Results 301  350 of 1824  
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Qian Huifeng H Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213,   2009
It has long been a major challenge to achieve synthetic control over size and monodispersity of gold thiolate nanoclusters. Among the reported Aun thiolate clusters, Au38 has been shown to be particularly stable but was only obtained as a minor product in previous syntheses. In this work, we report a ...


Chakraborty Hrishikesh   2009
Adequately powered sample size calculations for cluster randomized trials primarily depend on the event rate variability, effect size, average cluster size, and intracluster correlation (ICC). Furthermore, an ICC estimate depends on event rate variability among clusters, cluster size, and number of clusters. We evaluated the impact on ICC estimates of ...


Meng Jia J Department of ECE, University of Texas at San Antonio, Texas,   2009
Clustering is a popular data exploration technique widely used in microarray data analysis. When dealing with timeseries data, most conventional clustering algorithms, however, either use oneway clustering methods, which fail to consider the heterogeneity of temporary domain, or use twoway clustering methods that do not take into account the time ...


Ahn Chul C Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, TX 753909066, United States.   2009
In some cluster randomization trials, the number of clusters cannot exceed a specified maximum value due to cost constraints or other practical reasons. Donner and Klar [Donner A, and Klar N. Design and analysis of cluster randomization trials in health research. Oxford University Press 2000] provided the sample size formula ...


Collins Chris A   2009
The current consensus is that galaxies begin as small density fluctuations in the early Universe and grow by in situ star formation and hierarchical merging. Stars begin to form relatively quickly in subgalacticsized building blocks called haloes which are subsequently assembled into galaxies. However, exactly when this assembly takes place ...


Olives Casey   2009
Traditional lot quality assurance sampling (LQAS) methods require simple random sampling to guarantee valid results. However, cluster sampling has been proposed to reduce the number of random starting points. This study uses simulations to examine the classification error of two such designs, a 67x3 (67 clusters of three observations) and ...


Rempel Jane Y   2009
A model is presented for the colloidal synthesis of semiconductor nanocrystals capturing the reactions underlying nucleation and growth processes. The model combines an activation mechanism for precursor conversion to monomers, discrete rate equations for formation of smallsized clusters, and continuous FokkerPlanck equation for growth of largesized clusters. The model allows ...


Xuan Qi   2009
The country neighborhood network, where nodes represent countries and two nodes are considered linked if the corresponding countries are neighbors on territory, is created and its giant component, the Asia, Europe, and Africa (AEA) cluster, is carefully studied in this paper. It is found that, as common, the degree distribution ...


Olman Victor   2009
Large sets of bioinformatical data provide a challenge in time consumption while solving the cluster identification problem, and that is why a parallel algorithm is so needed for identifying dense clusters in a noisy background. Our algorithm works on a graph representation of the data set to be analyzed. It ...


Dawson K J   2009
Clustering problems (including the clustering of individuals into outcrossing populations, hybrid generations, fullsib families and selfing lines) have recently received much attention in population genetics. In these clustering problems, the parameter of interest is a partition of the set of sampled individualsthe sample partition. In a fully Bayesian approach to ...


Jiang Fan   2009
The proposed unusual video event detection method is based on unsupervised clustering of object trajectories, which are modeled by hidden Markov models (HMM). The novelty of the method includes a dynamic hierarchical process incorporated in the trajectory clustering algorithm to prevent model overfitting and a 2depth greedy search strategy for ...


Vignes Matthieu M BioSS at the Scottish Crop Research Institute, Invergowrie, Dundee, Scotland, UK.   2009
Clustering of genes into groups sharing common characteristics is a useful exploratory technique for a number of subsequent computational analysis. A wide range of clustering algorithms have been proposed in particular to analyze gene expression data, but most of them consider genes as independent entities or include relevant information on ...


Zhao Bin   2009
Support vector ordinal regression (SVOR) is a recently proposed ordinal regression (OR) algorithm. Despite its theoretical and empirical success, the method has one major bottleneck, which is the high computational complexity. In this brief, we propose a both practical and theoretical guaranteed algorithm, blockquantized support vector ordinal regression (BQSVOR), where ...


Cheng ShihSian   2009
In this paper, we consider the learning process of a probabilistic selforganizing map (PbSOM) as a modelbased data clustering procedure that preserves the topological relationships between data clusters in a neural network. Based on this concept, we develop a couplinglikelihood mixture model for the PbSOM that extends the reference vectors ...


Berchenko Yakir   2009
Standard techniques for analyzing network models usually break down in the presence of clustering. Here we introduce a new analytic tool, the "freeexcess degree" distribution, which extends the generating function framework, making it applicable for clustered networks (C>0). The methodology is general and provides a new expression for the threshold ...


Woodley S M   2009
We report a general method of constructing microporous, cubic frameworks from eight different high symmetry small clusters of ZnO, which were previously predicted via the application of an evolutionary algorithm. Using interatomic potentials, the lattice energies of the structures formed are computed. We analyse the relative stabilities within particular subsets ...


