Evolution of worldwide stock markets, correlation structure, and correlation-based graphs

NASA Astrophysics Data System (ADS)

Song, Dong-Ming; Tumminello, Michele; Zhou, Wei-Xing; Mantegna, Rosario N.

2011-08-01

We investigate the daily correlation present among market indices of stock exchanges located all over the world in the time period January 1996 to July 2009. We discover that the correlation among market indices presents both a fast and a slow dynamics. The slow dynamics reflects the development and consolidation of globalization. The fast dynamics is associated with critical events that originate in a specific country or region of the world and rapidly affect the global system. We provide evidence that the short term time scale of correlation among market indices is less than 3 trading months (about 60 trading days). The average values of the nondiagonal elements of the correlation matrix, correlation-based graphs, and the spectral properties of the largest eigenvalues and eigenvectors of the correlation matrix are carrying information about the fast and slow dynamics of the correlation of market indices. We introduce a measure of mutual information based on link co-occurrence in networks in order to detect the fast dynamics of successive changes of correlation-based graphs in a quantitative way.

Efficient Storage Scheme of Covariance Matrix during Inverse Modeling

NASA Astrophysics Data System (ADS)

Mao, D.; Yeh, T. J.

2013-12-01

During stochastic inverse modeling, the covariance matrix of geostatistical based methods carries the information about the geologic structure. Its update during iterations reflects the decrease of uncertainty with the incorporation of observed data. For large scale problem, its storage and update cost too much memory and computational resources. In this study, we propose a new efficient storage scheme for storage and update. Compressed Sparse Column (CSC) format is utilized to storage the covariance matrix, and users can assign how many data they prefer to store based on correlation scales since the data beyond several correlation scales are usually not very informative for inverse modeling. After every iteration, only the diagonal terms of the covariance matrix are updated. The off diagonal terms are calculated and updated based on shortened correlation scales with a pre-assigned exponential model. The correlation scales are shortened by a coefficient, i.e. 0.95, every iteration to show the decrease of uncertainty. There is no universal coefficient for all the problems and users are encouraged to try several times. This new scheme is tested with 1D examples first. The estimated results and uncertainty are compared with the traditional full storage method. In the end, a large scale numerical model is utilized to validate this new scheme.

A Method of Q-Matrix Validation for the Linear Logistic Test Model

PubMed Central

Baghaei, Purya; Hohensinn, Christine

2017-01-01

The linear logistic test model (LLTM) is a well-recognized psychometric model for examining the components of difficulty in cognitive tests and validating construct theories. The plausibility of the construct model, summarized in a matrix of weights, known as the Q-matrix or weight matrix, is tested by (1) comparing the fit of LLTM with the fit of the Rasch model (RM) using the likelihood ratio (LR) test and (2) by examining the correlation between the Rasch model item parameters and LLTM reconstructed item parameters. The problem with the LR test is that it is almost always significant and, consequently, LLTM is rejected. The drawback of examining the correlation coefficient is that there is no cut-off value or lower bound for the magnitude of the correlation coefficient. In this article we suggest a simulation method to set a minimum benchmark for the correlation between item parameters from the Rasch model and those reconstructed by the LLTM. If the cognitive model is valid then the correlation coefficient between the RM-based item parameters and the LLTM-reconstructed item parameters derived from the theoretical weight matrix should be greater than those derived from the simulated matrices. PMID:28611721

Group identification in Indonesian stock market

NASA Astrophysics Data System (ADS)

Nurriyadi Suparno, Ervano; Jo, Sung Kyun; Lim, Kyuseong; Purqon, Acep; Kim, Soo Yong

2016-08-01

The characteristic of Indonesian stock market is interesting especially because it represents developing countries. We investigate the dynamics and structures by using Random Matrix Theory (RMT). Here, we analyze the cross-correlation of the fluctuations of the daily closing price of stocks from the Indonesian Stock Exchange (IDX) between January 1, 2007, and October 28, 2014. The eigenvalue distribution of the correlation matrix consists of noise which is filtered out using the random matrix as a control. The bulk of the eigenvalue distribution conforms to the random matrix, allowing the separation of random noise from original data which is the deviating eigenvalues. From the deviating eigenvalues and the corresponding eigenvectors, we identify the intrinsic normal modes of the system and interpret their meaning based on qualitative and quantitative approach. The results show that the largest eigenvector represents the market-wide effect which has a predominantly common influence toward all stocks. The other eigenvectors represent highly correlated groups within the system. Furthermore, identification of the largest components of the eigenvectors shows the sector or background of the correlated groups. Interestingly, the result shows that there are mainly two clusters within IDX, natural and non-natural resource companies. We then decompose the correlation matrix to investigate the contribution of the correlated groups to the total correlation, and we find that IDX is still driven mainly by the market-wide effect.

Estimating and Identifying Unspecified Correlation Structure for Longitudinal Data

PubMed Central

Hu, Jianhua; Wang, Peng; Qu, Annie

2014-01-01

Identifying correlation structure is important to achieving estimation efficiency in analyzing longitudinal data, and is also crucial for drawing valid statistical inference for large size clustered data. In this paper, we propose a nonparametric method to estimate the correlation structure, which is applicable for discrete longitudinal data. We utilize eigenvector-based basis matrices to approximate the inverse of the empirical correlation matrix and determine the number of basis matrices via model selection. A penalized objective function based on the difference between the empirical and model approximation of the correlation matrices is adopted to select an informative structure for the correlation matrix. The eigenvector representation of the correlation estimation is capable of reducing the risk of model misspecification, and also provides useful information on the specific within-cluster correlation pattern of the data. We show that the proposed method possesses the oracle property and selects the true correlation structure consistently. The proposed method is illustrated through simulations and two data examples on air pollution and sonar signal studies. PMID:26361433

Noise-immune complex correlation for vasculature imaging based on standard and Jones-matrix optical coherence tomography

NASA Astrophysics Data System (ADS)

Makita, Shuichi; Kurokawa, Kazuhiro; Hong, Young-Joo; Li, En; Miura, Masahiro; Yasuno, Yoshiaki

2016-03-01

A new optical coherence angiography (OCA) method, called correlation mapping OCA (cmOCA), is presented by using the SNR-corrected complex correlation. An SNR-correction theory for the complex correlation calculation is presented. The method also integrates a motion-artifact-removal method for the sample motion induced decorrelation artifact. The theory is further extended to compute more reliable correlation by using multi- channel OCT systems, such as Jones-matrix OCT. The high contrast vasculature imaging of in vivo human posterior eye has been obtained. Composite imaging of cmOCA and degree of polarization uniformity indicates abnormalities of vasculature and pigmented tissues simultaneously.

Triple collocation-based estimation of spatially correlated observation error covariance in remote sensing soil moisture data assimilation

NASA Astrophysics Data System (ADS)

Wu, Kai; Shu, Hong; Nie, Lei; Jiao, Zhenhang

2018-01-01

Spatially correlated errors are typically ignored in data assimilation, thus degenerating the observation error covariance R to a diagonal matrix. We argue that a nondiagonal R carries more observation information making assimilation results more accurate. A method, denoted TC_Cov, was proposed for soil moisture data assimilation to estimate spatially correlated observation error covariance based on triple collocation (TC). Assimilation experiments were carried out to test the performance of TC_Cov. AMSR-E soil moisture was assimilated with a diagonal R matrix computed using the TC and assimilated using a nondiagonal R matrix, as estimated by proposed TC_Cov. The ensemble Kalman filter was considered as the assimilation method. Our assimilation results were validated against climate change initiative data and ground-based soil moisture measurements using the Pearson correlation coefficient and unbiased root mean square difference metrics. These experiments confirmed that deterioration of diagonal R assimilation results occurred when model simulation is more accurate than observation data. Furthermore, nondiagonal R achieved higher correlation coefficient and lower ubRMSD values over diagonal R in experiments and demonstrated the effectiveness of TC_Cov to estimate richly structuralized R in data assimilation. In sum, compared with diagonal R, nondiagonal R may relieve the detrimental effects of assimilation when simulated model results outperform observation data.

An attempt to determine the effect of increase of observation correlations on detectability and identifiability of a single gross error

NASA Astrophysics Data System (ADS)

Prószyński, Witold; Kwaśniak, Mieczysław

2016-12-01

The paper presents the results of investigating the effect of increase of observation correlations on detectability and identifiability of a single gross error, the outlier test sensitivity and also the response-based measures of internal reliability of networks. To reduce in a research a practically incomputable number of possible test options when considering all the non-diagonal elements of the correlation matrix as variables, its simplest representation was used being a matrix with all non-diagonal elements of equal values, termed uniform correlation. By raising the common correlation value incrementally, a sequence of matrix configurations could be obtained corresponding to the increasing level of observation correlations. For each of the measures characterizing the above mentioned features of network reliability the effect is presented in a diagram form as a function of the increasing level of observation correlations. The influence of observation correlations on sensitivity of the w-test for correlated observations (Förstner 1983, Teunissen 2006) is investigated in comparison with the original Baarda's w-test designated for uncorrelated observations, to determine the character of expected sensitivity degradation of the latter when used for correlated observations. The correlation effects obtained for different reliability measures exhibit mutual consistency in a satisfactory extent. As a by-product of the analyses, a simple formula valid for any arbitrary correlation matrix is proposed for transforming the Baarda's w-test statistics into the w-test statistics for correlated observations.

Thermal form-factor approach to dynamical correlation functions of integrable lattice models

NASA Astrophysics Data System (ADS)

Göhmann, Frank; Karbach, Michael; Klümper, Andreas; Kozlowski, Karol K.; Suzuki, Junji

2017-11-01

We propose a method for calculating dynamical correlation functions at finite temperature in integrable lattice models of Yang-Baxter type. The method is based on an expansion of the correlation functions as a series over matrix elements of a time-dependent quantum transfer matrix rather than the Hamiltonian. In the infinite Trotter-number limit the matrix elements become time independent and turn into the thermal form factors studied previously in the context of static correlation functions. We make this explicit with the example of the XXZ model. We show how the form factors can be summed utilizing certain auxiliary functions solving finite sets of nonlinear integral equations. The case of the XX model is worked out in more detail leading to a novel form-factor series representation of the dynamical transverse two-point function.

Noise-immune complex correlation for optical coherence angiography based on standard and Jones matrix optical coherence tomography

PubMed Central

Makita, Shuichi; Kurokawa, Kazuhiro; Hong, Young-Joo; Miura, Masahiro; Yasuno, Yoshiaki

2016-01-01

This paper describes a complex correlation mapping algorithm for optical coherence angiography (cmOCA). The proposed algorithm avoids the signal-to-noise ratio dependence and exhibits low noise in vasculature imaging. The complex correlation coefficient of the signals, rather than that of the measured data are estimated, and two-step averaging is introduced. Algorithms of motion artifact removal based on non perfusing tissue detection using correlation are developed. The algorithms are implemented with Jones-matrix OCT. Simultaneous imaging of pigmented tissue and vasculature is also achieved using degree of polarization uniformity imaging with cmOCA. An application of cmOCA to in vivo posterior human eyes is presented to demonstrate that high-contrast images of patients’ eyes can be obtained. PMID:27446673

Relationships Between Abrasive Wear, Hardness, and Surface Grinding Characteristics of Titanium-Based Metal Matrix Composites

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

Blau, Peter Julian; Jolly, Brian C

2009-01-01

The objective of this work was to support the development of grinding models for titanium metal-matrix composites (MMCs) by investigating possible relationships between their indentation hardness, low-stress belt abrasion, high-stress belt abrasion, and the surface grinding characteristics. Three Ti-based particulate composites were tested and compared with the popular titanium alloy Ti-6Al-4V. The three composites were a Ti-6Al-4V-based MMC with 5% TiB{sub 2} particles, a Ti-6Al-4V MMC with 10% TiC particles, and a Ti-6Al-4V/Ti-7.5%W binary alloy matrix that contained 7.5% TiC particles. Two types of belt abrasion tests were used: (a) a modified ASTM G164 low-stress loop abrasion test, and (b)more » a higher-stress test developed to quantify the grindability of ceramics. Results were correlated with G-ratios (ratio of stock removed to abrasives consumed) obtained from an instrumented surface grinder. Brinell hardness correlated better with abrasion characteristics than microindentation or scratch hardness. Wear volumes from low-stress and high-stress abrasive belt tests were related by a second-degree polynomial. Grindability numbers correlated with hard particle content but were also matrix-dependent.« less

Refractive index inversion based on Mueller matrix method

NASA Astrophysics Data System (ADS)

Fan, Huaxi; Wu, Wenyuan; Huang, Yanhua; Li, Zhaozhao

2016-03-01

Based on Stokes vector and Jones vector, the correlation between Mueller matrix elements and refractive index was studied with the result simplified, and through Mueller matrix way, the expression of refractive index inversion was deduced. The Mueller matrix elements, under different incident angle, are simulated through the expression of specular reflection so as to analyze the influence of the angle of incidence and refractive index on it, which is verified through the measure of the Mueller matrix elements of polished metal surface. Research shows that, under the condition of specular reflection, the result of Mueller matrix inversion is consistent with the experiment and can be used as an index of refraction of inversion method, and it provides a new way for target detection and recognition technology.

A micromechanics-based strength prediction methodology for notched metal matrix composites

NASA Technical Reports Server (NTRS)

Bigelow, C. A.

1992-01-01

An analytical micromechanics based strength prediction methodology was developed to predict failure of notched metal matrix composites. The stress-strain behavior and notched strength of two metal matrix composites, boron/aluminum (B/Al) and silicon-carbide/titanium (SCS-6/Ti-15-3), were predicted. The prediction methodology combines analytical techniques ranging from a three dimensional finite element analysis of a notched specimen to a micromechanical model of a single fiber. In the B/Al laminates, a fiber failure criteria based on the axial and shear stress in the fiber accurately predicted laminate failure for a variety of layups and notch-length to specimen-width ratios with both circular holes and sharp notches when matrix plasticity was included in the analysis. For the SCS-6/Ti-15-3 laminates, a fiber failure based on the axial stress in the fiber correlated well with experimental results for static and post fatigue residual strengths when fiber matrix debonding and matrix cracking were included in the analysis. The micromechanics based strength prediction methodology offers a direct approach to strength prediction by modeling behavior and damage on a constituent level, thus, explicitly including matrix nonlinearity, fiber matrix debonding, and matrix cracking.

A micromechanics-based strength prediction methodology for notched metal-matrix composites

NASA Technical Reports Server (NTRS)

Bigelow, C. A.

1993-01-01

An analytical micromechanics-based strength prediction methodology was developed to predict failure of notched metal matrix composites. The stress-strain behavior and notched strength of two metal matrix composites, boron/aluminum (B/Al) and silicon-carbide/titanium (SCS-6/Ti-15-3), were predicted. The prediction methodology combines analytical techniques ranging from a three-dimensional finite element analysis of a notched specimen to a micromechanical model of a single fiber. In the B/Al laminates, a fiber failure criteria based on the axial and shear stress in the fiber accurately predicted laminate failure for a variety of layups and notch-length to specimen-width ratios with both circular holes and sharp notches when matrix plasticity was included in the analysis. For the SCS-6/Ti-15-3 laminates, a fiber failure based on the axial stress in the fiber correlated well with experimental results for static and postfatigue residual strengths when fiber matrix debonding and matrix cracking were included in the analysis. The micromechanics-based strength prediction methodology offers a direct approach to strength prediction by modeling behavior and damage on a constituent level, thus, explicitly including matrix nonlinearity, fiber matrix debonding, and matrix cracking.

Estimation of positive semidefinite correlation matrices by using convex quadratic semidefinite programming.

PubMed

Fushiki, Tadayoshi

2009-07-01

The correlation matrix is a fundamental statistic that is used in many fields. For example, GroupLens, a collaborative filtering system, uses the correlation between users for predictive purposes. Since the correlation is a natural similarity measure between users, the correlation matrix may be used in the Gram matrix in kernel methods. However, the estimated correlation matrix sometimes has a serious defect: although the correlation matrix is originally positive semidefinite, the estimated one may not be positive semidefinite when not all ratings are observed. To obtain a positive semidefinite correlation matrix, the nearest correlation matrix problem has recently been studied in the fields of numerical analysis and optimization. However, statistical properties are not explicitly used in such studies. To obtain a positive semidefinite correlation matrix, we assume the approximate model. By using the model, an estimate is obtained as the optimal point of an optimization problem formulated with information on the variances of the estimated correlation coefficients. The problem is solved by a convex quadratic semidefinite program. A penalized likelihood approach is also examined. The MovieLens data set is used to test our approach.

Minimum number of measurements for evaluating soursop (Annona muricata L.) yield.

PubMed

Sánchez, C F B; Teodoro, P E; Londoño, S; Silva, L A; Peixoto, L A; Bhering, L L

2017-05-31

Repeatability studies on fruit species are of great importance to identify the minimum number of measurements necessary to accurately select superior genotypes. This study aimed to identify the most efficient method to estimate the repeatability coefficient (r) and predict the minimum number of measurements needed for a more accurate evaluation of soursop (Annona muricata L.) genotypes based on fruit yield. Sixteen measurements of fruit yield from 71 soursop genotypes were carried out between 2000 and 2016. In order to estimate r with the best accuracy, four procedures were used: analysis of variance, principal component analysis based on the correlation matrix, principal component analysis based on the phenotypic variance and covariance matrix, and structural analysis based on the correlation matrix. The minimum number of measurements needed to predict the actual value of individuals was estimated. Principal component analysis using the phenotypic variance and covariance matrix provided the most accurate estimates of both r and the number of measurements required for accurate evaluation of fruit yield in soursop. Our results indicate that selection of soursop genotypes with high fruit yield can be performed based on the third and fourth measurements in the early years and/or based on the eighth and ninth measurements at more advanced stages.

Matrix exponential-based closures for the turbulent subgrid-scale stress tensor.

PubMed

Li, Yi; Chevillard, Laurent; Eyink, Gregory; Meneveau, Charles

2009-01-01

Two approaches for closing the turbulence subgrid-scale stress tensor in terms of matrix exponentials are introduced and compared. The first approach is based on a formal solution of the stress transport equation in which the production terms can be integrated exactly in terms of matrix exponentials. This formal solution of the subgrid-scale stress transport equation is shown to be useful to explore special cases, such as the response to constant velocity gradient, but neglecting pressure-strain correlations and diffusion effects. The second approach is based on an Eulerian-Lagrangian change of variables, combined with the assumption of isotropy for the conditionally averaged Lagrangian velocity gradient tensor and with the recent fluid deformation approximation. It is shown that both approaches lead to the same basic closure in which the stress tensor is expressed as the matrix exponential of the resolved velocity gradient tensor multiplied by its transpose. Short-time expansions of the matrix exponentials are shown to provide an eddy-viscosity term and particular quadratic terms, and thus allow a reinterpretation of traditional eddy-viscosity and nonlinear stress closures. The basic feasibility of the matrix-exponential closure is illustrated by implementing it successfully in large eddy simulation of forced isotropic turbulence. The matrix-exponential closure employs the drastic approximation of entirely omitting the pressure-strain correlation and other nonlinear scrambling terms. But unlike eddy-viscosity closures, the matrix exponential approach provides a simple and local closure that can be derived directly from the stress transport equation with the production term, and using physically motivated assumptions about Lagrangian decorrelation and upstream isotropy.

Lanczos algorithm with matrix product states for dynamical correlation functions

NASA Astrophysics Data System (ADS)

Dargel, P. E.; Wöllert, A.; Honecker, A.; McCulloch, I. P.; Schollwöck, U.; Pruschke, T.

2012-05-01

The density-matrix renormalization group (DMRG) algorithm can be adapted to the calculation of dynamical correlation functions in various ways which all represent compromises between computational efficiency and physical accuracy. In this paper we reconsider the oldest approach based on a suitable Lanczos-generated approximate basis and implement it using matrix product states (MPS) for the representation of the basis states. The direct use of matrix product states combined with an ex post reorthogonalization method allows us to avoid several shortcomings of the original approach, namely the multitargeting and the approximate representation of the Hamiltonian inherent in earlier Lanczos-method implementations in the DMRG framework, and to deal with the ghost problem of Lanczos methods, leading to a much better convergence of the spectral weights and poles. We present results for the dynamic spin structure factor of the spin-1/2 antiferromagnetic Heisenberg chain. A comparison to Bethe ansatz results in the thermodynamic limit reveals that the MPS-based Lanczos approach is much more accurate than earlier approaches at minor additional numerical cost.

Noise sensitivity of portfolio selection in constant conditional correlation GARCH models

NASA Astrophysics Data System (ADS)

Varga-Haszonits, I.; Kondor, I.

2007-11-01

This paper investigates the efficiency of minimum variance portfolio optimization for stock price movements following the Constant Conditional Correlation GARCH process proposed by Bollerslev. Simulations show that the quality of portfolio selection can be improved substantially by computing optimal portfolio weights from conditional covariances instead of unconditional ones. Measurement noise can be further reduced by applying some filtering method on the conditional correlation matrix (such as Random Matrix Theory based filtering). As an empirical support for the simulation results, the analysis is also carried out for a time series of S&P500 stock prices.

Structure of a financial cross-correlation matrix under attack

NASA Astrophysics Data System (ADS)

Lim, Gyuchang; Kim, SooYong; Kim, Junghwan; Kim, Pyungsoo; Kang, Yoonjong; Park, Sanghoon; Park, Inho; Park, Sang-Bum; Kim, Kyungsik

2009-09-01

We investigate the structure of a perturbed stock market in terms of correlation matrices. For the purpose of perturbing a stock market, two distinct methods are used, namely local and global perturbation. The former involves replacing a correlation coefficient of the cross-correlation matrix with one calculated from two Gaussian-distributed time series while the latter reconstructs the cross-correlation matrix just after replacing the original return series with Gaussian-distributed time series. Concerning the local case, it is a technical study only and there is no attempt to model reality. The term ‘global’ means the overall effect of the replacement on other untouched returns. Through statistical analyses such as random matrix theory (RMT), network theory, and the correlation coefficient distributions, we show that the global structure of a stock market is vulnerable to perturbation. However, apart from in the analysis of inverse participation ratios (IPRs), the vulnerability becomes dull under a small-scale perturbation. This means that these analysis tools are inappropriate for monitoring the whole stock market due to the low sensitivity of a stock market to a small-scale perturbation. In contrast, when going down to the structure of business sectors, we confirm that correlation-based business sectors are regrouped in terms of IPRs. This result gives a clue about monitoring the effect of hidden intentions, which are revealed via portfolios taken mostly by large investors.

DBDA as a Novel Matrix for the Analyses of Small Molecules and Quantification of Fatty Acids by Negative Ion MALDI-TOF MS.

PubMed

Ling, Ling; Li, Ying; Wang, Sheng; Guo, Liming; Xiao, Chunsheng; Chen, Xuesi; Guo, Xinhua

2018-04-01

Matrix interference ions in low mass range has always been a concern when using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) to analyze small molecules (<500 Da). In this work, a novel matrix, N1,N4-dibenzylidenebenzene-1,4-diamine (DBDA) was synthesized for the analyses of small molecules by negative ion MALDI-TOF MS. Notably, only neat ions ([M-H] - ) of fatty acids without matrix interference appeared in the mass spectra and the limit of detection (LOD) reached 0.3 fmol. DBDA also has great performance towards other small molecules such as amino acids, peptides, and nucleotide. Furthermore, with this novel matrix, the free fatty acids in serum were quantitatively analyzed based on the correlation curves with correlation coefficient of 0.99. In addition, UV-Vis experiments and molecular orbital calculations were performed to explore mechanism about DBDA used as matrix in the negative ion mode. The present work shows that the DBDA matrix is a highly sensitive matrix with few interference ions for analysis of small molecules. Meanwhile, DBDA is able to precisely quantify the fatty acids in real biological samples. Graphical Abstract ᅟ.

Research on criticality analysis method of CNC machine tools components under fault rate correlation

NASA Astrophysics Data System (ADS)

Gui-xiang, Shen; Xian-zhuo, Zhao; Zhang, Ying-zhi; Chen-yu, Han

2018-02-01

In order to determine the key components of CNC machine tools under fault rate correlation, a system component criticality analysis method is proposed. Based on the fault mechanism analysis, the component fault relation is determined, and the adjacency matrix is introduced to describe it. Then, the fault structure relation is hierarchical by using the interpretive structure model (ISM). Assuming that the impact of the fault obeys the Markov process, the fault association matrix is described and transformed, and the Pagerank algorithm is used to determine the relative influence values, combined component fault rate under time correlation can obtain comprehensive fault rate. Based on the fault mode frequency and fault influence, the criticality of the components under the fault rate correlation is determined, and the key components are determined to provide the correct basis for equationting the reliability assurance measures. Finally, taking machining centers as an example, the effectiveness of the method is verified.

Electrical condition monitoring method for polymers

DOEpatents

Watkins, Jr., Kenneth S.; Morris, Shelby J [Hampton, VA; Masakowski, Daniel D [Worcester, MA; Wong, Ching Ping [Duluth, GA; Luo, Shijian [Boise, ID

2008-08-19

An electrical condition monitoring method utilizes measurement of electrical resistivity of an age sensor made of a conductive matrix or composite disposed in a polymeric structure such as an electrical cable. The conductive matrix comprises a base polymer and conductive filler. The method includes communicating the resistivity to a measuring instrument and correlating resistivity of the conductive matrix of the polymeric structure with resistivity of an accelerated-aged conductive composite.

Targeting functional motifs of a protein family

NASA Astrophysics Data System (ADS)

Bhadola, Pradeep; Deo, Nivedita

2016-10-01

The structural organization of a protein family is investigated by devising a method based on the random matrix theory (RMT), which uses the physiochemical properties of the amino acid with multiple sequence alignment. A graphical method to represent protein sequences using physiochemical properties is devised that gives a fast, easy, and informative way of comparing the evolutionary distances between protein sequences. A correlation matrix associated with each property is calculated, where the noise reduction and information filtering is done using RMT involving an ensemble of Wishart matrices. The analysis of the eigenvalue statistics of the correlation matrix for the β -lactamase family shows the universal features as observed in the Gaussian orthogonal ensemble (GOE). The property-based approach captures the short- as well as the long-range correlation (approximately following GOE) between the eigenvalues, whereas the previous approach (treating amino acids as characters) gives the usual short-range correlations, while the long-range correlations are the same as that of an uncorrelated series. The distribution of the eigenvector components for the eigenvalues outside the bulk (RMT bound) deviates significantly from RMT observations and contains important information about the system. The information content of each eigenvector of the correlation matrix is quantified by introducing an entropic estimate, which shows that for the β -lactamase family the smallest eigenvectors (low eigenmodes) are highly localized as well as informative. These small eigenvectors when processed gives clusters involving positions that have well-defined biological and structural importance matching with experiments. The approach is crucial for the recognition of structural motifs as shown in β -lactamase (and other families) and selectively identifies the important positions for targets to deactivate (activate) the enzymatic actions.

Invariant operators, orthogonal bases and correlators in general tensor models

NASA Astrophysics Data System (ADS)

Diaz, Pablo; Rey, Soo-Jong

2018-07-01

We study invariant operators in general tensor models. We show that representation theory provides an efficient framework to count and classify invariants in tensor models of (gauge) symmetry Gd = U (N1) ⊗ ⋯ ⊗ U (Nd). As a continuation and completion of our earlier work, we present two natural ways of counting invariants, one for arbitrary Gd and another valid for large rank of Gd. We construct bases of invariant operators based on the counting, and compute correlators of their elements. The basis associated with finite rank of Gd diagonalizes the two-point function of the free theory. It is analogous to the restricted Schur basis used in matrix models. We show that the constructions get almost identical as we swap the Littlewood-Richardson numbers in multi-matrix models with Kronecker coefficients in general tensor models. We explore the parallelism between matrix model and tensor model in depth from the perspective of representation theory and comment on several ideas for future investigation.

Cross-section fluctuations in chaotic scattering systems.

PubMed

Ericson, Torleif E O; Dietz, Barbara; Richter, Achim

2016-10-01

Exact analytical expressions for the cross-section correlation functions of chaotic scattering systems have hitherto been derived only under special conditions. The objective of the present article is to provide expressions that are applicable beyond these restrictions. The derivation is based on a statistical model of Breit-Wigner type for chaotic scattering amplitudes which has been shown to describe the exact analytical results for the scattering (S)-matrix correlation functions accurately. Our results are given in the energy and in the time representations and apply in the whole range from isolated to overlapping resonances. The S-matrix contributions to the cross-section correlations are obtained in terms of explicit irreducible and reducible correlation functions. Consequently, the model can be used for a detailed exploration of the key features of the cross-section correlations and the underlying physical mechanisms. In the region of isolated resonances, the cross-section correlations contain a dominant contribution from the self-correlation term. For narrow states the self-correlations originate predominantly from widely spaced states with exceptionally large partial width. In the asymptotic region of well-overlapping resonances, the cross-section autocorrelation functions are given in terms of the S-matrix autocorrelation functions. For inelastic correlations, in particular, the Ericson fluctuations rapidly dominate in that region. Agreement with known analytical and experimental results is excellent.

Micromechanical modeling of damage growth in titanium based metal-matrix composites

NASA Technical Reports Server (NTRS)

Sherwood, James A.; Quimby, Howard M.

1994-01-01

The thermomechanical behavior of continuous-fiber reinforced titanium based metal-matrix composites (MMC) is studied using the finite element method. A thermoviscoplastic unified state variable constitutive theory is employed to capture inelastic and strain-rate sensitive behavior in the Timetal-21s matrix. The SCS-6 fibers are modeled as thermoplastic. The effects of residual stresses generated during the consolidation process on the tensile response of the composites are investigated. Unidirectional and cross-ply geometries are considered. Differences between the tensile responses in composites with perfectly bonded and completely debonded fiber/matrix interfaces are discussed. Model simulations for the completely debonded-interface condition are shown to correlate well with experimental results.

Brueckner G -matrix approach for neutron-proton pairing correlations in the deformed BCS approach

NASA Astrophysics Data System (ADS)

Ha, Eunja; Cheoun, Myung-Ki; Šimkovic, F.

2015-10-01

Ground states of even-even Ge isotopes with mass number A =64 -76 have been studied in the deformed Bardeen-Cooper-Schrieffer (BCS) theory by taking neutron-proton (n p ) pairing correlations as well as neutron-neutron (n n ) and proton-proton (p p ) pairing correlations. The n p pairing has two different modes J =0 ,T =1 (isotriplet) and J =1 ,T =0 (isosinglet). In this work, the Brueckner G matrix, based on the CD-Bonn potential, has been exploited to reduce the ambiguity regarding nucleon-nucleon interactions inside nuclei compared to the results by a simple schematic phenomenological force. We found that the G matrix plays important roles to obtain reasonable descriptions of even-even nuclei compared to the schematic force. The n p pairing strength has been shown to have a clear correlation with quadrupole deformation parameter β2 for the isotopes, and affects the smearing of the Fermi surfaces of not only N =Z nuclei but also N ≠Z nuclei. In particular, the coexistence of the like particle (n n and p p ) and the n p pairing modes was found to become more salient by the G -matrix approach than by the schematic force approach.

Hydrophilic polyurethane matrix promotes chondrogenesis of mesenchymal stem cells☆

PubMed Central

Nalluri, Sandeep M.; Krishnan, G. Rajesh; Cheah, Calvin; Arzumand, Ayesha; Yuan, Yuan; Richardson, Caley A.; Yang, Shuying; Sarkar, Debanjan

2016-01-01

Segmental polyurethanes exhibit biphasic morphology and can control cell fate by providing distinct matrix guided signals to increase the chondrogenic potential of mesenchymal stem cells (MSCs). Polyethylene glycol (PEG) based hydrophilic polyurethanes can deliver differential signals to MSCs through their matrix phases where hard segments are cell-interactive domains and PEG based soft segments are minimally interactive with cells. These coordinated communications can modulate cell–matrix interactions to control cell shape and size for chondrogenesis. Biphasic character and hydrophilicity of polyurethanes with gel like architecture provide a synthetic matrix conducive for chondrogenesis of MSCs, as evidenced by deposition of cartilage-associated extracellular matrix. Compared to monophasic hydrogels, presence of cell interactive domains in hydrophilic polyurethanes gels can balance cell–cell and cell–matrix interactions. These results demonstrate the correlation between lineage commitment and the changes in cell shape, cell–matrix interaction, and cell–cell adhesion during chondrogenic differentiation which is regulated by polyurethane phase morphology, and thus, represent hydrophilic polyurethanes as promising synthetic matrices for cartilage regeneration. PMID:26046282

Doubly Nonparametric Sparse Nonnegative Matrix Factorization Based on Dependent Indian Buffet Processes.

PubMed

Xuan, Junyu; Lu, Jie; Zhang, Guangquan; Xu, Richard Yi Da; Luo, Xiangfeng

2018-05-01

Sparse nonnegative matrix factorization (SNMF) aims to factorize a data matrix into two optimized nonnegative sparse factor matrices, which could benefit many tasks, such as document-word co-clustering. However, the traditional SNMF typically assumes the number of latent factors (i.e., dimensionality of the factor matrices) to be fixed. This assumption makes it inflexible in practice. In this paper, we propose a doubly sparse nonparametric NMF framework to mitigate this issue by using dependent Indian buffet processes (dIBP). We apply a correlation function for the generation of two stick weights associated with each column pair of factor matrices while still maintaining their respective marginal distribution specified by IBP. As a consequence, the generation of two factor matrices will be columnwise correlated. Under this framework, two classes of correlation function are proposed: 1) using bivariate Beta distribution and 2) using Copula function. Compared with the single IBP-based NMF, this paper jointly makes two factor matrices nonparametric and sparse, which could be applied to broader scenarios, such as co-clustering. This paper is seen to be much more flexible than Gaussian process-based and hierarchial Beta process-based dIBPs in terms of allowing the two corresponding binary matrix columns to have greater variations in their nonzero entries. Our experiments on synthetic data show the merits of this paper compared with the state-of-the-art models in respect of factorization efficiency, sparsity, and flexibility. Experiments on real-world data sets demonstrate the efficiency of this paper in document-word co-clustering tasks.

Proceedings of the Japan - U.S. Conference on Composite Materials (6th) Held in Orlando, Florida on June 22-24, 1992

DTIC Science & Technology

1993-03-01

correlation was determined between the matrix microplastic flow and the global composite tensile stress-strain curve. Based on the knowledge of the...framentation of the elastic matrix to form remnant elastic pockets at Silw tip surrounded y the matrix plastic flow. The matrix microplasticity is also...Deformation of SiC-Al Composites.’ Mater. Sci. Engng., A131:55-68. 11. Hamann, R., P. F. Gobin, and R. Fougeres, 1990. "A Study of the Microplasticity of Some

DBDA as a Novel Matrix for the Analyses of Small Molecules and Quantification of Fatty Acids by Negative Ion MALDI-TOF MS

NASA Astrophysics Data System (ADS)

Ling, Ling; Li, Ying; Wang, Sheng; Guo, Liming; Xiao, Chunsheng; Chen, Xuesi; Guo, Xinhua

2018-01-01

Matrix interference ions in low mass range has always been a concern when using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) to analyze small molecules (<500 Da). In this work, a novel matrix, N1,N4-dibenzylidenebenzene-1,4-diamine (DBDA) was synthesized for the analyses of small molecules by negative ion MALDI-TOF MS. Notably, only neat ions ([M-H]-) of fatty acids without matrix interference appeared in the mass spectra and the limit of detection (LOD) reached 0.3 fmol. DBDA also has great performance towards other small molecules such as amino acids, peptides, and nucleotide. Furthermore, with this novel matrix, the free fatty acids in serum were quantitatively analyzed based on the correlation curves with correlation coefficient of 0.99. In addition, UV-Vis experiments and molecular orbital calculations were performed to explore mechanism about DBDA used as matrix in the negative ion mode. The present work shows that the DBDA matrix is a highly sensitive matrix with few interference ions for analysis of small molecules. Meanwhile, DBDA is able to precisely quantify the fatty acids in real biological samples. [Figure not available: see fulltext.

Effective correlator for RadioAstron project

NASA Astrophysics Data System (ADS)

Sergeev, Sergey

This paper presents the implementation of programme FX-correlator for Very Long Baseline Interferometry, adapted for the project "RadioAstron". Software correlator implemented for heterogeneous computing systems using graphics accelerators. It is shown that for the task interferometry implementation of the graphics hardware has a high efficiency. The host processor of heterogeneous computing system, performs the function of forming the data flow for graphics accelerators, the number of which corresponds to the number of frequency channels. So, for the Radioastron project, such channels is seven. Each accelerator is perform correlation matrix for all bases for a single frequency channel. Initial data is converted to the floating-point format, is correction for the corresponding delay function and computes the entire correlation matrix simultaneously. Calculation of the correlation matrix is performed using the sliding Fourier transform. Thus, thanks to the compliance of a solved problem for architecture graphics accelerators, managed to get a performance for one processor platform Kepler, which corresponds to the performance of this task, the computing cluster platforms Intel on four nodes. This task successfully scaled not only on a large number of graphics accelerators, but also on a large number of nodes with multiple accelerators.

Imaging of downward-looking linear array SAR using three-dimensional spatial smoothing MUSIC algorithm

NASA Astrophysics Data System (ADS)

Zhang, Siqian; Kuang, Gangyao

2014-10-01

In this paper, a novel three-dimensional imaging algorithm of downward-looking linear array SAR is presented. To improve the resolution, multiple signal classification (MUSIC) algorithm has been used. However, since the scattering centers are always correlated in real SAR system, the estimated covariance matrix becomes singular. To address the problem, a three-dimensional spatial smoothing method is proposed in this paper to restore the singular covariance matrix to a full-rank one. The three-dimensional signal matrix can be divided into a set of orthogonal three-dimensional subspaces. The main idea of the method is based on extracting the array correlation matrix as the average of all correlation matrices from the subspaces. In addition, the spectral height of the peaks contains no information with regard to the scattering intensity of the different scattering centers, thus it is difficulty to reconstruct the backscattering information. The least square strategy is used to estimate the amplitude of the scattering center in this paper. The above results of the theoretical analysis are verified by 3-D scene simulations and experiments on real data.

Rich structure in the correlation matrix spectra in non-equilibrium steady states

NASA Astrophysics Data System (ADS)

Biswas, Soham; Leyvraz, Francois; Monroy Castillero, Paulino; Seligman, Thomas H.

2017-01-01

It has been shown that, if a model displays long-range (power-law) spatial correlations, its equal-time correlation matrix will also have a power law tail in the distribution of its high-lying eigenvalues. The purpose of this paper is to show that the converse is generally incorrect: a power-law tail in the high-lying eigenvalues of the correlation matrix may exist even in the absence of equal-time power law correlations in the initial model. We may therefore view the study of the eigenvalue distribution of the correlation matrix as a more powerful tool than the study of spatial Correlations, one which may in fact uncover structure, that would otherwise not be apparent. Specifically, we show that in the Totally Asymmetric Simple Exclusion Process, whereas there are no clearly visible correlations in the steady state, the eigenvalues of its correlation matrix exhibit a rich structure which we describe in detail.

Rich structure in the correlation matrix spectra in non-equilibrium steady states.

PubMed

Biswas, Soham; Leyvraz, Francois; Monroy Castillero, Paulino; Seligman, Thomas H

2017-01-17

It has been shown that, if a model displays long-range (power-law) spatial correlations, its equal-time correlation matrix will also have a power law tail in the distribution of its high-lying eigenvalues. The purpose of this paper is to show that the converse is generally incorrect: a power-law tail in the high-lying eigenvalues of the correlation matrix may exist even in the absence of equal-time power law correlations in the initial model. We may therefore view the study of the eigenvalue distribution of the correlation matrix as a more powerful tool than the study of spatial Correlations, one which may in fact uncover structure, that would otherwise not be apparent. Specifically, we show that in the Totally Asymmetric Simple Exclusion Process, whereas there are no clearly visible correlations in the steady state, the eigenvalues of its correlation matrix exhibit a rich structure which we describe in detail.

Automated vessel segmentation using cross-correlation and pooled covariance matrix analysis.

PubMed

Du, Jiang; Karimi, Afshin; Wu, Yijing; Korosec, Frank R; Grist, Thomas M; Mistretta, Charles A

2011-04-01

Time-resolved contrast-enhanced magnetic resonance angiography (CE-MRA) provides contrast dynamics in the vasculature and allows vessel segmentation based on temporal correlation analysis. Here we present an automated vessel segmentation algorithm including automated generation of regions of interest (ROIs), cross-correlation and pooled sample covariance matrix analysis. The dynamic images are divided into multiple equal-sized regions. In each region, ROIs for artery, vein and background are generated using an iterative thresholding algorithm based on the contrast arrival time map and contrast enhancement map. Region-specific multi-feature cross-correlation analysis and pooled covariance matrix analysis are performed to calculate the Mahalanobis distances (MDs), which are used to automatically separate arteries from veins. This segmentation algorithm is applied to a dual-phase dynamic imaging acquisition scheme where low-resolution time-resolved images are acquired during the dynamic phase followed by high-frequency data acquisition at the steady-state phase. The segmented low-resolution arterial and venous images are then combined with the high-frequency data in k-space and inverse Fourier transformed to form the final segmented arterial and venous images. Results from volunteer and patient studies demonstrate the advantages of this automated vessel segmentation and dual phase data acquisition technique. Copyright © 2011 Elsevier Inc. All rights reserved.

A physiologically motivated sparse, compact, and smooth (SCS) approach to EEG source localization.

PubMed

Cao, Cheng; Akalin Acar, Zeynep; Kreutz-Delgado, Kenneth; Makeig, Scott

2012-01-01

Here, we introduce a novel approach to the EEG inverse problem based on the assumption that principal cortical sources of multi-channel EEG recordings may be assumed to be spatially sparse, compact, and smooth (SCS). To enforce these characteristics of solutions to the EEG inverse problem, we propose a correlation-variance model which factors a cortical source space covariance matrix into the multiplication of a pre-given correlation coefficient matrix and the square root of the diagonal variance matrix learned from the data under a Bayesian learning framework. We tested the SCS method using simulated EEG data with various SNR and applied it to a real ECOG data set. We compare the results of SCS to those of an established SBL algorithm.

Large-N and Bethe Ansatz

NASA Astrophysics Data System (ADS)

Jurčo, Branislav

We describe an integrable model, related to the Gaudin magnet, and its relation to the matrix model of Brézin, Itzykson, Parisi and Zuber. Relation is based on Bethe ansatz for the integrable model and its interpretation using orthogonal polynomials and saddle point approximation. Large-N limit of the matrix model corresponds to the thermodynamic limit of the integrable system. In this limit (functional) Bethe ansatz is the same as the generating function for correlators of the matrix models.

Photomask CD and LER characterization using Mueller matrix spectroscopic ellipsometry

NASA Astrophysics Data System (ADS)

Heinrich, A.; Dirnstorfer, I.; Bischoff, J.; Meiner, K.; Ketelsen, H.; Richter, U.; Mikolajick, T.

