focuses specifically on Cepheid and RR Lyrae variable-star tracer populations, as well as on distance estimates based on features in the observational Hertzsprung-Russell diagram. We conclude that strong publication bias is unlikely to have been the main driver of the majority of published LMC distance moduli. However, for a given distance tracer, the body of publications leading to the tightly clustered distances is based on highly non-independent tracer samples and analysis methods, hence leading to significant correlations among the LMC distances reported in subsequent articles. Based on a careful, weighted combination, in a statistical sense, of the main stellar population tracers, we recommend that a slightly adjusted canonical distance modulus of (m – M){sub 0} = 18.49 ± 0.09 mag be used for all practical purposes that require a general distance scale without the need for accuracies of better than a few percent.« less

Panoramic Views of Cluster Evolution Since z = 3

NASA Astrophysics Data System (ADS)

Kodama, Tadayuki; Tanaka, M.; Tanaka, Ichi; Kajisawa, M.

2007-05-01

We have been conducting PISCES project (Panoramic Imaging and Spectroscopy of Cluster Evolution with Subaru) with making use of the wide-field imaging capability of Subaru. Our motivations are first to map out large scale structure and local environment of galaxies therein, and then to investigate the variation in galaxy properties as a function of environment and mass. We have completed multi-colour imaging of 8 distant clusters between 0.4clusters across 10-20 Mpc. Three clusters have secure spectroscopic confirmation of the physical association of the structures to the main body of the clusters. We also show photometric and spectroscopic properties of galaxies along the filaments as a function of local density of galaxies, and argue that the truncation of galaxies is seen in the outskirts of clusters rather than in cluster cores. Down-sizing is another important issue and we discuss the environmental dependence of such phenomenon. We are now pushing the study of dense environment to higher redshift (z>2) by wide-field near-infrared imaging of proto-clusters around radio loud galaxies, some of which are known to show a large number of Lya/Ha emitters at the same redshift of the radio galaxies. We have seen clear excess of near-infrared selected galaxies (including DRG) around many of the radio galaxies, suggesting that these are indeed likely to be proto-clusters with not only young emitters but also evolved populations. Spatial distribution of such NIR selected galaxies is filamentary and track similar structures traced by the emitters, but showing little individual overlap. The above two wide-field studies of dense environments and their surroundings will tell us galaxy evolution during the course of cluster assembly over more than 80 per cent of the age of the Universe.

Dynamical Mass Measurements of Contaminated Galaxy Clusters Using Support Distribution Machines

NASA Astrophysics Data System (ADS)

Ntampaka, Michelle; Trac, Hy; Sutherland, Dougal; Fromenteau, Sebastien; Poczos, Barnabas; Schneider, Jeff

2018-01-01

We study dynamical mass measurements of galaxy clusters contaminated by interlopers and show that a modern machine learning (ML) algorithm can predict masses by better than a factor of two compared to a standard scaling relation approach. We create two mock catalogs from Multidark’s publicly available N-body MDPL1 simulation, one with perfect galaxy cluster membership infor- mation and the other where a simple cylindrical cut around the cluster center allows interlopers to contaminate the clusters. In the standard approach, we use a power-law scaling relation to infer cluster mass from galaxy line-of-sight (LOS) velocity dispersion. Assuming perfect membership knowledge, this unrealistic case produces a wide fractional mass error distribution, with a width E=0.87. Interlopers introduce additional scatter, significantly widening the error distribution further (E=2.13). We employ the support distribution machine (SDM) class of algorithms to learn from distributions of data to predict single values. Applied to distributions of galaxy observables such as LOS velocity and projected distance from the cluster center, SDM yields better than a factor-of-two improvement (E=0.67) for the contaminated case. Remarkably, SDM applied to contaminated clusters is better able to recover masses than even the scaling relation approach applied to uncon- taminated clusters. We show that the SDM method more accurately reproduces the cluster mass function, making it a valuable tool for employing cluster observations to evaluate cosmological models.

A hybrid monkey search algorithm for clustering analysis.