Trappey Amy J C   2009
This correspondence presents a novel hierarchical clustering approach for knowledge document selforganization, particularly for patent analysis. Current keywordbased methodologies for document content management tend to be inconsistent and ineffective when partial meanings of the technical content are used for cluster analysis. Thus, a new methodology to automatically interpret and cluster ...


Borghammer Per   2009
BACKGROUND: Global mean (GM) normalization is one of the most commonly used methods of normalization in PET and SPECT group comparison studies of neurodegenerative disorders. It requires that no betweengroup GM difference is present, which may be strongly violated in neurodegenerative disorders. Importantly, such GM differences often elude detection due ...


Tanamoto Tetsufumi   2009
While Isingtype interactions are ideal for implementing controlled phase flip gates in oneway quantum computing, natural interactions between solidstate qubits are most often described by either the XY or the Heisenberg models. We show an efficient way of generating cluster states directly using either the imaginary SWAP (iSWAP) gate for ...


Sharma Ashok   2009
MOTIVATION: As the number of publically available microarray experiments increases, the ability to analyze extremely large datasets across multiple experiments becomes critical. There is a requirement to develop algorithms which are fast and can cluster extremely large datasets without affecting the cluster quality. Clustering is an unsupervised exploratory technique applied ...


Saberi A A   2009
We investigate the statistics of isoheight lines of (2+1) dimensional KardarParisiZhang model at different level sets around the mean height in the saturation regime. We find that the exponent describing the distribution of the heightcluster size behaves differently for level cuts above and below the mean height, while the fractal ...


Wang Xiaogang   2009
We propose a novel unsupervised learning framework to model activities and interactions in crowded and complicated scenes. Hierarchical Bayesian models are used to connect three elements in visual surveillance: lowlevel visual features, simple "atomic" activities, and interactions. Atomic activities are modeled as distributions over lowlevel visual features, and multiagent interactions ...


Crespi Catherine M CM Department of Biostatistics, University of California, Los Angeles, CA 900951772, U.S.A.   2009
In clusterrandomized trials, it is commonly assumed that the magnitude of the correlation among subjects within a cluster is constant across clusters. However, the correlation may in fact be heterogeneous and depend on cluster characteristics. Accurate modeling of the correlation has the potential to improve inference. We use secondorder generalized ...


Burgarella C   2009
Powerful and accurate detection of firstgeneration (F1) hybrids and backcrosses in nature is needed to achieve a better understanding of the function and dynamics of introgression. To document the frequency of ongoing interspecific gene exchange between two Mediterranean evergreen oaks, the cork oak (Quercus suber) and the holm oak (Q. ...


Habbi Hacene   2009
In this paper, an efficient fuzzy modelbased leak detection algorithm is designed for a pilot heat exchanger. A dynamic fuzzy model of the physical plant is first derived from inputoutput measurements using a fuzzy clustering technique. This model is run in parallel to the process for symptom generation. The leak ...


Andreopoulos Bill   2009
Clustering is ubiquitously applied in bioinformatics with hierarchical clustering and kmeans partitioning being the most popular methods. Numerous improvements of these two clustering methods have been introduced, as well as completely different approaches such as gridbased, densitybased and modelbased clustering. For improved bioinformatics analysis of data, it is important to ...


Taşdemir Kadim   2009
The selforganizing map (SOM) is a powerful method for visualization, cluster extraction, and data mining. It has been used successfully for data of high dimensionality and complexity where traditional methods may often be insufficient. In order to analyze data structure and capture cluster boundaries from the SOM, one common approach ...


Hin LinYee   2009
Selecting an appropriate working correlation structure is pertinent to clustered data analysis using generalized estimating equations (GEE) because an inappropriate choice will lead to inefficient parameter estimation. We investigate the wellknown criterion of QIC for selecting a working correlation structure, and have found that performance of the QIC is deteriorated ...


McNally Richard J Q   2009
The aetiology of childhood cancer is poorly understood. Both genetic and environmental factors are likely to be involved. The presence of spatial clustering is indicative of a very localized environmental component to aetiology. Spatial clustering is present when there are a small number of areas with greatly increased incidence or ...


Gujrati P D   2009
The results presented in the abovementioned recent paper by Neto and Stilck [J. Chem. Phys.128, 184904 (2008)] represent special cases of a more general investigation by Gujrati on recursive lattices and have already appeared either in this journal or elsewhere. Even the methodology adopted by these authors is almost identical ...


Maugis Cathy   2009
This article is concerned with variable selection for cluster analysis. The problem is regarded as a model selection problem in the modelbased cluster analysis context. A model generalizing the model of Raftery and Dean (2006, Journal of the American Statistical Association 101, 168178) is proposed to specify the role of ...


LaBau, Vernon J.
Graduation date: 1967


Yi Grace Y   2009
Clustered data arise commonly in practice and it is often of interest to estimate the mean response parameters as well as the association parameters. However, most research has been directed to inference about the mean response parameters with the association parameters relegated to a nuisance role. There is little work ...