2014-10-01

Critical dimension and line edge roughness on photomask arrays are determined with Mueller matrix spectroscopic ellipsometry. Arrays with large sinusoidal perturbations are measured for different azimuth angels and compared with simulations based on rigorous coupled wave analysis. Experiment and simulation show that line edge roughness leads to characteristic changes in the different Mueller matrix elements. The influence of line edge roughness is interpreted as an increase of isotropic character of the sample. The changes in the Mueller matrix elements are very similar when the arrays are statistically perturbed with rms roughness values in the nanometer range suggesting that the results on the sinusoidal test structures are also relevant for "real" mask errors. Critical dimension errors and line edge roughness have similar impact on the SE MM measurement. To distinguish between both deviations, a strategy based on the calculation of sensitivities and correlation coefficients for all Mueller matrix elements is shown. The Mueller matrix elements M13/M31 and M34/M43 are the most suitable elements due to their high sensitivities to critical dimension errors and line edge roughness and, at the same time, to a low correlation coefficient between both influences. From the simulated sensitivities, it is estimated that the measurement accuracy has to be in the order of 0.01 and 0.001 for the detection of 1 nm critical dimension error and 1 nm line edge roughness, respectively.

Pre-Processed Recursive Lattice Reduction for Complexity Reduction in Spatially and Temporally Correlated MIMO Channels

NASA Astrophysics Data System (ADS)

An, Chan-Ho; Yang, Janghoon; Jang, Seunghun; Kim, Dong Ku

In this letter, a pre-processed lattice reduction (PLR) scheme is developed for the lattice reduction aided (LRA) detection of multiple input multiple-output (MIMO) systems in spatially correlated channel. The PLR computes the LLL-reduced matrix of the equivalent matrix, which is the product of the present channel matrix and unimodular transformation matrix for LR of spatial correlation matrix, rather than the present channel matrix itself. In conjunction with PLR followed by recursive lattice reduction (RLR) scheme [7], pre-processed RLR (PRLR) is shown to efficiently carry out the LR of the channel matrix, especially for the burst packet message in spatially and temporally correlated channel while matching the performance of conventional LRA detection.

Characteristics of global organic matrix in normal and pimpled chicken eggshells.

PubMed

Liu, Z; Song, L; Zhang, F; He, W; Linhardt, R J

2017-10-01

The organic matrix from normal and pimpled calcified chicken eggshells were dissociated into acid-insoluble, water-insoluble, and facultative-soluble (both acid- and water-soluble) components, to understand the influence of shell matrix on eggshell qualities. A linear correlation was shown among these 3 matrix components in normal eggshells but was not observed in pimpled eggshells. In pimpled eggshells, the percentage contents of all 4 groups of matrix (the total matrix, acid-insoluble matrix, water-insoluble matrix, and facultative-soluble matrix) were significantly higher than that in normal eggshells. The amounts of both total matrix and acid-insoluble matrix in individual pimpled calcified shells were high, even though their weight was much lower than a normal eggshell. In both normal and pimpled eggshells, the calcified eggshell weight and shell thickness significantly and positively correlated with the amounts of all 4 groups of matrix in an individual calcified shell. In normal eggshells, the calcified shell thickness and shell breaking strength showed no significant correlations with the percentage contents of all 4 groups of matrix. In normal eggshells, only the shell membrane weight significantly correlated with the constituent ratios of both acid-insoluble matrix and facultative-soluble matrix in the whole matrix. In pimpled eggshells, 3 variables (calcified shell weight, shell thickness, and breaking strength) were significantly correlated with the constituent proportions of both acid-insoluble matrix and facultative-matrix. This study suggests that mechanical properties of normal eggshells may not linearly depend on the organic matrix content in the calcified eggshells and that pimpled eggshells might result by the disequilibrium enrichment of some proteins with negative effects. © 2017 Poultry Science Association Inc.

The G matrix under fluctuating correlational mutation and selection.

PubMed

Revell, Liam J

2007-08-01

Theoretical quantitative genetics provides a framework for reconstructing past selection and predicting future patterns of phenotypic differentiation. However, the usefulness of the equations of quantitative genetics for evolutionary inference relies on the evolutionary stability of the additive genetic variance-covariance matrix (G matrix). A fruitful new approach for exploring the evolutionary dynamics of G involves the use of individual-based computer simulations. Previous studies have focused on the evolution of the eigenstructure of G. An alternative approach employed in this paper uses the multivariate response-to-selection equation to evaluate the stability of G. In this approach, I measure similarity by the correlation between response-to-selection vectors due to random selection gradients. I analyze the dynamics of G under several conditions of correlational mutation and selection. As found in a previous study, the eigenstructure of G is stabilized by correlational mutation and selection. However, over broad conditions, instability of G did not result in a decreased consistency of the response to selection. I also analyze the stability of G when the correlation coefficients of correlational mutation and selection and the effective population size change through time. To my knowledge, no prior study has used computer simulations to investigate the stability of G when correlational mutation and selection fluctuate. Under these conditions, the eigenstructure of G is unstable under some simulation conditions. Different results are obtained if G matrix stability is assessed by eigenanalysis or by the response to random selection gradients. In this case, the response to selection is most consistent when certain aspects of the eigenstructure of G are least stable and vice versa.

Sum-rule corrections: a route to error cancellations in correlation matrix renormalisation theory

NASA Astrophysics Data System (ADS)

Liu, C.; Liu, J.; Yao, Y. X.; Wang, C. Z.; Ho, K. M.

2017-03-01

We recently proposed the correlation matrix renormalisation (CMR) theory to efficiently and accurately calculate ground state total energy of molecular systems, based on the Gutzwiller variational wavefunction (GWF) to treat the electronic correlation effects. To help reduce numerical complications and better adapt the CMR to infinite lattice systems, we need to further refine the way to minimise the error originated from the approximations in the theory. This conference proceeding reports our recent progress on this key issue, namely, we obtained a simple analytical functional form for the one-electron renormalisation factors, and introduced a novel sum-rule correction for a more accurate description of the intersite electron correlations. Benchmark calculations are performed on a set of molecules to show the reasonable accuracy of the method.

Development and experimental design of a novel controlled-release matrix tablet formulation for indapamide hemihydrate.

PubMed

Antovska, Packa; Ugarkovic, Sonja; Petruševski, Gjorgji; Stefanova, Bosilka; Manchevska, Blagica; Petkovska, Rumenka; Makreski, Petre

2017-11-01

Development, experimental design and in vitro in vivo correlation (IVIVC) of controlled-release matrix formulation. Development of novel oral controlled delivery system for indapamide hemihydrate, optimization of the formulation by experimental design and evaluation regarding IVIVC on a pilot scale batch as a confirmation of a well-established formulation. In vitro dissolution profiles of controlled-release tablets of indapamide hemihydrate from four different matrices had been evaluated in comparison to the originator's product Natrilix (Servier) as a direction for further development and optimization of a hydroxyethylcellulose-based matrix controlled-release formulation. A central composite factorial design had been applied for the optimization of a chosen controlled-release tablet formulation. The controlled-release tablets with appropriate physical and technological properties had been obtained with a matrix: binder concentration variations in the range: 20-40w/w% for the matrix and 1-3w/w% for the binder. The experimental design had defined the design space for the formulation and was prerequisite for extraction of a particular formulation that would be a subject for transfer on pilot scale and IVIV correlation. The release model of the optimized formulation has shown best fit to the zero order kinetics depicted with the Hixson-Crowell erosion-dependent mechanism of release. Level A correlation was obtained.

Non-Linear Optimization Applied to Angle-of-Arrival Satellite-Based Geolocation with Correlated Measurements

DTIC Science & Technology

2015-03-01

General covariance intersection covariance matrix Σ1 Measurement 1’s covariance matrix I(X) Fisher information matrix g Confidence region L Lower... information in this chapter will discuss the motivation and background of the geolocation algorithm with the scope of the applications for this research. The...algorithm is able to produce the best description of an object given the information from a set of measurements. Determining a position requires the use of a

Hydrophilic polyurethane matrix promotes chondrogenesis of mesenchymal stem cells.

PubMed

Nalluri, Sandeep M; Krishnan, G Rajesh; Cheah, Calvin; Arzumand, Ayesha; Yuan, Yuan; Richardson, Caley A; Yang, Shuying; Sarkar, Debanjan

2015-09-01

Segmental polyurethanes exhibit biphasic morphology and can control cell fate by providing distinct matrix guided signals to increase the chondrogenic potential of mesenchymal stem cells (MSCs). Polyethylene glycol (PEG) based hydrophilic polyurethanes can deliver differential signals to MSCs through their matrix phases where hard segments are cell-interactive domains and PEG based soft segments are minimally interactive with cells. These coordinated communications can modulate cell-matrix interactions to control cell shape and size for chondrogenesis. Biphasic character and hydrophilicity of polyurethanes with gel like architecture provide a synthetic matrix conducive for chondrogenesis of MSCs, as evidenced by deposition of cartilage-associated extracellular matrix. Compared to monophasic hydrogels, presence of cell interactive domains in hydrophilic polyurethanes gels can balance cell-cell and cell-matrix interactions. These results demonstrate the correlation between lineage commitment and the changes in cell shape, cell-matrix interaction, and cell-cell adhesion during chondrogenic differentiation which is regulated by polyurethane phase morphology, and thus, represent hydrophilic polyurethanes as promising synthetic matrices for cartilage regeneration. Copyright © 2015 Elsevier B.V. All rights reserved.

Near-lossless multichannel EEG compression based on matrix and tensor decompositions.

PubMed

Dauwels, Justin; Srinivasan, K; Reddy, M Ramasubba; Cichocki, Andrzej

2013-05-01

A novel near-lossless compression algorithm for multichannel electroencephalogram (MC-EEG) is proposed based on matrix/tensor decomposition models. MC-EEG is represented in suitable multiway (multidimensional) forms to efficiently exploit temporal and spatial correlations simultaneously. Several matrix/tensor decomposition models are analyzed in view of efficient decorrelation of the multiway forms of MC-EEG. A compression algorithm is built based on the principle of “lossy plus residual coding,” consisting of a matrix/tensor decomposition-based coder in the lossy layer followed by arithmetic coding in the residual layer. This approach guarantees a specifiable maximum absolute error between original and reconstructed signals. The compression algorithm is applied to three different scalp EEG datasets and an intracranial EEG dataset, each with different sampling rate and resolution. The proposed algorithm achieves attractive compression ratios compared to compressing individual channels separately. For similar compression ratios, the proposed algorithm achieves nearly fivefold lower average error compared to a similar wavelet-based volumetric MC-EEG compression algorithm.

Not all that glitters is RMT in the forecasting of risk of portfolios in the Brazilian stock market

NASA Astrophysics Data System (ADS)

Sandoval, Leonidas; Bortoluzzo, Adriana Bruscato; Venezuela, Maria Kelly

2014-09-01

Using stocks of the Brazilian stock exchange (BM&F-Bovespa), we build portfolios of stocks based on Markowitz's theory and test the predicted and realized risks. This is done using the correlation matrices between stocks, and also using Random Matrix Theory in order to clean such correlation matrices from noise. We also calculate correlation matrices using a regression model in order to remove the effect of common market movements and their cleaned versions using Random Matrix Theory. This is done for years of both low and high volatility of the Brazilian stock market, from 2004 to 2012. The results show that the use of regression to subtract the market effect on returns greatly increases the accuracy of the prediction of risk, and that, although the cleaning of the correlation matrix often leads to portfolios that better predict risks, in periods of high volatility of the market this procedure may fail to do so. The results may be used in the assessment of the true risks when one builds a portfolio of stocks during periods of crisis.

Long-range corrected density functional through the density matrix expansion based semilocal exchange hole.

PubMed

Patra, Bikash; Jana, Subrata; Samal, Prasanjit

2018-03-28

The exchange hole, which is one of the principal constituents of the density functional formalism, can be used to design accurate range-separated hybrid functionals in association with appropriate correlation. In this regard, the exchange hole derived from the density matrix expansion has gained attention due to its fulfillment of some of the desired exact constraints. Thus, the new long-range corrected density functional proposed here combines the meta generalized gradient approximation level exchange functional designed from the density matrix expansion based exchange hole coupled with the ab initio Hartree-Fock exchange through the range separation of the Coulomb interaction operator using the standard error function technique. Then, in association with the Lee-Yang-Parr correlation functional, the assessment and benchmarking of the above newly constructed range-separated functional with various well-known test sets shows its reasonable performance for a broad range of molecular properties, such as thermochemistry, non-covalent interaction and barrier heights of the chemical reactions.

Kinematic matrix theory and universalities in self-propellers and active swimmers.

PubMed

Nourhani, Amir; Lammert, Paul E; Borhan, Ali; Crespi, Vincent H

2014-06-01

We describe an efficient and parsimonious matrix-based theory for studying the ensemble behavior of self-propellers and active swimmers, such as nanomotors or motile bacteria, that are typically studied by differential-equation-based Langevin or Fokker-Planck formalisms. The kinematic effects for elementary processes of motion are incorporated into a matrix, called the "kinematrix," from which we immediately obtain correlators and the mean and variance of angular and position variables (and thus effective diffusivity) by simple matrix algebra. The kinematrix formalism enables us recast the behaviors of a diverse range of self-propellers into a unified form, revealing universalities in their ensemble behavior in terms of new emergent time scales. Active fluctuations and hydrodynamic interactions can be expressed as an additive composition of separate self-propellers.

Coupled Cluster Method with Single and Double Excitations Tailored by Matrix Product State Wave Functions.

PubMed

Veis, Libor; Antalík, Andrej; Brabec, Jiří; Neese, Frank; Legeza, Örs; Pittner, Jiří

2016-10-03

In the past decade, the quantum chemical version of the density matrix renormalization group (DMRG) method has established itself as the method of choice for calculations of strongly correlated molecular systems. Despite its favorable scaling, it is in practice not suitable for computations of dynamic correlation. We present a novel method for accurate "post-DMRG" treatment of dynamic correlation based on the tailored coupled cluster (CC) theory in which the DMRG method is responsible for the proper description of nondynamic correlation, whereas dynamic correlation is incorporated through the framework of the CC theory. We illustrate the potential of this method on prominent multireference systems, in particular, N 2 and Cr 2 molecules and also oxo-Mn(Salen), for which we have performed the first post-DMRG computations in order to shed light on the energy ordering of the lowest spin states.

Sensitivity analysis of a sound absorption model with correlated inputs

NASA Astrophysics Data System (ADS)

Chai, W.; Christen, J.-L.; Zine, A.-M.; Ichchou, M.

2017-04-01

Sound absorption in porous media is a complex phenomenon, which is usually addressed with homogenized models, depending on macroscopic parameters. Since these parameters emerge from the structure at microscopic scale, they may be correlated. This paper deals with sensitivity analysis methods of a sound absorption model with correlated inputs. Specifically, the Johnson-Champoux-Allard model (JCA) is chosen as the objective model with correlation effects generated by a secondary micro-macro semi-empirical model. To deal with this case, a relatively new sensitivity analysis method Fourier Amplitude Sensitivity Test with Correlation design (FASTC), based on Iman's transform, is taken into application. This method requires a priori information such as variables' marginal distribution functions and their correlation matrix. The results are compared to the Correlation Ratio Method (CRM) for reference and validation. The distribution of the macroscopic variables arising from the microstructure, as well as their correlation matrix are studied. Finally the results of tests shows that the correlation has a very important impact on the results of sensitivity analysis. Assessment of correlation strength among input variables on the sensitivity analysis is also achieved.

Vast Volatility Matrix Estimation using High Frequency Data for Portfolio Selection*

PubMed Central

Fan, Jianqing; Li, Yingying; Yu, Ke

2012-01-01

Portfolio allocation with gross-exposure constraint is an effective method to increase the efficiency and stability of portfolios selection among a vast pool of assets, as demonstrated in Fan et al. (2011). The required high-dimensional volatility matrix can be estimated by using high frequency financial data. This enables us to better adapt to the local volatilities and local correlations among vast number of assets and to increase significantly the sample size for estimating the volatility matrix. This paper studies the volatility matrix estimation using high-dimensional high-frequency data from the perspective of portfolio selection. Specifically, we propose the use of “pairwise-refresh time” and “all-refresh time” methods based on the concept of “refresh time” proposed by Barndorff-Nielsen et al. (2008) for estimation of vast covariance matrix and compare their merits in the portfolio selection. We establish the concentration inequalities of the estimates, which guarantee desirable properties of the estimated volatility matrix in vast asset allocation with gross exposure constraints. Extensive numerical studies are made via carefully designed simulations. Comparing with the methods based on low frequency daily data, our methods can capture the most recent trend of the time varying volatility and correlation, hence provide more accurate guidance for the portfolio allocation in the next time period. The advantage of using high-frequency data is significant in our simulation and empirical studies, which consist of 50 simulated assets and 30 constituent stocks of Dow Jones Industrial Average index. PMID:23264708

Random matrix approach to the dynamics of stock inventory variations

NASA Astrophysics Data System (ADS)

Zhou, Wei-Xing; Mu, Guo-Hua; Kertész, János

2012-09-01

It is well accepted that investors can be classified into groups owing to distinct trading strategies, which forms the basic assumption of many agent-based models for financial markets when agents are not zero-intelligent. However, empirical tests of these assumptions are still very rare due to the lack of order flow data. Here we adopt the order flow data of Chinese stocks to tackle this problem by investigating the dynamics of inventory variations for individual and institutional investors that contain rich information about the trading behavior of investors and have a crucial influence on price fluctuations. We find that the distributions of cross-correlation coefficient Cij have power-law forms in the bulk that are followed by exponential tails, and there are more positive coefficients than negative ones. In addition, it is more likely that two individuals or two institutions have a stronger inventory variation correlation than one individual and one institution. We find that the largest and the second largest eigenvalues (λ1 and λ2) of the correlation matrix cannot be explained by random matrix theory and the projections of investors' inventory variations on the first eigenvector u(λ1) are linearly correlated with stock returns, where individual investors play a dominating role. The investors are classified into three categories based on the cross-correlation coefficients CV R between inventory variations and stock returns. A strong Granger causality is unveiled from stock returns to inventory variations, which means that a large proportion of individuals hold the reversing trading strategy and a small part of individuals hold the trending strategy. Our empirical findings have scientific significance in the understanding of investors' trading behavior and in the construction of agent-based models for emerging stock markets.

Sum-rule corrections: A route to error cancellations in correlation matrix renormalisation theory

DOE PAGES

Liu, C.; Liu, J.; Yao, Y. X.; ...

2017-01-16

Here, we recently proposed the correlation matrix renormalisation (CMR) theory to efficiently and accurately calculate ground state total energy of molecular systems, based on the Gutzwiller variational wavefunction (GWF) to treat the electronic correlation effects. To help reduce numerical complications and better adapt the CMR to infinite lattice systems, we need to further refine the way to minimise the error originated from the approximations in the theory. This conference proceeding reports our recent progress on this key issue, namely, we obtained a simple analytical functional form for the one-electron renormalisation factors, and introduced a novel sum-rule correction for a moremore » accurate description of the intersite electron correlations. Benchmark calculations are performed on a set of molecules to show the reasonable accuracy of the method.« less

Sum-rule corrections: A route to error cancellations in correlation matrix renormalisation theory

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

Liu, C.; Liu, J.; Yao, Y. X.

Here, we recently proposed the correlation matrix renormalisation (CMR) theory to efficiently and accurately calculate ground state total energy of molecular systems, based on the Gutzwiller variational wavefunction (GWF) to treat the electronic correlation effects. To help reduce numerical complications and better adapt the CMR to infinite lattice systems, we need to further refine the way to minimise the error originated from the approximations in the theory. This conference proceeding reports our recent progress on this key issue, namely, we obtained a simple analytical functional form for the one-electron renormalisation factors, and introduced a novel sum-rule correction for a moremore » accurate description of the intersite electron correlations. Benchmark calculations are performed on a set of molecules to show the reasonable accuracy of the method.« less

[Research on partial least squares for determination of impurities in the presence of high concentration of matrix by ICP-AES].

PubMed

Wang, Yan-peng; Gong, Qi; Yu, Sheng-rong; Liu, You-yan

2012-04-01

A method for detecting trace impurities in high concentration matrix by ICP-AES based on partial least squares (PLS) was established. The research showed that PLS could effectively correct the interference caused by high level of matrix concentration error and could withstand higher concentrations of matrix than multicomponent spectral fitting (MSF). When the mass ratios of matrix to impurities were from 1 000 : 1 to 20 000 : 1, the recoveries of standard addition were between 95% and 105% by PLS. For the system in which interference effect has nonlinear correlation with the matrix concentrations, the prediction accuracy of normal PLS method was poor, but it can be improved greatly by using LIN-PPLS, which was based on matrix transformation of sample concentration. The contents of Co, Pb and Ga in stream sediment (GBW07312) were detected by MSF, PLS and LIN-PPLS respectively. The results showed that the prediction accuracy of LIN-PPLS was better than PLS, and the prediction accuracy of PLS was better than MSF.

A generalization of random matrix theory and its application to statistical physics.

PubMed

Wang, Duan; Zhang, Xin; Horvatic, Davor; Podobnik, Boris; Eugene Stanley, H

2017-02-01

To study the statistical structure of crosscorrelations in empirical data, we generalize random matrix theory and propose a new method of cross-correlation analysis, known as autoregressive random matrix theory (ARRMT). ARRMT takes into account the influence of auto-correlations in the study of cross-correlations in multiple time series. We first analytically and numerically determine how auto-correlations affect the eigenvalue distribution of the correlation matrix. Then we introduce ARRMT with a detailed procedure of how to implement the method. Finally, we illustrate the method using two examples taken from inflation rates for air pressure data for 95 US cities.

Complex network analysis of resting-state fMRI of the brain.

PubMed

Anwar, Abdul Rauf; Hashmy, Muhammad Yousaf; Imran, Bilal; Riaz, Muhammad Hussnain; Mehdi, Sabtain Muhammad Muntazir; Muthalib, Makii; Perrey, Stephane; Deuschl, Gunther; Groppa, Sergiu; Muthuraman, Muthuraman

2016-08-01

Due to the fact that the brain activity hardly ever diminishes in healthy individuals, analysis of resting state functionality of the brain seems pertinent. Various resting state networks are active inside the idle brain at any time. Based on various neuro-imaging studies, it is understood that various structurally distant regions of the brain could be functionally connected. Regions of the brain, that are functionally connected, during rest constitutes to the resting state network. In the present study, we employed the complex network measures to estimate the presence of community structures within a network. Such estimate is named as modularity. Instead of using a traditional correlation matrix, we used a coherence matrix taken from the causality measure between different nodes. Our results show that in prolonged resting state the modularity starts to decrease. This decrease was observed in all the resting state networks and on both sides of the brain. Our study highlights the usage of coherence matrix instead of correlation matrix for complex network analysis.

System of Mueller-Jones matrix polarizing mapping of blood plasma films in breast pathology

NASA Astrophysics Data System (ADS)

Zabolotna, Natalia I.; Radchenko, Kostiantyn O.; Tarnovskiy, Mykola H.

2017-08-01

The combined method of Jones-Mueller matrix mapping and blood plasma films analysis based on the system that proposed in this paper. Based on the obtained data about the structure and state of blood plasma samples the diagnostic conclusions can be make about the state of breast cancer patients ("normal" or "pathology"). Then, by using the statistical analysis obtain statistical and correlational moments for every coordinate distributions; these indicators are served as diagnostic criterias. The final step is to comparing results and choosing the most effective diagnostic indicators. The paper presents the results of Mueller-Jones matrix mapping of optically thin (attenuation coefficient ,τ≤0,1) blood plasma layers.

A simple laminate theory using the orthotropic viscoplasticity theory based on overstress. I - In-plane stress-strain relationships for metal matrix composites

NASA Technical Reports Server (NTRS)

Krempl, Erhard; Hong, Bor Zen

1989-01-01

A macromechanics analysis is presented for the in-plane, anisotropic time-dependent behavior of metal matrix laminates. The small deformation, orthotropic viscoplasticity theory based on overstress represents lamina behavior in a modified simple laminate theory. Material functions and constants can be identified in principle from experiments with laminae. Orthotropic invariants can be repositories for tension-compression asymmetry and for linear elasticity in one direction while the other directions behave in a viscoplastic manner. Computer programs are generated and tested for either unidirectional or symmetric laminates under in-plane loading. Correlations with the experimental results on metal matrix composites are presented.

The local matrix distribution and the functional development of tissue engineered cartilage, a finite element study.

PubMed

Sengers, B G; Van Donkelaar, C C; Oomens, C W J; Baaijens, F P T

2004-12-01

Assessment of the functionality of tissue engineered cartilage constructs is hampered by the lack of correlation between global measurements of extra cellular matrix constituents and the global mechanical properties. Based on patterns of matrix deposition around individual cells, it has been hypothesized previously, that mechanical functionality arises when contact occurs between zones of matrix associated with individual cells. The objective of this study is to determine whether the local distribution of newly synthesized extracellular matrix components contributes to the evolution of the mechanical properties of tissue engineered cartilage constructs. A computational homogenization approach was adopted, based on the concept of a periodic representative volume element. Local transport and immobilization of newly synthesized matrix components were described. Mechanical properties were taken dependent on the local matrix concentration and subsequently the global aggregate modulus and hydraulic permeability were derived. The transport parameters were varied to assess the effect of the evolving matrix distribution during culture. The results indicate that the overall stiffness and permeability are to a large extent insensitive to differences in local matrix distribution. This emphasizes the need for caution in the visual interpretation of tissue functionality from histology and underlines the importance of complementary measurements of the matrix's intrinsic molecular organization.

Relativistic coupled cluster theory based on the no-pair Dirac-Coulomb-Breit Hamiltonian: Relativistic pair correlation energies of the Xe atom

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

Eliav, E.; Kaldor, U.; Ishikawa, Y.

1994-12-31

Relativistic pair correlation energies of Xe were computed by employing a recently developed relativistic coupled cluster theory based on the no-pair Dirac-Coulomb-Breit Hamiltonian. The matrix Dirac-Fock-Breit SCF and relativistic coupled cluster calculations were performed by means of expansion in basis sets of well-tempered Gaussian spinors. A detailed study of the pair correlation energies in Xe is performed, in order to investigate the effects of the low-frequency Breit interaction on the correlation energies of Xe. Nonadditivity of correlation and relativistic (particularly Breit) effects is discussed.

Estimated correlation matrices and portfolio optimization

NASA Astrophysics Data System (ADS)

Pafka, Szilárd; Kondor, Imre

2004-11-01

Correlations of returns on various assets play a central role in financial theory and also in many practical applications. From a theoretical point of view, the main interest lies in the proper description of the structure and dynamics of correlations, whereas for the practitioner the emphasis is on the ability of the models to provide adequate inputs for the numerous portfolio and risk management procedures used in the financial industry. The theory of portfolios, initiated by Markowitz, has suffered from the “curse of dimensions” from the very outset. Over the past decades a large number of different techniques have been developed to tackle this problem and reduce the effective dimension of large bank portfolios, but the efficiency and reliability of these procedures are extremely hard to assess or compare. In this paper, we propose a model (simulation)-based approach which can be used for the systematical testing of all these dimensional reduction techniques. To illustrate the usefulness of our framework, we develop several toy models that display some of the main characteristic features of empirical correlations and generate artificial time series from them. Then, we regard these time series as empirical data and reconstruct the corresponding correlation matrices which will inevitably contain a certain amount of noise, due to the finiteness of the time series. Next, we apply several correlation matrix estimators and dimension reduction techniques introduced in the literature and/or applied in practice. As in our artificial world the only source of error is the finite length of the time series and, in addition, the “true” model, hence also the “true” correlation matrix, are precisely known, therefore in sharp contrast with empirical studies, we can precisely compare the performance of the various noise reduction techniques. One of our recurrent observations is that the recently introduced filtering technique based on random matrix theory performs consistently well in all the investigated cases. Based on this experience, we believe that our simulation-based approach can also be useful for the systematic investigation of several related problems of current interest in finance.

A DS-UWB Cognitive Radio System Based on Bridge Function Smart Codes

NASA Astrophysics Data System (ADS)

Xu, Yafei; Hong, Sheng; Zhao, Guodong; Zhang, Fengyuan; di, Jinshan; Zhang, Qishan

This paper proposes a direct-sequence UWB Gaussian pulse of cognitive radio systems based on bridge function smart sequence matrix and the Gaussian pulse. As the system uses the spreading sequence code, that is the bridge function smart code sequence, the zero correlation zones (ZCZs) which the bridge function sequences' auto-correlation functions had, could reduce multipath fading of the pulse interference. The Modulated channel signal was sent into the IEEE 802.15.3a UWB channel. We analysis the ZCZs's inhibition to the interference multipath interference (MPI), as one of the main system sources interferences. The simulation in SIMULINK/MATLAB is described in detail. The result shows the system has better performance by comparison with that employing Walsh sequence square matrix, and it was verified by the formula in principle.

The improved Apriori algorithm based on matrix pruning and weight analysis

NASA Astrophysics Data System (ADS)

Lang, Zhenhong

2018-04-01

This paper uses the matrix compression algorithm and weight analysis algorithm for reference and proposes an improved matrix pruning and weight analysis Apriori algorithm. After the transactional database is scanned for only once, the algorithm will construct the boolean transaction matrix. Through the calculation of one figure in the rows and columns of the matrix, the infrequent item set is pruned, and a new candidate item set is formed. Then, the item's weight and the transaction's weight as well as the weight support for items are calculated, thus the frequent item sets are gained. The experimental result shows that the improved Apriori algorithm not only reduces the number of repeated scans of the database, but also improves the efficiency of data correlation mining.

HIGH DIMENSIONAL COVARIANCE MATRIX ESTIMATION IN APPROXIMATE FACTOR MODELS.

PubMed

Fan, Jianqing; Liao, Yuan; Mincheva, Martina

2011-01-01

The variance covariance matrix plays a central role in the inferential theories of high dimensional factor models in finance and economics. Popular regularization methods of directly exploiting sparsity are not directly applicable to many financial problems. Classical methods of estimating the covariance matrices are based on the strict factor models, assuming independent idiosyncratic components. This assumption, however, is restrictive in practical applications. By assuming sparse error covariance matrix, we allow the presence of the cross-sectional correlation even after taking out common factors, and it enables us to combine the merits of both methods. We estimate the sparse covariance using the adaptive thresholding technique as in Cai and Liu (2011), taking into account the fact that direct observations of the idiosyncratic components are unavailable. The impact of high dimensionality on the covariance matrix estimation based on the factor structure is then studied.

Signatures of bifurcation on quantum correlations: Case of the quantum kicked top

NASA Astrophysics Data System (ADS)

Bhosale, Udaysinh T.; Santhanam, M. S.

2017-01-01

Quantum correlations reflect the quantumness of a system and are useful resources for quantum information and computational processes. Measures of quantum correlations do not have a classical analog and yet are influenced by classical dynamics. In this work, by modeling the quantum kicked top as a multiqubit system, the effect of classical bifurcations on measures of quantum correlations such as the quantum discord, geometric discord, and Meyer and Wallach Q measure is studied. The quantum correlation measures change rapidly in the vicinity of a classical bifurcation point. If the classical system is largely chaotic, time averages of the correlation measures are in good agreement with the values obtained by considering the appropriate random matrix ensembles. The quantum correlations scale with the total spin of the system, representing its semiclassical limit. In the vicinity of trivial fixed points of the kicked top, the scaling function decays as a power law. In the chaotic limit, for large total spin, quantum correlations saturate to a constant, which we obtain analytically, based on random matrix theory, for the Q measure. We also suggest that it can have experimental consequences.

Utility of texture analysis for quantifying hepatic fibrosis on proton density MRI.

PubMed

Yu, HeiShun; Buch, Karen; Li, Baojun; O'Brien, Michael; Soto, Jorge; Jara, Hernan; Anderson, Stephan W

2015-11-01

To evaluate the potential utility of texture analysis of proton density maps for quantifying hepatic fibrosis in a murine model of hepatic fibrosis. Following Institutional Animal Care and Use Committee (IACUC) approval, a dietary model of hepatic fibrosis was used and 15 ex vivo murine liver tissues were examined. All images were acquired using a 30 mm bore 11.7T magnetic resonance imaging (MRI) scanner with a multiecho spin-echo sequence. A texture analysis was employed extracting multiple texture features including histogram-based, gray-level co-occurrence matrix-based (GLCM), gray-level run-length-based features (GLRL), gray level gradient matrix (GLGM), and Laws' features. Texture features were correlated with histopathologic and digital image analysis of hepatic fibrosis. Histogram features demonstrated very weak to moderate correlations (r = -0.29 to 0.51) with hepatic fibrosis. GLCM features correlation and contrast demonstrated moderate-to-strong correlations (r = -0.71 and 0.59, respectively) with hepatic fibrosis. Moderate correlations were seen between hepatic fibrosis and the GLRL feature short run low gray-level emphasis (SRLGE) (r = -0. 51). GLGM features demonstrate very weak to weak correlations with hepatic fibrosis (r = -0.27 to 0.09). Moderate correlations were seen between hepatic fibrosis and Laws' features L6 and L7 (r = 0.58). This study demonstrates the utility of texture analysis applied to proton density MRI in a murine liver fibrosis model and validates the potential utility of texture-based features for the noninvasive, quantitative assessment of hepatic fibrosis. © 2015 Wiley Periodicals, Inc.

Matched field localization based on CS-MUSIC algorithm

NASA Astrophysics Data System (ADS)

Guo, Shuangle; Tang, Ruichun; Peng, Linhui; Ji, Xiaopeng

2016-04-01

The problem caused by shortness or excessiveness of snapshots and by coherent sources in underwater acoustic positioning is considered. A matched field localization algorithm based on CS-MUSIC (Compressive Sensing Multiple Signal Classification) is proposed based on the sparse mathematical model of the underwater positioning. The signal matrix is calculated through the SVD (Singular Value Decomposition) of the observation matrix. The observation matrix in the sparse mathematical model is replaced by the signal matrix, and a new concise sparse mathematical model is obtained, which means not only the scale of the localization problem but also the noise level is reduced; then the new sparse mathematical model is solved by the CS-MUSIC algorithm which is a combination of CS (Compressive Sensing) method and MUSIC (Multiple Signal Classification) method. The algorithm proposed in this paper can overcome effectively the difficulties caused by correlated sources and shortness of snapshots, and it can also reduce the time complexity and noise level of the localization problem by using the SVD of the observation matrix when the number of snapshots is large, which will be proved in this paper.

On base station cooperation using statistical CSI in jointly correlated MIMO downlink channels

NASA Astrophysics Data System (ADS)

Zhang, Jun; Jiang, Bin; Jin, Shi; Gao, Xiqi; Wong, Kai-Kit

2012-12-01

This article studies the transmission of a single cell-edge user's signal using statistical channel state information at cooperative base stations (BSs) with a general jointly correlated multiple-input multiple-output (MIMO) channel model. We first present an optimal scheme to maximize the ergodic sum capacity with per-BS power constraints, revealing that the transmitted signals of all BSs are mutually independent and the optimum transmit directions for each BS align with the eigenvectors of the BS's own transmit correlation matrix of the channel. Then, we employ matrix permanents to derive a closed-form tight upper bound for the ergodic sum capacity. Based on these results, we develop a low-complexity power allocation solution using convex optimization techniques and a simple iterative water-filling algorithm (IWFA) for power allocation. Finally, we derive a necessary and sufficient condition for which a beamforming approach achieves capacity for all BSs. Simulation results demonstrate that the upper bound of ergodic sum capacity is tight and the proposed cooperative transmission scheme increases the downlink system sum capacity considerably.

Processing of Antenna-Array Signals on the Basis of the Interference Model Including a Rank-Deficient Correlation Matrix

NASA Astrophysics Data System (ADS)

Rodionov, A. A.; Turchin, V. I.

2017-06-01

We propose a new method of signal processing in antenna arrays, which is called the Maximum-Likelihood Signal Classification. The proposed method is based on the model in which interference includes a component with a rank-deficient correlation matrix. Using numerical simulation, we show that the proposed method allows one to ensure variance of the estimated arrival angle of the plane wave, which is close to the Cramer-Rao lower boundary and more efficient than the best-known MUSIC method. It is also shown that the proposed technique can be efficiently used for estimating the time dependence of the useful signal.

Fractal analysis of phasic laser images of the myocardium for the purpose of diagnostics of acute coronary insufficiency

NASA Astrophysics Data System (ADS)

Wanchuliak, O. Y.; Bachinskyi, V. T.

2011-09-01

In this work on the base of Mueller-matrix description of optical anisotropy, the possibility of monitoring of time changes of myocardium tissue birefringence, has been considered. The optical model of polycrystalline networks of myocardium is suggested. The results of investigating the interrelation between the values correlation (correlation area, asymmetry coefficient and autocorrelation function excess) and fractal (dispersion of logarithmic dependencies of power spectra) parameters are presented. They characterize the distributions of Mueller matrix elements in the points of laser images of myocardium histological sections. The criteria of differentiation of death coming reasons are determined.

Asymmetric correlation matrices: an analysis of financial data

NASA Astrophysics Data System (ADS)

Livan, G.; Rebecchi, L.

2012-06-01

We analyse the spectral properties of correlation matrices between distinct statistical systems. Such matrices are intrinsically non-symmetric, and lend themselves to extend the spectral analyses usually performed on standard Pearson correlation matrices to the realm of complex eigenvalues. We employ some recent random matrix theory results on the average eigenvalue density of this type of matrix to distinguish between noise and non-trivial correlation structures, and we focus on financial data as a case study. Namely, we employ daily prices of stocks belonging to the American and British stock exchanges, and look for the emergence of correlations between two such markets in the eigenvalue spectrum of their non-symmetric correlation matrix. We find several non trivial results when considering time-lagged correlations over short lags, and we corroborate our findings by additionally studying the asymmetric correlation matrix of the principal components of our datasets.

Comparison of co-expression measures: mutual information, correlation, and model based indices.

PubMed

Song, Lin; Langfelder, Peter; Horvath, Steve

2012-12-09

Co-expression measures are often used to define networks among genes. Mutual information (MI) is often used as a generalized correlation measure. It is not clear how much MI adds beyond standard (robust) correlation measures or regression model based association measures. Further, it is important to assess what transformations of these and other co-expression measures lead to biologically meaningful modules (clusters of genes). We provide a comprehensive comparison between mutual information and several correlation measures in 8 empirical data sets and in simulations. We also study different approaches for transforming an adjacency matrix, e.g. using the topological overlap measure. Overall, we confirm close relationships between MI and correlation in all data sets which reflects the fact that most gene pairs satisfy linear or monotonic relationships. We discuss rare situations when the two measures disagree. We also compare correlation and MI based approaches when it comes to defining co-expression network modules. We show that a robust measure of correlation (the biweight midcorrelation transformed via the topological overlap transformation) leads to modules that are superior to MI based modules and maximal information coefficient (MIC) based modules in terms of gene ontology enrichment. We present a function that relates correlation to mutual information which can be used to approximate the mutual information from the corresponding correlation coefficient. We propose the use of polynomial or spline regression models as an alternative to MI for capturing non-linear relationships between quantitative variables. The biweight midcorrelation outperforms MI in terms of elucidating gene pairwise relationships. Coupled with the topological overlap matrix transformation, it often leads to more significantly enriched co-expression modules. Spline and polynomial networks form attractive alternatives to MI in case of non-linear relationships. Our results indicate that MI networks can safely be replaced by correlation networks when it comes to measuring co-expression relationships in stationary data.

Dodecylamine functionalization of carbon nanotubes to improve dispersion, thermal and mechanical properties of polyethylene based nanocomposites

NASA Astrophysics Data System (ADS)

Ferreira, F. V.; Franceschi, W.; Menezes, B. R. C.; Brito, F. S.; Lozano, K.; Coutinho, A. R.; Cividanes, L. S.; Thim, G. P.

2017-07-01

This study presents the effect of dodecylamine (DDA) functionalization of carbon nanotubes (CNTs) on the thermo-physical and mechanical properties of high-density polyethylene (HDPE) based composites. Here, we showed that the functionalization with DDA improved the dispersion of the CNTs as well as the interfacial adhesion with the HDPE matrix via non-covalent interactions. The better dispersion and interaction of CNT in the HDPE matrix as a function of the surface chemistry was correlated with the improved thermo-physical and mechanical properties.

Visual Working Memory and Perception Speed of 3- to 6-Year-Old Children Tested with a Matrix Film Battery Test

ERIC Educational Resources Information Center

Pittorf, Martin L.; Lehmann, Wolfgang; Huckauf, Anke

2014-01-01

In this study the visual working memory (VWM) and perception speed of 60 children between the ages of three and six years were tested with an age-based, easy-to-handle Matrix Film Battery Test (reliability R?=?0.71). It was thereby affirmed that the VWM is age dependent (correlation coefficient r?=?0.66***) as expected. Furthermore, a significant…

Moving Sound Source Localization Based on Sequential Subspace Estimation in Actual Room Environments

NASA Astrophysics Data System (ADS)

Tsuji, Daisuke; Suyama, Kenji

This paper presents a novel method for moving sound source localization and its performance evaluation in actual room environments. The method is based on the MUSIC (MUltiple SIgnal Classification) which is one of the most high resolution localization methods. When using the MUSIC, a computation of eigenvectors of correlation matrix is required for the estimation. It needs often a high computational costs. Especially, in the situation of moving source, it becomes a crucial drawback because the estimation must be conducted at every the observation time. Moreover, since the correlation matrix varies its characteristics due to the spatial-temporal non-stationarity, the matrix have to be estimated using only a few observed samples. It makes the estimation accuracy degraded. In this paper, the PAST (Projection Approximation Subspace Tracking) is applied for sequentially estimating the eigenvectors spanning the subspace. In the PAST, the eigen-decomposition is not required, and therefore it is possible to reduce the computational costs. Several experimental results in the actual room environments are shown to present the superior performance of the proposed method.

Covariance, correlation matrix, and the multiscale community structure of networks.

PubMed

Shen, Hua-Wei; Cheng, Xue-Qi; Fang, Bin-Xing

2010-07-01

Empirical studies show that real world networks often exhibit multiple scales of topological descriptions. However, it is still an open problem how to identify the intrinsic multiple scales of networks. In this paper, we consider detecting the multiscale community structure of network from the perspective of dimension reduction. According to this perspective, a covariance matrix of network is defined to uncover the multiscale community structure through the translation and rotation transformations. It is proved that the covariance matrix is the unbiased version of the well-known modularity matrix. We then point out that the translation and rotation transformations fail to deal with the heterogeneous network, which is very common in nature and society. To address this problem, a correlation matrix is proposed through introducing the rescaling transformation into the covariance matrix. Extensive tests on real world and artificial networks demonstrate that the correlation matrix significantly outperforms the covariance matrix, identically the modularity matrix, as regards identifying the multiscale community structure of network. This work provides a novel perspective to the identification of community structure and thus various dimension reduction methods might be used for the identification of community structure. Through introducing the correlation matrix, we further conclude that the rescaling transformation is crucial to identify the multiscale community structure of network, as well as the translation and rotation transformations.