PubMed

Chen, Xin; Zhou, Yongquan; Luo, Qifang

2014-01-01

Clustering is a popular data analysis and data mining technique. The k-means clustering algorithm is one of the most commonly used methods. However, it highly depends on the initial solution and is easy to fall into local optimum solution. In view of the disadvantages of the k-means method, this paper proposed a hybrid monkey algorithm based on search operator of artificial bee colony algorithm for clustering analysis and experiment on synthetic and real life datasets to show that the algorithm has a good performance than that of the basic monkey algorithm for clustering analysis.

Investigation of spacial clustering of rare diseases: childhood malignancies in North Humberside.

PubMed

Alexander, F; Cartwright, R; McKinney, P A; Ricketts, T J

1990-03-01

The aims of the study were (1) to test for uniformity of distribution of childhood leukaemias and other malignancies; and (2) to consider the aetiological implications of unusual distributions. A test for spacial clustering was applied using a method which allows for unequal distribution of the population at risk and avoids using census data to provide population denominators. When clustering was identified, four possible aetiological links which had already been suggested to the Leukaemia Research Fund Centre were examined in a local area. The study was carried out in the Yorkshire Health Region in the north of England. 144 children under 15 years of age with a diagnosis of malignant disease known to the Yorkshire Regional Childhood Tumour Registry between 1974 and 1986 were included in the analysis. Of these 53 had leukaemias and nine had lymphomas. Significant localised clustering was found in North Humberside, though not in the whole of the Yorkshire Health Region. A number of clustered cases were identified, some of whom were in a post code sector, Hull 10, to the west of Kingston-upon-Hull, about which concern had been expressed since 1985. There was however no evidence that disease clustering was confined to this area. Four previously suggested hypotheses about causation in this particular area were examined but the results were negative or inconclusive. The identification of spacial clustering must be seen as only the first step in a series of investigations; it can only rarely lead to aetiological conclusions by itself, but it can motivate and target other investigations.

Density-Aware Clustering Based on Aggregated Heat Kernel and Its Transformation

DOE PAGES

Huang, Hao; Yoo, Shinjae; Yu, Dantong; ...

2015-06-01

Current spectral clustering algorithms suffer from the sensitivity to existing noise, and parameter scaling, and may not be aware of different density distributions across clusters. If these problems are left untreated, the consequent clustering results cannot accurately represent true data patterns, in particular, for complex real world datasets with heterogeneous densities. This paper aims to solve these problems by proposing a diffusion-based Aggregated Heat Kernel (AHK) to improve the clustering stability, and a Local Density Affinity Transformation (LDAT) to correct the bias originating from different cluster densities. AHK statistically\\ models the heat diffusion traces along the entire time scale, somore » it ensures robustness during clustering process, while LDAT probabilistically reveals local density of each instance and suppresses the local density bias in the affinity matrix. Our proposed framework integrates these two techniques systematically. As a result, not only does it provide an advanced noise-resisting and density-aware spectral mapping to the original dataset, but also demonstrates the stability during the processing of tuning the scaling parameter (which usually controls the range of neighborhood). Furthermore, our framework works well with the majority of similarity kernels, which ensures its applicability to many types of data and problem domains. The systematic experiments on different applications show that our proposed algorithms outperform state-of-the-art clustering algorithms for the data with heterogeneous density distributions, and achieve robust clustering performance with respect to tuning the scaling parameter and handling various levels and types of noise.« less

Waveforms clustering of small magnitude earthquakes recorded in the Northern Sicilian offshore: evidence of multiplets

NASA Astrophysics Data System (ADS)

D'Alessandro, A.; Mangano, G.; D'Anna, G.; Luzio, D.; Selvaggi, G.

2011-12-01

On September 6th 2002 the northern Sicily was hit by a strong earthquake (MW 5.9). In the following six months over a thousand aftershocks were located in the same area. On December 7th 2009, the INGV OBSLab deployed an OBS/H near the epicentral area of the main shock at a depth of 1500 m. The submarine station was recovered after 233 days. During the eight months of the experiment the OBS/H recorded about 250 small magnitude events of clear local origin. In order to identify seismic events generated by the same tectonic structure, we have applied a clustering technique based on the similarity of the waveforms. The similarity matrix was constructed using the maximum of the normalized cross-covariance function. To identify the multiplets, we used a clustering technique based on an agglomerative hierarchical algorithm, based on the nearest neighbor strategy. The results were summarized in the dendrogram of Fig. 1. The partitions have been obtained by "cutting" the dendrogram at a level of distance equal to 0.3. So we have identified 9 multiplets and some doublets and triplets. Fig. 2 shows as example the multiplet 1. The events of this cluster have a high level of similarity; 25 of the 31 micro-events are characterized by a similarity greater than 0.9. In order to locate the micro-earthquakes recorded by the OBS/H only a single station location technique was implemented and applied. Some multiplets have clouds of hypocenters overlapping each other. These clusters, indistinguishable without the application of a waveforms clustering technique, show differences in the waveforms that must be attributed to differences in focal mechanisms which generated the waveforms.

