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.

Derivation of the density functional theory from the cluster expansion.

PubMed

Hsu, J Y

2003-09-26

The density functional theory is derived from a cluster expansion by truncating the higher-order correlations in one and only one term in the kinetic energy. The formulation allows self-consistent calculation of the exchange correlation effect without imposing additional assumptions to generalize the local density approximation. The pair correlation is described as a two-body collision of bound-state electrons, and modifies the electron- electron interaction energy as well as the kinetic energy. The theory admits excited states, and has no self-interaction energy.

Cluster expansion modeling and Monte Carlo simulation of alnico 5-7 permanent magnets

NASA Astrophysics Data System (ADS)

Nguyen, Manh Cuong; Zhao, Xin; Wang, Cai-Zhuang; Ho, Kai-Ming

2015-03-01

The concerns about the supply and resource of rare earth (RE) metals have generated a lot of interests in searching for high performance RE-free permanent magnets. Alnico alloys are traditional non-RE permanent magnets and have received much attention recently due their good performance at high temperature. In this paper, we develop an accurate and efficient cluster expansion energy model for alnico 5-7. Monte Carlo simulations using the cluster expansion method are performed to investigate the structure of alnico 5-7 at atomistic and nano scales. The alnico 5-7 master alloy is found to decompose into FeCo-rich and NiAl-rich phases at low temperature. The boundary between these two phases is quite sharp (˜2 nm) for a wide range of temperature. The compositions of the main constituents in these two phases become higher when the temperature gets lower. Both FeCo-rich and NiAl-rich phases are in B2 ordering with Fe and Al on α-site and Ni and Co on β-site. The degree of order of the NiAl-rich phase is much higher than that of the FeCo-rich phase. A small magnetic moment is also observed in NiAl-rich phase but the moment reduces as the temperature is lowered, implying that the magnetic properties of alnico 5-7 could be improved by lowering annealing temperature to diminish the magnetism in NiAl-rich phase. The results from our Monte Carlo simulations are consistent with available experimental results.

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

Virial Expansion Bounds

NASA Astrophysics Data System (ADS)

Tate, Stephen James

2013-10-01

In the 1960s, the technique of using cluster expansion bounds in order to achieve bounds on the virial expansion was developed by Lebowitz and Penrose (J. Math. Phys. 5:841, 1964) and Ruelle (Statistical Mechanics: Rigorous Results. Benjamin, Elmsford, 1969). This technique is generalised to more recent cluster expansion bounds by Poghosyan and Ueltschi (J. Math. Phys. 50:053509, 2009), which are related to the work of Procacci (J. Stat. Phys. 129:171, 2007) and the tree-graph identity, detailed by Brydges (Phénomènes Critiques, Systèmes Aléatoires, Théories de Jauge. Les Houches 1984, pp. 129-183, 1986). The bounds achieved by Lebowitz and Penrose can also be sharpened by doing the actual optimisation and achieving expressions in terms of the Lambert W-function. The different bound from the cluster expansion shows some improvements for bounds on the convergence of the virial expansion in the case of positive potentials, which are allowed to have a hard core.

Membership determination of open clusters based on a spectral clustering method

NASA Astrophysics Data System (ADS)

Gao, Xin-Hua

2018-06-01

We present a spectral clustering (SC) method aimed at segregating reliable members of open clusters in multi-dimensional space. The SC method is a non-parametric clustering technique that performs cluster division using eigenvectors of the similarity matrix; no prior knowledge of the clusters is required. This method is more flexible in dealing with multi-dimensional data compared to other methods of membership determination. We use this method to segregate the cluster members of five open clusters (Hyades, Coma Ber, Pleiades, Praesepe, and NGC 188) in five-dimensional space; fairly clean cluster members are obtained. We find that the SC method can capture a small number of cluster members (weak signal) from a large number of field stars (heavy noise). Based on these cluster members, we compute the mean proper motions and distances for the Hyades, Coma Ber, Pleiades, and Praesepe clusters, and our results are in general quite consistent with the results derived by other authors. The test results indicate that the SC method is highly suitable for segregating cluster members of open clusters based on high-precision multi-dimensional astrometric data such as Gaia data.

Cluster expansion modeling and Monte Carlo simulation of alnico 5–7 permanent magnets

DOE PAGES

Nguyen, Manh Cuong; Zhao, Xin; Wang, Cai -Zhuang; ...

2015-03-05

The concerns about the supply and resource of rare earth (RE) metals have generated a lot of interests in searching for high performance RE-free permanent magnets. Alnico alloys are traditional non-RE permanent magnets and have received much attention recently due their good performance at high temperature. In this paper, we develop an accurate and efficient cluster expansion energy model for alnico 5–7. Monte Carlo simulations using the cluster expansion method are performed to investigate the structure of alnico 5–7 at atomistic and nano scales. The alnico 5–7 master alloy is found to decompose into FeCo-rich and NiAl-rich phases at lowmore » temperature. The boundary between these two phases is quite sharp (~2 nm) for a wide range of temperature. The compositions of the main constituents in these two phases become higher when the temperature gets lower. Both FeCo-rich and NiAl-rich phases are in B2 ordering with Fe and Al on α-site and Ni and Co on β-site. The degree of order of the NiAl-rich phase is much higher than that of the FeCo-rich phase. In addition, a small magnetic moment is also observed in NiAl-rich phase but the moment reduces as the temperature is lowered, implying that the magnetic properties of alnico 5–7 could be improved by lowering annealing temperature to diminish the magnetism in NiAl-rich phase. Furthermore, the results from our Monte Carlo simulations are consistent with available experimental results.« less

Multipole expansion method for supernova neutrino oscillations

DOE PAGES

Duan, Huaiyu; Shalgar, Shashank

2014-10-31

Here, we demonstrate a multipole expansion method to calculate collective neutrino oscillations in supernovae using the neutrino bulb model. We show that it is much more efficient to solve multi-angle neutrino oscillations in multipole basis than in angle basis. The multipole expansion method also provides interesting insights into multi-angle calculations that were accomplished previously in angle basis.

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.

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.

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

NASA Astrophysics Data System (ADS)

Borner, Arnaud

This research discusses the use of advanced kinetic particle methods such as Molecular Dynamics (MD) and direct simulation Monte Carlo (DSMC) to model space propulsion systems such as electrospray thrusters and their supersonic expansions. MD simulations are performed to model an electrospray thruster for the ionic liquid (IL) EMIM--BF4 using coarse-grained (CG) potentials. The model is initially featuring a constant electric field applied in the longitudinal direction. Two coarse-grained potentials are compared, and the effective-force CG (EFCG) potential is found to predict the formation of the Taylor cone, the cone-jet, and other extrusion modes for similar electric fields and mass flow rates observed in experiments of a IL fed capillary-tip-extractor system better than the simple CG potential. Later, one-dimensional and fully transient three-dimensional electric fields, the latter solving Poisson's equation to take into account the electric field due to space charge at each timestep, are computed by coupling the MD model to a Poisson solver. It is found that the inhomogeneous electric field as well as that of the IL space-charge improve agreement between modeling and experiment. The boundary conditions (BCs) are found to have a substantial impact on the potential and electric field, and the tip BC is introduced and compared to the two previous BCs, named plate and needle, showing good improvement by reducing unrealistically high radial electric fields generated in the vicinity of the capillary tip. The influence of the different boundary condition models on charged species currents as a function of the mass flow rate is studied, and it is found that a constant electric field model gives similar agreement to the more rigorous and computationally expensive tip boundary condition at lower flow rates. However, at higher mass flow rates the MD simulations with the constant electric field produces extruded particles with higher Coulomb energy per ion, consistent with

The Expansion of Clusters of Galaxies

DTIC Science & Technology

1961-01-01

investigators, currently we have usable data for a number of other clusters and groups. It is true that, with the exception of the Virgo Cluster , in each case...progress. Throughout this paper , the term "system of galaxies" is used as a synonym of the term " cluster " appearing in the theory of the spatial...mentioned at the beginning of the paper . Whether these outliers should be treated as members of the Coma Cluster or not is a subjective matter and

Locating Structural Centers: A Density-Based Clustering Method for Community Detection

PubMed Central

Liu, Gongshen; Li, Jianhua; Nees, Jan P.

2017-01-01

Uncovering underlying community structures in complex networks has received considerable attention because of its importance in understanding structural attributes and group characteristics of networks. The algorithmic identification of such structures is a significant challenge. Local expanding methods have proven to be efficient and effective in community detection, but most methods are sensitive to initial seeds and built-in parameters. In this paper, we present a local expansion method by density-based clustering, which aims to uncover the intrinsic network communities by locating the structural centers of communities based on a proposed structural centrality. The structural centrality takes into account local density of nodes and relative distance between nodes. The proposed algorithm expands a community from the structural center to the border with a single local search procedure. The local expanding procedure follows a heuristic strategy as allowing it to find complete community structures. Moreover, it can identify different node roles (cores and outliers) in communities by defining a border region. The experiments involve both on real-world and artificial networks, and give a comparison view to evaluate the proposed method. The result of these experiments shows that the proposed method performs more efficiently with a comparative clustering performance than current state of the art methods. PMID:28046030

Review of Maxillary Expansion Appliance Activation Methods: Engineering and Clinical Perspectives

PubMed Central

Romanyk, D. L.; Lagravere, M. O.; Toogood, R. W.; Major, P. W.; Carey, J. P.

2010-01-01

Objective. Review the reported activation methods of maxillary expansion devices for midpalatal suture separation from an engineering perspective and suggest areas of improvement. Materials and Methods. A literature search of Scopus and PubMed was used to determine current expansion methods. A U.S. and Canadian patent database search was also conducted using patent classification and keywords. Any paper presenting a new method of expansion was included. Results. Expansion methods in use, or patented, can be classified as either a screw- or spring-type, magnetic, or shape memory alloy expansion appliance. Conclusions. Each activation method presented unique advantages and disadvantages from both clinical and engineering perspectives. Areas for improvement still remain and are identified in the paper. PMID:20948570

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.

Review of methods for handling confounding by cluster and informative cluster size in clustered data

PubMed Central

Seaman, Shaun; Pavlou, Menelaos; Copas, Andrew

2014-01-01

Clustered data are common in medical research. Typically, one is interested in a regression model for the association between an outcome and covariates. Two complications that can arise when analysing clustered data are informative cluster size (ICS) and confounding by cluster (CBC). ICS and CBC mean that the outcome of a member given its covariates is associated with, respectively, the number of members in the cluster and the covariate values of other members in the cluster. Standard generalised linear mixed models for cluster-specific inference and standard generalised estimating equations for population-average inference assume, in general, the absence of ICS and CBC. Modifications of these approaches have been proposed to account for CBC or ICS. This article is a review of these methods. We express their assumptions in a common format, thus providing greater clarity about the assumptions that methods proposed for handling CBC make about ICS and vice versa, and about when different methods can be used in practice. We report relative efficiencies of methods where available, describe how methods are related, identify a previously unreported equivalence between two key methods, and propose some simple additional methods. Unnecessarily using a method that allows for ICS/CBC has an efficiency cost when ICS and CBC are absent. We review tools for identifying ICS/CBC. A strategy for analysis when CBC and ICS are suspected is demonstrated by examining the association between socio-economic deprivation and preterm neonatal death in Scotland. PMID:25087978

Bulk properties of two-phase disordered media. I. Cluster expansion for the effective dielectric constant of dispersions of penetrable spheres

NASA Astrophysics Data System (ADS)

Torquato, S.

1984-12-01

We derive a cluster expansion for the effective dielectric constant ɛ* of a dispersion of equal-sized spheres distributed with arbitrary degree of impenetrability. The degree of impenetrability is characterized by some parameter λ whose value varies between zero (in the case of randomly centered spheres, i.e., fully penetrable spheres) and unity (in the instance of totally impenetrable spheres). This generalizes the results of Felderhof, Ford, and Cohen who obtain a cluster expansion for ɛ* for the specific case of a dispersion of totally impenetrable spheres, i.e., the instance λ=1. We describe the physical significance of the contributions to the average polarization of the two-phase system which arise from inclusion-overlap effects. Using these results, we obtain a density expansion for ɛ*, which is exact through second order in the number density ρ, and give the physical interpretations of all of the cluster integrals that arise here. The use of a certain family of equilibrium sphere distributions is suggested in order to systematically study the effects of details of the microstructure on ɛ* through second order in ρ. We show, furthermore, that the second-order term can be written as a sum of the contribution from a reference system of totally impenetrable spheres and an excess contribution, which only involves effects due to overlap of pairs of inclusions. We also obtain an expansion for ɛ* which is exact through second order in φ2, where φ2 is the sphere volume fraction. We evaluate, for concreteness, some of the integrals that arise in this study, for arbitrary λ, in the permeable-sphere model and in the penetrable concentric-shell model introduced in this study.

Magnetic cluster expansion model for random and ordered magnetic face-centered cubic Fe-Ni-Cr alloys

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

Lavrentiev, M. Yu., E-mail: Mikhail.Lavrentiev@ukaea.uk; Nguyen-Manh, D.; Dudarev, S. L.

A Magnetic Cluster Expansion model for ternary face-centered cubic Fe-Ni-Cr alloys has been developed, using DFT data spanning binary and ternary alloy configurations. Using this Magnetic Cluster Expansion model Hamiltonian, we perform Monte Carlo simulations and explore magnetic structures of alloys over the entire range of compositions, considering both random and ordered alloy structures. In random alloys, the removal of magnetic collinearity constraint reduces the total magnetic moment but does not affect the predicted range of compositions where the alloys adopt low-temperature ferromagnetic configurations. During alloying of ordered fcc Fe-Ni compounds with Cr, chromium atoms tend to replace nickel rathermore » than iron atoms. Replacement of Ni by Cr in ordered alloys with high iron content increases the Curie temperature of the alloys. This can be explained by strong antiferromagnetic Fe-Cr coupling, similar to that found in bcc Fe-Cr solutions, where the Curie temperature increase, predicted by simulations as a function of Cr concentration, is confirmed by experimental observations. In random alloys, both magnetization and the Curie temperature decrease abruptly with increasing chromium content, in agreement with experiment.« less

Application of the Cluster Expansion to a Mathematical Model of the Long Memory Phenomenon in a Financial Market

NASA Astrophysics Data System (ADS)

Kuroda, Koji; Maskawa, Jun-ichi; Murai, Joshin

2013-08-01

Empirical studies of the high frequency data in stock markets show that the time series of trade signs or signed volumes has a long memory property. In this paper, we present a discrete time stochastic process for polymer model which describes trader's trading strategy, and show that a scale limit of the process converges to superposition of fractional Brownian motions with Hurst exponents and Brownian motion, provided that the index γ of the time scale about the trader's investment strategy coincides with the index δ of the interaction range in the discrete time process. The main tool for the investigation is the method of cluster expansion developed in the mathematical study of statistical mechanics.

First Principles Studies for Lithium Intercalation and Diffusion Behaviors in MoS2 treated with the Compressive Sensing Cluster Expansion

NASA Astrophysics Data System (ADS)

Liu, Chi-Ping; Zhou, Fei; Ozolins, Vidvuds

2014-03-01

Molybdenum disulfide (MoS2) is a good candidate electrode material for high capacity energy storage applications, such as lithium ion batteries and supercapacitors. In this work, we investigate lithium intercalation and diffusion kinetics in MoS2 by using first-principles density-functional theory (DFT) calculations. Two different lithium intercalation sites (1-H and 2-T) in MoS2 are found to be stable for lithium intercalation at different van der Waals' (vdW) gap distances. It is found that both thermodynamic and kinetic properties are highly related to the interlayer vdW gap distance, and that the optimal gap distance leads to effective solid-state diffusion in MoS2. Additionally, through the use of compressive sensing, we build accurate cluster expansion models to study the thermodynamic properties of MoS2 at high lithium content by truncating the higher order effective clusters with significant contributions. The results show that compressive sensing cluster expansion is a rigorous and powerful tool for model construction for advanced electrochemical applications in the future.

A first principles investigation of the oxygen adsorption on Zr(0001) surface using cluster expansions

NASA Astrophysics Data System (ADS)

Samin, Adib J.; Taylor, Christopher D.

2017-11-01

The design of corrosion resistant zircalloys is important for a variety of technological applications ranging from medicine to the nuclear industry. Since corrosion resistance is mainly attributed to the formation of a surface oxide layer, developing a detailed understanding of this process may assist in future corrosion resistance design. In this work, we conduct a systematic multi-scale investigation of the early stages of oxide formation. This was accomplished by first using a database of fully relaxed DFT calculations to build a cluster-expansion description of the potential function. The developed potential was reasonably good at predicting DFT energies as evidenced by the cross-validation score of 4.4 meV/site. The effective cluster expansion parameters were indicative of repulsive adsorbate interactions in the adlayer in agreement with the literature. The potential then allowed for a systematic investigation of the oxygen configurations on the Zr(0001) surface via Monte Carlo simulations. The adsorption energy was recorded as a function of coverage and an increasing trend was observed in agreement with DFT predictions and the repulsive nature of interactions in the adlayer. The convex hull diagram was recorded indicating the most stable configuration to occur around a coverage of 0.6 ML. The adsorption isotherm was also recorded and contrasted for two temperatures relevant for different applications.

Generalized quantum kinetic expansion: Higher-order corrections to multichromophoric Förster theory

NASA Astrophysics Data System (ADS)

Wu, Jianlan; Gong, Zhihao; Tang, Zhoufei

2015-08-01

For a general two-cluster energy transfer network, a new methodology of the generalized quantum kinetic expansion (GQKE) method is developed, which predicts an exact time-convolution equation for the cluster population evolution under the initial condition of the local cluster equilibrium state. The cluster-to-cluster rate kernel is expanded over the inter-cluster couplings. The lowest second-order GQKE rate recovers the multichromophoric Förster theory (MCFT) rate. The higher-order corrections to the MCFT rate are systematically included using the continued fraction resummation form, resulting in the resummed GQKE method. The reliability of the GQKE methodology is verified in two model systems, revealing the relevance of higher-order corrections.

Theoretical modelling on thermal expansion of Al, Ag and Cu nanomaterials

NASA Astrophysics Data System (ADS)

Manu, Mehul; Dubey, Vikash

2018-05-01

A simple theoretical model is developed for the calculating the coefficient of volume thermal expansion (CTE) and volume thermal expansion (VTE) of Al, Ag and Cu nanomaterials by considering the cubo-octahedral structure with the change of temperature and the cluster size. At the room temperature, the coefficient of volume thermal expansion decreases sharply below 20-25 nm and the decrement of the coefficient of volume thermal expansion becomes slower above 20-25 nm. We also saw a variation in the volume thermal expansion with the variation of temperature and cluster size. At a fixed cluster size, the volume thermal expansion increases with an increase of temperature at below the melting temperature and show a linear relation of volume thermal expansion with the temperature. At a constant temperature, the volume thermal expansion decreases rapidly with an increase in cluster size below 20-25 nm and after 20-25 nm the decrement of volume thermal expansion becomes slower with the increase of the size of the cluster. Thermal expansion is due to the anharmonicity of the atom interaction. As the temperature rises the amplitude of crystal lattice vibration increases, but the equilibrium distance shifts as the atom spend more time at distance greater than the original spacing due as the repulsion at short distance greater than the corresponding attraction at farther distance. In considering the cubo- octahedral structure with the cluster order, the model prediction on the CTE and the VTE are in good agreement with the available experimental data which demonstrate the validity of our work.

Magnetic cluster expansion simulation and experimental study of high temperature magnetic properties of Fe-Cr alloys.

PubMed

Lavrentiev, M Yu; Mergia, K; Gjoka, M; Nguyen-Manh, D; Apostolopoulos, G; Dudarev, S L

2012-08-15

We present a combined experimental and computational study of high temperature magnetic properties of Fe-Cr alloys with chromium content up to about 20 at.%. The magnetic cluster expansion method is applied to model the magnetic properties of random Fe-Cr alloys, and in particular the Curie transition temperature, as a function of alloy composition. We find that at low (3-6 at.%) Cr content the Curie temperature increases with the increase of Cr concentration. It is maximum at approximately 6 at.% Cr and then decreases for higher Cr content. The same feature is found in thermo-magnetic measurements performed on model Fe-Cr alloys, where a 5 at.% Cr alloy has a higher Curie temperature than pure Fe. The Curie temperatures of 10 and 15 at.% Cr alloys are found to be lower than the Curie temperature of pure Fe.

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.

Convergence of the Light-Front Coupled-Cluster Method in Scalar Yukawa Theory

NASA Astrophysics Data System (ADS)

Usselman, Austin

We use Fock-state expansions and the Light-Front Coupled-Cluster (LFCC) method to study mass eigenvalue problems in quantum field theory. Specifically, we study convergence of the method in scalar Yukawa theory. In this theory, a single charged particle is surrounded by a cloud of neutral particles. The charged particle can create or annihilate neutral particles, causing the n-particle state to depend on the n + 1 and n - 1-particle state. Fock state expansion leads to an infinite set of coupled equations where truncation is required. The wave functions for the particle states are expanded in a basis of symmetric polynomials and a generalized eigenvalue problem is solved for the mass eigenvalue. The mass eigenvalue problem is solved for multiple values for the coupling strength while the number of particle states and polynomial basis order are increased. Convergence of the mass eigenvalue solutions is then obtained. Three mass ratios between the charged particle and neutral particles were studied. This includes a massive charged particle, equal masses and massive neutral particles. Relative probability between states can also be explored for more detailed understanding of the process of convergence with respect to the number of Fock sectors. The reliance on higher order particle states depended on how large the mass of the charge particle was. The higher the mass of the charged particle, the more the system depended on higher order particle states. The LFCC method solves this same mass eigenvalue problem using an exponential operator. This exponential operator can then be truncated instead to form a finite system of equations that can be solved using a built in system solver provided in most computational environments, such as MatLab and Mathematica. First approximation in the LFCC method allows for only one particle to be created by the new operator and proved to be not powerful enough to match the Fock state expansion. The second order approximation allowed one

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.

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

Hybrid Tracking Algorithm Improvements and Cluster Analysis Methods.

DTIC Science & Technology

1982-02-26

UPGMA ), and Ward’s method. Ling’s papers describe a (k,r) clustering method. Each of these methods have individual characteristics which make them...Reference 7), UPGMA is probably the most frequently used clustering strategy. UPGMA tries to group new points into an existing cluster by using an

Self consistency grouping: a stringent clustering method

PubMed Central

2012-01-01

Background Numerous types of clustering like single linkage and K-means have been widely studied and applied to a variety of scientific problems. However, the existing methods are not readily applicable for the problems that demand high stringency. Methods Our method, self consistency grouping, i.e. SCG, yields clusters whose members are closer in rank to each other than to any member outside the cluster. We do not define a distance metric; we use the best known distance metric and presume that it measures the correct distance. SCG does not impose any restriction on the size or the number of the clusters that it finds. The boundaries of clusters are determined by the inconsistencies in the ranks. In addition to the direct implementation that finds the complete structure of the (sub)clusters we implemented two faster versions. The fastest version is guaranteed to find only the clusters that are not subclusters of any other clusters and the other version yields the same output as the direct implementation but does so more efficiently. Results Our tests have demonstrated that SCG yields very few false positives. This was accomplished by introducing errors in the distance measurement. Clustering of protein domain representatives by structural similarity showed that SCG could recover homologous groups with high precision. Conclusions SCG has potential for finding biological relationships under stringent conditions. PMID:23320864

Generalized moments expansion applied to the two-dimensional S= 1 /2 Heisenberg model

NASA Astrophysics Data System (ADS)

Mancini, Jay D.; Murawski, Robert K.; Fessatidis, Vassilios; Bowen, Samuel P.

2005-12-01

In this work we derive a generalized moments expansion (GMX), to third order, of which the well-established connected moments expansion and the alternate moments expansion are shown to be special cases. We discuss the benefits of the GMX with respect to the avoidance of singularities which are known to plague such moments methods. We then apply the GMX estimates for the ground-state energy for the two-dimensional S=1/2 Heisenberg square lattice and compare these results to those of both spin-wave theory and the linked-cluster expansion.

Series Expansion of Functions with He's Homotopy Perturbation Method

ERIC Educational Resources Information Center

Khattri, Sanjay Kumar

2012-01-01

Finding a series expansion, such as Taylor series, of functions is an important mathematical concept with many applications. Homotopy perturbation method (HPM) is a new, easy to use and effective tool for solving a variety of mathematical problems. In this study, we present how to apply HPM to obtain a series expansion of functions. Consequently,…

Method for assaying clustered DNA damages

DOEpatents

Sutherland, Betsy M.

2004-09-07

Disclosed is a method for detecting and quantifying clustered damages in DNA. In this method, a first aliquot of the DNA to be tested for clustered damages with one or more lesion-specific cleaving reagents under conditions appropriate for cleavage of the DNA to produce single-strand nicks in the DNA at sites of damage lesions. The number average molecular length (Ln) of double stranded DNA is then quantitatively determined for the treated DNA. The number average molecular length (Ln) of double stranded DNA is also quantitatively determined for a second, untreated aliquot of the DNA. The frequency of clustered damages (.PHI..sub.c) in the DNA is then calculated.

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.

Para-hydrogen and helium cluster size distributions in free jet expansions based on Smoluchowski theory with kernel scaling.

PubMed

Kornilov, Oleg; Toennies, J Peter

2015-02-21

The size distribution of para-H2 (pH2) clusters produced in free jet expansions at a source temperature of T0 = 29.5 K and pressures of P0 = 0.9-1.96 bars is reported and analyzed according to a cluster growth model based on the Smoluchowski theory with kernel scaling. Good overall agreement is found between the measured and predicted, Nk = A k(a) e(-bk), shape of the distribution. The fit yields values for A and b for values of a derived from simple collision models. The small remaining deviations between measured abundances and theory imply a (pH2)k magic number cluster of k = 13 as has been observed previously by Raman spectroscopy. The predicted linear dependence of b(-(a+1)) on source gas pressure was verified and used to determine the value of the basic effective agglomeration reaction rate constant. A comparison of the corresponding effective growth cross sections σ11 with results from a similar analysis of He cluster size distributions indicates that the latter are much larger by a factor 6-10. An analysis of the three body recombination rates, the geometric sizes and the fact that the He clusters are liquid independent of their size can explain the larger cross sections found for He.

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.

Mayer-cluster expansion of instanton partition functions and thermodynamic bethe ansatz

NASA Astrophysics Data System (ADS)

Meneghelli, Carlo; Yang, Gang

2014-05-01

In [19] Nekrasov and Shatashvili pointed out that the = 2 instanton partition function in a special limit of the Ω-deformation parameters is characterized by certain thermodynamic Bethe ansatz (TBA) like equations. In this work we present an explicit derivation of this fact as well as generalizations to quiver gauge theories. To do so we combine various techniques like the iterated Mayer expansion, the method of expansion by regions, and the path integral tricks for non-perturbative summation. The TBA equations derived entirely within gauge theory have been proposed to encode the spectrum of a large class of quantum integrable systems. We hope that the derivation presented in this paper elucidates further this completely new point of view on the origin, as well as on the structure, of TBA equations in integrable models.

Modeling laser beam diffraction and propagation by the mode-expansion method.

PubMed

Snyder, James J

2007-08-01

In the mode-expansion method for modeling propagation of a diffracted beam, the beam at the aperture can be expanded as a weighted set of orthogonal modes. The parameters of the expansion modes are chosen to maximize the weighting coefficient of the lowest-order mode. As the beam propagates, its field distribution can be reconstructed from the set of weighting coefficients and the Gouy phase of the lowest-order mode. We have developed a simple procedure to implement the mode-expansion method for propagation through an arbitrary ABCD matrix, and we have demonstrated that it is accurate in comparison with direct calculations of diffraction integrals and much faster.

A new method to prepare colloids of size-controlled clusters from a matrix assembly cluster source

NASA Astrophysics Data System (ADS)

Cai, Rongsheng; Jian, Nan; Murphy, Shane; Bauer, Karl; Palmer, Richard E.

2017-05-01

A new method for the production of colloidal suspensions of physically deposited clusters is demonstrated. A cluster source has been used to deposit size-controlled clusters onto water-soluble polymer films, which are then dissolved to produce colloidal suspensions of clusters encapsulated with polymer molecules. This process has been demonstrated using different cluster materials (Au and Ag) and polymers (polyvinylpyrrolidone, polyvinyl alcohol, and polyethylene glycol). Scanning transmission electron microscopy of the clusters before and after colloidal dispersion confirms that the polymers act as stabilizing agents. We propose that this method is suitable for the production of biocompatible colloids of ultraprecise clusters.

Comparing the performance of biomedical clustering methods.

PubMed

Wiwie, Christian; Baumbach, Jan; Röttger, Richard

2015-11-01

Identifying groups of similar objects is a popular first step in biomedical data analysis, but it is error-prone and impossible to perform manually. Many computational methods have been developed to tackle this problem. Here we assessed 13 well-known methods using 24 data sets ranging from gene expression to protein domains. Performance was judged on the basis of 13 common cluster validity indices. We developed a clustering analysis platform, ClustEval (http://clusteval.mpi-inf.mpg.de), to promote streamlined evaluation, comparison and reproducibility of clustering results in the future. This allowed us to objectively evaluate the performance of all tools on all data sets with up to 1,000 different parameter sets each, resulting in a total of more than 4 million calculated cluster validity indices. We observed that there was no universal best performer, but on the basis of this wide-ranging comparison we were able to develop a short guideline for biomedical clustering tasks. ClustEval allows biomedical researchers to pick the appropriate tool for their data type and allows method developers to compare their tool to the state of the art.

Controlled Expansion of a Strong-Field Iron Nitride Cluster: Multi-Site Ligand Substitution as a Strategy for Activating Interstitial Nitride Nucleophilicity.

PubMed

Drance, Myles J; Mokhtarzadeh, Charles C; Melaimi, Mohand; Agnew, Douglas W; Moore, Curtis E; Rheingold, Arnold L; Figueroa, Joshua S

2018-05-02

Multimetallic clusters have long been investigated as molecular surrogates for reactive sites on metal surfaces. In the case of the μ 4 -nitrido cluster [Fe 4 (μ 4 -N)(CO) 12 ] - , this analogy is limited owing to the electron-withdrawing effect of carbonyl ligands on the iron nitride core. Described here is the synthesis and reactivity of [Fe 4 (μ 4 -N)(CO) 8 (CNAr Mes2 ) 4 ] - , an electron-rich analogue of [Fe 4 (μ 4 -N)(CO) 12 ] - , where the interstitial nitride displays significant nucleophilicity. This characteristic enables rational expansion with main-group and transition-metal centers to yield unsaturated sites. The resulting clusters display surface-like reactivity through coordination-sphere-dependent atom rearrangement and metal-metal cooperativity. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Thermal expansion method for lining tantalum alloy tubing with tungsten

NASA Technical Reports Server (NTRS)

Watson, G. K.; Whittenberger, J. D.; Mattson, W. F.

1973-01-01

A differential-thermal expansion method was developed to line T-111 (tantalum - 8 percent tungsten - 2 percent hafnium) tubing with a tungsten diffusion barrier as part of a fuel element fabrication study for a space power nuclear reactor concept. This method uses a steel mandrel, which has a larger thermal expansion than T-111, to force the tungsten against the inside of the T-111 tube. Variables investigated include lining temperature, initial assembly gas size, and tube length. Linear integrity increased with increasing lining temperature and decreasing gap size. The method should have more general applicability where cylinders must be lined with a thin layer of a second material.

Identification of natural killer cell receptor clusters in the platypus genome reveals an expansion of C-type lectin genes.

PubMed

Wong, Emily S W; Sanderson, Claire E; Deakin, Janine E; Whittington, Camilla M; Papenfuss, Anthony T; Belov, Katherine

2009-08-01

Natural killer (NK) cell receptors belong to two unrelated, but functionally analogous gene families: the immunoglobulin superfamily, situated in the leukocyte receptor complex (LRC) and the C-type lectin superfamily, located in the natural killer complex (NKC). Here, we describe the largest NK receptor gene expansion seen to date. We identified 213 putative C-type lectin NK receptor homologs in the genome of the platypus. Many have arisen as the result of a lineage-specific expansion. Orthologs of OLR1, CD69, KLRE, CLEC12B, and CLEC16p genes were also identified. The NKC is split into at least two regions of the genome: 34 genes map to chromosome 7, two map to a small autosome, and the remainder are unanchored in the current genome assembly. No NK receptor genes from the LRC were identified. The massive C-type lectin expansion and lack of Ig-domain-containing NK receptors represents the most extreme polarization of NK receptors found to date. We have used this new data from platypus to trace the possible evolutionary history of the NK receptor clusters.

Differential cosmic expansion and the Hubble flow anisotropy

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

Bolejko, Krzysztof; Nazer, M. Ahsan; Wiltshire, David L., E-mail: bolejko@physics.usyd.edu.au, E-mail: ahsan.nazer@canterbury.ac.nz, E-mail: david.wiltshire@canterbury.ac.nz

2016-06-01

The Universe on scales 10–100 h {sup −1}Mpc is dominated by a cosmic web of voids, filaments, sheets and knots of galaxy clusters. These structures participate differently in the global expansion of the Universe: from non-expanding clusters to the above average expansion rate of voids. In this paper we characterize Hubble expansion anisotropies in the COMPOSITE sample of 4534 galaxies and clusters. We concentrate on the dipole and quadrupole in the rest frame of the Local Group. These both have statistically significant amplitudes. These anisotropies, and their redshift dependence, cannot be explained solely by a boost of the Local Groupmore » in the Friedmann-Lemaitre-Robertson-Walker (FLRW) model which expands isotropically in the rest frame of the cosmic microwave background (CMB) radiation. We simulate the local expansion of the Universe with inhomogeneous Szekeres solutions, which match the standard FLRW model on ∼> 100 h {sup −1}Mpc scales but exhibit nonkinematic relativistic differential expansion on small scales. We restrict models to be consistent with observed CMB temperature anisotropies, while simultaneously fitting the redshift variation of the Hubble expansion dipole. We include features to account for both the Local Void and the 'Great Attractor'. While this naturally accounts for the Hubble expansion and CMB dipoles, the simulated quadrupoles are smaller than observed. Further refinement to incorporate additional structures may improve this. This would enable a test of the hypothesis that some large angle CMB anomalies result from failing to treat the relativistic differential expansion of the background geometry; a natural feature of solutions to Einstein's equations not included in the current standard model of cosmology.« less

``Dressing'' lines and vertices in calculations of matrix elements with the coupled-cluster method and determination of Cs atomic properties

NASA Astrophysics Data System (ADS)

Derevianko, Andrei; Porsev, Sergey G.

2005-03-01

We consider evaluation of matrix elements with the coupled-cluster method. Such calculations formally involve infinite number of terms and we devise a method of partial summation (dressing) of the resulting series. Our formalism is built upon an expansion of the product C†C of cluster amplitudes C into a sum of n -body insertions. We consider two types of insertions: particle (hole) line insertion and two-particle (two-hole) random-phase-approximation-like insertion. We demonstrate how to “dress” these insertions and formulate iterative equations. We illustrate the dressing equations in the case when the cluster operator is truncated at single and double excitations. Using univalent systems as an example, we upgrade coupled-cluster diagrams for matrix elements with the dressed insertions and highlight a relation to pertinent fourth-order diagrams. We illustrate our formalism with relativistic calculations of the hyperfine constant A(6s) and the 6s1/2-6p1/2 electric-dipole transition amplitude for the Cs atom. Finally, we augment the truncated coupled-cluster calculations with otherwise omitted fourth order diagrams. The resulting analysis for Cs is complete through the fourth order of many-body perturbation theory and reveals an important role of triple and disconnected quadruple excitations.

Clustering PPI data by combining FA and SHC method.

PubMed

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

2015-01-01

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

Clustering PPI data by combining FA and SHC method

PubMed Central

2015-01-01

Clustering is one of main methods to identify functional modules from protein-protein interaction (PPI) data. Nevertheless traditional clustering methods may not be effective for clustering PPI data. In this paper, we proposed a novel method for clustering PPI data by combining firefly algorithm (FA) and synchronization-based hierarchical clustering (SHC) algorithm. Firstly, the PPI data are preprocessed via spectral clustering (SC) which transforms the high-dimensional similarity matrix into a low dimension matrix. Then the SHC algorithm is used to perform clustering. In SHC algorithm, hierarchical clustering is achieved by enlarging the neighborhood radius of synchronized objects continuously, while the hierarchical search is very difficult to find the optimal neighborhood radius of synchronization and the efficiency is not high. So we adopt the firefly algorithm to determine the optimal threshold of the neighborhood radius of synchronization automatically. The proposed algorithm is tested on the MIPS PPI dataset. The results show that our proposed algorithm is better than the traditional algorithms in precision, recall and f-measure value. PMID:25707632

Object-Oriented Image Clustering Method Using UAS Photogrammetric Imagery

NASA Astrophysics Data System (ADS)

Lin, Y.; Larson, A.; Schultz-Fellenz, E. S.; Sussman, A. J.; Swanson, E.; Coppersmith, R.

2016-12-01

Unmanned Aerial Systems (UAS) have been used widely as an imaging modality to obtain remotely sensed multi-band surface imagery, and are growing in popularity due to their efficiency, ease of use, and affordability. Los Alamos National Laboratory (LANL) has employed the use of UAS for geologic site characterization and change detection studies at a variety of field sites. The deployed UAS equipped with a standard visible band camera to collect imagery datasets. Based on the imagery collected, we use deep sparse algorithmic processing to detect and discriminate subtle topographic features created or impacted by subsurface activities. In this work, we develop an object-oriented remote sensing imagery clustering method for land cover classification. To improve the clustering and segmentation accuracy, instead of using conventional pixel-based clustering methods, we integrate the spatial information from neighboring regions to create super-pixels to avoid salt-and-pepper noise and subsequent over-segmentation. To further improve robustness of our clustering method, we also incorporate a custom digital elevation model (DEM) dataset generated using a structure-from-motion (SfM) algorithm together with the red, green, and blue (RGB) band data for clustering. In particular, we first employ an agglomerative clustering to create an initial segmentation map, from where every object is treated as a single (new) pixel. Based on the new pixels obtained, we generate new features to implement another level of clustering. We employ our clustering method to the RGB+DEM datasets collected at the field site. Through binary clustering and multi-object clustering tests, we verify that our method can accurately separate vegetation from non-vegetation regions, and are also able to differentiate object features on the surface.

Nearest neighbor-density-based clustering methods for large hyperspectral images

NASA Astrophysics Data System (ADS)

Cariou, Claude; Chehdi, Kacem

2017-10-01

We address the problem of hyperspectral image (HSI) pixel partitioning using nearest neighbor - density-based (NN-DB) clustering methods. NN-DB methods are able to cluster objects without specifying the number of clusters to be found. Within the NN-DB approach, we focus on deterministic methods, e.g. ModeSeek, knnClust, and GWENN (standing for Graph WatershEd using Nearest Neighbors). These methods only require the availability of a k-nearest neighbor (kNN) graph based on a given distance metric. Recently, a new DB clustering method, called Density Peak Clustering (DPC), has received much attention, and kNN versions of it have quickly followed and showed their efficiency. However, NN-DB methods still suffer from the difficulty of obtaining the kNN graph due to the quadratic complexity with respect to the number of pixels. This is why GWENN was embedded into a multiresolution (MR) scheme to bypass the computation of the full kNN graph over the image pixels. In this communication, we propose to extent the MR-GWENN scheme on three aspects. Firstly, similarly to knnClust, the original labeling rule of GWENN is modified to account for local density values, in addition to the labels of previously processed objects. Secondly, we set up a modified NN search procedure within the MR scheme, in order to stabilize of the number of clusters found from the coarsest to the finest spatial resolution. Finally, we show that these extensions can be easily adapted to the three other NN-DB methods (ModeSeek, knnClust, knnDPC) for pixel clustering in large HSIs. Experiments are conducted to compare the four NN-DB methods for pixel clustering in HSIs. We show that NN-DB methods can outperform a classical clustering method such as fuzzy c-means (FCM), in terms of classification accuracy, relevance of found clusters, and clustering speed. Finally, we demonstrate the feasibility and evaluate the performances of NN-DB methods on a very large image acquired by our AISA Eagle hyperspectral

CORM: An R Package Implementing the Clustering of Regression Models Method for Gene Clustering

PubMed Central

Shi, Jiejun; Qin, Li-Xuan

2014-01-01

We report a new R package implementing the clustering of regression models (CORM) method for clustering genes using gene expression data and provide data examples illustrating each clustering function in the package. The CORM package is freely available at CRAN from http://cran.r-project.org. PMID:25452684

Para-hydrogen and helium cluster size distributions in free jet expansions based on Smoluchowski theory with kernel scaling

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

Kornilov, Oleg; Toennies, J. Peter

The size distribution of para-H{sub 2} (pH{sub 2}) clusters produced in free jet expansions at a source temperature of T{sub 0} = 29.5 K and pressures of P{sub 0} = 0.9–1.96 bars is reported and analyzed according to a cluster growth model based on the Smoluchowski theory with kernel scaling. Good overall agreement is found between the measured and predicted, N{sub k} = A k{sup a} e{sup −bk}, shape of the distribution. The fit yields values for A and b for values of a derived from simple collision models. The small remaining deviations between measured abundances and theory imply a (pH{submore » 2}){sub k} magic number cluster of k = 13 as has been observed previously by Raman spectroscopy. The predicted linear dependence of b{sup −(a+1)} on source gas pressure was verified and used to determine the value of the basic effective agglomeration reaction rate constant. A comparison of the corresponding effective growth cross sections σ{sub 11} with results from a similar analysis of He cluster size distributions indicates that the latter are much larger by a factor 6-10. An analysis of the three body recombination rates, the geometric sizes and the fact that the He clusters are liquid independent of their size can explain the larger cross sections found for He.« less

The expansion of neighborhood and pattern formation on spatial prisoner's dilemma

NASA Astrophysics Data System (ADS)

Qian, Xiaolan; Xu, Fangqian; Yang, Junzhong; Kurths, Jürgen

2015-04-01

The prisoner's dilemma (PD), in which players can either cooperate or defect, is considered a paradigm for studying the evolution of cooperation in spatially structured populations. There the compact cooperator cluster is identified as a characteristic pattern and the probability of forming such pattern in turn depends on the features of the networks. In this paper, we investigate the influence of expansion of neighborhood on pattern formation by taking a weak PD game with one free parameter T, the temptation to defect. Two different expansion methods of neighborhood are considered. One is based on a square lattice and expanses along four directions generating networks with degree increasing with K = 4 m . The other is based on a lattice with Moore neighborhood and expanses along eight directions, generating networks with degree of K = 8 m . Individuals are placed on the nodes of the networks, interact with their neighbors and learn from the better one. We find that cooperator can survive for a broad degree 4 ≤ K ≤ 70 by taking a loose type of cooperator clusters. The former simple corresponding relationship between macroscopic patterns and the microscopic PD interactions is broken. Under a condition that is unfavorable for cooperators such as large T and K, systems prefer to evolve to a loose type of cooperator clusters to support cooperation. However, compared to the well-known compact pattern, it is a suboptimal strategy because it cannot help cooperators dominating the population and always corresponding to a low cooperation level.

Developing appropriate methods for cost-effectiveness analysis of cluster randomized trials.