Physically Based Failure Criteria for Transverse Matrix Cracking

NASA Technical Reports Server (NTRS)

Davila, Carlos G.; Camanho, Pedro P.

2003-01-01

A criterion for matrix failure of laminated composite plies in transverse tension and in-plane shear is developed by examining the mechanics of transverse matrix crack growth. Matrix cracks are assumed to initiate from manufacturing defects and can propagate within planes parallel to the fiber direction and normal to the ply mid-plane. Fracture mechanics models of cracks in unidirectional laminates, embedded plies and outer plies are developed to determine the onset and direction of propagation for unstable crack growth. The models for each ply configuration relate ply thickness and ply toughness to the corresponding in-situ ply strength. Calculated results for several materials are shown to correlate well with experimental results.

Heat transfer and flow friction correlations for perforated plate matrix heat exchangers

NASA Astrophysics Data System (ADS)

Ratna Raju, L.; Kumar, S. Sunil; Chowdhury, K.; Nandi, T. K.

2017-02-01

Perforated plate matrix heat exchangers (MHE) are constructed of high conductivity perforated plates stacked alternately with low conductivity spacers. They are being increasingly used in many cryogenic applications including Claude cycle or Reversed Brayton cycle cryo-refrigerators and liquefiers. Design of high NTU (number of (heat) transfer unit) cryogenic MHEs requires accurate heat transfer coefficient and flow friction factor. Thermo-hydraulic behaviour of perforated plates strongly depends on the geometrical parameters. Existing correlations, however, are mostly expressed as functions of Reynolds number only. This causes, for a given configuration, significant variations in coefficients from one correlation to the other. In this paper we present heat transfer and flow friction correlations as functions of all geometrical and other controlling variables. A FluentTM based numerical model has been developed for heat transfer and pressure drop studies over a stack of alternately arranged perforated plates and spacers. The model is validated with the data from literature. Generalized correlations are obtained through regression analysis over a large number of computed data.

Overcoming multicollinearity in multiple regression using correlation coefficient

NASA Astrophysics Data System (ADS)

Zainodin, H. J.; Yap, S. J.

2013-09-01

Multicollinearity happens when there are high correlations among independent variables. In this case, it would be difficult to distinguish between the contributions of these independent variables to that of the dependent variable as they may compete to explain much of the similar variance. Besides, the problem of multicollinearity also violates the assumption of multiple regression: that there is no collinearity among the possible independent variables. Thus, an alternative approach is introduced in overcoming the multicollinearity problem in achieving a well represented model eventually. This approach is accomplished by removing the multicollinearity source variables on the basis of the correlation coefficient values based on full correlation matrix. Using the full correlation matrix can facilitate the implementation of Excel function in removing the multicollinearity source variables. It is found that this procedure is easier and time-saving especially when dealing with greater number of independent variables in a model and a large number of all possible models. Hence, in this paper detailed insight of the procedure is shown, compared and implemented.

HIGH DIMENSIONAL COVARIANCE MATRIX ESTIMATION IN APPROXIMATE FACTOR MODELS

PubMed Central

Fan, Jianqing; Liao, Yuan; Mincheva, Martina

2012-01-01

The variance covariance matrix plays a central role in the inferential theories of high dimensional factor models in finance and economics. Popular regularization methods of directly exploiting sparsity are not directly applicable to many financial problems. Classical methods of estimating the covariance matrices are based on the strict factor models, assuming independent idiosyncratic components. This assumption, however, is restrictive in practical applications. By assuming sparse error covariance matrix, we allow the presence of the cross-sectional correlation even after taking out common factors, and it enables us to combine the merits of both methods. We estimate the sparse covariance using the adaptive thresholding technique as in Cai and Liu (2011), taking into account the fact that direct observations of the idiosyncratic components are unavailable. The impact of high dimensionality on the covariance matrix estimation based on the factor structure is then studied. PMID:22661790

Online blind source separation using incremental nonnegative matrix factorization with volume constraint.

PubMed

Zhou, Guoxu; Yang, Zuyuan; Xie, Shengli; Yang, Jun-Mei

2011-04-01

Online blind source separation (BSS) is proposed to overcome the high computational cost problem, which limits the practical applications of traditional batch BSS algorithms. However, the existing online BSS methods are mainly used to separate independent or uncorrelated sources. Recently, nonnegative matrix factorization (NMF) shows great potential to separate the correlative sources, where some constraints are often imposed to overcome the non-uniqueness of the factorization. In this paper, an incremental NMF with volume constraint is derived and utilized for solving online BSS. The volume constraint to the mixing matrix enhances the identifiability of the sources, while the incremental learning mode reduces the computational cost. The proposed method takes advantage of the natural gradient based multiplication updating rule, and it performs especially well in the recovery of dependent sources. Simulations in BSS for dual-energy X-ray images, online encrypted speech signals, and high correlative face images show the validity of the proposed method.

When do correlations increase with firing rates in recurrent networks?

PubMed Central

2017-01-01

A central question in neuroscience is to understand how noisy firing patterns are used to transmit information. Because neural spiking is noisy, spiking patterns are often quantified via pairwise correlations, or the probability that two cells will spike coincidentally, above and beyond their baseline firing rate. One observation frequently made in experiments, is that correlations can increase systematically with firing rate. Theoretical studies have determined that stimulus-dependent correlations that increase with firing rate can have beneficial effects on information coding; however, we still have an incomplete understanding of what circuit mechanisms do, or do not, produce this correlation-firing rate relationship. Here, we studied the relationship between pairwise correlations and firing rates in recurrently coupled excitatory-inhibitory spiking networks with conductance-based synapses. We found that with stronger excitatory coupling, a positive relationship emerged between pairwise correlations and firing rates. To explain these findings, we used linear response theory to predict the full correlation matrix and to decompose correlations in terms of graph motifs. We then used this decomposition to explain why covariation of correlations with firing rate—a relationship previously explained in feedforward networks driven by correlated input—emerges in some recurrent networks but not in others. Furthermore, when correlations covary with firing rate, this relationship is reflected in low-rank structure in the correlation matrix. PMID:28448499

Sector Identification in a Set of Stock Return Time Series Traded at the London Stock Exchange

NASA Astrophysics Data System (ADS)

Coronnello, C.; Tumminello, M.; Lillo, F.; Micciche, S.; Mantegna, R. N.

2005-09-01

We compare some methods recently used in the literature to detect the existence of a certain degree of common behavior of stock returns belonging to the same economic sector. Specifically, we discuss methods based on random matrix theory and hierarchical clustering techniques. We apply these methods to a portfolio of stocks traded at the London Stock Exchange. The investigated time series are recorded both at a daily time horizon and at a 5-minute time horizon. The correlation coefficient matrix is very different at different time horizons confirming that more structured correlation coefficient matrices are observed for long time horizons. All the considered methods are able to detect economic information and the presence of clusters characterized by the economic sector of stocks. However, different methods present a different degree of sensitivity with respect to different sectors. Our comparative analysis suggests that the application of just a single method could not be able to extract all the economic information present in the correlation coefficient matrix of a stock portfolio.

A Comparison of Methods for Estimating the Determinant of High-Dimensional Covariance Matrix.

PubMed

Hu, Zongliang; Dong, Kai; Dai, Wenlin; Tong, Tiejun

2017-09-21

The determinant of the covariance matrix for high-dimensional data plays an important role in statistical inference and decision. It has many real applications including statistical tests and information theory. Due to the statistical and computational challenges with high dimensionality, little work has been proposed in the literature for estimating the determinant of high-dimensional covariance matrix. In this paper, we estimate the determinant of the covariance matrix using some recent proposals for estimating high-dimensional covariance matrix. Specifically, we consider a total of eight covariance matrix estimation methods for comparison. Through extensive simulation studies, we explore and summarize some interesting comparison results among all compared methods. We also provide practical guidelines based on the sample size, the dimension, and the correlation of the data set for estimating the determinant of high-dimensional covariance matrix. Finally, from a perspective of the loss function, the comparison study in this paper may also serve as a proxy to assess the performance of the covariance matrix estimation.

Digital transceiver design for two-way AF-MIMO relay systems with imperfect CSI

NASA Astrophysics Data System (ADS)

Hu, Chia-Chang; Chou, Yu-Fei; Chen, Kui-He

2013-09-01

In the paper, combined optimization of the terminal precoders/equalizers and single-relay precoder is proposed for an amplify-and-forward (AF) multiple-input multiple-output (MIMO) two-way single-relay system with correlated channel uncertainties. Both terminal transceivers and relay precoding matrix are designed based on the minimum mean square error (MMSE) criterion when terminals are unable to erase completely self-interference due to imperfect correlated channel state information (CSI). This robust joint optimization problem of beamforming and precoding matrices under power constraints belongs to neither concave nor convex so that a nonlinear matrix-form conjugate gradient (MCG) algorithm is applied to explore local optimal solutions. Simulation results show that the robust transceiver design is able to overcome effectively the loss of bit-error-rate (BER) due to inclusion of correlated channel uncertainties and residual self-interference.

$$t\\bar{t}$$ Spin Correlations at D0

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

Peters, Yvonne

2013-01-01

The heaviest known elementary particle today, the top quark, has been discovered in 1995 by the CDF and D0 collaborations at the Tevatron collider at Fermilab. Its high mass and short lifetime, shorter than the timescale for hadronization, makes the top quark a special particle to study. Due to the short lifetime, the top quark's spin information is preserved in the decay products. In this article we discuss the studies of ttbar spin correlations at D0, testing the full chain from production to decay. In particular, we present a measurement using angular information and an analysis using a matrix-element basedmore » technique. The application of the matrix-element based technique to the ttbar dilepton and semileponic final state resulted in the first evidence for non-vanishing ttbar spin correlations.« less

Diagnosing Topological Edge States via Entanglement Monogamy.

PubMed

Meichanetzidis, K; Eisert, J; Cirio, M; Lahtinen, V; Pachos, J K

2016-04-01

Topological phases of matter possess intricate correlation patterns typically probed by entanglement entropies or entanglement spectra. In this Letter, we propose an alternative approach to assessing topologically induced edge states in free and interacting fermionic systems. We do so by focussing on the fermionic covariance matrix. This matrix is often tractable either analytically or numerically, and it precisely captures the relevant correlations of the system. By invoking the concept of monogamy of entanglement, we show that highly entangled states supported across a system bipartition are largely disentangled from the rest of the system, thus, usually appearing as gapless edge states. We then define an entanglement qualifier that identifies the presence of topological edge states based purely on correlations present in the ground states. We demonstrate the versatility of this qualifier by applying it to various free and interacting fermionic topological systems.

Diagnosing Topological Edge States via Entanglement Monogamy

NASA Astrophysics Data System (ADS)

Meichanetzidis, K.; Eisert, J.; Cirio, M.; Lahtinen, V.; Pachos, J. K.

2016-04-01

Topological phases of matter possess intricate correlation patterns typically probed by entanglement entropies or entanglement spectra. In this Letter, we propose an alternative approach to assessing topologically induced edge states in free and interacting fermionic systems. We do so by focussing on the fermionic covariance matrix. This matrix is often tractable either analytically or numerically, and it precisely captures the relevant correlations of the system. By invoking the concept of monogamy of entanglement, we show that highly entangled states supported across a system bipartition are largely disentangled from the rest of the system, thus, usually appearing as gapless edge states. We then define an entanglement qualifier that identifies the presence of topological edge states based purely on correlations present in the ground states. We demonstrate the versatility of this qualifier by applying it to various free and interacting fermionic topological systems.

Graph reconstruction using covariance-based methods.

PubMed

Sulaimanov, Nurgazy; Koeppl, Heinz

2016-12-01

Methods based on correlation and partial correlation are today employed in the reconstruction of a statistical interaction graph from high-throughput omics data. These dedicated methods work well even for the case when the number of variables exceeds the number of samples. In this study, we investigate how the graphs extracted from covariance and concentration matrix estimates are related by using Neumann series and transitive closure and through discussing concrete small examples. Considering the ideal case where the true graph is available, we also compare correlation and partial correlation methods for large realistic graphs. In particular, we perform the comparisons with optimally selected parameters based on the true underlying graph and with data-driven approaches where the parameters are directly estimated from the data.

ppcor: An R Package for a Fast Calculation to Semi-partial Correlation Coefficients.

PubMed

Kim, Seongho

2015-11-01

Lack of a general matrix formula hampers implementation of the semi-partial correlation, also known as part correlation, to the higher-order coefficient. This is because the higher-order semi-partial correlation calculation using a recursive formula requires an enormous number of recursive calculations to obtain the correlation coefficients. To resolve this difficulty, we derive a general matrix formula of the semi-partial correlation for fast computation. The semi-partial correlations are then implemented on an R package ppcor along with the partial correlation. Owing to the general matrix formulas, users can readily calculate the coefficients of both partial and semi-partial correlations without computational burden. The package ppcor further provides users with the level of the statistical significance with its test statistic.

Characteristics of group networks in the KOSPI and the KOSDAQ

NASA Astrophysics Data System (ADS)

Kim, Kyungsik; Ko, Jeung-Su; Yi, Myunggi

2012-02-01

We investigate the main feature of group networks in the KOSPI and KOSDAQ of Korean financial markets and analyze daily cross-correlations between price fluctuations for the 5-year time period from 2006 to 2010. We discuss the stabilities by undressing the market-wide effect using the Markowitz multi-factor model and the network-based approach. In particular we ascertain the explicit list of significant firms in the few largest eigenvectors from the undressed correlation matrix. Finally, we show the structure of group correlation by applying a network-based approach. In addition, the relation between market capitalizations and businesses is examined.

The predictive power of singular value decomposition entropy for stock market dynamics

NASA Astrophysics Data System (ADS)

Caraiani, Petre

2014-01-01

We use a correlation-based approach to analyze financial data from the US stock market, both daily and monthly observations from the Dow Jones. We compute the entropy based on the singular value decomposition of the correlation matrix for the components of the Dow Jones Industrial Index. Based on a moving window, we derive time varying measures of entropy for both daily and monthly data. We find that the entropy has a predictive ability with respect to stock market dynamics as indicated by the Granger causality tests.

The spatial-temporal characteristics of type I collagen-based extracellular matrix.

PubMed

Jones, Christopher Allen Rucksack; Liang, Long; Lin, Daniel; Jiao, Yang; Sun, Bo

2014-11-28

Type I collagen abounds in mammalian extracellular matrix (ECM) and is crucial to many biophysical processes. While previous studies have mostly focused on bulk averaged properties, here we provide a comprehensive and quantitative spatial-temporal characterization of the microstructure of type I collagen-based ECM as the gelation temperature varies. The structural characteristics including the density and nematic correlation functions are obtained by analyzing confocal images of collagen gels prepared at a wide range of gelation temperatures (from 16 °C to 36 °C). As temperature increases, the gel microstructure varies from a "bundled" network with strong orientational correlation between the fibers to an isotropic homogeneous network with no significant orientational correlation, as manifested by the decaying of length scales in the correlation functions. We develop a kinetic Monte-Carlo collagen growth model to better understand how ECM microstructure depends on various environmental or kinetic factors. We show that the nucleation rate, growth rate, and an effective hydrodynamic alignment of collagen fibers fully determines the spatiotemporal fluctuations of the density and orientational order of collagen gel microstructure. Also the temperature dependence of the growth rate and nucleation rate follow the prediction of classical nucleation theory.

Cultural interaction and biological distance in postclassic period Mexico.

PubMed

Ragsdale, Corey S; Edgar, Heather J H

2015-05-01

Economic, political, and cultural relationships connected virtually every population throughout Mexico during Postclassic period (AD 900-1520). Much of what is known about population interaction in prehistoric Mexico is based on archaeological or ethnohistoric data. What is unclear, especially for the Postclassic period, is how these data correlate with biological population structure. We address this by assessing biological (phenotypic) distances among 28 samples based upon a comparison of dental morphology trait frequencies, which serve as a proxy for genetic variation, from 810 individuals. These distances were compared with models representing geographic and cultural relationships among the same groups. Results of Mantel and partial Mantel matrix correlation tests show that shared migration and trade are correlated with biological distances, but geographic distance is not. Trade and political interaction are also correlated with biological distance when combined in a single matrix. These results indicate that trade and political relationships affected population structure among Postclassic Mexican populations. We suggest that trade likely played a major role in shaping patterns of interaction between populations. This study also shows that the biological distance data support the migration histories described in ethnohistoric sources. © 2015 Wiley Periodicals, Inc.

Simplified Estimation and Testing in Unbalanced Repeated Measures Designs.

PubMed

Spiess, Martin; Jordan, Pascal; Wendt, Mike

2018-05-07

In this paper we propose a simple estimator for unbalanced repeated measures design models where each unit is observed at least once in each cell of the experimental design. The estimator does not require a model of the error covariance structure. Thus, circularity of the error covariance matrix and estimation of correlation parameters and variances are not necessary. Together with a weak assumption about the reason for the varying number of observations, the proposed estimator and its variance estimator are unbiased. As an alternative to confidence intervals based on the normality assumption, a bias-corrected and accelerated bootstrap technique is considered. We also propose the naive percentile bootstrap for Wald-type tests where the standard Wald test may break down when the number of observations is small relative to the number of parameters to be estimated. In a simulation study we illustrate the properties of the estimator and the bootstrap techniques to calculate confidence intervals and conduct hypothesis tests in small and large samples under normality and non-normality of the errors. The results imply that the simple estimator is only slightly less efficient than an estimator that correctly assumes a block structure of the error correlation matrix, a special case of which is an equi-correlation matrix. Application of the estimator and the bootstrap technique is illustrated using data from a task switch experiment based on an experimental within design with 32 cells and 33 participants.

Prediction on the inhibition ratio of pyrrolidine derivatives on matrix metalloproteinase based on gene expression programming.

PubMed

Li, Yuqin; You, Guirong; Jia, Baoxiu; Si, Hongzong; Yao, Xiaojun

2014-01-01

Quantitative structure-activity relationships (QSAR) were developed to predict the inhibition ratio of pyrrolidine derivatives on matrix metalloproteinase via heuristic method (HM) and gene expression programming (GEP). The descriptors of 33 pyrrolidine derivatives were calculated by the software CODESSA, which can calculate quantum chemical, topological, geometrical, constitutional, and electrostatic descriptors. HM was also used for the preselection of 5 appropriate molecular descriptors. Linear and nonlinear QSAR models were developed based on the HM and GEP separately and two prediction models lead to a good correlation coefficient (R (2)) of 0.93 and 0.94. The two QSAR models are useful in predicting the inhibition ratio of pyrrolidine derivatives on matrix metalloproteinase during the discovery of new anticancer drugs and providing theory information for studying the new drugs.

A Bayesian method for detecting pairwise associations in compositional data

PubMed Central

Ventz, Steffen; Huttenhower, Curtis

2017-01-01

Compositional data consist of vectors of proportions normalized to a constant sum from a basis of unobserved counts. The sum constraint makes inference on correlations between unconstrained features challenging due to the information loss from normalization. However, such correlations are of long-standing interest in fields including ecology. We propose a novel Bayesian framework (BAnOCC: Bayesian Analysis of Compositional Covariance) to estimate a sparse precision matrix through a LASSO prior. The resulting posterior, generated by MCMC sampling, allows uncertainty quantification of any function of the precision matrix, including the correlation matrix. We also use a first-order Taylor expansion to approximate the transformation from the unobserved counts to the composition in order to investigate what characteristics of the unobserved counts can make the correlations more or less difficult to infer. On simulated datasets, we show that BAnOCC infers the true network as well as previous methods while offering the advantage of posterior inference. Larger and more realistic simulated datasets further showed that BAnOCC performs well as measured by type I and type II error rates. Finally, we apply BAnOCC to a microbial ecology dataset from the Human Microbiome Project, which in addition to reproducing established ecological results revealed unique, competition-based roles for Proteobacteria in multiple distinct habitats. PMID:29140991

Wideband Direction of Arrival Estimation in the Presence of Unknown Mutual Coupling

PubMed Central

Li, Weixing; Zhang, Yue; Lin, Jianzhi; Guo, Rui; Chen, Zengping

2017-01-01

This paper investigates a subarray based algorithm for direction of arrival (DOA) estimation of wideband uniform linear array (ULA), under the presence of frequency-dependent mutual coupling effects. Based on the Toeplitz structure of mutual coupling matrices, the whole array is divided into the middle subarray and the auxiliary subarray. Then two-sided correlation transformation is applied to the correlation matrix of the middle subarray instead of the whole array. In this way, the mutual coupling effects can be eliminated. Finally, the multiple signal classification (MUSIC) method is utilized to derive the DOAs. For the condition when the blind angles exist, we refine DOA estimation by using a simple approach based on the frequency-dependent mutual coupling matrixes (MCMs). The proposed method can achieve high estimation accuracy without any calibration sources. It has a low computational complexity because iterative processing is not required. Simulation results validate the effectiveness and feasibility of the proposed algorithm. PMID:28178177

Trust in Leadership DEOCS 4.1 Construct Validity Summary

DTIC Science & Technology

2017-08-01

Item Corrected Item- Total Correlation Cronbach’s Alpha if Item Deleted Four-point Scale Items I can depend on my immediate supervisor to meet...1974) were used to assess the fit between the data and the factor. The BTS hypothesizes that the correlation matrix is an identity matrix. The...to reject the null hypothesis that the correlation matrix is an identity, and to conclude that the factor analysis is an appropriate method to

Extension of latin hypercube samples with correlated variables.

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

Hora, Stephen Curtis; Helton, Jon Craig; Sallaberry, Cedric J. PhD.

2006-11-01

A procedure for extending the size of a Latin hypercube sample (LHS) with rank correlated variables is described and illustrated. The extension procedure starts with an LHS of size m and associated rank correlation matrix C and constructs a new LHS of size 2m that contains the elements of the original LHS and has a rank correlation matrix that is close to the original rank correlation matrix C. The procedure is intended for use in conjunction with uncertainty and sensitivity analysis of computationally demanding models in which it is important to make efficient use of a necessarily limited number ofmore » model evaluations.« less

Extracting factors for interest rate scenarios

NASA Astrophysics Data System (ADS)

Molgedey, L.; Galic, E.

2001-04-01

Factor based interest rate models are widely used for risk managing purposes, for option pricing and for identifying and capturing yield curve anomalies. The movements of a term structure of interest rates are commonly assumed to be driven by a small number of orthogonal factors such as SHIFT, TWIST and BUTTERFLY (BOW). These factors are usually obtained by a Principal Component Analysis (PCA) of historical bond prices (interest rates). Although PCA diagonalizes the covariance matrix of either the interest rates or the interest rate changes, it does not use both covariance matrices simultaneously. Furthermore higher linear and nonlinear correlations are neglected. These correlations as well as the mean reverting properties of the interest rates become crucial, if one is interested in a longer time horizon (infrequent hedging or trading). We will show that Independent Component Analysis (ICA) is a more appropriate tool than PCA, since ICA uses the covariance matrix of the interest rates as well as the covariance matrix of the interest rate changes simultaneously. Additionally higher linear and nonlinear correlations may be easily incorporated. The resulting factors are uncorrelated for various time delays, approximately independent but nonorthogonal. This is in contrast to the factors obtained from the PCA, which are orthogonal and uncorrelated for identical times only. Although factors from the ICA are nonorthogonal, it is sufficient to consider only a few factors in order to explain most of the variation in the original data. Finally we will present examples that ICA based hedges outperforms PCA based hedges specifically if the portfolio is sensitive to structural changes of the yield curve.

Combined analysis of magnetic and gravity anomalies using normalized source strength (NSS)

NASA Astrophysics Data System (ADS)

Li, L.; Wu, Y.

2017-12-01

Gravity field and magnetic field belong to potential fields which lead inherent multi-solution. Combined analysis of magnetic and gravity anomalies based on Poisson's relation is used to determinate homology gravity and magnetic anomalies and decrease the ambiguity. The traditional combined analysis uses the linear regression of the reduction to pole (RTP) magnetic anomaly to the first order vertical derivative of the gravity anomaly, and provides the quantitative or semi-quantitative interpretation by calculating the correlation coefficient, slope and intercept. In the calculation process, due to the effect of remanent magnetization, the RTP anomaly still contains the effect of oblique magnetization. In this case the homology gravity and magnetic anomalies display irrelevant results in the linear regression calculation. The normalized source strength (NSS) can be transformed from the magnetic tensor matrix, which is insensitive to the remanence. Here we present a new combined analysis using NSS. Based on the Poisson's relation, the gravity tensor matrix can be transformed into the pseudomagnetic tensor matrix of the direction of geomagnetic field magnetization under the homologous condition. The NSS of pseudomagnetic tensor matrix and original magnetic tensor matrix are calculated and linear regression analysis is carried out. The calculated correlation coefficient, slope and intercept indicate the homology level, Poisson's ratio and the distribution of remanent respectively. We test the approach using synthetic model under complex magnetization, the results show that it can still distinguish the same source under the condition of strong remanence, and establish the Poisson's ratio. Finally, this approach is applied in China. The results demonstrated that our approach is feasible.

Random matrix theory filters in portfolio optimisation: A stability and risk assessment

NASA Astrophysics Data System (ADS)

Daly, J.; Crane, M.; Ruskin, H. J.

2008-07-01

Random matrix theory (RMT) filters, applied to covariance matrices of financial returns, have recently been shown to offer improvements to the optimisation of stock portfolios. This paper studies the effect of three RMT filters on the realised portfolio risk, and on the stability of the filtered covariance matrix, using bootstrap analysis and out-of-sample testing. We propose an extension to an existing RMT filter, (based on Krzanowski stability), which is observed to reduce risk and increase stability, when compared to other RMT filters tested. We also study a scheme for filtering the covariance matrix directly, as opposed to the standard method of filtering correlation, where the latter is found to lower the realised risk, on average, by up to 6.7%. We consider both equally and exponentially weighted covariance matrices in our analysis, and observe that the overall best method out-of-sample was that of the exponentially weighted covariance, with our Krzanowski stability-based filter applied to the correlation matrix. We also find that the optimal out-of-sample decay factors, for both filtered and unfiltered forecasts, were higher than those suggested by Riskmetrics [J.P. Morgan, Reuters, Riskmetrics technical document, Technical Report, 1996. http://www.riskmetrics.com/techdoc.html], with those for the latter approaching a value of α=1. In conclusion, RMT filtering reduced the realised risk, on average, and in the majority of cases when tested out-of-sample, but increased the realised risk on a marked number of individual days-in some cases more than doubling it.

10Gbps 2D MGC OCDMA Code over FSO Communication System

NASA Astrophysics Data System (ADS)

Professor Urmila Bhanja, Associate, Dr.; Khuntia, Arpita; Alamasety Swati, (Student

2017-08-01

Currently, wide bandwidth signal dissemination along with low latency is a leading requisite in various applications. Free space optical wireless communication has introduced as a realistic technology for bridging the gap in present high data transmission fiber connectivity and as a provisional backbone for rapidly deployable wireless communication infrastructure. The manuscript highlights on the implementation of 10Gbps SAC-OCDMA FSO communications using modified two dimensional Golomb code (2D MGC) that possesses better auto correlation, minimum cross correlation and high cardinality. A comparison based on pseudo orthogonal (PSO) matrix code and modified two dimensional Golomb code (2D MGC) is developed in the proposed SAC OCDMA-FSO communication module taking different parameters into account. The simulative outcome signifies that the communication radius is bounded by the multiple access interference (MAI). In this work, a comparison is made in terms of bit error rate (BER), and quality factor (Q) based on modified two dimensional Golomb code (2D MGC) and PSO matrix code. It is observed that the 2D MGC yields better results compared to the PSO matrix code. The simulation results are validated using optisystem version 14.

Measures for the Dynamics in a Few-Body Quantum System with Harmonic Interactions

NASA Astrophysics Data System (ADS)

Nagy, I.; Pipek, J.; Glasser, M. L.

2018-01-01

We determine the exact time-dependent non-idempotent one-particle reduced density matrix and its spectral decomposition for a harmonically confined two-particle correlated one-dimensional system when the interaction terms in the Schrödinger Hamiltonian are changed abruptly. Based on this matrix in coordinate space we derive a precise condition for the equivalence of the purity and the overlap-square of the correlated and non-correlated wave functions as the model system with harmonic interactions evolves in time. This equivalence holds only if the interparticle interactions are affected, while the confinement terms are unaffected within the stability range of the system. Under this condition we analyze various time-dependent measures of entanglement and demonstrate that, depending on the magnitude of the changes made in the Hamiltonian, periodic, logarithmically increasing or constant value behavior of the von Neumann entropy can occur.

Minimum number of measurements for evaluating Bertholletia excelsa.

PubMed

Baldoni, A B; Tonini, H; Tardin, F D; Botelho, S C C; Teodoro, P E

2017-09-27

Repeatability studies on fruit species are of great importance to identify the minimum number of measurements necessary to accurately select superior genotypes. This study aimed to identify the most efficient method to estimate the repeatability coefficient (r) and predict the minimum number of measurements needed for a more accurate evaluation of Brazil nut tree (Bertholletia excelsa) genotypes based on fruit yield. For this, we assessed the number of fruits and dry mass of seeds of 75 Brazil nut genotypes, from native forest, located in the municipality of Itaúba, MT, for 5 years. To better estimate r, four procedures were used: analysis of variance (ANOVA), principal component analysis based on the correlation matrix (CPCOR), principal component analysis based on the phenotypic variance and covariance matrix (CPCOV), and structural analysis based on the correlation matrix (mean r - AECOR). There was a significant effect of genotypes and measurements, which reveals the need to study the minimum number of measurements for selecting superior Brazil nut genotypes for a production increase. Estimates of r by ANOVA were lower than those observed with the principal component methodology and close to AECOR. The CPCOV methodology provided the highest estimate of r, which resulted in a lower number of measurements needed to identify superior Brazil nut genotypes for the number of fruits and dry mass of seeds. Based on this methodology, three measurements are necessary to predict the true value of the Brazil nut genotypes with a minimum accuracy of 85%.

Fuzzy comprehensive evaluation for grid-connected performance of integrated distributed PV-ES systems

NASA Astrophysics Data System (ADS)

Lv, Z. H.; Li, Q.; Huang, R. W.; Liu, H. M.; Liu, D.

2016-08-01

Based on the discussion about topology structure of integrated distributed photovoltaic (PV) power generation system and energy storage (ES) in single or mixed type, this paper focuses on analyzing grid-connected performance of integrated distributed photovoltaic and energy storage (PV-ES) systems, and proposes a comprehensive evaluation index system. Then a multi-level fuzzy comprehensive evaluation method based on grey correlation degree is proposed, and the calculations for weight matrix and fuzzy matrix are presented step by step. Finally, a distributed integrated PV-ES power generation system connected to a 380 V low voltage distribution network is taken as the example, and some suggestions are made based on the evaluation results.

Postconcussive Symptoms in OEF-OIF Veterans: Factor Structure and Impact of Posttraumatic Stress

DTIC Science & Technology

2009-06-03

correlations between NSI full items are presented in Appendix A. Visual inspection of the correlation matrix, the Kaiser - Meyer - Olkin coefficient of .92, and...Spearman rho correlations between NSI residuals are pre- sented in Appendix B. Again, visual inspection of the correla- tion matrix, the Kaiser - Meyer ... Olkin coefficient of .83, and Bartlett’s test of sphericity (x2 5 1,936.0, p , .01) suggested that the matrix could be factored. Principal-components

Cluster and propensity based approximation of a network

PubMed Central

2013-01-01

Background The models in this article generalize current models for both correlation networks and multigraph networks. Correlation networks are widely applied in genomics research. In contrast to general networks, it is straightforward to test the statistical significance of an edge in a correlation network. It is also easy to decompose the underlying correlation matrix and generate informative network statistics such as the module eigenvector. However, correlation networks only capture the connections between numeric variables. An open question is whether one can find suitable decompositions of the similarity measures employed in constructing general networks. Multigraph networks are attractive because they support likelihood based inference. Unfortunately, it is unclear how to adjust current statistical methods to detect the clusters inherent in many data sets. Results Here we present an intuitive and parsimonious parametrization of a general similarity measure such as a network adjacency matrix. The cluster and propensity based approximation (CPBA) of a network not only generalizes correlation network methods but also multigraph methods. In particular, it gives rise to a novel and more realistic multigraph model that accounts for clustering and provides likelihood based tests for assessing the significance of an edge after controlling for clustering. We present a novel Majorization-Minimization (MM) algorithm for estimating the parameters of the CPBA. To illustrate the practical utility of the CPBA of a network, we apply it to gene expression data and to a bi-partite network model for diseases and disease genes from the Online Mendelian Inheritance in Man (OMIM). Conclusions The CPBA of a network is theoretically appealing since a) it generalizes correlation and multigraph network methods, b) it improves likelihood based significance tests for edge counts, c) it directly models higher-order relationships between clusters, and d) it suggests novel clustering algorithms. The CPBA of a network is implemented in Fortran 95 and bundled in the freely available R package PropClust. PMID:23497424

Imprints of spherical nontrivial topologies on the cosmic microwave background.

PubMed

Niarchou, Anastasia; Jaffe, Andrew

2007-08-24

The apparent low power in the cosmic microwave background (CMB) temperature anisotropy power spectrum derived from the Wilkinson Microwave Anisotropy Probe motivated us to consider the possibility of a nontrivial topology. We focus on simple spherical multiconnected manifolds and discuss their implications for the CMB in terms of the power spectrum, maps, and the correlation matrix. We perform a Bayesian model comparison against the fiducial best-fit cold dark matter model with a cosmological constant based both on the power spectrum and the correlation matrix to assess their statistical significance. We find that the first-year power spectrum shows a slight preference for the truncated cube space, but the three-year data show no evidence for any of these spaces.

Open-Ended Recursive Approach for the Calculation of Multiphoton Absorption Matrix Elements

PubMed Central

2015-01-01

We present an implementation of single residues for response functions to arbitrary order using a recursive approach. Explicit expressions in terms of density-matrix-based response theory for the single residues of the linear, quadratic, cubic, and quartic response functions are also presented. These residues correspond to one-, two-, three- and four-photon transition matrix elements. The newly developed code is used to calculate the one-, two-, three- and four-photon absorption cross sections of para-nitroaniline and para-nitroaminostilbene, making this the first treatment of four-photon absorption in the framework of response theory. We find that the calculated multiphoton absorption cross sections are not very sensitive to the size of the basis set as long as a reasonably large basis set with diffuse functions is used. The choice of exchange–correlation functional, however, significantly affects the calculated cross sections of both charge-transfer transitions and other transitions, in particular, for the larger para-nitroaminostilbene molecule. We therefore recommend the use of a range-separated exchange–correlation functional in combination with the augmented correlation-consistent double-ζ basis set aug-cc-pVDZ for the calculation of multiphoton absorption properties. PMID:25821415

Neutrinoless ββ decay mediated by the exchange of light and heavy neutrinos: the role of nuclear structure correlations

NASA Astrophysics Data System (ADS)

Menéndez, J.

2018-01-01

Neutrinoless β β decay nuclear matrix elements calculated with the shell model and energy-density functional theory typically disagree by more than a factor of two in the standard scenario of light-neutrino exchange. In contrast, for a decay mediated by sterile heavy neutrinos the deviations are reduced to about 50%, an uncertainty similar to the one due to short-range effects. We compare matrix elements in the light- and heavy-neutrino-exchange channels, exploring the radial, momentum transfer and angular momentum-parity matrix element distributions, and considering transitions that involve correlated and uncorrelated nuclear states. We argue that the shorter-range heavy-neutrino exchange is less sensitive to collective nuclear correlations, and that discrepancies in matrix elements are mostly due to the treatment of long-range correlations in many-body calculations. Our analysis supports previous studies suggesting that isoscalar pairing correlations, which affect mostly the longer-range part of the neutrinoless β β decay operator, are partially responsible for the differences between nuclear matrix elements in the standard light-neutrino-exchange mechanism.

Eigenvalue density of cross-correlations in Sri Lankan financial market

NASA Astrophysics Data System (ADS)

Nilantha, K. G. D. R.; Ranasinghe; Malmini, P. K. C.

2007-05-01

We apply the universal properties with Gaussian orthogonal ensemble (GOE) of random matrices namely spectral properties, distribution of eigenvalues, eigenvalue spacing predicted by random matrix theory (RMT) to compare cross-correlation matrix estimators from emerging market data. The daily stock prices of the Sri Lankan All share price index and Milanka price index from August 2004 to March 2005 were analyzed. Most eigenvalues in the spectrum of the cross-correlation matrix of stock price changes agree with the universal predictions of RMT. We find that the cross-correlation matrix satisfies the universal properties of the GOE of real symmetric random matrices. The eigen distribution follows the RMT predictions in the bulk but there are some deviations at the large eigenvalues. The nearest-neighbor spacing and the next nearest-neighbor spacing of the eigenvalues were examined and found that they follow the universality of GOE. RMT with deterministic correlations found that each eigenvalue from deterministic correlations is observed at values, which are repelled from the bulk distribution.

Robust Image Regression Based on the Extended Matrix Variate Power Exponential Distribution of Dependent Noise.

PubMed

Luo, Lei; Yang, Jian; Qian, Jianjun; Tai, Ying; Lu, Gui-Fu

2017-09-01

Dealing with partial occlusion or illumination is one of the most challenging problems in image representation and classification. In this problem, the characterization of the representation error plays a crucial role. In most current approaches, the error matrix needs to be stretched into a vector and each element is assumed to be independently corrupted. This ignores the dependence between the elements of error. In this paper, it is assumed that the error image caused by partial occlusion or illumination changes is a random matrix variate and follows the extended matrix variate power exponential distribution. This has the heavy tailed regions and can be used to describe a matrix pattern of l×m dimensional observations that are not independent. This paper reveals the essence of the proposed distribution: it actually alleviates the correlations between pixels in an error matrix E and makes E approximately Gaussian. On the basis of this distribution, we derive a Schatten p -norm-based matrix regression model with L q regularization. Alternating direction method of multipliers is applied to solve this model. To get a closed-form solution in each step of the algorithm, two singular value function thresholding operators are introduced. In addition, the extended Schatten p -norm is utilized to characterize the distance between the test samples and classes in the design of the classifier. Extensive experimental results for image reconstruction and classification with structural noise demonstrate that the proposed algorithm works much more robustly than some existing regression-based methods.

Heat Transfer Characteristics of Regenerator Matrix (Case of Packed Wire Gauzes)

NASA Technical Reports Server (NTRS)

Hamaguchi, K.; Takahashi, S.; Miyabe, H.

1984-01-01

The average heat transfer coefficient in the matrix of laminated wire screens (10 to 250 mesh) for a Stirling engine heat exchanger was studied experimentally. The data are correlated by N sub ud = 0.42 R sub ed 0.56 (3 or = R sub ed or = 400), and R sub ed are the Nusselt and Reynolds nubmers based on the wire diameter. The pressure drop decreased and the heat transfer increased as the wire diameter was decreased.

Thermal stress effects in intermetallic matrix composites

NASA Technical Reports Server (NTRS)

Wright, P. K.; Sensmeier, M. D.; Kupperman, D. S.; Wadley, H. N. G.

1993-01-01

Intermetallic matrix composites develop residual stresses from the large thermal expansion mismatch (delta-alpha) between the fibers and matrix. This work was undertaken to: establish improved techniques to measure these thermal stresses in IMC's; determine residual stresses in a variety of IMC systems by experiments and modeling; and, determine the effect of residual stresses on selected mechanical properties of an IMC. X ray diffraction (XRD), neutron diffraction (ND), synchrotron XRD (SXRD), and ultrasonics (US) techniques for measuring thermal stresses in IMC were examined and ND was selected as the most promising technique. ND was demonstrated on a variety of IMC systems encompassing Ti- and Ni-base matrices, SiC, W, and Al2O3 fibers, and different fiber fractions (Vf). Experimental results on these systems agreed with predictions of a concentric cylinder model. In SiC/Ti-base systems, little yielding was found and stresses were controlled primarily by delta-alpha and Vf. In Ni-base matrix systems, yield strength of the matrix and Vf controlled stress levels. The longitudinal residual stresses in SCS-6/Ti-24Al-llNb composite were modified by thermomechanical processing. Increasing residual stress decreased ultimate tensile strength in agreement with model predictions. Fiber pushout strength showed an unexpected inverse correlation with residual stress. In-plane shear yield strength showed no dependence on residual stress. Higher levels of residual tension led to higher fatigue crack growth rates, as suggested by matrix mean stress effects.

Linearized radiative transfer models for retrieval of cloud parameters from EPIC/DSCOVR measurements

NASA Astrophysics Data System (ADS)

Molina García, Víctor; Sasi, Sruthy; Efremenko, Dmitry S.; Doicu, Adrian; Loyola, Diego

2018-07-01

In this paper, we describe several linearized radiative transfer models which can be used for the retrieval of cloud parameters from EPIC (Earth Polychromatic Imaging Camera) measurements. The approaches under examination are (1) the linearized forward approach, represented in this paper by the linearized discrete ordinate and matrix operator methods with matrix exponential, and (2) the forward-adjoint approach based on the discrete ordinate method with matrix exponential. To enhance the performance of the radiative transfer computations, the correlated k-distribution method and the Principal Component Analysis (PCA) technique are used. We provide a compact description of the proposed methods, as well as a numerical analysis of their accuracy and efficiency when simulating EPIC measurements in the oxygen A-band channel at 764 nm. We found that the computation time of the forward-adjoint approach using the correlated k-distribution method in conjunction with PCA is approximately 13 s for simultaneously computing the derivatives with respect to cloud optical thickness and cloud top height.

Towards a nondestructive chemical characterization of biofilm matrix by Raman microscopy.

PubMed

Ivleva, Natalia P; Wagner, Michael; Horn, Harald; Niessner, Reinhard; Haisch, Christoph

2009-01-01

In this study, the applicability of Raman microscopy (RM) for nondestructive chemical analysis of biofilm matrix, including microbial constituents and extracellular polymeric substances (EPS), has been assessed. The examination of a wide range of reference samples such as biofilm-specific polysaccharides, proteins, microorganisms, and encapsulated bacteria revealed characteristic frequency regions and specific marker bands for different biofilm constituents. Based on received data, the assignment of Raman bands in spectra of multispecies biofilms was performed. The study of different multispecies biofilms showed that RM can correlate various structural appearances within the biofilm to variations in their chemical composition and provide chemical information about a complex biofilm matrix. The results of RM analysis of biofilms are in good agreement with data obtained by confocal laser scanning microscopy (CLSM). Thus, RM is a promising tool for a label-free chemical characterization of different biofilm constituents. Moreover, the combination of RM with CLSM analysis for the study of biofilms grown under different environmental conditions can provide new insights into the complex structure/function correlations in biofilms.