Groundwater source contamination mechanisms: Physicochemical profile clustering, risk factor analysis and multivariate modelling

NASA Astrophysics Data System (ADS)

Hynds, Paul; Misstear, Bruce D.; Gill, Laurence W.; Murphy, Heather M.

2014-04-01

An integrated domestic well sampling and "susceptibility assessment" programme was undertaken in the Republic of Ireland from April 2008 to November 2010. Overall, 211 domestic wells were sampled, assessed and collated with local climate data. Based upon groundwater physicochemical profile, three clusters have been identified and characterised by source type (borehole or hand-dug well) and local geological setting. Statistical analysis indicates that cluster membership is significantly associated with the prevalence of bacteria (p = 0.001), with mean Escherichia coli presence within clusters ranging from 15.4% (Cluster-1) to 47.6% (Cluster-3). Bivariate risk factor analysis shows that on-site septic tank presence was the only risk factor significantly associated (p < 0.05) with bacterial presence within all clusters. Point agriculture adjacency was significantly associated with both borehole-related clusters. Well design criteria were associated with hand-dug wells and boreholes in areas characterised by high permeability subsoils, while local geological setting was significant for hand-dug wells and boreholes in areas dominated by low/moderate permeability subsoils. Multivariate susceptibility models were developed for all clusters, with predictive accuracies of 84% (Cluster-1) to 91% (Cluster-2) achieved. Septic tank setback was a common variable within all multivariate models, while agricultural sources were also significant, albeit to a lesser degree. Furthermore, well liner clearance was a significant factor in all models, indicating that direct surface ingress is a significant well contamination mechanism. Identification and elucidation of cluster-specific contamination mechanisms may be used to develop improved overall risk management and wellhead protection strategies, while also informing future remediation and maintenance efforts.

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.

Merger types forming the Virgo cluster in recent gigayears

NASA Astrophysics Data System (ADS)

Olchanski, M.; Sorce, J. G.

2018-06-01

Context. As our closest cluster-neighbor, the Virgo cluster of galaxies is intensely studied by observers to unravel the mysteries of galaxy evolution within clusters. At this stage, cosmological numerical simulations of the cluster are useful to efficiently test theories and calibrate models. However, it is not trivial to select the perfect simulacrum of the Virgo cluster to fairly compare in detail its observed and simulated galaxy populations that are affected by the type and history of the cluster. Aims: Determining precisely the properties of Virgo for a later selection of simulated clusters becomes essential. It is still not clear how to access some of these properties, such as the past history of the Virgo cluster from current observations. Therefore, directly producing effective simulacra of the Virgo cluster is inevitable. Methods: Efficient simulacra of the Virgo cluster can be obtained via simulations that resemble the local Universe down to the cluster scale. In such simulations, Virgo-like halos form in the proper local environment and permit assessing the most probable formation history of the cluster. Studies based on these simulations have already revealed that the Virgo cluster has had a quiet merging history over the last seven gigayears and that the cluster accretes matter along a preferential direction. Results: This paper reveals that in addition such Virgo halos have had on average only one merger larger than about a tenth of their mass at redshift zero within the last four gigayears. This second branch (by opposition to main branch) formed in a given sub-region and merged recently (within the last gigayear). These properties are not shared with a set of random halos within the same mass range. Conclusions: This study extends the validity of the scheme used to produce the Virgo simulacra down to the largest sub-halos of the Virgo cluster. It opens up great prospects for detailed comparisons with observations, including substructures and

CC2 oscillator strengths within the local framework for calculating excitation energies (LoFEx).