PubMed

Gomes, Manuel; Ng, Edmond S-W; Grieve, Richard; Nixon, Richard; Carpenter, James; Thompson, Simon G

2012-01-01

Cost-effectiveness analyses (CEAs) may use data from cluster randomized trials (CRTs), where the unit of randomization is the cluster, not the individual. However, most studies use analytical methods that ignore clustering. This article compares alternative statistical methods for accommodating clustering in CEAs of CRTs. Our simulation study compared the performance of statistical methods for CEAs of CRTs with 2 treatment arms. The study considered a method that ignored clustering--seemingly unrelated regression (SUR) without a robust standard error (SE)--and 4 methods that recognized clustering--SUR and generalized estimating equations (GEEs), both with robust SE, a "2-stage" nonparametric bootstrap (TSB) with shrinkage correction, and a multilevel model (MLM). The base case assumed CRTs with moderate numbers of balanced clusters (20 per arm) and normally distributed costs. Other scenarios included CRTs with few clusters, imbalanced cluster sizes, and skewed costs. Performance was reported as bias, root mean squared error (rMSE), and confidence interval (CI) coverage for estimating incremental net benefits (INBs). We also compared the methods in a case study. Each method reported low levels of bias. Without the robust SE, SUR gave poor CI coverage (base case: 0.89 v. nominal level: 0.95). The MLM and TSB performed well in each scenario (CI coverage, 0.92-0.95). With few clusters, the GEE and SUR (with robust SE) had coverage below 0.90. In the case study, the mean INBs were similar across all methods, but ignoring clustering underestimated statistical uncertainty and the value of further research. MLMs and the TSB are appropriate analytical methods for CEAs of CRTs with the characteristics described. SUR and GEE are not recommended for studies with few clusters.

Testing prediction methods: Earthquake clustering versus the Poisson model

USGS Publications Warehouse

Michael, A.J.

1997-01-01

Testing earthquake prediction methods requires statistical techniques that compare observed success to random chance. One technique is to produce simulated earthquake catalogs and measure the relative success of predicting real and simulated earthquakes. The accuracy of these tests depends on the validity of the statistical model used to simulate the earthquakes. This study tests the effect of clustering in the statistical earthquake model on the results. Three simulation models were used to produce significance levels for a VLF earthquake prediction method. As the degree of simulated clustering increases, the statistical significance drops. Hence, the use of a seismicity model with insufficient clustering can lead to overly optimistic results. A successful method must pass the statistical tests with a model that fully replicates the observed clustering. However, a method can be rejected based on tests with a model that contains insufficient clustering. U.S. copyright. Published in 1997 by the American Geophysical Union.

The use of many-body expansions and geometry optimizations in fragment-based methods.

PubMed

Fedorov, Dmitri G; Asada, Naoya; Nakanishi, Isao; Kitaura, Kazuo

2014-09-16

Conspectus Chemists routinely work with complex molecular systems: solutions, biochemical molecules, and amorphous and composite materials provide some typical examples. The questions one often asks are what are the driving forces for a chemical phenomenon? How reasonable are our views of chemical systems in terms of subunits, such as functional groups and individual molecules? How can one quantify the difference in physicochemical properties of functional units found in a different chemical environment? Are various effects on functional units in molecular systems additive? Can they be represented by pairwise potentials? Are there effects that cannot be represented in a simple picture of pairwise interactions? How can we obtain quantitative values for these effects? Many of these questions can be formulated in the language of many-body effects. They quantify the properties of subunits (fragments), referred to as one-body properties, pairwise interactions (two-body properties), couplings of two-body interactions described by three-body properties, and so on. By introducing the notion of fragments in the framework of quantum chemistry, one obtains two immense benefits: (a) chemists can finally relate to quantum chemistry, which now speaks their language, by discussing chemically interesting subunits and their interactions and (b) calculations become much faster due to a reduced computational scaling. For instance, the somewhat academic sounding question of the importance of three-body effects in water clusters is actually another way of asking how two hydrogen bonds affect each other, when they involve three water molecules. One aspect of this is the many-body charge transfer (CT), because the charge transfers in the two hydrogen bonds are coupled to each other (not independent). In this work, we provide a generalized view on the use of many-body expansions in fragment-based methods, focusing on the general aspects of the property expansion and a contraction of a

A double expansion method for the frequency response of finite-length beams with periodic parameters

NASA Astrophysics Data System (ADS)

Ying, Z. G.; Ni, Y. Q.

2017-03-01

A double expansion method for the frequency response of finite-length beams with periodic distribution parameters is proposed. The vibration response of the beam with spatial periodic parameters under harmonic excitations is studied. The frequency response of the periodic beam is the function of parametric period and then can be expressed by the series with the product of periodic and non-periodic functions. The procedure of the double expansion method includes the following two main steps: first, the frequency response function and periodic parameters are expanded by using identical periodic functions based on the extension of the Floquet-Bloch theorem, and the period-parametric differential equation for the frequency response is converted into a series of linear differential equations with constant coefficients; second, the solutions to the linear differential equations are expanded by using modal functions which satisfy the boundary conditions, and the linear differential equations are converted into algebraic equations according to the Galerkin method. The expansion coefficients are obtained by solving the algebraic equations and then the frequency response function is finally determined. The proposed double expansion method can uncouple the effects of the periodic expansion and modal expansion so that the expansion terms are determined respectively. The modal number considered in the second expansion can be reduced remarkably in comparison with the direct expansion method. The proposed double expansion method can be extended and applied to the other structures with periodic distribution parameters for dynamics analysis. Numerical results on the frequency response of the finite-length periodic beam with various parametric wave numbers and wave amplitude ratios are given to illustrate the effective application of the proposed method and the new frequency response characteristics, including the parameter-excited modal resonance, doubling-peak frequency response

Thermodynamic Behavior of Nano-sized Gold Clusters on the (001) Surface

NASA Technical Reports Server (NTRS)

Paik, Sun M.; Yoo, Sung M.; Namkung, Min; Wincheski, Russell A.; Bushnell, Dennis M. (Technical Monitor)

2001-01-01

We have studied thermal expansion of the surface layers of the hexagonally reconstructed Au (001) surface using a classical Molecular Dynamics (MD) simulation technique with an Embedded Atomic Method (EAM) type many-body potential. We find that the top-most hexagonal layer contracts as temperature increases, whereas the second layer expands or contracts depending on the system size. The magnitude of expansion coefficient of the top layer is much larger than that of the other layers. The calculated thermal expansion coefficients of the top-most layer are about -4.93 x 10(exp -5)Angstroms/Kelvin for the (262 x 227)Angstrom cluster and -3.05 x 10(exp -5)Angstroms/Kelvin for (101 x 87)Angstrom cluster. The Fast Fourier Transform (FFT) image of the atomic density shows that there exists a rotated domain of the top-most hexagonal cluster with rotation angle close to 1 degree at temperature T less than 1000Kelvin. As the temperature increases this domain undergoes a surface orientational phase transition. These predictions are in good agreement with previous phenomenological theories and experimental studies.

Combining DFT, Cluster Expansions, and KMC to Model Point Defects in Alloys

NASA Astrophysics Data System (ADS)

Modine, N. A.; Wright, A. F.; Lee, S. R.; Foiles, S. M.; Battaile, C. C.; Thomas, J. C.; van der Ven, A.

In an alloy, defect energies are sensitive to the occupations of nearby atomic sites, which leads to a distribution of defect properties. When radiation-induced defects diffuse from their initially non-equilibrium locations, this distribution becomes time-dependent. The defects can become trapped in energetically favorable regions of the alloy leading to a diffusion rate that slows dramatically with time. Density Functional Theory (DFT) allows the accurate determination of ground state and transition state energies for a defect in a particular alloy environment but requires thousands of processing hours for each such calculation. Kinetic Monte-Carlo (KMC) can be used to model defect diffusion and the changing distribution of defect properties but requires energy evaluations for millions of local environments. We have used the Cluster Expansion (CE) formalism to ``glue'' together these seemingly incompatible methods. The occupation of each alloy site is represented by an Ising-like variable, and products of these variables are used to expand quantities of interest. Once a CE is fit to a training set of DFT energies, it allows very rapid evaluation of the energy for an arbitrary configuration, while maintaining the accuracy of the underlying DFT calculations. These energy evaluations are then used to drive our KMC simulations. We will demonstrate the application of our DFT/MC/KMC approach to model thermal and carrier-induced diffusion of intrinsic point defects in III-V alloys. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under Contract DE.

Clustering of longitudinal data by using an extended baseline: A new method for treatment efficacy clustering in longitudinal data.

PubMed

Schramm, Catherine; Vial, Céline; Bachoud-Lévi, Anne-Catherine; Katsahian, Sandrine

2018-01-01

Heterogeneity in treatment efficacy is a major concern in clinical trials. Clustering may help to identify the treatment responders and the non-responders. In the context of longitudinal cluster analyses, sample size and variability of the times of measurements are the main issues with the current methods. Here, we propose a new two-step method for the Clustering of Longitudinal data by using an Extended Baseline. The first step relies on a piecewise linear mixed model for repeated measurements with a treatment-time interaction. The second step clusters the random predictions and considers several parametric (model-based) and non-parametric (partitioning, ascendant hierarchical clustering) algorithms. A simulation study compares all options of the clustering of longitudinal data by using an extended baseline method with the latent-class mixed model. The clustering of longitudinal data by using an extended baseline method with the two model-based algorithms was the more robust model. The clustering of longitudinal data by using an extended baseline method with all the non-parametric algorithms failed when there were unequal variances of treatment effect between clusters or when the subgroups had unbalanced sample sizes. The latent-class mixed model failed when the between-patients slope variability is high. Two real data sets on neurodegenerative disease and on obesity illustrate the clustering of longitudinal data by using an extended baseline method and show how clustering may help to identify the marker(s) of the treatment response. The application of the clustering of longitudinal data by using an extended baseline method in exploratory analysis as the first stage before setting up stratified designs can provide a better estimation of treatment effect in future clinical trials.

A Web service substitution method based on service cluster nets

NASA Astrophysics Data System (ADS)

Du, YuYue; Gai, JunJing; Zhou, MengChu

2017-11-01

Service substitution is an important research topic in the fields of Web services and service-oriented computing. This work presents a novel method to analyse and substitute Web services. A new concept, called a Service Cluster Net Unit, is proposed based on Web service clusters. A service cluster is converted into a Service Cluster Net Unit. Then it is used to analyse whether the services in the cluster can satisfy some service requests. Meanwhile, the substitution methods of an atomic service and a composite service are proposed. The correctness of the proposed method is proved, and the effectiveness is shown and compared with the state-of-the-art method via an experiment. It can be readily applied to e-commerce service substitution to meet the business automation needs.

A general moment expansion method for stochastic kinetic models

NASA Astrophysics Data System (ADS)

Ale, Angelique; Kirk, Paul; Stumpf, Michael P. H.

2013-05-01

Moment approximation methods are gaining increasing attention for their use in the approximation of the stochastic kinetics of chemical reaction systems. In this paper we derive a general moment expansion method for any type of propensities and which allows expansion up to any number of moments. For some chemical reaction systems, more than two moments are necessary to describe the dynamic properties of the system, which the linear noise approximation is unable to provide. Moreover, also for systems for which the mean does not have a strong dependence on higher order moments, moment approximation methods give information about higher order moments of the underlying probability distribution. We demonstrate the method using a dimerisation reaction, Michaelis-Menten kinetics and a model of an oscillating p53 system. We show that for the dimerisation reaction and Michaelis-Menten enzyme kinetics system higher order moments have limited influence on the estimation of the mean, while for the p53 system, the solution for the mean can require several moments to converge to the average obtained from many stochastic simulations. We also find that agreement between lower order moments does not guarantee that higher moments will agree. Compared to stochastic simulations, our approach is numerically highly efficient at capturing the behaviour of stochastic systems in terms of the average and higher moments, and we provide expressions for the computational cost for different system sizes and orders of approximation. We show how the moment expansion method can be employed to efficiently quantify parameter sensitivity. Finally we investigate the effects of using too few moments on parameter estimation, and provide guidance on how to estimate if the distribution can be accurately approximated using only a few moments.

Clustering Methods with Qualitative Data: A Mixed Methods Approach for Prevention Research with Small Samples

PubMed Central

Henry, David; Dymnicki, Allison B.; Mohatt, Nathaniel; Allen, James; Kelly, James G.

2016-01-01

Qualitative methods potentially add depth to prevention research, but can produce large amounts of complex data even with small samples. Studies conducted with culturally distinct samples often produce voluminous qualitative data, but may lack sufficient sample sizes for sophisticated quantitative analysis. Currently lacking in mixed methods research are methods allowing for more fully integrating qualitative and quantitative analysis techniques. Cluster analysis can be applied to coded qualitative data to clarify the findings of prevention studies by aiding efforts to reveal such things as the motives of participants for their actions and the reasons behind counterintuitive findings. By clustering groups of participants with similar profiles of codes in a quantitative analysis, cluster analysis can serve as a key component in mixed methods research. This article reports two studies. In the first study, we conduct simulations to test the accuracy of cluster assignment using three different clustering methods with binary data as produced when coding qualitative interviews. Results indicated that hierarchical clustering, K-Means clustering, and latent class analysis produced similar levels of accuracy with binary data, and that the accuracy of these methods did not decrease with samples as small as 50. Whereas the first study explores the feasibility of using common clustering methods with binary data, the second study provides a “real-world” example using data from a qualitative study of community leadership connected with a drug abuse prevention project. We discuss the implications of this approach for conducting prevention research, especially with small samples and culturally distinct communities. PMID:25946969

Clustering Methods with Qualitative Data: a Mixed-Methods Approach for Prevention Research with Small Samples.

PubMed

Henry, David; Dymnicki, Allison B; Mohatt, Nathaniel; Allen, James; Kelly, James G

2015-10-01

Qualitative methods potentially add depth to prevention research but can produce large amounts of complex data even with small samples. Studies conducted with culturally distinct samples often produce voluminous qualitative data but may lack sufficient sample sizes for sophisticated quantitative analysis. Currently lacking in mixed-methods research are methods allowing for more fully integrating qualitative and quantitative analysis techniques. Cluster analysis can be applied to coded qualitative data to clarify the findings of prevention studies by aiding efforts to reveal such things as the motives of participants for their actions and the reasons behind counterintuitive findings. By clustering groups of participants with similar profiles of codes in a quantitative analysis, cluster analysis can serve as a key component in mixed-methods research. This article reports two studies. In the first study, we conduct simulations to test the accuracy of cluster assignment using three different clustering methods with binary data as produced when coding qualitative interviews. Results indicated that hierarchical clustering, K-means clustering, and latent class analysis produced similar levels of accuracy with binary data and that the accuracy of these methods did not decrease with samples as small as 50. Whereas the first study explores the feasibility of using common clustering methods with binary data, the second study provides a "real-world" example using data from a qualitative study of community leadership connected with a drug abuse prevention project. We discuss the implications of this approach for conducting prevention research, especially with small samples and culturally distinct communities.

Developing Appropriate Methods for Cost-Effectiveness Analysis of Cluster Randomized Trials

PubMed Central

Gomes, Manuel; Ng, Edmond S.-W.; Nixon, Richard; Carpenter, James; Thompson, Simon G.

2012-01-01

Aim. Cost-effectiveness analyses (CEAs) may use data from cluster randomized trials (CRTs), where the unit of randomization is the cluster, not the individual. However, most studies use analytical methods that ignore clustering. This article compares alternative statistical methods for accommodating clustering in CEAs of CRTs. Methods. Our simulation study compared the performance of statistical methods for CEAs of CRTs with 2 treatment arms. The study considered a method that ignored clustering—seemingly unrelated regression (SUR) without a robust standard error (SE)—and 4 methods that recognized clustering—SUR and generalized estimating equations (GEEs), both with robust SE, a “2-stage” nonparametric bootstrap (TSB) with shrinkage correction, and a multilevel model (MLM). The base case assumed CRTs with moderate numbers of balanced clusters (20 per arm) and normally distributed costs. Other scenarios included CRTs with few clusters, imbalanced cluster sizes, and skewed costs. Performance was reported as bias, root mean squared error (rMSE), and confidence interval (CI) coverage for estimating incremental net benefits (INBs). We also compared the methods in a case study. Results. Each method reported low levels of bias. Without the robust SE, SUR gave poor CI coverage (base case: 0.89 v. nominal level: 0.95). The MLM and TSB performed well in each scenario (CI coverage, 0.92–0.95). With few clusters, the GEE and SUR (with robust SE) had coverage below 0.90. In the case study, the mean INBs were similar across all methods, but ignoring clustering underestimated statistical uncertainty and the value of further research. Conclusions. MLMs and the TSB are appropriate analytical methods for CEAs of CRTs with the characteristics described. SUR and GEE are not recommended for studies with few clusters. PMID:22016450

Visual cluster analysis and pattern recognition methods

DOEpatents

Osbourn, Gordon Cecil; Martinez, Rubel Francisco

2001-01-01

A method of clustering using a novel template to define a region of influence. Using neighboring approximation methods, computation times can be significantly reduced. The template and method are applicable and improve pattern recognition techniques.

Brome isotope selective control of CF3Br molecule clustering by IR laser radiation in gas-dynamic expansion of CF3Br - Ar mixture

NASA Astrophysics Data System (ADS)

Apatin, V. M.; Lokhman, V. N.; Makarov, G. N.; Ogurok, N.-D. D.; Ryabov, E. A.

2018-02-01

We report the results of research on the experimental control of CF3Br molecule clustering under gas-dynamic expansion of the CF3Br - Ar mixture at a nozzle exit by using IR laser radiation. A cw CO2 laser is used for exciting molecules and clusters in the beam and a time-of-flight mass-spectrometer with laser UV ionisation of particles for their detection. The parameters of the gas above the nozzle are determined (compositions and pressure) at which intensive molecule clustering occurs. It is found that in the case of the CF3Br gas without carrier when the pressure P0 above the nozzle does not exceed 4 atm, molecular clusters actually are not generated in the beam. If the gas mixture of CF3Br with argon is used at a pressure ratio 1 : N, where N >= 3, and the total pressure above the nozzle is P0 >= 2 atm, then there occurs molecule clustering. We study the dependences of the efficiency of suppressing the molecule clustering on parameters of the exciting pulse, gas parameters above the nozzle, and on a distance of the molecule irradiation zone from the nozzle exit section. It is shown that in the case of resonant vibrational excitation of gas-dynamically cooled CF3Br molecules at the nozzle exit one can realise isotope-selective suppression of molecule clustering with respect to bromine isotopes. With the CF3Br - Ar mixtures having the pressure ratio 1 : 3 and 1 : 15, the enrichment factors obtained with respect to bromine isotopes are kenr ≈ 1.05 ± 0.005 and kenr ≈ 1.06 ± 0.007, respectively, under jet irradiation by laser emission in the 9R(30) line (1084.635 cm-1). The results obtained let us assume that this method can be used to control clustering of molecules comprising heavy element isotopes, which have a small isotopic shift in IR absorption spectra.

Understanding the many-body expansion for large systems. I. Precision considerations

NASA Astrophysics Data System (ADS)

Richard, Ryan M.; Lao, Ka Un; Herbert, John M.

2014-07-01

Electronic structure methods based on low-order "n-body" expansions are an increasingly popular means to defeat the highly nonlinear scaling of ab initio quantum chemistry calculations, taking advantage of the inherently distributable nature of the numerous subsystem calculations. Here, we examine how the finite precision of these subsystem calculations manifests in applications to large systems, in this case, a sequence of water clusters ranging in size up to (H_2O)_{47}. Using two different computer implementations of the n-body expansion, one fully integrated into a quantum chemistry program and the other written as a separate driver routine for the same program, we examine the reproducibility of total binding energies as a function of cluster size. The combinatorial nature of the n-body expansion amplifies subtle differences between the two implementations, especially for n ⩾ 4, leading to total energies that differ by as much as several kcal/mol between two implementations of what is ostensibly the same method. This behavior can be understood based on a propagation-of-errors analysis applied to a closed-form expression for the n-body expansion, which is derived here for the first time. Discrepancies between the two implementations arise primarily from the Coulomb self-energy correction that is required when electrostatic embedding charges are implemented by means of an external driver program. For reliable results in large systems, our analysis suggests that script- or driver-based implementations should read binary output files from an electronic structure program, in full double precision, or better yet be fully integrated in a way that avoids the need to compute the aforementioned self-energy. Moreover, four-body and higher-order expansions may be too sensitive to numerical thresholds to be of practical use in large systems.

Understanding the many-body expansion for large systems. I. Precision considerations

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

Richard, Ryan M.; Lao, Ka Un; Herbert, John M., E-mail: herbert@chemistry.ohio-state.edu

2014-07-07

Electronic structure methods based on low-order “n-body” expansions are an increasingly popular means to defeat the highly nonlinear scaling of ab initio quantum chemistry calculations, taking advantage of the inherently distributable nature of the numerous subsystem calculations. Here, we examine how the finite precision of these subsystem calculations manifests in applications to large systems, in this case, a sequence of water clusters ranging in size up to (H{sub 2}O){sub 47}. Using two different computer implementations of the n-body expansion, one fully integrated into a quantum chemistry program and the other written as a separate driver routine for the same program,more » we examine the reproducibility of total binding energies as a function of cluster size. The combinatorial nature of the n-body expansion amplifies subtle differences between the two implementations, especially for n ⩾ 4, leading to total energies that differ by as much as several kcal/mol between two implementations of what is ostensibly the same method. This behavior can be understood based on a propagation-of-errors analysis applied to a closed-form expression for the n-body expansion, which is derived here for the first time. Discrepancies between the two implementations arise primarily from the Coulomb self-energy correction that is required when electrostatic embedding charges are implemented by means of an external driver program. For reliable results in large systems, our analysis suggests that script- or driver-based implementations should read binary output files from an electronic structure program, in full double precision, or better yet be fully integrated in a way that avoids the need to compute the aforementioned self-energy. Moreover, four-body and higher-order expansions may be too sensitive to numerical thresholds to be of practical use in large systems.« less

Cluster-based query expansion using external collections in medical information retrieval.

PubMed

Oh, Heung-Seon; Jung, Yuchul

2015-12-01

Utilizing external collections to improve retrieval performance is challenging research because various test collections are created for different purposes. Improving medical information retrieval has also gained much attention as various types of medical documents have become available to researchers ever since they started storing them in machine processable formats. In this paper, we propose an effective method of utilizing external collections based on the pseudo relevance feedback approach. Our method incorporates the structure of external collections in estimating individual components in the final feedback model. Extensive experiments on three medical collections (TREC CDS, CLEF eHealth, and OHSUMED) were performed, and the results were compared with a representative expansion approach utilizing the external collections to show the superiority of our method. Copyright © 2015 Elsevier Inc. All rights reserved.

Performance Analysis of Entropy Methods on K Means in Clustering Process

NASA Astrophysics Data System (ADS)

Dicky Syahputra Lubis, Mhd.; Mawengkang, Herman; Suwilo, Saib

2017-12-01

K Means is a non-hierarchical data clustering method that attempts to partition existing data into one or more clusters / groups. This method partitions the data into clusters / groups so that data that have the same characteristics are grouped into the same cluster and data that have different characteristics are grouped into other groups.The purpose of this data clustering is to minimize the objective function set in the clustering process, which generally attempts to minimize variation within a cluster and maximize the variation between clusters. However, the main disadvantage of this method is that the number k is often not known before. Furthermore, a randomly chosen starting point may cause two points to approach the distance to be determined as two centroids. Therefore, for the determination of the starting point in K Means used entropy method where this method is a method that can be used to determine a weight and take a decision from a set of alternatives. Entropy is able to investigate the harmony in discrimination among a multitude of data sets. Using Entropy criteria with the highest value variations will get the highest weight. Given this entropy method can help K Means work process in determining the starting point which is usually determined at random. Thus the process of clustering on K Means can be more quickly known by helping the entropy method where the iteration process is faster than the K Means Standard process. Where the postoperative patient dataset of the UCI Repository Machine Learning used and using only 12 data as an example of its calculations is obtained by entropy method only with 2 times iteration can get the desired end result.

Breaking the Link between Environmental Degradation and Oil Palm Expansion: A Method for Enabling Sustainable Oil Palm Expansion

PubMed Central

Smit, Hans Harmen; Meijaard, Erik; van der Laan, Carina; Mantel, Stephan; Budiman, Arif; Verweij, Pita

2013-01-01

Land degradation is a global concern. In tropical areas it primarily concerns the conversion of forest into non-forest lands and the associated losses of environmental services. Defining such degradation is not straightforward hampering effective reduction in degradation and use of already degraded lands for more productive purposes. To facilitate the processes of avoided degradation and land rehabilitation, we have developed a methodology in which we have used international environmental and social sustainability standards to determine the suitability of lands for sustainable agricultural expansion. The method was developed and tested in one of the frontiers of agricultural expansion, West Kalimantan province in Indonesia. The focus was on oil palm expansion, which is considered as a major driver for deforestation in tropical regions globally. The results suggest that substantial changes in current land-use planning are necessary for most new plantations to comply with international sustainability standards. Through visualizing options for sustainable expansion with our methodology, we demonstrate that the link between oil palm expansion and degradation can be broken. Application of the methodology with criteria and thresholds similar to ours could help the Indonesian government and the industry to achieve its pro-growth, pro-job, pro-poor and pro-environment development goals. For sustainable agricultural production, context specific guidance has to be developed in areas suitable for expansion. Our methodology can serve as a template for designing such commodity and country specific tools and deliver such guidance. PMID:24039700

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

Magnus expansion method for two-level atom interacting with few-cycle pulse

NASA Astrophysics Data System (ADS)

Begzjav, T.; Ben-Benjamin, J. S.; Eleuch, H.; Nessler, R.; Rostovtsev, Y.; Shchedrin, G.

2018-06-01

Using the Magnus expansion to the fourth order, we obtain analytic expressions for the atomic state of a two-level system driven by a laser pulse of arbitrary shape with small pulse area. We also determine the limitation of our obtained formulas due to limited range of convergence of the Magnus series. We compare our method to the recently developed method of Rostovtsev et al. (PRA 2009, 79, 063833) for several detunings. Our analysis shows that our technique based on the Magnus expansion can be used as a complementary method to the one in PRA 2009.

A cluster merging method for time series microarray with production values.

PubMed

Chira, Camelia; Sedano, Javier; Camara, Monica; Prieto, Carlos; Villar, Jose R; Corchado, Emilio

2014-09-01

A challenging task in time-course microarray data analysis is to cluster genes meaningfully combining the information provided by multiple replicates covering the same key time points. This paper proposes a novel cluster merging method to accomplish this goal obtaining groups with highly correlated genes. The main idea behind the proposed method is to generate a clustering starting from groups created based on individual temporal series (representing different biological replicates measured in the same time points) and merging them by taking into account the frequency by which two genes are assembled together in each clustering. The gene groups at the level of individual time series are generated using several shape-based clustering methods. This study is focused on a real-world time series microarray task with the aim to find co-expressed genes related to the production and growth of a certain bacteria. The shape-based clustering methods used at the level of individual time series rely on identifying similar gene expression patterns over time which, in some models, are further matched to the pattern of production/growth. The proposed cluster merging method is able to produce meaningful gene groups which can be naturally ranked by the level of agreement on the clustering among individual time series. The list of clusters and genes is further sorted based on the information correlation coefficient and new problem-specific relevant measures. Computational experiments and results of the cluster merging method are analyzed from a biological perspective and further compared with the clustering generated based on the mean value of time series and the same shape-based algorithm.

Methods for sample size determination in cluster randomized trials

PubMed Central

Rutterford, Clare; Copas, Andrew; Eldridge, Sandra

2015-01-01

Background: The use of cluster randomized trials (CRTs) is increasing, along with the variety in their design and analysis. The simplest approach for their sample size calculation is to calculate the sample size assuming individual randomization and inflate this by a design effect to account for randomization by cluster. The assumptions of a simple design effect may not always be met; alternative or more complicated approaches are required. Methods: We summarise a wide range of sample size methods available for cluster randomized trials. For those familiar with sample size calculations for individually randomized trials but with less experience in the clustered case, this manuscript provides formulae for a wide range of scenarios with associated explanation and recommendations. For those with more experience, comprehensive summaries are provided that allow quick identification of methods for a given design, outcome and analysis method. Results: We present first those methods applicable to the simplest two-arm, parallel group, completely randomized design followed by methods that incorporate deviations from this design such as: variability in cluster sizes; attrition; non-compliance; or the inclusion of baseline covariates or repeated measures. The paper concludes with methods for alternative designs. Conclusions: There is a large amount of methodology available for sample size calculations in CRTs. This paper gives the most comprehensive description of published methodology for sample size calculation and provides an important resource for those designing these trials. PMID:26174515

Density-functional expansion methods: Grand challenges.

PubMed

Giese, Timothy J; York, Darrin M

2012-03-01

We discuss the source of errors in semiempirical density functional expansion (VE) methods. In particular, we show that VE methods are capable of well-reproducing their standard Kohn-Sham density functional method counterparts, but suffer from large errors upon using one or more of these approximations: the limited size of the atomic orbital basis, the Slater monopole auxiliary basis description of the response density, and the one- and two-body treatment of the core-Hamiltonian matrix elements. In the process of discussing these approximations and highlighting their symptoms, we introduce a new model that supplements the second-order density-functional tight-binding model with a self-consistent charge-dependent chemical potential equalization correction; we review our recently reported method for generalizing the auxiliary basis description of the atomic orbital response density; and we decompose the first-order potential into a summation of additive atomic components and many-body corrections, and from this examination, we provide new insights and preliminary results that motivate and inspire new approximate treatments of the core-Hamiltonian.

Coma cluster of galaxies

NASA Technical Reports Server (NTRS)

1999-01-01

Atlas Image mosaic, covering 34' x 34' on the sky, of the Coma cluster, aka Abell 1656. This is a particularly rich cluster of individual galaxies (over 1000 members), most prominently the two giant ellipticals, NGC 4874 (right) and NGC 4889 (left). The remaining members are mostly smaller ellipticals, but spiral galaxies are also evident in the 2MASS image. The cluster is seen toward the constellation Coma Berenices, but is actually at a distance of about 100 Mpc (330 million light years, or a redshift of 0.023) from us. At this distance, the cluster is in what is known as the 'Hubble flow,' or the overall expansion of the Universe. As such, astronomers can measure the Hubble Constant, or the universal expansion rate, based on the distance to this cluster. Large, rich clusters, such as Coma, allow astronomers to measure the 'missing mass,' i.e., the matter in the cluster that we cannot see, since it gravitationally influences the motions of the member galaxies within the cluster. The near-infrared maps the overall luminous mass content of the member galaxies, since the light at these wavelengths is dominated by the more numerous older stellar populations. Galaxies, as seen by 2MASS, look fairly smooth and homogeneous, as can be seen from the Hubble 'tuning fork' diagram of near-infrared galaxy morphology. Image mosaic by S. Van Dyk (IPAC).

Improvements in Ionized Cluster-Beam Deposition

NASA Technical Reports Server (NTRS)

Fitzgerald, D. J.; Compton, L. E.; Pawlik, E. V.

1986-01-01

Lower temperatures result in higher purity and fewer equipment problems. In cluster-beam deposition, clusters of atoms formed by adiabatic expansion nozzle and with proper nozzle design, expanding vapor cools sufficiently to become supersaturated and form clusters of material deposited. Clusters are ionized and accelerated in electric field and then impacted on substrate where films form. Improved cluster-beam technique useful for deposition of refractory metals.

SU-E-J-221: A Novel Expansion Method for MRI Based Target Delineation in Prostate Radiotherapy

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

Ruiz, B; East Carolina University, Greenville, NC; Feng, Y

Purpose: To compare a novel bladder/rectum carveout expansion method on MRI delineated prostate to standard CT and expansion based methods for maintaining prostate coverage while providing superior bladder and rectal sparing. Methods: Ten prostate cases were planned to include four trials: MRI vs CT delineated prostate/proximal seminal vesicles, and each image modality compared to both standard expansions (8mm 3D expansion and 5mm posterior, i.e. ∼8mm) and carveout method expansions (5mm 3D expansion, 4mm posterior for GTV-CTV excluding expansion into bladder/rectum followed by additional 5mm 3D expansion to PTV, i.e. ∼1cm). All trials were planned to total dose 7920 cGy viamore » IMRT. Evaluation and comparison was made using the following criteria: QUANTEC constraints for bladder/rectum including analysis of low dose regions, changes in PTV volume, total control points, and maximum hot spot. Results: ∼8mm MRI expansion consistently produced the most optimal plan with lowest total control points and best bladder/rectum sparing. However, this scheme had the smallest prostate (average 22.9% reduction) and subsequent PTV volume, consistent with prior literature. ∼1cm MRI had an average PTV volume comparable to ∼8mm CT at 3.79% difference. Bladder QUANTEC constraints were on average less for the ∼1cm MRI as compared to the ∼8mm CT and observed as statistically significant with 2.64% reduction in V65. Rectal constraints appeared to follow the same trend. Case-by-case analysis showed variation in rectal V30 with MRI delineated prostate being most favorable regardless of expansion type. ∼1cm MRI and ∼8mm CT had comparable plan quality. Conclusion: MRI delineated prostate with standard expansions had the smallest PTV leading to margins that may be too tight. Bladder/rectum carveout expansion method on MRI delineated prostate was found to be superior to standard CT based methods in terms of bladder and rectal sparing while maintaining prostate coverage

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

PubMed

Alexander, Nathan; Woetzel, Nils; Meiler, Jens

2011-02-01

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

Geographic Expansion of Lyme Disease in the Southeastern United States, 2000-2014.

PubMed

Lantos, Paul M; Nigrovic, Lise E; Auwaerter, Paul G; Fowler, Vance G; Ruffin, Felicia; Brinkerhoff, R Jory; Reber, Jodi; Williams, Carl; Broyhill, James; Pan, William K; Gaines, David N

2015-12-01

Background.  The majority of Lyme disease cases in the United States are acquired on the east coast between northern Virginia and New England. In recent years the geographic extent of Lyme disease has been expanding, raising the prospect of Lyme disease becoming endemic in the southeast. Methods.  We collected confirmed and probable cases of Lyme disease from 2000 through 2014 from the Virginia Department of Health and North Carolina Department of Public Health and entered them in a geographic information system. We performed spatial and spatiotemporal cluster analyses to characterize Lyme disease expansion. Results.  There was a marked increase in Lyme disease cases in Virginia, particularly from 2007 onwards. Northern Virginia experienced intensification and geographic expansion of Lyme disease cases. The most notable area of expansion was to the southwest along the Appalachian Mountains with development of a new disease cluster in the southern Virginia mountain region. Conclusions.  The geographic distribution of Lyme disease cases significantly expanded in Virginia between 2000 and 2014, particularly southward in the Virginia mountain ranges. If these trends continue, North Carolina can expect autochthonous Lyme disease transmission in its mountain region in the coming years.

An improved method to detect correct protein folds using partial clustering.

PubMed

Zhou, Jianjun; Wishart, David S

2013-01-16

Structure-based clustering is commonly used to identify correct protein folds among candidate folds (also called decoys) generated by protein structure prediction programs. However, traditional clustering methods exhibit a poor runtime performance on large decoy sets. We hypothesized that a more efficient "partial" clustering approach in combination with an improved scoring scheme could significantly improve both the speed and performance of existing candidate selection methods. We propose a new scheme that performs rapid but incomplete clustering on protein decoys. Our method detects structurally similar decoys (measured using either C(α) RMSD or GDT-TS score) and extracts representatives from them without assigning every decoy to a cluster. We integrated our new clustering strategy with several different scoring functions to assess both the performance and speed in identifying correct or near-correct folds. Experimental results on 35 Rosetta decoy sets and 40 I-TASSER decoy sets show that our method can improve the correct fold detection rate as assessed by two different quality criteria. This improvement is significantly better than two recently published clustering methods, Durandal and Calibur-lite. Speed and efficiency testing shows that our method can handle much larger decoy sets and is up to 22 times faster than Durandal and Calibur-lite. The new method, named HS-Forest, avoids the computationally expensive task of clustering every decoy, yet still allows superior correct-fold selection. Its improved speed, efficiency and decoy-selection performance should enable structure prediction researchers to work with larger decoy sets and significantly improve their ab initio structure prediction performance.

An improved method to detect correct protein folds using partial clustering

PubMed Central

2013-01-01

Background Structure-based clustering is commonly used to identify correct protein folds among candidate folds (also called decoys) generated by protein structure prediction programs. However, traditional clustering methods exhibit a poor runtime performance on large decoy sets. We hypothesized that a more efficient “partial“ clustering approach in combination with an improved scoring scheme could significantly improve both the speed and performance of existing candidate selection methods. Results We propose a new scheme that performs rapid but incomplete clustering on protein decoys. Our method detects structurally similar decoys (measured using either Cα RMSD or GDT-TS score) and extracts representatives from them without assigning every decoy to a cluster. We integrated our new clustering strategy with several different scoring functions to assess both the performance and speed in identifying correct or near-correct folds. Experimental results on 35 Rosetta decoy sets and 40 I-TASSER decoy sets show that our method can improve the correct fold detection rate as assessed by two different quality criteria. This improvement is significantly better than two recently published clustering methods, Durandal and Calibur-lite. Speed and efficiency testing shows that our method can handle much larger decoy sets and is up to 22 times faster than Durandal and Calibur-lite. Conclusions The new method, named HS-Forest, avoids the computationally expensive task of clustering every decoy, yet still allows superior correct-fold selection. Its improved speed, efficiency and decoy-selection performance should enable structure prediction researchers to work with larger decoy sets and significantly improve their ab initio structure prediction performance. PMID:23323835

Fast optimization of binary clusters using a novel dynamic lattice searching method.

PubMed

Wu, Xia; Cheng, Wen

2014-09-28

Global optimization of binary clusters has been a difficult task despite of much effort and many efficient methods. Directing toward two types of elements (i.e., homotop problem) in binary clusters, two classes of virtual dynamic lattices are constructed and a modified dynamic lattice searching (DLS) method, i.e., binary DLS (BDLS) method, is developed. However, it was found that the BDLS can only be utilized for the optimization of binary clusters with small sizes because homotop problem is hard to be solved without atomic exchange operation. Therefore, the iterated local search (ILS) method is adopted to solve homotop problem and an efficient method based on the BDLS method and ILS, named as BDLS-ILS, is presented for global optimization of binary clusters. In order to assess the efficiency of the proposed method, binary Lennard-Jones clusters with up to 100 atoms are investigated. Results show that the method is proved to be efficient. Furthermore, the BDLS-ILS method is also adopted to study the geometrical structures of (AuPd)79 clusters with DFT-fit parameters of Gupta potential.

Expansion patterns and parallaxes for planetary nebulae

NASA Astrophysics Data System (ADS)

Schönberner, D.; Balick, B.; Jacob, R.

2018-02-01

Aims: We aim to determine individual distances to a small number of rather round, quite regularly shaped planetary nebulae by combining their angular expansion in the plane of the sky with a spectroscopically measured expansion along the line of sight. Methods: We combined up to three epochs of Hubble Space Telescope imaging data and determined the angular proper motions of rim and shell edges and of other features. These results are combined with measured expansion speeds to determine individual distances by assuming that line of sight and sky-plane expansions are equal. We employed 1D radiation-hydrodynamics simulations of nebular evolution to correct for the difference between the spectroscopically measured expansion velocities of rim and shell and of their respective shock fronts. Results: Rim and shell are two independently expanding entities, driven by different physical mechanisms, although their model-based expansion timescales are quite similar. We derive good individual distances for 15 objects, and the main results are as follows: (i) distances derived from rim and shell agree well; (ii) comparison with the statistical distances in the literature gives reasonable agreement; (iii) our distances disagree with those derived by spectroscopic methods; (iv) central-star "plateau" luminosities range from about 2000 L⊙ to well below 10 000 L⊙, with a mean value at about 5000 L⊙, in excellent agreement with other samples of known distance (Galactic bulge, Magellanic Clouds, and K648 in the globular cluster M 15); (v) the central-star mass range is rather restricted: from about 0.53 to about 0.56 M⊙, with a mean value of 0.55 M⊙. Conclusions: The expansion measurements of nebular rim and shell edges confirm the predictions of radiation-hydrodynamics simulations and offer a reliable method for the evaluation of distances to suited objects. Results of this paper are based on observations made with the NASA/ESA Hubble Space Telescope in Cycle 16 (GO11122

Boson expansion based on the extended commutator method in the Tamm-Dancoff representation

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

Pedrocchi, V.G.; Tamura, T.

1983-07-01

Formal aspects of boson expansions in the Tamm-Dancoff representation are investigated in detail. This is carried out in the framework of the extended commutator method by solving in complete generality the coefficient equations, searching for Hermitian as well as non-Hermitian boson expansions. The solutions for the expansion coefficients are obtained in a new form, called the square root realization, which is then applied to carry out an analysis of the relationship between the type of expansion and the boson space in which the expansion is defined. It is shown that this new realization is reduced to various well-known boson theoriesmore » when the boson space is chosen in an appropriate manner. Further discussed, still on the basis of the square root realization, is the equivalence, on a practical level, of a few boson expansion approaches when the Tamm-Dancoff space is truncated to a single quadrupole collective component.« less

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.

Voltage-dependent cluster expansion for electrified solid-liquid interfaces: Application to the electrochemical deposition of transition metals

NASA Astrophysics Data System (ADS)

Weitzner, Stephen E.; Dabo, Ismaila

2017-11-01

The detailed atomistic modeling of electrochemically deposited metal monolayers is challenging due to the complex structure of the metal-solution interface and the critical effects of surface electrification during electrode polarization. Accurate models of interfacial electrochemical equilibria are further challenged by the need to include entropic effects to obtain accurate surface chemical potentials. We present an embedded quantum-continuum model of the interfacial environment that addresses each of these challenges and study the underpotential deposition of silver on the gold (100) surface. We leverage these results to parametrize a cluster expansion of the electrified interface and show through grand canonical Monte Carlo calculations the crucial need to account for variations in the interfacial dipole when modeling electrodeposited metals under finite-temperature electrochemical conditions.

Thermal expansion of composites: Methods and results. [large space structures

NASA Technical Reports Server (NTRS)

Bowles, D. E.; Tenney, D. R.

1981-01-01

The factors controlling the dimensional stability of various components of large space structures were investigated. Cyclic, thermal and mechanical loading were identified as the primary controlling factors of the dimensional stability of cables. For organic matrix composites, such as graphite-epoxy, it was found that these factors include moisture desorption in the space environment, thermal expansion as the structure moves from the sunlight to shadow in its orbit, mechanical loading, and microyielding of the material caused by microcracking of the matrix material. The major focus was placed on the thermal expansion of composites and in particular the development and testing of a method for its measurement.

A two-stage method for microcalcification cluster segmentation in mammography by deformable models

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

Arikidis, N.; Kazantzi, A.; Skiadopoulos, S.