Volatility and correlation-based systemic risk measures in the US market

NASA Astrophysics Data System (ADS)

Civitarese, Jamil

2016-10-01

This paper deals with the problem of how to use simple systemic risk measures to assess portfolio risk characteristics. Using three simple examples taken from previous literature, one based on raw and partial correlations, another based on the eigenvalue decomposition of the covariance matrix and the last one based on an eigenvalue entropy, a Granger-causation analysis revealed some of them are not always a good measure of risk in the S&P 500 and in the VIX. The measures selected do not Granger-cause the VIX index in all windows selected; therefore, in the sense of risk as volatility, the indicators are not always suitable. Nevertheless, their results towards returns are similar to previous works that accept them. A deeper analysis has shown that any symmetric measure based on eigenvalue decomposition of correlation matrices, however, is not useful as a measure of "correlation" risk. The empirical counterpart analysis of this proposition stated that negative correlations are usually small and, therefore, do not heavily distort the behavior of the indicator.

Modified signed-digit arithmetic based on redundant bit representation.

PubMed

Huang, H; Itoh, M; Yatagai, T

1994-09-10

Fully parallel modified signed-digit arithmetic operations are realized based on redundant bit representation of the digits proposed. A new truth-table minimizing technique is presented based on redundant-bitrepresentation coding. It is shown that only 34 minterms are enough for implementing one-step modified signed-digit addition and subtraction with this new representation. Two optical implementation schemes, correlation and matrix multiplication, are described. Experimental demonstrations of the correlation architecture are presented. Both architectures use fixed minterm masks for arbitrary-length operands, taking full advantage of the parallelism of the modified signed-digit number system and optics.

Scale-Free Nonparametric Factor Analysis: A User-Friendly Introduction with Concrete Heuristic Examples.

ERIC Educational Resources Information Center

Mittag, Kathleen Cage

Most researchers using factor analysis extract factors from a matrix of Pearson product-moment correlation coefficients. A method is presented for extracting factors in a non-parametric way, by extracting factors from a matrix of Spearman rho (rank correlation) coefficients. It is possible to factor analyze a matrix of association such that…

Accurate mask-based spatially regularized correlation filter for visual tracking

NASA Astrophysics Data System (ADS)

Gu, Xiaodong; Xu, Xinping

2017-01-01

Recently, discriminative correlation filter (DCF)-based trackers have achieved extremely successful results in many competitions and benchmarks. These methods utilize a periodic assumption of the training samples to efficiently learn a classifier. However, this assumption will produce unwanted boundary effects, which severely degrade the tracking performance. Correlation filters with limited boundaries and spatially regularized DCFs were proposed to reduce boundary effects. However, their methods used the fixed mask or predesigned weights function, respectively, which was unsuitable for large appearance variation. We propose an accurate mask-based spatially regularized correlation filter for visual tracking. Our augmented objective can reduce the boundary effect even in large appearance variation. In our algorithm, the masking matrix is converted into the regularized function that acts on the correlation filter in frequency domain, which makes the algorithm fast convergence. Our online tracking algorithm performs favorably against state-of-the-art trackers on OTB-2015 Benchmark in terms of efficiency, accuracy, and robustness.

Systematic Correlation Matrix Evaluation (SCoMaE) - a bottom-up, science-led approach to identifying indicators

NASA Astrophysics Data System (ADS)

Mengis, Nadine; Keller, David P.; Oschlies, Andreas

2018-01-01

This study introduces the Systematic Correlation Matrix Evaluation (SCoMaE) method, a bottom-up approach which combines expert judgment and statistical information to systematically select transparent, nonredundant indicators for a comprehensive assessment of the state of the Earth system. The methods consists of two basic steps: (1) the calculation of a correlation matrix among variables relevant for a given research question and (2) the systematic evaluation of the matrix, to identify clusters of variables with similar behavior and respective mutually independent indicators. Optional further analysis steps include (3) the interpretation of the identified clusters, enabling a learning effect from the selection of indicators, (4) testing the robustness of identified clusters with respect to changes in forcing or boundary conditions, (5) enabling a comparative assessment of varying scenarios by constructing and evaluating a common correlation matrix, and (6) the inclusion of expert judgment, for example, to prescribe indicators, to allow for considerations other than statistical consistency. The example application of the SCoMaE method to Earth system model output forced by different CO2 emission scenarios reveals the necessity of reevaluating indicators identified in a historical scenario simulation for an accurate assessment of an intermediate-high, as well as a business-as-usual, climate change scenario simulation. This necessity arises from changes in prevailing correlations in the Earth system under varying climate forcing. For a comparative assessment of the three climate change scenarios, we construct and evaluate a common correlation matrix, in which we identify robust correlations between variables across the three considered scenarios.

Correlating PMC-MMC Bonded Joint 3D FEA with Test

NASA Technical Reports Server (NTRS)

Jacobson, Mindy; Rodini, Benjamin; Chen, Wayne C.; Flom, Yury A.; Posey, Alan J.

2005-01-01

A viewgraph presentation on the correlation of Polymer Matrix Composites (PMC) and Metal Matrix Composites (MMC) bonded joints using three dimensional finite element analyses with materials tests is shown.

Age-Related Effects of Advanced Glycation End Products (Ages) in Bone Matrix on Osteoclastic Resorption.

PubMed

Yang, Xiao; Gandhi, Chintan; Rahman, Md Mizanur; Appleford, Mark; Sun, Lian-Wen; Wang, Xiaodu

2015-12-01

Advanced glycation end products (AGEs) accumulate in bone extracellular matrix as people age. Previous studies have shown controversial results regarding the role of in situ AGEs accumulation in osteoclastic resorption. To address this issue, this study cultured human osteoclast cells directly on human cadaveric bone slices from different age groups (young and elderly) to warrant its relevance to in vivo conditions. The cell culture was terminated on the 3rd, 7th, and 10th day, respectively, to assess temporal changes in the number of differentiated osteoclasts, the number and size of osteoclastic resorption pits, the amount of bone resorbed, as well as the amount of matrix AGEs released in the medium by resorption. In addition, the in situ concentration of matrix AGEs at each resorption pit was also estimated based on its AGEs autofluorescent intensity. The results indicated that (1) osteoclastic resorption activities were significantly correlated with the donor age, showing larger but shallower resorption pits on the elderly bone substrates than on the younger ones; (2) osteoclast resorption activities were not significantly dependent on the in situ AGEs concentration in bone matrix, and (3) a correlation was observed between osteoclast activities and the concentration of AGEs released by the resorption. These results suggest that osteoclasts tend to migrate away from initial anchoring sites on elderly bone substrate during resorption compared to younger bone substrates. However, such behavior is not directly related to the in situ concentration of AGEs in bone matrix at the resorption sites.

Using phase information to enhance speckle noise reduction in the ultrasonic NDE of coarse grain materials

NASA Astrophysics Data System (ADS)

Lardner, Timothy; Li, Minghui; Gachagan, Anthony

2014-02-01

Materials with a coarse grain structure are becoming increasingly prevalent in industry due to their resilience to stress and corrosion. These materials are difficult to inspect with ultrasound because reflections from the grains lead to high noise levels which hinder the echoes of interest. Spatially Averaged Sub-Aperture Correlation Imaging (SASACI) is an advanced array beamforming technique that uses the cross-correlation between images from array sub-apertures to generate an image weighting matrix, in order to reduce noise levels. This paper presents a method inspired by SASACI to further improve imaging using phase information to refine focusing and reduce noise. A-scans from adjacent array elements are cross-correlated using both signal amplitude and phase to refine delay laws and minimize phase aberration. The phase-based and amplitude-based corrected images are used as inputs to a two-dimensional cross-correlation algorithm that will output a weighting matrix that can be applied to any conventional image. This approach was validated experimentally using a 5MHz array a coarse grained Inconel 625 step wedge, and compared to the Total Focusing Method (TFM). Initial results have seen SNR improvements of over 20dB compared to TFM, and a resolution that is much higher.

MICRO-SCALE CFD MODELING OF OSCILLATING FLOW IN A REGENERATOR

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

Cheadle, M. J.; Nellis, G. F.; Klein, S. A.

2010-04-09

Regenerator models used by designers are macro-scale models that do not explicitly consider interactions between the fluid and the solid matrix. Rather, the heat transfer coefficient and pressure drop are calculated using correlations for Nusselt number and friction factor. These correlations are typically based on steady flow data. The error associated with using steady flow correlations to characterize the oscillatory flow that is actually present in the regenerator is not well understood. Oscillating flow correlations based on experimental data do exist in the literature; however, these results are often conflicting. This paper uses a micro-scale computational fluid dynamic (CFD) modelmore » of a unit-cell of a regenerator matrix to determine the conditions for which oscillating flow affects friction factor. These conditions are compared to those found in typical pulse tube regenerators to determine whether oscillatory flow is of practical importance. CFD results clearly show a transition Valensi number beyond which oscillating flow significantly increases the friction factor. This transition Valensi number increases with Reynolds number. Most practical pulse tube regenerators will operate below this Valensi transition number and therefore this study suggests that the effect of flow oscillation on pressure drop can be neglected in macro-scale regenerator models.« less

Improving performances of suboptimal greedy iterative biclustering heuristics via localization.

PubMed

Erten, Cesim; Sözdinler, Melih

2010-10-15

Biclustering gene expression data is the problem of extracting submatrices of genes and conditions exhibiting significant correlation across both the rows and the columns of a data matrix of expression values. Even the simplest versions of the problem are computationally hard. Most of the proposed solutions therefore employ greedy iterative heuristics that locally optimize a suitably assigned scoring function. We provide a fast and simple pre-processing algorithm called localization that reorders the rows and columns of the input data matrix in such a way as to group correlated entries in small local neighborhoods within the matrix. The proposed localization algorithm takes its roots from effective use of graph-theoretical methods applied to problems exhibiting a similar structure to that of biclustering. In order to evaluate the effectivenesss of the localization pre-processing algorithm, we focus on three representative greedy iterative heuristic methods. We show how the localization pre-processing can be incorporated into each representative algorithm to improve biclustering performance. Furthermore, we propose a simple biclustering algorithm, Random Extraction After Localization (REAL) that randomly extracts submatrices from the localization pre-processed data matrix, eliminates those with low similarity scores, and provides the rest as correlated structures representing biclusters. We compare the proposed localization pre-processing with another pre-processing alternative, non-negative matrix factorization. We show that our fast and simple localization procedure provides similar or even better results than the computationally heavy matrix factorization pre-processing with regards to H-value tests. We next demonstrate that the performances of the three representative greedy iterative heuristic methods improve with localization pre-processing when biological correlations in the form of functional enrichment and PPI verification constitute the main performance criteria. The fact that the random extraction method based on localization REAL performs better than the representative greedy heuristic methods under same criteria also confirms the effectiveness of the suggested pre-processing method. Supplementary material including code implementations in LEDA C++ library, experimental data, and the results are available at http://code.google.com/p/biclustering/ cesim@khas.edu.tr; melihsozdinler@boun.edu.tr Supplementary data are available at Bioinformatics online.

Performance analysis of structured gradient algorithm. [for adaptive beamforming linear arrays

NASA Technical Reports Server (NTRS)

Godara, Lal C.

1990-01-01

The structured gradient algorithm uses a structured estimate of the array correlation matrix (ACM) to estimate the gradient required for the constrained least-mean-square (LMS) algorithm. This structure reflects the structure of the exact array correlation matrix for an equispaced linear array and is obtained by spatial averaging of the elements of the noisy correlation matrix. In its standard form the LMS algorithm does not exploit the structure of the array correlation matrix. The gradient is estimated by multiplying the array output with the receiver outputs. An analysis of the two algorithms is presented to show that the covariance of the gradient estimated by the structured method is less sensitive to the look direction signal than that estimated by the standard method. The effect of the number of elements on the signal sensitivity of the two algorithms is studied.

Concurrent release of admixed antimicrobials and salicylic acid from salicylate-based poly(anhydride-esters)

PubMed Central

Johnson, Michelle L.; Uhrich, Kathryn E.

2008-01-01

A polymer blend consisting of antimicrobials (chlorhexidine, clindamycin, and minocycline) physically admixed at 10% by weight into a salicylic acid-based poly (anhydride-ester) (SA-based PAE) was developed as an adjunct treatment for periodontal disease. The SA-based PAE/antimicrobial blends were characterized by multiple methods, including contact angle measurements and differential scanning calorimetry. Static contact angle measurements showed no significant differences in hydrophobicity between the polymer and antimicrobial matrix surfaces. Notable decreases in the polymer glass transition temperature (Tg) and the antimicrobials' melting points (Tm) were observed indicating that the antimicrobials act as plasticizers within the polymer matrix. In vitro drug release of salicylic acid from the polymer matrix and for each physically admixed antimicrobial was concurrently monitored by high pressure liquid chromatography during the course of polymer degradation and erosion. Although the polymer/antimicrobial blends were immiscible, the initial 24 h of drug release correlated to the erosion profiles. The SA-based PAE/antimicrobial blends are being investigated as an improvement on current localized drug therapies used to treat periodontal disease. PMID:19180627

Statistics of time delay and scattering correlation functions in chaotic systems. I. Random matrix theory

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

Novaes, Marcel

2015-06-15

We consider the statistics of time delay in a chaotic cavity having M open channels, in the absence of time-reversal invariance. In the random matrix theory approach, we compute the average value of polynomial functions of the time delay matrix Q = − iħS{sup †}dS/dE, where S is the scattering matrix. Our results do not assume M to be large. In a companion paper, we develop a semiclassical approximation to S-matrix correlation functions, from which the statistics of Q can also be derived. Together, these papers contribute to establishing the conjectured equivalence between the random matrix and the semiclassical approaches.

Comparative Controller Design for a Marine Gas Turbine Propulsion Plant

DTIC Science & Technology

1988-09-01

ADDRESS (City State, and ZIP Code) ,onterey, California 93943-5000 Monterey, California 93943-5000 Sa. NAME OF FUNDING/SPONSORING 8b OFFICE SYMBOL 9 ...155 7. A21 MATRIX COEFFICIENT CORRELATION DATA ----------- 156 8. A22 MATRIX COEFFICIENT CORRELATION DATA ----------- 157 9 . A23 MATRIX...Flowchart of VRD Algorithm ----------------------- 29 8. Steady State Convergence Map --------------------- 34 9 . Smoothed Dynamic Transition Map

POLYMAT-C: a comprehensive SPSS program for computing the polychoric correlation matrix.

PubMed

Lorenzo-Seva, Urbano; Ferrando, Pere J

2015-09-01

We provide a free noncommercial SPSS program that implements procedures for (a) obtaining the polychoric correlation matrix between a set of ordered categorical measures, so that it can be used as input for the SPSS factor analysis (FA) program; (b) testing the null hypothesis of zero population correlation for each element of the matrix by using appropriate simulation procedures; (c) obtaining valid and accurate confidence intervals via bootstrap resampling for those correlations found to be significant; and (d) performing, if necessary, a smoothing procedure that makes the matrix amenable to any FA estimation procedure. For the main purpose (a), the program uses a robust unified procedure that allows four different types of estimates to be obtained at the user's choice. Overall, we hope the program will be a very useful tool for the applied researcher, not only because it provides an appropriate input matrix for FA, but also because it allows the researcher to carefully check the appropriateness of the matrix for this purpose. The SPSS syntax, a short manual, and data files related to this article are available as Supplemental materials that are available for download with this article.

Functional correlation approach to operational risk in banking organizations

NASA Astrophysics Data System (ADS)

Kühn, Reimer; Neu, Peter

2003-05-01

A Value-at-Risk-based model is proposed to compute the adequate equity capital necessary to cover potential losses due to operational risks, such as human and system process failures, in banking organizations. Exploring the analogy to a lattice gas model from physics, correlations between sequential failures are modeled by as functionally defined, heterogeneous couplings between mutually supportive processes. In contrast to traditional risk models for market and credit risk, where correlations are described as equal-time-correlations by a covariance matrix, the dynamics of the model shows collective phenomena such as bursts and avalanches of process failures.

Evolution of correlation structure of industrial indices of U.S. equity markets.

PubMed

Buccheri, Giuseppe; Marmi, Stefano; Mantegna, Rosario N

2013-07-01

We investigate the dynamics of correlations present between pairs of industry indices of U.S. stocks traded in U.S. markets by studying correlation-based networks and spectral properties of the correlation matrix. The study is performed by using 49 industry index time series computed by K. French and E. Fama during the time period from July 1969 to December 2011, which spans more than 40 years. We show that the correlation between industry indices presents both a fast and a slow dynamics. The slow dynamics has a time scale longer than 5 years, showing that a different degree of diversification of the investment is possible in different periods of time. Moreover, we also detect a fast dynamics associated with exogenous or endogenous events. The fast time scale we use is a monthly time scale and the evaluation time period is a 3-month time period. By investigating the correlation dynamics monthly, we are able to detect two examples of fast variations in the first and second eigenvalue of the correlation matrix. The first occurs during the dot-com bubble (from March 1999 to April 2001) and the second occurs during the period of highest impact of the subprime crisis (from August 2008 to August 2009).

Evolution of correlation structure of industrial indices of U.S. equity markets

NASA Astrophysics Data System (ADS)

Buccheri, Giuseppe; Marmi, Stefano; Mantegna, Rosario N.

2013-07-01

We investigate the dynamics of correlations present between pairs of industry indices of U.S. stocks traded in U.S. markets by studying correlation-based networks and spectral properties of the correlation matrix. The study is performed by using 49 industry index time series computed by K. French and E. Fama during the time period from July 1969 to December 2011, which spans more than 40 years. We show that the correlation between industry indices presents both a fast and a slow dynamics. The slow dynamics has a time scale longer than 5 years, showing that a different degree of diversification of the investment is possible in different periods of time. Moreover, we also detect a fast dynamics associated with exogenous or endogenous events. The fast time scale we use is a monthly time scale and the evaluation time period is a 3-month time period. By investigating the correlation dynamics monthly, we are able to detect two examples of fast variations in the first and second eigenvalue of the correlation matrix. The first occurs during the dot-com bubble (from March 1999 to April 2001) and the second occurs during the period of highest impact of the subprime crisis (from August 2008 to August 2009).

Statistical properties of cross-correlation in the Korean stock market

NASA Astrophysics Data System (ADS)

Oh, G.; Eom, C.; Wang, F.; Jung, W.-S.; Stanley, H. E.; Kim, S.

2011-01-01

We investigate the statistical properties of the cross-correlation matrix between individual stocks traded in the Korean stock market using the random matrix theory (RMT) and observe how these affect the portfolio weights in the Markowitz portfolio theory. We find that the distribution of the cross-correlation matrix is positively skewed and changes over time. We find that the eigenvalue distribution of original cross-correlation matrix deviates from the eigenvalues predicted by the RMT, and the largest eigenvalue is 52 times larger than the maximum value among the eigenvalues predicted by the RMT. The β_{473} coefficient, which reflect the largest eigenvalue property, is 0.8, while one of the eigenvalues in the RMT is approximately zero. Notably, we show that the entropy function E(σ) with the portfolio risk σ for the original and filtered cross-correlation matrices are consistent with a power-law function, E( σ) σ^{-γ}, with the exponent γ 2.92 and those for Asian currency crisis decreases significantly.

A bias correction for covariance estimators to improve inference with generalized estimating equations that use an unstructured correlation matrix.

PubMed

Westgate, Philip M

2013-07-20

Generalized estimating equations (GEEs) are routinely used for the marginal analysis of correlated data. The efficiency of GEE depends on how closely the working covariance structure resembles the true structure, and therefore accurate modeling of the working correlation of the data is important. A popular approach is the use of an unstructured working correlation matrix, as it is not as restrictive as simpler structures such as exchangeable and AR-1 and thus can theoretically improve efficiency. However, because of the potential for having to estimate a large number of correlation parameters, variances of regression parameter estimates can be larger than theoretically expected when utilizing the unstructured working correlation matrix. Therefore, standard error estimates can be negatively biased. To account for this additional finite-sample variability, we derive a bias correction that can be applied to typical estimators of the covariance matrix of parameter estimates. Via simulation and in application to a longitudinal study, we show that our proposed correction improves standard error estimation and statistical inference. Copyright © 2012 John Wiley & Sons, Ltd.

A determinant-based criterion for working correlation structure selection in generalized estimating equations.

PubMed

Jaman, Ajmery; Latif, Mahbub A H M; Bari, Wasimul; Wahed, Abdus S

2016-05-20

In generalized estimating equations (GEE), the correlation between the repeated observations on a subject is specified with a working correlation matrix. Correct specification of the working correlation structure ensures efficient estimators of the regression coefficients. Among the criteria used, in practice, for selecting working correlation structure, Rotnitzky-Jewell, Quasi Information Criterion (QIC) and Correlation Information Criterion (CIC) are based on the fact that if the assumed working correlation structure is correct then the model-based (naive) and the sandwich (robust) covariance estimators of the regression coefficient estimators should be close to each other. The sandwich covariance estimator, used in defining the Rotnitzky-Jewell, QIC and CIC criteria, is biased downward and has a larger variability than the corresponding model-based covariance estimator. Motivated by this fact, a new criterion is proposed in this paper based on the bias-corrected sandwich covariance estimator for selecting an appropriate working correlation structure in GEE. A comparison of the proposed and the competing criteria is shown using simulation studies with correlated binary responses. The results revealed that the proposed criterion generally performs better than the competing criteria. An example of selecting the appropriate working correlation structure has also been shown using the data from Madras Schizophrenia Study. Copyright © 2015 John Wiley & Sons, Ltd.

Correlation analysis of the Korean stock market: Revisited to consider the influence of foreign exchange rate

NASA Astrophysics Data System (ADS)

Jo, Sang Kyun; Kim, Min Jae; Lim, Kyuseong; Kim, Soo Yong

2018-02-01

We investigated the effect of foreign exchange rate in a correlation analysis of the Korean stock market using both random matrix theory and minimum spanning tree. We collected data sets which were divided into two types of stock price, the original stock price in Korean Won and the price converted into US dollars at contemporary foreign exchange rates. Comparing the random matrix theory based on the two different prices, a few particular sectors exhibited substantial differences while other sectors changed little. The particular sectors were closely related to economic circumstances and the influence of foreign financial markets during that period. The method introduced in this paper offers a way to pinpoint the effect of exchange rate on an emerging stock market.

Modeling the Nonlinear, Strain Rate Dependent Deformation of Woven Ceramic Matrix Composites With Hydrostatic Stress Effects Included

NASA Technical Reports Server (NTRS)

Goldberg, Robert K.; Carney, Kelly S.

2004-01-01

An analysis method based on a deformation (as opposed to damage) approach has been developed to model the strain rate dependent, nonlinear deformation of woven ceramic matrix composites with a plain weave fiber architecture. In the developed model, the differences in the tension and compression response have also been considered. State variable based viscoplastic equations originally developed for metals have been modified to analyze the ceramic matrix composites. To account for the tension/compression asymmetry in the material, the effective stress and effective inelastic strain definitions have been modified. The equations have also been modified to account for the fact that in an orthotropic composite the in-plane shear stiffness is independent of the stiffness in the normal directions. The developed equations have been implemented into a commercially available transient dynamic finite element code, LS-DYNA, through the use of user defined subroutines (UMATs). The tensile, compressive, and shear deformation of a representative plain weave woven ceramic matrix composite are computed and compared to experimental results. The computed values correlate well to the experimental data, demonstrating the ability of the model to accurately compute the deformation response of woven ceramic matrix composites.

A Ranking Analysis/An Interlinking Approach of New Triangular Fuzzy Cognitive Maps and Combined Effective Time Dependent Matrix

NASA Astrophysics Data System (ADS)

Adiga, Shreemathi; Saraswathi, A.; Praveen Prakash, A.

2018-04-01

This paper aims an interlinking approach of new Triangular Fuzzy Cognitive Maps (TrFCM) and Combined Effective Time Dependent (CETD) matrix to find the ranking of the problems of Transgenders. Section one begins with an introduction that briefly describes the scope of Triangular Fuzzy Cognitive Maps (TrFCM) and CETD Matrix. Section two provides the process of causes of problems faced by Transgenders using Fuzzy Triangular Fuzzy Cognitive Maps (TrFCM) method and performs the calculations using the collected data among the Transgender. In Section 3, the reasons for the main causes for the problems of the Transgenders. Section 4 describes the Charles Spearmans coefficients of rank correlation method by interlinking of Triangular Fuzzy Cognitive Maps (TrFCM) Method and CETD Matrix. Section 5 shows the results based on our study.

A node-wise analysis of the uterine muscle networks for pregnancy monitoring.

PubMed

Nader, N; Hassan, M; Falou, W; Marque, C; Khalil, M

2016-08-01

The recent past years have seen a noticeable increase of interest in the correlation analysis of electrohysterographic (EHG) signals in the perspective of improving the pregnancy monitoring. Here we propose a new approach based on the functional connectivity between multichannel (4×4 matrix) EHG signals recorded from the women's abdomen. The proposed pipeline includes i) the computation of the statistical couplings between the multichannel EHG signals, ii) the characterization of the connectivity matrices, computed by using the imaginary part of the coherence, based on the graph-theory analysis and iii) the use of these measures for pregnancy monitoring. The method was evaluated on a dataset of EHGs, in order to track the correlation between EHGs collected by each electrode of the matrix (called `node-wise' analysis) and follow their evolution along weeks before labor. Results showed that the strength of each node significantly increases from pregnancy to labor. Electrodes located on the median vertical axis of the uterus seemed to be the more discriminant. We speculate that the network-based analysis can be a very promising tool to improve pregnancy monitoring.

Phone camera detection of glucose blood level based on magnetic particles entrapped inside bubble wrap.

PubMed

Martinkova, Pavla; Pohanka, Miroslav

2016-12-18

Glucose is an important diagnostic biochemical marker of diabetes but also for organophosphates, carbamates, acetaminophens or salicylates poisoning. Hence, innovation of accurate and fast detection assay is still one of priorities in biomedical research. Glucose sensor based on magnetic particles (MPs) with immobilized enzymes glucose oxidase (GOx) and horseradish peroxidase (HRP) was developed and the GOx catalyzed reaction was visualized by a smart-phone-integrated camera. Exponential decay concentration curve with correlation coefficient 0.997 and with limit of detection 0.4 mmol/l was achieved. Interfering and matrix substances were measured due to possibility of assay influencing and no effect of the tested substances was observed. Spiked plasma samples were also measured and no influence of plasma matrix on the assay was proved. The presented assay showed complying results with reference method (standard spectrophotometry based on enzymes glucose oxidase and peroxidase inside plastic cuvettes) with linear dependence and correlation coefficient 0.999 in concentration range between 0 and 4 mmol/l. On the grounds of measured results, method was considered as highly specific, accurate and fast assay for detection of glucose.

Correlation and volatility in an Indian stock market: A random matrix approach

NASA Astrophysics Data System (ADS)

Kulkarni, Varsha; Deo, Nivedita

2007-11-01

We examine the volatility of an Indian stock market in terms of correlation of stocks and quantify the volatility using the random matrix approach. First we discuss trends observed in the pattern of stock prices in the Bombay Stock Exchange for the three-year period 2000 2002. Random matrix analysis is then applied to study the relationship between the coupling of stocks and volatility. The study uses daily returns of 70 stocks for successive time windows of length 85 days for the year 2001. We compare the properties of matrix C of correlations between price fluctuations in time regimes characterized by different volatilities. Our analyses reveal that (i) the largest (deviating) eigenvalue of C correlates highly with the volatility of the index, (ii) there is a shift in the distribution of the components of the eigenvector corresponding to the largest eigenvalue across regimes of different volatilities, (iii) the inverse participation ratio for this eigenvector anti-correlates significantly with the market fluctuations and finally, (iv) this eigenvector of C can be used to set up a Correlation Index, CI whose temporal evolution is significantly correlated with the volatility of the overall market index.

ChromAlign: A two-step algorithmic procedure for time alignment of three-dimensional LC-MS chromatographic surfaces.

PubMed

Sadygov, Rovshan G; Maroto, Fernando Martin; Hühmer, Andreas F R

2006-12-15

We present an algorithmic approach to align three-dimensional chromatographic surfaces of LC-MS data of complex mixture samples. The approach consists of two steps. In the first step, we prealign chromatographic profiles: two-dimensional projections of chromatographic surfaces. This is accomplished by correlation analysis using fast Fourier transforms. In this step, a temporal offset that maximizes the overlap and dot product between two chromatographic profiles is determined. In the second step, the algorithm generates correlation matrix elements between full mass scans of the reference and sample chromatographic surfaces. The temporal offset from the first step indicates a range of the mass scans that are possibly correlated, then the correlation matrix is calculated only for these mass scans. The correlation matrix carries information on highly correlated scans, but it does not itself determine the scan or time alignment. Alignment is determined as a path in the correlation matrix that maximizes the sum of the correlation matrix elements. The computational complexity of the optimal path generation problem is reduced by the use of dynamic programming. The program produces time-aligned surfaces. The use of the temporal offset from the first step in the second step reduces the computation time for generating the correlation matrix and speeds up the process. The algorithm has been implemented in a program, ChromAlign, developed in C++ language for the .NET2 environment in WINDOWS XP. In this work, we demonstrate the applications of ChromAlign to alignment of LC-MS surfaces of several datasets: a mixture of known proteins, samples from digests of surface proteins of T-cells, and samples prepared from digests of cerebrospinal fluid. ChromAlign accurately aligns the LC-MS surfaces we studied. In these examples, we discuss various aspects of the alignment by ChromAlign, such as constant time axis shifts and warping of chromatographic surfaces.

Comparison Of Eigenvector-Based Statistical Pattern Recognition Algorithms For Hybrid Processing

NASA Astrophysics Data System (ADS)

Tian, Q.; Fainman, Y.; Lee, Sing H.

1989-02-01

The pattern recognition algorithms based on eigenvector analysis (group 2) are theoretically and experimentally compared in this part of the paper. Group 2 consists of Foley-Sammon (F-S) transform, Hotelling trace criterion (HTC), Fukunaga-Koontz (F-K) transform, linear discriminant function (LDF) and generalized matched filter (GMF). It is shown that all eigenvector-based algorithms can be represented in a generalized eigenvector form. However, the calculations of the discriminant vectors are different for different algorithms. Summaries on how to calculate the discriminant functions for the F-S, HTC and F-K transforms are provided. Especially for the more practical, underdetermined case, where the number of training images is less than the number of pixels in each image, the calculations usually require the inversion of a large, singular, pixel correlation (or covariance) matrix. We suggest solving this problem by finding its pseudo-inverse, which requires inverting only the smaller, non-singular image correlation (or covariance) matrix plus multiplying several non-singular matrices. We also compare theoretically the effectiveness for classification with the discriminant functions from F-S, HTC and F-K with LDF and GMF, and between the linear-mapping-based algorithms and the eigenvector-based algorithms. Experimentally, we compare the eigenvector-based algorithms using a set of image data bases each image consisting of 64 x 64 pixels.

Matrix diffusion coefficients in volcanic rocks at the Nevada test site: influence of matrix porosity, matrix permeability, and fracture coating minerals.

PubMed

Reimus, Paul W; Callahan, Timothy J; Ware, S Doug; Haga, Marc J; Counce, Dale A

2007-08-15

Diffusion cell experiments were conducted to measure nonsorbing solute matrix diffusion coefficients in forty-seven different volcanic rock matrix samples from eight different locations (with multiple depth intervals represented at several locations) at the Nevada Test Site. The solutes used in the experiments included bromide, iodide, pentafluorobenzoate (PFBA), and tritiated water ((3)HHO). The porosity and saturated permeability of most of the diffusion cell samples were measured to evaluate the correlation of these two variables with tracer matrix diffusion coefficients divided by the free-water diffusion coefficient (D(m)/D*). To investigate the influence of fracture coating minerals on matrix diffusion, ten of the diffusion cells represented paired samples from the same depth interval in which one sample contained a fracture surface with mineral coatings and the other sample consisted of only pure matrix. The log of (D(m)/D*) was found to be positively correlated with both the matrix porosity and the log of matrix permeability. A multiple linear regression analysis indicated that both parameters contributed significantly to the regression at the 95% confidence level. However, the log of the matrix diffusion coefficient was more highly-correlated with the log of matrix permeability than with matrix porosity, which suggests that matrix diffusion coefficients, like matrix permeabilities, have a greater dependence on the interconnectedness of matrix porosity than on the matrix porosity itself. The regression equation for the volcanic rocks was found to provide satisfactory predictions of log(D(m)/D*) for other types of rocks with similar ranges of matrix porosity and permeability as the volcanic rocks, but it did a poorer job predicting log(D(m)/D*) for rocks with lower porosities and/or permeabilities. The presence of mineral coatings on fracture walls did not appear to have a significant effect on matrix diffusion in the ten paired diffusion cell experiments.

Matrix diffusion coefficients in volcanic rocks at the Nevada test site: Influence of matrix porosity, matrix permeability, and fracture coating minerals

NASA Astrophysics Data System (ADS)

Reimus, Paul W.; Callahan, Timothy J.; Ware, S. Doug; Haga, Marc J.; Counce, Dale A.

2007-08-01

Diffusion cell experiments were conducted to measure nonsorbing solute matrix diffusion coefficients in forty-seven different volcanic rock matrix samples from eight different locations (with multiple depth intervals represented at several locations) at the Nevada Test Site. The solutes used in the experiments included bromide, iodide, pentafluorobenzoate (PFBA), and tritiated water ( 3HHO). The porosity and saturated permeability of most of the diffusion cell samples were measured to evaluate the correlation of these two variables with tracer matrix diffusion coefficients divided by the free-water diffusion coefficient ( Dm/ D*). To investigate the influence of fracture coating minerals on matrix diffusion, ten of the diffusion cells represented paired samples from the same depth interval in which one sample contained a fracture surface with mineral coatings and the other sample consisted of only pure matrix. The log of ( Dm/ D*) was found to be positively correlated with both the matrix porosity and the log of matrix permeability. A multiple linear regression analysis indicated that both parameters contributed significantly to the regression at the 95% confidence level. However, the log of the matrix diffusion coefficient was more highly-correlated with the log of matrix permeability than with matrix porosity, which suggests that matrix diffusion coefficients, like matrix permeabilities, have a greater dependence on the interconnectedness of matrix porosity than on the matrix porosity itself. The regression equation for the volcanic rocks was found to provide satisfactory predictions of log( Dm/ D*) for other types of rocks with similar ranges of matrix porosity and permeability as the volcanic rocks, but it did a poorer job predicting log( Dm/ D*) for rocks with lower porosities and/or permeabilities. The presence of mineral coatings on fracture walls did not appear to have a significant effect on matrix diffusion in the ten paired diffusion cell experiments.

Multivariate localization methods for ensemble Kalman filtering

NASA Astrophysics Data System (ADS)

Roh, S.; Jun, M.; Szunyogh, I.; Genton, M. G.

2015-05-01

In ensemble Kalman filtering (EnKF), the small number of ensemble members that is feasible to use in a practical data assimilation application leads to sampling variability of the estimates of the background error covariances. The standard approach to reducing the effects of this sampling variability, which has also been found to be highly efficient in improving the performance of EnKF, is the localization of the estimates of the covariances. One family of localization techniques is based on taking the Schur (entry-wise) product of the ensemble-based sample covariance matrix and a correlation matrix whose entries are obtained by the discretization of a distance-dependent correlation function. While the proper definition of the localization function for a single state variable has been extensively investigated, a rigorous definition of the localization function for multiple state variables has been seldom considered. This paper introduces two strategies for the construction of localization functions for multiple state variables. The proposed localization functions are tested by assimilating simulated observations experiments into the bivariate Lorenz 95 model with their help.

Failure Models and Criteria for FRP Under In-Plane or Three-Dimensional Stress States Including Shear Non-Linearity

NASA Technical Reports Server (NTRS)

Pinho, Silvestre T.; Davila, C. G.; Camanho, P. P.; Iannucci, L.; Robinson, P.

2005-01-01

A set of three-dimensional failure criteria for laminated fiber-reinforced composites, denoted LaRC04, is proposed. The criteria are based on physical models for each failure mode and take into consideration non-linear matrix shear behaviour. The model for matrix compressive failure is based on the Mohr-Coulomb criterion and it predicts the fracture angle. Fiber kinking is triggered by an initial fiber misalignment angle and by the rotation of the fibers during compressive loading. The plane of fiber kinking is predicted by the model. LaRC04 consists of 6 expressions that can be used directly for design purposes. Several applications involving a broad range of load combinations are presented and compared to experimental data and other existing criteria. Predictions using LaRC04 correlate well with the experimental data, arguably better than most existing criteria. The good correlation seems to be attributable to the physical soundness of the underlying failure models.

Exploring Market State and Stock Interactions on the Minute Timescale

PubMed Central

Tan, Lei; Chen, Jun-Jie; Zheng, Bo; Ouyang, Fang-Yan

2016-01-01

A stock market is a non-stationary complex system. The stock interactions are important for understanding the state of the market. However, our knowledge on the stock interactions on the minute timescale is limited. Here we apply the random matrix theory and methods in complex networks to study the stock interactions and sector interactions. Further, we construct a new kind of cross-correlation matrix to investigate the correlation between the stock interactions at different minutes within one trading day. Based on 50 million minute-to-minute price data in the Shanghai stock market, we discover that the market states in the morning and afternoon are significantly different. The differences mainly exist in three aspects, i.e. the co-movement of stock prices, interactions of sectors and correlation between the stock interactions at different minutes. In the afternoon, the component stocks of sectors are more robust and the structure of sectors is firmer. Therefore, the market state in the afternoon is more stable. Furthermore, we reveal that the information of the sector interactions can indicate the financial crisis in the market, and the indicator based on the empirical data in the afternoon is more effective. PMID:26900948

Exploring Market State and Stock Interactions on the Minute Timescale.

PubMed

Tan, Lei; Chen, Jun-Jie; Zheng, Bo; Ouyang, Fang-Yan

2016-01-01

A stock market is a non-stationary complex system. The stock interactions are important for understanding the state of the market. However, our knowledge on the stock interactions on the minute timescale is limited. Here we apply the random matrix theory and methods in complex networks to study the stock interactions and sector interactions. Further, we construct a new kind of cross-correlation matrix to investigate the correlation between the stock interactions at different minutes within one trading day. Based on 50 million minute-to-minute price data in the Shanghai stock market, we discover that the market states in the morning and afternoon are significantly different. The differences mainly exist in three aspects, i.e. the co-movement of stock prices, interactions of sectors and correlation between the stock interactions at different minutes. In the afternoon, the component stocks of sectors are more robust and the structure of sectors is firmer. Therefore, the market state in the afternoon is more stable. Furthermore, we reveal that the information of the sector interactions can indicate the financial crisis in the market, and the indicator based on the empirical data in the afternoon is more effective.

Handling of computational in vitro/in vivo correlation problems by Microsoft Excel: IV. Generalized matrix analysis of linear compartment systems.

PubMed

Langenbucher, Frieder

2005-01-01

A linear system comprising n compartments is completely defined by the rate constants between any of the compartments and the initial condition in which compartment(s) the drug is present at the beginning. The generalized solution is the time profiles of drug amount in each compartment, described by polyexponential equations. Based on standard matrix operations, an Excel worksheet computes the rate constants and the coefficients, finally the full time profiles for a specified range of time values.

Structure of local interactions in complex financial dynamics

PubMed Central

Jiang, X. F.; Chen, T. T.; Zheng, B.

2014-01-01

With the network methods and random matrix theory, we investigate the interaction structure of communities in financial markets. In particular, based on the random matrix decomposition, we clarify that the local interactions between the business sectors (subsectors) are mainly contained in the sector mode. In the sector mode, the average correlation inside the sectors is positive, while that between the sectors is negative. Further, we explore the time evolution of the interaction structure of the business sectors, and observe that the local interaction structure changes dramatically during a financial bubble or crisis. PMID:24936906

Distributed Storage Algorithm for Geospatial Image Data Based on Data Access Patterns.

PubMed

Pan, Shaoming; Li, Yongkai; Xu, Zhengquan; Chong, Yanwen

2015-01-01

Declustering techniques are widely used in distributed environments to reduce query response time through parallel I/O by splitting large files into several small blocks and then distributing those blocks among multiple storage nodes. Unfortunately, however, many small geospatial image data files cannot be further split for distributed storage. In this paper, we propose a complete theoretical system for the distributed storage of small geospatial image data files based on mining the access patterns of geospatial image data using their historical access log information. First, an algorithm is developed to construct an access correlation matrix based on the analysis of the log information, which reveals the patterns of access to the geospatial image data. Then, a practical heuristic algorithm is developed to determine a reasonable solution based on the access correlation matrix. Finally, a number of comparative experiments are presented, demonstrating that our algorithm displays a higher total parallel access probability than those of other algorithms by approximately 10-15% and that the performance can be further improved by more than 20% by simultaneously applying a copy storage strategy. These experiments show that the algorithm can be applied in distributed environments to help realize parallel I/O and thereby improve system performance.

Methods for converging correlation energies within the dielectric matrix formalism

NASA Astrophysics Data System (ADS)

Dixit, Anant; Claudot, Julien; Gould, Tim; Lebègue, Sébastien; Rocca, Dario

2018-03-01

Within the dielectric matrix formalism, the random-phase approximation (RPA) and analogous methods that include exchange effects are promising approaches to overcome some of the limitations of traditional density functional theory approximations. The RPA-type methods however have a significantly higher computational cost, and, similarly to correlated quantum-chemical methods, are characterized by a slow basis set convergence. In this work we analyzed two different schemes to converge the correlation energy, one based on a more traditional complete basis set extrapolation and one that converges energy differences by accounting for the size-consistency property. These two approaches have been systematically tested on the A24 test set, for six points on the potential-energy surface of the methane-formaldehyde complex, and for reaction energies involving the breaking and formation of covalent bonds. While both methods converge to similar results at similar rates, the computation of size-consistent energy differences has the advantage of not relying on the choice of a specific extrapolation model.

Comparative analysis of genetic diversity among Indian populations of Scirpophaga incertulas by ISSR-PCR and RAPD-PCR.

PubMed

Kumar, L S; Sawant, A S; Gupta, V S; Ranjekar, P K

2001-10-01

Genetic variation between 28 Indian populations of the rice pest, Scirpophaga incertulas was evaluated using inter-simple sequence repeats (ISSR)-PCR assay. Nine SSR primers gave rise to 79 amplification products of which 67 were polymorphic. A dendrogram constructed from this data indicates that there is no geographical bias to the clustering and that gene flow between populations appears to be relatively unrestricted, substantiating our earlier conclusion based on the RAPD (random amplified polymorphic DNA) data. The dendrograms obtained using each of these marker systems were poorly correlated with each other as determined by Mantel's test for matrix correlation. Estimates of expected heterozygosity and marker index for each of these marker systems suggests that both these marker systems are equally efficient in determining polymorphisms. Matrix correlation analyses suggest that reliable estimates of genetic variation among the S. incertulas pest populations can be obtained by using RAPDs alone or in combination with ISSRs, but ISSRs alone cannot be used for this purpose.