PubMed

Baudin, Pablo; Kjærgaard, Thomas; Kristensen, Kasper

2017-04-14

In a recent work [P. Baudin and K. Kristensen, J. Chem. Phys. 144, 224106 (2016)], we introduced a local framework for calculating excitation energies (LoFEx), based on second-order approximated coupled cluster (CC2) linear-response theory. LoFEx is a black-box method in which a reduced excitation orbital space (XOS) is optimized to provide coupled cluster (CC) excitation energies at a reduced computational cost. In this article, we present an extension of the LoFEx algorithm to the calculation of CC2 oscillator strengths. Two different strategies are suggested, in which the size of the XOS is determined based on the excitation energy or the oscillator strength of the targeted transitions. The two strategies are applied to a set of medium-sized organic molecules in order to assess both the accuracy and the computational cost of the methods. The results show that CC2 excitation energies and oscillator strengths can be calculated at a reduced computational cost, provided that the targeted transitions are local compared to the size of the molecule. To illustrate the potential of LoFEx for large molecules, both strategies have been successfully applied to the lowest transition of the bivalirudin molecule (4255 basis functions) and compared with time-dependent density functional theory.

Noncontact Measurement of the Local Mechanical Properties of Living Cells Using Pressure Applied via a Pipette

PubMed Central

Sánchez, Daniel; Johnson, Nick; Li, Chao; Novak, Pavel; Rheinlaender, Johannes; Zhang, Yanjun; Anand, Uma; Anand, Praveen; Gorelik, Julia; Frolenkov, Gregory I.; Benham, Christopher; Lab, Max; Ostanin, Victor P.; Schäffer, Tilman E.; Klenerman, David; Korchev, Yuri E.

2008-01-01

Mechanosensitivity in living biological tissue is a study area of increasing importance, but investigative tools are often inadequate. We have developed a noncontact nanoscale method to apply quantified positive and negative force at defined positions to the soft responsive surface of living cells. The method uses applied hydrostatic pressure (0.1–150 kPa) through a pipette, while the pipette-sample separation is kept constant above the cell surface using ion conductance based distance feedback. This prevents any surface contact, or contamination of the pipette, allowing repeated measurements. We show that we can probe the local mechanical properties of living cells using increasing pressure, and hence measure the nanomechanical properties of the cell membrane and the underlying cytoskeleton in a variety of cells (erythrocytes, epithelium, cardiomyocytes and neurons). Because the cell surface can first be imaged without pressure, it is possible to relate the mechanical properties to the local cell topography. This method is well suited to probe the nanomechanical properties and mechanosensitivity of living cells. PMID:18515369

Spatial clustering of pixels of a multispectral image

DOEpatents

Conger, James Lynn

2014-08-19

A method and system for clustering the pixels of a multispectral image is provided. A clustering system computes a maximum spectral similarity score for each pixel that indicates the similarity between that pixel and the most similar neighboring. To determine the maximum similarity score for a pixel, the clustering system generates a similarity score between that pixel and each of its neighboring pixels and then selects the similarity score that represents the highest similarity as the maximum similarity score. The clustering system may apply a filtering criterion based on the maximum similarity score so that pixels with similarity scores below a minimum threshold are not clustered. The clustering system changes the current pixel values of the pixels in a cluster based on an averaging of the original pixel values of the pixels in the cluster.

An optical catalog of galaxy clusters obtained from an adaptive matched filter finder applied to SDSS DR9 data

NASA Astrophysics Data System (ADS)

Banerjee, P.; Szabo, T.; Pierpaoli, E.; Franco, G.; Ortiz, M.; Oramas, A.; Tornello, B.