Purpose: Segmentation of microcalcification (MC) clusters in x-ray mammography is a difficult task for radiologists. Accurate segmentation is prerequisite for quantitative image analysis of MC clusters and subsequent feature extraction and classification in computer-aided diagnosis schemes. Methods: In this study, a two-stage semiautomated segmentation method of MC clusters is investigated. The first stage is targeted to accurate and time efficient segmentation of the majority of the particles of a MC cluster, by means of a level set method. The second stage is targeted to shape refinement of selected individual MCs, by means of an active contour model. Both methods aremore » applied in the framework of a rich scale-space representation, provided by the wavelet transform at integer scales. Segmentation reliability of the proposed method in terms of inter and intraobserver agreements was evaluated in a case sample of 80 MC clusters originating from the digital database for screening mammography, corresponding to 4 morphology types (punctate: 22, fine linear branching: 16, pleomorphic: 18, and amorphous: 24) of MC clusters, assessing radiologists’ segmentations quantitatively by two distance metrics (Hausdorff distance—HDIST{sub cluster}, average of minimum distance—AMINDIST{sub cluster}) and the area overlap measure (AOM{sub cluster}). The effect of the proposed segmentation method on MC cluster characterization accuracy was evaluated in a case sample of 162 pleomorphic MC clusters (72 malignant and 90 benign). Ten MC cluster features, targeted to capture morphologic properties of individual MCs in a cluster (area, major length, perimeter, compactness, and spread), were extracted and a correlation-based feature selection method yielded a feature subset to feed in a support vector machine classifier. Classification performance of the MC cluster features was estimated by means of the area under receiver operating characteristic curve (Az ± Standard Error

A second-order shock-expansion method applicable to bodies of revolution near zero lift

NASA Technical Reports Server (NTRS)

1957-01-01

A second-order shock-expansion method applicable to bodies of revolution is developed by the use of the predictions of the generalized shock-expansion method in combination with characteristics theory. Equations defining the zero-lift pressure distributions and the normal-force and pitching-moment derivatives are derived. Comparisons with experimental results show that the method is applicable at values of the similarity parameter, the ratio of free-stream Mach number to nose fineness ratio, from about 0.4 to 2.

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.

Using SVD on Clusters to Improve Precision of Interdocument Similarity Measure.

PubMed

Zhang, Wen; Xiao, Fan; Li, Bin; Zhang, Siguang

2016-01-01

Recently, LSI (Latent Semantic Indexing) based on SVD (Singular Value Decomposition) is proposed to overcome the problems of polysemy and homonym in traditional lexical matching. However, it is usually criticized as with low discriminative power for representing documents although it has been validated as with good representative quality. In this paper, SVD on clusters is proposed to improve the discriminative power of LSI. The contribution of this paper is three manifolds. Firstly, we make a survey of existing linear algebra methods for LSI, including both SVD based methods and non-SVD based methods. Secondly, we propose SVD on clusters for LSI and theoretically explain that dimension expansion of document vectors and dimension projection using SVD are the two manipulations involved in SVD on clusters. Moreover, we develop updating processes to fold in new documents and terms in a decomposed matrix by SVD on clusters. Thirdly, two corpora, a Chinese corpus and an English corpus, are used to evaluate the performances of the proposed methods. Experiments demonstrate that, to some extent, SVD on clusters can improve the precision of interdocument similarity measure in comparison with other SVD based LSI methods.

Using SVD on Clusters to Improve Precision of Interdocument Similarity Measure

PubMed Central

Xiao, Fan; Li, Bin; Zhang, Siguang

2016-01-01

Recently, LSI (Latent Semantic Indexing) based on SVD (Singular Value Decomposition) is proposed to overcome the problems of polysemy and homonym in traditional lexical matching. However, it is usually criticized as with low discriminative power for representing documents although it has been validated as with good representative quality. In this paper, SVD on clusters is proposed to improve the discriminative power of LSI. The contribution of this paper is three manifolds. Firstly, we make a survey of existing linear algebra methods for LSI, including both SVD based methods and non-SVD based methods. Secondly, we propose SVD on clusters for LSI and theoretically explain that dimension expansion of document vectors and dimension projection using SVD are the two manipulations involved in SVD on clusters. Moreover, we develop updating processes to fold in new documents and terms in a decomposed matrix by SVD on clusters. Thirdly, two corpora, a Chinese corpus and an English corpus, are used to evaluate the performances of the proposed methods. Experiments demonstrate that, to some extent, SVD on clusters can improve the precision of interdocument similarity measure in comparison with other SVD based LSI methods. PMID:27579031

Understanding the many-body expansion for large systems. II. Accuracy considerations

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

Lao, Ka Un; Liu, Kuan-Yu; Richard, Ryan M.

2016-04-28

To complement our study of the role of finite precision in electronic structure calculations based on a truncated many-body expansion (MBE, or “n-body expansion”), we examine the accuracy of such methods in the present work. Accuracy may be defined either with respect to a supersystem calculation computed at the same level of theory as the n-body calculations, or alternatively with respect to high-quality benchmarks. Both metrics are considered here. In applications to a sequence of water clusters, (H{sub 2}O){sub N=6−55} described at the B3LYP/cc-pVDZ level, we obtain mean absolute errors (MAEs) per H{sub 2}O monomer of ∼1.0 kcal/mol for two-bodymore » expansions, where the benchmark is a B3LYP/cc-pVDZ calculation on the entire cluster. Three- and four-body expansions exhibit MAEs of 0.5 and 0.1 kcal/mol/monomer, respectively, without resort to charge embedding. A generalized many-body expansion truncated at two-body terms [GMBE(2)], using 3–4 H{sub 2}O molecules per fragment, outperforms all of these methods and affords a MAE of ∼0.02 kcal/mol/monomer, also without charge embedding. GMBE(2) requires significantly fewer (although somewhat larger) subsystem calculations as compared to MBE(4), reducing problems associated with floating-point roundoff errors. When compared to high-quality benchmarks, we find that error cancellation often plays a critical role in the success of MBE(n) calculations, even at the four-body level, as basis-set superposition error can compensate for higher-order polarization interactions. A many-body counterpoise correction is introduced for the GMBE, and its two-body truncation [GMBCP(2)] is found to afford good results without error cancellation. Together with a method such as ωB97X-V/aug-cc-pVTZ that can describe both covalent and non-covalent interactions, the GMBE(2)+GMBCP(2) approach provides an accurate, stable, and tractable approach for large systems.« less

Constrained variation in Jastrow method at high density

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

Owen, J.C.; Bishop, R.F.; Irvine, J.M.

1976-11-01

A method is derived for constraining the correlation function in a Jastrow variational calculation which permits the truncation of the cluster expansion after two-body terms, and which permits exact minimization of the two-body cluster by functional variation. This method is compared with one previously proposed by Pandharipande and is found to be superior both theoretically and practically. The method is tested both on liquid /sup 3/He, by using the Lennard--Jones potential, and on the model system of neutrons treated as Boltzmann particles (''homework'' problem). Good agreement is found both with experiment and with other calculations involving the explicit evaluation ofmore » higher-order terms in the cluster expansion. The method is then applied to a more realistic model of a neutron gas up to a density of 4 neutrons per F/sup 3/, and is found to give ground-state energies considerably lower than those of Pandharipande. (AIP)« less

Diagrammatic analysis of correlations in polymer fluids: Cluster diagrams via Edwards' field theory

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

Morse, David C.

2006-10-15

Edwards' functional integral approach to the statistical mechanics of polymer liquids is amenable to a diagrammatic analysis in which free energies and correlation functions are expanded as infinite sums of Feynman diagrams. This analysis is shown to lead naturally to a perturbative cluster expansion that is closely related to the Mayer cluster expansion developed for molecular liquids by Chandler and co-workers. Expansion of the functional integral representation of the grand-canonical partition function yields a perturbation theory in which all quantities of interest are expressed as functionals of a monomer-monomer pair potential, as functionals of intramolecular correlation functions of non-interacting molecules,more » and as functions of molecular activities. In different variants of the theory, the pair potential may be either a bare or a screened potential. A series of topological reductions yields a renormalized diagrammatic expansion in which collective correlation functions are instead expressed diagrammatically as functionals of the true single-molecule correlation functions in the interacting fluid, and as functions of molecular number density. Similar renormalized expansions are also obtained for a collective Ornstein-Zernicke direct correlation function, and for intramolecular correlation functions. A concise discussion is given of the corresponding Mayer cluster expansion, and of the relationship between the Mayer and perturbative cluster expansions for liquids of flexible molecules. The application of the perturbative cluster expansion to coarse-grained models of dense multi-component polymer liquids is discussed, and a justification is given for the use of a loop expansion. As an example, the formalism is used to derive a new expression for the wave-number dependent direct correlation function and recover known expressions for the intramolecular two-point correlation function to first-order in a renormalized loop expansion for coarse

Sensitivity evaluation of dynamic speckle activity measurements using clustering methods.

PubMed

Etchepareborda, Pablo; Federico, Alejandro; Kaufmann, Guillermo H

2010-07-01

We evaluate and compare the use of competitive neural networks, self-organizing maps, the expectation-maximization algorithm, K-means, and fuzzy C-means techniques as partitional clustering methods, when the sensitivity of the activity measurement of dynamic speckle images needs to be improved. The temporal history of the acquired intensity generated by each pixel is analyzed in a wavelet decomposition framework, and it is shown that the mean energy of its corresponding wavelet coefficients provides a suited feature space for clustering purposes. The sensitivity obtained by using the evaluated clustering techniques is also compared with the well-known methods of Konishi-Fujii, weighted generalized differences, and wavelet entropy. The performance of the partitional clustering approach is evaluated using simulated dynamic speckle patterns and also experimental data.

An extended affinity propagation clustering method based on different data density types.

PubMed

Zhao, XiuLi; Xu, WeiXiang

2015-01-01

Affinity propagation (AP) algorithm, as a novel clustering method, does not require the users to specify the initial cluster centers in advance, which regards all data points as potential exemplars (cluster centers) equally and groups the clusters totally by the similar degree among the data points. But in many cases there exist some different intensive areas within the same data set, which means that the data set does not distribute homogeneously. In such situation the AP algorithm cannot group the data points into ideal clusters. In this paper, we proposed an extended AP clustering algorithm to deal with such a problem. There are two steps in our method: firstly the data set is partitioned into several data density types according to the nearest distances of each data point; and then the AP clustering method is, respectively, used to group the data points into clusters in each data density type. Two experiments are carried out to evaluate the performance of our algorithm: one utilizes an artificial data set and the other uses a real seismic data set. The experiment results show that groups are obtained more accurately by our algorithm than OPTICS and AP clustering algorithm itself.

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

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.

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

NASA Astrophysics Data System (ADS)

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

2010-03-01

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

Local expansion flows of galaxies: quantifying acceleration effect of dark energy

NASA Astrophysics Data System (ADS)

Chernin, A. D.; Teerikorpi, P.

2013-08-01

The nearest expansion flow of galaxies observed around the Local group is studied as an archetypical example of the newly discovered local expansion flows around groups and clusters of galaxies in the nearby Universe. The flow is accelerating due to the antigravity produced by the universal dark energy background. We introduce a new acceleration measure of the flow which is the dimensionless ``acceleration parameter" Q (x) = x - x-2 depending on the normalized distance x only. The parameter is zero at the zero-gravity distance x = 1, and Q(x) ∝ x, when x ≫ 1. At the distance x = 3, the parameter Q = 2.9. Since the expansion flows have a self-similar structure in normalized variables, we expect that the result is valid as well for all the other expansion flows around groups and clusters of galaxies on the spatial scales from ˜ 1 to ˜ 10 Mpc everywhere in the Universe.

Relation between financial market structure and the real economy: comparison between clustering methods.

PubMed

Musmeci, Nicoló; Aste, Tomaso; Di Matteo, T

2015-01-01

We quantify the amount of information filtered by different hierarchical clustering methods on correlations between stock returns comparing the clustering structure with the underlying industrial activity classification. We apply, for the first time to financial data, a novel hierarchical clustering approach, the Directed Bubble Hierarchical Tree and we compare it with other methods including the Linkage and k-medoids. By taking the industrial sector classification of stocks as a benchmark partition, we evaluate how the different methods retrieve this classification. The results show that the Directed Bubble Hierarchical Tree can outperform other methods, being able to retrieve more information with fewer clusters. Moreover,we show that the economic information is hidden at different levels of the hierarchical structures depending on the clustering method. The dynamical analysis on a rolling window also reveals that the different methods show different degrees of sensitivity to events affecting financial markets, like crises. These results can be of interest for all the applications of clustering methods to portfolio optimization and risk hedging [corrected].

Relation between Financial Market Structure and the Real Economy: Comparison between Clustering Methods

PubMed Central

Musmeci, Nicoló; Aste, Tomaso; Di Matteo, T.

2015-01-01

We quantify the amount of information filtered by different hierarchical clustering methods on correlations between stock returns comparing the clustering structure with the underlying industrial activity classification. We apply, for the first time to financial data, a novel hierarchical clustering approach, the Directed Bubble Hierarchical Tree and we compare it with other methods including the Linkage and k-medoids. By taking the industrial sector classification of stocks as a benchmark partition, we evaluate how the different methods retrieve this classification. The results show that the Directed Bubble Hierarchical Tree can outperform other methods, being able to retrieve more information with fewer clusters. Moreover, we show that the economic information is hidden at different levels of the hierarchical structures depending on the clustering method. The dynamical analysis on a rolling window also reveals that the different methods show different degrees of sensitivity to events affecting financial markets, like crises. These results can be of interest for all the applications of clustering methods to portfolio optimization and risk hedging. PMID:25786703

Optimizing the ionization and energy absorption of laser-irradiated clusters

NASA Astrophysics Data System (ADS)

Kundu, M.; Bauer, D.

2008-03-01

It is known that rare-gas or metal clusters absorb incident laser energy very efficiently. However, due to the intricate dependencies on all the laser and cluster parameters, it is difficult to predict under which circumstances ionization and energy absorption are optimal. With the help of three-dimensional particle-in-cell simulations of xenon clusters (up to 17256 atoms), it is shown that for a given laser pulse energy and cluster, an optimum wavelength exists that corresponds to the approximate wavelength of the transient, linear Mie-resonance of the ionizing cluster at an early stage of negligible expansion. In a single ultrashort laser pulse, the linear resonance at this optimum wavelength yields much higher absorption efficiency than in the conventional, dual-pulse pump-probe setup of linear resonance during cluster expansion.

Three-cluster dynamics within an ab initio framework

DOE PAGES

Quaglioni, Sofia; Romero-Redondo, Carolina; Navratil, Petr

2013-09-26

In this study, we introduce a fully antisymmetrized treatment of three-cluster dynamics within the ab initio framework of the no-core shell model/resonating-group method. Energy-independent nonlocal interactions among the three nuclear fragments are obtained from realistic nucleon-nucleon interactions and consistent ab initio many-body wave functions of the clusters. The three-cluster Schrödinger equation is solved with bound-state boundary conditions by means of the hyperspherical-harmonic method on a Lagrange mesh. We discuss the formalism in detail and give algebraic expressions for systems of two single nucleons plus a nucleus. Using a soft similarity-renormalization-group evolved chiral nucleon-nucleon potential, we apply the method to amore » 4He+n+n description of 6He and compare the results to experiment and to a six-body diagonalization of the Hamiltonian performed within the harmonic-oscillator expansions of the no-core shell model. Differences between the two calculations provide a measure of core ( 4He) polarization effects.« less

Clustering Scientific Publications Based on Citation Relations: A Systematic Comparison of Different Methods.

PubMed

Šubelj, Lovro; van Eck, Nees Jan; Waltman, Ludo

2016-01-01

Clustering methods are applied regularly in the bibliometric literature to identify research areas or scientific fields. These methods are for instance used to group publications into clusters based on their relations in a citation network. In the network science literature, many clustering methods, often referred to as graph partitioning or community detection techniques, have been developed. Focusing on the problem of clustering the publications in a citation network, we present a systematic comparison of the performance of a large number of these clustering methods. Using a number of different citation networks, some of them relatively small and others very large, we extensively study the statistical properties of the results provided by different methods. In addition, we also carry out an expert-based assessment of the results produced by different methods. The expert-based assessment focuses on publications in the field of scientometrics. Our findings seem to indicate that there is a trade-off between different properties that may be considered desirable for a good clustering of publications. Overall, map equation methods appear to perform best in our analysis, suggesting that these methods deserve more attention from the bibliometric community.

Clustering Scientific Publications Based on Citation Relations: A Systematic Comparison of Different Methods

PubMed Central

Šubelj, Lovro; van Eck, Nees Jan; Waltman, Ludo

2016-01-01

Clustering methods are applied regularly in the bibliometric literature to identify research areas or scientific fields. These methods are for instance used to group publications into clusters based on their relations in a citation network. In the network science literature, many clustering methods, often referred to as graph partitioning or community detection techniques, have been developed. Focusing on the problem of clustering the publications in a citation network, we present a systematic comparison of the performance of a large number of these clustering methods. Using a number of different citation networks, some of them relatively small and others very large, we extensively study the statistical properties of the results provided by different methods. In addition, we also carry out an expert-based assessment of the results produced by different methods. The expert-based assessment focuses on publications in the field of scientometrics. Our findings seem to indicate that there is a trade-off between different properties that may be considered desirable for a good clustering of publications. Overall, map equation methods appear to perform best in our analysis, suggesting that these methods deserve more attention from the bibliometric community. PMID:27124610

The Views of Turkish Pre-Service Teachers about Effectiveness of Cluster Method as a Teaching Writing Method

ERIC Educational Resources Information Center

Kitis, Emine; Türkel, Ali

2017-01-01

The aim of this study is to find out Turkish pre-service teachers' views on effectiveness of cluster method as a writing teaching method. The Cluster Method can be defined as a connotative creative writing method. The way the method works is that the person who brainstorms on connotations of a word or a concept in abscence of any kind of…

Visual cluster analysis and pattern recognition template and methods

DOEpatents

Osbourn, Gordon Cecil; Martinez, Rubel Francisco

1999-01-01

A method of clustering using a novel template to define a region of influence. Using neighboring approximation methods, computation times can be significantly reduced. The template and method are applicable and improve pattern recognition techniques.

Character expansion methods for matrix models of dually weighted graphs

NASA Astrophysics Data System (ADS)

Kazakov, Vladimir A.; Staudacher, Matthias; Wynter, Thomas

1996-04-01

We consider generalized one-matrix models in which external fields allow control over the coordination numbers on both the original and dual lattices. We rederive in a simple fashion a character expansion formula for these models originally due to Itzykson and Di Francesco, and then demonstrate how to take the large N limit of this expansion. The relationship to the usual matrix model resolvent is elucidated. Our methods give as a by-product an extremely simple derivation of the Migdal integral equation describing the large N limit of the Itzykson-Zuber formula. We illustrate and check our methods by analysing a number of models solvable by traditional means. We then proceed to solve a new model: a sum over planar graphys possessing even coordination numbers on both the original and the dual lattice. We conclude by formulating the equations for the case of arbitrary sets of even, self-dual coupling constants. This opens the way for studying the deep problems of phase transitions from random to flat lattices. January 1995

Hierarchic Agglomerative Clustering Methods for Automatic Document Classification.

ERIC Educational Resources Information Center

Griffiths, Alan; And Others

1984-01-01

Considers classifications produced by application of single linkage, complete linkage, group average, and word clustering methods to Keen and Cranfield document test collections, and studies structure of hierarchies produced, extent to which methods distort input similarity matrices during classification generation, and retrieval effectiveness…

Developing cluster strategy of apples dodol SMEs by integration K-means clustering and analytical hierarchy process method

NASA Astrophysics Data System (ADS)

Mustaniroh, S. A.; Effendi, U.; Silalahi, R. L. R.; Sari, T.; Ala, M.

2018-03-01

The purposes of this research were to determine the grouping of apples dodol small and medium enterprises (SMEs) in Batu City and to determine an appropriate development strategy for each cluster. The methods used for clustering SMEs was k-means. The Analytical Hierarchy Process (AHP) approach was then applied to determine the development strategy priority for each cluster. The variables used in grouping include production capacity per month, length of operation, investment value, average sales revenue per month, amount of SMEs assets, and the number of workers. Several factors were considered in AHP include industry cluster, government, as well as related and supporting industries. Data was collected using the methods of questionaire and interviews. SMEs respondents were selected among SMEs appels dodol in Batu City using purposive sampling. The result showed that two clusters were formed from five apples dodol SMEs. The 1stcluster of apples dodol SMEs, classified as small enterprises, included SME A, SME C, and SME D. The 2ndcluster of SMEs apples dodol, classified as medium enterprises, consisted of SME B and SME E. The AHP results indicated that the priority development strategy for the 1stcluster of apples dodol SMEs was improving quality and the product standardisation, while for the 2nd cluster was increasing the marketing access.

Expansion method in secondary total ear reconstruction for undesirable reconstructed ear.

PubMed

Liu, Tun; Hu, Jintian; Zhou, Xu; Zhang, Qingguo

2014-09-01

Ear reconstruction by autologous costal cartilage grafting is the most widely applied technique with fewer complications. However, undesirable ear reconstruction brings more problems to plastic surgeons. Some authors resort to free flap or osseointegration technique with prosthetic ear. In this article, we introduce a secondary total ear reconstruction with expanded skin flap method. From July 2010 to April 2012, 7 cases of undesirable ear reconstruction were repaired by tissue expansion method. Procedures including removal of previous cartilage framework, soft tissue expander insertion, and second stage of cartilage framework insertion were performed to each case regarding their local conditions. The follow-up time ranged from 6 months to 2.5 years. All of the cases recovered well with good 3-dimensional forms, symmetrical auriculocephalic angle, and stable fixation. All these evidence showed that this novel expansion method is safe, stable, and less traumatic for secondary total ear reconstruction. With sufficient expanded skin flap and refabricated cartilage framework, lifelike appearance of reconstructed ear could be acquired without causing additional injury.

Bootstrap-based methods for estimating standard errors in Cox's regression analyses of clustered event times.

PubMed

Xiao, Yongling; Abrahamowicz, Michal

2010-03-30

We propose two bootstrap-based methods to correct the standard errors (SEs) from Cox's model for within-cluster correlation of right-censored event times. The cluster-bootstrap method resamples, with replacement, only the clusters, whereas the two-step bootstrap method resamples (i) the clusters, and (ii) individuals within each selected cluster, with replacement. In simulations, we evaluate both methods and compare them with the existing robust variance estimator and the shared gamma frailty model, which are available in statistical software packages. We simulate clustered event time data, with latent cluster-level random effects, which are ignored in the conventional Cox's model. For cluster-level covariates, both proposed bootstrap methods yield accurate SEs, and type I error rates, and acceptable coverage rates, regardless of the true random effects distribution, and avoid serious variance under-estimation by conventional Cox-based standard errors. However, the two-step bootstrap method over-estimates the variance for individual-level covariates. We also apply the proposed bootstrap methods to obtain confidence bands around flexible estimates of time-dependent effects in a real-life analysis of cluster event times.

Consensus of satellite cluster flight using an energy-matching optimal control method

NASA Astrophysics Data System (ADS)

Luo, Jianjun; Zhou, Liang; Zhang, Bo

2017-11-01

This paper presents an optimal control method for consensus of satellite cluster flight under a kind of energy matching condition. Firstly, the relation between energy matching and satellite periodically bounded relative motion is analyzed, and the satellite energy matching principle is applied to configure the initial conditions. Then, period-delayed errors are adopted as state variables to establish the period-delayed errors dynamics models of a single satellite and the cluster. Next a novel satellite cluster feedback control protocol with coupling gain is designed, so that the satellite cluster periodically bounded relative motion consensus problem (period-delayed errors state consensus problem) is transformed to the stability of a set of matrices with the same low dimension. Based on the consensus region theory in the research of multi-agent system consensus issues, the coupling gain can be obtained to satisfy the requirement of consensus region and decouple the satellite cluster information topology and the feedback control gain matrix, which can be determined by Linear quadratic regulator (LQR) optimal method. This method can realize the consensus of satellite cluster period-delayed errors, leading to the consistency of semi-major axes (SMA) and the energy-matching of satellite cluster. Then satellites can emerge the global coordinative cluster behavior. Finally the feasibility and effectiveness of the present energy-matching optimal consensus for satellite cluster flight is verified through numerical simulations.

Visual cluster analysis and pattern recognition template and methods

DOEpatents

Osbourn, G.C.; Martinez, R.F.

1999-05-04

A method of clustering using a novel template to define a region of influence is disclosed. Using neighboring approximation methods, computation times can be significantly reduced. The template and method are applicable and improve pattern recognition techniques. 30 figs.

A new method to search for high-redshift clusters using photometric redshifts

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

Castignani, G.; Celotti, A.; Chiaberge, M.

2014-09-10

We describe a new method (Poisson probability method, PPM) to search for high-redshift galaxy clusters and groups by using photometric redshift information and galaxy number counts. The method relies on Poisson statistics and is primarily introduced to search for megaparsec-scale environments around a specific beacon. The PPM is tailored to both the properties of the FR I radio galaxies in the Chiaberge et al. sample, which are selected within the COSMOS survey, and to the specific data set used. We test the efficiency of our method of searching for cluster candidates against simulations. Two different approaches are adopted. (1) Wemore » use two z ∼ 1 X-ray detected cluster candidates found in the COSMOS survey and we shift them to higher redshift up to z = 2. We find that the PPM detects the cluster candidates up to z = 1.5, and it correctly estimates both the redshift and size of the two clusters. (2) We simulate spherically symmetric clusters of different size and richness, and we locate them at different redshifts (i.e., z = 1.0, 1.5, and 2.0) in the COSMOS field. We find that the PPM detects the simulated clusters within the considered redshift range with a statistical 1σ redshift accuracy of ∼0.05. The PPM is an efficient alternative method for high-redshift cluster searches that may also be applied to both present and future wide field surveys such as SDSS Stripe 82, LSST, and Euclid. Accurate photometric redshifts and a survey depth similar or better than that of COSMOS (e.g., I < 25) are required.« less

Least squares regression methods for clustered ROC data with discrete covariates.

PubMed

Tang, Liansheng Larry; Zhang, Wei; Li, Qizhai; Ye, Xuan; Chan, Leighton

2016-07-01

The receiver operating characteristic (ROC) curve is a popular tool to evaluate and compare the accuracy of diagnostic tests to distinguish the diseased group from the nondiseased group when test results from tests are continuous or ordinal. A complicated data setting occurs when multiple tests are measured on abnormal and normal locations from the same subject and the measurements are clustered within the subject. Although least squares regression methods can be used for the estimation of ROC curve from correlated data, how to develop the least squares methods to estimate the ROC curve from the clustered data has not been studied. Also, the statistical properties of the least squares methods under the clustering setting are unknown. In this article, we develop the least squares ROC methods to allow the baseline and link functions to differ, and more importantly, to accommodate clustered data with discrete covariates. The methods can generate smooth ROC curves that satisfy the inherent continuous property of the true underlying curve. The least squares methods are shown to be more efficient than the existing nonparametric ROC methods under appropriate model assumptions in simulation studies. We apply the methods to a real example in the detection of glaucomatous deterioration. We also derive the asymptotic properties of the proposed methods. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Method of preparing size-selected metal clusters

DOEpatents

Elam, Jeffrey W.; Pellin, Michael J.; Stair, Peter C.

2010-05-11

The invention provides a method for depositing catalytic clusters on a surface, the method comprising confining the surface to a controlled atmosphere; contacting the surface with catalyst containing vapor for a first period of time; removing the vapor from the controlled atmosphere; and contacting the surface with a reducing agent for a second period of time so as to produce catalyst-containing nucleation sites.

Agent-based method for distributed clustering of textual information

DOEpatents

Potok, Thomas E [Oak Ridge, TN; Reed, Joel W [Knoxville, TN; Elmore, Mark T [Oak Ridge, TN; Treadwell, Jim N [Louisville, TN

2010-09-28

A computer method and system for storing, retrieving and displaying information has a multiplexing agent (20) that calculates a new document vector (25) for a new document (21) to be added to the system and transmits the new document vector (25) to master cluster agents (22) and cluster agents (23) for evaluation. These agents (22, 23) perform the evaluation and return values upstream to the multiplexing agent (20) based on the similarity of the document to documents stored under their control. The multiplexing agent (20) then sends the document (21) and the document vector (25) to the master cluster agent (22), which then forwards it to a cluster agent (23) or creates a new cluster agent (23) to manage the document (21). The system also searches for stored documents according to a search query having at least one term and identifying the documents found in the search, and displays the documents in a clustering display (80) of similarity so as to indicate similarity of the documents to each other.

a Probabilistic Embedding Clustering Method for Urban Structure Detection

NASA Astrophysics Data System (ADS)

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

2017-09-01

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

Communication: Time-dependent optimized coupled-cluster method for multielectron dynamics

NASA Astrophysics Data System (ADS)

Sato, Takeshi; Pathak, Himadri; Orimo, Yuki; Ishikawa, Kenichi L.

2018-02-01

Time-dependent coupled-cluster method with time-varying orbital functions, called time-dependent optimized coupled-cluster (TD-OCC) method, is formulated for multielectron dynamics in an intense laser field. We have successfully derived the equations of motion for CC amplitudes and orthonormal orbital functions based on the real action functional, and implemented the method including double excitations (TD-OCCD) and double and triple excitations (TD-OCCDT) within the optimized active orbitals. The present method is size extensive and gauge invariant, a polynomial cost-scaling alternative to the time-dependent multiconfiguration self-consistent-field method. The first application of the TD-OCC method of intense-laser driven correlated electron dynamics in Ar atom is reported.

Communication: Time-dependent optimized coupled-cluster method for multielectron dynamics.

PubMed

Sato, Takeshi; Pathak, Himadri; Orimo, Yuki; Ishikawa, Kenichi L

2018-02-07

Time-dependent coupled-cluster method with time-varying orbital functions, called time-dependent optimized coupled-cluster (TD-OCC) method, is formulated for multielectron dynamics in an intense laser field. We have successfully derived the equations of motion for CC amplitudes and orthonormal orbital functions based on the real action functional, and implemented the method including double excitations (TD-OCCD) and double and triple excitations (TD-OCCDT) within the optimized active orbitals. The present method is size extensive and gauge invariant, a polynomial cost-scaling alternative to the time-dependent multiconfiguration self-consistent-field method. The first application of the TD-OCC method of intense-laser driven correlated electron dynamics in Ar atom is reported.

A nonperturbative light-front coupled-cluster method

NASA Astrophysics Data System (ADS)

Hiller, J. R.

2012-10-01

The nonperturbative Hamiltonian eigenvalue problem for bound states of a quantum field theory is formulated in terms of Dirac's light-front coordinates and then approximated by the exponential-operator technique of the many-body coupled-cluster method. This approximation eliminates any need for the usual approximation of Fock-space truncation. Instead, the exponentiated operator is truncated, and the terms retained are determined by a set of nonlinear integral equations. These equations are solved simultaneously with an effective eigenvalue problem in the valence sector, where the number of constituents is small. Matrix elements can be calculated, with extensions of techniques from standard coupled-cluster theory, to obtain form factors and other observables.

A robust and efficient stepwise regression method for building sparse polynomial chaos expansions

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

Abraham, Simon, E-mail: Simon.Abraham@ulb.ac.be; Raisee, Mehrdad; Ghorbaniasl, Ghader

2017-03-01

Polynomial Chaos (PC) expansions are widely used in various engineering fields for quantifying uncertainties arising from uncertain parameters. The computational cost of classical PC solution schemes is unaffordable as the number of deterministic simulations to be calculated grows dramatically with the number of stochastic dimension. This considerably restricts the practical use of PC at the industrial level. A common approach to address such problems is to make use of sparse PC expansions. This paper presents a non-intrusive regression-based method for building sparse PC expansions. The most important PC contributions are detected sequentially through an automatic search procedure. The variable selectionmore » criterion is based on efficient tools relevant to probabilistic method. Two benchmark analytical functions are used to validate the proposed algorithm. The computational efficiency of the method is then illustrated by a more realistic CFD application, consisting of the non-deterministic flow around a transonic airfoil subject to geometrical uncertainties. To assess the performance of the developed methodology, a detailed comparison is made with the well established LAR-based selection technique. The results show that the developed sparse regression technique is able to identify the most significant PC contributions describing the problem. Moreover, the most important stochastic features are captured at a reduced computational cost compared to the LAR method. The results also demonstrate the superior robustness of the method by repeating the analyses using random experimental designs.« less

Giant Spherical Cluster with I-C140 Fullerene Topology**

PubMed Central

Heinl, Sebastian; Peresypkina, Eugenia; Sutter, Jörg; Scheer, Manfred

2015-01-01

We report on an effective cluster expansion of CuBr-linked aggregates by the increase of the steric bulk of the CpR ligand in the pentatopic molecules [CpRFe(η5-P5)]. Using [CpBIGFe(η5-P5)] (CpBIG=C5(4-nBuC6H4)5), the novel multishell aggregate [{CpBIGFe(η5:2:1:1:1:1:1-P5)}12(CuBr)92] is obtained. It shows topological analogy to the theoretically predicted I-C140 fullerene molecule. The spherical cluster was comprehensively characterized by various methods in solution and in the solid state. PMID:26411255

A new method to generate large order low temperature expansions for discrete spin models

NASA Astrophysics Data System (ADS)

Bhanot, Gyan

1993-03-01

I describe work done in collaboration with Michael Creutz at BNL and Jan Lacki at IAS Princeton. We have developed a method to generate very high order low temperature (weak coupling) expansions for discrete spin systems. For the 3-d and 4-d Ising model, we give results for the low temperature expansion of the average free energy to 50 and 44 excited bonds respectively.

Does query expansion limit our learning? A comparison of social-based expansion to content-based expansion for medical queries on the internet.

PubMed

Pentoney, Christopher; Harwell, Jeff; Leroy, Gondy

2014-01-01

Searching for medical information online is a common activity. While it has been shown that forming good queries is difficult, Google's query suggestion tool, a type of query expansion, aims to facilitate query formation. However, it is unknown how this expansion, which is based on what others searched for, affects the information gathering of the online community. To measure the impact of social-based query expansion, this study compared it with content-based expansion, i.e., what is really in the text. We used 138,906 medical queries from the AOL User Session Collection and expanded them using Google's Autocomplete method (social-based) and the content of the Google Web Corpus (content-based). We evaluated the specificity and ambiguity of the expansion terms for trigram queries. We also looked at the impact on the actual results using domain diversity and expansion edit distance. Results showed that the social-based method provided more precise expansion terms as well as terms that were less ambiguous. Expanded queries do not differ significantly in diversity when expanded using the social-based method (6.72 different domains returned in the first ten results, on average) vs. content-based method (6.73 different domains, on average).

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

PubMed Central

Liu, Jingxian; Wu, Kefeng

2017-01-01

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

Atomistic cluster alignment method for local order mining in liquids and glasses

NASA Astrophysics Data System (ADS)

Fang, X. W.; Wang, C. Z.; Yao, Y. X.; Ding, Z. J.; Ho, K. M.

2010-11-01

An atomistic cluster alignment method is developed to identify and characterize the local atomic structural order in liquids and glasses. With the “order mining” idea for structurally disordered systems, the method can detect the presence of any type of local order in the system and can quantify the structural similarity between a given set of templates and the aligned clusters in a systematic and unbiased manner. Moreover, population analysis can also be carried out for various types of clusters in the system. The advantages of the method in comparison with other previously developed analysis methods are illustrated by performing the structural analysis for four prototype systems (i.e., pure Al, pure Zr, Zr35Cu65 , and Zr36Ni64 ). The results show that the cluster alignment method can identify various types of short-range orders (SROs) in these systems correctly while some of these SROs are difficult to capture by most of the currently available analysis methods (e.g., Voronoi tessellation method). Such a full three-dimensional atomistic analysis method is generic and can be applied to describe the magnitude and nature of noncrystalline ordering in many disordered systems.

Comparison of integrated clustering methods for accurate and stable prediction of building energy consumption data

DOE PAGES

Hsu, David

2015-09-27

Clustering methods are often used to model energy consumption for two reasons. First, clustering is often used to process data and to improve the predictive accuracy of subsequent energy models. Second, stable clusters that are reproducible with respect to non-essential changes can be used to group, target, and interpret observed subjects. However, it is well known that clustering methods are highly sensitive to the choice of algorithms and variables. This can lead to misleading assessments of predictive accuracy and mis-interpretation of clusters in policymaking. This paper therefore introduces two methods to the modeling of energy consumption in buildings: clusterwise regression,more » also known as latent class regression, which integrates clustering and regression simultaneously; and cluster validation methods to measure stability. Using a large dataset of multifamily buildings in New York City, clusterwise regression is compared to common two-stage algorithms that use K-means and model-based clustering with linear regression. Predictive accuracy is evaluated using 20-fold cross validation, and the stability of the perturbed clusters is measured using the Jaccard coefficient. These results show that there seems to be an inherent tradeoff between prediction accuracy and cluster stability. This paper concludes by discussing which clustering methods may be appropriate for different analytical purposes.« less

Robustness of serial clustering of extra-tropical cyclones to the choice of tracking method

NASA Astrophysics Data System (ADS)

Pinto, Joaquim G.; Ulbrich, Sven; Karremann, Melanie K.; Stephenson, David B.; Economou, Theodoros; Shaffrey, Len C.

2016-04-01

Cyclone families are a frequent synoptic weather feature in the Euro-Atlantic area in winter. Given appropriate large-scale conditions, the occurrence of such series (clusters) of storms may lead to large socio-economic impacts and cumulative losses. Recent studies analyzing Reanalysis data using single cyclone tracking methods have shown that serial clustering of cyclones occurs on both flanks and downstream regions of the North Atlantic storm track. This study explores the sensitivity of serial clustering to the choice of tracking method. With this aim, the IMILAST cyclone track database based on ERA-interim data is analysed. Clustering is estimated by the dispersion (ratio of variance to mean) of winter (DJF) cyclones passages near each grid point over the Euro-Atlantic area. Results indicate that while the general pattern of clustering is identified for all methods, there are considerable differences in detail. This can primarily be attributed to the differences in the variance of cyclone counts between the methods, which range up to one order of magnitude. Nevertheless, clustering over the Eastern North Atlantic and Western Europe can be identified for all methods and can thus be generally considered as a robust feature. The statistical links between large-scale patterns like the NAO and clustering are obtained for all methods, though with different magnitudes. We conclude that the occurrence of cyclone clustering over the Eastern North Atlantic and Western Europe is largely independent from the choice of tracking method and hence from the definition of a cyclone.

An effective trust-based recommendation method using a novel graph clustering algorithm

NASA Astrophysics Data System (ADS)

Moradi, Parham; Ahmadian, Sajad; Akhlaghian, Fardin

2015-10-01

Recommender systems are programs that aim to provide personalized recommendations to users for specific items (e.g. music, books) in online sharing communities or on e-commerce sites. Collaborative filtering methods are important and widely accepted types of recommender systems that generate recommendations based on the ratings of like-minded users. On the other hand, these systems confront several inherent issues such as data sparsity and cold start problems, caused by fewer ratings against the unknowns that need to be predicted. Incorporating trust information into the collaborative filtering systems is an attractive approach to resolve these problems. In this paper, we present a model-based collaborative filtering method by applying a novel graph clustering algorithm and also considering trust statements. In the proposed method first of all, the problem space is represented as a graph and then a sparsest subgraph finding algorithm is applied on the graph to find the initial cluster centers. Then, the proposed graph clustering algorithm is performed to obtain the appropriate users/items clusters. Finally, the identified clusters are used as a set of neighbors to recommend unseen items to the current active user. Experimental results based on three real-world datasets demonstrate that the proposed method outperforms several state-of-the-art recommender system methods.

Differences Between Ward's and UPGMA Methods of Cluster Analysis: Implications for School Psychology.

ERIC Educational Resources Information Center

Hale, Robert L.; Dougherty, Donna

1988-01-01

Compared the efficacy of two methods of cluster analysis, the unweighted pair-groups method using arithmetic averages (UPGMA) and Ward's method, for students grouped on intelligence, achievement, and social adjustment by both clustering methods. Found UPGMA more efficacious based on output, on cophenetic correlation coefficients generated by each…

Method and apparatus for the production of cluster ions

DOEpatents

Friedman, Lewis; Beuhler, Robert J.

1988-01-01

A method and apparatus for the production of cluster ions, and preferably isotopic hydrogen cluster ions is disclosed. A gas, preferably comprising a carrier gas and a substrate gas, is cooled to about its boiling point and expanded through a supersonic nozzle into a region maintained at a low pressure. Means are provided for the generation of a plasma in the gas before or just as it enters the nozzle.

Method and apparatus for the production of cluster ions

DOEpatents

Friedman, L.; Beuhler, R.J.

A method and apparatus for the production of cluster ions, and preferably isotopic hydrogen cluster ions is disclosed. A gas, preferably comprising a carrier gas and a substrate gas, is cooled to about its boiling point and expanded through a supersonic nozzle into a region maintained at a low pressure. Means are provided for the generation of a plasma in the gas before or just as it enters the nozzle.

Scaling Symmetries in Elastic-Plastic Dynamic Cavity Expansion Equations Using the Isovector Method

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

Albright, Eric Jason; Ramsey, Scott D.; Schmidt, Joseph H.

Cavity-expansion approximations are widely-used in the study of penetration mechanics and indentation phenomena. We apply the isovector method to a well-established model in the literature for elastic-plastic cavity-expansion to systematically demonstrate the existence of Lie symmetries corresponding to scale-invariant solutions. Here we use the symmetries obtained from the equations of motion to determine compatible auxiliary conditions describing the cavity wall trajectory and the elastic-plastic material interface. The admissible conditions are then compared with specific similarity solutions in the literature.

Scaling Symmetries in Elastic-Plastic Dynamic Cavity Expansion Equations Using the Isovector Method

DOE PAGES

Albright, Eric Jason; Ramsey, Scott D.; Schmidt, Joseph H.; ...

2017-09-16

Cavity-expansion approximations are widely-used in the study of penetration mechanics and indentation phenomena. We apply the isovector method to a well-established model in the literature for elastic-plastic cavity-expansion to systematically demonstrate the existence of Lie symmetries corresponding to scale-invariant solutions. Here we use the symmetries obtained from the equations of motion to determine compatible auxiliary conditions describing the cavity wall trajectory and the elastic-plastic material interface. The admissible conditions are then compared with specific similarity solutions in the literature.

Method for cancelling expansion waves in a wave rotor

NASA Astrophysics Data System (ADS)

Paxson, Daniel E.

1994-03-01

A wave rotor system includes a wave rotor coupled to first and second end plates. Special ports are provided, one in each of the first and second end plates, to cancel expansion waves generated by the release of working fluid from the wave rotor. One of the expansion waves is reflected in the wave rotor from a reflecting portion, and provided to the special port in the second end plate. Fluid present at the special port in the second end plate has a stagnation pressure and mass flow which is substantially the same as that of the cells of the wave rotor communicating with such special port. This allows for cancellation of the expansion wave generated by the release of working fluid from the wave rotor. The special port in the second end plate has a first end corresponding substantially to the head of the expansion wave, and a second end corresponding substantially to the tail of the expansion wave. Also, the special port is configured to continually change along the circumference of the second end plate to affect expansion wave cancellation. An expansion wave generated by a second release of working fluid from the wave rotor is cancelled in a similar manner to that described above using a special port in the first end plate. Preferably the cycle of operation of the wave rotor system is designed so that the stagnation pressure and mass flow of the fluid present at the special ports is the same so that the special ports may be connected by a common duct.

Ancient Expansion of the Hox Cluster in Lepidoptera Generated Four Homeobox Genes Implicated in Extra-Embryonic Tissue Formation

PubMed Central

Taylor, William R.; Gibbs, Melanie; Breuker, Casper J.; Holland, Peter W. H.