A covariance correction that accounts for correlation estimation to improve finite-sample inference with generalized estimating equations: A study on its applicability with structured correlation matrices

PubMed Central

Westgate, Philip M.

2016-01-01

When generalized estimating equations (GEE) incorporate an unstructured working correlation matrix, the variances of regression parameter estimates can inflate due to the estimation of the correlation parameters. In previous work, an approximation for this inflation that results in a corrected version of the sandwich formula for the covariance matrix of regression parameter estimates was derived. Use of this correction for correlation structure selection also reduces the over-selection of the unstructured working correlation matrix. In this manuscript, we conduct a simulation study to demonstrate that an increase in variances of regression parameter estimates can occur when GEE incorporates structured working correlation matrices as well. Correspondingly, we show the ability of the corrected version of the sandwich formula to improve the validity of inference and correlation structure selection. We also study the relative influences of two popular corrections to a different source of bias in the empirical sandwich covariance estimator. PMID:27818539

A covariance correction that accounts for correlation estimation to improve finite-sample inference with generalized estimating equations: A study on its applicability with structured correlation matrices.

PubMed

Westgate, Philip M

2016-01-01

When generalized estimating equations (GEE) incorporate an unstructured working correlation matrix, the variances of regression parameter estimates can inflate due to the estimation of the correlation parameters. In previous work, an approximation for this inflation that results in a corrected version of the sandwich formula for the covariance matrix of regression parameter estimates was derived. Use of this correction for correlation structure selection also reduces the over-selection of the unstructured working correlation matrix. In this manuscript, we conduct a simulation study to demonstrate that an increase in variances of regression parameter estimates can occur when GEE incorporates structured working correlation matrices as well. Correspondingly, we show the ability of the corrected version of the sandwich formula to improve the validity of inference and correlation structure selection. We also study the relative influences of two popular corrections to a different source of bias in the empirical sandwich covariance estimator.

SU-D-207B-07: Development of a CT-Radiomics Based Early Response Prediction Model During Delivery of Chemoradiation Therapy for Pancreatic Cancer

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

Klawikowski, S; Christian, J; Schott, D

Purpose: Pilot study developing a CT-texture based model for early assessment of treatment response during the delivery of chemoradiation therapy (CRT) for pancreatic cancer. Methods: Daily CT data acquired for 24 pancreatic head cancer patients using CT-on-rails, during the routine CT-guided CRT delivery with a radiation dose of 50.4 Gy in 28 fractions, were analyzed. The pancreas head was contoured on each daily CT. Texture analysis was performed within the pancreas head contour using a research tool (IBEX). Over 1300 texture metrics including: grey level co-occurrence, run-length, histogram, neighborhood intensity difference, and geometrical shape features were calculated for each dailymore » CT. Metric-trend information was established by finding the best fit of either a linear, quadratic, or exponential function for each metric value verses accumulated dose. Thus all the daily CT texture information was consolidated into a best-fit trend type for a given patient and texture metric. Linear correlation was performed between the patient histological response vector (good, medium, poor) and all combinations of 23 patient subgroups (statistical jackknife) determining which metrics were most correlated to response and repeatedly reliable across most patients. Control correlations against CT scanner, reconstruction kernel, and gated/nongated CT images were also calculated. Euclidean distance measure was used to group/sort patient vectors based on the data of these trend-response metrics. Results: We found four specific trend-metrics (Gray Level Coocurence Matrix311-1InverseDiffMomentNorm, Gray Level Coocurence Matrix311-1InverseDiffNorm, Gray Level Coocurence Matrix311-1 Homogeneity2, and Intensity Direct Local StdMean) that were highly correlated with patient response and repeatedly reliable. Our four trend-metric model successfully ordered our pilot response dataset (p=0.00070). We found no significant correlation to our control parameters: gating (p=0.7717), scanner (p=0.9741), and kernel (p=0.8586). Conclusion: We have successfully created a CT-texture based early treatment response prediction model using the CTs acquired during the delivery of chemoradiation therapy for pancreatic cancer. Future testing is required to validate the model with more patient data.« less

Failure Criteria for FRP Laminates in Plane Stress

NASA Technical Reports Server (NTRS)

Davila, Carlos G.; Camanho, Pedro P.

2003-01-01

A new set of six failure criteria for fiber reinforced polymer laminates is described. Derived from Dvorak's fracture mechanics analyses of cracked plies and from Puck's action plane concept, the physically-based criteria, denoted LaRC03, predict matrix and fiber failure accurately without requiring curve-fitting parameters. For matrix failure under transverse compression, the fracture plane is calculated by maximizing the Mohr-Coulomb effective stresses. A criterion for fiber kinking is obtained by calculating the fiber misalignment under load, and applying the matrix failure criterion in the coordinate frame of the misalignment. Fracture mechanics models of matrix cracks are used to develop a criterion for matrix in tension and to calculate the associated in-situ strengths. The LaRC03 criteria are applied to a few examples to predict failure load envelopes and to predict the failure mode for each region of the envelope. The analysis results are compared to the predictions using other available failure criteria and with experimental results. Predictions obtained with LaRC03 correlate well with the experimental results.

Random matrix theory and portfolio optimization in Moroccan stock exchange

NASA Astrophysics Data System (ADS)

El Alaoui, Marwane

2015-09-01

In this work, we use random matrix theory to analyze eigenvalues and see if there is a presence of pertinent information by using Marčenko-Pastur distribution. Thus, we study cross-correlation among stocks of Casablanca Stock Exchange. Moreover, we clean correlation matrix from noisy elements to see if the gap between predicted risk and realized risk would be reduced. We also analyze eigenvectors components distributions and their degree of deviations by computing the inverse participation ratio. This analysis is a way to understand the correlation structure among stocks of Casablanca Stock Exchange portfolio.

Analysis on the hot spot and trend of the foreign assembly building research

NASA Astrophysics Data System (ADS)

Bi, Xiaoqing; Luo, Yanbing

2017-03-01

First of all, the paper analyzes the research on the front of the assembly building in the past 15 years. This article mainly adopts the method of CO word analysis, construct the co word matrix, correlation matrix, and then into a dissimilarity matrix, and on this basis, using factor analysis, cluster analysis and multi scale analysis method to study the structure of prefabricated construction field display. Finally, the results of the analysis are discussed, and summarized the current research focus of foreign prefabricated construction mainly concentrated in 7 aspects: embankment construction, wood construction, bridge construction, crane layout, PCM wall and glass system, based on neural network test, energy saving and recycling, and forecast the future trend of development study.

Integrating Nonadditive Genomic Relationship Matrices into the Study of Genetic Architecture of Complex Traits.

PubMed

Nazarian, Alireza; Gezan, Salvador A

2016-03-01

The study of genetic architecture of complex traits has been dramatically influenced by implementing genome-wide analytical approaches during recent years. Of particular interest are genomic prediction strategies which make use of genomic information for predicting phenotypic responses instead of detecting trait-associated loci. In this work, we present the results of a simulation study to improve our understanding of the statistical properties of estimation of genetic variance components of complex traits, and of additive, dominance, and genetic effects through best linear unbiased prediction methodology. Simulated dense marker information was used to construct genomic additive and dominance matrices, and multiple alternative pedigree- and marker-based models were compared to determine if including a dominance term into the analysis may improve the genetic analysis of complex traits. Our results showed that a model containing a pedigree- or marker-based additive relationship matrix along with a pedigree-based dominance matrix provided the best partitioning of genetic variance into its components, especially when some degree of true dominance effects was expected to exist. Also, we noted that the use of a marker-based additive relationship matrix along with a pedigree-based dominance matrix had the best performance in terms of accuracy of correlations between true and estimated additive, dominance, and genetic effects. © The American Genetic Association 2015. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Band selection method based on spectrum difference in targets of interest in hyperspectral imagery

NASA Astrophysics Data System (ADS)

Zhang, Xiaohan; Yang, Guang; Yang, Yongbo; Huang, Junhua

2016-10-01

While hyperspectral data shares rich spectrum information, it has numbers of bands with high correlation coefficients, causing great data redundancy. A reasonable band selection is important for subsequent processing. Bands with large amount of information and low correlation should be selected. On this basis, according to the needs of target detection applications, the spectral characteristics of the objects of interest are taken into consideration in this paper, and a new method based on spectrum difference is proposed. Firstly, according to the spectrum differences of targets of interest, a difference matrix which represents the different spectral reflectance of different targets in different bands is structured. By setting a threshold, the bands satisfying the conditions would be left, constituting a subset of bands. Then, the correlation coefficients between bands are calculated and correlation matrix is given. According to the size of the correlation coefficient, the bands can be set into several groups. At last, the conception of normalized variance is used on behalf of the information content of each band. The bands are sorted by the value of its normalized variance. Set needing number of bands, and the optimum band combination solution can be get by these three steps. This method retains the greatest degree of difference between the target of interest and is easy to achieve by computer automatically. Besides, false color image synthesis experiment is carried out using the bands selected by this method as well as other 3 methods to show the performance of method in this paper.

Cooperative Activated Transport of Dilute Penetrants in Viscous Molecular and Polymer Liquids

NASA Astrophysics Data System (ADS)

Schweizer, Kenneth; Zhang, Rui

We generalize the force-level Elastically Collective Nonlinear Langevin Equation theory of activated relaxation in one-component supercooled liquids to treat the hopping transport of a dilute penetrant in a dense hard sphere fluid. The new idea is to explicitly account for the coupling between penetrant displacement and a local matrix cage re-arrangement which facilitates its hopping. A temporal casuality condition is employed to self-consistently determine a dimensionless degree of matrix distortion relative to the penetrant jump distance using the dynamic free energy concept. Penetrant diffusion becomes increasingly coupled to the correlated matrix displacements for larger penetrant to matrix particle size ratio (R) and/or attraction strength (physical bonds), but depends weakly on matrix packing fraction. In the absence of attractions, a nearly exponential dependence of penetrant diffusivity on R is predicted in the intermediate range of 0.2

Inelastic response of metal matrix composites under biaxial loading

NASA Technical Reports Server (NTRS)

Lissenden, C. J.; Mirzadeh, F.; Pindera, M.-J.; Herakovich, C. T.

1991-01-01

Theoretical predictions and experimental results were obtained for inelastic response of unidirectional and angle ply composite tubes subjected to axial and torsional loading. The composite material consist of silicon carbide fibers in a titanium alloy matrix. This material is known to be susceptible to fiber matrix interfacial damage. A method to distinguish between matrix yielding and fiber matrix interfacial damage is suggested. Biaxial tests were conducted on the two different layup configurations using an MTS Axial/Torsional load frame with a PC based data acquisition system. The experimentally determined elastic moduli of the SiC/Ti system are compared with those predicted by a micromechanics model. The test results indicate that fiber matrix interfacial damage occurs at relatively low load levels and is a local phenomenon. The micromechanics model used is the method of cells originally proposed by Aboudi. Finite element models using the ABACUS finite element program were used to study end effects and fixture specimen interactions. The results to date have shown good correlation between theory and experiment for response prior to damage initiation.

Linear data mining the Wichita clinical matrix suggests sleep and allostatic load involvement in chronic fatigue syndrome.

PubMed

Gurbaxani, Brian M; Jones, James F; Goertzel, Benjamin N; Maloney, Elizabeth M

2006-04-01

To provide a mathematical introduction to the Wichita (KS, USA) clinical dataset, which is all of the nongenetic data (no microarray or single nucleotide polymorphism data) from the 2-day clinical evaluation, and show the preliminary findings and limitations, of popular, matrix algebra-based data mining techniques. An initial matrix of 440 variables by 227 human subjects was reduced to 183 variables by 164 subjects. Variables were excluded that strongly correlated with chronic fatigue syndrome (CFS) case classification by design (for example, the multidimensional fatigue inventory [MFI] data), that were otherwise self reporting in nature and also tended to correlate strongly with CFS classification, or were sparse or nonvarying between case and control. Subjects were excluded if they did not clearly fall into well-defined CFS classifications, had comorbid depression with melancholic features, or other medical or psychiatric exclusions. The popular data mining techniques, principle components analysis (PCA) and linear discriminant analysis (LDA), were used to determine how well the data separated into groups. Two different feature selection methods helped identify the most discriminating parameters. Although purely biological features (variables) were found to separate CFS cases from controls, including many allostatic load and sleep-related variables, most parameters were not statistically significant individually. However, biological correlates of CFS, such as heart rate and heart rate variability, require further investigation. Feature selection of a limited number of variables from the purely biological dataset produced better separation between groups than a PCA of the entire dataset. Feature selection highlighted the importance of many of the allostatic load variables studied in more detail by Maloney and colleagues in this issue [1] , as well as some sleep-related variables. Nonetheless, matrix linear algebra-based data mining approaches appeared to be of limited utility when compared with more sophisticated nonlinear analyses on richer data types, such as those found in Maloney and colleagues [1] and Goertzel and colleagues [2] in this issue.

On using the Hilbert transform for blind identification of complex modes: A practical approach

NASA Astrophysics Data System (ADS)

Antunes, Jose; Debut, Vincent; Piteau, Pilippe; Delaune, Xavier; Borsoi, Laurent

2018-01-01

The modal identification of dynamical systems under operational conditions, when subjected to wide-band unmeasured excitations, is today a viable alternative to more traditional modal identification approaches based on processing sets of measured FRFs or impulse responses. Among current techniques for performing operational modal identification, the so-called blind identification methods are the subject of considerable investigation. In particular, the SOBI (Second-Order Blind Identification) method was found to be quite efficient. SOBI was originally developed for systems with normal modes. To address systems with complex modes, various extension approaches have been proposed, in particular: (a) Using a first-order state-space formulation for the system dynamics; (b) Building complex analytic signals from the measured responses using the Hilbert transform. In this paper we further explore the latter option, which is conceptually interesting while preserving the model order and size. Focus is on applicability of the SOBI technique for extracting the modal responses from analytic signals built from a set of vibratory responses. The novelty of this work is to propose a straightforward computational procedure for obtaining the complex cross-correlation response matrix to be used for the modal identification procedure. After clarifying subtle aspects of the general theoretical framework, we demonstrate that the correlation matrix of the analytic responses can be computed through a Hilbert transform of the real correlation matrix, so that the actual time-domain responses are no longer required for modal identification purposes. The numerical validation of the proposed technique is presented based on time-domain simulations of a conceptual physical multi-modal system, designed to display modes ranging from normal to highly complex, while keeping modal damping low and nearly independent of the modal complexity, and which can prove very interesting in test bench applications. Numerical results for complex modal identifications are presented, and the quality of the identified modal matrix and modal responses, extracted using the complex SOBI technique and implementing the proposed formulation, is assessed.

Bone Proteoglycan Changes During Skeletal Unloading

NASA Technical Reports Server (NTRS)

Yamauchi, M.; Uzawa, K.; Pornprasertsuk, S.; Arnaud, S.; Grindeland, R.; Grzesik, W.

1999-01-01

Skeletal adaptability to mechanical loads is well known since the last century. Disuse osteopenia due to the microgravity environment is one of the major concerns for space travelers. Several studies have indicated that a retardation of the mineralization process and a delay in matrix maturation occur during the space flight. Mineralizing fibrillar type I collagen possesses distinct cross-linking chemistries and their dynamic changes during mineralization correlate well with its function as a mineral organizer. Our previous studies suggested that a certain group of matrix proteoglycans in bone play an inhibitory role in the mineralization process through their interaction with collagen. Based on these studies, we hypothesized that the altered mineralization during spaceflight is due in part to changes in matrix components secreted by cells in response to microgravity. In this study, we employed hindlimb elevation (tail suspension) rat model to study the effects of skeletal unloading on matrix proteoglycans in bone.

0{nu}{beta}{beta}-decay nuclear matrix elements with self-consistent short-range correlations

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

Simkovic, Fedor; Bogoliubov Laboratory of Theoretical Physics, JINR, RU-141 980 Dubna, Moscow region; Department of Nuclear Physics, Comenius University, Mlynska dolina F1, SK-842 15 Bratislava

A self-consistent calculation of nuclear matrix elements of the neutrinoless double-beta decays (0{nu}{beta}{beta}) of {sup 76}Ge, {sup 82}Se, {sup 96}Zr, {sup 100}Mo, {sup 116}Cd, {sup 128}Te, {sup 130}Te, and {sup 136}Xe is presented in the framework of the renormalized quasiparticle random phase approximation (RQRPA) and the standard QRPA. The pairing and residual interactions as well as the two-nucleon short-range correlations are for the first time derived from the same modern realistic nucleon-nucleon potentials, namely, from the charge-dependent Bonn potential (CD-Bonn) and the Argonne V18 potential. In a comparison with the traditional approach of using the Miller-Spencer Jastrow correlations, matrix elementsmore » for the 0{nu}{beta}{beta} decay are obtained that are larger in magnitude. We analyze the differences among various two-nucleon correlations including those of the unitary correlation operator method (UCOM) and quantify the uncertainties in the calculated 0{nu}{beta}{beta}-decay matrix elements.« less

A Biomimetic Structural Health Monitoring Approach Using Carbon Nanotubes

NASA Astrophysics Data System (ADS)

Liu, Yingtao; Rajadas, Abhishek; Chattopadhyay, Aditi

2012-07-01

A self-sensing nanocomposite material has been developed to track the presence of damage in complex composite structures. Multiwalled carbon nanotubes are integrated with polymer matrix to develop a novel bonding material with sensing capabilities. The changes of the piezoresistance in the presence of damage are used to monitor the condition of bonded joints, where the usual bonding material is replaced by the self-sensing nanocomposite. The feasibility of this concept is investigated through experiments conducted on single-lap joints subject to monotonic tensile loading conditions. The results show that the self-sensing nanocomposite is sensitive to crack propagation within the matrix material. An acoustic emission-based sensing technique has been used to validate these results and shows good correlation with damage growth. A digital image correlation system is used to measure the shear strain field in the joint area.

Monitoring damage growth in titanium matrix composites using acoustic emission

NASA Technical Reports Server (NTRS)

Bakuckas, J. G., Jr.; Prosser, W. H.; Johnson, W. S.

1993-01-01

The application of the acoustic emission (AE) technique to locate and monitor damage growth in titanium matrix composites (TMC) was investigated. Damage growth was studied using several optical techniques including a long focal length, high magnification microscope system with image acquisition capabilities. Fracture surface examinations were conducted using a scanning electron microscope (SEM). The AE technique was used to locate damage based on the arrival times of AE events between two sensors. Using model specimens exhibiting a dominant failure mechanism, correlations were established between the observed damage growth mechanisms and the AE results in terms of the events amplitude. These correlations were used to monitor the damage growth process in laminates exhibiting multiple modes of damage. Results revealed that the AE technique is a viable and effective tool to monitor damage growth in TMC.

Vapor-liquid phase behavior of a size-asymmetric model of ionic fluids confined in a disordered matrix: The collective-variables-based approach

NASA Astrophysics Data System (ADS)

Patsahan, O. V.; Patsahan, T. M.; Holovko, M. F.

2018-02-01

We develop a theory based on the method of collective variables to study the vapor-liquid equilibrium of asymmetric ionic fluids confined in a disordered porous matrix. The approach allows us to formulate the perturbation theory using an extension of the scaled particle theory for a description of a reference system presented as a two-component hard-sphere fluid confined in a hard-sphere matrix. Treating an ionic fluid as a size- and charge-asymmetric primitive model (PM) we derive an explicit expression for the relevant chemical potential of a confined ionic system which takes into account the third-order correlations between ions. Using this expression, the phase diagrams for a size-asymmetric PM are calculated for different matrix porosities as well as for different sizes of matrix and fluid particles. It is observed that general trends of the coexistence curves with the matrix porosity are similar to those of simple fluids under disordered confinement, i.e., the coexistence region gets narrower with a decrease of porosity and, simultaneously, the reduced critical temperature Tc* and the critical density ρi,c * become lower. At the same time, our results suggest that an increase in size asymmetry of oppositely charged ions considerably affects the vapor-liquid diagrams leading to a faster decrease of Tc* and ρi,c * and even to a disappearance of the phase transition, especially for the case of small matrix particles.

The High School & Beyond Data Set: Academic Self-Concept Measures.

ERIC Educational Resources Information Center

Strein, William

A series of confirmatory factor analyses using both LISREL VI (maximum likelihood method) and LISCOMP (weighted least squares method using covariance matrix based on polychoric correlations) and including cross-validation on independent samples were applied to items from the High School and Beyond data set to explore the measurement…

Liquid chromatography/electrospray ionization/isotopic dilution mass spectrometry analysis of n-(phosphonomethyl) glycine and mass spectrometry analysis of aminomethyl phosphonic acid in environmental water and vegetation matrixes.

PubMed

Grey, L; Nguyen, B; Yang, P

2001-01-01

A liquid chromatography/electrospray/mass spectrometry (LC/ES/MS) method was developed for the analysis of glyphosate (n-phosphonomethyl glycine) and its metabolite, aminomethyl phosphonic acid (AMPA) using isotope-labelled glyphosate as a method surrogate. Optimized parameters were achieved to derivatize glyphosate and AMPA using 9-fluorenylmethyl chloroformate (FMOC-Cl) in borate buffer prior to a reversed-phase LC analysis. Method spike recovery data obtained using laboratory and real world sample matrixes indicated an excellent correlation between the recovery of the native and isotope-labelled glyphosate. Hence, the first performance-based, isotope dilution MS method with superior precision, accuracy, and data quality was developed for the analysis of glyphosate. There was, however, no observable correlation between the isotope-labelled glyphosate and AMPA. Thus, the use of this procedure for the accurate analysis of AMPA was not supported. Method detection limits established using standard U.S. Environmental Protection Agency protocol were 0.06 and 0.30 microg/L, respectively, for glyphosate and AMPA in water matrixes and 0.11 and 0.53 microg/g, respectively, in vegetation matrixes. Problems, solutions, and the method performance data related to the analysis of chlorine-treated drinking water samples are discussed. Applying this method to other environmental matrixes, e.g., soil, with minimum modifications is possible, assuring accurate, multimedia studies of glyphosate concentration in the environment and the delivery of useful multimedia information for regulatory applications.

Extracellular matrix-associated proteins form an integral and dynamic system during Pseudomonas aeruginosa biofilm development.

PubMed

Zhang, Weipeng; Sun, Jin; Ding, Wei; Lin, Jinshui; Tian, Renmao; Lu, Liang; Liu, Xiaofen; Shen, Xihui; Qian, Pei-Yuan

2015-01-01

Though the essential role of extracellular matrix in biofilm development has been extensively documented, the function of matrix-associated proteins is elusive. Determining the dynamics of matrix-associated proteins would be a useful way to reveal their functions in biofilm development. Therefore, we applied iTRAQ-based quantitative proteomics to evaluate matrix-associated proteins isolated from different phases of Pseudomonas aeruginosa ATCC27853 biofilms. Among the identified 389 proteins, 54 changed their abundance significantly. The increased abundance of stress resistance and nutrient metabolism-related proteins over the period of biofilm development was consistent with the hypothesis that biofilm matrix forms micro-environments in which cells are optimally organized to resist stress and use available nutrients. Secreted proteins, including novel putative effectors of the type III secretion system were identified, suggesting that the dynamics of pathogenesis-related proteins in the matrix are associated with biofilm development. Interestingly, there was a good correlation between the abundance changes of matrix-associated proteins and their expression. Further analysis revealed complex interactions among these modulated proteins, and the mutation of selected proteins attenuated biofilm development. Collectively, this work presents the first dynamic picture of matrix-associated proteins during biofilm development, and provides evidences that the matrix-associated proteins may form an integral and well regulated system that contributes to stress resistance, nutrient acquisition, pathogenesis and the stability of the biofilm.

A random matrix approach to credit risk.

PubMed

Münnix, Michael C; Schäfer, Rudi; Guhr, Thomas

2014-01-01

We estimate generic statistical properties of a structural credit risk model by considering an ensemble of correlation matrices. This ensemble is set up by Random Matrix Theory. We demonstrate analytically that the presence of correlations severely limits the effect of diversification in a credit portfolio if the correlations are not identically zero. The existence of correlations alters the tails of the loss distribution considerably, even if their average is zero. Under the assumption of randomly fluctuating correlations, a lower bound for the estimation of the loss distribution is provided.

A Random Matrix Approach to Credit Risk

PubMed Central

Guhr, Thomas

2014-01-01

We estimate generic statistical properties of a structural credit risk model by considering an ensemble of correlation matrices. This ensemble is set up by Random Matrix Theory. We demonstrate analytically that the presence of correlations severely limits the effect of diversification in a credit portfolio if the correlations are not identically zero. The existence of correlations alters the tails of the loss distribution considerably, even if their average is zero. Under the assumption of randomly fluctuating correlations, a lower bound for the estimation of the loss distribution is provided. PMID:24853864

LC-MS/MS signal suppression effects in the analysis of pesticides in complex environmental matrices.

PubMed

Choi, B K; Hercules, D M; Gusev, A I

2001-02-01

The application of LC separation and mobile phase additives in addressing LC-MS/MS matrix signal suppression effects for the analysis of pesticides in a complex environmental matrix was investigated. It was shown that signal suppression is most significant for analytes eluting early in the LC-MS analysis. Introduction of different buffers (e.g. ammonium formate, ammonium hydroxide, formic acid) into the LC mobile phase was effective in improving signal correlation between the matrix and standard samples. The signal improvement is dependent on buffer concentration as well as LC separation of the matrix components. The application of LC separation alone was not effective in addressing suppression effects when characterizing complex matrix samples. Overloading of the LC column by matrix components was found to significantly contribute to analyte-matrix co-elution and suppression of signal. This signal suppression effect can be efficiently compensated by 2D LC (LC-LC) separation techniques. The effectiveness of buffers and LC separation in improving signal correlation between standard and matrix samples is discussed.

Multivariate analysis: A statistical approach for computations

NASA Astrophysics Data System (ADS)

Michu, Sachin; Kaushik, Vandana

2014-10-01

Multivariate analysis is a type of multivariate statistical approach commonly used in, automotive diagnosis, education evaluating clusters in finance etc and more recently in the health-related professions. The objective of the paper is to provide a detailed exploratory discussion about factor analysis (FA) in image retrieval method and correlation analysis (CA) of network traffic. Image retrieval methods aim to retrieve relevant images from a collected database, based on their content. The problem is made more difficult due to the high dimension of the variable space in which the images are represented. Multivariate correlation analysis proposes an anomaly detection and analysis method based on the correlation coefficient matrix. Anomaly behaviors in the network include the various attacks on the network like DDOs attacks and network scanning.

Separable decompositions of bipartite mixed states

NASA Astrophysics Data System (ADS)

Li, Jun-Li; Qiao, Cong-Feng

2018-04-01

We present a practical scheme for the decomposition of a bipartite mixed state into a sum of direct products of local density matrices, using the technique developed in Li and Qiao (Sci. Rep. 8:1442, 2018). In the scheme, the correlation matrix which characterizes the bipartite entanglement is first decomposed into two matrices composed of the Bloch vectors of local states. Then, we show that the symmetries of Bloch vectors are consistent with that of the correlation matrix, and the magnitudes of the local Bloch vectors are lower bounded by the correlation matrix. Concrete examples for the separable decompositions of bipartite mixed states are presented for illustration.

Testing Pattern Hypotheses for Correlation Matrices

ERIC Educational Resources Information Center

McDonald, Roderick P.

1975-01-01

The treatment of covariance matrices given by McDonald (1974) can be readily modified to cover hypotheses prescribing zeros and equalities in the correlation matrix rather than the covariance matrix, still with the convenience of the closed-form Least Squares solution and the classical Newton method. (Author/RC)

Structure-function correlations in glaucoma using matrix and standard automated perimetry versus time-domain and spectral-domain OCT devices.

PubMed

Pinto, Luciano Moreira; Costa, Elaine Fiod; Melo, Luiz Alberto S; Gross, Paula Blasco; Sato, Eduardo Toshio; Almeida, Andrea Pereira; Maia, Andre; Paranhos, Augusto

2014-04-10

We examined the structure-function relationship between two perimetric tests, the frequency doubling technology (FDT) matrix and standard automated perimetry (SAP), and two optical coherence tomography (OCT) devices (time-domain and spectral-domain). This cross-sectional study included 97 eyes from 29 healthy individuals, and 68 individuals with early, moderate, or advanced primary open-angle glaucoma. The correlations between overall and sectorial parameters of retinal nerve fiber layer thickness (RNFL) measured with Stratus and Spectralis OCT, and the visual field sensitivity obtained with FDT matrix and SAP were assessed. The relationship also was evaluated using a previously described linear model. The correlation coefficients for the threshold sensitivity measured with SAP and Stratus OCT ranged from 0.44 to 0.79, and those for Spectralis OCT ranged from 0.30 to 0.75. Regarding FDT matrix, the correlation ranged from 0.40 to 0.79 with Stratus OCT and from 0.39 to 0.79 with Spectralis OCT. Stronger correlations were found in the overall measurements and the arcuate sectors for both visual fields and OCT devices. A linear relationship was observed between FDT matrix sensitivity and the OCT devices. The previously described linear model fit the data from SAP and the OCT devices well, particularly in the inferotemporal sector. The FDT matrix and SAP visual sensitivities were related strongly to the RNFL thickness measured with the Stratus and Spectralis OCT devices, particularly in the overall and arcuate sectors. Copyright 2014 The Association for Research in Vision and Ophthalmology, Inc.

Variable tunneling barriers in FEBID based PtC metal-matrix nanocomposites as a transducing element for humidity sensing.

PubMed

Kolb, Florian; Schmoltner, Kerstin; Huth, Michael; Hohenau, Andreas; Krenn, Joachim; Klug, Andreas; List, Emil J W; Plank, Harald

2013-08-02

The development of simple gas sensing concepts is still of great interest for science and technology. The demands on an ideal device would be a single-step fabrication method providing a device which is sensitive, analyte-selective, quantitative, and reversible without special operating/reformation conditions such as high temperatures or special environments. In this study we demonstrate a new gas sensing concept based on a nanosized PtC metal-matrix system fabricated in a single step via focused electron beam induced deposition (FEBID). The sensors react selectively on polar H2O molecules quantitatively and reversibly without any special reformation conditions after detection events, whereas non-polar species (O2, CO2, N2) produce no response. The key elements are isolated Pt nanograins (2-3 nm) which are embedded in a dielectric carbon matrix. The electrical transport in such materials is based on tunneling effects in the correlated variable range hopping regime, where the dielectric carbon matrix screens the electric field between the particles, which governs the final conductivity. The specific change of these dielectric properties by the physisorption of polar gas molecules (H2O) can change the tunneling probability and thus the overall conductivity, allowing their application as a simple and straightforward sensing concept.

Principal regression analysis and the index leverage effect

NASA Astrophysics Data System (ADS)

Reigneron, Pierre-Alain; Allez, Romain; Bouchaud, Jean-Philippe

2011-09-01

We revisit the index leverage effect, that can be decomposed into a volatility effect and a correlation effect. We investigate the latter using a matrix regression analysis, that we call ‘Principal Regression Analysis' (PRA) and for which we provide some analytical (using Random Matrix Theory) and numerical benchmarks. We find that downward index trends increase the average correlation between stocks (as measured by the most negative eigenvalue of the conditional correlation matrix), and makes the market mode more uniform. Upward trends, on the other hand, also increase the average correlation between stocks but rotates the corresponding market mode away from uniformity. There are two time scales associated to these effects, a short one on the order of a month (20 trading days), and a longer time scale on the order of a year. We also find indications of a leverage effect for sectorial correlations as well, which reveals itself in the second and third mode of the PRA.

Extension of the spatial autocorrelation (SPAC) method to mixed-component correlations of surface waves

USGS Publications Warehouse

Haney, Matthew M.; Mikesell, T. Dylan; van Wijk, Kasper; Nakahara, Hisashi

2012-01-01

Using ambient seismic noise for imaging subsurface structure dates back to the development of the spatial autocorrelation (SPAC) method in the 1950s. We present a theoretical analysis of the SPAC method for multicomponent recordings of surface waves to determine the complete 3 × 3 matrix of correlations between all pairs of three-component motions, called the correlation matrix. In the case of isotropic incidence, when either Rayleigh or Love waves arrive from all directions with equal power, the only non-zero off-diagonal terms in the matrix are the vertical–radial (ZR) and radial–vertical (RZ) correlations in the presence of Rayleigh waves. Such combinations were not considered in the development of the SPAC method. The method originally addressed the vertical–vertical (ZZ), RR and TT correlations, hence the name spatial autocorrelation. The theoretical expressions we derive for the ZR and RZ correlations offer additional ways to measure Rayleigh wave dispersion within the SPAC framework. Expanding on the results for isotropic incidence, we derive the complete correlation matrix in the case of generally anisotropic incidence. We show that the ZR and RZ correlations have advantageous properties in the presence of an out-of-plane directional wavefield compared to ZZ and RR correlations. We apply the results for mixed-component correlations to a data set from Akutan Volcano, Alaska and find consistent estimates of Rayleigh wave phase velocity from ZR compared to ZZ correlations. This work together with the recently discovered connections between the SPAC method and time-domain correlations of ambient noise provide further insights into the retrieval of surface wave Green’s functions from seismic noise.

Prediction of thermal cycling induced matrix cracking

NASA Technical Reports Server (NTRS)

Mcmanus, Hugh L.

1992-01-01

Thermal fatigue has been observed to cause matrix cracking in laminated composite materials. A method is presented to predict transverse matrix cracks in composite laminates subjected to cyclic thermal load. Shear lag stress approximations and a simple energy-based fracture criteria are used to predict crack densities as a function of temperature. Prediction of crack densities as a function of thermal cycling is accomplished by assuming that fatigue degrades the material's inherent resistance to cracking. The method is implemented as a computer program. A simple experiment provides data on progressive cracking of a laminate with decreasing temperature. Existing data on thermal fatigue is also used. Correlations of the analytical predictions to the data are very good. A parametric study using the analytical method is presented which provides insight into material behavior under cyclical thermal loads.

Modeling cometary photopolarimetric characteristics with Sh-matrix method

NASA Astrophysics Data System (ADS)

Kolokolova, L.; Petrov, D.

2017-12-01

Cometary dust is dominated by particles of complex shape and structure, which are often considered as fractal aggregates. Rigorous modeling of light scattering by such particles, even using parallelized codes and NASA supercomputer resources, is very computer time and memory consuming. We are presenting a new approach to modeling cometary dust that is based on the Sh-matrix technique (e.g., Petrov et al., JQSRT, 112, 2012). This method is based on the T-matrix technique (e.g., Mishchenko et al., JQSRT, 55, 1996) and was developed after it had been found that the shape-dependent factors could be separated from the size- and refractive-index-dependent factors and presented as a shape matrix, or Sh-matrix. Size and refractive index dependences are incorporated through analytical operations on the Sh-matrix to produce the elements of T-matrix. Sh-matrix method keeps all advantages of the T-matrix method, including analytical averaging over particle orientation. Moreover, the surface integrals describing the Sh-matrix elements themselves can be solvable analytically for particles of any shape. This makes Sh-matrix approach an effective technique to simulate light scattering by particles of complex shape and surface structure. In this paper, we present cometary dust as an ensemble of Gaussian random particles. The shape of these particles is described by a log-normal distribution of their radius length and direction (Muinonen, EMP, 72, 1996). Changing one of the parameters of this distribution, the correlation angle, from 0 to 90 deg., we can model a variety of particles from spheres to particles of a random complex shape. We survey the angular and spectral dependencies of intensity and polarization resulted from light scattering by such particles, studying how they depend on the particle shape, size, and composition (including porous particles to simulate aggregates) to find the best fit to the cometary observations.

Characterization of extracellular polymeric substances in biofilms under long-term exposure to ciprofloxacin antibiotic using fluorescence excitation-emission matrix and parallel factor analysis.

PubMed

Gu, Chaochao; Gao, Pin; Yang, Fan; An, Dongxuan; Munir, Mariya; Jia, Hanzhong; Xue, Gang; Ma, Chunyan

2017-05-01

The presence of antibiotic residues in the environment has been regarded as an emerging concern due to their potential adverse environmental consequences such as antibiotic resistance. However, the interaction between antibiotics and extracellular polymeric substances (EPSs) of biofilms in wastewater treatment systems is not entirely clear. In this study, the effect of ciprofloxacin (CIP) antibiotic on biofilm EPS matrix was investigated and characterized using fluorescence excitation-emission matrix (EEM) and parallel factor (PARAFAC) analysis. Physicochemical analysis showed that the proteins were the major EPS fraction, and their contents increased gradually with an increase in CIP concentration (0-300 μg/L). Based on the characterization of biofilm tightly bound EPS (TB-EPS) by EEM, three fluorescent components were identified by PARAFAC analysis. Component C1 was associated with protein-like substances, and components C2 and C3 belonged to humic-like substances. Component C1 exhibited an increasing trend as the CIP addition increased. Pearson's correlation results showed that CIP correlated significantly with the protein contents and component C1, while strong correlations were also found among UV 254 , dissolved organic carbon, humic acids, and component C3. A combined use of EEM-PARAFAC analysis and chemical measurements was demonstrated as a favorable approach for the characterization of variations in biofilm EPS in the presence of CIP antibiotic.

Determining the Number of Components from the Matrix of Partial Correlations

ERIC Educational Resources Information Center

Velicer, Wayne F.

1976-01-01

A method is presented for determining the number of components to retain in a principal components or image components analysis which utilizes a matrix of partial correlations. Advantages and uses of the method are discussed and a comparison of the proposed method with existing methods is presented. (JKS)

Asymptotic coincidence of the statistics for degenerate and non-degenerate correlated real Wishart ensembles

NASA Astrophysics Data System (ADS)

Wirtz, Tim; Kieburg, Mario; Guhr, Thomas

2017-06-01

The correlated Wishart model provides the standard benchmark when analyzing time series of any kind. Unfortunately, the real case, which is the most relevant one in applications, poses serious challenges for analytical calculations. Often these challenges are due to square root singularities which cannot be handled using common random matrix techniques. We present a new way to tackle this issue. Using supersymmetry, we carry out an anlaytical study which we support by numerical simulations. For large but finite matrix dimensions, we show that statistical properties of the fully correlated real Wishart model generically approach those of a correlated real Wishart model with doubled matrix dimensions and doubly degenerate empirical eigenvalues. This holds for the local and global spectral statistics. With Monte Carlo simulations we show that this is even approximately true for small matrix dimensions. We explicitly investigate the k-point correlation function as well as the distribution of the largest eigenvalue for which we find a surprisingly compact formula in the doubly degenerate case. Moreover we show that on the local scale the k-point correlation function exhibits the sine and the Airy kernel in the bulk and at the soft edges, respectively. We also address the positions and the fluctuations of the possible outliers in the data.

Anatomical relationships between serotonin 5-HT2A and dopamine D2 receptors in living human brain.

PubMed

Ishii, Tatsuya; Kimura, Yasuyuki; Ichise, Masanori; Takahata, Keisuke; Kitamura, Soichiro; Moriguchi, Sho; Kubota, Manabu; Zhang, Ming-Rong; Yamada, Makiko; Higuchi, Makoto; Okubo, Yoshinori; Suhara, Tetsuya

2017-01-01

Seven healthy volunteers underwent PET scans with [18F]altanserin and [11C]FLB 457 for 5-HT2A and D2 receptors, respectively. As a measure of receptor density, a binding potential (BP) was calculated from PET data for 76 cerebral cortical regions. A correlation matrix was calculated between the binding potentials of [18F]altanserin and [11C]FLB 457 for those regions. The regional relationships were investigated using a bicluster analysis of the correlation matrix with an iterative signature algorithm. We identified two clusters of regions. The first cluster identified a distinct profile of correlation coefficients between 5-HT2A and D2 receptors, with the former in regions related to sensorimotor integration (supplementary motor area, superior parietal gyrus, and paracentral lobule) and the latter in most cortical regions. The second cluster identified another distinct profile of correlation coefficients between 5-HT2A receptors in the bilateral hippocampi and D2 receptors in most cortical regions. The observation of two distinct clusters in the correlation matrix suggests regional interactions between 5-HT2A and D2 receptors in sensorimotor integration and hippocampal function. A bicluster analysis of the correlation matrix of these neuroreceptors may be beneficial in understanding molecular networks in the human brain.

Density matrix embedding in an antisymmetrized geminal power bath

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

Tsuchimochi, Takashi; Welborn, Matthew; Van Voorhis, Troy, E-mail: tvan@mit.edu

2015-07-14

Density matrix embedding theory (DMET) has emerged as a powerful tool for performing wave function-in-wave function embedding for strongly correlated systems. In traditional DMET, an accurate calculation is performed on a small impurity embedded in a mean field bath. Here, we extend the original DMET equations to account for correlation in the bath via an antisymmetrized geminal power (AGP) wave function. The resulting formalism has a number of advantages. First, it allows one to properly treat the weak correlation limit of independent pairs, which DMET is unable to do with a mean-field bath. Second, it associates a size extensive correlationmore » energy with a given density matrix (for the models tested), which AGP by itself is incapable of providing. Third, it provides a reasonable description of charge redistribution in strongly correlated but non-periodic systems. Thus, AGP-DMET appears to be a good starting point for describing electron correlation in molecules, which are aperiodic and possess both strong and weak electron correlation.« less

Direct determination of neonicotinoid insecticides in an analytically challenging crop such as Chinese chives using selective ELISAs.

PubMed

Watanabe, Eiki; Miyake, Shiro

2018-06-05

Easy-to-use commercial kit-based enzyme-linked immunosorbent assays (ELISAs) have been used to detect neonicotinoid dinotefuran, clothianidin and imidacloprid in Chinese chives, which are considered a troublesome matrix for chromatographic techniques. Based on their high water solubility, water was used as an extractant. Matrix interference could be avoided substantially just diluting sample extracts. Average recoveries of insecticides from spiked samples were 85-113%, with relative standard deviation of <15%. The concentrations of insecticides detected from the spiked samples with the proposed ELISA methods correlated well with those by the reference high-performance liquid chromatography (HPLC) method. The residues analyzed by the ELISA methods were consistently 1.24 times that found by the HPLC method, attributable to loss of analyte during sample clean-up for HPLC analyses. It was revealed that the ELISA methods can be applied easily to pesticide residue analysis in troublesome matrix such as Chinese chives.