Niu Jianwei   2009
The purpose of this study was to develop a 3D anthropometric sizing method based on a clustering algorithm combined with a multiresolution description and demonstrate the method with 3D head data. Wavelet decomposition was adopted to provide flexible descriptions of 3D shapes on different resolution levels. A blockdivision technique was ...


Chu ChiaWei   2009
Standardization is used to ensure that the variables in a similarity calculation make an equal contribution to the computed similarity value. This paper compares the use of seven different methods that have been suggested previously for the standardization of integervalued or realvalued data, comparing the results with unstandardized data. Sets ...


Yi Grace Y   2009
Clustered data arise commonly in practice and it is often of interest to estimate the mean response parameters as well as the association parameters. However, most research has been directed to address the mean response parameters with the association parameters relegated to a nuisance role. There is relatively little work ...


Miglioretti Diana L   2009
Although much research has been conducted to understand the influence of interpretive volume on radiologists' performance of mammography interpretation, the published literature has been unable to achieve consensus on the volume standards required for optimal mammography accuracy. One potential contributing factor is that studies have used different statistical approaches to ...


Bushel Pierre R   2009
Most clustering techniques do not incorporate phenotypic data. Limited biological interpretation is garnered from the informal process of clustering biological samples and then labeling groups with the phenotypes of the samples. A more formal approach of clustering samples is presented. The method utilizes simulated annealing of the Modkprototypes objective function. ...


Phan Vinhthuy V Department of Computer Science, The University of Memphis, Memphis, TN 38152, USA.   2009
Post hoc assignment of patterns determined by all pairwise comparisons in microarray experiments with multiple treatments has been proven to be useful in assessing treatment effects. We propose the usage of transitive directed acyclic graphs (tDAG) as the representation of these patterns and show that such representation can be useful ...


Zhu Lin   2009
The fuzziness index m has important influence on the clustering result of fuzzy clustering algorithms, and it should not be forced to fix at the usual value m = 2. In view of its distinctive features in applications and its limitation in having m = 2 only, a recent advance ...


Hoad K A   2009
For some diseases, the transmission of infection can cause spatial clustering of disease cases. This clustering has an impact on how one estimates the rate of the spread of the disease and on the design of control strategies. It is, however, difficult to assess such clustering, (local effects on transmission), ...


Ahn Chul   2009
Cluster randomization trials are increasingly popular among healthcare researchers. Intact groups (called 'clusters') of subjects are randomized to receive different interventions and all subjects within a cluster receive the same intervention. In cluster randomized trials, a cluster is the unit of randomization and a subject is the unit of analysis. ...


Eterovic A   2009
Genetic models of sex and caste determination in eusocial stingless bees suggest specific patterns of male, worker and gyne cell distribution in the brood comb. Conflict between queen and laying workers over male parentage and centerperiphery gradients of conditions, such as food and temperature, could also contribute to nonrandom spatial ...


Ziyan Ulas   2009
We propose an integrated registration and clustering algorithm, called "consistency clustering", that automatically constructs a probabilistic whitematter atlas from a set of multisubject diffusion weighted MR images. We formulate the atlas creation as a maximum likelihood problem which the proposed method solves using a generalized Expectation Maximization (EM) framework. Additionally, ...


Yip Kevin Y   2009
Recent studies have suggested that extremely low dimensional projected clusters exist in real datasets. Here, we propose a new algorithm for identifying them. It combines object clustering and dimension selection, and allows the input of domain knowledge in guiding the clustering process. Theoretical and experimental results show that even a ...


Ullrich Johannes   2009
This article extends research on leader procedural fairness as well as the social identity model of leadership effectiveness (SIMOL) by demonstrating that leader prototypicality can act as a substitute for procedural fairness. Although procedural fairness in general and voice in particular have been found to have a robust positive influence ...


Yona Golan   2009
Clustering is a popular technique commonly used to search for groups of similarly expressed genes using mRNA expression data. There are many different clustering algorithms and the application of each one will usually produce different results. Without additional evaluation, it is difficult to determine which solutions are better.In this chapter ...


Godde K   2009
Many authors have speculated on Nubian biological evolution. Because of the contact Nubians had with other peoples, migration and/or invasion (biological diffusion) were originally thought to be the biological mechanism for skeletal changes in Nubians. Later, a new hypothesis was put forth, the in situ hypothesis. The new hypothesis postulated ...


Woodring Jonathan   2009
Timevarying data is usually explored by animation or arrays of static images. Neither is particularly effective for classifying data by different temporal activities. Important temporal trends can be missed due to the lack of ability to find them with current visualization methods. In this paper, we propose a method to ...


Zhou Hui   2009
Clustering is one of the most useful tools for highdimensional analysis, e.g., for microarray data. It becomes challenging in presence of a large number of noise variables, which may mask underlying clustering structures. Therefore, noise removal through variable selection is necessary. One effective way is regularization for simultaneous parameter estimation ...


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