2014-01-01

Gene duplications within the conserved Hox cluster are rare in animal evolution, but in Lepidoptera an array of divergent Hox-related genes (Shx genes) has been reported between pb and zen. Here, we use genome sequencing of five lepidopteran species (Polygonia c-album, Pararge aegeria, Callimorpha dominula, Cameraria ohridella, Hepialus sylvina) plus a caddisfly outgroup (Glyphotaelius pellucidus) to trace the evolution of the lepidopteran Shx genes. We demonstrate that Shx genes originated by tandem duplication of zen early in the evolution of large clade Ditrysia; Shx are not found in a caddisfly and a member of the basally diverging Hepialidae (swift moths). Four distinct Shx genes were generated early in ditrysian evolution, and were stably retained in all descendent Lepidoptera except the silkmoth which has additional duplications. Despite extensive sequence divergence, molecular modelling indicates that all four Shx genes have the potential to encode stable homeodomains. The four Shx genes have distinct spatiotemporal expression patterns in early development of the Speckled Wood butterfly (Pararge aegeria), with ShxC demarcating the future sites of extraembryonic tissue formation via strikingly localised maternal RNA in the oocyte. All four genes are also expressed in presumptive serosal cells, prior to the onset of zen expression. Lepidopteran Shx genes represent an unusual example of Hox cluster expansion and integration of novel genes into ancient developmental regulatory networks. PMID:25340822

A Method for Measuring Collection Expansion Rates and Shelf Space Capacities.

ERIC Educational Resources Information Center

Sapp, Gregg; Suttle, George

1994-01-01

Describes an effort to quantify annual collection expansion and shelf space capacities with a computer spreadsheet program. Methods used to quantify the space taken at the beginning of the project; to estimate annual rate of collection growth; and to plot stack space and usage, volume equivalents and usage, and growth capacity are covered.…

Method for exploratory cluster analysis and visualisation of single-trial ERP ensembles.

PubMed

Williams, N J; Nasuto, S J; Saddy, J D

2015-07-30

The validity of ensemble averaging on event-related potential (ERP) data has been questioned, due to its assumption that the ERP is identical across trials. Thus, there is a need for preliminary testing for cluster structure in the data. We propose a complete pipeline for the cluster analysis of ERP data. To increase the signal-to-noise (SNR) ratio of the raw single-trials, we used a denoising method based on Empirical Mode Decomposition (EMD). Next, we used a bootstrap-based method to determine the number of clusters, through a measure called the Stability Index (SI). We then used a clustering algorithm based on a Genetic Algorithm (GA) to define initial cluster centroids for subsequent k-means clustering. Finally, we visualised the clustering results through a scheme based on Principal Component Analysis (PCA). After validating the pipeline on simulated data, we tested it on data from two experiments - a P300 speller paradigm on a single subject and a language processing study on 25 subjects. Results revealed evidence for the existence of 6 clusters in one experimental condition from the language processing study. Further, a two-way chi-square test revealed an influence of subject on cluster membership. Our analysis operates on denoised single-trials, the number of clusters are determined in a principled manner and the results are presented through an intuitive visualisation. Given the cluster structure in some experimental conditions, we suggest application of cluster analysis as a preliminary step before ensemble averaging. Copyright © 2015 Elsevier B.V. All rights reserved.

Segmentation of clustered cells in negative phase contrast images with integrated light intensity and cell shape information.

PubMed

Wang, Y; Wang, C; Zhang, Z

2018-05-01

Automated cell segmentation plays a key role in characterisations of cell behaviours for both biology research and clinical practices. Currently, the segmentation of clustered cells still remains as a challenge and is the main reason for false segmentation. In this study, the emphasis was put on the segmentation of clustered cells in negative phase contrast images. A new method was proposed to combine both light intensity and cell shape information through the construction of grey-weighted distance transform (GWDT) within preliminarily segmented areas. With the constructed GWDT, the clustered cells can be detected and then separated with a modified region skeleton-based method. Moreover, a contour expansion operation was applied to get optimised detection of cell boundaries. In this paper, the working principle and detailed procedure of the proposed method are described, followed by the evaluation of the method on clustered cell segmentation. Results show that the proposed method achieves an improved performance in clustered cell segmentation compared with other methods, with 85.8% and 97.16% accuracy rate for clustered cells and all cells, respectively. © 2017 The Authors Journal of Microscopy © 2017 Royal Microscopical Society.

QoE collaborative evaluation method based on fuzzy clustering heuristic algorithm.

PubMed

Bao, Ying; Lei, Weimin; Zhang, Wei; Zhan, Yuzhuo

2016-01-01

At present, to realize or improve the quality of experience (QoE) is a major goal for network media transmission service, and QoE evaluation is the basis for adjusting the transmission control mechanism. Therefore, a kind of QoE collaborative evaluation method based on fuzzy clustering heuristic algorithm is proposed in this paper, which is concentrated on service score calculation at the server side. The server side collects network transmission quality of service (QoS) parameter, node location data, and user expectation value from client feedback information. Then it manages the historical data in database through the "big data" process mode, and predicts user score according to heuristic rules. On this basis, it completes fuzzy clustering analysis, and generates service QoE score and management message, which will be finally fed back to clients. Besides, this paper mainly discussed service evaluation generative rules, heuristic evaluation rules and fuzzy clustering analysis methods, and presents service-based QoE evaluation processes. The simulation experiments have verified the effectiveness of QoE collaborative evaluation method based on fuzzy clustering heuristic rules.

Interactive K-Means Clustering Method Based on User Behavior for Different Analysis Target in Medicine.

PubMed

Lei, Yang; Yu, Dai; Bin, Zhang; Yang, Yang

2017-01-01

Clustering algorithm as a basis of data analysis is widely used in analysis systems. However, as for the high dimensions of the data, the clustering algorithm may overlook the business relation between these dimensions especially in the medical fields. As a result, usually the clustering result may not meet the business goals of the users. Then, in the clustering process, if it can combine the knowledge of the users, that is, the doctor's knowledge or the analysis intent, the clustering result can be more satisfied. In this paper, we propose an interactive K -means clustering method to improve the user's satisfactions towards the result. The core of this method is to get the user's feedback of the clustering result, to optimize the clustering result. Then, a particle swarm optimization algorithm is used in the method to optimize the parameters, especially the weight settings in the clustering algorithm to make it reflect the user's business preference as possible. After that, based on the parameter optimization and adjustment, the clustering result can be closer to the user's requirement. Finally, we take an example in the breast cancer, to testify our method. The experiments show the better performance of our algorithm.

An improved K-means clustering method for cDNA microarray image segmentation.

PubMed

Wang, T N; Li, T J; Shao, G F; Wu, S X

2015-07-14

Microarray technology is a powerful tool for human genetic research and other biomedical applications. Numerous improvements to the standard K-means algorithm have been carried out to complete the image segmentation step. However, most of the previous studies classify the image into two clusters. In this paper, we propose a novel K-means algorithm, which first classifies the image into three clusters, and then one of the three clusters is divided as the background region and the other two clusters, as the foreground region. The proposed method was evaluated on six different data sets. The analyses of accuracy, efficiency, expression values, special gene spots, and noise images demonstrate the effectiveness of our method in improving the segmentation quality.

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

NASA Astrophysics Data System (ADS)

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

2015-07-01

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

An empirical method to cluster objective nebulizer adherence data among adults with cystic fibrosis.

PubMed

Hoo, Zhe H; Campbell, Michael J; Curley, Rachael; Wildman, Martin J

2017-01-01

The purpose of using preventative inhaled treatments in cystic fibrosis is to improve health outcomes. Therefore, understanding the relationship between adherence to treatment and health outcome is crucial. Temporal variability, as well as absolute magnitude of adherence affects health outcomes, and there is likely to be a threshold effect in the relationship between adherence and outcomes. We therefore propose a pragmatic algorithm-based clustering method of objective nebulizer adherence data to better understand this relationship, and potentially, to guide clinical decisions. This clustering method consists of three related steps. The first step is to split adherence data for the previous 12 months into four 3-monthly sections. The second step is to calculate mean adherence for each section and to score the section based on mean adherence. The third step is to aggregate the individual scores to determine the final cluster ("cluster 1" = very low adherence; "cluster 2" = low adherence; "cluster 3" = moderate adherence; "cluster 4" = high adherence), and taking into account adherence trend as represented by sequential individual scores. The individual scores should be displayed along with the final cluster for clinicians to fully understand the adherence data. We present three cases to illustrate the use of the proposed clustering method. This pragmatic clustering method can deal with adherence data of variable duration (ie, can be used even if 12 months' worth of data are unavailable) and can cluster adherence data in real time. Empirical support for some of the clustering parameters is not yet available, but the suggested classifications provide a structure to investigate parameters in future prospective datasets in which there are accurate measurements of nebulizer adherence and health outcomes.

Thermal expansion and magnetostriction measurements at cryogenic temperature using the strain gage method

NASA Astrophysics Data System (ADS)

Wang, Wei; Liu, Huiming; Huang, Rongjin; Zhao, Yuqiang; Huang, Chuangjun; Guo, Shibin; Shan, Yi; Li, Laifeng

2018-03-01

Thermal expansion and magnetostriction, the strain responses of a material to temperature and a magnetic field, especially properties at low temperature, are extremely useful to study electronic and phononic properties, phase transitions, quantum criticality, and other interesting phenomena in cryogenic engineering and materials science. However, traditional dilatometers cannot provide magnetic field and ultra low temperature (＜77 K) environment easily. This paper describes the design and test results of thermal expansion and magnetostriction at cryogenic temperature using the strain gage method based on a Physical Properties Measurements System (PPMS). The interfacing software and automation were developed using LabVIEW. The sample temperature range can be tuned continuously between 1.8 K and 400 K. With this PPMS-aided measuring system, we can observe temperature and magnetic field dependence of the linear thermal expansion of different solid materials easily and accurately.

Analytical Energy Gradients for Excited-State Coupled-Cluster Methods

NASA Astrophysics Data System (ADS)

Wladyslawski, Mark; Nooijen, Marcel

The equation-of-motion coupled-cluster (EOM-CC) and similarity transformed equation-of-motion coupled-cluster (STEOM-CC) methods have been firmly established as accurate and routinely applicable extensions of single-reference coupled-cluster theory to describe electronically excited states. An overview of these methods is provided, with emphasis on the many-body similarity transform concept that is the key to a rationalization of their accuracy. The main topic of the paper is the derivation of analytical energy gradients for such non-variational electronic structure approaches, with an ultimate focus on obtaining their detailed algebraic working equations. A general theoretical framework using Lagrange's method of undetermined multipliers is presented, and the method is applied to formulate the EOM-CC and STEOM-CC gradients in abstract operator terms, following the previous work in [P.G. Szalay, Int. J. Quantum Chem. 55 (1995) 151] and [S.R. Gwaltney, R.J. Bartlett, M. Nooijen, J. Chem. Phys. 111 (1999) 58]. Moreover, the systematics of the Lagrange multiplier approach is suitable for automation by computer, enabling the derivation of the detailed derivative equations through a standardized and direct procedure. To this end, we have developed the SMART (Symbolic Manipulation and Regrouping of Tensors) package of automated symbolic algebra routines, written in the Mathematica programming language. The SMART toolkit provides the means to expand, differentiate, and simplify equations by manipulation of the detailed algebraic tensor expressions directly. The Lagrangian multiplier formulation establishes a uniform strategy to perform the automated derivation in a standardized manner: A Lagrange multiplier functional is constructed from the explicit algebraic equations that define the energy in the electronic method; the energy functional is then made fully variational with respect to all of its parameters, and the symbolic differentiations directly yield the explicit

Giant Spherical Cluster with I-C140 Fullerene Topology.

PubMed

Heinl, Sebastian; Peresypkina, Eugenia; Sutter, Jörg; Scheer, Manfred

2015-11-02

We report on an effective cluster expansion of CuBr-linked aggregates by the increase of the steric bulk of the Cp(R) ligand in the pentatopic molecules [Cp(R)Fe(η(5)-P5)]. Using [Cp(BIG)Fe(η(5)-P5)] (Cp(BIG)=C5(4-nBuC6H4)5), the novel multishell aggregate [{Cp(BIG)Fe(η(5:2:1:1:1:1:1)-P5)}12(CuBr)92] is obtained. It shows topological analogy to the theoretically predicted I-C140 fullerene molecule. The spherical cluster was comprehensively characterized by various methods in solution and in the solid state. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

WEIGHING GALAXY CLUSTERS WITH GAS. I. ON THE METHODS OF COMPUTING HYDROSTATIC MASS BIAS

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

Lau, Erwin T.; Nagai, Daisuke; Nelson, Kaylea, E-mail: erwin.lau@yale.edu

2013-11-10

Mass estimates of galaxy clusters from X-ray and Sunyeav-Zel'dovich observations assume the intracluster gas is in hydrostatic equilibrium with their gravitational potential. However, since galaxy clusters are dynamically active objects whose dynamical states can deviate significantly from the equilibrium configuration, the departure from the hydrostatic equilibrium assumption is one of the largest sources of systematic uncertainties in cluster cosmology. In the literature there have been two methods for computing the hydrostatic mass bias based on the Euler and the modified Jeans equations, respectively, and there has been some confusion about the validity of these two methods. The word 'Jeans' wasmore » a misnomer, which incorrectly implies that the gas is collisionless. To avoid further confusion, we instead refer these methods as 'summation' and 'averaging' methods respectively. In this work, we show that these two methods for computing the hydrostatic mass bias are equivalent by demonstrating that the equation used in the second method can be derived from taking spatial averages of the Euler equation. Specifically, we identify the correspondences of individual terms in these two methods mathematically and show that these correspondences are valid to within a few percent level using hydrodynamical simulations of galaxy cluster formation. In addition, we compute the mass bias associated with the acceleration of gas and show that its contribution is small in the virialized regions in the interior of galaxy clusters, but becomes non-negligible in the outskirts of massive galaxy clusters. We discuss future prospects of understanding and characterizing biases in the mass estimate of galaxy clusters using both hydrodynamical simulations and observations and their implications for cluster cosmology.« less

Weighing Galaxy Clusters with Gas. I. On the Methods of Computing Hydrostatic Mass Bias

NASA Astrophysics Data System (ADS)

Lau, Erwin T.; Nagai, Daisuke; Nelson, Kaylea

2013-11-01

Mass estimates of galaxy clusters from X-ray and Sunyeav-Zel'dovich observations assume the intracluster gas is in hydrostatic equilibrium with their gravitational potential. However, since galaxy clusters are dynamically active objects whose dynamical states can deviate significantly from the equilibrium configuration, the departure from the hydrostatic equilibrium assumption is one of the largest sources of systematic uncertainties in cluster cosmology. In the literature there have been two methods for computing the hydrostatic mass bias based on the Euler and the modified Jeans equations, respectively, and there has been some confusion about the validity of these two methods. The word "Jeans" was a misnomer, which incorrectly implies that the gas is collisionless. To avoid further confusion, we instead refer these methods as "summation" and "averaging" methods respectively. In this work, we show that these two methods for computing the hydrostatic mass bias are equivalent by demonstrating that the equation used in the second method can be derived from taking spatial averages of the Euler equation. Specifically, we identify the correspondences of individual terms in these two methods mathematically and show that these correspondences are valid to within a few percent level using hydrodynamical simulations of galaxy cluster formation. In addition, we compute the mass bias associated with the acceleration of gas and show that its contribution is small in the virialized regions in the interior of galaxy clusters, but becomes non-negligible in the outskirts of massive galaxy clusters. We discuss future prospects of understanding and characterizing biases in the mass estimate of galaxy clusters using both hydrodynamical simulations and observations and their implications for cluster cosmology.

Brillouin corrosion expansion sensors for steel reinforced concrete structures using a fiber optic coil winding method.

PubMed

Zhao, Xuefeng; Gong, Peng; Qiao, Guofu; Lu, Jie; Lv, Xingjun; Ou, Jinping

2011-01-01

In this paper, a novel kind of method to monitor corrosion expansion of steel rebars in steel reinforced concrete structures named fiber optic coil winding method is proposed, discussed and tested. It is based on the fiber optical Brillouin sensing technique. Firstly, a strain calibration experiment is designed and conducted to obtain the strain coefficient of single mode fiber optics. Results have shown that there is a good linear relationship between Brillouin frequency and applied strain. Then, three kinds of novel fiber optical Brillouin corrosion expansion sensors with different fiber optic coil winding packaging schemes are designed. Sensors were embedded into concrete specimens to monitor expansion strain caused by steel rebar corrosion, and their performance was studied in a designed electrochemical corrosion acceleration experiment. Experimental results have shown that expansion strain along the fiber optic coil winding area can be detected and measured by the three kinds of sensors with different measurement range during development the corrosion. With the assumption of uniform corrosion, diameters of corrosion steel rebars were obtained using calculated average strains. A maximum expansion strain of 6,738 με was monitored. Furthermore, the uniform corrosion analysis model was established and the evaluation formula to evaluate mass loss rate of steel rebar under a given corrosion rust expansion rate was derived. The research has shown that three kinds of Brillouin sensors can be used to monitor the steel rebar corrosion expansion of reinforced concrete structures with good sensitivity, accuracy and monitoring range, and can be applied to monitor different levels of corrosion. By means of this kind of monitoring technique, quantitative corrosion expansion monitoring can be carried out, with the virtues of long durability, real-time monitoring and quasi-distribution monitoring.

Brillouin Corrosion Expansion Sensors for Steel Reinforced Concrete Structures Using a Fiber Optic Coil Winding Method

PubMed Central

Zhao, Xuefeng; Gong, Peng; Qiao, Guofu; Lu, Jie; Lv, Xingjun; Ou, Jinping

2011-01-01

In this paper, a novel kind of method to monitor corrosion expansion of steel rebars in steel reinforced concrete structures named fiber optic coil winding method is proposed, discussed and tested. It is based on the fiber optical Brillouin sensing technique. Firstly, a strain calibration experiment is designed and conducted to obtain the strain coefficient of single mode fiber optics. Results have shown that there is a good linear relationship between Brillouin frequency and applied strain. Then, three kinds of novel fiber optical Brillouin corrosion expansion sensors with different fiber optic coil winding packaging schemes are designed. Sensors were embedded into concrete specimens to monitor expansion strain caused by steel rebar corrosion, and their performance was studied in a designed electrochemical corrosion acceleration experiment. Experimental results have shown that expansion strain along the fiber optic coil winding area can be detected and measured by the three kinds of sensors with different measurement range during development the corrosion. With the assumption of uniform corrosion, diameters of corrosion steel rebars were obtained using calculated average strains. A maximum expansion strain of 6,738 με was monitored. Furthermore, the uniform corrosion analysis model was established and the evaluation formula to evaluate mass loss rate of steel rebar under a given corrosion rust expansion rate was derived. The research has shown that three kinds of Brillouin sensors can be used to monitor the steel rebar corrosion expansion of reinforced concrete structures with good sensitivity, accuracy and monitoring range, and can be applied to monitor different levels of corrosion. By means of this kind of monitoring technique, quantitative corrosion expansion monitoring can be carried out, with the virtues of long durability, real-time monitoring and quasi-distribution monitoring. PMID:22346672

Minimum number of clusters and comparison of analysis methods for cross sectional stepped wedge cluster randomised trials with binary outcomes: A simulation study.

PubMed

Barker, Daniel; D'Este, Catherine; Campbell, Michael J; McElduff, Patrick

2017-03-09

Stepped wedge cluster randomised trials frequently involve a relatively small number of clusters. The most common frameworks used to analyse data from these types of trials are generalised estimating equations and generalised linear mixed models. A topic of much research into these methods has been their application to cluster randomised trial data and, in particular, the number of clusters required to make reasonable inferences about the intervention effect. However, for stepped wedge trials, which have been claimed by many researchers to have a statistical power advantage over the parallel cluster randomised trial, the minimum number of clusters required has not been investigated. We conducted a simulation study where we considered the most commonly used methods suggested in the literature to analyse cross-sectional stepped wedge cluster randomised trial data. We compared the per cent bias, the type I error rate and power of these methods in a stepped wedge trial setting with a binary outcome, where there are few clusters available and when the appropriate adjustment for a time trend is made, which by design may be confounding the intervention effect. We found that the generalised linear mixed modelling approach is the most consistent when few clusters are available. We also found that none of the common analysis methods for stepped wedge trials were both unbiased and maintained a 5% type I error rate when there were only three clusters. Of the commonly used analysis approaches, we recommend the generalised linear mixed model for small stepped wedge trials with binary outcomes. We also suggest that in a stepped wedge design with three steps, at least two clusters be randomised at each step, to ensure that the intervention effect estimator maintains the nominal 5% significance level and is also reasonably unbiased.

Impact of missing data imputation methods on gene expression clustering and classification.

PubMed

de Souto, Marcilio C P; Jaskowiak, Pablo A; Costa, Ivan G

2015-02-26

Several missing value imputation methods for gene expression data have been proposed in the literature. In the past few years, researchers have been putting a great deal of effort into presenting systematic evaluations of the different imputation algorithms. Initially, most algorithms were assessed with an emphasis on the accuracy of the imputation, using metrics such as the root mean squared error. However, it has become clear that the success of the estimation of the expression value should be evaluated in more practical terms as well. One can consider, for example, the ability of the method to preserve the significant genes in the dataset, or its discriminative/predictive power for classification/clustering purposes. We performed a broad analysis of the impact of five well-known missing value imputation methods on three clustering and four classification methods, in the context of 12 cancer gene expression datasets. We employed a statistical framework, for the first time in this field, to assess whether different imputation methods improve the performance of the clustering/classification methods. Our results suggest that the imputation methods evaluated have a minor impact on the classification and downstream clustering analyses. Simple methods such as replacing the missing values by mean or the median values performed as well as more complex strategies. The datasets analyzed in this study are available at http://costalab.org/Imputation/ .

Comparison of Bayesian clustering and edge detection methods for inferring boundaries in landscape genetics

USGS Publications Warehouse

Safner, T.; Miller, M.P.; McRae, B.H.; Fortin, M.-J.; Manel, S.

2011-01-01

Recently, techniques available for identifying clusters of individuals or boundaries between clusters using genetic data from natural populations have expanded rapidly. Consequently, there is a need to evaluate these different techniques. We used spatially-explicit simulation models to compare three spatial Bayesian clustering programs and two edge detection methods. Spatially-structured populations were simulated where a continuous population was subdivided by barriers. We evaluated the ability of each method to correctly identify boundary locations while varying: (i) time after divergence, (ii) strength of isolation by distance, (iii) level of genetic diversity, and (iv) amount of gene flow across barriers. To further evaluate the methods' effectiveness to detect genetic clusters in natural populations, we used previously published data on North American pumas and a European shrub. Our results show that with simulated and empirical data, the Bayesian spatial clustering algorithms outperformed direct edge detection methods. All methods incorrectly detected boundaries in the presence of strong patterns of isolation by distance. Based on this finding, we support the application of Bayesian spatial clustering algorithms for boundary detection in empirical datasets, with necessary tests for the influence of isolation by distance. ?? 2011 by the authors; licensee MDPI, Basel, Switzerland.

Volume shift and charge instability of simple-metal clusters

NASA Astrophysics Data System (ADS)

Brajczewska, M.; Vieira, A.; Fiolhais, C.; Perdew, J. P.

1996-12-01

Experiment indicates that small clusters show changes (mostly contractions) of the bond lengths with respect to bulk values. We use the stabilized jellium model to study the self-expansion and self-compression of spherical clusters (neutral or ionized) of simple metals. Results from Kohn - Sham density functional theory are presented for small clusters of Al and Na, including negatively-charged ones. We also examine the stability of clusters with respect to charging.

A new method to unveil embedded stellar clusters

NASA Astrophysics Data System (ADS)

Lombardi, Marco; Lada, Charles J.; Alves, João

2017-11-01

In this paper we present a novel method to identify and characterize stellar clusters deeply embedded in a dark molecular cloud. The method is based on measuring stellar surface density in wide-field infrared images using star counting techniques. It takes advantage of the differing H-band luminosity functions (HLFs) of field stars and young stellar populations and is able to statistically associate each star in an image as a member of either the background stellar population or a young stellar population projected on or near the cloud. Moreover, the technique corrects for the effects of differential extinction toward each individual star. We have tested this method against simulations as well as observations. In particular, we have applied the method to 2MASS point sources observed in the Orion A and B complexes, and the results obtained compare very well with those obtained from deep Spitzer and Chandra observations where presence of infrared excess or X-ray emission directly determines membership status for every star. Additionally, our method also identifies unobscured clusters and a low resolution version of the Orion stellar surface density map shows clearly the relatively unobscured and diffuse OB 1a and 1b sub-groups and provides useful insights on their spatial distribution.

Engineered high expansion glass-ceramics having near linear thermal strain and methods thereof

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

Dai, Steve Xunhu; Rodriguez, Mark A.; Lyon, Nathanael L.

The present invention relates to glass-ceramic compositions, as well as methods for forming such composition. In particular, the compositions include various polymorphs of silica that provide beneficial thermal expansion characteristics (e.g., a near linear thermal strain). Also described are methods of forming such compositions, as well as connectors including hermetic seals containing such compositions.

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

NASA Astrophysics Data System (ADS)

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

2015-11-01

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

Distinguishing Functional DNA Words; A Method for Measuring Clustering Levels

NASA Astrophysics Data System (ADS)

Moghaddasi, Hanieh; Khalifeh, Khosrow; Darooneh, Amir Hossein

2017-01-01

Functional DNA sub-sequences and genome elements are spatially clustered through the genome just as keywords in literary texts. Therefore, some of the methods for ranking words in texts can also be used to compare different DNA sub-sequences. In analogy with the literary texts, here we claim that the distribution of distances between the successive sub-sequences (words) is q-exponential which is the distribution function in non-extensive statistical mechanics. Thus the q-parameter can be used as a measure of words clustering levels. Here, we analyzed the distribution of distances between consecutive occurrences of 16 possible dinucleotides in human chromosomes to obtain their corresponding q-parameters. We found that CG as a biologically important two-letter word concerning its methylation, has the highest clustering level. This finding shows the predicting ability of the method in biology. We also proposed that chromosome 18 with the largest value of q-parameter for promoters of genes is more sensitive to dietary and lifestyle. We extended our study to compare the genome of some selected organisms and concluded that the clustering level of CGs increases in higher evolutionary organisms compared to lower ones.

Percolation of the site random-cluster model by Monte Carlo method

NASA Astrophysics Data System (ADS)

Wang, Songsong; Zhang, Wanzhou; Ding, Chengxiang

2015-08-01

We propose a site random-cluster model by introducing an additional cluster weight in the partition function of the traditional site percolation. To simulate the model on a square lattice, we combine the color-assignation and the Swendsen-Wang methods to design a highly efficient cluster algorithm with a small critical slowing-down phenomenon. To verify whether or not it is consistent with the bond random-cluster model, we measure several quantities, such as the wrapping probability Re, the percolating cluster density P∞, and the magnetic susceptibility per site χp, as well as two exponents, such as the thermal exponent yt and the fractal dimension yh of the percolating cluster. We find that for different exponents of cluster weight q =1.5 , 2, 2.5 , 3, 3.5 , and 4, the numerical estimation of the exponents yt and yh are consistent with the theoretical values. The universalities of the site random-cluster model and the bond random-cluster model are completely identical. For larger values of q , we find obvious signatures of the first-order percolation transition by the histograms and the hysteresis loops of percolating cluster density and the energy per site. Our results are helpful for the understanding of the percolation of traditional statistical models.

Galaxy cluster center detection methods with weak lensing

NASA Astrophysics Data System (ADS)

Simet, Melanie

The precise location of galaxy cluster centers is a persistent problem in weak lensing mass estimates and in interpretations of clusters in a cosmological context. In this work, we test methods of centroid determination from weak lensing data and examine the effects of such self-calibration on the measured masses. Drawing on lensing data from the Sloan Digital Sky Survey Stripe 82, a 275 square degree region of coadded data in the Southern Galactic Cap, together with a catalog of MaxBCG clusters, we show that halo substructure as well as shape noise and stochasticity in galaxy positions limit the precision of such a self-calibration (in the context of Stripe 82, to ˜ 500 h-1 kpc or larger) and bias the mass estimates around these points to a level that is likely unacceptable for the purposes of making cosmological measurements. We also project the usefulness of this technique in future surveys.

Unbiased methods for removing systematics from galaxy clustering measurements

NASA Astrophysics Data System (ADS)

Elsner, Franz; Leistedt, Boris; Peiris, Hiranya V.

2016-02-01

Measuring the angular clustering of galaxies as a function of redshift is a powerful method for extracting information from the three-dimensional galaxy distribution. The precision of such measurements will dramatically increase with ongoing and future wide-field galaxy surveys. However, these are also increasingly sensitive to observational and astrophysical contaminants. Here, we study the statistical properties of three methods proposed for controlling such systematics - template subtraction, basic mode projection, and extended mode projection - all of which make use of externally supplied template maps, designed to characterize and capture the spatial variations of potential systematic effects. Based on a detailed mathematical analysis, and in agreement with simulations, we find that the template subtraction method in its original formulation returns biased estimates of the galaxy angular clustering. We derive closed-form expressions that should be used to correct results for this shortcoming. Turning to the basic mode projection algorithm, we prove it to be free of any bias, whereas we conclude that results computed with extended mode projection are biased. Within a simplified setup, we derive analytical expressions for the bias and discuss the options for correcting it in more realistic configurations. Common to all three methods is an increased estimator variance induced by the cleaning process, albeit at different levels. These results enable unbiased high-precision clustering measurements in the presence of spatially varying systematics, an essential step towards realizing the full potential of current and planned galaxy surveys.

A New Soft Computing Method for K-Harmonic Means Clustering.

PubMed

Yeh, Wei-Chang; Jiang, Yunzhi; Chen, Yee-Fen; Chen, Zhe

2016-01-01

The K-harmonic means clustering algorithm (KHM) is a new clustering method used to group data such that the sum of the harmonic averages of the distances between each entity and all cluster centroids is minimized. Because it is less sensitive to initialization than K-means (KM), many researchers have recently been attracted to studying KHM. In this study, the proposed iSSO-KHM is based on an improved simplified swarm optimization (iSSO) and integrates a variable neighborhood search (VNS) for KHM clustering. As evidence of the utility of the proposed iSSO-KHM, we present extensive computational results on eight benchmark problems. From the computational results, the comparison appears to support the superiority of the proposed iSSO-KHM over previously developed algorithms for all experiments in the literature.

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.

Newspaper Reporting on a Cluster of Suicides in the UK.

PubMed

John, Ann; Hawton, Keith; Gunnell, David; Lloyd, Keith; Scourfield, Jonathan; Jones, Phillip A; Luce, Ann; Marchant, Amanda; Platt, Steve; Price, Sian; Dennis, Michael S

2017-01-01

Media reporting may influence suicide clusters through imitation or contagion. In 2008 there was extensive national and international newspaper coverage of a cluster of suicides in young people in the Bridgend area of South Wales, UK. To explore the quantity and quality of newspaper reporting during the identified cluster. Searches were conducted for articles on suicide in Bridgend for 6 months before and after the defined cluster (June 26, 2007, to September 16, 2008). Frequency, quality (using the PRINTQUAL instrument), and sensationalism were examined. In all, 577 newspaper articles were identified. One in seven articles included the suicide method in the headline, 47.3% referred to earlier suicides, and 44% used phrases that guidelines suggest should be avoided. Only 13% included sources of information or advice. A high level of poor-quality and sensationalist reporting was found during an ongoing suicide cluster at the very time when good-quality reporting could be considered important. A broad awareness of media guidelines and expansion and adherence to press codes of practice are required by journalists to ensure ethical reporting.

Prediction of Solvent Physical Properties using the Hierarchical Clustering Method

EPA Science Inventory

Recently a QSAR (Quantitative Structure Activity Relationship) method, the hierarchical clustering method, was developed to estimate acute toxicity values for large, diverse datasets. This methodology has now been applied to the estimate solvent physical properties including sur...

A quasiparticle-based multi-reference coupled-cluster method.

PubMed

Rolik, Zoltán; Kállay, Mihály

2014-10-07

The purpose of this paper is to introduce a quasiparticle-based multi-reference coupled-cluster (MRCC) approach. The quasiparticles are introduced via a unitary transformation which allows us to represent a complete active space reference function and other elements of an orthonormal multi-reference (MR) basis in a determinant-like form. The quasiparticle creation and annihilation operators satisfy the fermion anti-commutation relations. On the basis of these quasiparticles, a generalization of the normal-ordered operator products for the MR case can be introduced as an alternative to the approach of Mukherjee and Kutzelnigg [Recent Prog. Many-Body Theor. 4, 127 (1995); Mukherjee and Kutzelnigg, J. Chem. Phys. 107, 432 (1997)]. Based on the new normal ordering any quasiparticle-based theory can be formulated using the well-known diagram techniques. Beyond the general quasiparticle framework we also present a possible realization of the unitary transformation. The suggested transformation has an exponential form where the parameters, holding exclusively active indices, are defined in a form similar to the wave operator of the unitary coupled-cluster approach. The definition of our quasiparticle-based MRCC approach strictly follows the form of the single-reference coupled-cluster method and retains several of its beneficial properties. Test results for small systems are presented using a pilot implementation of the new approach and compared to those obtained by other MR methods.

Health state evaluation of shield tunnel SHM using fuzzy cluster method

NASA Astrophysics Data System (ADS)

Zhou, Fa; Zhang, Wei; Sun, Ke; Shi, Bin

2015-04-01

Shield tunnel SHM is in the path of rapid development currently while massive monitoring data processing and quantitative health grading remain a real challenge, since multiple sensors belonging to different types are employed in SHM system. This paper addressed the fuzzy cluster method based on fuzzy equivalence relationship for the health evaluation of shield tunnel SHM. The method was optimized by exporting the FSV map to automatically generate the threshold value. A new holistic health score(HHS) was proposed and its effectiveness was validated by conducting a pilot test. A case study on Nanjing Yangtze River Tunnel was presented to apply this method. Three types of indicators, namely soil pressure, pore pressure and steel strain, were used to develop the evaluation set U. The clustering results were verified by analyzing the engineering geological conditions; the applicability and validity of the proposed method was also demonstrated. Besides, the advantage of multi-factor evaluation over single-factor model was discussed by using the proposed HHS. This investigation indicated the fuzzy cluster method and HHS is capable of characterizing the fuzziness of tunnel health, and it is beneficial to clarify the tunnel health evaluation uncertainties.

HUBBLE SPACE TELESCOPE ON TRACK FOR MEASURING THE EXPANSION RATE OF THE UNIVERSE

NASA Technical Reports Server (NTRS)

2002-01-01

rate with an estimate of how much matter is in space. The younger age values from each team assume the Universe is at a critical density where it contains just enough matter to expand indefinitely. The higher age estimates are calculated based on a low density of matter in space. (See 'Science Background' for more information on the expanding Universe.) 'A point of great interest is whether the age of the Universe arrived at this way is really older than the independently derived ages of the oldest stars,' said Saha, an investigator on both Hubble teams. 'The numbers lean on the side that the stellar ages are a little lower, or that the hypothesis that we live in a critical density universe needs to be questioned,' said Saha. 'As further results accumulate over the next few years, we hope to tighten the constraints on these issues.' THE OBSERVATIONS The Key Project team is midway along in their three-year program to derive the expansion rate of the Universe based on precise distance measurements to galaxies. They have now measured Cepheid distances to a dozen galaxies, and are about halfway through their overall program. The Key Project team also presented a preliminary estimate of the distance to the Fornax cluster of galaxies. The estimate was obtained through the detection and measurement with the Hubble Space Telescope of pulsating stars known as Cepheid variables found in the Fornax cluster. The Fornax cluster is measured to be approximately as far away as the Virgo cluster of galaxies -- about 60 million light-years. The Key Project team member who led this effort, Caltech astronomer Barry Madore said, 'This cluster allows us to make independent estimates of the expansion rate of the Universe using a number of different techniques. All of these methods are now in excellent agreement. With Fornax we are now at turning point in this field.' The team is measuring Cepheid distances to the Virgo and Fornax clusters of galaxies as a complementary test. Their strategy

Ultrafast Method for the Analysis of Fluorescence Lifetime Imaging Microscopy Data Based on the Laguerre Expansion Technique

PubMed Central

Jo, Javier A.; Fang, Qiyin; Marcu, Laura

2007-01-01

We report a new deconvolution method for fluorescence lifetime imaging microscopy (FLIM) based on the Laguerre expansion technique. The performance of this method was tested on synthetic and real FLIM images. The following interesting properties of this technique were demonstrated. 1) The fluorescence intensity decay can be estimated simultaneously for all pixels, without a priori assumption of the decay functional form. 2) The computation speed is extremely fast, performing at least two orders of magnitude faster than current algorithms. 3) The estimated maps of Laguerre expansion coefficients provide a new domain for representing FLIM information. 4) The number of images required for the analysis is relatively small, allowing reduction of the acquisition time. These findings indicate that the developed Laguerre expansion technique for FLIM analysis represents a robust and extremely fast deconvolution method that enables practical applications of FLIM in medicine, biology, biochemistry, and chemistry. PMID:19444338

Characterization of micron-size hydrogen clusters using Mie scattering.

PubMed

Jinno, S; Tanaka, H; Matsui, R; Kanasaki, M; Sakaki, H; Kando, M; Kondo, K; Sugiyama, A; Uesaka, M; Kishimoto, Y; Fukuda, Y

2017-08-07

Hydrogen clusters with diameters of a few micrometer range, composed of 10 8-10 hydrogen molecules, have been produced for the first time in an expansion of supercooled, high-pressure hydrogen gas into a vacuum through a conical nozzle connected to a cryogenic pulsed solenoid valve. The size distribution of the clusters has been evaluated by measuring the angular distribution of laser light scattered from the clusters. The data were analyzed based on the Mie scattering theory combined with the Tikhonov regularization method including the instrumental functions, the validity of which was assessed by performing a calibration study using a reference target consisting of standard micro-particles with two different sizes. The size distribution of the clusters was found discrete peaked at 0.33 ± 0.03, 0.65 ± 0.05, 0.81 ± 0.06, 1.40 ± 0.06 and 2.00 ± 0.13 µm in diameter. The highly reproducible and impurity-free nature of the micron-size hydrogen clusters can be a promising target for laser-driven multi-MeV proton sources with the currently available high power lasers.

A Network-Based Algorithm for Clustering Multivariate Repeated Measures Data

NASA Technical Reports Server (NTRS)

Koslovsky, Matthew; Arellano, John; Schaefer, Caroline; Feiveson, Alan; Young, Millennia; Lee, Stuart

2017-01-01

The National Aeronautics and Space Administration (NASA) Astronaut Corps is a unique occupational cohort for which vast amounts of measures data have been collected repeatedly in research or operational studies pre-, in-, and post-flight, as well as during multiple clinical care visits. In exploratory analyses aimed at generating hypotheses regarding physiological changes associated with spaceflight exposure, such as impaired vision, it is of interest to identify anomalies and trends across these expansive datasets. Multivariate clustering algorithms for repeated measures data may help parse the data to identify homogeneous groups of astronauts that have higher risks for a particular physiological change. However, available clustering methods may not be able to accommodate the complex data structures found in NASA data, since the methods often rely on strict model assumptions, require equally-spaced and balanced assessment times, cannot accommodate missing data or differing time scales across variables, and cannot process continuous and discrete data simultaneously. To fill this gap, we propose a network-based, multivariate clustering algorithm for repeated measures data that can be tailored to fit various research settings. Using simulated data, we demonstrate how our method can be used to identify patterns in complex data structures found in practice.

Physics of Galaxy Clusters and How it Affects Cosmological Tests

NASA Technical Reports Server (NTRS)

Vikhlinin, Alexey; Oliversen, Ronald J. (Technical Monitor)

2002-01-01

We have worked on the analysis of the Chandra observations of the nearby and distant clusters of galaxies, and on the expansion of the sample of distant X-ray clusters based on the archival ROSAT PSPC data. Some of the scientific results are discussed.

A fixed mass method for the Kramers-Moyal expansion--application to time series with outliers.

PubMed

Petelczyc, M; Żebrowski, J J; Orłowska-Baranowska, E

2015-03-01

Extraction of stochastic and deterministic components from empirical data-necessary for the reconstruction of the dynamics of the system-is discussed. We determine both components using the Kramers-Moyal expansion. In our earlier papers, we obtained large fluctuations in the magnitude of both terms for rare or extreme valued events in the data. Calculations for such events are burdened by an unsatisfactory quality of the statistics. In general, the method is sensitive to the binning procedure applied for the construction of histograms. Instead of the commonly used constant width of bins, we use here a constant number of counts for each bin. This approach-the fixed mass method-allows to include in the calculation events, which do not yield satisfactory statistics in the fixed bin width method. The method developed is general. To demonstrate its properties, here, we present the modified Kramers-Moyal expansion method and discuss its properties by the application of the fixed mass method to four representative heart rate variability recordings with different numbers of ectopic beats. These beats may be rare events as well as outlying, i.e., very small or very large heart cycle lengths. The properties of ectopic beats are important not only for medical diagnostic purposes but the occurrence of ectopic beats is a general example of the kind of variability that occurs in a signal with outliers. To show that the method is general, we also present results for two examples of data from very different areas of science: daily temperatures at a large European city and recordings of traffics on a highway. Using the fixed mass method, to assess the dynamics leading to the outlying events we studied the occurrence of higher order terms of the Kramers-Moyal expansion in the recordings. We found that the higher order terms of the Kramers-Moyal expansion are negligible for heart rate variability. This finding opens the possibility of the application of the Langevin equation to the

a Three-Step Spatial-Temporal Clustering Method for Human Activity Pattern Analysis

NASA Astrophysics Data System (ADS)

Huang, W.; Li, S.; Xu, S.

2016-06-01

How people move in cities and what they do in various locations at different times form human activity patterns. Human activity pattern plays a key role in in urban planning, traffic forecasting, public health and safety, emergency response, friend recommendation, and so on. Therefore, scholars from different fields, such as social science, geography, transportation, physics and computer science, have made great efforts in modelling and analysing human activity patterns or human mobility patterns. One of the essential tasks in such studies is to find the locations or places where individuals stay to perform some kind of activities before further activity pattern analysis. In the era of Big Data, the emerging of social media along with wearable devices enables human activity data to be collected more easily and efficiently. Furthermore, the dimension of the accessible human activity data has been extended from two to three (space or space-time) to four dimensions (space, time and semantics). More specifically, not only a location and time that people stay and spend are collected, but also what people "say" for in a location at a time can be obtained. The characteristics of these datasets shed new light on the analysis of human mobility, where some of new methodologies should be accordingly developed to handle them. Traditional methods such as neural networks, statistics and clustering have been applied to study human activity patterns using geosocial media data. Among them, clustering methods have been widely used to analyse spatiotemporal patterns. However, to our best knowledge, few of clustering algorithms are specifically developed for handling the datasets that contain spatial, temporal and semantic aspects all together. In this work, we propose a three-step human activity clustering method based on space, time and semantics to fill this gap. One-year Twitter data, posted in Toronto, Canada, is used to test the clustering-based method. The results show that the

Numerical methods on European option second order asymptotic expansions for multiscale stochastic volatility

NASA Astrophysics Data System (ADS)

Canhanga, Betuel; Ni, Ying; Rančić, Milica; Malyarenko, Anatoliy; Silvestrov, Sergei

2017-01-01

After Black-Scholes proposed a model for pricing European Options in 1973, Cox, Ross and Rubinstein in 1979, and Heston in 1993, showed that the constant volatility assumption made by Black-Scholes was one of the main reasons for the model to be unable to capture some market details. Instead of constant volatilities, they introduced stochastic volatilities to the asset dynamic modeling. In 2009, Christoffersen empirically showed "why multifactor stochastic volatility models work so well". Four years later, Chiarella and Ziveyi solved the model proposed by Christoffersen. They considered an underlying asset whose price is governed by two factor stochastic volatilities of mean reversion type. Applying Fourier transforms, Laplace transforms and the method of characteristics they presented a semi-analytical formula to compute an approximate price for American options. The huge calculation involved in the Chiarella and Ziveyi approach motivated the authors of this paper in 2014 to investigate another methodology to compute European Option prices on a Christoffersen type model. Using the first and second order asymptotic expansion method we presented a closed form solution for European option, and provided experimental and numerical studies on investigating the accuracy of the approximation formulae given by the first order asymptotic expansion. In the present paper we will perform experimental and numerical studies for the second order asymptotic expansion and compare the obtained results with results presented by Chiarella and Ziveyi.