A wideband analog correlator system for AMiBA

NASA Astrophysics Data System (ADS)

Li, Chao-Te; Kubo, Derek; Han, Chih-Chiang; Chen, Chung-Cheng; Chen, Ming-Tang; Lien, Chun-Hsien; Wang, Huei; Wei, Ray-Ming; Yang, Chia-Hsiang; Chiueh, Tzi-Dar; Peterson, Jeffrey; Kesteven, Michael; Wilson, Warwick

2004-10-01

A wideband correlator system with a bandwidth of 16 GHz or more is required for Array for Microwave Background Anisotropy (AMiBA) to achieve the sensitivity of 10μK in one hour of observation. Double-balanced diode mixers were used as multipliers in 4-lag correlator modules. Several wideband modules were developed for IF signal distribution between receivers and correlators. Correlator outputs were amplified, and digitized by voltage-to-frequency converters. Data acquisition circuits were designed using field programmable gate arrays (FPGA). Subsequent data transfer and control software were based on the configuration for Australia Telescope Compact Array. Transform matrix method will be adopted during calibration to take into account the phase and amplitude variations of analog devices across the passband.

Expression Levels of Myostatin and Matrix Metalloproteinase 14 mRNAs in Uterine Leiomyoma are Correlated With Dysmenorrhea.

PubMed

Tsigkou, Anastasia; Reis, Fernando M; Ciarmela, Pasquapina; Lee, Meng H; Jiang, Bingjie; Tosti, Claudia; Shen, Fang-Rong; Shi, Zhendan; Chen, You-Guo; Petraglia, Felice

2015-12-01

Uterine leiomyoma is the most common benign neoplasm of female reproductive system, found in about 50% of women in reproductive age. The mechanisms of leiomyoma growth include cell proliferation, which is modulated by growth factors, and deposition of extracellular matrix (ECM). Activin A and myostatin are growth factors that play a role in proliferation of leiomyoma cells. Matrix metalloproteinases (MMPs) are known for their ability to remodel the ECM in different biological systems. The aim of this study was to evaluate the expression levels of activin βA-subunit, myostatin, and MMP14 messenger RNAs (mRNAs) in uterine leiomyomas and the possible correlation of these factors with clinical features of the disease. Matrix metalloproteinase 14 was highly expressed in uterine leiomyoma and correlated with myostatin and activin A mRNA expression. Moreover, MMP14 and myostatin mRNA expression correlated significantly and directly with the intensity of dysmenorrhea. Overall, the present findings showed that MMP14 mRNA is highly expressed in uterine leiomyoma, where it correlates with the molecular expression of growth factors and is further increased in cases of intense dysmenorrhea. © The Author(s) 2015.

Polymer matrix and graphite fiber interface study

NASA Technical Reports Server (NTRS)

Adams, D. F.; Zimmerman, R. S.; Odom, E. M.

1985-01-01

Hercules AS4 graphite fiber, unsized, or with EPON 828, PVA, or polysulfone sizing, was combined with three different polymer matrices. These included Hercules 3501-6 epoxy, Hercules 4001 bismaleimide, and Hexcel F155 rubber toughened epoxy. Unidirectional composites in all twelve combinations were fabricated and tested in transverse tension and axial compression. Quasi-isotropic laminates were tested in axial tension and compression, flexure, interlaminar shear, and tensile impact. All tests were conducted at both room temperature, dry and elevated temperature, and wet conditions. Single fiber pullout testing was also performed. Extensive scanning electron microphotographs of fracture surfaces are included, along with photographs of single fiber pullout failures. Analytical/experimental correlations are presented, based on the results of a finite element micromechanics analysis. Correlations between matrix type, fiber sizing, hygrothermal environment, and loading mode are presented. Results indicate that the various composite properties were only moderately influenced by the fiber sizings utilized.

Strongly contracted canonical transformation theory

NASA Astrophysics Data System (ADS)

Neuscamman, Eric; Yanai, Takeshi; Chan, Garnet Kin-Lic

2010-01-01

Canonical transformation (CT) theory describes dynamic correlation in multireference systems with large active spaces. Here we discuss CT theory's intruder state problem and why our previous approach of overlap matrix truncation becomes infeasible for sufficiently large active spaces. We propose the use of strongly and weakly contracted excitation operators as alternatives for dealing with intruder states in CT theory. The performance of these operators is evaluated for the H2O, N2, and NiO molecules, with comparisons made to complete active space second order perturbation theory and Davidson-corrected multireference configuration interaction theory. Finally, using a combination of strongly contracted CT theory and orbital-optimized density matrix renormalization group theory, we evaluate the singlet-triplet gap of free base porphin using an active space containing all 24 out-of-plane 2p orbitals. Modeling dynamic correlation with an active space of this size is currently only possible using CT theory.

Repeated decompositions reveal the stability of infomax decomposition of fMRI data

PubMed Central

Duann, Jeng-Ren; Jung, Tzyy-Ping; Sejnowski, Terrence J.; Makeig, Scott

2010-01-01

In this study, we decomposed 12 fMRI data sets from six subjects each 101 times using the infomax algorithm. The first decomposition was taken as a reference decomposition; the others were used to form a component matrix of 100 by 100 components. Equivalence relations between components in this matrix, defined as maximum spatial correlations to the components of the reference decomposition, were found by the Hungarian sorting method and used to form 100 equivalence classes for each data set. We then tested the reproducibility of the matched components in the equivalence classes using uncertainty measures based on component distributions, time courses, and ROC curves. Infomax ICA rarely failed to derive nearly the same components in different decompositions. Very few components per data set were poorly reproduced, even using vector angle uncertainty measures stricter than correlation and detection theory measures. PMID:17281453

Multivariate localization methods for ensemble Kalman filtering

NASA Astrophysics Data System (ADS)

Roh, S.; Jun, M.; Szunyogh, I.; Genton, M. G.

2015-12-01

In ensemble Kalman filtering (EnKF), the small number of ensemble members that is feasible to use in a practical data assimilation application leads to sampling variability of the estimates of the background error covariances. The standard approach to reducing the effects of this sampling variability, which has also been found to be highly efficient in improving the performance of EnKF, is the localization of the estimates of the covariances. One family of localization techniques is based on taking the Schur (element-wise) product of the ensemble-based sample covariance matrix and a correlation matrix whose entries are obtained by the discretization of a distance-dependent correlation function. While the proper definition of the localization function for a single state variable has been extensively investigated, a rigorous definition of the localization function for multiple state variables that exist at the same locations has been seldom considered. This paper introduces two strategies for the construction of localization functions for multiple state variables. The proposed localization functions are tested by assimilating simulated observations experiments into the bivariate Lorenz 95 model with their help.

Difference-based ridge-type estimator of parameters in restricted partial linear model with correlated errors.

PubMed

Wu, Jibo

2016-01-01

In this article, a generalized difference-based ridge estimator is proposed for the vector parameter in a partial linear model when the errors are dependent. It is supposed that some additional linear constraints may hold to the whole parameter space. Its mean-squared error matrix is compared with the generalized restricted difference-based estimator. Finally, the performance of the new estimator is explained by a simulation study and a numerical example.

A novel image encryption algorithm based on synchronized random bit generated in cascade-coupled chaotic semiconductor ring lasers

NASA Astrophysics Data System (ADS)

Li, Jiafu; Xiang, Shuiying; Wang, Haoning; Gong, Junkai; Wen, Aijun

2018-03-01

In this paper, a novel image encryption algorithm based on synchronization of physical random bit generated in a cascade-coupled semiconductor ring lasers (CCSRL) system is proposed, and the security analysis is performed. In both transmitter and receiver parts, the CCSRL system is a master-slave configuration consisting of a master semiconductor ring laser (M-SRL) with cross-feedback and a solitary SRL (S-SRL). The proposed image encryption algorithm includes image preprocessing based on conventional chaotic maps, pixel confusion based on control matrix extracted from physical random bit, and pixel diffusion based on random bit stream extracted from physical random bit. Firstly, the preprocessing method is used to eliminate the correlation between adjacent pixels. Secondly, physical random bit with verified randomness is generated based on chaos in the CCSRL system, and is used to simultaneously generate the control matrix and random bit stream. Finally, the control matrix and random bit stream are used for the encryption algorithm in order to change the position and the values of pixels, respectively. Simulation results and security analysis demonstrate that the proposed algorithm is effective and able to resist various typical attacks, and thus is an excellent candidate for secure image communication application.

Elevated Neutrophil Elastase in Tears of Ocular Graft-Versus-Host Disease Patients.

PubMed

Arafat, Samer N; Robert, Marie-Claude; Abud, Tulio; Spurr-Michaud, Sandra; Amparo, Francisco; Dohlman, Claes H; Dana, Reza; Gipson, Ilene K

2017-04-01

To investigate the levels of neutrophil elastase (NE), matrix metalloproteinases (MMPs), and myeloperoxidase (MPO) in tear washes of patients with ocular graft-vs-host disease (oGVHD). Case-control study. Based on established criteria, oGVHD patients (n = 14; 28 eyes) and age-/sex-matched healthy controls (n = 14; 28 eyes) were enrolled. Tear washes were collected and analyzed for NE using a single-analyte enzyme-linked immunosorbent assay (ELISA). MMPs (1, 2, 3, 7, 8, 9, 12), MPO, and tissue inhibitor of matrix metalloproteinase (TIMP)-1 were analyzed using multianalyte bead-based ELISA assays. Total MMP activity was measured using a fluorimetric assay. Correlation studies were performed between NE, MMP-8, MMP-9, and MPO within study groups. NE, MMP-8, MMP-9, and MPO levels were elevated in oGVHD tears when compared with controls (P < .0001). NE was the most elevated analyte. MMP activity was higher and TIMP-1 levels were lower in oGVHD than in control (P < .0001). In oGVHD, NE significantly correlated with MMP-8 (r = 0.92), MMP-9 (r = 0.90), and MPO (r = 0.79) (P < .0001). MMP-8 correlated with MMP-9 (r = 0.96, P < .0001), and MPO (r = 0.60, P = .001). MMP-9 correlated with MPO (r = 0.55, P = .002). In controls, NE, MMP-9, and MPO significantly correlated with each other (P < .0001). The marked increase in NE in oGVHD tears that correlated strongly with elevated MMP-8, MMP-9, and MPO suggests a common neutrophilic source and provides evidence of neutrophil activity on the ocular surface of oGVHD patients. Copyright © 2017 Elsevier Inc. All rights reserved.

Quantification of focal adhesion dynamics of cell movement based on cell-induced collagen matrix deformation using second-harmonic generation microscopy.

PubMed

Kang, Yong Guk; Jang, Hwanseok; Yang, Taeseok Daniel; Notbohm, Jacob; Choi, Youngwoon; Park, Yongdoo; Kim, Beop-Min

2018-06-01

Mechanical interactions of living cells with the surrounding environment via focal adhesion (FA) in three dimensions (3-D) play a key role in dynamic biological events, such as tissue regeneration, wound healing, and cancer invasion. Recently, several methods for observing 3-D cell-extracellular matrix (ECM) interactions have been reported, lacking solid and quantitative analysis on the dynamics of the physical interaction between the cell and the ECM. We measured the submicron displacements of ECM deformation in 3-D due to protrusion-retraction dynamics during cell migration, using second-harmonic generation without labeling the matrix structures. We then quantitatively analyzed the mechanical deformation between the ECM and the cells based on spatiotemporal volumetric correlations. The greatest deformations within the collagen matrix were found to occur at sites of colocalization of the FA site-related proteins vinculin and actin, which confirms that FA sites play a critical role in living cells within the ECM as a point for adhesion, traction, and migration. We believe that this modality can be used in studies of cell-ECM interaction during angiogenesis, wound healing, and metastasis. (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Influence of matrix and filler fraction on biofilm formation on the surface of experimental resin-based composites.

PubMed

Ionescu, Andrei; Brambilla, Eugenio; Wastl, Daniel S; Giessibl, Franz J; Cazzaniga, Gloria; Schneider-Feyrer, Sibylle; Hahnel, Sebastian

2015-01-01

The aim of this study was to investigate the impact of resin matrix chemistry and filler fraction on biofilm formation on the surface of experimental resin-based composites (RBCs). Specimens were prepared from eight experimental RBC formulations differing in resin matrix blend (BisGMA/TEGDMA in a 7:3 wt% ratio or UDMA/aliphatic dimethacrylate in a 1:1 wt% ratio) and filler fraction (no fillers; 65 wt% dental glass with an average diameter of 7 or 0.7 µm or 65 wt% SiO2 with an average diameter of 20 nm). Surface roughness, surface free energy, and chemical surface composition were determined; surface topography was visualized using atomic force microscopy. Biofilm formation was simulated under continuous flow conditions for a 48 h period using a monospecies Streptococcus mutans and a multispecies biofilm model. In the monospecies biofilm model, the impact of the filler fraction overruled the influence of the resin matrix, indicating lowest biofilm formation on RBCs with nano-scaled filler particles and those manufactured from the neat resin blends. The multispecies model suggested a more pronounced effect of the resin matrix blend, as significantly higher biofilm formation was identified on RBCs with a UDMA/dimethacrylate matrix blend than on those including a BisGMA/TEGDMA matrix blend but analogous filler fractions. Although significant differences in surface properties between the various materials were identified, correlations between the surface properties and biofilm formation were poor, which highlights the relevance of surface topography and chemistry. These results may help to tailor novel RBC formulations which feature reduced biofilm formation on their surface.

Recovering hidden diagonal structures via non-negative matrix factorization with multiple constraints.

PubMed

Yang, Xi; Han, Guoqiang; Cai, Hongmin; Song, Yan

2017-03-31

Revealing data with intrinsically diagonal block structures is particularly useful for analyzing groups of highly correlated variables. Earlier researches based on non-negative matrix factorization (NMF) have been shown to be effective in representing such data by decomposing the observed data into two factors, where one factor is considered to be the feature and the other the expansion loading from a linear algebra perspective. If the data are sampled from multiple independent subspaces, the loading factor would possess a diagonal structure under an ideal matrix decomposition. However, the standard NMF method and its variants have not been reported to exploit this type of data via direct estimation. To address this issue, a non-negative matrix factorization with multiple constraints model is proposed in this paper. The constraints include an sparsity norm on the feature matrix and a total variational norm on each column of the loading matrix. The proposed model is shown to be capable of efficiently recovering diagonal block structures hidden in observed samples. An efficient numerical algorithm using the alternating direction method of multipliers model is proposed for optimizing the new model. Compared with several benchmark models, the proposed method performs robustly and effectively for simulated and real biological data.

Commander and User Perceptions of the Army’s Intransit Visibility (ITV) Architecture

DTIC Science & Technology

2007-03-01

covariance matrix; (c) Bartlett’s test of Sphericity; and (d) Kaiser-Meyer- Olkin ( KMO ) measure of sampling adequacy. The inter-item correlation matrix...001), and all diagonal terms had a value of 1 while off-diagonal terms were 0. The KMO measure of sampling adequacy reflects the homogeneity...amongst the variables and serves as an index for comparing the magnitudes of correlation coefficients to partial correlation coefficients. KMO values at

Assessment of Infrared Sounder Radiometric Noise from Analysis of Spectral Residuals

NASA Astrophysics Data System (ADS)

Dufour, E.; Klonecki, A.; Standfuss, C.; Tournier, B.; Serio, C.; Masiello, G.; Tjemkes, S.; Stuhlmann, R.

2016-08-01

For the preparation and performance monitoring of the future generation of hyperspectral InfraRed sounders dedicated to the precise vertical profiling of the atmospheric state, such as the Meteosat Third Generation hyperspectral InfraRed Sounder, a reliable assessment of the instrument radiometric error covariance matrix is needed.Ideally, an inflight estimation of the radiometrric noise is recommended as certain sources of noise can be driven by the spectral signature of the observed Earth/ atmosphere radiance. Also, unknown correlated noise sources, generally related to incomplete knowledge of the instrument state, can be present, so a caracterisation of the noise spectral correlation is also neeed.A methodology, relying on the analysis of post-retreival spectral residuals, is designed and implemented to derive in-flight the covariance matrix on the basis of Earth scenes measurements. This methodology is successfully demonstrated using IASI observations as MTG-IRS proxy data and made it possible to highlight anticipated correlation structures explained by apodization and micro-vibration effects (ghost). This analysis is corroborated by a parallel estimation based on an IASI black body measurement dataset and the results of an independent micro-vibration model.

Hypothesis testing for differentially correlated features.

PubMed

Sheng, Elisa; Witten, Daniela; Zhou, Xiao-Hua

2016-10-01

In a multivariate setting, we consider the task of identifying features whose correlations with the other features differ across conditions. Such correlation shifts may occur independently of mean shifts, or differences in the means of the individual features across conditions. Previous approaches for detecting correlation shifts consider features simultaneously, by computing a correlation-based test statistic for each feature. However, since correlations involve two features, such approaches do not lend themselves to identifying which feature is the culprit. In this article, we instead consider a serial testing approach, by comparing columns of the sample correlation matrix across two conditions, and removing one feature at a time. Our method provides a novel perspective and favorable empirical results compared with competing approaches. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Characterization of porous glass fiber-reinforced composite (FRC) implant structures: porosity and mechanical properties.

PubMed

Ylä-Soininmäki, Anne; Moritz, Niko; Lassila, Lippo V J; Peltola, Matti; Aro, Hannu T; Vallittu, Pekka K

2013-12-01

The aim of this study was to characterize the microstructure and mechanical properties of porous fiber-reinforced composites (FRC). Implants made of the FRC structures are intended for cranial applications. The FRC specimens were prepared by impregnating E-glass fiber sheet with non-resorbable bifunctional bis-phenyl glycidyl dimethacrylate and triethylene glycol dimethacrylate resin matrix. Four groups of porous FRC specimens were prepared with a different amount of resin matrix. Control group contained specimens of fibers, which were bound together with sizing only. Microstructure of the specimens was analyzed using a micro computed tomography (micro-CT) based method. Mechanical properties of the specimens were measured with a tensile test. The amount of resin matrix in the specimens had an effect on the microstructure. Total porosity was 59.5 % (median) in the group with the lowest resin content and 11.2 % (median) in the group with the highest resin content. In control group, total porosity was 94.2 % (median). Correlations with resin content were obtained for all micro-CT based parameters except TbPf. The tensile strength of the composites was 21.3 MPa (median) in the group with the highest resin content and 43.4 MPa (median) in the group with the highest resin content. The tensile strength in control group was 18.9 MPa (median). There were strong correlations between the tensile strength of the specimens and most of the micro-CT based parameters. This experiment suggests that porous FRC structures may have the potential for use in implants for cranial bone reconstructions, provided further relevant in vitro and in vivo tests are performed.

Scale Free Reduced Rank Image Analysis.

ERIC Educational Resources Information Center

Horst, Paul

In the traditional Guttman-Harris type image analysis, a transformation is applied to the data matrix such that each column of the transformed data matrix is the best least squares estimate of the corresponding column of the data matrix from the remaining columns. The model is scale free. However, it assumes (1) that the correlation matrix is…

Effect of nano-SiO{sub 2} particles and curing time on development of fiber-matrix bond properties and microstructure of ultra-high strength concrete

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

Wu, Zemei; Department of Civil, Architectural and Environmental Engineering, Missouri University of Science and Technology, Rolla 65409, MO; Khayat, Kamal Henri, E-mail: khayatk@mst.edu

Bond properties between fibers and cementitious matrix have significant effect on the mechanical behavior of composite materials. In this study, the development of steel fiber-matrix interfacial bond properties in ultra-high strength concrete (UHSC) proportioned with nano-SiO{sub 2} varying between 0 and 2%, by mass of cementitious materials, was investigated. A statistical model relating either bond strength or pullout energy to curing time and nano-SiO{sub 2} content was proposed by using the response surface methodology. Mercury intrusion porosimetry (MIP) and backscatter scanning electron microscopy (BSEM) were used to characterize the microstructure of the matrix and the fiber-matrix interface, respectively. Micro-hardness aroundmore » the embedded fiber and hydration products of the matrix were evaluated as well. Test results indicated that the optimal nano-SiO{sub 2} dosage was 1% in terms of the bond properties and the microstructure. The proposed quadratic model efficiently predicted the bond strength and pullout energy with consideration of curing time and nano-SiO{sub 2} content. The improvement in bond properties associated with nano-silica was correlated with denser matrix and/or interface and stronger bond and greater strength of hydration products based on microstructural analysis.« less

Parameterizing correlations between hydrometeor species in mixed-phase Arctic clouds

NASA Astrophysics Data System (ADS)

Larson, Vincent E.; Nielsen, Brandon J.; Fan, Jiwen; Ovchinnikov, Mikhail

2011-01-01

Mixed-phase Arctic clouds, like other clouds, contain small-scale variability in hydrometeor fields, such as cloud water or snow mixing ratio. This variability may be worth parameterizing in coarse-resolution numerical models. In particular, for modeling multispecies processes such as accretion and aggregation, it would be useful to parameterize subgrid correlations among hydrometeor species. However, one difficulty is that there exist many hydrometeor species and many microphysical processes, leading to complexity and computational expense. Existing lower and upper bounds on linear correlation coefficients are too loose to serve directly as a method to predict subgrid correlations. Therefore, this paper proposes an alternative method that begins with the spherical parameterization framework of Pinheiro and Bates (1996), which expresses the correlation matrix in terms of its Cholesky factorization. The values of the elements of the Cholesky matrix are populated here using a "cSigma" parameterization that we introduce based on the aforementioned bounds on correlations. The method has three advantages: (1) the computational expense is tolerable; (2) the correlations are, by construction, guaranteed to be consistent with each other; and (3) the methodology is fairly general and hence may be applicable to other problems. The method is tested noninteractively using simulations of three Arctic mixed-phase cloud cases from two field experiments: the Indirect and Semi-Direct Aerosol Campaign and the Mixed-Phase Arctic Cloud Experiment. Benchmark simulations are performed using a large-eddy simulation (LES) model that includes a bin microphysical scheme. The correlations estimated by the new method satisfactorily approximate the correlations produced by the LES.

Non-negative Matrix Factorization for Self-calibration of Photometric Redshift Scatter in Weak-lensing Surveys

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

Zhang, Le; Yu, Yu; Zhang, Pengjie, E-mail: lezhang@sjtu.edu.cn

Photo- z error is one of the major sources of systematics degrading the accuracy of weak-lensing cosmological inferences. Zhang et al. proposed a self-calibration method combining galaxy–galaxy correlations and galaxy–shear correlations between different photo- z bins. Fisher matrix analysis shows that it can determine the rate of photo- z outliers at a level of 0.01%–1% merely using photometric data and do not rely on any prior knowledge. In this paper, we develop a new algorithm to implement this method by solving a constrained nonlinear optimization problem arising in the self-calibration process. Based on the techniques of fixed-point iteration and non-negativemore » matrix factorization, the proposed algorithm can efficiently and robustly reconstruct the scattering probabilities between the true- z and photo- z bins. The algorithm has been tested extensively by applying it to mock data from simulated stage IV weak-lensing projects. We find that the algorithm provides a successful recovery of the scatter rates at the level of 0.01%–1%, and the true mean redshifts of photo- z bins at the level of 0.001, which may satisfy the requirements in future lensing surveys.« less

Sparsistency and Rates of Convergence in Large Covariance Matrix Estimation.

PubMed

Lam, Clifford; Fan, Jianqing

2009-01-01

This paper studies the sparsistency and rates of convergence for estimating sparse covariance and precision matrices based on penalized likelihood with nonconvex penalty functions. Here, sparsistency refers to the property that all parameters that are zero are actually estimated as zero with probability tending to one. Depending on the case of applications, sparsity priori may occur on the covariance matrix, its inverse or its Cholesky decomposition. We study these three sparsity exploration problems under a unified framework with a general penalty function. We show that the rates of convergence for these problems under the Frobenius norm are of order (s(n) log p(n)/n)(1/2), where s(n) is the number of nonzero elements, p(n) is the size of the covariance matrix and n is the sample size. This explicitly spells out the contribution of high-dimensionality is merely of a logarithmic factor. The conditions on the rate with which the tuning parameter λ(n) goes to 0 have been made explicit and compared under different penalties. As a result, for the L(1)-penalty, to guarantee the sparsistency and optimal rate of convergence, the number of nonzero elements should be small: sn'=O(pn) at most, among O(pn2) parameters, for estimating sparse covariance or correlation matrix, sparse precision or inverse correlation matrix or sparse Cholesky factor, where sn' is the number of the nonzero elements on the off-diagonal entries. On the other hand, using the SCAD or hard-thresholding penalty functions, there is no such a restriction.

Analytical Modeling of the High Strain Rate Deformation of Polymer Matrix Composites

NASA Technical Reports Server (NTRS)

Goldberg, Robert K.; Roberts, Gary D.; Gilat, Amos

2003-01-01

The results presented here are part of an ongoing research program to develop strain rate dependent deformation and failure models for the analysis of polymer matrix composites subject to high strain rate impact loads. State variable constitutive equations originally developed for metals have been modified in order to model the nonlinear, strain rate dependent deformation of polymeric matrix materials. To account for the effects of hydrostatic stresses, which are significant in polymers, the classical 5 plasticity theory definitions of effective stress and effective plastic strain are modified by applying variations of the Drucker-Prager yield criterion. To verify the revised formulation, the shear and tensile deformation of a representative toughened epoxy is analyzed across a wide range of strain rates (from quasi-static to high strain rates) and the results are compared to experimentally obtained values. For the analyzed polymers, both the tensile and shear stress-strain curves computed using the analytical model correlate well with values obtained through experimental tests. The polymer constitutive equations are implemented within a strength of materials based micromechanics method to predict the nonlinear, strain rate dependent deformation of polymer matrix composites. In the micromechanics, the unit cell is divided up into a number of independently analyzed slices, and laminate theory is then applied to obtain the effective deformation of the unit cell. The composite mechanics are verified by analyzing the deformation of a representative polymer matrix composite (composed using the representative polymer analyzed for the correlation of the polymer constitutive equations) for several fiber orientation angles across a variety of strain rates. The computed values compare favorably to experimentally obtained results.

Multireference configuration interaction theory using cumulant reconstruction with internal contraction of density matrix renormalization group wave function.

PubMed

Saitow, Masaaki; Kurashige, Yuki; Yanai, Takeshi

2013-07-28

We report development of the multireference configuration interaction (MRCI) method that can use active space scalable to much larger size references than has previously been possible. The recent development of the density matrix renormalization group (DMRG) method in multireference quantum chemistry offers the ability to describe static correlation in a large active space. The present MRCI method provides a critical correction to the DMRG reference by including high-level dynamic correlation through the CI treatment. When the DMRG and MRCI theories are combined (DMRG-MRCI), the full internal contraction of the reference in the MRCI ansatz, including contraction of semi-internal states, plays a central role. However, it is thought to involve formidable complexity because of the presence of the five-particle rank reduced-density matrix (RDM) in the Hamiltonian matrix elements. To address this complexity, we express the Hamiltonian matrix using commutators, which allows the five-particle rank RDM to be canceled out without any approximation. Then we introduce an approximation to the four-particle rank RDM by using a cumulant reconstruction from lower-particle rank RDMs. A computer-aided approach is employed to derive the exceedingly complex equations of the MRCI in tensor-contracted form and to implement them into an efficient parallel computer code. This approach extends to the size-consistency-corrected variants of MRCI, such as the MRCI+Q, MR-ACPF, and MR-AQCC methods. We demonstrate the capability of the DMRG-MRCI method in several benchmark applications, including the evaluation of single-triplet gap of free-base porphyrin using 24 active orbitals.

Random matrix theory and fund of funds portfolio optimisation

NASA Astrophysics Data System (ADS)

Conlon, T.; Ruskin, H. J.; Crane, M.

2007-08-01

The proprietary nature of Hedge Fund investing means that it is common practise for managers to release minimal information about their returns. The construction of a fund of hedge funds portfolio requires a correlation matrix which often has to be estimated using a relatively small sample of monthly returns data which induces noise. In this paper, random matrix theory (RMT) is applied to a cross-correlation matrix C, constructed using hedge fund returns data. The analysis reveals a number of eigenvalues that deviate from the spectrum suggested by RMT. The components of the deviating eigenvectors are found to correspond to distinct groups of strategies that are applied by hedge fund managers. The inverse participation ratio is used to quantify the number of components that participate in each eigenvector. Finally, the correlation matrix is cleaned by separating the noisy part from the non-noisy part of C. This technique is found to greatly reduce the difference between the predicted and realised risk of a portfolio, leading to an improved risk profile for a fund of hedge funds.

Covalent bond orders and atomic valences from correlated wavefunctions

NASA Astrophysics Data System (ADS)

Ángyán, János G.; Rosta, Edina; Surján, Péter R.

1999-01-01

A comparison is made between two alternative definitions for covalent bond orders: one derived from the exchange part of the two-particle density matrix and the other expressed as the correlation of fluctuations (covariance) of the number of electrons between the atomic centers. Although these definitions lead to identical formulae for mono-determinantal SCF wavefunctions, they predict different bond orders for correlated wavefunctions. It is shown that, in this case, the fluctuation-based definition leads to slightly lower values of the bond order than does the exchange-based definition, provided one uses an appropriate space-partitioning technique like that of Bader's topological theory of atoms in a molecule; however, use of Mulliken partitioning in this context leads to unphysical behaviour. The example of H 2 is discussed in detail.

Development of an unsteady aerodynamics model to improve correlation of computed blade stresses with test data

NASA Technical Reports Server (NTRS)

Gangwani, S. T.

1985-01-01

A reliable rotor aeroelastic analysis operational that correctly predicts the vibration levels for a helicopter is utilized to test various unsteady aerodynamics models with the objective of improving the correlation between test and theory. This analysis called Rotor Aeroelastic Vibration (RAVIB) computer program is based on a frequency domain forced response analysis which utilizes the transfer matrix techniques to model helicopter/rotor dynamic systems of varying degrees of complexity. The results for the AH-1G helicopter rotor were compared with the flight test data during high speed operation and they indicated a reasonably good correlation for the beamwise and chordwise blade bending moments, but for torsional moments the correlation was poor. As a result, a new aerodynamics model based on unstalled synthesized data derived from the large amplitude oscillating airfoil experiments was developed and tested.

Automated flow quantification in valvular heart disease based on backscattered Doppler power analysis: implementation on matrix-array ultrasound imaging systems.

PubMed

Buck, Thomas; Hwang, Shawn M; Plicht, Björn; Mucci, Ronald A; Hunold, Peter; Erbel, Raimund; Levine, Robert A

2008-06-01

Cardiac ultrasound imaging systems are limited in the noninvasive quantification of valvular regurgitation due to indirect measurements and inaccurate hemodynamic assumptions. We recently demonstrated that the principle of integration of backscattered acoustic Doppler power times velocity can be used for flow quantification in valvular regurgitation directly at the vena contracta of a regurgitant flow jet. We now aimed to accomplish implementation of automated Doppler power flow analysis software on a standard cardiac ultrasound system utilizing novel matrix-array transducer technology with detailed description of system requirements, components and software contributing to the system. This system based on a 3.5 MHz, matrix-array cardiac ultrasound scanner (Sonos 5500, Philips Medical Systems) was validated by means of comprehensive experimental signal generator trials, in vitro flow phantom trials and in vivo testing in 48 patients with mitral regurgitation of different severity and etiology using magnetic resonance imaging (MRI) for reference. All measurements displayed good correlation to the reference values, indicating successful implementation of automated Doppler power flow analysis on a matrix-array ultrasound imaging system. Systematic underestimation of effective regurgitant orifice areas >0.65 cm(2) and volumes >40 ml was found due to currently limited Doppler beam width that could be readily overcome by the use of new generation 2D matrix-array technology. Automated flow quantification in valvular heart disease based on backscattered Doppler power can be fully implemented on board a routinely used matrix-array ultrasound imaging systems. Such automated Doppler power flow analysis of valvular regurgitant flow directly, noninvasively, and user independent overcomes the practical limitations of current techniques.

Relationship between second-generation frequency doubling technology and standard automated perimetry in patients with glaucoma.

PubMed

Zarkovic, Andrea; Mora, Justin; McKelvie, James; Gamble, Greg

2007-12-01

The aim of the study was to establish the correlation between visual filed loss as shown by second-generation Frequency Doubling Technology (Humphrey Matrix) and Standard Automated Perimetry (Humphrey Field Analyser) in patients with glaucoma. Also, compared were the test duration and reliability. Forty right eyes from glaucoma patients from a private ophthalmology practice were included in this prospective study. All participants had tests within an 8-month period. Pattern deviation plots and mean deviation were compared to establish the correlation between the two perimetry tests. Overall correlation and correlation between hemifields, quadrants and individual test locations were assessed. Humphrey Field Analyser tests were slightly more reliable (37/40 vs. 34/40 for Matrix)) but overall of longer duration. There was good correlation (0.69) between mean deviations. Superior hemifields and superonasal quadrants had the highest correlation (0.88 [95% CI 0.79, 0.94]). Correlation between individual points was independent of distance from the macula. Generally, the Matrix and Humphrey Field Analyser perimetry correlate well; however, each machine utilizes a different method of analysing data and thus the direct comparison should be made with caution.

Detection of Nitrogen Content in Rubber Leaves Using Near-Infrared (NIR) Spectroscopy with Correlation-Based Successive Projections Algorithm (SPA).

PubMed

Tang, Rongnian; Chen, Xupeng; Li, Chuang

2018-05-01

Near-infrared spectroscopy is an efficient, low-cost technology that has potential as an accurate method in detecting the nitrogen content of natural rubber leaves. Successive projections algorithm (SPA) is a widely used variable selection method for multivariate calibration, which uses projection operations to select a variable subset with minimum multi-collinearity. However, due to the fluctuation of correlation between variables, high collinearity may still exist in non-adjacent variables of subset obtained by basic SPA. Based on analysis to the correlation matrix of the spectra data, this paper proposed a correlation-based SPA (CB-SPA) to apply the successive projections algorithm in regions with consistent correlation. The result shows that CB-SPA can select variable subsets with more valuable variables and less multi-collinearity. Meanwhile, models established by the CB-SPA subset outperform basic SPA subsets in predicting nitrogen content in terms of both cross-validation and external prediction. Moreover, CB-SPA is assured to be more efficient, for the time cost in its selection procedure is one-twelfth that of the basic SPA.

A test of the hypothesis that correlational selection generates genetic correlations.

PubMed

Roff, Derek A; Fairbairn, Daphne J

2012-09-01

Theory predicts that correlational selection on two traits will cause the major axis of the bivariate G matrix to orient itself in the same direction as the correlational selection gradient. Two testable predictions follow from this: for a given pair of traits, (1) the sign of correlational selection gradient should be the same as that of the genetic correlation, and (2) the correlational selection gradient should be positively correlated with the value of the genetic correlation. We test this hypothesis with a meta-analysis utilizing empirical estimates of correlational selection gradients and measures of the correlation between the two focal traits. Our results are consistent with both predictions and hence support the underlying hypothesis that correlational selection generates a genetic correlation between the two traits and hence orients the bivariate G matrix. © 2012 The Author(s). Evolution© 2012 The Society for the Study of Evolution.

Matrix product algorithm for stochastic dynamics on networks applied to nonequilibrium Glauber dynamics

NASA Astrophysics Data System (ADS)

Barthel, Thomas; De Bacco, Caterina; Franz, Silvio

2018-01-01

We introduce and apply an efficient method for the precise simulation of stochastic dynamical processes on locally treelike graphs. Networks with cycles are treated in the framework of the cavity method. Such models correspond, for example, to spin-glass systems, Boolean networks, neural networks, or other technological, biological, and social networks. Building upon ideas from quantum many-body theory, our approach is based on a matrix product approximation of the so-called edge messages—conditional probabilities of vertex variable trajectories. Computation costs and accuracy can be tuned by controlling the matrix dimensions of the matrix product edge messages (MPEM) in truncations. In contrast to Monte Carlo simulations, the algorithm has a better error scaling and works for both single instances as well as the thermodynamic limit. We employ it to examine prototypical nonequilibrium Glauber dynamics in the kinetic Ising model. Because of the absence of cancellation effects, observables with small expectation values can be evaluated accurately, allowing for the study of decay processes and temporal correlations.

Coupled-cluster based R-matrix codes (CCRM): Recent developments

NASA Astrophysics Data System (ADS)

Sur, Chiranjib; Pradhan, Anil K.

2008-05-01

We report the ongoing development of the new coupled-cluster R-matrix codes (CCRM) for treating electron-ion scattering and radiative processes within the framework of the relativistic coupled-cluster method (RCC), interfaced with the standard R-matrix methodology. The RCC method is size consistent and in principle equivalent to an all-order many-body perturbation theory. The RCC method is one of the most accurate many-body theories, and has been applied for several systems. This project should enable the study of electron-interactions with heavy atoms/ions, utilizing not only high speed computing platforms but also improved theoretical description of the relativistic and correlation effects for the target atoms/ions as treated extensively within the RCC method. Here we present a comprehensive outline of the newly developed theoretical method and a schematic representation of the new suite of CCRM codes. We begin with the flowchart and description of various stages involved in this development. We retain the notations and nomenclature of different stages as analogous to the standard R-matrix codes.

Multi-Target Angle Tracking Algorithm for Bistatic MIMO Radar Based on the Elements of the Covariance Matrix

PubMed Central

Zhang, Zhengyan; Zhang, Jianyun; Zhou, Qingsong; Li, Xiaobo

2018-01-01

In this paper, we consider the problem of tracking the direction of arrivals (DOA) and the direction of departure (DOD) of multiple targets for bistatic multiple-input multiple-output (MIMO) radar. A high-precision tracking algorithm for target angle is proposed. First, the linear relationship between the covariance matrix difference and the angle difference of the adjacent moment was obtained through three approximate relations. Then, the proposed algorithm obtained the relationship between the elements in the covariance matrix difference. On this basis, the performance of the algorithm was improved by averaging the covariance matrix element. Finally, the least square method was used to estimate the DOD and DOA. The algorithm realized the automatic correlation of the angle and provided better performance when compared with the adaptive asymmetric joint diagonalization (AAJD) algorithm. The simulation results demonstrated the effectiveness of the proposed algorithm. The algorithm provides the technical support for the practical application of MIMO radar. PMID:29518957

Multi-Target Angle Tracking Algorithm for Bistatic Multiple-Input Multiple-Output (MIMO) Radar Based on the Elements of the Covariance Matrix.

PubMed

Zhang, Zhengyan; Zhang, Jianyun; Zhou, Qingsong; Li, Xiaobo

2018-03-07

In this paper, we consider the problem of tracking the direction of arrivals (DOA) and the direction of departure (DOD) of multiple targets for bistatic multiple-input multiple-output (MIMO) radar. A high-precision tracking algorithm for target angle is proposed. First, the linear relationship between the covariance matrix difference and the angle difference of the adjacent moment was obtained through three approximate relations. Then, the proposed algorithm obtained the relationship between the elements in the covariance matrix difference. On this basis, the performance of the algorithm was improved by averaging the covariance matrix element. Finally, the least square method was used to estimate the DOD and DOA. The algorithm realized the automatic correlation of the angle and provided better performance when compared with the adaptive asymmetric joint diagonalization (AAJD) algorithm. The simulation results demonstrated the effectiveness of the proposed algorithm. The algorithm provides the technical support for the practical application of MIMO radar.

Low-Power Architectures for Large Radio Astronomy Correlators

NASA Technical Reports Server (NTRS)

D'Addario, Larry R.

2011-01-01

The architecture of a cross-correlator for a synthesis radio telescope with N greater than 1000 antennas is studied with the objective of minimizing power consumption. It is found that the optimum architecture minimizes memory operations, and this implies preference for a matrix structure over a pipeline structure and avoiding the use of memory banks as accumulation registers when sharing multiply-accumulators among baselines. A straw-man design for N = 2000 and bandwidth of 1 GHz, based on ASICs fabricated in a 90 nm CMOS process, is presented. The cross-correlator proper (excluding per-antenna processing) is estimated to consume less than 35 kW.

Aggrecan-based extracellular matrix shows unique cortical features and conserved subcortical principles of mammalian brain organization in the Madagascan lesser hedgehog tenrec (Echinops telfairi Martin, 1838).

PubMed

Morawski, M; Brückner, G; Jäger, C; Seeger, G; Künzle, H; Arendt, T

2010-02-03

The Madagascan tenrecs (Afrotheria), an ancient mammalian clade, are characterized by unique brain anatomy. Striking features are an expanded paleocortex but a small and poorly differentiated neocortex devoid of a distinct granular layer IV. To investigate the organization of cortical areas we analyzed extracellular matrix components in perineuronal nets (PNs) using antibodies to aggrecan, lectin staining and hyaluronan-binding protein. Selected subcortical regions were studied to correlate the cortical patterns with features in evolutionary conserved systems. In the neocortex, paleocortex and hippocampus PNs were associated with nonpyramidal neurons. Quantitative analysis in the cerebral cortex revealed area-specific proportions and laminar distribution patterns of neurons ensheathed by PNs. Cortical PNs showed divergent structural phenotypes. Diffuse PNs forming a cotton wool-like perisomatic rim were characteristic of the paleocortex. These PNs were associated with a dense pericellular plexus of calretinin-immunoreactive fibres. Clearly contoured PNs were devoid of a calretinin-positive plexus and predominated in the neocortex and hippocampus. The organization of the extracellular matrix in subcortical nuclei followed the widely distributed mammalian type. We conclude that molecular properties of the aggrecan-based extracellular matrix are conserved during evolution of mammals; however, the matrix scaffold is adapted to specific wiring patterns of cortical and subcortical neuronal networks. Copyright 2010 IBRO. Published by Elsevier Ltd. All rights reserved.

Simulation of speckle patterns with pre-defined correlation distributions.

PubMed

Song, Lipei; Zhou, Zhen; Wang, Xueyan; Zhao, Xing; Elson, Daniel S

2016-03-01

We put forward a method to easily generate a single or a sequence of fully developed speckle patterns with pre-defined correlation distribution by utilizing the principle of coherent imaging. The few-to-one mapping between the input correlation matrix and the correlation distribution between simulated speckle patterns is realized and there is a simple square relationship between the values of these two correlation coefficient sets. This method is demonstrated both theoretically and experimentally. The square relationship enables easy conversion from any desired correlation distribution. Since the input correlation distribution can be defined by a digital matrix or a gray-scale image acquired experimentally, this method provides a convenient way to simulate real speckle-related experiments and to evaluate data processing techniques.

Simulation of speckle patterns with pre-defined correlation distributions

PubMed Central

Song, Lipei; Zhou, Zhen; Wang, Xueyan; Zhao, Xing; Elson, Daniel S.