2018-01-01

We present a new galaxy cluster catalog constructed from the Sloan Digital Sky Survey Data Release 9 (SDSS DR9) using an Adaptive Matched Filter (AMF) technique. Our catalog has 46,479 galaxy clusters with richness Λ200 > 20 in the redshift range 0.045 ≤ z < 0.641 in ∼11,500 deg2 of the sky. Angular position, richness, core and virial radii and redshift estimates for these clusters, as well as their error analysis, are provided as part of this catalog. In addition to the main version of the catalog, we also provide an extended version with a lower richness cut, containing 79,368 clusters. This version, in addition to the clusters in the main catalog, also contains those clusters (with richness 10 < Λ200 < 20) which have a one-to-one match in the DR8 catalog developed by Wen et al.(WHL). We obtain probabilities for cluster membership for each galaxy and implement several procedures for the identification and removal of false cluster detections. We cross-correlate the main AMF DR9 catalog with a number of cluster catalogs in different wavebands (Optical, X-ray). We compare our catalog with other SDSS-based ones such as the redMaPPer (26,350 clusters) and the Wen et al. (WHL) (132,684 clusters) in the same area of the sky and in the overlapping redshift range. We match 97% of the richest Abell clusters (Richness group 3), the same as WHL, while redMaPPer matches ∼ 90% of these clusters. Considering AMF DR9 richness bins, redMaPPer does not have one-to-one matches for 70% of our lowest richness clusters (20 < Λ200 < 40), while WHL matches 54% of these missed clusters (not present in redMaPPer). redMaPPer consistently does not possess one-to-one matches for ∼ 20% AMF DR9 clusters with Λ200 > 40, while WHL matches ≥ 70% of these missed clusters on average. For comparisons with X-ray clusters, we match the AMF catalog with BAX, MCXC and a combined catalog from NORAS and REFLEX. We consistently obtain a greater number of one-to-one matches for X-ray clusters

Swarm: robust and fast clustering method for amplicon-based studies

PubMed Central

Rognes, Torbjørn; Quince, Christopher; de Vargas, Colomban; Dunthorn, Micah

2014-01-01

Popular de novo amplicon clustering methods suffer from two fundamental flaws: arbitrary global clustering thresholds, and input-order dependency induced by centroid selection. Swarm was developed to address these issues by first clustering nearly identical amplicons iteratively using a local threshold, and then by using clusters’ internal structure and amplicon abundances to refine its results. This fast, scalable, and input-order independent approach reduces the influence of clustering parameters and produces robust operational taxonomic units. PMID:25276506

Cluster formation in precompound nuclei in the time-dependent framework

NASA Astrophysics Data System (ADS)

Schuetrumpf, B.; Nazarewicz, W.

2017-12-01

Background: Modern applications of nuclear time-dependent density functional theory (TDDFT) are often capable of providing quantitative description of heavy ion reactions. However, the structures of precompound (preequilibrium, prefission) states produced in heavy ion reactions are difficult to assess theoretically in TDDFT as the single-particle density alone is a weak indicator of shell structure and cluster states. Purpose: We employ the time-dependent nucleon localization function (NLF) to reveal the structure of precompound states in nuclear reactions involving light and medium-mass ions. We primarily focus on spin saturated systems with N =Z . Furthermore, we study reactions with oxygen and carbon ions, for which some experimental evidence for α clustering in precompound states exists. Method: We utilize the symmetry-free TDDFT approach with the Skyrme energy density functional UNEDF1 and compute the time-dependent NLFs to describe 16O + 16O,40Ca + 16O, 40Ca + 40Ca, and O,1816 + 12C collisions at energies above the Coulomb barrier. Results: We show that NLFs reveal a variety of time-dependent modes involving cluster structures. For instance, the 16O + 16O collision results in a vibrational mode of a quasimolecular α - 12C - 12C-α state. For heavier ions, a variety of cluster configurations are predicted. For the collision of O,1816 + 12C, we showed that the precompound system has a tendency to form α clusters. This result supports the experimental findings that the presence of cluster structures in the projectile and target nuclei gives rise to strong entrance channel effects and enhanced α emission. Conclusion: The time-dependent nucleon localization measure is a very good indicator of cluster structures in complex precompound states formed in heavy-ion fusion reactions. The localization reveals the presence of collective vibrations involving cluster structures, which dominate the initial dynamics of the fusing system.

Using cluster analysis for medical resource decision making.

PubMed

Dilts, D; Khamalah, J; Plotkin, A

1995-01-01

Escalating costs of health care delivery have in the recent past often made the health care industry investigate, adapt, and apply those management techniques relating to budgeting, resource control, and forecasting that have long been used in the manufacturing sector. A strategy that has contributed much in this direction is the definition and classification of a hospital's output into "products" or groups of patients that impose similar resource or cost demands on the hospital. Existing classification schemes have frequently employed cluster analysis in generating these groupings. Unfortunately, the myriad articles and books on clustering and classification contain few formalized selection methodologies for choosing a technique for solving a particular problem, hence they often leave the novice investigator at a loss. This paper reviews the literature on clustering, particularly as it has been applied in the medical resource-utilization domain, addresses the critical choices facing an investigator in the medical field using cluster analysis, and offers suggestions (using the example of clustering low-vision patients) for how such choices can be made.