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

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

Assessment of the further improved (G'/G)-expansion method and the extended tanh-method in probing exact solutions of nonlinear PDEs.

PubMed

Akbar, M Ali; Ali, Norhashidah Hj Mohd; Mohyud-Din, Syed Tauseef

2013-01-01

The (G'/G)-expansion method is one of the most direct and effective method for obtaining exact solutions of nonlinear partial differential equations (PDEs). In the present article, we construct the exact traveling wave solutions of nonlinear evolution equations in mathematical physics via the (2 + 1)-dimensional breaking soliton equation by using two methods: namely, a further improved (G'/G)-expansion method, where G(ξ) satisfies the auxiliary ordinary differential equation (ODE) [G'(ξ)](2) = p G (2)(ξ) + q G (4)(ξ) + r G (6)(ξ); p, q and r are constants and the well known extended tanh-function method. We demonstrate, nevertheless some of the exact solutions bring out by these two methods are analogous, but they are not one and the same. It is worth mentioning that the first method has not been exercised anybody previously which gives further exact solutions than the second one. PACS numbers 02.30.Jr, 05.45.Yv, 02.30.Ik.

An experimental study of arch perimeter and arch width increase with mandibular expansion: a finite element method.

PubMed

Baswaraj; Hemanth, M; Jayasudha; Patil, Chandrashekhargouda; Sunilkumar, P; Raghuveer, H P; Chandralekha, B

2013-01-01

The objective of this study was to estimate the increase in arch perimeter associated with mandibular lateral expansion, To estimate the increase in intermolar width with mandibular lateral expansion and to find out the changes of tooth inclination with mandibular expansion. The mandibular bone with dentition of indian skeletal specimen was obtained. The computer tomogram (CT) slices of the mandible were taken. Finite element model (FEM): Numerical representation of the geometry was created by dividing the geometry into finite number of elements and the elements were connected together with nodes at the junction. The result of the study showed when 10° of lateral expansion was applied to the lower buccal segment at the center of rotation found at 4.3 mm below the root apex of first molar, a space of 1.3 mm between the canine and first premolar, and thus an increase in arch perimeter of 2.6 mm. The tip of the mesiolingual cusp of the first molar moved 4.2 mm laterally, resulting in a change in intermolar width by 8.4 mm. Three-dimensional simulation showed that 1 mm of intermolar expansion increased the arch perimeter by 0.30 mm. As the finite element method evolves and scientists are able to more clearly define physical properties of biological tissues, more accurate information can be generated at the level that other analytical methods cannot fully provide data.This result would be of value clinically for prediction of the effects of mandibular expansion.

Extensive regularization of the coupled cluster methods based on the generating functional formalism: application to gas-phase benchmarks and to the S(N)2 reaction of CHCl3 and OH- in water

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

Kowalski, Karol; Valiev, Marat

2009-12-21

The recently introduced energy expansion based on the use of generating functional (GF) [K. Kowalski, P.D. Fan, J. Chem. Phys. 130, 084112 (2009)] provides a way of constructing size-consistent non-iterative coupled-cluster (CC) corrections in terms of moments of the CC equations. To take advantage of this expansion in a strongly interacting regime, the regularization of the cluster amplitudes is required in order to counteract the effect of excessive growth of the norm of the CC wavefunction. Although proven to be effcient, the previously discussed form of the regularization does not lead to rigorously size-consistent corrections. In this paper we addressmore » the issue of size-consistent regularization of the GF expansion by redefning the equations for the cluster amplitudes. The performance and basic features of proposed methodology is illustrated on several gas-phase benchmark systems. Moreover, the regularized GF approaches are combined with QM/MM module and applied to describe the SN2 reaction of CHCl3 and OH- in aqueous solution.« less

Is It Feasible to Identify Natural Clusters of TSC-Associated Neuropsychiatric Disorders (TAND)?

PubMed

Leclezio, Loren; Gardner-Lubbe, Sugnet; de Vries, Petrus J

2018-04-01

Tuberous sclerosis complex (TSC) is a genetic disorder with multisystem involvement. The lifetime prevalence of TSC-Associated Neuropsychiatric Disorders (TAND) is in the region of 90% in an apparently unique, individual pattern. This "uniqueness" poses significant challenges for diagnosis, psycho-education, and intervention planning. To date, no studies have explored whether there may be natural clusters of TAND. The purpose of this feasibility study was (1) to investigate the practicability of identifying natural TAND clusters, and (2) to identify appropriate multivariate data analysis techniques for larger-scale studies. TAND Checklist data were collected from 56 individuals with a clinical diagnosis of TSC (n = 20 from South Africa; n = 36 from Australia). Using R, the open-source statistical platform, mean squared contingency coefficients were calculated to produce a correlation matrix, and various cluster analyses and exploratory factor analysis were examined. Ward's method rendered six TAND clusters with good face validity and significant convergence with a six-factor exploratory factor analysis solution. The "bottom-up" data-driven strategies identified a "scholastic" cluster of TAND manifestations, an "autism spectrum disorder-like" cluster, a "dysregulated behavior" cluster, a "neuropsychological" cluster, a "hyperactive/impulsive" cluster, and a "mixed/mood" cluster. These feasibility results suggest that a combination of cluster analysis and exploratory factor analysis methods may be able to identify clinically meaningful natural TAND clusters. Findings require replication and expansion in larger dataset, and could include quantification of cluster or factor scores at an individual level. Copyright © 2018 Elsevier Inc. All rights reserved.

Coherent Anomaly Method Calculation on the Cluster Variation Method. II.

NASA Astrophysics Data System (ADS)

Wada, Koh; Watanabe, Naotosi; Uchida, Tetsuya

The critical exponents of the bond percolation model are calculated in the D(= 2,3,…)-dimensional simple cubic lattice on the basis of Suzuki's coherent anomaly method (CAM) by making use of a series of the pair, the square-cactus and the square approximations of the cluster variation method (CVM) in the s-state Potts model. These simple approximations give reasonable values of critical exponents α, β, γ and ν in comparison with ones estimated by other methods. It is also shown that the results of the pair and the square-cactus approximations can be derived as exact results of the bond percolation model on the Bethe and the square-cactus lattice, respectively, in the presence of ghost field without recourse to the s→1 limit of the s-state Potts model.

Expansion and melting of Xe nanocrystals in Si

NASA Astrophysics Data System (ADS)

Faraci, Giuseppe; Pennisi, Agata R.; Zontone, Federico; Li, Boquan; Petrov, Ivan

2006-12-01

Xe agglomerates confined in a Si matrix by ion implantation were synthesized with different size depending on the implantation process and/or the thermal treatment. At low temperature Xe nanocrystals are formed, whose expansion and melting were studied in the range 15- 300K . Previous high resolution x-ray diffraction spectra were corroborated with complementary techniques such as two-dimensional imaging plate patterns and transmission electron microscopy. We detected fcc Xe nanocrystals whose properties were size dependent. The experiments showed that in annealed samples epitaxial condensation of small Xe clusters, on the cavities of the Si matrix, gave in fact expanded and oriented Xe, suggesting a possible preferential growth of Xe(311) planes oriented orthogonally to the Si[02-2] direction. On the contrary, small Xe clusters in an amorphous Si matrix have a fcc lattice contracted as a consequence of surface tension. Furthermore, a solid-to-liquid phase transition size dependent was found. Expansion of fcc Xe lattice was accurately determined as a function of the temperature. Overpressurized nanocrystals and/or binary size distributions were disproved.

Cluster Size Statistic and Cluster Mass Statistic: Two Novel Methods for Identifying Changes in Functional Connectivity Between Groups or Conditions

PubMed Central

Ing, Alex; Schwarzbauer, Christian

2014-01-01

Functional connectivity has become an increasingly important area of research in recent years. At a typical spatial resolution, approximately 300 million connections link each voxel in the brain with every other. This pattern of connectivity is known as the functional connectome. Connectivity is often compared between experimental groups and conditions. Standard methods used to control the type 1 error rate are likely to be insensitive when comparisons are carried out across the whole connectome, due to the huge number of statistical tests involved. To address this problem, two new cluster based methods – the cluster size statistic (CSS) and cluster mass statistic (CMS) – are introduced to control the family wise error rate across all connectivity values. These methods operate within a statistical framework similar to the cluster based methods used in conventional task based fMRI. Both methods are data driven, permutation based and require minimal statistical assumptions. Here, the performance of each procedure is evaluated in a receiver operator characteristic (ROC) analysis, utilising a simulated dataset. The relative sensitivity of each method is also tested on real data: BOLD (blood oxygen level dependent) fMRI scans were carried out on twelve subjects under normal conditions and during the hypercapnic state (induced through the inhalation of 6% CO2 in 21% O2 and 73%N2). Both CSS and CMS detected significant changes in connectivity between normal and hypercapnic states. A family wise error correction carried out at the individual connection level exhibited no significant changes in connectivity. PMID:24906136

Cluster size statistic and cluster mass statistic: two novel methods for identifying changes in functional connectivity between groups or conditions.

PubMed

Ing, Alex; Schwarzbauer, Christian

2014-01-01

Functional connectivity has become an increasingly important area of research in recent years. At a typical spatial resolution, approximately 300 million connections link each voxel in the brain with every other. This pattern of connectivity is known as the functional connectome. Connectivity is often compared between experimental groups and conditions. Standard methods used to control the type 1 error rate are likely to be insensitive when comparisons are carried out across the whole connectome, due to the huge number of statistical tests involved. To address this problem, two new cluster based methods--the cluster size statistic (CSS) and cluster mass statistic (CMS)--are introduced to control the family wise error rate across all connectivity values. These methods operate within a statistical framework similar to the cluster based methods used in conventional task based fMRI. Both methods are data driven, permutation based and require minimal statistical assumptions. Here, the performance of each procedure is evaluated in a receiver operator characteristic (ROC) analysis, utilising a simulated dataset. The relative sensitivity of each method is also tested on real data: BOLD (blood oxygen level dependent) fMRI scans were carried out on twelve subjects under normal conditions and during the hypercapnic state (induced through the inhalation of 6% CO2 in 21% O2 and 73%N2). Both CSS and CMS detected significant changes in connectivity between normal and hypercapnic states. A family wise error correction carried out at the individual connection level exhibited no significant changes in connectivity.

Comparison of cluster-based and source-attribution methods for estimating transmission risk using large HIV sequence databases.

PubMed

Le Vu, Stéphane; Ratmann, Oliver; Delpech, Valerie; Brown, Alison E; Gill, O Noel; Tostevin, Anna; Fraser, Christophe; Volz, Erik M

2018-06-01

Phylogenetic clustering of HIV sequences from a random sample of patients can reveal epidemiological transmission patterns, but interpretation is hampered by limited theoretical support and statistical properties of clustering analysis remain poorly understood. Alternatively, source attribution methods allow fitting of HIV transmission models and thereby quantify aspects of disease transmission. A simulation study was conducted to assess error rates of clustering methods for detecting transmission risk factors. We modeled HIV epidemics among men having sex with men and generated phylogenies comparable to those that can be obtained from HIV surveillance data in the UK. Clustering and source attribution approaches were applied to evaluate their ability to identify patient attributes as transmission risk factors. We find that commonly used methods show a misleading association between cluster size or odds of clustering and covariates that are correlated with time since infection, regardless of their influence on transmission. Clustering methods usually have higher error rates and lower sensitivity than source attribution method for identifying transmission risk factors. But neither methods provide robust estimates of transmission risk ratios. Source attribution method can alleviate drawbacks from phylogenetic clustering but formal population genetic modeling may be required to estimate quantitative transmission risk factors. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Energetics of charged metal clusters containing vacancies

NASA Astrophysics Data System (ADS)

Pogosov, Valentin V.; Reva, Vitalii I.

2018-01-01

We study theoretically large metal clusters containing vacancies. We propose an approach, which combines the Kohn-Sham results for monovacancy in a bulk of metal and analytical expansions in small parameters cv (relative concentration of vacancies) and RN,v -1, RN ,v being cluster radii. We obtain expressions of the ionization potential and electron affinity in the form of corrections to electron work function, which require only the characteristics of 3D defect-free metal. The Kohn-Sham method is used to calculate the electron profiles, ionization potential, electron affinity, electrical capacitance; dissociation, cohesion, and monovacancy-formation energies of the small perfect clusters NaN, MgN, AlN (N ≤ 270) and the clusters containing a monovacancy (N ≥ 12) in the stabilized-jellium model. The quantum-sized dependences for monovacancy-formation energies are calculated for the Schottky scenario and the "bubble blowing" scenario, and their asymptotic behavior is also determined. It is shown that the asymptotical behaviors of size dependences for these two mechanisms differ from each other and weakly depend on the number of atoms in the cluster. The contribution of monovacancy to energetics of charged clusters and the size dependences of their characteristics and asymptotics are discussed. It is shown that the difference between the characteristics for the neutral and charged clusters is entirely determined by size dependences of ionization potential and electron affinity. Obtained analytical dependences may be useful for the analysis of the results of photoionization experiments and for the estimation of the size dependences of the vacancy concentration including the vicinity of the melting point.

Structural parameters of young star clusters: fractal analysis

NASA Astrophysics Data System (ADS)

Hetem, A.

2017-07-01

A unified view of star formation in the Universe demand detailed and in-depth studies of young star clusters. This work is related to our previous study of fractal statistics estimated for a sample of young stellar clusters (Gregorio-Hetem et al. 2015, MNRAS 448, 2504). The structural properties can lead to significant conclusions about the early stages of cluster formation: 1) virial conditions can be used to distinguish warm collapsed; 2) bound or unbound behaviour can lead to conclusions about expansion; and 3) fractal statistics are correlated to the dynamical evolution and age. The technique of error bars estimation most used in the literature is to adopt inferential methods (like bootstrap) to estimate deviation and variance, which are valid only for an artificially generated cluster. In this paper, we expanded the number of studied clusters, in order to enhance the investigation of the cluster properties and dynamic evolution. The structural parameters were compared with fractal statistics and reveal that the clusters radial density profile show a tendency of the mean separation of the stars increase with the average surface density. The sample can be divided into two groups showing different dynamic behaviour, but they have the same dynamic evolution, since the entire sample was revealed as being expanding objects, for which the substructures do not seem to have been completely erased. These results are in agreement with the simulations adopting low surface densities and supervirial conditions.

Traveling wave solutions of the Boussinesq equation via the new approach of generalized (G'/G)-expansion method.

PubMed

Alam, Md Nur; Akbar, M Ali; Roshid, Harun-Or-

2014-01-01

Exact solutions of nonlinear evolution equations (NLEEs) play a vital role to reveal the internal mechanism of complex physical phenomena. In this work, the exact traveling wave solutions of the Boussinesq equation is studied by using the new generalized (G'/G)-expansion method. Abundant traveling wave solutions with arbitrary parameters are successfully obtained by this method and the wave solutions are expressed in terms of the hyperbolic, trigonometric, and rational functions. It is shown that the new approach of generalized (G'/G)-expansion method is a powerful and concise mathematical tool for solving nonlinear partial differential equations in mathematical physics and engineering. 05.45.Yv, 02.30.Jr, 02.30.Ik.

Study of methods to increase cluster/dislocation loop densities in electrodes

NASA Astrophysics Data System (ADS)

Yang, Xiaoling; Miley, George H.

2009-03-01

Recent research has developed a technique for imbedding ultra-high density deuterium ``clusters'' (50 to 100 atoms per cluster) in various metals such as Palladium (Pd), Beryllium (Be) and Lithium (Li). It was found the thermally dehydrogenated PdHx retained the clusters and exhibited up to 12 percent lower resistance compared to the virginal Pd samplesootnotetextA. G. Lipson, et al. Phys. Solid State. 39 (1997) 1891. SQUID measurements showed that in Pd these condensed matter clusters approach metallic conditions, exhibiting superconducting propertiesootnotetextA. Lipson, et al. Phys. Rev. B 72, 212507 (2005ootnotetextA. G. Lipson, et al. Phys. Lett. A 339, (2005) 414-423. If the fabrication methods under study are successful, a large packing fraction of nuclear reactive clusters can be developed in the electrodes by electrolyte or high pressure gas loading. This will provide a much higher low-energy-nuclear- reaction (LENR) rate than achieved with earlier electrodeootnotetextCastano, C.H., et al. Proc. ICCF-9, Beijing, China 19-24 May, 2002..

Expansion: A Plan for Success.

ERIC Educational Resources Information Center

Callahan, A.P.

This report provides selling brokers' guidelines for the successful expansion of their operations outlining a basic method of preparing an expansion plan. Topic headings are: The Pitfalls of Expansion (The Language of Business, Timely Financial Reporting, Regulatory Agencies of Government, Preoccupation with the Facade of Business, A Business Is a…

Hydrodynamic clustering of droplets in turbulence

NASA Astrophysics Data System (ADS)

Kunnen, Rudie; Yavuz, Altug; van Heijst, Gertjan; Clercx, Herman

2017-11-01

Small, inertial particles are known to cluster in turbulent flows: particles are centrifuged out of eddies and gather in the strain-dominated regions. This so-called preferential concentration is reflected in the radial distribution function (RDF; a quantitative measure of clustering). We study clustering of water droplets in a loudspeaker-driven turbulence chamber. We track the motion of droplets in 3D and calculate the RDF. At moderate scales (a few Kolmogorov lengths) we find the typical power-law scaling of preferential concentration in the RDF. However, at even smaller scales (a few droplet diameters), we encounter a hitherto unobserved additional clustering. We postulate that the additional clustering is due to hydrodynamic interactions, an effect which is typically disregarded in modeling. Using a perturbative expansion of inertial effects in a Stokes-flow description of two interacting spheres, we obtain an expression for the RDF which indeed includes the additional clustering. The additional clustering enhances the collision probability of droplets, which enhances their growth rate due to coalescence. The additional clustering is thus an essential effect in precipitation modeling.

Approximate solution of coupled cluster equations: application to the coupled cluster doubles method and non-covalent interacting systems.

PubMed

Smiga, Szymon; Fabiano, Eduardo

2017-11-15

We have developed a simplified coupled cluster (SCC) methodology, using the basic idea of scaled MP2 methods. The scheme has been applied to the coupled cluster double equations and implemented in three different non-iterative variants. This new method (especially the SCCD[3] variant, which utilizes a spin-resolved formalism) has been found to be very efficient and to yield an accurate approximation of the reference CCD results for both total and interaction energies of different atoms and molecules. Furthermore, we demonstrate that the equations determining the scaling coefficients for the SCCD[3] approach can generate non-empirical SCS-MP2 scaling coefficients which are in good agreement with previous theoretical investigations.

A forward-advancing wave expansion method for numerical solution of large-scale sound propagation problems

NASA Astrophysics Data System (ADS)

Rolla, L. Barrera; Rice, H. J.

2006-09-01

In this paper a "forward-advancing" field discretization method suitable for solving the Helmholtz equation in large-scale problems is proposed. The forward wave expansion method (FWEM) is derived from a highly efficient discretization procedure based on interpolation of wave functions known as the wave expansion method (WEM). The FWEM computes the propagated sound field by means of an exclusively forward advancing solution, neglecting the backscattered field. It is thus analogous to methods such as the (one way) parabolic equation method (PEM) (usually discretized using standard finite difference or finite element methods). These techniques do not require the inversion of large system matrices and thus enable the solution of large-scale acoustic problems where backscatter is not of interest. Calculations using FWEM are presented for two propagation problems and comparisons to data computed with analytical and theoretical solutions and show this forward approximation to be highly accurate. Examples of sound propagation over a screen in upwind and downwind refracting atmospheric conditions at low nodal spacings (0.2 per wavelength in the propagation direction) are also included to demonstrate the flexibility and efficiency of the method.

Cluster beam targets for laser plasma extreme ultraviolet and soft x-ray sources

DOEpatents

Kublak, G.D.; Richardson, M.C.

1996-11-19

Method and apparatus for producing extreme ultraviolet (EUV) and soft x-ray radiation from an ultra-low debris plasma source are disclosed. Targets are produced by the free jet expansion of various gases through a temperature controlled nozzle to form molecular clusters. These target clusters are subsequently irradiated with commercially available lasers of moderate intensity (10{sup 11}--10{sup 12} watts/cm{sup 2}) to produce a plasma radiating in the region of 0.5 to 100 nanometers. By appropriate adjustment of the experimental conditions the laser focus can be moved 10--30 mm from the nozzle thereby eliminating debris produced by plasma erosion of the nozzle. 5 figs.

Cluster beam targets for laser plasma extreme ultraviolet and soft x-ray sources

DOEpatents

Kublak, Glenn D.; Richardson, Martin C. (CREOL

1996-01-01

Method and apparatus for producing extreme ultra violet (EUV) and soft x-ray radiation from an ultra-low debris plasma source are disclosed. Targets are produced by the free jet expansion of various gases through a temperature controlled nozzle to form molecular clusters. These target clusters are subsequently irradiated with commercially available lasers of moderate intensity (10.sup.11 -10.sup.12 watts/cm.sup.2) to produce a plasma radiating in the region of 0.5 to 100 nanometers. By appropriate adjustment of the experimental conditions the laser focus can be moved 10-30 mm from the nozzle thereby eliminating debris produced by plasma erosion of the nozzle.

Recombination-enhanced surface expansion of clusters in intense soft x-ray laser pulses

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

Rupp, Daniela; Flückiger, Leonie; Adolph, Marcus

Here, we studied the nanoplasma formation and explosion dynamics of single large xenon clusters in ultrashort, intense x-ray free-electron laser pulses via ion spectroscopy. The simultaneous measurement of single-shot diffraction images enabled a single-cluster analysis that is free from any averaging over the cluster size and laser intensity distributions. The measured charge state-resolved ion energy spectra show narrow distributions with peak positions that scale linearly with final ion charge state. These two distinct signatures are attributed to highly efficient recombination that eventually leads to the dominant formation of neutral atoms in the cluster. The measured mean ion energies exceed themore » value expected without recombination by more than an order of magnitude, indicating that the energy release resulting from electron-ion recombination constitutes a previously unnoticed nanoplasma heating process. This conclusion is supported by results from semiclassical molecular dynamics simulations.« less

Recombination-enhanced surface expansion of clusters in intense soft x-ray laser pulses

DOE PAGES

Rupp, Daniela; Flückiger, Leonie; Adolph, Marcus; ...

2016-10-07

Here, we studied the nanoplasma formation and explosion dynamics of single large xenon clusters in ultrashort, intense x-ray free-electron laser pulses via ion spectroscopy. The simultaneous measurement of single-shot diffraction images enabled a single-cluster analysis that is free from any averaging over the cluster size and laser intensity distributions. The measured charge state-resolved ion energy spectra show narrow distributions with peak positions that scale linearly with final ion charge state. These two distinct signatures are attributed to highly efficient recombination that eventually leads to the dominant formation of neutral atoms in the cluster. The measured mean ion energies exceed themore » value expected without recombination by more than an order of magnitude, indicating that the energy release resulting from electron-ion recombination constitutes a previously unnoticed nanoplasma heating process. This conclusion is supported by results from semiclassical molecular dynamics simulations.« less

Energy and charge transfer in ionized argon coated water clusters.

PubMed

Kočišek, J; Lengyel, J; Fárník, M; Slavíček, P

2013-12-07

We investigate the electron ionization of clusters generated in mixed Ar-water expansions. The electron energy dependent ion yields reveal the neutral cluster composition and structure: water clusters fully covered with the Ar solvation shell are formed under certain expansion conditions. The argon atoms shield the embedded (H2O)n clusters resulting in the ionization threshold above ≈15 eV for all fragments. The argon atoms also mediate more complex reactions in the clusters: e.g., the charge transfer between Ar(+) and water occurs above the threshold; at higher electron energies above ~28 eV, an excitonic transfer process between Ar(+)* and water opens leading to new products Ar(n)H(+) and (H2O)(n)H(+). On the other hand, the excitonic transfer from the neutral Ar* state at lower energies is not observed although this resonant process was demonstrated previously in a photoionization experiment. Doubly charged fragments (H2O)(n)H2(2+) and (H2O)(n)(2+) ions are observed and Intermolecular Coulomb decay (ICD) processes are invoked to explain their thresholds. The Coulomb explosion of the doubly charged cluster formed within the ICD process is prevented by the stabilization effect of the argon solvent.

Numerical analysis of bubble-cluster formation in an ultrasonic field

NASA Astrophysics Data System (ADS)

Kim, Donghyun; Son, Gihun

2016-11-01

Bubble-cluster formation in an ultrasonic field is investigated numerically solving the conservation equations of mass, momentum and energy. The liquid-gas interface is calculated using the volume-of-fluid method with variable gas density to consider the bubble compressibility. The effect of liquid-gas phase change is also included as the interface source terms of the mass and energy equations. The numerical approach is tested through the simulation of the expansion and contraction motion of a compressed bubble adjacent to a wall. When the bubble is placed in an ultrasonic field, it oscillates radially and then collapses violently. Numerical simulation is also performed for bubble-cluster formation induced by an ultrasonic generator, where the generated bubbles are merged into a macrostructure along the acoustic flow field. The effects of ultrasonic power and frequency, liquid properties and pool temperature on the bubble-cluster formation are investigated. This work was supported by the Korea Institute of Energy Research.

Applicability of the polynomial chaos expansion method for personalization of a cardiovascular pulse wave propagation model.

PubMed

Huberts, W; Donders, W P; Delhaas, T; van de Vosse, F N

2014-12-01

Patient-specific modeling requires model personalization, which can be achieved in an efficient manner by parameter fixing and parameter prioritization. An efficient variance-based method is using generalized polynomial chaos expansion (gPCE), but it has not been applied in the context of model personalization, nor has it ever been compared with standard variance-based methods for models with many parameters. In this work, we apply the gPCE method to a previously reported pulse wave propagation model and compare the conclusions for model personalization with that of a reference analysis performed with Saltelli's efficient Monte Carlo method. We furthermore differentiate two approaches for obtaining the expansion coefficients: one based on spectral projection (gPCE-P) and one based on least squares regression (gPCE-R). It was found that in general the gPCE yields similar conclusions as the reference analysis but at much lower cost, as long as the polynomial metamodel does not contain unnecessary high order terms. Furthermore, the gPCE-R approach generally yielded better results than gPCE-P. The weak performance of the gPCE-P can be attributed to the assessment of the expansion coefficients using the Smolyak algorithm, which might be hampered by the high number of model parameters and/or by possible non-smoothness in the output space. Copyright © 2014 John Wiley & Sons, Ltd.

On star formation in stellar systems. I - Photoionization effects in protoglobular clusters

NASA Technical Reports Server (NTRS)

Tenorio-Tagle, G.; Bodenheimer, P.; Lin, D. N. C.; Noriega-Crespo, A.

1986-01-01

The progressive ionization and subsequent dynamical evolution of nonhomogeneously distributed low-metal-abundance diffuse gas after star formation in globular clusters are investigated analytically, taking the gravitational acceleration due to the stars into account. The basic equations are derived; the underlying assumptions, input parameters, and solution methods are explained; and numerical results for three standard cases (ionization during star formation, ionization during expansion, and evolution resulting in a stable H II region at its equilibrium Stromgren radius) are presented in graphs and characterized in detail. The time scale of residual-gas loss in typical clusters is found to be about the same as the lifetime of a massive star on the main sequence.

Convex Clustering: An Attractive Alternative to Hierarchical Clustering

PubMed Central

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

2015-01-01

The primary goal in cluster analysis is to discover natural groupings of objects. The field of cluster analysis is crowded with diverse methods that make special assumptions about data and address different scientific aims. Despite its shortcomings in accuracy, hierarchical clustering is the dominant clustering method in bioinformatics. Biologists find the trees constructed by hierarchical clustering visually appealing and in tune with their evolutionary perspective. Hierarchical clustering operates on multiple scales simultaneously. This is essential, for instance, in transcriptome data, where one may be interested in making qualitative inferences about how lower-order relationships like gene modules lead to higher-order relationships like pathways or biological processes. The recently developed method of convex clustering preserves the visual appeal of hierarchical clustering while ameliorating its propensity to make false inferences in the presence of outliers and noise. The solution paths generated by convex clustering reveal relationships between clusters that are hidden by static methods such as k-means clustering. The current paper derives and tests a novel proximal distance algorithm for minimizing the objective function of convex clustering. The algorithm separates parameters, accommodates missing data, and supports prior information on relationships. Our program CONVEXCLUSTER incorporating the algorithm is implemented on ATI and nVidia graphics processing units (GPUs) for maximal speed. Several biological examples illustrate the strengths of convex clustering and the ability of the proximal distance algorithm to handle high-dimensional problems. CONVEXCLUSTER can be freely downloaded from the UCLA Human Genetics web site at http://www.genetics.ucla.edu/software/ PMID:25965340

Convex clustering: an attractive alternative to hierarchical clustering.

PubMed

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

2015-05-01

The primary goal in cluster analysis is to discover natural groupings of objects. The field of cluster analysis is crowded with diverse methods that make special assumptions about data and address different scientific aims. Despite its shortcomings in accuracy, hierarchical clustering is the dominant clustering method in bioinformatics. Biologists find the trees constructed by hierarchical clustering visually appealing and in tune with their evolutionary perspective. Hierarchical clustering operates on multiple scales simultaneously. This is essential, for instance, in transcriptome data, where one may be interested in making qualitative inferences about how lower-order relationships like gene modules lead to higher-order relationships like pathways or biological processes. The recently developed method of convex clustering preserves the visual appeal of hierarchical clustering while ameliorating its propensity to make false inferences in the presence of outliers and noise. The solution paths generated by convex clustering reveal relationships between clusters that are hidden by static methods such as k-means clustering. The current paper derives and tests a novel proximal distance algorithm for minimizing the objective function of convex clustering. The algorithm separates parameters, accommodates missing data, and supports prior information on relationships. Our program CONVEXCLUSTER incorporating the algorithm is implemented on ATI and nVidia graphics processing units (GPUs) for maximal speed. Several biological examples illustrate the strengths of convex clustering and the ability of the proximal distance algorithm to handle high-dimensional problems. CONVEXCLUSTER can be freely downloaded from the UCLA Human Genetics web site at http://www.genetics.ucla.edu/software/.

Quantum structural fluctuation in para-hydrogen clusters revealed by the variational path integral method.

PubMed

Miura, Shinichi

2018-03-14

In this paper, the ground state of para-hydrogen clusters for size regime N ≤ 40 has been studied by our variational path integral molecular dynamics method. Long molecular dynamics calculations have been performed to accurately evaluate ground state properties. The chemical potential of the hydrogen molecule is found to have a zigzag size dependence, indicating the magic number stability for the clusters of the size N = 13, 26, 29, 34, and 39. One-body density of the hydrogen molecule is demonstrated to have a structured profile, not a melted one. The observed magic number stability is examined using the inherent structure analysis. We also have developed a novel method combining our variational path integral hybrid Monte Carlo method with the replica exchange technique. We introduce replicas of the original system bridging from the structured to the melted cluster, which is realized by scaling the potential energy of the system. Using the enhanced sampling method, the clusters are demonstrated to have the structured density profile in the ground state.

Quantum structural fluctuation in para-hydrogen clusters revealed by the variational path integral method

NASA Astrophysics Data System (ADS)

Miura, Shinichi

2018-03-01

In this paper, the ground state of para-hydrogen clusters for size regime N ≤ 40 has been studied by our variational path integral molecular dynamics method. Long molecular dynamics calculations have been performed to accurately evaluate ground state properties. The chemical potential of the hydrogen molecule is found to have a zigzag size dependence, indicating the magic number stability for the clusters of the size N = 13, 26, 29, 34, and 39. One-body density of the hydrogen molecule is demonstrated to have a structured profile, not a melted one. The observed magic number stability is examined using the inherent structure analysis. We also have developed a novel method combining our variational path integral hybrid Monte Carlo method with the replica exchange technique. We introduce replicas of the original system bridging from the structured to the melted cluster, which is realized by scaling the potential energy of the system. Using the enhanced sampling method, the clusters are demonstrated to have the structured density profile in the ground state.

The Formation of Galaxies and Clusters.

ERIC Educational Resources Information Center

Gregory, Stephen; Morrison, Nancy D.

1985-01-01

Summarizes recent research on the formation of galaxies and clusters, focusing on research examining how the materials in galaxies seen today separated from the universal expansion and collapsed into stable bodies. A list of six nontechnical books and articles for readers with less background is included. (JN)

System and method for cancelling expansion waves in a wave rotor

NASA Astrophysics Data System (ADS)

Paxson, Daniel E.

1993-12-01

A wave rotor system that is comprised of a wave rotor coupled to first and second plates is described. Special ports are provided, one in each of the first and second end plates, to cancel expansion waves generated by the release of working fluid from the wave rotor. One of the expansion waves is reflected in the wave rotor from a reflecting portion and provided to the special port in the second end plate. Fluid present at the special port in the second end plate has a stagnation pressure and mass flow which is the same as that of the cells of the wave rotor communicating with such special port. This allows for cancellation of the expansion wave generated by the release of working fluid from the wave rotor. The special port in the second end plate has a first end corresponding to the head of the expansion wave and a second end corresponding to the tail of the expansion wave. Also, the special port is configured to continually change along the circumference of the second end plate to affect expansion wave cancellation. An expansion wave generated by a second release of working fluid from the wave rotor is cancelled in a similar manner to that described above using a special port in the first end plate. The cycle of operation of the wave rotor system is designed so that the stagnation pressure and mass flow of the fluid present at the special ports is the same so that the special ports may be connected by a common duct.

The potential of clustering methods to define intersection test scenarios: Assessing real-life performance of AEB.

PubMed

Sander, Ulrich; Lubbe, Nils

2018-04-01

Intersection accidents are frequent and harmful. The accident types 'straight crossing path' (SCP), 'left turn across path - oncoming direction' (LTAP/OD), and 'left-turn across path - lateral direction' (LTAP/LD) represent around 95% of all intersection accidents and one-third of all police-reported car-to-car accidents in Germany. The European New Car Assessment Program (Euro NCAP) have announced that intersection scenarios will be included in their rating from 2020; however, how these scenarios are to be tested has not been defined. This study investigates whether clustering methods can be used to identify a small number of test scenarios sufficiently representative of the accident dataset to evaluate Intersection Automated Emergency Braking (AEB). Data from the German In-Depth Accident Study (GIDAS) and the GIDAS-based Pre-Crash Matrix (PCM) from 1999 to 2016, containing 784 SCP and 453 LTAP/OD accidents, were analyzed with principal component methods to identify variables that account for the relevant total variances of the sample. Three different methods for data clustering were applied to each of the accident types, two similarity-based approaches, namely Hierarchical Clustering (HC) and Partitioning Around Medoids (PAM), and the probability-based Latent Class Clustering (LCC). The optimum number of clusters was derived for HC and PAM with the silhouette method. The PAM algorithm was both initiated with random start medoid selection and medoids from HC. For LCC, the Bayesian Information Criterion (BIC) was used to determine the optimal number of clusters. Test scenarios were defined from optimal cluster medoids weighted by their real-life representation in GIDAS. The set of variables for clustering was further varied to investigate the influence of variable type and character. We quantified how accurately each cluster variation represents real-life AEB performance using pre-crash simulations with PCM data and a generic algorithm for AEB intervention. The

Unsupervised Learning —A Novel Clustering Method for Rolling Bearing Faults Identification

NASA Astrophysics Data System (ADS)

Kai, Li; Bo, Luo; Tao, Ma; Xuefeng, Yang; Guangming, Wang

2017-12-01

To promptly process the massive fault data and automatically provide accurate diagnosis results, numerous studies have been conducted on intelligent fault diagnosis of rolling bearing. Among these studies, such as artificial neural networks, support vector machines, decision trees and other supervised learning methods are used commonly. These methods can detect the failure of rolling bearing effectively, but to achieve better detection results, it often requires a lot of training samples. Based on above, a novel clustering method is proposed in this paper. This novel method is able to find the correct number of clusters automatically the effectiveness of the proposed method is validated using datasets from rolling element bearings. The diagnosis results show that the proposed method can accurately detect the fault types of small samples. Meanwhile, the diagnosis results are also relative high accuracy even for massive samples.

Investigation of the cluster formation in lithium niobate crystals by computer modeling method

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

Voskresenskii, V. M.; Starodub, O. R., E-mail: ol-star@mail.ru; Sidorov, N. V.

The processes occurring upon the formation of energetically equilibrium oxygen-octahedral clusters in the ferroelectric phase of a stoichiometric lithium niobate (LiNbO{sub 3}) crystal have been investigated by the computer modeling method within the semiclassical atomistic model. An energetically favorable cluster size (at which a structure similar to that of a congruent crystal is organized) is shown to exist. A stoichiometric cluster cannot exist because of the electroneutrality loss. The most energetically favorable cluster is that with a Li/Nb ratio of about 0.945, a value close to the lithium-to-niobium ratio for a congruent crystal.

Threshold selection for classification of MR brain images by clustering method

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

Moldovanu, Simona; Dumitru Moţoc High School, 15 Milcov St., 800509, Galaţi; Obreja, Cristian

Given a grey-intensity image, our method detects the optimal threshold for a suitable binarization of MR brain images. In MR brain image processing, the grey levels of pixels belonging to the object are not substantially different from the grey levels belonging to the background. Threshold optimization is an effective tool to separate objects from the background and further, in classification applications. This paper gives a detailed investigation on the selection of thresholds. Our method does not use the well-known method for binarization. Instead, we perform a simple threshold optimization which, in turn, will allow the best classification of the analyzedmore » images into healthy and multiple sclerosis disease. The dissimilarity (or the distance between classes) has been established using the clustering method based on dendrograms. We tested our method using two classes of images: the first consists of 20 T2-weighted and 20 proton density PD-weighted scans from two healthy subjects and from two patients with multiple sclerosis. For each image and for each threshold, the number of the white pixels (or the area of white objects in binary image) has been determined. These pixel numbers represent the objects in clustering operation. The following optimum threshold values are obtained, T = 80 for PD images and T = 30 for T2w images. Each mentioned threshold separate clearly the clusters that belonging of the studied groups, healthy patient and multiple sclerosis disease.« less

Threshold selection for classification of MR brain images by clustering method

NASA Astrophysics Data System (ADS)

Moldovanu, Simona; Obreja, Cristian; Moraru, Luminita

2015-12-01

Given a grey-intensity image, our method detects the optimal threshold for a suitable binarization of MR brain images. In MR brain image processing, the grey levels of pixels belonging to the object are not substantially different from the grey levels belonging to the background. Threshold optimization is an effective tool to separate objects from the background and further, in classification applications. This paper gives a detailed investigation on the selection of thresholds. Our method does not use the well-known method for binarization. Instead, we perform a simple threshold optimization which, in turn, will allow the best classification of the analyzed images into healthy and multiple sclerosis disease. The dissimilarity (or the distance between classes) has been established using the clustering method based on dendrograms. We tested our method using two classes of images: the first consists of 20 T2-weighted and 20 proton density PD-weighted scans from two healthy subjects and from two patients with multiple sclerosis. For each image and for each threshold, the number of the white pixels (or the area of white objects in binary image) has been determined. These pixel numbers represent the objects in clustering operation. The following optimum threshold values are obtained, T = 80 for PD images and T = 30 for T2w images. Each mentioned threshold separate clearly the clusters that belonging of the studied groups, healthy patient and multiple sclerosis disease.

Early dynamical evolution of substructured stellar clusters

NASA Astrophysics Data System (ADS)

Dorval, Julien; Boily, Christian

2015-08-01

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

Source clustering in the Hi-GAL survey determined using a minimum spanning tree method

NASA Astrophysics Data System (ADS)

Beuret, M.; Billot, N.; Cambrésy, L.; Eden, D. J.; Elia, D.; Molinari, S.; Pezzuto, S.; Schisano, E.

2017-01-01

Aims: The aims are to investigate the clustering of the far-infrared sources from the Herschel infrared Galactic Plane Survey (Hi-GAL) in the Galactic longitude range of -71 to 67 deg. These clumps, and their spatial distribution, are an imprint of the original conditions within a molecular cloud. This will produce a catalogue of over-densities. Methods: The minimum spanning tree (MST) method was used to identify the over-densities in two dimensions. The catalogue was further refined by folding in heliocentric distances, resulting in more reliable over-densities, which are cluster candidates. Results: We found 1633 over-densities with more than ten members. Of these, 496 are defined as cluster candidates because of the reliability of the distances, with a further 1137 potential cluster candidates. The spatial distributions of the cluster candidates are different in the first and fourth quadrants, with all clusters following the spiral structure of the Milky Way. The cluster candidates are fractal. The clump mass functions of the clustered and isolated are statistically indistinguishable from each other and are consistent with Kroupa's initial mass function. Hi-GAL is a key-project of the Herschel Space Observatory survey (Pilbratt et al. 2010) and uses the PACS (Poglitsch et al. 2010) and SPIRE (Griffin et al. 2010) cameras in parallel mode.The catalogues of cluster candidates and potential clusters are 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/597/A114

Population Genetics of Three Dimensional Range Expansions

NASA Astrophysics Data System (ADS)

Lavrentovich, Maxim; Nelson, David

2014-03-01

We develop a simple model of genetic diversity in growing spherical cell clusters, where the growth is confined to the cluster surface. This kind of growth occurs in cells growing in soft agar, and can also serve as a simple model of avascular tumors. Mutation-selection balance in these radial expansions is strongly influenced by scaling near a neutral, voter model critical point and by the inflating frontier. We develop a scaling theory to describe how the dynamics of mutation-selection balance is cut off by inflation. Genetic drift, i.e., local fluctuations in the genetic diversity, also plays an important role, and can lead to the extinction even of selectively advantageous strains. We calculate this extinction probability, taking into account the effect of rough population frontiers.

Testing Gravity and Cosmic Acceleration with Galaxy Clustering

NASA Astrophysics Data System (ADS)

Kazin, Eyal; Tinker, J.; Sanchez, A. G.; Blanton, M.