2016-01-01

We put forward a method to easily generate a single or a sequence of fully developed speckle patterns with pre-defined correlation distribution by utilizing the principle of coherent imaging. The few-to-one mapping between the input correlation matrix and the correlation distribution between simulated speckle patterns is realized and there is a simple square relationship between the values of these two correlation coefficient sets. This method is demonstrated both theoretically and experimentally. The square relationship enables easy conversion from any desired correlation distribution. Since the input correlation distribution can be defined by a digital matrix or a gray-scale image acquired experimentally, this method provides a convenient way to simulate real speckle-related experiments and to evaluate data processing techniques. PMID:27231589

Neutrino CP violation and sign of baryon asymmetry in the minimal seesaw model

NASA Astrophysics Data System (ADS)

Shimizu, Yusuke; Takagi, Kenta; Tanimoto, Morimitsu

2018-03-01

We discuss the correlation between the CP violating Dirac phase of the lepton mixing matrix and the cosmological baryon asymmetry based on the leptogenesis in the minimal seesaw model with two right-handed Majorana neutrinos and the trimaximal mixing for neutrino flavors. The sign of the CP violating Dirac phase at low energy is fixed by the observed cosmological baryon asymmetry since there is only one phase parameter in the model. According to the recent T2K and NOνA data of the CP violation, the Dirac neutrino mass matrix of our model is fixed only for the normal hierarchy of neutrino masses.

Quantifying Complexity in Quantum Phase Transitions via Mutual Information Complex Networks

NASA Astrophysics Data System (ADS)

Valdez, Marc Andrew; Jaschke, Daniel; Vargas, David L.; Carr, Lincoln D.

2017-12-01

We quantify the emergent complexity of quantum states near quantum critical points on regular 1D lattices, via complex network measures based on quantum mutual information as the adjacency matrix, in direct analogy to quantifying the complexity of electroencephalogram or functional magnetic resonance imaging measurements of the brain. Using matrix product state methods, we show that network density, clustering, disparity, and Pearson's correlation obtain the critical point for both quantum Ising and Bose-Hubbard models to a high degree of accuracy in finite-size scaling for three classes of quantum phase transitions, Z2, mean field superfluid to Mott insulator, and a Berzinskii-Kosterlitz-Thouless crossover.

Birefringence and vascular imaging of in vivo human skin by Jones-matrix optical coherence tomography

NASA Astrophysics Data System (ADS)

Li, En; Makita, Shuichi; Hong, Young-Joo; Kasaragod, Deepa; Yasuno, Yoshiaki

2017-02-01

A customized 1310-nm Jones-matrix optical coherence tomography (JM-OCT) for dermatological investigation was constructed and used for in vivo normal human skin tissue imaging. This system can simultaneously measure the threedimensional depth-resolved local birefringence, complex-correlation based OCT angiography (OCT-A), degree-ofpolarization- uniformity (DOPU) and scattering OCT intensity. By obtaining these optical properties of tissue, the morphology, vasculature, and collagen content of skin can be deduced and visualized. Structures in the deep layers of the epithelium were observed with depth-resolved local birefringence and polarization uniformity images. These results suggest high diagnostic and investigative potential of JM-OCT for dermatology.

Multireference quantum chemistry through a joint density matrix renormalization group and canonical transformation theory.

PubMed

Yanai, Takeshi; Kurashige, Yuki; Neuscamman, Eric; Chan, Garnet Kin-Lic

2010-01-14

We describe the joint application of the density matrix renormalization group and canonical transformation theory to multireference quantum chemistry. The density matrix renormalization group provides the ability to describe static correlation in large active spaces, while the canonical transformation theory provides a high-order description of the dynamic correlation effects. We demonstrate the joint theory in two benchmark systems designed to test the dynamic and static correlation capabilities of the methods, namely, (i) total correlation energies in long polyenes and (ii) the isomerization curve of the [Cu(2)O(2)](2+) core. The largest complete active spaces and atomic orbital basis sets treated by the joint DMRG-CT theory in these systems correspond to a (24e,24o) active space and 268 atomic orbitals in the polyenes and a (28e,32o) active space and 278 atomic orbitals in [Cu(2)O(2)](2+).

Model correlation and damage location for large space truss structures: Secant method development and evaluation

NASA Technical Reports Server (NTRS)

Smith, Suzanne Weaver; Beattie, Christopher A.

1991-01-01

On-orbit testing of a large space structure will be required to complete the certification of any mathematical model for the structure dynamic response. The process of establishing a mathematical model that matches measured structure response is referred to as model correlation. Most model correlation approaches have an identification technique to determine structural characteristics from the measurements of the structure response. This problem is approached with one particular class of identification techniques - matrix adjustment methods - which use measured data to produce an optimal update of the structure property matrix, often the stiffness matrix. New methods were developed for identification to handle problems of the size and complexity expected for large space structures. Further development and refinement of these secant-method identification algorithms were undertaken. Also, evaluation of these techniques is an approach for model correlation and damage location was initiated.

Aggregation of carbon dioxide sequestration storage assessment units

USGS Publications Warehouse

Blondes, Madalyn S.; Schuenemeyer, John H.; Olea, Ricardo A.; Drew, Lawrence J.

2013-01-01

The U.S. Geological Survey is currently conducting a national assessment of carbon dioxide (CO2) storage resources, mandated by the Energy Independence and Security Act of 2007. Pre-emission capture and storage of CO2 in subsurface saline formations is one potential method to reduce greenhouse gas emissions and the negative impact of global climate change. Like many large-scale resource assessments, the area under investigation is split into smaller, more manageable storage assessment units (SAUs), which must be aggregated with correctly propagated uncertainty to the basin, regional, and national scales. The aggregation methodology requires two types of data: marginal probability distributions of storage resource for each SAU, and a correlation matrix obtained by expert elicitation describing interdependencies between pairs of SAUs. Dependencies arise because geologic analogs, assessment methods, and assessors often overlap. The correlation matrix is used to induce rank correlation, using a Cholesky decomposition, among the empirical marginal distributions representing individually assessed SAUs. This manuscript presents a probabilistic aggregation method tailored to the correlations and dependencies inherent to a CO2 storage assessment. Aggregation results must be presented at the basin, regional, and national scales. A single stage approach, in which one large correlation matrix is defined and subsets are used for different scales, is compared to a multiple stage approach, in which new correlation matrices are created to aggregate intermediate results. Although the single-stage approach requires determination of significantly more correlation coefficients, it captures geologic dependencies among similar units in different basins and it is less sensitive to fluctuations in low correlation coefficients than the multiple stage approach. Thus, subsets of one single-stage correlation matrix are used to aggregate to basin, regional, and national scales.

Static and dynamic factors in an information-based multi-asset artificial stock market

NASA Astrophysics Data System (ADS)

Ponta, Linda; Pastore, Stefano; Cincotti, Silvano

2018-02-01

An information-based multi-asset artificial stock market characterized by different types of stocks and populated by heterogeneous agents is presented. In the market, agents trade risky assets in exchange for cash. Beside the amount of cash and of stocks owned, each agent is characterized by sentiments and agents share their sentiments by means of interactions that are determined by sparsely connected networks. A central market maker (clearing house mechanism) determines the price processes for each stock at the intersection of the demand and the supply curves. Single stock price processes exhibit volatility clustering and fat-tailed distribution of returns whereas multivariate price process exhibits both static and dynamic stylized facts, i.e., the presence of static factors and common trends. Static factors are studied making reference to the cross-correlation of returns of different stocks. The common trends are investigated considering the variance-covariance matrix of prices. Results point out that the probability distribution of eigenvalues of the cross-correlation matrix of returns shows the presence of sectors, similar to those observed on real empirical data. As regarding the dynamic factors, the variance-covariance matrix of prices point out a limited number of assets prices series that are independent integrated processes, in close agreement with the empirical evidence of asset price time series of real stock markets. These results remarks the crucial dependence of statistical properties of multi-assets stock market on the agents' interaction structure.

Content and distribution of noncollagenous matrix proteins in bone and cementum: relationship to speed of formation and collagen packing density.

PubMed

Nanci, A

1999-06-30

The organic matrix of collagen-based calcified tissues consists of a supporting collagen meshwork and various noncollagenous matrix proteins (NCPs). Together, they contribute to determining the structure and biomechanical properties of the tissue. Their respective organization and interrelation can advantageously be examined by immunocytochemistry, an approach which allows correlation of composition with structure. The aim of this article is to review postembedding immuno- and lectin-gold-labeling data on the characterization of the noncollagenous compartment in rat and human bone and cementum, and on its relationship to collagen. The two major NCPs, bone sialoprotein and osteopontin, generally codistribute and accumulate in cement lines and in the spaces among the mineralized collagen fibrils. However, there are variations in their distribution and density of labeling throughout the tissue. Indeed, bone and cementum can form in environments that are either poor or enriched in NCPs. The amount of NCPs generally correlates with bone and cementum types and with speed of formation of the tissue and packing density of collagen fibrils. Taken together, the data suggest that production of both collagenous and noncollagenous constituents can be "modulated" during formation of collagen-based calcified tissues. It is concluded that, in addition to structural and compositional parameters, tissue dynamics must be taken into consideration in order to understand the significance of the apparent accumulation of NCPs at some sites and to determine the mechanisms of normal and pathological calcified tissue formation. Copyright 1999 Academic Press.

Light-Cone and Diffusive Propagation of Correlations in a Many-Body Dissipative System.

PubMed

Bernier, Jean-Sébastien; Tan, Ryan; Bonnes, Lars; Guo, Chu; Poletti, Dario; Kollath, Corinna

2018-01-12

We analyze the propagation of correlations after a sudden interaction change in a strongly interacting quantum system in contact with an environment. In particular, we consider an interaction quench in the Bose-Hubbard model, deep within the Mott-insulating phase, under the effect of dephasing. We observe that dissipation effectively speeds up the propagation of single-particle correlations while reducing their coherence. In contrast, for two-point density correlations, the initial ballistic propagation regime gives way to diffusion at intermediate times. Numerical simulations, based on a time-dependent matrix product state algorithm, are supplemented by a quantitatively accurate fermionic quasiparticle approach providing an intuitive description of the initial dynamics in terms of holon and doublon excitations.

Cross-Correlations and Structures of Aero-Engine Gas Path System Based on DCCA Coefficient and Rooted Tree

NASA Astrophysics Data System (ADS)

Dong, Keqiang; Fan, Jie; Gao, You

2015-12-01

Identifying the mutual interaction is a crucial problem that facilitates the understanding of emerging structures in complex system. We here focus on aero-engine dynamic as an example of complex system. By applying the detrended cross-correlation analysis (DCCA) coefficient method to aero-engine gas path system, we find that the low-spool rotor speed (N1) and high-spool rotor speed (N2) fluctuation series exhibit cross-correlation characteristic. Further, we employ detrended cross-correlation coefficient matrix and rooted tree to investigate the mutual interactions of other gas path variables. The results can infer that the exhaust gas temperature (EGT), N1, N2, fuel flow (WF) and engine pressure ratio (EPR) are main gas path parameters.

Network trending; leadership, followership and neutrality among companies: A random matrix approach

NASA Astrophysics Data System (ADS)

Mobarhan, N. S. Safavi; Saeedi, A.; Roodposhti, F. Rahnamay; Jafari, G. R.

2016-11-01

In this article, we analyze the cross-correlation between returns of different stocks to answer the following important questions. The first one is: If there exists collective behavior in a financial market, how could we detect it? And the second question is: Is there a particular company among the companies of a market as the leader of the collective behavior? Or is there no specified leadership governing the system similar to some complex systems? We use the method of random matrix theory to answer the mentioned questions. Cross-correlation matrix of index returns of four different markets is analyzed. The participation ratio quantity related to each matrices' eigenvectors and the eigenvalue spectrum is calculated. We introduce shuffled-matrix created of cross correlation matrix in such a way that the elements of the later one are displaced randomly. Comparing the participation ratio quantities obtained from a correlation matrix of a market and its related shuffled-one, on the bulk distribution region of the eigenvalues, we detect a meaningful deviation between the mentioned quantities indicating the collective behavior of the companies forming the market. By calculating the relative deviation of participation ratios, we obtain a measure to compare the markets according to their collective behavior. Answering the second question, we show there are three groups of companies: The first group having higher impact on the market trend called leaders, the second group is followers and the third one is the companies who have not a considerable role in the trend. The results can be utilized in portfolio construction.

Fast-GPU-PCC: A GPU-Based Technique to Compute Pairwise Pearson's Correlation Coefficients for Time Series Data-fMRI Study.

PubMed

Eslami, Taban; Saeed, Fahad

2018-04-20

Functional magnetic resonance imaging (fMRI) is a non-invasive brain imaging technique, which has been regularly used for studying brain’s functional activities in the past few years. A very well-used measure for capturing functional associations in brain is Pearson’s correlation coefficient. Pearson’s correlation is widely used for constructing functional network and studying dynamic functional connectivity of the brain. These are useful measures for understanding the effects of brain disorders on connectivities among brain regions. The fMRI scanners produce huge number of voxels and using traditional central processing unit (CPU)-based techniques for computing pairwise correlations is very time consuming especially when large number of subjects are being studied. In this paper, we propose a graphics processing unit (GPU)-based algorithm called Fast-GPU-PCC for computing pairwise Pearson’s correlation coefficient. Based on the symmetric property of Pearson’s correlation, this approach returns N ( N − 1 ) / 2 correlation coefficients located at strictly upper triangle part of the correlation matrix. Storing correlations in a one-dimensional array with the order as proposed in this paper is useful for further usage. Our experiments on real and synthetic fMRI data for different number of voxels and varying length of time series show that the proposed approach outperformed state of the art GPU-based techniques as well as the sequential CPU-based versions. We show that Fast-GPU-PCC runs 62 times faster than CPU-based version and about 2 to 3 times faster than two other state of the art GPU-based methods.

Isolation and characterization of chicken bile matrix metalloproteinase

USDA-ARS?s Scientific Manuscript database

Avian bile is rich in matrix metalloproteinases (MMP), the enzymes that cleave extracellular matrix (ECM) proteins such as collagens and proteoglycans. Changes in bile MMP expression have been correlated with hepatic and gall bladder pathologies but the significance of their expression in normal, he...

Partial covariance based functional connectivity computation using Ledoit-Wolf covariance regularization.

PubMed

Brier, Matthew R; Mitra, Anish; McCarthy, John E; Ances, Beau M; Snyder, Abraham Z

2015-11-01

Functional connectivity refers to shared signals among brain regions and is typically assessed in a task free state. Functional connectivity commonly is quantified between signal pairs using Pearson correlation. However, resting-state fMRI is a multivariate process exhibiting a complicated covariance structure. Partial covariance assesses the unique variance shared between two brain regions excluding any widely shared variance, hence is appropriate for the analysis of multivariate fMRI datasets. However, calculation of partial covariance requires inversion of the covariance matrix, which, in most functional connectivity studies, is not invertible owing to rank deficiency. Here we apply Ledoit-Wolf shrinkage (L2 regularization) to invert the high dimensional BOLD covariance matrix. We investigate the network organization and brain-state dependence of partial covariance-based functional connectivity. Although RSNs are conventionally defined in terms of shared variance, removal of widely shared variance, surprisingly, improved the separation of RSNs in a spring embedded graphical model. This result suggests that pair-wise unique shared variance plays a heretofore unrecognized role in RSN covariance organization. In addition, application of partial correlation to fMRI data acquired in the eyes open vs. eyes closed states revealed focal changes in uniquely shared variance between the thalamus and visual cortices. This result suggests that partial correlation of resting state BOLD time series reflect functional processes in addition to structural connectivity. Copyright © 2015 Elsevier Inc. All rights reserved.

Partial covariance based functional connectivity computation using Ledoit-Wolf covariance regularization

PubMed Central

Brier, Matthew R.; Mitra, Anish; McCarthy, John E.; Ances, Beau M.; Snyder, Abraham Z.

2015-01-01

Functional connectivity refers to shared signals among brain regions and is typically assessed in a task free state. Functional connectivity commonly is quantified between signal pairs using Pearson correlation. However, resting-state fMRI is a multivariate process exhibiting a complicated covariance structure. Partial covariance assesses the unique variance shared between two brain regions excluding any widely shared variance, hence is appropriate for the analysis of multivariate fMRI datasets. However, calculation of partial covariance requires inversion of the covariance matrix, which, in most functional connectivity studies, is not invertible owing to rank deficiency. Here we apply Ledoit-Wolf shrinkage (L2 regularization) to invert the high dimensional BOLD covariance matrix. We investigate the network organization and brain-state dependence of partial covariance-based functional connectivity. Although RSNs are conventionally defined in terms of shared variance, removal of widely shared variance, surprisingly, improved the separation of RSNs in a spring embedded graphical model. This result suggests that pair-wise unique shared variance plays a heretofore unrecognized role in RSN covariance organization. In addition, application of partial correlation to fMRI data acquired in the eyes open vs. eyes closed states revealed focal changes in uniquely shared variance between the thalamus and visual cortices. This result suggests that partial correlation of resting state BOLD time series reflect functional processes in addition to structural connectivity. PMID:26208872

Decay of correlations between cross-polarized electromagnetic waves in a two-dimensional random medium.

PubMed

Gorodnichev, E E

2018-04-01

The problem of multiple scattering of polarized light in a two-dimensional medium composed of fiberlike inhomogeneities is studied. The attenuation lengths for the density matrix elements are calculated. For a highly absorbing medium it is found that, as the sample thickness increases, the intensity of waves polarized along the fibers decays faster than the other density matrix elements. With further increase in the sample thickness, the off-diagonal elements which are responsible for correlations between the cross-polarized waves disappear. In the asymptotic limit of very thick samples the scattered light proves to be polarized perpendicular to the fibers. The difference in the attenuation lengths between the density matrix elements results in a nonmonotonic depth dependence of the degree of polarization. In the opposite case of a weakly absorbing medium, the off-diagonal element of the density matrix and, correspondingly, the correlations between the cross-polarized fields are shown to decay faster than the intensity of waves polarized along and perpendicular to the fibers.

A Planning Approach of Engineering Characteristics Based on QFD-TRIZ Integrated

NASA Astrophysics Data System (ADS)

Liu, Shang; Shi, Dongyan; Zhang, Ying

Traditional QFD planning method compromises contradictions between engineering characteristics to achieve higher customer satisfaction. However, this compromise trade-off can not eliminate the contradictions existing among the engineering characteristics which limited the overall customer satisfaction. QFD (Quality function deployment) integrated with TRIZ (the Russian acronym of the Theory of Inventive Problem Solving) becomes hot research recently for TRIZ can be used to solve contradictions between engineering characteristics which construct the roof of HOQ (House of quality). But, the traditional QFD planning approach is not suitable for QFD integrated with TRIZ for that TRIZ requires emphasizing the contradictions between engineering characteristics at problem definition stage instead of compromising trade-off. So, a new planning approach based on QFD / TRIZ integration is proposed in this paper, which based on the consideration of the correlation matrix of engineering characteristics and customer satisfaction on the basis of cost. The proposed approach suggests that TRIZ should be applied to solve contradictions at the first step, and the correlation matrix of engineering characteristics should be amended at the second step, and at next step IFR (ideal final result) must be validated, then planning execute. An example is used to illustrate the proposed approach. The application indicated that higher customer satisfaction can be met and the contradictions between the characteristic parameters are eliminated.

Ground reaction force analysed with correlation coefficient matrix in group of stroke patients.

PubMed

Szczerbik, Ewa; Krawczyk, Maciej; Syczewska, Małgorzata

2014-01-01

Stroke is the third cause of death in contemporary society and causes many disorders. Clinical scales, ground reaction force (GRF) and objective gait analysis are used for assessment of patient's rehabilitation progress during treatment. The goal of this paper is to assess whether signal correlation coefficient matrix applied to GRF can be used for evaluation of the status of post-stroke patients. A group of patients underwent clinical assessment and instrumented gait analysis simultaneously three times. The difference between components of patient's GRF (vertical, fore/aft, med/lat) and normal ones (reference GRF of healthy subjects) was calculated as correlation coefficient. Patients were divided into two groups ("worse" and "better") based on the clinical functional scale tests done at the beginning of rehabilitation process. The results obtained by these two groups were compared using statistical analysis. An increase of median value of correlation coefficient is observed in all components of GRF, but only in non-paretic leg. Analysis of GRF signal can be helpful in assessment of post-stroke patients during rehabilitation. Improvement in stroke patients was observed in non-paretic leg of the "worse" group. GRF analysis should not be the only tool for objective validation of patient's improvement, but could be used as additional source of information.

Matrix elements of N-particle explicitly correlated Gaussian basis functions with complex exponential parameters

NASA Astrophysics Data System (ADS)

Bubin, Sergiy; Adamowicz, Ludwik

2006-06-01

In this work we present analytical expressions for Hamiltonian matrix elements with spherically symmetric, explicitly correlated Gaussian basis functions with complex exponential parameters for an arbitrary number of particles. The expressions are derived using the formalism of matrix differential calculus. In addition, we present expressions for the energy gradient that includes derivatives of the Hamiltonian integrals with respect to the exponential parameters. The gradient is used in the variational optimization of the parameters. All the expressions are presented in the matrix form suitable for both numerical implementation and theoretical analysis. The energy and gradient formulas have been programed and used to calculate ground and excited states of the He atom using an approach that does not involve the Born-Oppenheimer approximation.

Matrix elements of N-particle explicitly correlated Gaussian basis functions with complex exponential parameters.

PubMed

Bubin, Sergiy; Adamowicz, Ludwik

2006-06-14

In this work we present analytical expressions for Hamiltonian matrix elements with spherically symmetric, explicitly correlated Gaussian basis functions with complex exponential parameters for an arbitrary number of particles. The expressions are derived using the formalism of matrix differential calculus. In addition, we present expressions for the energy gradient that includes derivatives of the Hamiltonian integrals with respect to the exponential parameters. The gradient is used in the variational optimization of the parameters. All the expressions are presented in the matrix form suitable for both numerical implementation and theoretical analysis. The energy and gradient formulas have been programmed and used to calculate ground and excited states of the He atom using an approach that does not involve the Born-Oppenheimer approximation.

System of multifunctional Jones matrix tomography of phase anisotropy in diagnostics of endometriosis

NASA Astrophysics Data System (ADS)

Ushenko, V. O.; Koval, G. D.; Ushenko, Yu. O.; Pidkamin, L. Y.; Sidor, M. I.; Vanchuliak, O.; Motrich, A. V.; Gorsky, M. P.; Meglinskiy, I.

2017-09-01

The paper presents the results of Jones-matrix mapping of uterine wall histological sections with second-degree and third-degree endometriosis. The technique of experimental measurement of coordinate distributions of the modulus and phase values of Jones matrix elements is suggested. Within the statistical and cross-correlation approaches the modulus and phase maps of Jones matrix images of optically thin biological layers of polycrystalline films of plasma and cerebrospinal fluid are analyzed. A set of objective parameters (statistical and generalized correlation moments), which are the most sensitive to changes in the phase of anisotropy, associated with the features of polycrystalline structure of uterine wall histological sections with second-degree and third-degree endometriosis are determined.

Biodistance analysis of the Moche sacrificial victims from Huaca de la Luna plaza 3C: Matrix method test of their origins.

PubMed

Sutter, Richard C; Verano, John W

2007-02-01

The purpose of this study is to test two competing models regarding the origins of Early Intermediate Period (AD 200-750) sacrificial victims from the Huacas de Moche site using the matrix correlation method. The first model posits the sacrificial victims represent local elites who lost competitions in ritual battles with one another, while the other model suggests the victims were nonlocal warriors captured during warfare with nearby polities. We estimate biodistances for sacrificial victims from Huaca de la Luna Plaza 3C (AD 300-550) with eight previously reported samples from the north coast of Peru using both the mean measure of divergence (MMD) and Mahalanobis' distance (d2). Hypothetical matrices are developed based upon the assumptions of each of the two competing models regarding the origins of Moche sacrificial victims. When the MMD matrix is compared to the two hypothetical matrices using a partial-Mantel test (Smouse et al.: Syst Zool 35 (1986) 627-632), the ritual combat model (i.e. local origins) has a low and nonsignificant correlation (r = 0.134, P = 0.163), while the nonlocal origins model is highly correlated and significant (r = 0.688, P = 0.001). Comparisons of the d2 results and the two hypothetical matrices also produced low and nonsignificant correlation for the ritual combat model (r = 0.210, P = 0.212), while producing a higher and statistically significant result with the nonlocal origins model (r = 0.676, P = 0.002). We suggest that the Moche sacrificial victims represent nonlocal warriors captured in territorial combat with nearby competing polities. Copyright 2006 Wiley-Liss, Inc.

Protein structure similarity from Principle Component Correlation analysis.

PubMed

Zhou, Xiaobo; Chou, James; Wong, Stephen T C

2006-01-25

Owing to rapid expansion of protein structure databases in recent years, methods of structure comparison are becoming increasingly effective and important in revealing novel information on functional properties of proteins and their roles in the grand scheme of evolutionary biology. Currently, the structural similarity between two proteins is measured by the root-mean-square-deviation (RMSD) in their best-superimposed atomic coordinates. RMSD is the golden rule of measuring structural similarity when the structures are nearly identical; it, however, fails to detect the higher order topological similarities in proteins evolved into different shapes. We propose new algorithms for extracting geometrical invariants of proteins that can be effectively used to identify homologous protein structures or topologies in order to quantify both close and remote structural similarities. We measure structural similarity between proteins by correlating the principle components of their secondary structure interaction matrix. In our approach, the Principle Component Correlation (PCC) analysis, a symmetric interaction matrix for a protein structure is constructed with relationship parameters between secondary elements that can take the form of distance, orientation, or other relevant structural invariants. When using a distance-based construction in the presence or absence of encoded N to C terminal sense, there are strong correlations between the principle components of interaction matrices of structurally or topologically similar proteins. The PCC method is extensively tested for protein structures that belong to the same topological class but are significantly different by RMSD measure. The PCC analysis can also differentiate proteins having similar shapes but different topological arrangements. Additionally, we demonstrate that when using two independently defined interaction matrices, comparison of their maximum eigenvalues can be highly effective in clustering structurally or topologically similar proteins. We believe that the PCC analysis of interaction matrix is highly flexible in adopting various structural parameters for protein structure comparison.

Human aqueous humor levels of transforming growth factor-β2: Association with matrix metalloproteinases/tissue inhibitors of matrix metalloproteinases

PubMed Central

Jia, Yan; Yue, Yu; Hu, Dan-Ning; Chen, Ji-Li; Zhou, Ji-Bo

2017-01-01

The present study aims to investigate the association of transforming growth factor-β2 (TGF-β2) and matrix metalloproteinases (MMPs), MMP-2 and MMP-3, and tissue inhibitors of matrix metalloproteinases (TIMPs), TIMP-1, TIMP-2 and TIMP-3 in the aqueous humor of patients with high myopia or cataracts. The levels of TGF-β2 and MMPs/TIMPs were measured with the Luminex xMAP Technology using commercially available Milliplex xMAP kits. The association between TGF-β2 and MMPs/TIMPs levels was analyzed using the Spearmans correlation test. The levels of TGF-β2 were identified to be positively correlated with the levels of TIMP-1 and TIMP-3 (TIMP-1: r=0.334; P=0.007; TIMP-3: r=0.309; P=0.012). The levels of MMP-2, MMP-3 and TIMP-2 did not significantly correlate with TGF-β2 levels (P>0.05). A positive correlation was identified between TGF-β2 and TIMPs in the aqueous humor of human eyes with elongated axial length. It appears that TGF-β2 stimulates the expression of TIMPs as a compensatory reaction to the development of high myopia. PMID:29188062

Investigation of damage mechanisms in a cross-ply metal-matrix composite under thermomechanical loading. Master's thesis

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

Schubbe, J.J.

1990-12-01

Metal matrix composites (MMCs) are rapidly becoming strong candidates for high temperature and high stiffness structural applications such as the Advanced Tactical Fighter (ATF). This study systematically investigated the failure modes and associated damage in a cross-ply, (0/90)2s SCS6/Ti-15-3 metal matrix composite under in-phase and out-of-phase thermomechanic fatigue. Initiation and progression of fatigue damage were recorded and correlated to changes in Young's Modulus of the composite material. Experimental results show an internal stabilization of reaction zone size but degradation and separation from constituent materials under extended cyclic thermal loading. Critical to damage were transverse cracks initiating in the 90 degreesmore » plies, growing and coalescing from fiber/matrix interfaces internal to the specimen, progressing outward through the 0 degree plies before failure. Maximum mechanical strain at failure was determined to be approximately 0.0075 mm/mm. A correlation was made relating maximum matrix stress to failure life, resulting in a fatigue threshold limit of 280 MPa. An attempt was made to correlate the degradation in Young's Modulus (Damage=1-E/Eo) with the applied life cycles from different TMF tests.« less

Interleukin-8 is associated with adhesion, migration and invasion in human gastric cancer SCG-7901 cells.

PubMed

Ju, Dawei; Sun, Dazhi; Xiu, Lijuan; Meng, Xianze; Zhang, Cian; Wei, Pinkang

2012-03-01

Interleukin-8 is known as an important chemokine involved in tumor angiogenesis and progression. Overexpression of interleukin-8 has been detected in a variety of human tumors, including gastric cancer, and is negatively correlated with prognosis. The aim of our study is to determine the effects of interleukin-8 on proliferation, adhesion, migration and invasion abilities and correlated molecular mechanisms in gastric cancer. We made recombinant interleukin-8 ranged from 0 ng/ml to 100 ng/ml interferes in human gastric cancer SCG-7901 cells in vitro. The results shown that interleukin-8 did not change cell proliferation, but promoted cell adhesion to endothelial cell and extracellular matrix components (collagen, laminin and fibronectin) as detected by Cell Counting Kit-8. And it induced migration and invasion ability based on scratch and transwell-chamber assays. Also, interleukin-8 regulated the protein and mRNA expression of matrix metalloproteinase-9, intercellular adhesion molecule-1 and E-cad and there was obviously a dose-dependent relationship, but the protein or mRNA expression of matrix metalloproteinase-2 was not obviously changed under the tested conditions. Our findings indicate that interleukin-8 is associated with adhesion, migration and invasion in gastric cancer and the regulation of matrix metalloproteinase-9, intercellular adhesion molecule-1 and E-cad expression is one of the potential molecule mechanisms. The studies imply interleukin-8 may be an alternative treatment strategy against gastric cancer.

Numericware i: Identical by State Matrix Calculator

PubMed Central

Kim, Bongsong; Beavis, William D

2017-01-01

We introduce software, Numericware i, to compute identical by state (IBS) matrix based on genotypic data. Calculating an IBS matrix with a large dataset requires large computer memory and takes lengthy processing time. Numericware i addresses these challenges with 2 algorithmic methods: multithreading and forward chopping. The multithreading allows computational routines to concurrently run on multiple central processing unit (CPU) processors. The forward chopping addresses memory limitation by dividing a dataset into appropriately sized subsets. Numericware i allows calculation of the IBS matrix for a large genotypic dataset using a laptop or a desktop computer. For comparison with different software, we calculated genetic relationship matrices using Numericware i, SPAGeDi, and TASSEL with the same genotypic dataset. Numericware i calculates IBS coefficients between 0 and 2, whereas SPAGeDi and TASSEL produce different ranges of values including negative values. The Pearson correlation coefficient between the matrices from Numericware i and TASSEL was high at .9972, whereas SPAGeDi showed low correlation with Numericware i (.0505) and TASSEL (.0587). With a high-dimensional dataset of 500 entities by 10 000 000 SNPs, Numericware i spent 382 minutes using 19 CPU threads and 64 GB memory by dividing the dataset into 3 pieces, whereas SPAGeDi and TASSEL failed with the same dataset. Numericware i is freely available for Windows and Linux under CC-BY 4.0 license at https://figshare.com/s/f100f33a8857131eb2db. PMID:28469375

Parameterizing correlations between hydrometeor species in mixed-phase Arctic clouds

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

Larson, Vincent E.; Nielsen, Brandon J.; Fan, Jiwen

2011-08-16

Mixed-phase Arctic clouds, like other clouds, contain small-scale variability in hydrometeor fields, such as cloud water or snow mixing ratio. This variability may be worth parameterizing in coarse-resolution numerical models. In particular, for modeling processes such as accretion and aggregation, it would be useful to parameterize subgrid correlations among hydrometeor species. However, one difficulty is that there exist many hydrometeor species and many microphysical processes, leading to complexity and computational expense.Existing lower and upper bounds (inequalities) on linear correlation coefficients provide useful guidance, but these bounds are too loose to serve directly as a method to predict subgrid correlations. Therefore,more » this paper proposes an alternative method that is based on a blend of theory and empiricism. The method begins with the spherical parameterization framework of Pinheiro and Bates (1996), which expresses the correlation matrix in terms of its Cholesky factorization. The values of the elements of the Cholesky matrix are parameterized here using a cosine row-wise formula that is inspired by the aforementioned bounds on correlations. The method has three advantages: 1) the computational expense is tolerable; 2) the correlations are, by construction, guaranteed to be consistent with each other; and 3) the methodology is fairly general and hence may be applicable to other problems. The method is tested non-interactively using simulations of three Arctic mixed-phase cloud cases from two different field experiments: the Indirect and Semi-Direct Aerosol Campaign (ISDAC) and the Mixed-Phase Arctic Cloud Experiment (M-PACE). Benchmark simulations are performed using a large-eddy simulation (LES) model that includes a bin microphysical scheme. The correlations estimated by the new method satisfactorily approximate the correlations produced by the LES.« less

Combined infrared and ultraviolet-visible spectroscopy matrix-isolated carbon vapor

NASA Technical Reports Server (NTRS)

Kurtz, Joe; Huffman, Donald R.

1990-01-01

Infrared and UV-visible absorption spectra have been measured on the same sample of matrix-isolated carbon vapor in order to establish correlations between absorption intensities of vibrational and electronic transitions as a function of sample annealing. A high degree of correlation has been found between the IR feature at 1998/cm recently assigned to C8 and a UV absorption feature at about 3100 A. Thus, for the first time, direct evidence is given for the assignment of one of the unknown UV-visible features of the long-studied matrix-isolated carbon vapor spectrum.

Characterization of Al/crystallized Al-based metallic glass composites produced by repeated roll bonding process

NASA Astrophysics Data System (ADS)

Alizadeh, Morteza; Khoramkhorshid, Saba; Taghvaei, Amir Hossein; Gokuldoss, Prashanth Konda

2017-07-01

Devitrified Al84Gd6Ni7Co3 glassy particles have been used to reinforce Al-matrix composites through repeated roll bonding (RRB) process. Microstructural characterization of the produced composites after various rolling cycles was performed by scanning electron microscopy. Mechanical properties of the fabricated composites were evaluated by the tensile and microhardness tests. The results indicate that the RRB process is successful to produce composites with the negligible amount of flaws and porosity, and it is followed by homogeneous distribution of Al84Gd6Ni7Co3 particles in the Al matrix after nine rolling passes. Elongation of the composites improves significantly upon RRB cycles and the tensile strength and microhardness of them increase more than two times compared to unreinforced Al. According to fractography results, the enhanced mechanical properties are correlated with formation of excellent bonding at the interface of Al84Gd6Ni7Co3 particles and Al matrix. The theoretical values of composites hardness and yield strength calculated based on iso-strain model show a good agreement with respect to the experimental results.

A comparison of phenotypic variation and covariation patterns and the role of phylogeny, ecology, and ontogeny during cranial evolution of new world monkeys.

PubMed

Marroig, G; Cheverud, J M

2001-12-01

Similarity of genetic and phenotypic variation patterns among populations is important for making quantitative inferences about past evolutionary forces acting to differentiate populations and for evaluating the evolution of relationships among traits in response to new functional and developmental relationships. Here, phenotypic co variance and correlation structure is compared among Platyrrhine Neotropical primates. Comparisons range from among species within a genus to the superfamily level. Matrix correlation followed by Mantel's test and vector correlation among responses to random natural selection vectors (random skewers) were used to compare correlation and variance/covariance matrices of 39 skull traits. Sampling errors involved in matrix estimates were taken into account in comparisons using matrix repeatability to set upper limits for each pairwise comparison. Results indicate that covariance structure is not strictly constant but that the amount of variance pattern divergence observed among taxa is generally low and not associated with taxonomic distance. Specific instances of divergence are identified. There is no correlation between the amount of divergence in covariance patterns among the 16 genera and their phylogenetic distance derived from a conjoint analysis of four already published nuclear gene datasets. In contrast, there is a significant correlation between phylogenetic distance and morphological distance (Mahalanobis distance among genus centroids). This result indicates that while the phenotypic means were evolving during the last 30 millions years of New World monkey evolution, phenotypic covariance structures of Neotropical primate skulls have remained relatively consistent. Neotropical primates can be divided into four major groups based on their feeding habits (fruit-leaves, seed-fruits, insect-fruits, and gum-insect-fruits). Differences in phenotypic covariance structure are correlated with differences in feeding habits, indicating that to some extent changes in interrelationships among skull traits are associated with changes in feeding habits. Finally, common patterns and levels of morphological integration are found among Platyrrhine primates, suggesting that functional/developmental integration could be one major factor keeping covariance structure relatively stable during evolutionary diversification of South American monkeys.

In-situ measurement of thermoset resin degree of cure using embedded fiber optic

NASA Astrophysics Data System (ADS)

Breglio, Giovanni; Cusano, Andrea; Cutolo, Antonello; Calabro, Antonio M.; Cantoni, Stefania; Di Vita, Gandolfo; Buonocore, Vincenzo; Giordano, Michele; Nicolais, Luigi, II

1999-12-01

In this work, a fiber optic sensor based on Fresnel principle is presented. It is used to monitor the variations of the refractive index due to the cure process of an epoxy based resin. These materials are widely used in polymer- matrix composites. The process of thermoset matrix based composite involves mass and heat transfer coupled with irreversible chemical reactions inducing physical changes: the transformation of a fluid resin into a rubber and then into a solid glass. To improve the quality and the reliability of these materials key points are the cure monitoring and the optimization of the manufacturing process. To this aim, the fiber optic embedded sensor has been designed, developed and tested. Preliminary results on sensor capability to monitor the cure kinetics are shown. Correlation between the sensor output and conversion advancement has been proposed following the Lorentz-Lorenz law. Isothermal data form the sensor have been compared with calorimetric analysis of an epoxy based resin.

Exact diagonalization library for quantum electron models

NASA Astrophysics Data System (ADS)

Iskakov, Sergei; Danilov, Michael

2018-04-01

We present an exact diagonalization C++ template library (EDLib) for solving quantum electron models, including the single-band finite Hubbard cluster and the multi-orbital impurity Anderson model. The observables that can be computed using EDLib are single particle Green's functions and spin-spin correlation functions. This code provides three different types of Hamiltonian matrix storage that can be chosen based on the model.

Evaluation for Bearing Wear States Based on Online Oil Multi-Parameters Monitoring

PubMed Central

Hu, Hai-Feng

2018-01-01

As bearings are critical components of a mechanical system, it is important to characterize their wear states and evaluate health conditions. In this paper, a novel approach for analyzing the relationship between online oil multi-parameter monitoring samples and bearing wear states has been proposed based on an improved gray k-means clustering model (G-KCM). First, an online monitoring system with multiple sensors for bearings is established, obtaining oil multi-parameter data and vibration signals for bearings through the whole lifetime. Secondly, a gray correlation degree distance matrix is generated using a gray correlation model (GCM) to express the relationship of oil monitoring samples at different times and then a KCM is applied to cluster the matrix. Analysis and experimental results show that there is an obvious correspondence that state changing coincides basically in time between the lubricants’ multi-parameters and the bearings’ wear states. It also has shown that online oil samples with multi-parameters have early wear failure prediction ability for bearings superior to vibration signals. It is expected to realize online oil monitoring and evaluation for bearing health condition and to provide a novel approach for early identification of bearing-related failure modes. PMID:29621175

Evaluation for Bearing Wear States Based on Online Oil Multi-Parameters Monitoring.

PubMed

Wang, Si-Yuan; Yang, Ding-Xin; Hu, Hai-Feng

2018-04-05

As bearings are critical components of a mechanical system, it is important to characterize their wear states and evaluate health conditions. In this paper, a novel approach for analyzing the relationship between online oil multi-parameter monitoring samples and bearing wear states has been proposed based on an improved gray k-means clustering model (G-KCM). First, an online monitoring system with multiple sensors for bearings is established, obtaining oil multi-parameter data and vibration signals for bearings through the whole lifetime. Secondly, a gray correlation degree distance matrix is generated using a gray correlation model (GCM) to express the relationship of oil monitoring samples at different times and then a KCM is applied to cluster the matrix. Analysis and experimental results show that there is an obvious correspondence that state changing coincides basically in time between the lubricants' multi-parameters and the bearings' wear states. It also has shown that online oil samples with multi-parameters have early wear failure prediction ability for bearings superior to vibration signals. It is expected to realize online oil monitoring and evaluation for bearing health condition and to provide a novel approach for early identification of bearing-related failure modes.

Evaluating wilderness recreational opportunities: application of an impact matrix

USGS Publications Warehouse

Stohlgren, Thomas J.; Parsons, David J.

1992-01-01

An inventory of the severity and spatial distribution of wilderness campsite impacts in Sequoia and Kings Canyon National Parks identified a total of 273 distinct nodes of campsites or “management areas.” A campsite impact matrix was developed to evaluate management areas based on total impacts (correlated to the total area of campsite development) and the density, or concentration, of impacts relative to each area's potentially campable area. The matrix is used to quantify potential recreational opportunities for wilderness visitors in a spectrum from areas offering low impact-dispersed camping to those areas offering high impact-concentrated camping. Wilderness managers can use this type of information to evaluate use distribution patterns, identify areas to increase or decrease use, and to identify areas needing site-specific regulations (e.g., one-night camping limits) to preserve wilderness resources and guarantee outstanding opportunities for solitude.

Joint estimation of 2D-DOA and frequency based on space-time matrix and conformal array.

PubMed

Wan, Liang-Tian; Liu, Lu-Tao; Si, Wei-Jian; Tian, Zuo-Xi

2013-01-01

Each element in the conformal array has a different pattern, which leads to the performance deterioration of the conventional high resolution direction-of-arrival (DOA) algorithms. In this paper, a joint frequency and two-dimension DOA (2D-DOA) estimation algorithm for conformal array are proposed. The delay correlation function is used to suppress noise. Both spatial and time sampling are utilized to construct the spatial-time matrix. The frequency and 2D-DOA estimation are accomplished based on parallel factor (PARAFAC) analysis without spectral peak searching and parameter pairing. The proposed algorithm needs only four guiding elements with precise positions to estimate frequency and 2D-DOA. Other instrumental elements can be arranged flexibly on the surface of the carrier. Simulation results demonstrate the effectiveness of the proposed algorithm.

Large Covariance Estimation by Thresholding Principal Orthogonal Complements

PubMed Central

Fan, Jianqing; Liao, Yuan; Mincheva, Martina

2012-01-01

This paper deals with the estimation of a high-dimensional covariance with a conditional sparsity structure and fast-diverging eigenvalues. By assuming sparse error covariance matrix in an approximate factor model, we allow for the presence of some cross-sectional correlation even after taking out common but unobservable factors. We introduce the Principal Orthogonal complEment Thresholding (POET) method to explore such an approximate factor structure with sparsity. The POET estimator includes the sample covariance matrix, the factor-based covariance matrix (Fan, Fan, and Lv, 2008), the thresholding estimator (Bickel and Levina, 2008) and the adaptive thresholding estimator (Cai and Liu, 2011) as specific examples. We provide mathematical insights when the factor analysis is approximately the same as the principal component analysis for high-dimensional data. The rates of convergence of the sparse residual covariance matrix and the conditional sparse covariance matrix are studied under various norms. It is shown that the impact of estimating the unknown factors vanishes as the dimensionality increases. The uniform rates of convergence for the unobserved factors and their factor loadings are derived. The asymptotic results are also verified by extensive simulation studies. Finally, a real data application on portfolio allocation is presented. PMID:24348088

Large Covariance Estimation by Thresholding Principal Orthogonal Complements.