Mutation Clusters from Cancer Exome.

PubMed

Kakushadze, Zura; Yu, Willie

2017-08-15

We apply our statistically deterministic machine learning/clustering algorithm *K-means (recently developed in https://ssrn.com/abstract=2908286) to 10,656 published exome samples for 32 cancer types. A majority of cancer types exhibit a mutation clustering structure. Our results are in-sample stable. They are also out-of-sample stable when applied to 1389 published genome samples across 14 cancer types. In contrast, we find in- and out-of-sample instabilities in cancer signatures extracted from exome samples via nonnegative matrix factorization (NMF), a computationally-costly and non-deterministic method. Extracting stable mutation structures from exome data could have important implications for speed and cost, which are critical for early-stage cancer diagnostics, such as novel blood-test methods currently in development.

Mutation Clusters from Cancer Exome

PubMed Central

Kakushadze, Zura; Yu, Willie

2017-01-01

We apply our statistically deterministic machine learning/clustering algorithm *K-means (recently developed in https://ssrn.com/abstract=2908286) to 10,656 published exome samples for 32 cancer types. A majority of cancer types exhibit a mutation clustering structure. Our results are in-sample stable. They are also out-of-sample stable when applied to 1389 published genome samples across 14 cancer types. In contrast, we find in- and out-of-sample instabilities in cancer signatures extracted from exome samples via nonnegative matrix factorization (NMF), a computationally-costly and non-deterministic method. Extracting stable mutation structures from exome data could have important implications for speed and cost, which are critical for early-stage cancer diagnostics, such as novel blood-test methods currently in development. PMID:28809811

Deriving physical parameters of unresolved star clusters. V. M 31 PHAT star clusters

NASA Astrophysics Data System (ADS)

de Meulenaer, P.; Stonkutė, R.; Vansevičius, V.

2017-06-01

Context. This study is the fifth of a series that investigates the degeneracy and stochasticity problems present in the determination of physical parameters such as age, mass, extinction, and metallicity of partially resolved or unresolved star cluster populations in external galaxies when using HST broad-band photometry. Aims: In this work we aim to derive parameters of star clusters using models with fixed and free metallicity based on the HST WFC3+ACS photometric system. The method is applied to derive parameters of a subsample of 1363 star clusters in the Andromeda galaxy observed with the HST. Methods: Following Paper III, we derive the star cluster parameters using a large grid of stochastic models that are compared to the six observed integrated broad-band WFC3+ACS magnitudes of star clusters. Results: We show that the age, mass, and extinction of the M 31 star clusters, derived assuming fixed solar metallicity, are in agreement with previous studies. We also demonstrate the ability of the WFC3+ACS photometric system to derive metallicity of star clusters older than 1 Gyr. We show that the metallicity derived using broad-band photometry of 36 massive M 31 star clusters is in good agreement with the metallicity derived using spectroscopy. Table 1 is only available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (http://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/602/A112

Hybrid fuzzy cluster ensemble framework for tumor clustering from biomolecular data.