2012-01-01

The large-scale structure contains vast amounts of cosmological information that can help understand the accelerating nature of the Universe and test gravity on large scales. Ongoing and future sky surveys are designed to test these using various techniques applied on clustering measurements of galaxies. We present redshift distortion measurements of the Sloan Digital Sky Survey II Luminous Red Galaxy sample. We find that when combining the normalized quadrupole Q with the projected correlation function wp(rp) along with cluster counts (Rapetti et al. 2010), results are consistent with General Relativity. The advantage of combining Q and wp is the addition of the bias information, when using the Halo Occupation Distribution framework. We also present improvements to the standard technique of measuring Hubble expansion rates H(z) and angular diameter distances DA(z) when using the baryonic acoustic feature as a standard ruler. We introduce clustering wedges as an alternative basis to the multipole expansion and show that it yields similar constraints. This alternative basis serves as a useful technique to test for systematics, and ultimately improve measurements of the cosmic acceleration.

Spatial temporal clustering for hotspot using kulldorff scan statistic method (KSS): A case in Riau Province

NASA Astrophysics Data System (ADS)

Hudjimartsu, S. A.; Djatna, T.; Ambarwari, A.; Apriliantono

2017-01-01

The forest fires in Indonesia occurs frequently in the dry season. Almost all the causes of forest fires are caused by the human activity itself. The impact of forest fires is the loss of biodiversity, pollution hazard and harm the economy of surrounding communities. To prevent fires required the method, one of them with spatial temporal clustering. Spatial temporal clustering formed grouping data so that the results of these groupings can be used as initial information on fire prevention. To analyze the fires, used hotspot data as early indicator of fire spot. Hotspot data consists of spatial and temporal dimensions can be processed using the Spatial Temporal Clustering with Kulldorff Scan Statistic (KSS). The result of this research is to the effectiveness of KSS method to cluster spatial hotspot in a case within Riau Province and produces two types of clusters, most cluster and secondary cluster. This cluster can be used as an early fire warning information.

Fast Electron Correlation Methods for Molecular Clusters without Basis Set Superposition Errors

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

Kamiya, Muneaki; Hirata, So; Valiev, Marat

2008-02-19

Two critical extensions to our fast, accurate, and easy-to-implement binary or ternary interaction method for weakly-interacting molecular clusters [Hirata et al. Mol. Phys. 103, 2255 (2005)] have been proposed, implemented, and applied to water hexamers, hydrogen fluoride chains and rings, and neutral and zwitterionic glycine–water clusters with an excellent result for an initial performance assessment. Our original method included up to two- or three-body Coulomb, exchange, and correlation energies exactly and higher-order Coulomb energies in the dipole–dipole approximation. In this work, the dipole moments are replaced by atom-centered point charges determined so that they reproduce the electrostatic potentials of themore » cluster subunits as closely as possible and also self-consistently with one another in the cluster environment. They have been shown to lead to dramatic improvement in the description of short-range electrostatic potentials not only of large, charge-separated subunits like zwitterionic glycine but also of small subunits. Furthermore, basis set superposition errors (BSSE) known to plague direct evaluation of weak interactions have been eliminated by com-bining the Valiron–Mayer function counterpoise (VMFC) correction with our binary or ternary interaction method in an economical fashion (quadratic scaling n2 with respect to the number of subunits n when n is small and linear scaling when n is large). A new variant of VMFC has also been proposed in which three-body and all higher-order Coulomb effects on BSSE are estimated approximately. The BSSE-corrected ternary interaction method with atom-centered point charges reproduces the VMFC-corrected results of conventional electron correlation calculations within 0.1 kcal/mol. The proposed method is significantly more accurate and also efficient than conventional correlation methods uncorrected of BSSE.« less

Puzzle of magnetic moments of Ni clusters revisited using quantum Monte Carlo method.

PubMed

Lee, Hung-Wen; Chang, Chun-Ming; Hsing, Cheng-Rong

2017-02-28

The puzzle of the magnetic moments of small nickel clusters arises from the discrepancy between values predicted using density functional theory (DFT) and experimental measurements. Traditional DFT approaches underestimate the magnetic moments of nickel clusters. Two fundamental problems are associated with this puzzle, namely, calculating the exchange-correlation interaction accurately and determining the global minimum structures of the clusters. Theoretically, the two problems can be solved using quantum Monte Carlo (QMC) calculations and the ab initio random structure searching (AIRSS) method correspondingly. Therefore, we combined the fixed-moment AIRSS and QMC methods to investigate the magnetic properties of Ni n (n = 5-9) clusters. The spin moments of the diffusion Monte Carlo (DMC) ground states are higher than those of the Perdew-Burke-Ernzerhof ground states and, in the case of Ni 8-9 , two new ground-state structures have been discovered using the DMC calculations. The predicted results are closer to the experimental findings, unlike the results predicted in previous standard DFT studies.

Serial Tissue Expansion at the Same Site in Pediatric Patients: Is the Subsequent Expansion Faster?

PubMed Central

Lee, Moon Ki; Park, Seong Oh; Choi, Tae Hyun

2017-01-01

Background Serial tissue expansion is performed to remove giant congenital melanocytic nevi. However, there have been no studies comparing the expansion rate between the subsequent and preceding expansions. In this study, we analyzed the rate of expansion in accordance with the number of surgeries, expander location, expander size, and sex. Methods A retrospective analysis was performed in pediatric patients who underwent tissue expansion for giant congenital melanocytic nevi. We tested four factors that may influence the expansion rate: The number of surgeries, expander location, expander size, and sex. The rate of expansion was calculated by dividing the ‘inflation amount’ by the ‘expander size’. Results The expansion rate, compared with the first-time group, was 1.25 times higher in the second-or-more group (P=0.04) and 1.84 times higher in the third-or-more group (P<0.01). The expansion rate was higher at the trunk than at other sites (P<0.01). There was a tendency of lower expansion rate for larger expanders (P=0.03). Sex did not affect the expansion rate. Conclusions There was a positive correlation between the number of surgeries and the expansion rate, a positive correlation between the expander location and the expansion rate, and a negative correlation between the expander size and the expansion rate. PMID:29076319

Iterative expansion microscopy.

PubMed

Chang, Jae-Byum; Chen, Fei; Yoon, Young-Gyu; Jung, Erica E; Babcock, Hazen; Kang, Jeong Seuk; Asano, Shoh; Suk, Ho-Jun; Pak, Nikita; Tillberg, Paul W; Wassie, Asmamaw T; Cai, Dawen; Boyden, Edward S

2017-06-01

We recently developed a method called expansion microscopy, in which preserved biological specimens are physically magnified by embedding them in a densely crosslinked polyelectrolyte gel, anchoring key labels or biomolecules to the gel, mechanically homogenizing the specimen, and then swelling the gel-specimen composite by ∼4.5× in linear dimension. Here we describe iterative expansion microscopy (iExM), in which a sample is expanded ∼20×. After preliminary expansion a second swellable polymer mesh is formed in the space newly opened up by the first expansion, and the sample is expanded again. iExM expands biological specimens ∼4.5 × 4.5, or ∼20×, and enables ∼25-nm-resolution imaging of cells and tissues on conventional microscopes. We used iExM to visualize synaptic proteins, as well as the detailed architecture of dendritic spines, in mouse brain circuitry.

Not all stars form in clusters - measuring the kinematics of OB associations with Gaia

NASA Astrophysics Data System (ADS)

Ward, Jacob L.; Kruijssen, J. M. Diederik

2018-04-01

It is often stated that star clusters are the fundamental units of star formation and that most (if not all) stars form in dense stellar clusters. In this monolithic formation scenario, low-density OB associations are formed from the expansion of gravitationally bound clusters following gas expulsion due to stellar feedback. N-body simulations of this process show that OB associations formed this way retain signs of expansion and elevated radial anisotropy over tens of Myr. However, recent theoretical and observational studies suggest that star formation is a hierarchical process, following the fractal nature of natal molecular clouds and allowing the formation of large-scale associations in situ. We distinguish between these two scenarios by characterizing the kinematics of OB associations using the Tycho-Gaia Astrometric Solution catalogue. To this end, we quantify four key kinematic diagnostics: the number ratio of stars with positive radial velocities to those with negative radial velocities, the median radial velocity, the median radial velocity normalized by the tangential velocity, and the radial anisotropy parameter. Each quantity presents a useful diagnostic of whether the association was more compact in the past. We compare these diagnostics to models representing random motion and the expanding products of monolithic cluster formation. None of these diagnostics show evidence of expansion, either from a single cluster or multiple clusters, and the observed kinematics are better represented by a random velocity distribution. This result favours the hierarchical star formation model in which a minority of stars forms in bound clusters and large-scale, hierarchically structured associations are formed in situ.

Weighing the Giants - I. Weak-lensing masses for 51 massive galaxy clusters: project overview, data analysis methods and cluster images

NASA Astrophysics Data System (ADS)

von der Linden, Anja; Allen, Mark T.; Applegate, Douglas E.; Kelly, Patrick L.; Allen, Steven W.; Ebeling, Harald; Burchat, Patricia R.; Burke, David L.; Donovan, David; Morris, R. Glenn; Blandford, Roger; Erben, Thomas; Mantz, Adam

2014-03-01

uncertainty for our weak-lensing mass measurements. In accompanying papers, we discuss the key aspects of our photometric calibration and photometric redshift measurements (Kelly et al.), and measure cluster masses using two methods, including a novel Bayesian weak-lensing approach that makes full use of the photometric redshift probability distributions for individual background galaxies (Applegate et al.). In subsequent papers, we will incorporate these weak-lensing mass measurements into a self-consistent framework to simultaneously determine cluster scaling relations and cosmological parameters.

Electron attenuation in free, neutral ethane clusters.

PubMed

Winkler, M; Myrseth, V; Harnes, J; Børve, K J

2014-10-28

The electron effective attenuation length (EAL) in free, neutral ethane clusters has been determined at 40 eV kinetic energy by combining carbon 1s x-ray photoelectron spectroscopy and theoretical lineshape modeling. More specifically, theory is employed to form model spectra on a grid in cluster size (N) and EAL (λ), allowing N and λ to be determined by optimizing the goodness-of-fit χ(2)(N, λ) between model and observed spectra. Experimentally, the clusters were produced in an adiabatic-expansion setup using helium as the driving gas, spanning a range of 100-600 molecules in mean cluster size. The effective attenuation length was determined to be 8.4 ± 1.9 Å, in good agreement with an independent estimate of 10 Å formed on the basis of molecular electron-scattering data and Monte Carlo simulations. The aggregation state of the clusters as well as the cluster temperature and its importance to the derived EAL value are discussed in some depth.

Initial conditions of formation of starburst clusters: constraints from stellar dynamics

NASA Astrophysics Data System (ADS)

Banerjee, Sambaran

2017-03-01

How starburst clusters form out of molecular clouds is still an open question. In this article, I highlight some of the key constraints in this regard, that one can get from the dynamical evolutionary properties of dense stellar systems. I particularly focus on secular expansion of massive star clusters and hierarchical merging of sub-clusters, and discuss their implications vis-á-vis the observed properties of young massive clusters. The analysis suggests that residual gas expulsion is necessary for shaping these clusters as we see them today, irrespective of their monolithic or hierarchical mode of formation.

Rigid geometry solves “curse of dimensionality” effects in clustering methods: An application to omics data

PubMed Central

2017-01-01

The quality of samples preserved long term at ultralow temperatures has not been adequately studied. To improve our understanding, we need a strategy to analyze protein degradation and metabolism at subfreezing temperatures. To do this, we obtained liquid chromatography-mass spectrometry (LC/MS) data of calculated protein signal intensities in HEK-293 cells. Our first attempt at directly clustering the values failed, most likely due to the so-called “curse of dimensionality”. The clusters were not reproducible, and the outputs differed with different methods. By utilizing rigid geometry with a prime ideal I-adic (p-adic) metric, however, we rearranged the sample clusters into a meaningful and reproducible order, and the results were the same with each of the different clustering methods tested. Furthermore, we have also succeeded in application of this method to expression array data in similar situations. Thus, we eliminated the “curse of dimensionality” from the data set, at least in clustering methods. It is possible that our approach determines a characteristic value of systems that follow a Boltzmann distribution. PMID:28614363

Isentropic expansion and related thermodynamic properties of non-ionic amphiphile-water mixtures.

PubMed

Reis, João Carlos R; Douhéret, Gérard; Davis, Michael I; Fjellanger, Inger Johanne; Høiland, Harald

2008-01-28

A concise thermodynamic formalism is developed for the molar isentropic thermal expansion, ES,m = ( partial differential Vm/ partial differential T)(Sm,x), and the ideal and excess quantities for the molar, apparent molar and partial molar isentropic expansions of binary liquid mixtures. Ultrasound speeds were determined by means of the pulse-echo-overlap method in aqueous mixtures of 2-methylpropan-2-ol at 298.15 K over the entire composition range. These data complement selected extensive literature data on density, isobaric heat capacity and ultrasound speed for 9 amphiphile (methanol, ethanol, propan-1-ol, propan-2-ol, 2-methylpropan-2-ol, ethane-1,2-diol, 2-methoxyethanol, 2-ethoxyethanol or 2-butoxyethanol)-water binary systems, which form the basis of tables listing molar and excess molar isobaric expansions and heat capacities, and molar and excess molar isentropic compressions and expansions at 298.15 K and at 65 fixed mole fractions spanning the entire composition range and fine-grained in the water-rich region. The dependence on composition of these 9 systems is graphically depicted for the excess molar isobaric and isentropic expansions and for the excess partial molar isobaric and isentropic expansions of the amphiphile. The analysis shows that isentropic thermal expansion properties give a much stronger response to amphiphile-water molecular interactions than do their isobaric counterparts. Depending on the pair property-system, the maximum excess molar isentropic value is generally twenty- to a hundred-fold greater than the corresponding maximum isobaric value, and occurs at a lower mole fraction of the amphiphile. Values at infinite dilution of the 9 amphiphiles in water are given for the excess partial molar isobaric heat capacity, isentropic compression, isobaric expansion and isentropic expansion. These values are interpreted in terms of the changes occurring when amphiphile molecules cluster into an oligomeric form. Present results are discussed

Application of clustering methods: Regularized Markov clustering (R-MCL) for analyzing dengue virus similarity

NASA Astrophysics Data System (ADS)

Lestari, D.; Raharjo, D.; Bustamam, A.; Abdillah, B.; Widhianto, W.

2017-07-01

Dengue virus consists of 10 different constituent proteins and are classified into 4 major serotypes (DEN 1 - DEN 4). This study was designed to perform clustering against 30 protein sequences of dengue virus taken from Virus Pathogen Database and Analysis Resource (VIPR) using Regularized Markov Clustering (R-MCL) algorithm and then we analyze the result. By using Python program 3.4, R-MCL algorithm produces 8 clusters with more than one centroid in several clusters. The number of centroid shows the density level of interaction. Protein interactions that are connected in a tissue, form a complex protein that serves as a specific biological process unit. The analysis of result shows the R-MCL clustering produces clusters of dengue virus family based on the similarity role of their constituent protein, regardless of serotypes.

Research progress on expansive soil cracks under changing environment.

PubMed

Shi, Bei-xiao; Zheng, Cheng-feng; Wu, Jin-kun

2014-01-01

Engineering problems shunned previously rise to the surface gradually with the activities of reforming the natural world in depth, the problem of expansive soil crack under the changing environment becoming a control factor of expansive soil slope stability. The problem of expansive soil crack has gradually become a research hotspot, elaborates the occurrence and development of cracks from the basic properties of expansive soil, and points out the role of controlling the crack of expansive soil strength. We summarize the existing research methods and results of expansive soil crack characteristics. Improving crack measurement and calculation method and researching the crack depth measurement, statistical analysis method, crack depth and surface feature relationship will be the future direction.

The cosmological analysis of X-ray cluster surveys - I. A new method for interpreting number counts

NASA Astrophysics Data System (ADS)

Clerc, N.; Pierre, M.; Pacaud, F.; Sadibekova, T.

2012-07-01

We present a new method aimed at simplifying the cosmological analysis of X-ray cluster surveys. It is based on purely instrumental observable quantities considered in a two-dimensional X-ray colour-magnitude diagram (hardness ratio versus count rate). The basic principle is that even in rather shallow surveys, substantial information on cluster redshift and temperature is present in the raw X-ray data and can be statistically extracted; in parallel, such diagrams can be readily predicted from an ab initio cosmological modelling. We illustrate the methodology for the case of a 100-deg2XMM survey having a sensitivity of ˜10-14 erg s-1 cm-2 and fit at the same time, the survey selection function, the cluster evolutionary scaling relations and the cosmology; our sole assumption - driven by the limited size of the sample considered in the case study - is that the local cluster scaling relations are known. We devote special attention to the realistic modelling of the count-rate measurement uncertainties and evaluate the potential of the method via a Fisher analysis. In the absence of individual cluster redshifts, the count rate and hardness ratio (CR-HR) method appears to be much more efficient than the traditional approach based on cluster counts (i.e. dn/dz, requiring redshifts). In the case where redshifts are available, our method performs similar to the traditional mass function (dn/dM/dz) for the purely cosmological parameters, but constrains better parameters defining the cluster scaling relations and their evolution. A further practical advantage of the CR-HR method is its simplicity: this fully top-down approach totally bypasses the tedious steps consisting in deriving cluster masses from X-ray temperature measurements.

Form gene clustering method about pan-ethnic-group products based on emotional semantic

NASA Astrophysics Data System (ADS)

Chen, Dengkai; Ding, Jingjing; Gao, Minzhuo; Ma, Danping; Liu, Donghui

2016-09-01

The use of pan-ethnic-group products form knowledge primarily depends on a designer's subjective experience without user participation. The majority of studies primarily focus on the detection of the perceptual demands of consumers from the target product category. A pan-ethnic-group products form gene clustering method based on emotional semantic is constructed. Consumers' perceptual images of the pan-ethnic-group products are obtained by means of product form gene extraction and coding and computer aided product form clustering technology. A case of form gene clustering about the typical pan-ethnic-group products is investigated which indicates that the method is feasible. This paper opens up a new direction for the future development of product form design which improves the agility of product design process in the era of Industry 4.0.

Implementation of K-Means Clustering Method for Electronic Learning Model

NASA Astrophysics Data System (ADS)

Latipa Sari, Herlina; Suranti Mrs., Dewi; Natalia Zulita, Leni

2017-12-01

Teaching and Learning process at SMK Negeri 2 Bengkulu Tengah has applied e-learning system for teachers and students. The e-learning was based on the classification of normative, productive, and adaptive subjects. SMK Negeri 2 Bengkulu Tengah consisted of 394 students and 60 teachers with 16 subjects. The record of e-learning database was used in this research to observe students’ activity pattern in attending class. K-Means algorithm in this research was used to classify students’ learning activities using e-learning, so that it was obtained cluster of students’ activity and improvement of student’s ability. Implementation of K-Means Clustering method for electronic learning model at SMK Negeri 2 Bengkulu Tengah was conducted by observing 10 students’ activities, namely participation of students in the classroom, submit assignment, view assignment, add discussion, view discussion, add comment, download course materials, view article, view test, and submit test. In the e-learning model, the testing was conducted toward 10 students that yielded 2 clusters of membership data (C1 and C2). Cluster 1: with membership percentage of 70% and it consisted of 6 members, namely 1112438 Anggi Julian, 1112439 Anis Maulita, 1112441 Ardi Febriansyah, 1112452 Berlian Sinurat, 1112460 Dewi Anugrah Anwar and 1112467 Eka Tri Oktavia Sari. Cluster 2:with membership percentage of 30% and it consisted of 4 members, namely 1112463 Dosita Afriyani, 1112471 Erda Novita, 1112474 Eskardi and 1112477 Fachrur Rozi.

Multiple imputation methods for bivariate outcomes in cluster randomised trials.

PubMed

DiazOrdaz, K; Kenward, M G; Gomes, M; Grieve, R

2016-09-10

Missing observations are common in cluster randomised trials. The problem is exacerbated when modelling bivariate outcomes jointly, as the proportion of complete cases is often considerably smaller than the proportion having either of the outcomes fully observed. Approaches taken to handling such missing data include the following: complete case analysis, single-level multiple imputation that ignores the clustering, multiple imputation with a fixed effect for each cluster and multilevel multiple imputation. We contrasted the alternative approaches to handling missing data in a cost-effectiveness analysis that uses data from a cluster randomised trial to evaluate an exercise intervention for care home residents. We then conducted a simulation study to assess the performance of these approaches on bivariate continuous outcomes, in terms of confidence interval coverage and empirical bias in the estimated treatment effects. Missing-at-random clustered data scenarios were simulated following a full-factorial design. Across all the missing data mechanisms considered, the multiple imputation methods provided estimators with negligible bias, while complete case analysis resulted in biased treatment effect estimates in scenarios where the randomised treatment arm was associated with missingness. Confidence interval coverage was generally in excess of nominal levels (up to 99.8%) following fixed-effects multiple imputation and too low following single-level multiple imputation. Multilevel multiple imputation led to coverage levels of approximately 95% throughout. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

A comparison of latent class, K-means, and K-median methods for clustering dichotomous data.

PubMed

Brusco, Michael J; Shireman, Emilie; Steinley, Douglas

2017-09-01

The problem of partitioning a collection of objects based on their measurements on a set of dichotomous variables is a well-established problem in psychological research, with applications including clinical diagnosis, educational testing, cognitive categorization, and choice analysis. Latent class analysis and K-means clustering are popular methods for partitioning objects based on dichotomous measures in the psychological literature. The K-median clustering method has recently been touted as a potentially useful tool for psychological data and might be preferable to its close neighbor, K-means, when the variable measures are dichotomous. We conducted simulation-based comparisons of the latent class, K-means, and K-median approaches for partitioning dichotomous data. Although all 3 methods proved capable of recovering cluster structure, K-median clustering yielded the best average performance, followed closely by latent class analysis. We also report results for the 3 methods within the context of an application to transitive reasoning data, in which it was found that the 3 approaches can exhibit profound differences when applied to real data. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Gas Dynamics in Galaxy Clusters

NASA Astrophysics Data System (ADS)

McCourt, Michael Kingsley, Jr.

Galaxy clusters are the most massive structures in the universe and, in the hierarchical pattern of cosmological structure formation, the largest objects in the universe form last. Galaxy clusters are thus interesting objects for a number of reasons. Three examples relevant to this thesis are: 1. Constraining the properties of dark energy: Due to the hierarchical nature of structure formation, the largest objects in the universe form last. The cluster mass function is thus sensitive to the entire expansion history of the universe and can be used to constrain the properties of dark energy. This constraint complements others derived from the CMB or from Type Ia supernovae and provides an important, independent confirmation of such methods. In particular, clusters provide detailed information about the equation of state parameter w because they sample a large redshift range z ˜ 0 - 1. 2. Probing galaxy formation: Clusters contain the most massive galaxies in the uni- verse, and the most massive black holes; because clusters form so late, we can still witness the assembly of these objects in the nearby universe. Clusters thus provide a more detailed view of galaxy formation than is possible in studies of lower-mass ob- jects. An important example comes from x-ray studies of clusters, which unexpectedly found that star formation in massive galaxies in clusters is closely correlated with the properties of the hot, virialized gas in their halos. This correlation persists despite the enormous separation in temperature, in dynamical time-scales, and in length-scales between the virialized gas in the halo and the star-forming regions in the galaxy. This remains a challenge to interpret theoretically. 3. Developing our knowledge of dilute plasmas: The masses and sizes of galaxy clusters imply that the plasma which permeates them is both very hot (˜ 108 K) and very dilute (˜ 10 -2 cm-3). This plasma is collisional enough to be considered a fluid, but collisionless enough to

Open-Source Sequence Clustering Methods Improve the State Of the Art.

PubMed

Kopylova, Evguenia; Navas-Molina, Jose A; Mercier, Céline; Xu, Zhenjiang Zech; Mahé, Frédéric; He, Yan; Zhou, Hong-Wei; Rognes, Torbjørn; Caporaso, J Gregory; Knight, Rob

2016-01-01

Sequence clustering is a common early step in amplicon-based microbial community analysis, when raw sequencing reads are clustered into operational taxonomic units (OTUs) to reduce the run time of subsequent analysis steps. Here, we evaluated the performance of recently released state-of-the-art open-source clustering software products, namely, OTUCLUST, Swarm, SUMACLUST, and SortMeRNA, against current principal options (UCLUST and USEARCH) in QIIME, hierarchical clustering methods in mothur, and USEARCH's most recent clustering algorithm, UPARSE. All the latest open-source tools showed promising results, reporting up to 60% fewer spurious OTUs than UCLUST, indicating that the underlying clustering algorithm can vastly reduce the number of these derived OTUs. Furthermore, we observed that stringent quality filtering, such as is done in UPARSE, can cause a significant underestimation of species abundance and diversity, leading to incorrect biological results. Swarm, SUMACLUST, and SortMeRNA have been included in the QIIME 1.9.0 release. IMPORTANCE Massive collections of next-generation sequencing data call for fast, accurate, and easily accessible bioinformatics algorithms to perform sequence clustering. A comprehensive benchmark is presented, including open-source tools and the popular USEARCH suite. Simulated, mock, and environmental communities were used to analyze sensitivity, selectivity, species diversity (alpha and beta), and taxonomic composition. The results demonstrate that recent clustering algorithms can significantly improve accuracy and preserve estimated diversity without the application of aggressive filtering. Moreover, these tools are all open source, apply multiple levels of multithreading, and scale to the demands of modern next-generation sequencing data, which is essential for the analysis of massive multidisciplinary studies such as the Earth Microbiome Project (EMP) (J. A. Gilbert, J. K. Jansson, and R. Knight, BMC Biol 12:69, 2014, http

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.

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.

A method for determining the radius of an open cluster from stellar proper motions

NASA Astrophysics Data System (ADS)

Sánchez, Néstor; Alfaro, Emilio J.; López-Martínez, Fátima

2018-04-01

We propose a method for calculating the radius of an open cluster in an objective way from an astrometric catalogue containing, at least, positions and proper motions. It uses the minimum spanning tree in the proper motion space to discriminate cluster stars from field stars and it quantifies the strength of the cluster-field separation by means of a statistical parameter defined for the first time in this paper. This is done for a range of different sampling radii from where the cluster radius is obtained as the size at which the best cluster-field separation is achieved. The novelty of this strategy is that the cluster radius is obtained independently of how its stars are spatially distributed. We test the reliability and robustness of the method with both simulated and real data from a well-studied open cluster (NGC 188), and apply it to UCAC4 data for five other open clusters with different catalogued radius values. NGC 188, NGC 1647, NGC 6603, and Ruprecht 155 yielded unambiguous radius values of 15.2 ± 1.8, 29.4 ± 3.4, 4.2 ± 1.7, and 7.0 ± 0.3 arcmin, respectively. ASCC 19 and Collinder 471 showed more than one possible solution, but it is not possible to know whether this is due to the involved uncertainties or due to the presence of complex patterns in their proper motion distributions, something that could be inherent to the physical object or due to the way in which the catalogue was sampled.

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

NASA Technical Reports Server (NTRS)

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

1975-01-01

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

The evidence of the rugoscopy effectiveness as a human identification method in patients submitted to rapid palatal expansion.

PubMed

Barbieri, Ana A; Scoralick, Raquel A; Naressi, Suely C M; Moraes, Mari E L; Daruge, Eduardo; Daruge, Eduardo

2013-01-01

The objective of this study was to demonstrate the effectiveness of rugoscopy as a human identification method, even when the patient is submitted to rapid palatal expansion, which in theory would introduce doubt. With this intent, the Rugoscopic Identity was obtained for each subject using the classification formula proposed by Santos based on the intra-oral casts made before and after treatment from patients who were subjected to palatal expansion. The casts were labeled with the patients' initials and randomly arranged for studying. The palatine rugae kept the same patterns in every case studied. The technical error of the intra-evaluator measurement provided a confidence interval of 95%, making rugoscopy a reliable identification method for patients who were submitted to rapid palatal expansion, because even in the presence of intra-oral changes owing to the use of palatal expanders, the palatine rugae retained the biological and technical requirements for the human identification process. © 2012 American Academy of Forensic Sciences.

Ecotype-specific and chromosome-specific expansion of variant centromeric satellites in Arabidopsis thaliana.

PubMed

Ito, Hidetaka; Miura, Asuka; Takashima, Kazuya; Kakutani, Tetsuji

2007-01-01

Despite the conserved roles and conserved protein machineries of centromeres, their nucleotide sequences can be highly diverse even among related species. The diversity reflects rapid evolution, but the underlying mechanism is largely unknown. One approach to monitor rapid evolution is examination of intra-specific variation. Here we report variant centromeric satellites of Arabidopsis thaliana found through survey of 103 natural accessions (ecotypes). Among them, a cluster of variant centromeric satellites was detected in one ecotype, Cape Verde Islands (Cvi). Recombinant inbred mapping revealed that the variant satellites are distributed in centromeric region of the chromosome 5 (CEN5) of this ecotype. This apparently recent variant accumulation is associated with large deletion of a pericentromeric region and the expansion of satellite region. The variant satellite was bound to HTR12 (centromeric variant histone H3), although expansion of the satellite was not associated with comparable increase in the HTR12 binding. The results suggest that variant satellites with centromere function can rapidly accumulate in one centromere, supporting the model that the satellite repeats in the array are homogenized by occasional unequal crossing-over, which has a potential to generate an expansion of local sequence variants within a centromere cluster.

High Performance Computing of Meshless Time Domain Method on Multi-GPU Cluster

NASA Astrophysics Data System (ADS)

Ikuno, Soichiro; Nakata, Susumu; Hirokawa, Yuta; Itoh, Taku

2015-01-01

High performance computing of Meshless Time Domain Method (MTDM) on multi-GPU using the supercomputer HA-PACS (Highly Accelerated Parallel Advanced system for Computational Sciences) at University of Tsukuba is investigated. Generally, the finite difference time domain (FDTD) method is adopted for the numerical simulation of the electromagnetic wave propagation phenomena. However, the numerical domain must be divided into rectangle meshes, and it is difficult to adopt the problem in a complexed domain to the method. On the other hand, MTDM can be easily adept to the problem because MTDM does not requires meshes. In the present study, we implement MTDM on multi-GPU cluster to speedup the method, and numerically investigate the performance of the method on multi-GPU cluster. To reduce the computation time, the communication time between the decomposed domain is hided below the perfect matched layer (PML) calculation procedure. The results of computation show that speedup of MTDM on 128 GPUs is 173 times faster than that of single CPU calculation.

Symptom Clusters in Advanced Cancer Patients: An Empirical Comparison of Statistical Methods and the Impact on Quality of Life.

PubMed

Dong, Skye T; Costa, Daniel S J; Butow, Phyllis N; Lovell, Melanie R; Agar, Meera; Velikova, Galina; Teckle, Paulos; Tong, Allison; Tebbutt, Niall C; Clarke, Stephen J; van der Hoek, Kim; King, Madeleine T; Fayers, Peter M

2016-01-01

Symptom clusters in advanced cancer can influence patient outcomes. There is large heterogeneity in the methods used to identify symptom clusters. To investigate the consistency of symptom cluster composition in advanced cancer patients using different statistical methodologies for all patients across five primary cancer sites, and to examine which clusters predict functional status, a global assessment of health and global quality of life. Principal component analysis and exploratory factor analysis (with different rotation and factor selection methods) and hierarchical cluster analysis (with different linkage and similarity measures) were used on a data set of 1562 advanced cancer patients who completed the European Organization for the Research and Treatment of Cancer Quality of Life Questionnaire-Core 30. Four clusters consistently formed for many of the methods and cancer sites: tense-worry-irritable-depressed (emotional cluster), fatigue-pain, nausea-vomiting, and concentration-memory (cognitive cluster). The emotional cluster was a stronger predictor of overall quality of life than the other clusters. Fatigue-pain was a stronger predictor of overall health than the other clusters. The cognitive cluster and fatigue-pain predicted physical functioning, role functioning, and social functioning. The four identified symptom clusters were consistent across statistical methods and cancer types, although there were some noteworthy differences. Statistical derivation of symptom clusters is in need of greater methodological guidance. A psychosocial pathway in the management of symptom clusters may improve quality of life. Biological mechanisms underpinning symptom clusters need to be delineated by future research. A framework for evidence-based screening, assessment, treatment, and follow-up of symptom clusters in advanced cancer is essential. Copyright © 2016 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.

A comparison of three clustering methods for finding subgroups in MRI, SMS or clinical data: SPSS TwoStep Cluster analysis, Latent Gold and SNOB.

PubMed

Kent, Peter; Jensen, Rikke K; Kongsted, Alice

2014-10-02

There are various methodological approaches to identifying clinically important subgroups and one method is to identify clusters of characteristics that differentiate people in cross-sectional and/or longitudinal data using Cluster Analysis (CA) or Latent Class Analysis (LCA). There is a scarcity of head-to-head comparisons that can inform the choice of which clustering method might be suitable for particular clinical datasets and research questions. Therefore, the aim of this study was to perform a head-to-head comparison of three commonly available methods (SPSS TwoStep CA, Latent Gold LCA and SNOB LCA). The performance of these three methods was compared: (i) quantitatively using the number of subgroups detected, the classification probability of individuals into subgroups, the reproducibility of results, and (ii) qualitatively using subjective judgments about each program's ease of use and interpretability of the presentation of results.We analysed five real datasets of varying complexity in a secondary analysis of data from other research projects. Three datasets contained only MRI findings (n = 2,060 to 20,810 vertebral disc levels), one dataset contained only pain intensity data collected for 52 weeks by text (SMS) messaging (n = 1,121 people), and the last dataset contained a range of clinical variables measured in low back pain patients (n = 543 people). Four artificial datasets (n = 1,000 each) containing subgroups of varying complexity were also analysed testing the ability of these clustering methods to detect subgroups and correctly classify individuals when subgroup membership was known. The results from the real clinical datasets indicated that the number of subgroups detected varied, the certainty of classifying individuals into those subgroups varied, the findings had perfect reproducibility, some programs were easier to use and the interpretability of the presentation of their findings also varied. The results from the artificial datasets

Measurement of the spectral signature of small carbon clusters at near and far infrared wavelengths

NASA Technical Reports Server (NTRS)

Tarter, J.; Saykally, R.

1991-01-01

A significant percentage of the carbon inventory of the circumstellar and interstellar media may be in the form of large refractory molecules (or small grains) referred to as carbon clusters. At the small end, uneven numbers of carbon atoms seem to be preferred, whereas above 12 atoms, clusters containing an even number of carbon atoms appear to be preferred in laboratory chemistry. In the lab, the cluster C-60 appears to be a particularly stable form and has been nicknamed Bucky Balls because of its resemblance to a soccer ball and to geodesic domes designed by Buckminster Fuller. In order to investigate the prevalence of these clusters, and their relationship to the polycyclic aromatic hydrocarbons (PAHs) that have become the newest focus of IR astronomy, it is necessary to determine the spectroscopic characteristics of these clusters at near and far infrared wavelengths. Described here is the construction of a near to far IR laser magnetic resonance spectrometer that has been built at the University of California Berkeley in order to detect and characterize these spectra. The equipment produces carbon clusters by laser evaporation of a graphitic target. The clusters are then cooled in a supersonic expansion beam in order to simulate conditions in the interstellar medium (ISM). The expansion beam feeds into the spectrometer chamber and permits concentrations of clusters sufficiently high as to permit ultra-high resolution spectroscopy at near and far IR wavelengths. The first successful demonstration of this apparatus occurred last year when the laboratory studies permitted the observational detection of C-5 in the stellar outflow surrounding IRC+10216 in the near-IR. Current efforts focus on reducing the temperature of the supersonic expansion beam that transport the C clusters evaporated from a graphite target into the spectrometer down to temperatures as low as 1 K.

Ab initio structures and polarizabilities of sodium clusters

NASA Astrophysics Data System (ADS)

Kronik, Leeor; Vasiliev, Igor; Jain, Manish; Chelikowsky, James R.

2001-09-01

We present quantitative ab initio calculations for Na cluster structures and polarizabilities, for all cluster sizes up to 20 atoms. Our calculations are performed by combining an ab initio core-corrected pseudopotential and a gradient-corrected density functional within a real space approach. We find the cluster bonding to be very floppy and catalog a host of low-energy quasi-degenerate isomers for all second-decade clusters. The existence of these isomers results in a band of polarizability values for each cluster size even at zero temperature. This eliminates any finer structure in the polarizability curve. We further show that the experimental polarizability values are consistently underestimated by calculations at zero temperature. By computing the effects of structure expansion and distortion due to a finite temperature we arrive at a quantitative agreement between theory and experiment.

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

Water Quality Evaluation of the Yellow River Basin Based on Gray Clustering Method

NASA Astrophysics Data System (ADS)

Fu, X. Q.; Zou, Z. H.

2018-03-01

Evaluating the water quality of 12 monitoring sections in the Yellow River Basin comprehensively by grey clustering method based on the water quality monitoring data from the Ministry of environmental protection of China in May 2016 and the environmental quality standard of surface water. The results can reflect the water quality of the Yellow River Basin objectively. Furthermore, the evaluation results are basically the same when compared with the fuzzy comprehensive evaluation method. The results also show that the overall water quality of the Yellow River Basin is good and coincident with the actual situation of the Yellow River basin. Overall, gray clustering method for water quality evaluation is reasonable and feasible and it is also convenient to calculate.

Applications of cluster analysis to the creation of perfectionism profiles: a comparison of two clustering approaches.

PubMed

Bolin, Jocelyn H; Edwards, Julianne M; Finch, W Holmes; Cassady, Jerrell C

2014-01-01

Although traditional clustering methods (e.g., K-means) have been shown to be useful in the social sciences it is often difficult for such methods to handle situations where clusters in the population overlap or are ambiguous. Fuzzy clustering, a method already recognized in many disciplines, provides a more flexible alternative to these traditional clustering methods. Fuzzy clustering differs from other traditional clustering methods in that it allows for a case to belong to multiple clusters simultaneously. Unfortunately, fuzzy clustering techniques remain relatively unused in the social and behavioral sciences. The purpose of this paper is to introduce fuzzy clustering to these audiences who are currently relatively unfamiliar with the technique. In order to demonstrate the advantages associated with this method, cluster solutions of a common perfectionism measure were created using both fuzzy clustering and K-means clustering, and the results compared. Results of these analyses reveal that different cluster solutions are found by the two methods, and the similarity between the different clustering solutions depends on the amount of cluster overlap allowed for in fuzzy clustering.

Applications of cluster analysis to the creation of perfectionism profiles: a comparison of two clustering approaches

PubMed Central

Bolin, Jocelyn H.; Edwards, Julianne M.; Finch, W. Holmes; Cassady, Jerrell C.

2014-01-01

Although traditional clustering methods (e.g., K-means) have been shown to be useful in the social sciences it is often difficult for such methods to handle situations where clusters in the population overlap or are ambiguous. Fuzzy clustering, a method already recognized in many disciplines, provides a more flexible alternative to these traditional clustering methods. Fuzzy clustering differs from other traditional clustering methods in that it allows for a case to belong to multiple clusters simultaneously. Unfortunately, fuzzy clustering techniques remain relatively unused in the social and behavioral sciences. The purpose of this paper is to introduce fuzzy clustering to these audiences who are currently relatively unfamiliar with the technique. In order to demonstrate the advantages associated with this method, cluster solutions of a common perfectionism measure were created using both fuzzy clustering and K-means clustering, and the results compared. Results of these analyses reveal that different cluster solutions are found by the two methods, and the similarity between the different clustering solutions depends on the amount of cluster overlap allowed for in fuzzy clustering. PMID:24795683

Clustering self-organizing maps (SOM) method for human papillomavirus (HPV) DNA as the main cause of cervical cancer disease

NASA Astrophysics Data System (ADS)

Bustamam, A.; Aldila, D.; Fatimah, Arimbi, M. D.

2017-07-01

One of the most widely used clustering method, since it has advantage on its robustness, is Self-Organizing Maps (SOM) method. This paper discusses the application of SOM method on Human Papillomavirus (HPV) DNA which is the main cause of cervical cancer disease, the most dangerous cancer in developing countries. We use 18 types of HPV DNA-based on the newest complete genome. By using open-source-based program R, clustering process can separate 18 types of HPV into two different clusters. There are two types of HPV in the first cluster while 16 others in the second cluster. The analyzing result of 18 types HPV based on the malignancy of the virus (the difficultness to cure). Two of HPV types the first cluster can be classified as tame HPV, while 16 others in the second cluster are classified as vicious HPV.

The modified alternative (G'/G)-expansion method to nonlinear evolution equation: application to the (1+1)-dimensional Drinfel'd-Sokolov-Wilson equation.

PubMed

Akbar, M Ali; Mohd Ali, Norhashidah Hj; Mohyud-Din, Syed Tauseef

2013-01-01

Over the years, (G'/G)-expansion method is employed to generate traveling wave solutions to various wave equations in mathematical physics. In the present paper, the alternative (G'/G)-expansion method has been further modified by introducing the generalized Riccati equation to construct new exact solutions. In order to illustrate the novelty and advantages of this approach, the (1+1)-dimensional Drinfel'd-Sokolov-Wilson (DSW) equation is considered and abundant new exact traveling wave solutions are obtained in a uniform way. These solutions may be imperative and significant for the explanation of some practical physical phenomena. It is shown that the modified alternative (G'/G)-expansion method an efficient and advance mathematical tool for solving nonlinear partial differential equations in mathematical physics.

Tunable thermal expansion in framework materials through redox intercalation

NASA Astrophysics Data System (ADS)

Chen, Jun; Gao, Qilong; Sanson, Andrea; Jiang, Xingxing; Huang, Qingzhen; Carnera, Alberto; Rodriguez, Clara Guglieri; Olivi, Luca; Wang, Lei; Hu, Lei; Lin, Kun; Ren, Yang; Lin, Zheshuai; Wang, Cong; Gu, Lin; Deng, Jinxia; Attfield, J. Paul; Xing, Xianran

2017-02-01

Thermal expansion properties of solids are of fundamental interest and control of thermal expansion is important for practical applications but can be difficult to achieve. Many framework-type materials show negative thermal expansion when internal cages are empty but positive thermal expansion when additional atoms or molecules fill internal voids present. Here we show that redox intercalation offers an effective method to control thermal expansion from positive to zero to negative by insertion of Li ions into the simple negative thermal expansion framework material ScF3, doped with 10% Fe to enable reduction. The small concentration of intercalated Li ions has a strong influence through steric hindrance of transverse fluoride ion vibrations, which directly controls the thermal expansion. Redox intercalation of guest ions is thus likely to be a general and effective method for controlling thermal expansion in the many known framework materials with phonon-driven negative thermal expansion.

Clustering method for counting passengers getting in a bus with single camera

NASA Astrophysics Data System (ADS)

Yang, Tao; Zhang, Yanning; Shao, Dapei; Li, Ying

2010-03-01

Automatic counting of passengers is very important for both business and security applications. We present a single-camera-based vision system that is able to count passengers in a highly crowded situation at the entrance of a traffic bus. The unique characteristics of the proposed system include, First, a novel feature-point-tracking- and online clustering-based passenger counting framework, which performs much better than those of background-modeling-and foreground-blob-tracking-based methods. Second, a simple and highly accurate clustering algorithm is developed that projects the high-dimensional feature point trajectories into a 2-D feature space by their appearance and disappearance times and counts the number of people through online clustering. Finally, all test video sequences in the experiment are captured from a real traffic bus in Shanghai, China. The results show that the system can process two 320×240 video sequences at a frame rate of 25 fps simultaneously, and can count passengers reliably in various difficult scenarios with complex interaction and occlusion among people. The method achieves high accuracy rates up to 96.5%.

Cluster Physics with Merging Galaxy Clusters

NASA Astrophysics Data System (ADS)

Molnar, Sandor

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

A review of recent advances in the spherical harmonics expansion method for semiconductor device simulation.

PubMed

Rupp, K; Jungemann, C; Hong, S-M; Bina, M; Grasser, T; Jüngel, A

The Boltzmann transport equation is commonly considered to be the best semi-classical description of carrier transport in semiconductors, providing precise information about the distribution of carriers with respect to time (one dimension), location (three dimensions), and momentum (three dimensions). However, numerical solutions for the seven-dimensional carrier distribution functions are very demanding. The most common solution approach is the stochastic Monte Carlo method, because the gigabytes of memory requirements of deterministic direct solution approaches has not been available until recently. As a remedy, the higher accuracy provided by solutions of the Boltzmann transport equation is often exchanged for lower computational expense by using simpler models based on macroscopic quantities such as carrier density and mean carrier velocity. Recent developments for the deterministic spherical harmonics expansion method have reduced the computational cost for solving the Boltzmann transport equation, enabling the computation of carrier distribution functions even for spatially three-dimensional device simulations within minutes to hours. We summarize recent progress for the spherical harmonics expansion method and show that small currents, reasonable execution times, and rare events such as low-frequency noise, which are all hard or even impossible to simulate with the established Monte Carlo method, can be handled in a straight-forward manner. The applicability of the method for important practical applications is demonstrated for noise simulation, small-signal analysis, hot-carrier degradation, and avalanche breakdown.

Clustering Methods; Part IV of Scientific Report No. ISR-18, Information Storage and Retrieval...

ERIC Educational Resources Information Center

Cornell Univ., Ithaca, NY. Dept. of Computer Science.

Two papers are included as Part Four of this report on Salton's Magical Automatic Retriever of Texts (SMART) project report. The first paper: "A Controlled Single Pass Classification Algorithm with Application to Multilevel Clustering" by D. B. Johnson and J. M. Laferente presents a single pass clustering method which compares favorably…

Selection of representative embankments based on rough set - fuzzy clustering method

NASA Astrophysics Data System (ADS)

Bin, Ou; Lin, Zhi-xiang; Fu, Shu-yan; Gao, Sheng-song

2018-02-01

The premise condition of comprehensive evaluation of embankment safety is selection of representative unit embankment, on the basis of dividing the unit levee the influencing factors and classification of the unit embankment are drafted.Based on the rough set-fuzzy clustering, the influence factors of the unit embankment are measured by quantitative and qualitative indexes.Construct to fuzzy similarity matrix of standard embankment then calculate fuzzy equivalent matrix of fuzzy similarity matrix by square method. By setting the threshold of the fuzzy equivalence matrix, the unit embankment is clustered, and the representative unit embankment is selected from the classification of the embankment.

Independent-Cluster Parametrizations of Wave Functions in Model Field Theories III. The Coupled-Cluster Phase Spaces and Their Geometrical Structure

NASA Astrophysics Data System (ADS)

Arponen, J. S.; Bishop, R. F.

1993-11-01

In this third paper of a series we study the structure of the phase spaces of the independent-cluster methods. These phase spaces are classical symplectic manifolds which provide faithful descriptions of the quantum mechanical pure states of an arbitrary system. They are "superspaces" in the sense that the full physical many-body or field-theoretic system is described by a point of the space, in contrast to "ordinary" spaces for which the state of the physical system is described rather by the whole space itself. We focus attention on the normal and extended coupled-cluster methods (NCCM and ECCM). Both methods provide parametrizations of the Hilbert space which take into account in increasing degrees of completeness the connectivity properties of the associated perturbative diagram structure. This corresponds to an increasing incorporation of locality into the description of the quantum system. As a result the degree of nonlinearity increases in the dynamical equations that govern the temporal evolution and determine the equilibrium state. Because of the nonlinearity, the structure of the manifold becomes geometrically complicated. We analyse the neighbourhood of the ground state of the one-mode anharmonic bosonic field theory and derive the nonlinear expansion beyond the linear response regime. The expansion is given in terms of normal-mode amplitudes, which provide the best local coordinate system close to the ground state. We generalize the treatment to other nonequilibrium states by considering the similarly defined normal coordinates around the corresponding phase space point. It is pointed out that the coupled-cluster method (CCM) maps display such features as (an)holonomy, or geometric phase. For example, a physical state may be represented by a number of different points on the CCM manifold. For this reason the whole phase spaces in the NCCM or ECCM cannot be covered by a single chart. To account for this non-Euclidean nature we introduce a suitable pseudo

Tunable thermal expansion in framework materials through redox intercalation

PubMed Central

Chen, Jun; Gao, Qilong; Sanson, Andrea; Jiang, Xingxing; Huang, Qingzhen; Carnera, Alberto; Rodriguez, Clara Guglieri; Olivi, Luca; Wang, Lei; Hu, Lei; Lin, Kun; Ren, Yang; Lin, Zheshuai; Wang, Cong; Gu, Lin; Deng, Jinxia; Attfield, J. Paul; Xing, Xianran

2017-01-01

Thermal expansion properties of solids are of fundamental interest and control of thermal expansion is important for practical applications but can be difficult to achieve. Many framework-type materials show negative thermal expansion when internal cages are empty but positive thermal expansion when additional atoms or molecules fill internal voids present. Here we show that redox intercalation offers an effective method to control thermal expansion from positive to zero to negative by insertion of Li ions into the simple negative thermal expansion framework material ScF3, doped with 10% Fe to enable reduction. The small concentration of intercalated Li ions has a strong influence through steric hindrance of transverse fluoride ion vibrations, which directly controls the thermal expansion. Redox intercalation of guest ions is thus likely to be a general and effective method for controlling thermal expansion in the many known framework materials with phonon-driven negative thermal expansion. PMID:28181576

Tunable thermal expansion in framework materials through redox intercalation

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

Chen, Jun; Gao, Qilong; Sanson, Andrea

Thermal expansion properties of solids are of fundamental interest and control of thermal expansion is important for practical applications but can be difficult to achieve. Many framework type materials show negative thermal expansion when internal cages are empty but positive thermal expansion when additional atoms or molecules fill internal voids present, offering a potential route for control. Here we show that redox intercalation offers an effective method to control thermal expansion from positive to zero to negative by insertion of Li ions into the simple negative thermal expansion framework material ScF 3, doped with 10% Fe to enable reduction. Themore » small concentration of intercalated Li ions has a strong influence through steric hindrance of transverse fluoride ion vibrations, which directly controls the thermal expansion. As a result, redox intercalation of guest ions is thus likely to be a general and effective method for controlling thermal expansion in the many known framework materials with phonon-driven negative thermal expansion.« less

Tunable thermal expansion in framework materials through redox intercalation

DOE PAGES

Chen, Jun; Gao, Qilong; Sanson, Andrea; ...

2017-02-09

Thermal expansion properties of solids are of fundamental interest and control of thermal expansion is important for practical applications but can be difficult to achieve. Many framework type materials show negative thermal expansion when internal cages are empty but positive thermal expansion when additional atoms or molecules fill internal voids present, offering a potential route for control. Here we show that redox intercalation offers an effective method to control thermal expansion from positive to zero to negative by insertion of Li ions into the simple negative thermal expansion framework material ScF 3, doped with 10% Fe to enable reduction. Themore » small concentration of intercalated Li ions has a strong influence through steric hindrance of transverse fluoride ion vibrations, which directly controls the thermal expansion. As a result, redox intercalation of guest ions is thus likely to be a general and effective method for controlling thermal expansion in the many known framework materials with phonon-driven negative thermal expansion.« less

Astrophysics. Multiple images of a highly magnified supernova formed by an early-type cluster galaxy lens.

PubMed

Kelly, Patrick L; Rodney, Steven A; Treu, Tommaso; Foley, Ryan J; Brammer, Gabriel; Schmidt, Kasper B; Zitrin, Adi; Sonnenfeld, Alessandro; Strolger, Louis-Gregory; Graur, Or; Filippenko, Alexei V; Jha, Saurabh W; Riess, Adam G; Bradac, Marusa; Weiner, Benjamin J; Scolnic, Daniel; Malkan, Matthew A; von der Linden, Anja; Trenti, Michele; Hjorth, Jens; Gavazzi, Raphael; Fontana, Adriano; Merten, Julian C; McCully, Curtis; Jones, Tucker; Postman, Marc; Dressler, Alan; Patel, Brandon; Cenko, S Bradley; Graham, Melissa L; Tucker, Bradley E

2015-03-06

In 1964, Refsdal hypothesized that a supernova whose light traversed multiple paths around a strong gravitational lens could be used to measure the rate of cosmic expansion. We report the discovery of such a system. In Hubble Space Telescope imaging, we have found four images of a single supernova forming an Einstein cross configuration around a redshift z = 0.54 elliptical galaxy in the MACS J1149.6+2223 cluster. The cluster's gravitational potential also creates multiple images of the z = 1.49 spiral supernova host galaxy, and a future appearance of the supernova elsewhere in the cluster field is expected. The magnifications and staggered arrivals of the supernova images probe the cosmic expansion rate, as well as the distribution of matter in the galaxy and cluster lenses. Copyright © 2015, American Association for the Advancement of Science.

Y-chromosome descent clusters and male differential reproductive success: young lineage expansions dominate Asian pastoral nomadic populations

PubMed Central

Balaresque, Patricia; Poulet, Nicolas; Cussat-Blanc, Sylvain; Gerard, Patrice; Quintana-Murci, Lluis; Heyer, Evelyne; Jobling, Mark A

2015-01-01

High-frequency microsatellite haplotypes of the male-specific Y-chromosome can signal past episodes of high reproductive success of particular men and their patrilineal descendants. Previously, two examples of such successful Y-lineages have been described in Asia, both associated with Altaic-speaking pastoral nomadic societies, and putatively linked to dynasties descending, respectively, from Genghis Khan and Giocangga. Here we surveyed a total of 5321 Y-chromosomes from 127 Asian populations, including novel Y-SNP and microsatellite data on 461 Central Asian males, to ask whether additional lineage expansions could be identified. Based on the most frequent eight-microsatellite haplotypes, we objectively defined 11 descent clusters (DCs), each within a specific haplogroup, that represent likely past instances of high male reproductive success, including the two previously identified cases. Analysis of the geographical patterns and ages of these DCs and their associated cultural characteristics showed that the most successful lineages are found both among sedentary agriculturalists and pastoral nomads, and expanded between 2100 BCE and 1100 CE. However, those with recent origins in the historical period are almost exclusively found in Altaic-speaking pastoral nomadic populations, which may reflect a shift in political organisation in pastoralist economies and a greater ease of transmission of Y-chromosomes through time and space facilitated by the use of horses. PMID:25585703

F-Expansion Method and New Exact Solutions of the Schrödinger-KdV Equation

PubMed Central

Filiz, Ali; Ekici, Mehmet; Sonmezoglu, Abdullah

2014-01-01

F-expansion method is proposed to seek exact solutions of nonlinear evolution equations. With the aid of symbolic computation, we choose the Schrödinger-KdV equation with a source to illustrate the validity and advantages of the proposed method. A number of Jacobi-elliptic function solutions are obtained including the Weierstrass-elliptic function solutions. When the modulus m of Jacobi-elliptic function approaches to 1 and 0, soliton-like solutions and trigonometric-function solutions are also obtained, respectively. The proposed method is a straightforward, short, promising, and powerful method for the nonlinear evolution equations in mathematical physics. PMID:24672327

F-expansion method and new exact solutions of the Schrödinger-KdV equation.

PubMed

Filiz, Ali; Ekici, Mehmet; Sonmezoglu, Abdullah

2014-01-01

F-expansion method is proposed to seek exact solutions of nonlinear evolution equations. With the aid of symbolic computation, we choose the Schrödinger-KdV equation with a source to illustrate the validity and advantages of the proposed method. A number of Jacobi-elliptic function solutions are obtained including the Weierstrass-elliptic function solutions. When the modulus m of Jacobi-elliptic function approaches to 1 and 0, soliton-like solutions and trigonometric-function solutions are also obtained, respectively. The proposed method is a straightforward, short, promising, and powerful method for the nonlinear evolution equations in mathematical physics.

Clustering of attitudes towards obesity: a mixed methods study of Australian parents and children

PubMed Central

2013-01-01

Background Current population-based anti-obesity campaigns often target individuals based on either weight or socio-demographic characteristics, and give a ‘mass’ message about personal responsibility. There is a recognition that attempts to influence attitudes and opinions may be more effective if they resonate with the beliefs that different groups have about the causes of, and solutions for, obesity. Limited research has explored how attitudinal factors may inform the development of both upstream and downstream social marketing initiatives. Methods Computer-assisted face-to-face interviews were conducted with 159 parents and 184 of their children (aged 9–18 years old) in two Australian states. A mixed methods approach was used to assess attitudes towards obesity, and elucidate why different groups held various attitudes towards obesity. Participants were quantitatively assessed on eight dimensions relating to the severity and extent, causes and responsibility, possible remedies, and messaging strategies. Cluster analysis was used to determine attitudinal clusters. Participants were also able to qualify each answer. Qualitative responses were analysed both within and across attitudinal clusters using a constant comparative method. Results Three clusters were identified. Concerned Internalisers (27% of the sample) judged that obesity was a serious health problem, that Australia had among the highest levels of obesity in the world and that prevalence was rapidly increasing. They situated the causes and remedies for the obesity crisis in individual choices. Concerned Externalisers (38% of the sample) held similar views about the severity and extent of the obesity crisis. However, they saw responsibility and remedies as a societal rather than an individual issue. The final cluster, the Moderates, which contained significantly more children and males, believed that obesity was not such an important public health issue, and judged the extent of obesity to be

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

NASA Astrophysics Data System (ADS)

Yevsyukov, N. N.

1985-09-01

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

Evaluation of sliding baseline methods for spatial estimation for cluster detection in the biosurveillance system

PubMed Central

Xing, Jian; Burkom, Howard; Moniz, Linda; Edgerton, James; Leuze, Michael; Tokars, Jerome

2009-01-01

Background The Centers for Disease Control and Prevention's (CDC's) BioSense system provides near-real time situational awareness for public health monitoring through analysis of electronic health data. Determination of anomalous spatial and temporal disease clusters is a crucial part of the daily disease monitoring task. Our study focused on finding useful anomalies at manageable alert rates according to available BioSense data history. Methods The study dataset included more than 3 years of daily counts of military outpatient clinic visits for respiratory and rash syndrome groupings. We applied four spatial estimation methods in implementations of space-time scan statistics cross-checked in Matlab and C. We compared the utility of these methods according to the resultant background cluster rate (a false alarm surrogate) and sensitivity to injected cluster signals. The comparison runs used a spatial resolution based on the facility zip code in the patient record and a finer resolution based on the residence zip code. Results Simple estimation methods that account for day-of-week (DOW) data patterns yielded a clear advantage both in background cluster rate and in signal sensitivity. A 28-day baseline gave the most robust results for this estimation; the preferred baseline is long enough to remove daily fluctuations but short enough to reflect recent disease trends and data representation. Background cluster rates were lower for the rash syndrome counts than for the respiratory counts, likely because of seasonality and the large scale of the respiratory counts. Conclusion The spatial estimation method should be chosen according to characteristics of the selected data streams. In this dataset with strong day-of-week effects, the overall best detection performance was achieved using subregion averages over a 28-day baseline stratified by weekday or weekend/holiday behavior. Changing the estimation method for particular scenarios involving different spatial resolution

A NOVEL ENVIRONMENT FRIENDLY METHOD FOR EXPANSION AND MOLDING OF POLYMERIC FOAM

EPA Science Inventory

The objective of the project is to develop an environment friendly, novel and efficient alternative process for expansion and molding of polymeric foam. Spherical, expandable polymer beads are prepared from liquid monomer suspended in an aqueous medium, containing an expansion...

Method of assembling a thermal expansion compensator

NASA Technical Reports Server (NTRS)

Matejczyk, Daniel Edward (Inventor); Determan, William (Inventor)

2012-01-01

A thermal expansion compensator is provided and includes a first electrode structure having a first surface, a second electrode structure having a second surface facing the first surface and an elastic element bonded to the first and second surfaces and including a conductive element by which the first and second electrode structures electrically and/or thermally communicate, the conductive element having a length that is not substantially longer than a distance between the first and second surfaces.

Monte Carlo-based fluorescence molecular tomography reconstruction method accelerated by a cluster of graphic processing units.

PubMed

Quan, Guotao; Gong, Hui; Deng, Yong; Fu, Jianwei; Luo, Qingming

2011-02-01

High-speed fluorescence molecular tomography (FMT) reconstruction for 3-D heterogeneous media is still one of the most challenging problems in diffusive optical fluorescence imaging. In this paper, we propose a fast FMT reconstruction method that is based on Monte Carlo (MC) simulation and accelerated by a cluster of graphics processing units (GPUs). Based on the Message Passing Interface standard, we modified the MC code for fast FMT reconstruction, and different Green's functions representing the flux distribution in media are calculated simultaneously by different GPUs in the cluster. A load-balancing method was also developed to increase the computational efficiency. By applying the Fréchet derivative, a Jacobian matrix is formed to reconstruct the distribution of the fluorochromes using the calculated Green's functions. Phantom experiments have shown that only 10 min are required to get reconstruction results with a cluster of 6 GPUs, rather than 6 h with a cluster of multiple dual opteron CPU nodes. Because of the advantages of high accuracy and suitability for 3-D heterogeneity media with refractive-index-unmatched boundaries from the MC simulation, the GPU cluster-accelerated method provides a reliable approach to high-speed reconstruction for FMT imaging.

Fierz Convergence Criterion: A Controlled Approach to Strongly Interacting Systems with Small Embedded Clusters.

PubMed

Ayral, Thomas; Vučičević, Jaksa; Parcollet, Olivier

2017-10-20

We present an embedded-cluster method, based on the triply irreducible local expansion formalism. It turns the Fierz ambiguity, inherent to approaches based on a bosonic decoupling of local fermionic interactions, into a convergence criterion. It is based on the approximation of the three-leg vertex by a coarse-grained vertex computed from a self-consistently determined cluster impurity model. The computed self-energies are, by construction, continuous functions of momentum. We show that, in three interaction and doping regimes of the two-dimensional Hubbard model, self-energies obtained with clusters of size four only are very close to numerically exact benchmark results. We show that the Fierz parameter, which parametrizes the freedom in the Hubbard-Stratonovich decoupling, can be used as a quality control parameter. By contrast, the GW+extended dynamical mean field theory approximation with four cluster sites is shown to yield good results only in the weak-coupling regime and for a particular decoupling. Finally, we show that the vertex has spatially nonlocal components only at low Matsubara frequencies.

Perturbative universal state-selective correction for state-specific multi-reference coupled cluster methods

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

Brabec, Jiri; Banik, Subrata; Kowalski, Karol

2016-10-28

The implementation details of the universal state-selective (USS) multi-reference coupled cluster (MRCC) formalism with singles and doubles (USS(2)) are discussed on the example of several benchmark systems. We demonstrate that the USS(2) formalism is capable of improving accuracies of state specific multi-reference coupled-cluster (MRCC) methods based on the Brillouin-Wigner and Mukherjee’s sufficiency conditions. Additionally, it is shown that the USS(2) approach significantly alleviates problems associated with the lack of invariance of MRCC theories upon the rotation of active orbitals. We also discuss the perturbative USS(2) formulations that significantly reduce numerical overhead of the full USS(2) method.

Method of precisely modifying predetermined surface layers of a workpiece by cluster ion impact therewith

DOEpatents

Friedman, L.; Beuhler, R.J.; Matthew, M.W.; Ledbetter, M.

1984-06-25

A method of precisely modifying a selected area of a workpiece by producing a beam of charged cluster ions that is narrowly mass selected to a predetermined mean size of cluster ions within a range of 25 to 10/sup 6/ atoms per cluster ion, and accelerated in a beam to a critical velocity. The accelerated beam is used to impact a selected area of an outer surface of the workpiece at a preselected rate of impacts of cluster ions/cm/sup 2//sec in order to effect a precise modification in that selected area of the workpiece.

Method of precisely modifying predetermined surface layers of a workpiece by cluster ion impact therewith

DOEpatents

Friedman, Lewis; Buehler, Robert J.; Matthew, Michael W.; Ledbetter, Myron

1985-01-01

A method of precisely modifying a selected area of a workpiece by producing a beam of charged cluster ions that is narrowly mass selected to a predetermined mean size of cluster ions within a range of 25 to 10.sup.6 atoms per cluster ion, and accelerated in a beam to a critical velocity. The accelerated beam is used to impact a selected area of an outer surface of the workpiece at a preselected rate of impacts of cluster ions/cm.sup.2 /sec. in order to effect a precise modification in that selected area of the workpiece.

Pre-crash scenarios at road junctions: A clustering method for car crash data.

PubMed

Nitsche, Philippe; Thomas, Pete; Stuetz, Rainer; Welsh, Ruth

2017-10-01

Given the recent advancements in autonomous driving functions, one of the main challenges is safe and efficient operation in complex traffic situations such as road junctions. There is a need for comprehensive testing, either in virtual simulation environments or on real-world test tracks. This paper presents a novel data analysis method including the preparation, analysis and visualization of car crash data, to identify the critical pre-crash scenarios at T- and four-legged junctions as a basis for testing the safety of automated driving systems. The presented method employs k-medoids to cluster historical junction crash data into distinct partitions and then applies the association rules algorithm to each cluster to specify the driving scenarios in more detail. The dataset used consists of 1056 junction crashes in the UK, which were exported from the in-depth "On-the-Spot" database. The study resulted in thirteen crash clusters for T-junctions, and six crash clusters for crossroads. Association rules revealed common crash characteristics, which were the basis for the scenario descriptions. The results support existing findings on road junction accidents and provide benchmark situations for safety performance tests in order to reduce the possible number parameter combinations. Copyright © 2017 Elsevier Ltd. All rights reserved.

Solution of a Nonlinear Heat Conduction Equation for a Curvilinear Region with Dirichlet Conditions by the Fast-Expansion Method

NASA Astrophysics Data System (ADS)

Chernyshov, A. D.

2018-05-01

The analytical solution of the nonlinear heat conduction problem for a curvilinear region is obtained with the use of the fast-expansion method together with the method of extension of boundaries and pointwise technique of computing Fourier coefficients.

Method and system for data clustering for very large databases

NASA Technical Reports Server (NTRS)

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

1998-01-01

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

Postcollapse Evolution of Globular Clusters

NASA Astrophysics Data System (ADS)

Makino, Junichiro

1996-11-01

A number of globular clusters appear to have undergone core collapse, in the sense that their predicted collapse times are much shorter than their current ages. Simulations with gas models and the Fokker-Planck approximation have shown that the central density of a globular cluster after the collapse undergoes nonlinear oscillation with a large amplitude (gravothermal oscillation). However, the question whether such an oscillation actually takes place in real N-body systems has remained unsolved because an N-body simulation with a sufficiently high resolution would have required computing resources of the order of several GFLOPS-yr. In the present paper, we report the results of such a simulation performed on a dedicated special-purpose computer, GRAPE-4. We have simulated the evolution of isolated point-mass systems with up to 32,768 particles. The largest number of particles reported previously is 10,000. We confirm that gravothermal oscillation takes place in an N-body system. The expansion phase shows all the signatures that are considered to be evidence of the gravothermal nature of the oscillation. At the maximum expansion, the core radius is ˜1% of the half-mass radius for the run with 32,768 particles. The maximum core size, rc, depends on N as ∝ N-1/3.

Choosing appropriate analysis methods for cluster randomised cross-over trials with a binary outcome.

PubMed

Morgan, Katy E; Forbes, Andrew B; Keogh, Ruth H; Jairath, Vipul; Kahan, Brennan C

2017-01-30

In cluster randomised cross-over (CRXO) trials, clusters receive multiple treatments in a randomised sequence over time. In such trials, there is usual correlation between patients in the same cluster. In addition, within a cluster, patients in the same period may be more similar to each other than to patients in other periods. We demonstrate that it is necessary to account for these correlations in the analysis to obtain correct Type I error rates. We then use simulation to compare different methods of analysing a binary outcome from a two-period CRXO design. Our simulations demonstrated that hierarchical models without random effects for period-within-cluster, which do not account for any extra within-period correlation, performed poorly with greatly inflated Type I errors in many scenarios. In scenarios where extra within-period correlation was present, a hierarchical model with random effects for cluster and period-within-cluster only had correct Type I errors when there were large numbers of clusters; with small numbers of clusters, the error rate was inflated. We also found that generalised estimating equations did not give correct error rates in any scenarios considered. An unweighted cluster-level summary regression performed best overall, maintaining an error rate close to 5% for all scenarios, although it lost power when extra within-period correlation was present, especially for small numbers of clusters. Results from our simulation study show that it is important to model both levels of clustering in CRXO trials, and that any extra within-period correlation should be accounted for. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Two worlds collide: Image analysis methods for quantifying structural variation in cluster molecular dynamics

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

Steenbergen, K. G., E-mail: kgsteen@gmail.com; Gaston, N.

2014-02-14

Inspired by methods of remote sensing image analysis, we analyze structural variation in cluster molecular dynamics (MD) simulations through a unique application of the principal component analysis (PCA) and Pearson Correlation Coefficient (PCC). The PCA analysis characterizes the geometric shape of the cluster structure at each time step, yielding a detailed and quantitative measure of structural stability and variation at finite temperature. Our PCC analysis captures bond structure variation in MD, which can be used to both supplement the PCA analysis as well as compare bond patterns between different cluster sizes. Relying only on atomic position data, without requirement formore » a priori structural input, PCA and PCC can be used to analyze both classical and ab initio MD simulations for any cluster composition or electronic configuration. Taken together, these statistical tools represent powerful new techniques for quantitative structural characterization and isomer identification in cluster MD.« less

Two worlds collide: image analysis methods for quantifying structural variation in cluster molecular dynamics.

PubMed

Steenbergen, K G; Gaston, N

2014-02-14

Inspired by methods of remote sensing image analysis, we analyze structural variation in cluster molecular dynamics (MD) simulations through a unique application of the principal component analysis (PCA) and Pearson Correlation Coefficient (PCC). The PCA analysis characterizes the geometric shape of the cluster structure at each time step, yielding a detailed and quantitative measure of structural stability and variation at finite temperature. Our PCC analysis captures bond structure variation in MD, which can be used to both supplement the PCA analysis as well as compare bond patterns between different cluster sizes. Relying only on atomic position data, without requirement for a priori structural input, PCA and PCC can be used to analyze both classical and ab initio MD simulations for any cluster composition or electronic configuration. Taken together, these statistical tools represent powerful new techniques for quantitative structural characterization and isomer identification in cluster MD.

Globular clusters and environmental effects in galaxy clusters

NASA Astrophysics Data System (ADS)

Sales, Laura

2016-10-01

Globular clusters are old compact stellar systems orbiting around galaxies of all types. Tens of thousands of them can also be found populating the intra-cluster regions of nearby galaxy clusters like Virgo and Coma. Thanks to the HST Frontier Fields program, GCs are starting now to be detected also in intermediate redshift clusters. Yet, despite their ubiquity, a theoretical model for the formation and evolution of GCs is still missing, especially within the cosmological context.Here we propose to use cosmological hydrodynamical simulations of 18 galaxy clusters coupled to a post-processing GC formation model to explore the assembly of galaxies in clusters together with their expected GC population. The method, which has already been implemented and tested, will allow us to characterize for the first time the number, radial distribution and kinematics of GCs in clusters, with products directly comparable to observational maps. We will explore cluster-to-cluster variations and also characterize the build up of the intra-cluster component of GCs with time.As the method relies on a detailed study of the star-formation history of galaxies, we will jointly constrain the predicted quenching time-scales for satellites and the occurrence of starburst events associated to infall and orbital pericenters of galaxies in massive clusters. This will inform further studies on the distribution, velocity and properties of post-starburst galaxies in past, ongoing and future HST programs.

Comprehensive cluster analysis with Transitivity Clustering.

PubMed

Wittkop, Tobias; Emig, Dorothea; Truss, Anke; Albrecht, Mario; Böcker, Sebastian; Baumbach, Jan

2011-03-01

Transitivity Clustering is a method for the partitioning of biological data into groups of similar objects, such as genes, for instance. It provides integrated access to various functions addressing each step of a typical cluster analysis. To facilitate this, Transitivity Clustering is accessible online and offers three user-friendly interfaces: a powerful stand-alone version, a web interface, and a collection of Cytoscape plug-ins. In this paper, we describe three major workflows: (i) protein (super)family detection with Cytoscape, (ii) protein homology detection with incomplete gold standards and (iii) clustering of gene expression data. This protocol guides the user through the most important features of Transitivity Clustering and takes ∼1 h to complete.

Controlled Expansion of Supercritical Solution: A Robust Method to Produce Pure Drug Nanoparticles With Narrow Size-Distribution.

PubMed

Pessi, Jenni; Lassila, Ilkka; Meriläinen, Antti; Räikkönen, Heikki; Hæggström, Edward; Yliruusi, Jouko

2016-08-01

We introduce a robust, stable, and reproducible method to produce nanoparticles based on expansion of supercritical solutions using carbon dioxide as a solvent. The method, controlled expansion of supercritical solution (CESS), uses controlled mass transfer, flow, pressure reduction, and particle collection in dry ice. CESS offers control over the crystallization process as the pressure in the system is reduced according to a specific profile. Particle formation takes place before the exit nozzle, and condensation is the main mechanism for postnucleation particle growth. A 2-step gradient pressure reduction is used to prevent Mach disk formation and particle growth by coagulation. Controlled particle growth keeps the production process stable. With CESS, we produced piroxicam nanoparticles, 60 mg/h, featuring narrow size distribution (176 ± 53 nm). Copyright © 2016 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Functional connectivity analysis of the neural bases of emotion regulation: A comparison of independent component method with density-based k-means clustering method.

PubMed

Zou, Ling; Guo, Qian; Xu, Yi; Yang, Biao; Jiao, Zhuqing; Xiang, Jianbo

2016-04-29

Functional magnetic resonance imaging (fMRI) is an important tool in neuroscience for assessing connectivity and interactions between distant areas of the brain. To find and characterize the coherent patterns of brain activity as a means of identifying brain systems for the cognitive reappraisal of the emotion task, both density-based k-means clustering and independent component analysis (ICA) methods can be applied to characterize the interactions between brain regions involved in cognitive reappraisal of emotion. Our results reveal that compared with the ICA method, the density-based k-means clustering method provides a higher sensitivity of polymerization. In addition, it is more sensitive to those relatively weak functional connection regions. Thus, the study concludes that in the process of receiving emotional stimuli, the relatively obvious activation areas are mainly distributed in the frontal lobe, cingulum and near the hypothalamus. Furthermore, density-based k-means clustering method creates a more reliable method for follow-up studies of brain functional connectivity.

Method and apparatus for detecting and/or imaging clusters of small scattering centers in the body

DOEpatents

Perez-Mendez, V.; Sommer, F.G.

1982-07-13

An ultrasonic method and apparatus are provided for detecting and imaging clusters of small scattering centers in the breast wherein periodic pulses are applied to an ultrasound emitting transducer and projected into the body, thereafter being received by at least one receiving transducer positioned to receive scattering from the scattering center clusters. The signals are processed to provide an image showing cluster extent and location. 6 figs.

Method and apparatus for detecting and/or imaging clusters of small scattering centers in the body

DOEpatents

Perez-Mendez, Victor; Sommer, Frank G.

1982-01-01

An ultrasonic method and apparatus are provided for detecting and imaging clusters of small scattering centers in the breast wherein periodic pulses are applied to an ultrasound emitting transducer and projected into the body, thereafter being received by at least one receiving transducer positioned to receive scattering from the scattering center clusters. The signals are processed to provide an image showing cluster extent and location.

Early dynamical evolution of young substructured clusters

NASA Astrophysics Data System (ADS)

Dorval, Julien; Boily, Christian

2017-03-01

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

Exact traveling wave solutions of the KP-BBM equation by using the new approach of generalized (G'/G)-expansion method.

PubMed

Alam, Md Nur; Akbar, M Ali

2013-01-01

The new approach of the generalized (G'/G)-expansion method is an effective and powerful mathematical tool in finding exact traveling wave solutions of nonlinear evolution equations (NLEEs) in science, engineering and mathematical physics. In this article, the new approach of the generalized (G'/G)-expansion method is applied to construct traveling wave solutions of the Kadomtsev-Petviashvili-Benjamin-Bona-Mahony (KP-BBM) equation. The solutions are expressed in terms of the hyperbolic functions, the trigonometric functions and the rational functions. By means of this scheme, we found some new traveling wave solutions of the above mentioned equation.

Validation of the activity expansion method with ultrahigh pressure shock equations of state

NASA Astrophysics Data System (ADS)

Rogers, Forrest J.; Young, David A.

1997-11-01

Laser shock experiments have recently been used to measure the equation of state (EOS) of matter in the ultrahigh pressure region between condensed matter and a weakly coupled plasma. Some ultrahigh pressure data from nuclear-generated shocks are also available. Matter at these conditions has proven very difficult to treat theoretically. The many-body activity expansion method (ACTEX) has been used for some time to calculate EOS and opacity data in this region, for use in modeling inertial confinement fusion and stellar interior plasmas. In the present work, we carry out a detailed comparison with the available experimental data in order to validate the method. The agreement is good, showing that ACTEX adequately describes strongly shocked matter.

Modification of 2-D Time-Domain Shallow Water Wave Equation using Asymptotic Expansion Method

NASA Astrophysics Data System (ADS)

Khairuman, Teuku; Nasruddin, MN; Tulus; Ramli, Marwan

2018-01-01

Generally, research on the tsunami wave propagation model can be conducted by using a linear model of shallow water theory, where a non-linear side on high order is ignored. In line with research on the investigation of the tsunami waves, the Boussinesq equation model underwent a change aimed to obtain an improved quality of the dispersion relation and non-linearity by increasing the order to be higher. To solve non-linear sides at high order is used a asymptotic expansion method. This method can be used to solve non linear partial differential equations. In the present work, we found that this method needs much computational time and memory with the increase of the number of elements.

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.

Data Clustering

NASA Astrophysics Data System (ADS)

Wagstaff, Kiri L.

2012-03-01

On obtaining a new data set, the researcher is immediately faced with the challenge of obtaining a high-level understanding from the observations. What does a typical item look like? What are the dominant trends? How many distinct groups are included in the data set, and how is each one characterized? Which observable values are common, and which rarely occur? Which items stand out as anomalies or outliers from the rest of the data? This challenge is exacerbated by the steady growth in data set size [11] as new instruments push into new frontiers of parameter space, via improvements in temporal, spatial, and spectral resolution, or by the desire to "fuse" observations from different modalities and instruments into a larger-picture understanding of the same underlying phenomenon. Data clustering algorithms provide a variety of solutions for this task. They can generate summaries, locate outliers, compress data, identify dense or sparse regions of feature space, and build data models. It is useful to note up front that "clusters" in this context refer to groups of items within some descriptive feature space, not (necessarily) to "galaxy clusters" which are dense regions in physical space. The goal of this chapter is to survey a variety of data clustering methods, with an eye toward their applicability to astronomical data analysis. In addition to improving the individual researcher’s understanding of a given data set, clustering has led directly to scientific advances, such as the discovery of new subclasses of stars [14] and gamma-ray bursts (GRBs) [38]. All clustering algorithms seek to identify groups within a data set that reflect some observed, quantifiable structure. Clustering is traditionally an unsupervised approach to data analysis, in the sense that it operates without any direct guidance about which items should be assigned to which clusters. There has been a recent trend in the clustering literature toward supporting semisupervised or constrained

Consumers' Kansei Needs Clustering Method for Product Emotional Design Based on Numerical Design Structure Matrix and Genetic Algorithms.

PubMed

Yang, Yan-Pu; Chen, Deng-Kai; Gu, Rong; Gu, Yu-Feng; Yu, Sui-Huai

2016-01-01

Consumers' Kansei needs reflect their perception about a product and always consist of a large number of adjectives. Reducing the dimension complexity of these needs to extract primary words not only enables the target product to be explicitly positioned, but also provides a convenient design basis for designers engaging in design work. Accordingly, this study employs a numerical design structure matrix (NDSM) by parameterizing a conventional DSM and integrating genetic algorithms to find optimum Kansei clusters. A four-point scale method is applied to assign link weights of every two Kansei adjectives as values of cells when constructing an NDSM. Genetic algorithms are used to cluster the Kansei NDSM and find optimum clusters. Furthermore, the process of the proposed method is presented. The details of the proposed approach are illustrated using an example of electronic scooter for Kansei needs clustering. The case study reveals that the proposed method is promising for clustering Kansei needs adjectives in product emotional design.

Consumers' Kansei Needs Clustering Method for Product Emotional Design Based on Numerical Design Structure Matrix and Genetic Algorithms

PubMed Central

Chen, Deng-kai; Gu, Rong; Gu, Yu-feng; Yu, Sui-huai

2016-01-01

Consumers' Kansei needs reflect their perception about a product and always consist of a large number of adjectives. Reducing the dimension complexity of these needs to extract primary words not only enables the target product to be explicitly positioned, but also provides a convenient design basis for designers engaging in design work. Accordingly, this study employs a numerical design structure matrix (NDSM) by parameterizing a conventional DSM and integrating genetic algorithms to find optimum Kansei clusters. A four-point scale method is applied to assign link weights of every two Kansei adjectives as values of cells when constructing an NDSM. Genetic algorithms are used to cluster the Kansei NDSM and find optimum clusters. Furthermore, the process of the proposed method is presented. The details of the proposed approach are illustrated using an example of electronic scooter for Kansei needs clustering. The case study reveals that the proposed method is promising for clustering Kansei needs adjectives in product emotional design. PMID:27630709

Cluster analysis of European Y-chromosomal STR haplotypes using the discrete Laplace method.

PubMed

Andersen, Mikkel Meyer; Eriksen, Poul Svante; Morling, Niels

2014-07-01

The European Y-chromosomal short tandem repeat (STR) haplotype distribution has previously been analysed in various ways. Here, we introduce a new way of analysing population substructure using a new method based on clustering within the discrete Laplace exponential family that models the probability distribution of the Y-STR haplotypes. Creating a consistent statistical model of the haplotypes enables us to perform a wide range of analyses. Previously, haplotype frequency estimation using the discrete Laplace method has been validated. In this paper we investigate how the discrete Laplace method can be used for cluster analysis to further validate the discrete Laplace method. A very important practical fact is that the calculations can be performed on a normal computer. We identified two sub-clusters of the Eastern and Western European Y-STR haplotypes similar to results of previous studies. We also compared pairwise distances (between geographically separated samples) with those obtained using the AMOVA method and found good agreement. Further analyses that are impossible with AMOVA were made using the discrete Laplace method: analysis of the homogeneity in two different ways and calculating marginal STR distributions. We found that the Y-STR haplotypes from e.g. Finland were relatively homogeneous as opposed to the relatively heterogeneous Y-STR haplotypes from e.g. Lublin, Eastern Poland and Berlin, Germany. We demonstrated that the observed distributions of alleles at each locus were similar to the expected ones. We also compared pairwise distances between geographically separated samples from Africa with those obtained using the AMOVA method and found good agreement. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Multi-Optimisation Consensus Clustering

NASA Astrophysics Data System (ADS)

Li, Jian; Swift, Stephen; Liu, Xiaohui

Ensemble Clustering has been developed to provide an alternative way of obtaining more stable and accurate clustering results. It aims to avoid the biases of individual clustering algorithms. However, it is still a challenge to develop an efficient and robust method for Ensemble Clustering. Based on an existing ensemble clustering method, Consensus Clustering (CC), this paper introduces an advanced Consensus Clustering algorithm called Multi-Optimisation Consensus Clustering (MOCC), which utilises an optimised Agreement Separation criterion and a Multi-Optimisation framework to improve the performance of CC. Fifteen different data sets are used for evaluating the performance of MOCC. The results reveal that MOCC can generate more accurate clustering results than the original CC algorithm.

Discrete range clustering using Monte Carlo methods

NASA Technical Reports Server (NTRS)

Chatterji, G. B.; Sridhar, B.

1993-01-01

For automatic obstacle avoidance guidance during rotorcraft low altitude flight, a reliable model of the nearby environment is needed. Such a model may be constructed by applying surface fitting techniques to the dense range map obtained by active sensing using radars. However, for covertness, passive sensing techniques using electro-optic sensors are desirable. As opposed to the dense range map obtained via active sensing, passive sensing algorithms produce reliable range at sparse locations, and therefore, surface fitting techniques to fill the gaps in the range measurement are not directly applicable. Both for automatic guidance and as a display for aiding the pilot, these discrete ranges need to be grouped into sets which correspond to objects in the nearby environment. The focus of this paper is on using Monte Carlo methods for clustering range points into meaningful groups. One of the aims of the paper is to explore whether simulated annealing methods offer significant advantage over the basic Monte Carlo method for this class of problems. We compare three different approaches and present application results of these algorithms to a laboratory image sequence and a helicopter flight sequence.

The IMACS Cluster Building Survey. I. Description of the Survey and Analysis Methods

NASA Technical Reports Server (NTRS)

Oemler Jr., Augustus; Dressler, Alan; Gladders, Michael G.; Rigby, Jane R.; Bai, Lei; Kelson, Daniel; Villanueva, Edward; Fritz, Jacopo; Rieke, George; Poggianti, Bianca M.;

Derivatized gold clusters and antibody-gold cluster conjugates

DOEpatents

Hainfeld, James F.; Furuya, Frederic R.

1994-11-01

Antibody- or antibody fragment-gold cluster conjugates are shown wherein the conjugate size can be as small as 5.0 nm. Methods and reagents are disclosed in which antibodies, Fab' or F(ab').sub.2 fragments thereof are covalently bound to a stable cluster of gold atoms. The gold clusters may contain 6, 8, 9, 11, 13, 55 or 67 gold atoms in their inner core. The clusters may also contain radioactive gold. The antibody-cluster conjugates are useful in electron microscopy applications as well as in clinical applications that include imaging, diagnosis and therapy.

A Comparison of Single Sample and Bootstrap Methods to Assess Mediation in Cluster Randomized Trials

ERIC Educational Resources Information Center

Pituch, Keenan A.; Stapleton, Laura M.; Kang, Joo Youn

2006-01-01

A Monte Carlo study examined the statistical performance of single sample and bootstrap methods that can be used to test and form confidence interval estimates of indirect effects in two cluster randomized experimental designs. The designs were similar in that they featured random assignment of clusters to one of two treatment conditions and…

Solution of Linearized Drift Kinetic Equations in Neoclassical Transport Theory by the Method of Matched Asymptotic Expansions

NASA Astrophysics Data System (ADS)

Wong, S. K.; Chan, V. S.; Hinton, F. L.

2001-10-01

The classic solution of the linearized drift kinetic equations in neoclassical transport theory for large-aspect-ratio tokamak flux-surfaces relies on the variational principle and the choice of ``localized" distribution functions as trialfunctions.(M.N. Rosenbluth, et al., Phys. Fluids 15) (1972) 116. Somewhat unclear in this approach are the nature and the origin of the ``localization" and whether the results obtained represent the exact leading terms in an asymptotic expansion int he inverse aspect ratio. Using the method of matched asymptotic expansions, we were able to derive the leading approximations to the distribution functions and demonstrated the asymptotic exactness of the existing results. The method is also applied to the calculation of angular momentum transport(M.N. Rosenbluth, et al., Plasma Phys. and Contr. Nucl. Fusion Research, 1970, Vol. 1 (IAEA, Vienna, 1971) p. 495.) and the current driven by electron cyclotron waves.