PubMed

Yu, Zhiwen; Chen, Hantao; You, Jane; Han, Guoqiang; Li, Le

2013-01-01

Cancer class discovery using biomolecular data is one of the most important tasks for cancer diagnosis and treatment. Tumor clustering from gene expression data provides a new way to perform cancer class discovery. Most of the existing research works adopt single-clustering algorithms to perform tumor clustering is from biomolecular data that lack robustness, stability, and accuracy. To further improve the performance of tumor clustering from biomolecular data, we introduce the fuzzy theory into the cluster ensemble framework for tumor clustering from biomolecular data, and propose four kinds of hybrid fuzzy cluster ensemble frameworks (HFCEF), named as HFCEF-I, HFCEF-II, HFCEF-III, and HFCEF-IV, respectively, to identify samples that belong to different types of cancers. The difference between HFCEF-I and HFCEF-II is that they adopt different ensemble generator approaches to generate a set of fuzzy matrices in the ensemble. Specifically, HFCEF-I applies the affinity propagation algorithm (AP) to perform clustering on the sample dimension and generates a set of fuzzy matrices in the ensemble based on the fuzzy membership function and base samples selected by AP. HFCEF-II adopts AP to perform clustering on the attribute dimension, generates a set of subspaces, and obtains a set of fuzzy matrices in the ensemble by performing fuzzy c-means on subspaces. Compared with HFCEF-I and HFCEF-II, HFCEF-III and HFCEF-IV consider the characteristics of HFCEF-I and HFCEF-II. HFCEF-III combines HFCEF-I and HFCEF-II in a serial way, while HFCEF-IV integrates HFCEF-I and HFCEF-II in a concurrent way. HFCEFs adopt suitable consensus functions, such as the fuzzy c-means algorithm or the normalized cut algorithm (Ncut), to summarize generated fuzzy matrices, and obtain the final results. The experiments on real data sets from UCI machine learning repository and cancer gene expression profiles illustrate that 1) the proposed hybrid fuzzy cluster ensemble frameworks work well on real

Rapid and coordinated processing of global motion images by local clusters of retinal ganglion cells.

PubMed

Matsumoto, Akihiro; Tachibana, Masao

2017-01-01

Even when the body is stationary, the whole retinal image is always in motion by fixational eye movements and saccades that move the eye between fixation points. Accumulating evidence indicates that the brain is equipped with specific mechanisms for compensating for the global motion induced by these eye movements. However, it is not yet fully understood how the retina processes global motion images during eye movements. Here we show that global motion images evoke novel coordinated firing in retinal ganglion cells (GCs). We simultaneously recorded the firing of GCs in the goldfish isolated retina using a multi-electrode array, and classified each GC based on the temporal profile of its receptive field (RF). A moving target that accompanied the global motion (simulating a saccade following a period of fixational eye movements) modulated the RF properties and evoked synchronized and correlated firing among local clusters of the specific GCs. Our findings provide a novel concept for retinal information processing during eye movements.

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.

Application of Scan Statistics to Detect Suicide Clusters in Australia

PubMed Central

Cheung, Yee Tak Derek; Spittal, Matthew J.; Williamson, Michelle Kate; Tung, Sui Jay; Pirkis, Jane

2013-01-01

Background Suicide clustering occurs when multiple suicide incidents take place in a small area or/and within a short period of time. In spite of the multi-national research attention and particular efforts in preparing guidelines for tackling suicide clusters, the broader picture of epidemiology of suicide clustering remains unclear. This study aimed to develop techniques in using scan statistics to detect clusters, with the detection of suicide clusters in Australia as example. Methods and Findings Scan statistics was applied to detect clusters among suicides occurring between 2004 and 2008. Manipulation of parameter settings and change of area for scan statistics were performed to remedy shortcomings in existing methods. In total, 243 suicides out of 10,176 (2.4%) were identified as belonging to 15 suicide clusters. These clusters were mainly located in the Northern Territory, the northern part of Western Australia, and the northern part of Queensland. Among the 15 clusters, 4 (26.7%) were detected by both national and state cluster detections, 8 (53.3%) were only detected by the state cluster detection, and 3 (20%) were only detected by the national cluster detection. Conclusions These findings illustrate that the majority of spatial-temporal clusters of suicide were located in the inland northern areas, with socio-economic deprivation and higher proportions of indigenous people. Discrepancies between national and state/territory cluster detection by scan statistics were due to the contrast of the underlying suicide rates across states/territories. Performing both small-area and large-area analyses, and applying multiple parameter settings may yield the maximum benefits for exploring clusters. PMID:23342098

The smart cluster method. Adaptive earthquake cluster identification and analysis in strong seismic regions

NASA Astrophysics Data System (ADS)