Construction and application of Red5 cluster based on OpenStack

NASA Astrophysics Data System (ADS)

Wang, Jiaqing; Song, Jianxin

2017-08-01

With the application and development of cloud computing technology in various fields, the resource utilization rate of the data center has been improved obviously, and the system based on cloud computing platform has also improved the expansibility and stability. In the traditional way, Red5 cluster resource utilization is low and the system stability is poor. This paper uses cloud computing to efficiently calculate the resource allocation ability, and builds a Red5 server cluster based on OpenStack. Multimedia applications can be published to the Red5 cloud server cluster. The system achieves the flexible construction of computing resources, but also greatly improves the stability of the cluster and service efficiency.

Unequal cluster sizes in stepped-wedge cluster randomised trials: a systematic review.

PubMed

Kristunas, Caroline; Morris, Tom; Gray, Laura

2017-11-15

To investigate the extent to which cluster sizes vary in stepped-wedge cluster randomised trials (SW-CRT) and whether any variability is accounted for during the sample size calculation and analysis of these trials. Any, not limited to healthcare settings. Any taking part in an SW-CRT published up to March 2016. The primary outcome is the variability in cluster sizes, measured by the coefficient of variation (CV) in cluster size. Secondary outcomes include the difference between the cluster sizes assumed during the sample size calculation and those observed during the trial, any reported variability in cluster sizes and whether the methods of sample size calculation and methods of analysis accounted for any variability in cluster sizes. Of the 101 included SW-CRTs, 48% mentioned that the included clusters were known to vary in size, yet only 13% of these accounted for this during the calculation of the sample size. However, 69% of the trials did use a method of analysis appropriate for when clusters vary in size. Full trial reports were available for 53 trials. The CV was calculated for 23 of these: the median CV was 0.41 (IQR: 0.22-0.52). Actual cluster sizes could be compared with those assumed during the sample size calculation for 14 (26%) of the trial reports; the cluster sizes were between 29% and 480% of that which had been assumed. Cluster sizes often vary in SW-CRTs. Reporting of SW-CRTs also remains suboptimal. The effect of unequal cluster sizes on the statistical power of SW-CRTs needs further exploration and methods appropriate to studies with unequal cluster sizes need to be employed. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Unequal cluster sizes in stepped-wedge cluster randomised trials: a systematic review

PubMed Central

Morris, Tom; Gray, Laura

2017-01-01

Objectives To investigate the extent to which cluster sizes vary in stepped-wedge cluster randomised trials (SW-CRT) and whether any variability is accounted for during the sample size calculation and analysis of these trials. Setting Any, not limited to healthcare settings. Participants Any taking part in an SW-CRT published up to March 2016. Primary and secondary outcome measures The primary outcome is the variability in cluster sizes, measured by the coefficient of variation (CV) in cluster size. Secondary outcomes include the difference between the cluster sizes assumed during the sample size calculation and those observed during the trial, any reported variability in cluster sizes and whether the methods of sample size calculation and methods of analysis accounted for any variability in cluster sizes. Results Of the 101 included SW-CRTs, 48% mentioned that the included clusters were known to vary in size, yet only 13% of these accounted for this during the calculation of the sample size. However, 69% of the trials did use a method of analysis appropriate for when clusters vary in size. Full trial reports were available for 53 trials. The CV was calculated for 23 of these: the median CV was 0.41 (IQR: 0.22–0.52). Actual cluster sizes could be compared with those assumed during the sample size calculation for 14 (26%) of the trial reports; the cluster sizes were between 29% and 480% of that which had been assumed. Conclusions Cluster sizes often vary in SW-CRTs. Reporting of SW-CRTs also remains suboptimal. The effect of unequal cluster sizes on the statistical power of SW-CRTs needs further exploration and methods appropriate to studies with unequal cluster sizes need to be employed. PMID:29146637

Conformal expansions and renormalons

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

Rathsman, J.

2000-02-07

The coefficients in perturbative expansions in gauge theories are factorially increasing, predominantly due to renormalons. This type of factorial increase is not expected in conformal theories. In QCD conformal relations between observables can be defined in the presence of a perturbative infrared fixed-point. Using the Banks-Zaks expansion the authors study the effect of the large-order behavior of the perturbative series on the conformal coefficients. The authors find that in general these coefficients become factorially increasing. However, when the factorial behavior genuinely originates in a renormalon integral, as implied by a postulated skeleton expansion, it does not affect the conformal coefficients.more » As a consequence, the conformal coefficients will indeed be free of renormalon divergence, in accordance with previous observations concerning the smallness of these coefficients for specific observables. The authors further show that the correspondence of the BLM method with the skeleton expansion implies a unique scale-setting procedure. The BLM coefficients can be interpreted as the conformal coefficients in the series relating the fixed-point value of the observable with that of the skeleton effective charge. Through the skeleton expansion the relevance of renormalon-free conformal coefficients extends to real-world QCD.« less

Node-Expansion Operators for the UCT Algorithm

NASA Astrophysics Data System (ADS)

Yajima, Takayuki; Hashimoto, Tsuyoshi; Matsui, Toshiki; Hashimoto, Junichi; Spoerer, Kristian

Recent works on the MCTS and UCT framework in the domain of Go focused on introducing knowledge to the playout and on pruning variations from the tree, but so far node expansion has not been investigated. In this paper we show that delaying expansion according to the number of the siblings delivers a gain of more than 92% when compared to normal expansion. We propose three improvements; one that uses domain knowledge and two that are domain-independent methods. Experimental results show that all advanced operators significantly improve the UCT performance when compared to the basic delaying expansion. From the results we may conclude that the new expansion operators are an appropriate means to improve the UCT algorithm.

New Target for an Old Method: Hubble Measures Globular Cluster Parallax

NASA Astrophysics Data System (ADS)

Hensley, Kerry

2018-05-01

Measuring precise distances to faraway objects has long been a challenge in astrophysics. Now, one of the earliest techniques used to measure the distance to astrophysical objects has been applied to a metal-poor globular cluster for the first time.A Classic TechniqueAn artists impression of the European Space Agencys Gaia spacecraft. Gaia is on track to map the positions and motions of a billion stars. [ESA]Distances to nearby stars are often measured using the parallax technique tracing the tiny apparent motion of a target star against the background of more distant stars as Earth orbits the Sun. This technique has come a long way since it was first used in the 1800s to measure the distance to stars a few tens of light-years away; with the advent of space observatories like Hipparcos and Gaia, parallax can now be used to map the positions of stars out to thousands of light-years.Precise distance measurements arent only important for setting the scale of the universe, however; they can also help us better understand stellar evolution over the course of cosmic history. Stellar evolution models are often anchored to a reference star cluster, the properties of which must be known precisely. These precise properties can be readily determined for young, nearby open clusters using parallax measurements. But stellar evolution models that anchor on themore-distant, ancient, metal-poor globular clusters have been hampered by theless-precise indirect methods used tomeasure distance to these faraway clusters until now.Top: An image of NGC 6397 overlaid with the area scanned by Hubble (dashed green) and the footprint of the camera (solid green). The blue ellipse represents the parallax motion of a star in the cluster, exaggerated by a factor of ten thousand. Bottom: An example scan from this field. [Adapted from Brown et al. 2018]New Measurement to an Old ClusterThomas Brown (Space Telescope Science Institute) and collaborators used the Hubble Space Telescope todetermine the

The expansion of the metazoan microRNA repertoire

PubMed Central

Hertel, Jana; Lindemeyer, Manuela; Missal, Kristin; Fried, Claudia; Tanzer, Andrea; Flamm, Christoph; Hofacker, Ivo L; Stadler, Peter F

2006-01-01

Background MicroRNAs have been identified as crucial regulators in both animals and plants. Here we report on a comprehensive comparative study of all known miRNA families in animals. We expand the MicroRNA Registry 6.0 by more than 1000 new homologs of miRNA precursors whose expression has been verified in at least one species. Using this uniform data basis we analyze their evolutionary history in terms of individual gene phylogenies and in terms of preservation of genomic nearness across species. This allows us to reliably identify microRNA clusters that are derived from a common transcript. Results We identify three episodes of microRNA innovation that correspond to major developmental innovations: A class of about 20 miRNAs is common to protostomes and deuterostomes and might be related to the advent of bilaterians. A second large wave of innovations maps to the branch leading to the vertebrates. The third significant outburst of miRNA innovation coincides with placental (eutherian) mammals. In addition, we observe the expected expansion of the microRNA inventory due to genome duplications in early vertebrates and in an ancestral teleost. The non-local duplications in the vertebrate ancestor are predated by local (tandem) duplications leading to the formation of about a dozen ancient microRNA clusters. Conclusion Our results suggest that microRNA innovation is an ongoing process. Major expansions of the metazoan miRNA repertoire coincide with the advent of bilaterians, vertebrates, and (placental) mammals. PMID:16480513

The Tolman Surface Brightness Test for the Reality of the Expansion. V. Provenance of the Test and a New Representation of the Data for Three Remote Hubble Space Telescope Galaxy Clusters

NASA Astrophysics Data System (ADS)

Sandage, Allan

2010-02-01

A new reduction is made of the Hubble Space Telescope (HST) photometric data for E galaxies in three remote clusters at redshifts near z = 0.85 in search for the Tolman surface brightness (SB) signal for the reality of the expansion. Because of the strong variation of SB of such galaxies with intrinsic size, and because the Tolman test is about SB, we must account for the variation. In an earlier version of the test, Lubin & Sandage calibrated the variation out. In contrast, the test is made here using fixed radius bins for both the local and remote samples. Homologous positions in the galaxy image at which to compare the SB values are defined by radii at five Petrosian η values ranging from 1.0 to 2.0. Sérsic luminosity profiles are used to generate two diagnostic diagrams that define the mean SB distribution across the galaxy image. A Sérsic exponent, defined by the rn family of Sérsic profiles, of n = 0.46 fits both the local and remote samples, on average, with only a small spread from 0.4 to 0.6. Diagrams of the dimming of the langSBrang with redshift over the range of Petrosian η radii shows a highly significant Tolman signal but degraded by luminosity evolution in the look-back time. The expansion is real and a luminosity evolution exists at the mean redshift of the HST clusters of 0.8 mag in R cape and 0.4 mag in the I cape photometric rest-frame bands, consistent with the evolution models of Bruzual & Charlot.

An improved method for design of expansion-chamber mufflers with application to an operational helicopter

NASA Technical Reports Server (NTRS)

Parrott, T. L.

1973-01-01

An improved method for the design of expansion-chamber mufflers is described and applied to the task of reducing exhaust noise generated by a helicopter. The method is an improvement of standard transmission-line theory in that it accounts for the effect of the mean exhaust-gas flow on the acoustic-transmission properties of a muffler system, including the termination boundary condition. The method has been computerized, and the computer program includes an optimization procedure that adjusts muffler component lengths to achieve a minimum specified desired transmission loss over a specified frequency range. A printout of the program is included together with a user-oriented description.

Dendritic cells control fibroblastic reticular network tension and lymph node expansion.

PubMed

Acton, Sophie E; Farrugia, Aaron J; Astarita, Jillian L; Mourão-Sá, Diego; Jenkins, Robert P; Nye, Emma; Hooper, Steven; van Blijswijk, Janneke; Rogers, Neil C; Snelgrove, Kathryn J; Rosewell, Ian; Moita, Luis F; Stamp, Gordon; Turley, Shannon J; Sahai, Erik; Reis e Sousa, Caetano

2014-10-23

After immunogenic challenge, infiltrating and dividing lymphocytes markedly increase lymph node cellularity, leading to organ expansion. Here we report that the physical elasticity of lymph nodes is maintained in part by podoplanin (PDPN) signalling in stromal fibroblastic reticular cells (FRCs) and its modulation by CLEC-2 expressed on dendritic cells. We show in mouse cells that PDPN induces actomyosin contractility in FRCs via activation of RhoA/C and downstream Rho-associated protein kinase (ROCK). Engagement by CLEC-2 causes PDPN clustering and rapidly uncouples PDPN from RhoA/C activation, relaxing the actomyosin cytoskeleton and permitting FRC stretching. Notably, administration of CLEC-2 protein to immunized mice augments lymph node expansion. In contrast, lymph node expansion is significantly constrained in mice selectively lacking CLEC-2 expression in dendritic cells. Thus, the same dendritic cells that initiate immunity by presenting antigens to T lymphocytes also initiate remodelling of lymph nodes by delivering CLEC-2 to FRCs. CLEC-2 modulation of PDPN signalling permits FRC network stretching and allows for the rapid lymph node expansion--driven by lymphocyte influx and proliferation--that is the critical hallmark of adaptive immunity.

Similarity-transformed equation-of-motion vibrational coupled-cluster theory.

PubMed

Faucheaux, Jacob A; Nooijen, Marcel; Hirata, So

2018-02-07

A similarity-transformed equation-of-motion vibrational coupled-cluster (STEOM-XVCC) method is introduced as a one-mode theory with an effective vibrational Hamiltonian, which is similarity transformed twice so that its lower-order operators are dressed with higher-order anharmonic effects. The first transformation uses an exponential excitation operator, defining the equation-of-motion vibrational coupled-cluster (EOM-XVCC) method, and the second uses an exponential excitation-deexcitation operator. From diagonalization of this doubly similarity-transformed Hamiltonian in the small one-mode excitation space, the method simultaneously computes accurate anharmonic vibrational frequencies of all fundamentals, which have unique significance in vibrational analyses. We establish a diagrammatic method of deriving the working equations of STEOM-XVCC and prove their connectedness and thus size-consistency as well as the exact equality of its frequencies with the corresponding roots of EOM-XVCC. We furthermore elucidate the similarities and differences between electronic and vibrational STEOM methods and between STEOM-XVCC and vibrational many-body Green's function theory based on the Dyson equation, which is also an anharmonic one-mode theory. The latter comparison inspires three approximate STEOM-XVCC methods utilizing the common approximations made in the Dyson equation: the diagonal approximation, a perturbative expansion of the Dyson self-energy, and the frequency-independent approximation. The STEOM-XVCC method including up to the simultaneous four-mode excitation operator in a quartic force field and its three approximate variants are formulated and implemented in computer codes with the aid of computer algebra, and they are applied to small test cases with varied degrees of anharmonicity.

Similarity-transformed equation-of-motion vibrational coupled-cluster theory

NASA Astrophysics Data System (ADS)

Faucheaux, Jacob A.; Nooijen, Marcel; Hirata, So

2018-02-01

A similarity-transformed equation-of-motion vibrational coupled-cluster (STEOM-XVCC) method is introduced as a one-mode theory with an effective vibrational Hamiltonian, which is similarity transformed twice so that its lower-order operators are dressed with higher-order anharmonic effects. The first transformation uses an exponential excitation operator, defining the equation-of-motion vibrational coupled-cluster (EOM-XVCC) method, and the second uses an exponential excitation-deexcitation operator. From diagonalization of this doubly similarity-transformed Hamiltonian in the small one-mode excitation space, the method simultaneously computes accurate anharmonic vibrational frequencies of all fundamentals, which have unique significance in vibrational analyses. We establish a diagrammatic method of deriving the working equations of STEOM-XVCC and prove their connectedness and thus size-consistency as well as the exact equality of its frequencies with the corresponding roots of EOM-XVCC. We furthermore elucidate the similarities and differences between electronic and vibrational STEOM methods and between STEOM-XVCC and vibrational many-body Green's function theory based on the Dyson equation, which is also an anharmonic one-mode theory. The latter comparison inspires three approximate STEOM-XVCC methods utilizing the common approximations made in the Dyson equation: the diagonal approximation, a perturbative expansion of the Dyson self-energy, and the frequency-independent approximation. The STEOM-XVCC method including up to the simultaneous four-mode excitation operator in a quartic force field and its three approximate variants are formulated and implemented in computer codes with the aid of computer algebra, and they are applied to small test cases with varied degrees of anharmonicity.

Derivatized gold clusters and antibody-gold cluster conjugates

DOEpatents

Hainfeld, J.F.; Furuya, F.R.

1994-11-01

Antibody- or antibody fragment-gold cluster conjugates are shown wherein the conjugate size can be as small as 5.0 nm. Methods and reagents are disclosed in which antibodies, Fab' or F(ab')[sub 2] fragments are covalently bound to a stable cluster of gold atoms. The gold clusters may contain 6, 8, 9, 11, 13, 55 or 67 gold atoms in their inner core. The clusters may also contain radioactive gold. The antibody-cluster conjugates are useful in electron microscopy applications as well as in clinical applications that include imaging, diagnosis and therapy. 7 figs.

Determination of Cluster Distances from Chandra Imaging Spectroscopy and Sunyaev-Zeldovich Effect Measurements. I; Analysis Methods and Initial Results

NASA Technical Reports Server (NTRS)

Bonamente, Massimiliano; Joy, Marshall K.; Carlstrom, John E.; LaRoque, Samuel J.

2004-01-01

X-ray and Sunyaev-Zeldovich Effect data ca,n be combined to determine the distance to galaxy clusters. High-resolution X-ray data are now available from the Chandra Observatory, which provides both spatial and spectral information, and interferometric radio measurements of the Sunyam-Zeldovich Effect are available from the BIMA and 0VR.O arrays. We introduce a Monte Carlo Markov chain procedure for the joint analysis of X-ray and Sunyaev-Zeldovich Effect data. The advantages of this method are the high computational efficiency and the ability to measure the full probability distribution of all parameters of interest, such as the spatial and spectral properties of the cluster gas and the cluster distance. We apply this technique to the Chandra X-ray data and the OVRO radio data for the galaxy cluster Abell 611. Comparisons with traditional likelihood-ratio methods reveal the robustness of the method. This method will be used in a follow-up paper to determine the distance of a large sample of galaxy clusters for which high-resolution Chandra X-ray and BIMA/OVRO radio data are available.

Accompanying coordinate expansion and recurrence relation method using a transfer relation scheme for electron repulsion integrals with high angular momenta and long contractions

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

Hayami, Masao; Seino, Junji; Nakai, Hiromi, E-mail: nakai@waseda.jp

An efficient algorithm for the rapid evaluation of electron repulsion integrals is proposed. The present method, denoted by accompanying coordinate expansion and transferred recurrence relation (ACE-TRR), is constructed using a transfer relation scheme based on the accompanying coordinate expansion and recurrence relation method. Furthermore, the ACE-TRR algorithm is extended for the general-contraction basis sets. Numerical assessments clarify the efficiency of the ACE-TRR method for the systems including heavy elements, whose orbitals have long contractions and high angular momenta, such as f- and g-orbitals.

An Energy-Efficient Cluster-Based Vehicle Detection on Road Network Using Intention Numeration Method

PubMed Central

Devasenapathy, Deepa; Kannan, Kathiravan

2015-01-01

The traffic in the road network is progressively increasing at a greater extent. Good knowledge of network traffic can minimize congestions using information pertaining to road network obtained with the aid of communal callers, pavement detectors, and so on. Using these methods, low featured information is generated with respect to the user in the road network. Although the existing schemes obtain urban traffic information, they fail to calculate the energy drain rate of nodes and to locate equilibrium between the overhead and quality of the routing protocol that renders a great challenge. Thus, an energy-efficient cluster-based vehicle detection in road network using the intention numeration method (CVDRN-IN) is developed. Initially, sensor nodes that detect a vehicle are grouped into separate clusters. Further, we approximate the strength of the node drain rate for a cluster using polynomial regression function. In addition, the total node energy is estimated by taking the integral over the area. Finally, enhanced data aggregation is performed to reduce the amount of data transmission using digital signature tree. The experimental performance is evaluated with Dodgers loop sensor data set from UCI repository and the performance evaluation outperforms existing work on energy consumption, clustering efficiency, and node drain rate. PMID:25793221

An energy-efficient cluster-based vehicle detection on road network using intention numeration method.

PubMed

Devasenapathy, Deepa; Kannan, Kathiravan

2015-01-01

The traffic in the road network is progressively increasing at a greater extent. Good knowledge of network traffic can minimize congestions using information pertaining to road network obtained with the aid of communal callers, pavement detectors, and so on. Using these methods, low featured information is generated with respect to the user in the road network. Although the existing schemes obtain urban traffic information, they fail to calculate the energy drain rate of nodes and to locate equilibrium between the overhead and quality of the routing protocol that renders a great challenge. Thus, an energy-efficient cluster-based vehicle detection in road network using the intention numeration method (CVDRN-IN) is developed. Initially, sensor nodes that detect a vehicle are grouped into separate clusters. Further, we approximate the strength of the node drain rate for a cluster using polynomial regression function. In addition, the total node energy is estimated by taking the integral over the area. Finally, enhanced data aggregation is performed to reduce the amount of data transmission using digital signature tree. The experimental performance is evaluated with Dodgers loop sensor data set from UCI repository and the performance evaluation outperforms existing work on energy consumption, clustering efficiency, and node drain rate.

The effects of micro-implant assisted rapid palatal expansion (MARPE) on the nasomaxillary complex--a finite element method (FEM) analysis.

PubMed

MacGinnis, Matt; Chu, Howard; Youssef, George; Wu, Kimberley W; Machado, Andre Wilson; Moon, Won

2014-08-29

Orthodontic palatal expansion appliances have been widely used with satisfactory and, most often, predictable clinical results. Recently, clinicians have successfully utilized micro-implants with palatal expander designs to work as anchors to the palate to achieve more efficient skeletal expansion and to decrease undesired dental effects. The purpose of the study was to use finite element method (FEM) to determine the stress distribution and displacement within the craniofacial complex when simulated conventional and micro-implant-assisted rapid palatal expansion (MARPE) expansion forces are applied to the maxilla. The simulated stress distribution produced within the palate and maxillary buttresses in addition to the displacement and rotation of the maxilla could then be analyzed to determine if micro-implants aid in skeletal expansion. A three-dimensional (3D) mesh model of the cranium with associated maxillary sutures was developed using computed tomography (CT) images and Mimics modeling software. To compare transverse expansion stresses in rapid palatal expansion (RPE) and MARPE, expansion forces were distributed to differing points on the maxilla and evaluated with ANSYS simulation software. The stresses distributed from forces applied to the maxillary teeth are distributed mainly along the trajectories of the three maxillary buttresses. In comparison, the MARPE showed tension and compression directed to the palate, while showing less rotation, and tipping of the maxillary complex. In addition, the conventional hyrax displayed a rotation of the maxilla around the teeth as opposed to the midpalatal suture of the MARPE. This data suggests that the MARPE causes the maxilla to bend laterally, while preventing unwanted rotation of the complex. In conclusion, the MARPE may be beneficial for hyperdivergent patients, or those that have already experienced closure of the midpalatal suture, who require palatal expansion and would worsen from buccal tipping of the teeth

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…

A comparison of confidence interval methods for the intraclass correlation coefficient in community-based cluster randomization trials with a binary outcome.

PubMed

Braschel, Melissa C; Svec, Ivana; Darlington, Gerarda A; Donner, Allan

2016-04-01

Many investigators rely on previously published point estimates of the intraclass correlation coefficient rather than on their associated confidence intervals to determine the required size of a newly planned cluster randomized trial. Although confidence interval methods for the intraclass correlation coefficient that can be applied to community-based trials have been developed for a continuous outcome variable, fewer methods exist for a binary outcome variable. The aim of this study is to evaluate confidence interval methods for the intraclass correlation coefficient applied to binary outcomes in community intervention trials enrolling a small number of large clusters. Existing methods for confidence interval construction are examined and compared to a new ad hoc approach based on dividing clusters into a large number of smaller sub-clusters and subsequently applying existing methods to the resulting data. Monte Carlo simulation is used to assess the width and coverage of confidence intervals for the intraclass correlation coefficient based on Smith's large sample approximation of the standard error of the one-way analysis of variance estimator, an inverted modified Wald test for the Fleiss-Cuzick estimator, and intervals constructed using a bootstrap-t applied to a variance-stabilizing transformation of the intraclass correlation coefficient estimate. In addition, a new approach is applied in which clusters are randomly divided into a large number of smaller sub-clusters with the same methods applied to these data (with the exception of the bootstrap-t interval, which assumes large cluster sizes). These methods are also applied to a cluster randomized trial on adolescent tobacco use for illustration. When applied to a binary outcome variable in a small number of large clusters, existing confidence interval methods for the intraclass correlation coefficient provide poor coverage. However, confidence intervals constructed using the new approach combined with Smith

A novel alignment-free method to classify protein folding types by combining spectral graph clustering with Chou's pseudo amino acid composition.

PubMed

Tripathi, Pooja; Pandey, Paras N

2017-07-07

The present work employs pseudo amino acid composition (PseAAC) for encoding the protein sequences in their numeric form. Later this will be arranged in the similarity matrix, which serves as input for spectral graph clustering method. Spectral methods are used previously also for clustering of protein sequences, but they uses pair wise alignment scores of protein sequences, in similarity matrix. The alignment score depends on the length of sequences, so clustering short and long sequences together may not good idea. Therefore the idea of introducing PseAAC with spectral clustering algorithm came into scene. We extensively tested our method and compared its performance with other existing machine learning methods. It is consistently observed that, the number of clusters that we obtained for a given set of proteins is close to the number of superfamilies in that set and PseAAC combined with spectral graph clustering shows the best classification results. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

Clustering of attitudes towards obesity: a mixed methods study of Australian parents and children.

PubMed

Olds, Tim; Thomas, Samantha; Lewis, Sophie; Petkov, John

2013-10-12

Current population-based anti-obesity campaigns often target individuals based on either weight or socio-demographic characteristics, and give a 'mass' message about personal responsibility. There is a recognition that attempts to influence attitudes and opinions may be more effective if they resonate with the beliefs that different groups have about the causes of, and solutions for, obesity. Limited research has explored how attitudinal factors may inform the development of both upstream and downstream social marketing initiatives. Computer-assisted face-to-face interviews were conducted with 159 parents and 184 of their children (aged 9-18 years old) in two Australian states. A mixed methods approach was used to assess attitudes towards obesity, and elucidate why different groups held various attitudes towards obesity. Participants were quantitatively assessed on eight dimensions relating to the severity and extent, causes and responsibility, possible remedies, and messaging strategies. Cluster analysis was used to determine attitudinal clusters. Participants were also able to qualify each answer. Qualitative responses were analysed both within and across attitudinal clusters using a constant comparative method. Three clusters were identified. Concerned Internalisers (27% of the sample) judged that obesity was a serious health problem, that Australia had among the highest levels of obesity in the world and that prevalence was rapidly increasing. They situated the causes and remedies for the obesity crisis in individual choices. Concerned Externalisers (38% of the sample) held similar views about the severity and extent of the obesity crisis. However, they saw responsibility and remedies as a societal rather than an individual issue. The final cluster, the Moderates, which contained significantly more children and males, believed that obesity was not such an important public health issue, and judged the extent of obesity to be less extreme than the other clusters

Integrated management of thesis using clustering method

NASA Astrophysics Data System (ADS)

Astuti, Indah Fitri; Cahyadi, Dedy

2017-02-01

Thesis is one of major requirements for student in pursuing their bachelor degree. In fact, finishing the thesis involves a long process including consultation, writing manuscript, conducting the chosen method, seminar scheduling, searching for references, and appraisal process by the board of mentors and examiners. Unfortunately, most of students find it hard to match all the lecturers' free time to sit together in a seminar room in order to examine the thesis. Therefore, seminar scheduling process should be on the top of priority to be solved. Manual mechanism for this task no longer fulfills the need. People in campus including students, staffs, and lecturers demand a system in which all the stakeholders can interact each other and manage the thesis process without conflicting their timetable. A branch of computer science named Management Information System (MIS) could be a breakthrough in dealing with thesis management. This research conduct a method called clustering to distinguish certain categories using mathematics formulas. A system then be developed along with the method to create a well-managed tool in providing some main facilities such as seminar scheduling, consultation and review process, thesis approval, assessment process, and also a reliable database of thesis. The database plays an important role in present and future purposes.

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

A note on improved F-expansion method combined with Riccati equation applied to nonlinear evolution equations.

PubMed

Islam, Md Shafiqul; Khan, Kamruzzaman; Akbar, M Ali; Mastroberardino, Antonio

2014-10-01

The purpose of this article is to present an analytical method, namely the improved F-expansion method combined with the Riccati equation, for finding exact solutions of nonlinear evolution equations. The present method is capable of calculating all branches of solutions simultaneously, even if multiple solutions are very close and thus difficult to distinguish with numerical techniques. To verify the computational efficiency, we consider the modified Benjamin-Bona-Mahony equation and the modified Korteweg-de Vries equation. Our results reveal that the method is a very effective and straightforward way of formulating the exact travelling wave solutions of nonlinear wave equations arising in mathematical physics and engineering.

A note on improved F-expansion method combined with Riccati equation applied to nonlinear evolution equations

PubMed Central

Islam, Md. Shafiqul; Khan, Kamruzzaman; Akbar, M. Ali; Mastroberardino, Antonio

2014-01-01

The purpose of this article is to present an analytical method, namely the improved F-expansion method combined with the Riccati equation, for finding exact solutions of nonlinear evolution equations. The present method is capable of calculating all branches of solutions simultaneously, even if multiple solutions are very close and thus difficult to distinguish with numerical techniques. To verify the computational efficiency, we consider the modified Benjamin–Bona–Mahony equation and the modified Korteweg-de Vries equation. Our results reveal that the method is a very effective and straightforward way of formulating the exact travelling wave solutions of nonlinear wave equations arising in mathematical physics and engineering. PMID:26064530

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

Pressurized heat treatment of glass-ceramic to control thermal expansion

DOEpatents

Kramer, Daniel P.

1985-01-01

A method of producing a glass-ceramic having a specified thermal expansion value is disclosed. The method includes the step of pressurizing the parent glass material to a predetermined pressure during heat treatment so that the glass-ceramic produced has a specified thermal expansion value. Preferably, the glass-ceramic material is isostatically pressed. A method for forming a strong glass-ceramic to metal seal is also disclosed in which the glass-ceramic is fabricated to have a thermal expansion value equal to that of the metal. The determination of the thermal expansion value of a parent glass material placed in a high-temperature environment is also used to determine the pressure in the environment.

Pressurized electrolysis stack with thermal expansion capability

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

Bourgeois, Richard Scott

The present techniques provide systems and methods for mounting an electrolyzer stack in an outer shell so as to allow for differential thermal expansion of the electrolyzer stack and shell. Generally, an electrolyzer stack may be formed from a material with a high coefficient of thermal expansion, while the shell may be formed from a material having a lower coefficient of thermal expansion. The differences between the coefficients of thermal expansion may lead to damage to the electrolyzer stack as the shell may restrain the thermal expansion of the electrolyzer stack. To allow for the differences in thermal expansion, themore » electrolyzer stack may be mounted within the shell leaving a space between the electrolyzer stack and shell. The space between the electrolyzer stack and the shell may be filled with a non-conductive fluid to further equalize pressure inside and outside of the electrolyzer stack.« less

Lattice cluster theory of associating polymers. I. Solutions of linear telechelic polymer chains.

PubMed

Dudowicz, Jacek; Freed, Karl F

2012-02-14

The lattice cluster theory (LCT) for the thermodynamics of a wide array of polymer systems has been developed by using an analogy to Mayer's virial expansions for non-ideal gases. However, the high-temperature expansion inherent to the LCT has heretofore precluded its application to systems exhibiting strong, specific "sticky" interactions. The present paper describes a reformulation of the LCT necessary to treat systems with both weak and strong, "sticky" interactions. This initial study concerns solutions of linear telechelic chains (with stickers at the chain ends) as the self-assembling system. The main idea behind this extension of the LCT lies in the extraction of terms associated with the strong interactions from the cluster expansion. The generalized LCT for sticky systems reduces to the quasi-chemical theory of hydrogen bonding of Panyioutou and Sanchez when correlation corrections are neglected in the LCT. A diagrammatic representation is employed to facilitate the evaluation of the corrections to the zeroth-order approximation from short range correlations. © 2012 American Institute of Physics

Data-driven modeling and predictive control for boiler-turbine unit using fuzzy clustering and subspace methods.

PubMed

Wu, Xiao; Shen, Jiong; Li, Yiguo; Lee, Kwang Y

2014-05-01

This paper develops a novel data-driven fuzzy modeling strategy and predictive controller for boiler-turbine unit using fuzzy clustering and subspace identification (SID) methods. To deal with the nonlinear behavior of boiler-turbine unit, fuzzy clustering is used to provide an appropriate division of the operation region and develop the structure of the fuzzy model. Then by combining the input data with the corresponding fuzzy membership functions, the SID method is extended to extract the local state-space model parameters. Owing to the advantages of the both methods, the resulting fuzzy model can represent the boiler-turbine unit very closely, and a fuzzy model predictive controller is designed based on this model. As an alternative approach, a direct data-driven fuzzy predictive control is also developed following the same clustering and subspace methods, where intermediate subspace matrices developed during the identification procedure are utilized directly as the predictor. Simulation results show the advantages and effectiveness of the proposed approach. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Parity among interpretation methods of MLEE patterns and disparity among clustering methods in epidemiological typing of Candida albicans.

PubMed

Boriollo, Marcelo Fabiano Gomes; Rosa, Edvaldo Antonio Ribeiro; Gonçalves, Reginaldo Bruno; Höfling, José Francisco

2006-03-01

The typing of C. albicans by MLEE (multilocus enzyme electrophoresis) is dependent on the interpretation of enzyme electrophoretic patterns, and the study of the epidemiological relationships of these yeasts can be conducted by cluster analysis. Therefore, the aims of the present study were to first determine the discriminatory power of genetic interpretation (deduction of the allelic composition of diploid organisms) and numerical interpretation (mere determination of the presence and absence of bands) of MLEE patterns, and then to determine the concordance (Pearson product-moment correlation coefficient) and similarity (Jaccard similarity coefficient) of the groups of strains generated by three cluster analysis models, and the discriminatory power of such models as well [model A: genetic interpretation, genetic distance matrix of Nei (d(ij)) and UPGMA dendrogram; model B: genetic interpretation, Dice similarity matrix (S(D1)) and UPGMA dendrogram; model C: numerical interpretation, Dice similarity matrix (S(D2)) and UPGMA dendrogram]. MLEE was found to be a powerful and reliable tool for the typing of C. albicans due to its high discriminatory power (>0.9). Discriminatory power indicated that numerical interpretation is a method capable of discriminating a greater number of strains (47 versus 43 subtypes), but also pointed to model B as a method capable of providing a greater number of groups, suggesting its use for the typing of C. albicans by MLEE and cluster analysis. Very good agreement was only observed between the elements of the matrices S(D1) and S(D2), but a large majority of the groups generated in the three UPGMA dendrograms showed similarity S(J) between 4.8% and 75%, suggesting disparities in the conclusions obtained by the cluster assays.

A New Cluster Analysis-Marker-Controlled Watershed Method for Separating Particles of Granular Soils.

PubMed

Alam, Md Ferdous; Haque, Asadul

2017-10-18

An accurate determination of particle-level fabric of granular soils from tomography data requires a maximum correct separation of particles. The popular marker-controlled watershed separation method is widely used to separate particles. However, the watershed method alone is not capable of producing the maximum separation of particles when subjected to boundary stresses leading to crushing of particles. In this paper, a new separation method, named as Monash Particle Separation Method (MPSM), has been introduced. The new method automatically determines the optimal contrast coefficient based on cluster evaluation framework to produce the maximum accurate separation outcomes. Finally, the particles which could not be separated by the optimal contrast coefficient were separated by integrating cuboid markers generated from the clustering by Gaussian mixture models into the routine watershed method. The MPSM was validated on a uniformly graded sand volume subjected to one-dimensional compression loading up to 32 MPa. It was demonstrated that the MPSM is capable of producing the best possible separation of particles required for the fabric analysis.

A New Cluster Analysis-Marker-Controlled Watershed Method for Separating Particles of Granular Soils

PubMed Central

Alam, Md Ferdous

2017-01-01

An accurate determination of particle-level fabric of granular soils from tomography data requires a maximum correct separation of particles. The popular marker-controlled watershed separation method is widely used to separate particles. However, the watershed method alone is not capable of producing the maximum separation of particles when subjected to boundary stresses leading to crushing of particles. In this paper, a new separation method, named as Monash Particle Separation Method (MPSM), has been introduced. The new method automatically determines the optimal contrast coefficient based on cluster evaluation framework to produce the maximum accurate separation outcomes. Finally, the particles which could not be separated by the optimal contrast coefficient were separated by integrating cuboid markers generated from the clustering by Gaussian mixture models into the routine watershed method. The MPSM was validated on a uniformly graded sand volume subjected to one-dimensional compression loading up to 32 MPa. It was demonstrated that the MPSM is capable of producing the best possible separation of particles required for the fabric analysis. PMID:29057823

Slope angle estimation method based on sparse subspace clustering for probe safe landing

NASA Astrophysics Data System (ADS)

Li, Haibo; Cao, Yunfeng; Ding, Meng; Zhuang, Likui

2018-06-01

To avoid planetary probes landing on steep slopes where they may slip or tip over, a new method of slope angle estimation based on sparse subspace clustering is proposed to improve accuracy. First, a coordinate system is defined and established to describe the measured data of light detection and ranging (LIDAR). Second, this data is processed and expressed with a sparse representation. Third, on this basis, the data is made to cluster to determine which subspace it belongs to. Fourth, eliminating outliers in subspace, the correct data points are used for the fitting planes. Finally, the vectors normal to the planes are obtained using the plane model, and the angle between the normal vectors is obtained through calculation. Based on the geometric relationship, this angle is equal in value to the slope angle. The proposed method was tested in a series of experiments. The experimental results show that this method can effectively estimate the slope angle, can overcome the influence of noise and obtain an exact slope angle. Compared with other methods, this method can minimize the measuring errors and further improve the estimation accuracy of the slope angle.

Choosing the Number of Clusters in K-Means Clustering

ERIC Educational Resources Information Center

Steinley, Douglas; Brusco, Michael J.

2011-01-01

Steinley (2007) provided a lower bound for the sum-of-squares error criterion function used in K-means clustering. In this article, on the basis of the lower bound, the authors propose a method to distinguish between 1 cluster (i.e., a single distribution) versus more than 1 cluster. Additionally, conditional on indicating there are multiple…

Bacterial community comparisons by taxonomy-supervised analysis independent of sequence alignment and clustering

PubMed Central

Sul, Woo Jun; Cole, James R.; Jesus, Ederson da C.; Wang, Qiong; Farris, Ryan J.; Fish, Jordan A.; Tiedje, James M.

2011-01-01

High-throughput sequencing of 16S rRNA genes has increased our understanding of microbial community structure, but now even higher-throughput methods to the Illumina scale allow the creation of much larger datasets with more samples and orders-of-magnitude more sequences that swamp current analytic methods. We developed a method capable of handling these larger datasets on the basis of assignment of sequences into an existing taxonomy using a supervised learning approach (taxonomy-supervised analysis). We compared this method with a commonly used clustering approach based on sequence similarity (taxonomy-unsupervised analysis). We sampled 211 different bacterial communities from various habitats and obtained ∼1.3 million 16S rRNA sequences spanning the V4 hypervariable region by pyrosequencing. Both methodologies gave similar ecological conclusions in that β-diversity measures calculated by using these two types of matrices were significantly correlated to each other, as were the ordination configurations and hierarchical clustering dendrograms. In addition, our taxonomy-supervised analyses were also highly correlated with phylogenetic methods, such as UniFrac. The taxonomy-supervised analysis has the advantages that it is not limited by the exhaustive computation required for the alignment and clustering necessary for the taxonomy-unsupervised analysis, is more tolerant of sequencing errors, and allows comparisons when sequences are from different regions of the 16S rRNA gene. With the tremendous expansion in 16S rRNA data acquisition underway, the taxonomy-supervised approach offers the potential to provide more rapid and extensive community comparisons across habitats and samples. PMID:21873204

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

PubMed

Orsi, Rebecca

2017-02-01

Concept mapping is now a commonly-used technique for articulating and evaluating programmatic outcomes. However, research regarding validity of knowledge and outcomes produced with concept mapping is sparse. The current study describes quantitative validity analyses using a concept mapping dataset. We sought to increase the validity of concept mapping evaluation results by running multiple cluster analysis methods and then using several metrics to choose from among solutions. We present four different clustering methods based on analyses using the R statistical software package: partitioning around medoids (PAM), fuzzy analysis (FANNY), agglomerative nesting (AGNES) and divisive analysis (DIANA). We then used the Dunn and Davies-Bouldin indices to assist in choosing a valid cluster solution for a concept mapping outcomes evaluation. We conclude that the validity of the outcomes map is high, based on the analyses described. Finally, we discuss areas for further concept mapping methods research. Copyright © 2016 Elsevier Ltd. All rights reserved.

A unified perturbation expansion for surface scattering

NASA Technical Reports Server (NTRS)

Rodriguez, Ernesto; Kim, Yunjin

1992-01-01

Starting with the extinction theorem, a perturbation expansion which, to first and second orders, converges over a wider domain than the small perturbation expansion and the momentum transfer expansion is presented. It is shown that, in the appropriate limits, both of these theories, as well as the two-scale expansion, are recovered. There is no adjustable parameter, such as a spectral split, in the theory. This theory is applied to random rough surfaces and derive analytic expressions for the coherent field and the bistatic cross section. Finally, a numerical test of the theory against method of moments results for Gaussian random rough surfaces with a power law spectrum is given. These results show that the expansion is ramarkably accurate over a large range of surface heights and slopes for both horizontal and vertical polarization.

Molecular Evolution and Expansion Analysis of the NAC Transcription Factor in Zea mays

PubMed Central

Fan, Kai; Wang, Ming; Miao, Ying; Ni, Mi; Bibi, Noreen; Yuan, Shuna; Li, Feng; Wang, Xuede

2014-01-01

NAC (NAM, ATAF1, 2 and CUC2) family is a plant-specific transcription factor and it controls various plant developmental processes. In the current study, 124 NAC members were identified in Zea mays and were phylogenetically clustered into 13 distinct subfamilies. The whole genome duplication (WGD), especially an additional WGD event, may lead to expanding ZmNAC members. Different subfamily has different expansion rate, and NAC subfamily preference was found during the expansion in maize. Moreover, the duplication events might occur after the divergence of the lineages of Z. mays and S. italica, and segmental duplication seemed to be the dominant pattern for the gene duplication in maize. Furthermore, the expansion of ZmNAC members may be also related to gain and loss of introns. Besides, the restriction of functional divergence was discovered after most of the gene duplication events. These results could provide novel insights into molecular evolution and expansion analysis of NAC family in maize, and advance the NAC researches in other plants, especially polyploid plants. PMID:25369196

Methods of Conceptual Clustering and their Relation to Numerical Taxonomy.

DTIC Science & Technology

1985-07-22

the conceptual clustering problem is to first solve theaggregation problem, and then the characterization problem. In machine learning, the...cluster- ings by first generating some number of possible clusterings. For each clustering generated, one calls a learning from examples subroutine, which...class 1 from class 2, and vice versa, only the first combination implies a partition over the set of theoretically possible objects. The first

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).