Schaefer, Andreas M.; Daniell, James E.; Wenzel, Friedemann

2017-07-01

Earthquake clustering is an essential part of almost any statistical analysis of spatial and temporal properties of seismic activity. The nature of earthquake clusters and subsequent declustering of earthquake catalogues plays a crucial role in determining the magnitude-dependent earthquake return period and its respective spatial variation for probabilistic seismic hazard assessment. This study introduces the Smart Cluster Method (SCM), a new methodology to identify earthquake clusters, which uses an adaptive point process for spatio-temporal cluster identification. It utilises the magnitude-dependent spatio-temporal earthquake density to adjust the search properties, subsequently analyses the identified clusters to determine directional variation and adjusts its search space with respect to directional properties. In the case of rapid subsequent ruptures like the 1992 Landers sequence or the 2010-2011 Darfield-Christchurch sequence, a reclassification procedure is applied to disassemble subsequent ruptures using near-field searches, nearest neighbour classification and temporal splitting. The method is capable of identifying and classifying earthquake clusters in space and time. It has been tested and validated using earthquake data from California and New Zealand. A total of more than 1500 clusters have been found in both regions since 1980 with M m i n = 2.0. Utilising the knowledge of cluster classification, the method has been adjusted to provide an earthquake declustering algorithm, which has been compared to existing methods. Its performance is comparable to established methodologies. The analysis of earthquake clustering statistics lead to various new and updated correlation functions, e.g. for ratios between mainshock and strongest aftershock and general aftershock activity metrics.

Cluster Formation of Anchored Proteins Induced by Membrane-Mediated Interaction

PubMed Central

Li, Shuangyang; Zhang, Xianren; Wang, Wenchuan

2010-01-01

Abstract Computer simulations were used to study the cluster formation of anchored proteins in a membrane. The rate and extent of clustering was found to be dependent upon the hydrophobic length of the anchored proteins embedded in the membrane. The cluster formation mechanism of anchored proteins in our work was ascribed to the different local perturbations on the upper and lower monolayers of the membrane and the intermonolayer coupling. Simulation results demonstrated that only when the penetration depth of anchored proteins was larger than half the membrane thickness, could the structure of the lower monolayer be significantly deformed. Additionally, studies on the local structures of membranes indicated weak perturbation of bilayer thickness for a shallowly inserted protein, while there was significant perturbation for a more deeply inserted protein. The origin of membrane-mediated protein-protein interaction is therefore due to the local perturbation of the membrane thickness, and the entropy loss—both of which are caused by the conformation restriction on the lipid chains and the enhanced intermonolayer coupling for a deeply inserted protein. Finally, in this study we addressed the difference of cluster formation mechanisms between anchored proteins and transmembrane proteins. PMID:20513399

Cluster analysis applied to the spatial and temporal variability of monthly rainfall in Mato Grosso do Sul State, Brazil

NASA Astrophysics Data System (ADS)

Teodoro, Paulo Eduardo; de Oliveira-Júnior, José Francisco; da Cunha, Elias Rodrigues; Correa, Caio Cezar Guedes; Torres, Francisco Eduardo; Bacani, Vitor Matheus; Gois, Givanildo; Ribeiro, Larissa Pereira

2016-04-01

The State of Mato Grosso do Sul (MS) located in Brazil Midwest is devoid of climatological studies, mainly in the characterization of rainfall regime and producers' meteorological systems and rain inhibitors. This state has different soil and climatic characteristics distributed among three biomes: Cerrado, Atlantic Forest and Pantanal. This study aimed to apply the cluster analysis using Ward's algorithm and identify those meteorological systems that affect the rainfall regime in the biomes. The rainfall data of 32 stations (sites) of the MS State were obtained from the Agência Nacional de Águas (ANA) database, collected from 1954 to 2013. In each of the 384 monthly rainfall temporal series was calculated the average and applied the Ward's algorithm to identify spatial and temporal variability of rainfall. Bartlett's test revealed only in January homogeneous variance at all sites. Run test showed that there was no increase or decrease in trend of monthly rainfall. Cluster analysis identified five rainfall homogeneous regions in the MS State, followed by three seasons (rainy, transitional and dry). The rainy season occurs during the months of November, December, January, February and March. The transitional season ranges between the months of April and May, September and October. The dry season occurs in June, July and August. The groups G1, G4 and G5 are influenced by South Atlantic Subtropical Anticyclone (SASA), Chaco's Low (CL), Bolivia's High (BH), Low Levels Jet (LLJ) and South Atlantic Convergence Zone (SACZ) and Maden-Julian Oscillation (MJO). Group G2 is influenced by Upper Tropospheric Cyclonic Vortex (UTCV) and Front Systems (FS). The group G3 is affected by UTCV, FS and SACZ. The meteorological systems' interaction that operates in each biome and the altitude causes the rainfall spatial and temporal diversity in MS State.