PubMed

Fan, Jianqing; Liao, Yuan; Mincheva, Martina

2013-09-01

This paper deals with the estimation of a high-dimensional covariance with a conditional sparsity structure and fast-diverging eigenvalues. By assuming sparse error covariance matrix in an approximate factor model, we allow for the presence of some cross-sectional correlation even after taking out common but unobservable factors. We introduce the Principal Orthogonal complEment Thresholding (POET) method to explore such an approximate factor structure with sparsity. The POET estimator includes the sample covariance matrix, the factor-based covariance matrix (Fan, Fan, and Lv, 2008), the thresholding estimator (Bickel and Levina, 2008) and the adaptive thresholding estimator (Cai and Liu, 2011) as specific examples. We provide mathematical insights when the factor analysis is approximately the same as the principal component analysis for high-dimensional data. The rates of convergence of the sparse residual covariance matrix and the conditional sparse covariance matrix are studied under various norms. It is shown that the impact of estimating the unknown factors vanishes as the dimensionality increases. The uniform rates of convergence for the unobserved factors and their factor loadings are derived. The asymptotic results are also verified by extensive simulation studies. Finally, a real data application on portfolio allocation is presented.

Raman and Fourier Transform Infrared (FT-IR) Mineral to Matrix Ratios Correlate with Physical Chemical Properties of Model Compounds and Native Bone Tissue.

PubMed

Taylor, Erik A; Lloyd, Ashley A; Salazar-Lara, Carolina; Donnelly, Eve

2017-10-01

Raman and Fourier transform infrared (FT-IR) spectroscopic imaging techniques can be used to characterize bone composition. In this study, our objective was to validate the Raman mineral:matrix ratios (ν 1 PO 4 :amide III, ν 1 PO 4 :amide I, ν 1 PO 4 :Proline + hydroxyproline, ν 1 PO 4 :Phenylalanine, ν 1 PO 4 :δ CH 2 peak area ratios) by correlating them to ash fraction and the IR mineral:matrix ratio (ν 3 PO 4 :amide I peak area ratio) in chemical standards and native bone tissue. Chemical standards consisting of varying ratios of synthetic hydroxyapatite (HA) and collagen, as well as bone tissue from humans, sheep, and mice, were characterized with confocal Raman spectroscopy and FT-IR spectroscopy and gravimetric analysis. Raman and IR mineral:matrix ratio values from chemical standards increased reciprocally with ash fraction (Raman ν 1 PO 4 /Amide III: P < 0.01, R 2  = 0.966; Raman ν 1 PO 4 /Amide I: P < 0.01, R 2  = 0.919; Raman ν 1 PO 4 /Proline + Hydroxyproline: P < 0.01, R 2  = 0.976; Raman ν 1 PO 4 /Phenylalanine: P < 0.01, R 2  = 0.911; Raman ν 1 PO 4 /δ CH 2 : P < 0.01, R 2  = 0.894; IR P < 0.01, R 2  = 0.91). Fourier transform infrared mineral:matrix ratio values from native bone tissue were also similar to theoretical mineral:matrix ratio values for a given ash fraction. Raman and IR mineral:matrix ratio values were strongly correlated ( P < 0.01, R 2  = 0.82). These results were confirmed by calculating the mineral:matrix ratio for theoretical IR spectra, developed by applying the Beer-Lambert law to calculate the relative extinction coefficients of HA and collagen over the same range of wavenumbers (800-1800 cm -1 ). The results confirm that the Raman mineral:matrix bone composition parameter correlates strongly to ash fraction and to its IR counterpart. Finally, the mineral:matrix ratio values of the native bone tissue are similar to those of both chemical standards and theoretical values, confirming the biological relevance of the chemical standards and the characterization techniques.

Comparison of Matrix Frequency-Doubling Technology (FDT) Perimetry with the SWEDISH Interactive Thresholding Algorithm (SITA) Standard Automated Perimetry (SAP) in Mild Glaucoma.

PubMed

Doozandeh, Azadeh; Irandoost, Farnoosh; Mirzajani, Ali; Yazdani, Shahin; Pakravan, Mohammad; Esfandiari, Hamed

2017-01-01

This study aimed to compare second-generation frequency-doubling technology (FDT) perimetry with standard automated perimetry (SAP) in mild glaucoma. Forty-seven eyes of 47 participants who had mild visual field defect by SAP were included in this study. All participants were examined using SITA 24-2 (SITA-SAP) and matrix 24-2 (Matrix-FDT). The correlations of global indices and the number of defects on pattern deviation (PD) plots were determined. Agreement between two sets regarding the stage of visual field damage was assessed. Pearson's correlation, intra-cluster comparison, paired t-test, and 95% limit of agreement were calculated. Although there was no significant difference between global indices, the agreement between the two devices regarding the global indices was weak (the limit of agreement for mean deviation was -6.08 to 6.08 and that for pattern standard deviation was -4.42 to 3.42). The agreement between SITA-SAP and Matrix-FDT regarding the Glaucoma Hemifield Test (GHT) and the number of defective points in each quadrant and staging of the visual field damage was also weak. Because the correlation between SITA-SAP and Matrix-FDT regarding global indices, GHT, number of defective points, and stage of the visual field damage in mild glaucoma is weak, Matrix-FDT cannot be used interchangeably with SITA-SAP in the early stages of glaucoma.

Modification of surface morphology of Ti6Al4V alloy manufactured by Laser Sintering

NASA Astrophysics Data System (ADS)

Draganovská, Dagmar; Ižariková, Gabriela; Guzanová, Anna; Brezinová, Janette; Koncz, Juraj

2016-06-01

The paper deals with the evaluation of relation between roughness parameters of Ti6Al4V alloy produced by DMLS and modified by abrasive blasting. There were two types of blasting abrasives that were used - white corundum and Zirblast at three levels of air pressure. The effect of pressure on the value of individual roughness parameters and an influence of blasting media on the parameters for samples blasted by white corundum and Zirblast were evaluated by ANOVA. Based on the measured values, the correlation matrix was set and the standard of correlation statistic importance between the monitored parameters was determined from it. The correlation coefficient was also set.

A predictive assessment of genetic correlations between traits in chickens using markers.

PubMed

Momen, Mehdi; Mehrgardi, Ahmad Ayatollahi; Sheikhy, Ayoub; Esmailizadeh, Ali; Fozi, Masood Asadi; Kranis, Andreas; Valente, Bruno D; Rosa, Guilherme J M; Gianola, Daniel

2017-02-01

Genomic selection has been successfully implemented in plant and animal breeding programs to shorten generation intervals and accelerate genetic progress per unit of time. In practice, genomic selection can be used to improve several correlated traits simultaneously via multiple-trait prediction, which exploits correlations between traits. However, few studies have explored multiple-trait genomic selection. Our aim was to infer genetic correlations between three traits measured in broiler chickens by exploring kinship matrices based on a linear combination of measures of pedigree and marker-based relatedness. A predictive assessment was used to gauge genetic correlations. A multivariate genomic best linear unbiased prediction model was designed to combine information from pedigree and genome-wide markers in order to assess genetic correlations between three complex traits in chickens, i.e. body weight at 35 days of age (BW), ultrasound area of breast meat (BM) and hen-house egg production (HHP). A dataset with 1351 birds that were genotyped with the 600 K Affymetrix platform was used. A kinship kernel (K) was constructed as K = λ G + (1 - λ)A, where A is the numerator relationship matrix, measuring pedigree-based relatedness, and G is a genomic relationship matrix. The weight (λ) assigned to each source of information varied over the grid λ = (0, 0.2, 0.4, 0.6, 0.8, 1). Maximum likelihood estimates of heritability and genetic correlations were obtained at each λ, and the "optimum" λ was determined using cross-validation. Estimates of genetic correlations were affected by the weight placed on the source of information used to build K. For example, the genetic correlation between BW-HHP and BM-HHP changed markedly when λ varied from 0 (only A used for measuring relatedness) to 1 (only genomic information used). As λ increased, predictive correlations (correlation between observed phenotypes and predicted breeding values) increased and mean-squared predictive error decreased. However, the improvement in predictive ability was not monotonic, with an optimum found at some 0 < λ < 1, i.e., when both sources of information were used together. Our findings indicate that multiple-trait prediction may benefit from combining pedigree and marker information. Also, it appeared that expected correlated responses to selection computed from standard theory may differ from realized responses. The predictive assessment provided a metric for performance evaluation as well as a means for expressing uncertainty of outcomes of multiple-trait selection.

Robust alignment of chromatograms by statistically analyzing the shifts matrix generated by moving window fast Fourier transform cross-correlation.

PubMed

Zhang, Mingjing; Wen, Ming; Zhang, Zhi-Min; Lu, Hongmei; Liang, Yizeng; Zhan, Dejian

2015-03-01

Retention time shift is one of the most challenging problems during the preprocessing of massive chromatographic datasets. Here, an improved version of the moving window fast Fourier transform cross-correlation algorithm is presented to perform nonlinear and robust alignment of chromatograms by analyzing the shifts matrix generated by moving window procedure. The shifts matrix in retention time can be estimated by fast Fourier transform cross-correlation with a moving window procedure. The refined shift of each scan point can be obtained by calculating the mode of corresponding column of the shifts matrix. This version is simple, but more effective and robust than the previously published moving window fast Fourier transform cross-correlation method. It can handle nonlinear retention time shift robustly if proper window size has been selected. The window size is the only one parameter needed to adjust and optimize. The properties of the proposed method are investigated by comparison with the previous moving window fast Fourier transform cross-correlation and recursive alignment by fast Fourier transform using chromatographic datasets. The pattern recognition results of a gas chromatography mass spectrometry dataset of metabolic syndrome can be improved significantly after preprocessing by this method. Furthermore, the proposed method is available as an open source package at https://github.com/zmzhang/MWFFT2. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Development and Characterization of Embedded Sensory Particles Using Multi-Scale 3D Digital Image Correlation

NASA Technical Reports Server (NTRS)

Cornell, Stephen R.; Leser, William P.; Hochhalter, Jacob D.; Newman, John A.; Hartl, Darren J.

2014-01-01

A method for detecting fatigue cracks has been explored at NASA Langley Research Center. Microscopic NiTi shape memory alloy (sensory) particles were embedded in a 7050 aluminum alloy matrix to detect the presence of fatigue cracks. Cracks exhibit an elevated stress field near their tip inducing a martensitic phase transformation in nearby sensory particles. Detectable levels of acoustic energy are emitted upon particle phase transformation such that the existence and location of fatigue cracks can be detected. To test this concept, a fatigue crack was grown in a mode-I single-edge notch fatigue crack growth specimen containing sensory particles. As the crack approached the sensory particles, measurements of particle strain, matrix-particle debonding, and phase transformation behavior of the sensory particles were performed. Full-field deformation measurements were performed using a novel multi-scale optical 3D digital image correlation (DIC) system. This information will be used in a finite element-based study to determine optimal sensory material behavior and density.

Improving acoustic beamforming maps in a reverberant environment by modifying the cross-correlation matrix

NASA Astrophysics Data System (ADS)

Fischer, J.; Doolan, C.

2017-12-01

A method to improve the quality of acoustic beamforming in reverberant environments is proposed in this paper. The processing is based on a filtering of the cross-correlation matrix of the microphone signals obtained using a microphone array. The main advantage of the proposed method is that it does not require information about the geometry of the reverberant environment and thus it can be applied to any configuration. The method is applied to the particular example of aeroacoustic testing in a hard-walled low-speed wind tunnel; however, the technique can be used in any reverberant environment. Two test cases demonstrate the technique. The first uses a speaker placed in the hard-walled working section with no wind tunnel flow. In the second test case, an airfoil is placed in a flow and acoustic beamforming maps are obtained. The acoustic maps have been improved, as the reflections observed in the conventional maps have been removed after application of the proposed method.

Application of partial least squares near-infrared spectral classification in diabetic identification

NASA Astrophysics Data System (ADS)

Yan, Wen-juan; Yang, Ming; He, Guo-quan; Qin, Lin; Li, Gang

2014-11-01

In order to identify the diabetic patients by using tongue near-infrared (NIR) spectrum - a spectral classification model of the NIR reflectivity of the tongue tip is proposed, based on the partial least square (PLS) method. 39sample data of tongue tip's NIR spectra are harvested from healthy people and diabetic patients , respectively. After pretreatment of the reflectivity, the spectral data are set as the independent variable matrix, and information of classification as the dependent variables matrix, Samples were divided into two groups - i.e. 53 samples as calibration set and 25 as prediction set - then the PLS is used to build the classification model The constructed modelfrom the 53 samples has the correlation of 0.9614 and the root mean square error of cross-validation (RMSECV) of 0.1387.The predictions for the 25 samples have the correlation of 0.9146 and the RMSECV of 0.2122.The experimental result shows that the PLS method can achieve good classification on features of healthy people and diabetic patients.

Matrix elements of explicitly correlated Gaussian basis functions with arbitrary angular momentum

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

Joyce, Tennesse; Varga, Kálmán

2016-05-14

A new algorithm for calculating the Hamiltonian matrix elements with all-electron explicitly correlated Gaussian functions for quantum-mechanical calculations of atoms with arbitrary angular momentum is presented. The calculations are checked on several excited states of three and four electron systems. The presented formalism can be used as unified framework for high accuracy calculations of properties of small atoms and molecules.

Impact of an autologous oxygenating matrix culture system on rat islet transplantation outcome.

PubMed

Schaschkow, A; Mura, C; Bietiger, W; Peronet, C; Langlois, A; Bodin, F; Dissaux, C; Bruant-Rodier, C; Pinget, M; Jeandidier, N; Juszczak, M T; Sigrist, S; Maillard, E

2015-06-01

Disruption of the pancreatic islet environment combined with the decrease in oxygen supply that occurs during isolation leads to poor islet survival. The aim of this study was to validate the benefit of using a plasma-based scaffold supplemented with perfluorodecalin to improve islet transplantation outcome. Rat islets were cultured in three conditions: i) control group, ii) plasma based-matrix (P-matrix), and iii) P-matrix supplemented with emulsified perfluorodecalin. After 24 h culture, matrix/cell contacts (Integrinβ1, p-FAK/FAK, p-Akt/Akt), survival (caspase 3, TUNEL, FDA/PI), function, and HIF-1α translocation were assessed. Afterwards, P-matrices were dissolved and the islets were intraportally transplanted. Graft function was monitored for 31 days with glycaemia and C-peptide follow up. Inflammation was assessed by histology (macrophage and granulocyte staining) and thrombin/anti-thrombin complex measurement. Islet survival correlated with an increase in integrin, FAK, and Akt activation in P-matrices and function was maintained. Perfluorodecalin supplementation decreased translocation of HIF-1α in the nucleus and post-transplantation islet structure was better preserved in P-matrices, but a quicker activation of IBMIR resulted in early loss of graft function. "Oxygenating" P-matrices provided a real benefit to islet survival and resistance in vivo. However, intraportal transplantation is not suitable for this kind of culture due to IBMIR; thus, alternative sites must be explored. Copyright © 2015 Elsevier Ltd. All rights reserved.

Exchange-correlation approximations for reduced-density-matrix-functional theory at finite temperature: Capturing magnetic phase transitions in the homogeneous electron gas

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

Baldsiefen, Tim; Cangi, Attila; Eich, F. G.

Here, we derive an intrinsically temperature-dependent approximation to the correlation grand potential for many-electron systems in thermodynamical equilibrium in the context of finite-temperature reduced-density-matrix-functional theory (FT-RDMFT). We demonstrate its accuracy by calculating the magnetic phase diagram of the homogeneous electron gas. We compare it to known limits from highly accurate quantum Monte Carlo calculations as well as to phase diagrams obtained within existing exchange-correlation approximations from density functional theory and zero-temperature RDMFT.

Exchange-correlation approximations for reduced-density-matrix-functional theory at finite temperature: Capturing magnetic phase transitions in the homogeneous electron gas

DOE PAGES

Baldsiefen, Tim; Cangi, Attila; Eich, F. G.; ...

2017-12-18

Here, we derive an intrinsically temperature-dependent approximation to the correlation grand potential for many-electron systems in thermodynamical equilibrium in the context of finite-temperature reduced-density-matrix-functional theory (FT-RDMFT). We demonstrate its accuracy by calculating the magnetic phase diagram of the homogeneous electron gas. We compare it to known limits from highly accurate quantum Monte Carlo calculations as well as to phase diagrams obtained within existing exchange-correlation approximations from density functional theory and zero-temperature RDMFT.

Characterization of the dynamics of surface stabilized ferroelectric liquid crystal under electric field by full optical snapshot matrix Mueller polarimeter

NASA Astrophysics Data System (ADS)

Silva, Vinicius N. H.; Babilotte, Philippe; Rivet, Sylvain; Dubreuil, Mathieu; Le Jeune, Bernard; Dupont, Laurent

2012-12-01

We investigated the layer dynamics of a conventional surface-stabilized ferroelectric liquid crystal (SSFLC) using a full-optical snapshot Mueller matrix polarimeter (SMMP) based on wavelength polarization coding. Time-resolved polarimetric measurements were performed with different SSFLC samples, and a strong correlation between the polarimetric parameters and the SSFLC under electric field at different exposure times was found. It has been shown that the SMMP polarimeter is able to determine the evolution of the trajectory of the liquid crystal director between the two addressed states, the reversible motion of the smectic layer while switching, as well as the irreversible transition from chevron to bookshelf texture.

Modeling PSInSAR time series without phase unwrapping

USGS Publications Warehouse

Zhang, L.; Ding, X.; Lu, Z.

2011-01-01

In this paper, we propose a least-squares-based method for multitemporal synthetic aperture radar interferometry that allows one to estimate deformations without the need of phase unwrapping. The method utilizes a series of multimaster wrapped differential interferograms with short baselines and focuses on arcs at which there are no phase ambiguities. An outlier detector is used to identify and remove the arcs with phase ambiguities, and a pseudoinverse of the variance-covariance matrix is used as the weight matrix of the correlated observations. The deformation rates at coherent points are estimated with a least squares model constrained by reference points. The proposed approach is verified with a set of simulated data.

Nondestructive evaluation of a new hydrolytically degradable and photo-clickable PEG hydrogel for cartilage tissue engineering.

PubMed

Neumann, Alexander J; Quinn, Timothy; Bryant, Stephanie J

2016-07-15

Photopolymerizable and hydrolytically labile poly(ethylene glycol) (PEG) hydrogels formed from photo-clickable reactions were investigated as cell delivery platforms for cartilage tissue engineering (TE). PEG hydrogels were formed from thiol-norbornene PEG macromers whereby the crosslinks contained caprolactone segments with hydrolytically labile ester linkages. Juvenile bovine chondrocytes encapsulated in the hydrogels were cultured for up to four weeks and assessed biochemically and histologically, using standard destructive assays, and for mechanical and ultrasound properties, as nondestructive assays. Bulk degradation of acellular hydrogels was confirmed by a decrease in compressive modulus and an increase in mass swelling ratio over time. Chondrocytes deposited increasing amounts of sulfated glycosaminoglycans and collagens in the hydrogels with time. Spatially, collagen type II and aggrecan were present in the neotissue with formation of a territorial matrix beginning at day 21. Nondestructive measurements revealed an 8-fold increase in compressive modulus from days 7 to 28, which correlated with total collagen content. Ultrasound measurements revealed changes in the constructs over time, which differed from the mechanical properties, and appeared to correlate with ECM structure and organization shown by immunohistochemical analysis. Overall, non-destructive and destructive measurements show that this new hydrolytically degradable PEG hydrogel is promising for cartilage TE. Designing synthetic hydrogels whose degradation matches tissue growth is critical to maintaining mechanical integrity as the hydrogel degrades and new tissue forms, but is challenging due to the nature of the hydrogel crosslinks that inhibit diffusion of tissue matrix molecules. This study details a promising, new, photo-clickable and synthetic hydrogel whose degradation supports cartilaginous tissue matrix growth leading to the formation of a territorial matrix, concomitant with an increase in mechanical properties. Nondestructive assays based on mechanical and ultrasonic properties were also investigated using a novel instrument and found to correlate with matrix deposition and evolution. In sum, this study presents a new hydrogel platform combined with nondestructive assessments, which together have potential for in vitro cartilage tissue engineering. Copyright © 2016 Acta Materialia Inc. All rights reserved.

Correlation, Cost Risk, and Geometry

NASA Technical Reports Server (NTRS)

Dean, Edwin B.

1992-01-01

The geometric viewpoint identifies the choice of a correlation matrix for the simulation of cost risk with the pairwise choice of data vectors corresponding to the parameters used to obtain cost risk. The correlation coefficient is the cosine of the angle between the data vectors after translation to an origin at the mean and normalization for magnitude. Thus correlation is equivalent to expressing the data in terms of a non orthogonal basis. To understand the many resulting phenomena requires the use of the tensor concept of raising the index to transform the measured and observed covariant components into contravariant components before vector addition can be applied. The geometric viewpoint also demonstrates that correlation and covariance are geometric properties, as opposed to purely statistical properties, of the variates. Thus, variates from different distributions may be correlated, as desired, after selection from independent distributions. By determining the principal components of the correlation matrix, variates with the desired mean, magnitude, and correlation can be generated through linear transforms which include the eigenvalues and the eigenvectors of the correlation matrix. The conversion of the data to a non orthogonal basis uses a compound linear transformation which distorts or stretches the data space. Hence, the correlated data does not have the same properties as the uncorrelated data used to generate it. This phenomena is responsible for seemingly strange observations such as the fact that the marginal distributions of the correlated data can be quite different from the distributions used to generate the data. The joint effect of statistical distributions and correlation remains a fertile area for further research. In terms of application to cost estimating, the geometric approach demonstrates that the estimator must have data and must understand that data in order to properly choose the correlation matrix appropriate for a given estimate. There is a general feeling by employers and managers that the field of cost requires little technical or mathematical background. Contrary to that opinion, this paper demonstrates that a background in mathematics equivalent to that needed for typical engineering and scientific disciplines at the masters or doctorate level is appropriate within the field of cost risk.

Matrix isolation studies of hydrogen bonding - An historical perspective

NASA Astrophysics Data System (ADS)

Barnes, Austin J.

2018-07-01

An historical introduction sets matrix isolation in perspective with other spectroscopic techniques for studying hydrogen-bonded complexes. This is followed by detailed accounts of various aspects of hydrogen-bonded complexes that have been studied using matrix isolation spectroscopy: Matrix effects: stabilisation of complexes. Strongly hydrogen-bonded molecular complexes: the vibrational correlation diagram. Anomalous spectra: the Ratajczak-Yaremko model. Metastable complexes. Csbnd H hydrogen bonding and blue shifting hydrogen bonds.

Structure-performance relationships of phenyl cinnamic acid derivatives as MALDI-MS matrices for sulfatide detection.

PubMed

Tambe, Suparna; Blott, Henning; Fülöp, Annabelle; Spang, Nils; Flottmann, Dirk; Bräse, Stefan; Hopf, Carsten; Junker, Hans-Dieter

2017-02-01

A key aspect for the further development of matrix-assisted laser desorption ionization (MALDI)-mass spectrometry (MS) is a better understanding of the working principles of MALDI matrices. To address this issue, a chemical compound library of 59 structurally related cinnamic acid derivatives was synthesized. Potential MALDI matrices were evaluated with sulfatides, a class of anionic lipids which are abundant in complex brain lipid mixtures. For each matrix relative mean S/N ratios of sulfatides were determined against 9-aminoacridine as a reference matrix using negative ion mass spectrometry with 355 and 337 nm laser systems. The comparison of matrix features with their corresponding relative mean S/N ratios for sulfatide detection identified correlations between matrix substitution patterns, their chemical functionality, and their MALDI-MS performance. Crystal structures of six selected matrices provided structural insight in hydrogen bond interactions in the solid state. Principal component analysis allowed the additional identification of correlation trends between structural and physical matrix properties like number of exchangeable protons at the head group, MW, logP, UV-Vis, and sulfatide detection sensitivity. Graphical abstract Design, synthesis and mass spectrometric evaluation of MALDI-MS matrix compound libraries allows the identification of matrix structure - MALDI-MS performance relationships using multivariate statistics as a tool.

Factor Structure of the TOEFL® Internet-Based Test (iBT): Exploration in a Field Trial Sample. TOEFL iBT Research Report. TOEFL iBT-04. ETS RR-08-09

ERIC Educational Resources Information Center

Sawaki, Yasuyo; Stricker, Lawrence; Oranje, Andreas

2008-01-01

The present study investigated the factor structure of a field trial sample of the Test of English as a Foreign Language™ Internet-based test (TOEFL® iBT). An item-level confirmatory factor analysis (CFA) was conducted for a polychoric correlation matrix of items on a test form completed by 2,720 participants in the 2003-2004 TOEFL iBT Field…

Combining Correlation Matrices: Simulation Analysis of Improved Fixed-Effects Methods

ERIC Educational Resources Information Center

Hafdahl, Adam R.

2007-01-01

The originally proposed multivariate meta-analysis approach for correlation matrices--analyze Pearson correlations, with each study's observed correlations replacing their population counterparts in its conditional-covariance matrix--performs poorly. Two refinements are considered: Analyze Fisher Z-transformed correlations, and substitute better…

Speeding Up Non-Parametric Bootstrap Computations for Statistics Based on Sample Moments in Small/Moderate Sample Size Applications

PubMed Central

Chaibub Neto, Elias

2015-01-01

In this paper we propose a vectorized implementation of the non-parametric bootstrap for statistics based on sample moments. Basically, we adopt the multinomial sampling formulation of the non-parametric bootstrap, and compute bootstrap replications of sample moment statistics by simply weighting the observed data according to multinomial counts instead of evaluating the statistic on a resampled version of the observed data. Using this formulation we can generate a matrix of bootstrap weights and compute the entire vector of bootstrap replications with a few matrix multiplications. Vectorization is particularly important for matrix-oriented programming languages such as R, where matrix/vector calculations tend to be faster than scalar operations implemented in a loop. We illustrate the application of the vectorized implementation in real and simulated data sets, when bootstrapping Pearson’s sample correlation coefficient, and compared its performance against two state-of-the-art R implementations of the non-parametric bootstrap, as well as a straightforward one based on a for loop. Our investigations spanned varying sample sizes and number of bootstrap replications. The vectorized bootstrap compared favorably against the state-of-the-art implementations in all cases tested, and was remarkably/considerably faster for small/moderate sample sizes. The same results were observed in the comparison with the straightforward implementation, except for large sample sizes, where the vectorized bootstrap was slightly slower than the straightforward implementation due to increased time expenditures in the generation of weight matrices via multinomial sampling. PMID:26125965

Classification of Regional Radiographic Emphysematous Patterns Using Low-Attenuation Gap Length Matrix

NASA Astrophysics Data System (ADS)

Tan, Kok Liang; Tanaka, Toshiyuki; Nakamura, Hidetoshi; Shirahata, Toru; Sugiura, Hiroaki

The standard computer-tomography-based method for measuring emphysema uses percentage of area of low attenuation which is called the pixel index (PI). However, the PI method is susceptible to the problem of averaging effect and this causes the discrepancy between what the PI method describes and what radiologists observe. Knowing that visual recognition of the different types of regional radiographic emphysematous tissues in a CT image can be fuzzy, this paper proposes a low-attenuation gap length matrix (LAGLM) based algorithm for classifying the regional radiographic lung tissues into four emphysema types distinguishing, in particular, radiographic patterns that imply obvious or subtle bullous emphysema from those that imply diffuse emphysema or minor destruction of airway walls. Neural network is used for discrimination. The proposed LAGLM method is inspired by, but different from, former texture-based methods like gray level run length matrix (GLRLM) and gray level gap length matrix (GLGLM). The proposed algorithm is successfully validated by classifying 105 lung regions that are randomly selected from 270 images. The lung regions are hand-annotated by radiologists beforehand. The average four-class classification accuracies in the form of the proposed algorithm/PI/GLRLM/GLGLM methods are: 89.00%/82.97%/52.90%/51.36%, respectively. The p-values from the correlation analyses between the classification results of 270 images and pulmonary function test results are generally less than 0.01. The classification results are useful for a followup study especially for monitoring morphological changes with progression of pulmonary disease.

Digital halftoning methods for selectively partitioning error into achromatic and chromatic channels

NASA Technical Reports Server (NTRS)

Mulligan, Jeffrey B.

1990-01-01

A method is described for reducing the visibility of artifacts arising in the display of quantized color images on CRT displays. The method is based on the differential spatial sensitivity of the human visual system to chromatic and achromatic modulations. Because the visual system has the highest spatial and temporal acuity for the luminance component of an image, a technique which will reduce luminance artifacts at the expense of introducing high-frequency chromatic errors is sought. A method based on controlling the correlations between the quantization errors in the individual phosphor images is explored. The luminance component is greatest when the phosphor errors are positively correlated, and is minimized when the phosphor errors are negatively correlated. The greatest effect of the correlation is obtained when the intensity quantization step sizes of the individual phosphors have equal luminances. For the ordered dither algorithm, a version of the method can be implemented by simply inverting the matrix of thresholds for one of the color components.

A KST framework for correlation network construction from time series signals

NASA Astrophysics Data System (ADS)

Qi, Jin-Peng; Gu, Quan; Zhu, Ying; Zhang, Ping

2018-04-01

A KST (Kolmogorov-Smirnov test and T statistic) method is used for construction of a correlation network based on the fluctuation of each time series within the multivariate time signals. In this method, each time series is divided equally into multiple segments, and the maximal data fluctuation in each segment is calculated by a KST change detection procedure. Connections between each time series are derived from the data fluctuation matrix, and are used for construction of the fluctuation correlation network (FCN). The method was tested with synthetic simulations and the result was compared with those from using KS or T only for detection of data fluctuation. The novelty of this study is that the correlation analyses was based on the data fluctuation in each segment of each time series rather than on the original time signals, which would be more meaningful for many real world applications and for analysis of large-scale time signals where prior knowledge is uncertain.

Carbon matrix based magnetic nanocomposites for potential biomedical applications.

PubMed

Izydorzak-Wozniak, M; Leonowicz, M

2014-03-01

It was found that by varying the pyrolysis temperature of the polymeric precursor, carbon matrix magnetic nanocomposites with different constitution and fractions of magnetic component were made. X-ray diffraction, transmission electron microscopy and Raman spectroscopy revealed the presence of nanocrystallites (NCs) of Co, Fe3C and Ni embedded in porous, partially-graphitized carbon matrix. Vibrating sample magnetometer measurements enabled to determine the correlation between NCs size distribution and magnetic properties. The magnetic studies confirmed that the coercivity, saturation and remanent magnetizations, as well as fraction of the magnetic component depend on the pyrolysis temperature. The Co#C and Fe3C#C composites exhibited ferromagnetic behavior with a remanent to saturation magnetization (M(R)/M(S)) ratio ranging from 0.25 to 0.3, whereas in the Ni containing samples a relatively small M(R)/M(S) ratio point to significant contribution of superparamagnetic interactions. As the carbon matrix magnetic nanocomposites are proposed for biomedical application the basic cytotoxicity test were performed to evaluate a potential toxic effect of the materials on MG-63 cells line.

Synchronized and sustained release of multiple components in silymarin from erodible glyceryl monostearate matrix system.

PubMed

Lu, Cheng; Lu, Yi; Chen, Jian; Zhang, Wentong; Wu, Wei

2007-05-01

Development of sustained delivery systems for herbal medicines was very difficult because of their complexity in composition. The concept of synchronized release from sustained release systems, which is characterized by release of multiple components in their original ratio that defines a herbal medicine, served as the basis for keeping the original pharmacological activity. In this study, erodible matrix systems based on glyceryl monostearate and polyethylene glycol 6000 or poloxamer 188 were prepared to perform strict control on synchronized release of the five active components of silymarin, i.e. taxifolin, silychrystin, silydianin, isosilybin and silybin. The matrix system was prepared by a melt fusion method. Synchronized release was achieved with high similarity factor f(2) values between each two of the five components. Erosion profiles of the matrix were in good correlation with release profiles of the five components, showing erosion-controlled release mechanisms. Through tuning some of the formulation variables, the system can be adjusted for synchronized and sustained release of silymarin for oral administration. In vitro hemolysis study indicated that the synchronized release samples showed a much better stabilizing effect on erythrocyte membrane.

Random matrix theory analysis of cross-correlations in the US stock market: Evidence from Pearson’s correlation coefficient and detrended cross-correlation coefficient

NASA Astrophysics Data System (ADS)

Wang, Gang-Jin; Xie, Chi; Chen, Shou; Yang, Jiao-Jiao; Yang, Ming-Yan

2013-09-01

In this study, we first build two empirical cross-correlation matrices in the US stock market by two different methods, namely the Pearson’s correlation coefficient and the detrended cross-correlation coefficient (DCCA coefficient). Then, combining the two matrices with the method of random matrix theory (RMT), we mainly investigate the statistical properties of cross-correlations in the US stock market. We choose the daily closing prices of 462 constituent stocks of S&P 500 index as the research objects and select the sample data from January 3, 2005 to August 31, 2012. In the empirical analysis, we examine the statistical properties of cross-correlation coefficients, the distribution of eigenvalues, the distribution of eigenvector components, and the inverse participation ratio. From the two methods, we find some new results of the cross-correlations in the US stock market in our study, which are different from the conclusions reached by previous studies. The empirical cross-correlation matrices constructed by the DCCA coefficient show several interesting properties at different time scales in the US stock market, which are useful to the risk management and optimal portfolio selection, especially to the diversity of the asset portfolio. It will be an interesting and meaningful work to find the theoretical eigenvalue distribution of a completely random matrix R for the DCCA coefficient because it does not obey the Marčenko-Pastur distribution.

Empirical data and the variance-covariance matrix for the 1969 Smithsonian Standard Earth (2)

NASA Technical Reports Server (NTRS)

Gaposchkin, E. M.

1972-01-01

The empirical data used in the 1969 Smithsonian Standard Earth (2) are presented. The variance-covariance matrix, or the normal equations, used for correlation analysis, are considered. The format and contents of the matrix, available on magnetic tape, are described and a sample printout is given.

Low-temperature matrix effects on orientational motion of Methyl radical trapped in gas solids: Angular tunneling vs. libration

NASA Astrophysics Data System (ADS)

Dmitriev, Yurij A.; Zelenetckii, Ilia A.; Benetis, Nikolas P.

2018-05-01

EPR investigation of the lineshape of matrix -isolated methyl radical, CH3, spectra recorded in solid N2O and CO2 was carried out. Reversible temperature-dependent line width anisotropy was observed in both matrices. This effect is a fingerprint of the extra-slow radical rotation about the in-plane C2 axes. The rotation was found to be anisotropic and closely correlated to the orientational dynamics of the matrix molecules. It was suggested that a recently discovered "hoping precession" effect of matrix molecules in solid CO2 is a common feature of matrices of the linear molecules CO, N2O, and CO2. A new low-temperature matrix effect, referred to as "libration trap", was proposed which accounts for the changing CH3 reorientational motion about the radical C3-axis from rotation to libration. Temperature dependence of the intensity of the EPR satellites produced by these nonrotating-but librating methyls was presented. This allowed for a rough estimation of the rotation hindering potential due to correlation mismatch between the radical and the nearest matrix molecules' librations.

S-matrix analysis of the baryon electric charge correlation

NASA Astrophysics Data System (ADS)

Lo, Pok Man; Friman, Bengt; Redlich, Krzysztof; Sasaki, Chihiro

2018-03-01

We compute the correlation of the net baryon number with the electric charge (χBQ) for an interacting hadron gas using the S-matrix formulation of statistical mechanics. The observable χBQ is particularly sensitive to the details of the pion-nucleon interaction, which are consistently incorporated in the current scheme via the empirical scattering phase shifts. Comparing to the recent lattice QCD studies in the (2 + 1)-flavor system, we find that the natural implementation of interactions and the proper treatment of resonances in the S-matrix approach lead to an improved description of the lattice data over that obtained in the hadron resonance gas model.

Comprehensive Deployment Method for Technical Characteristics Base on Multi-failure Modes Correlation Analysis

NASA Astrophysics Data System (ADS)

Zheng, W.; Gao, J. M.; Wang, R. X.; Chen, K.; Jiang, Y.

2017-12-01

This paper put forward a new method of technical characteristics deployment based on Reliability Function Deployment (RFD) by analysing the advantages and shortages of related research works on mechanical reliability design. The matrix decomposition structure of RFD was used to describe the correlative relation between failure mechanisms, soft failures and hard failures. By considering the correlation of multiple failure modes, the reliability loss of one failure mode to the whole part was defined, and a calculation and analysis model for reliability loss was presented. According to the reliability loss, the reliability index value of the whole part was allocated to each failure mode. On the basis of the deployment of reliability index value, the inverse reliability method was employed to acquire the values of technology characteristics. The feasibility and validity of proposed method were illustrated by a development case of machining centre’s transmission system.

Two-Way Gene Interaction From Microarray Data Based on Correlation Methods.

PubMed

Alavi Majd, Hamid; Talebi, Atefeh; Gilany, Kambiz; Khayyer, Nasibeh

2016-06-01

Gene networks have generated a massive explosion in the development of high-throughput techniques for monitoring various aspects of gene activity. Networks offer a natural way to model interactions between genes, and extracting gene network information from high-throughput genomic data is an important and difficult task. The purpose of this study is to construct a two-way gene network based on parametric and nonparametric correlation coefficients. The first step in constructing a Gene Co-expression Network is to score all pairs of gene vectors. The second step is to select a score threshold and connect all gene pairs whose scores exceed this value. In the foundation-application study, we constructed two-way gene networks using nonparametric methods, such as Spearman's rank correlation coefficient and Blomqvist's measure, and compared them with Pearson's correlation coefficient. We surveyed six genes of venous thrombosis disease, made a matrix entry representing the score for the corresponding gene pair, and obtained two-way interactions using Pearson's correlation, Spearman's rank correlation, and Blomqvist's coefficient. Finally, these methods were compared with Cytoscape, based on BIND, and Gene Ontology, based on molecular function visual methods; R software version 3.2 and Bioconductor were used to perform these methods. Based on the Pearson and Spearman correlations, the results were the same and were confirmed by Cytoscape and GO visual methods; however, Blomqvist's coefficient was not confirmed by visual methods. Some results of the correlation coefficients are not the same with visualization. The reason may be due to the small number of data.

Mars Mission Analysis Trades Based on Legacy and Future Nuclear Propulsion Options

NASA Astrophysics Data System (ADS)

Joyner, Russell; Lentati, Andrea; Cichon, Jaclyn

2007-01-01

The purpose of this paper is to discuss the results of mission-based system trades when using a nuclear thermal propulsion (NTP) system for Solar System exploration. The results are based on comparing reactor designs that use a ceramic-metallic (CERMET), graphite matrix, graphite composite matrix, or carbide matrix fuel element designs. The composite graphite matrix and CERMET designs have been examined for providing power as well as propulsion. Approaches to the design of the NTP to be discussed will include an examination of graphite, composite, carbide, and CERMET core designs and the attributes of each in regards to performance and power generation capability. The focus is on NTP approaches based on tested fuel materials within a prismatic fuel form per the Argonne National Laboratory testing and the ROVER/NERVA program. NTP concepts have been examined for several years at Pratt & Whitney Rocketdyne for use as the primary propulsion for human missions beyond earth. Recently, an approach was taken to examine the design trades between specific NTP concepts; NERVA-based (UC)C-Graphite, (UC,ZrC)C-Composite, (U,Zr)C-Solid Carbide and UO2-W CERMET. Using Pratt & Whitney Rocketdyne's multidisciplinary design analysis capability, a detailed mission and vehicle model has been used to examine how several of these NTP designs impact a human Mars mission. Trends for the propulsion system mass as a function of power level (i.e. thrust size) for the graphite-carbide and CERMET designs were established and correlated against data created over the past forty years. These were used for the mission trade study. The resulting mission trades presented in this paper used a comprehensive modeling approach that captures the mission, vehicle subsystems, and NTP sizing.

Tensor models, Kronecker coefficients and permutation centralizer algebras

NASA Astrophysics Data System (ADS)

Geloun, Joseph Ben; Ramgoolam, Sanjaye

2017-11-01

We show that the counting of observables and correlators for a 3-index tensor model are organized by the structure of a family of permutation centralizer algebras. These algebras are shown to be semi-simple and their Wedderburn-Artin decompositions into matrix blocks are given in terms of Clebsch-Gordan coefficients of symmetric groups. The matrix basis for the algebras also gives an orthogonal basis for the tensor observables which diagonalizes the Gaussian two-point functions. The centres of the algebras are associated with correlators which are expressible in terms of Kronecker coefficients (Clebsch-Gordan multiplicities of symmetric groups). The color-exchange symmetry present in the Gaussian model, as well as a large class of interacting models, is used to refine the description of the permutation centralizer algebras. This discussion is extended to a general number of colors d: it is used to prove the integrality of an infinite family of number sequences related to color-symmetrizations of colored graphs, and expressible in terms of symmetric group representation theory data. Generalizing a connection between matrix models and Belyi maps, correlators in Gaussian tensor models are interpreted in terms of covers of singular 2-complexes. There is an intriguing difference, between matrix and higher rank tensor models, in the computational complexity of superficially comparable correlators of observables parametrized by Young diagrams.

Correlative imaging reveals physiochemical heterogeneity of microcalcifications in human breast carcinomas.

PubMed

Kunitake, Jennie A M R; Choi, Siyoung; Nguyen, Kayla X; Lee, Meredith M; He, Frank; Sudilovsky, Daniel; Morris, Patrick G; Jochelson, Maxine S; Hudis, Clifford A; Muller, David A; Fratzl, Peter; Fischbach, Claudia; Masic, Admir; Estroff, Lara A

2018-04-01

Microcalcifications (MCs) are routinely used to detect breast cancer in mammography. Little is known, however, about their materials properties and associated organic matrix, or their correlation to breast cancer prognosis. We combine histopathology, Raman microscopy, and electron microscopy to image MCs within snap-frozen human breast tissue and generate micron-scale resolution correlative maps of crystalline phase, trace metals, particle morphology, and organic matrix chemical signatures within high grade ductal carcinoma in situ (DCIS) and invasive cancer. We reveal the heterogeneity of mineral-matrix pairings, including punctate apatitic particles (<2 µm) with associated trace elements (e.g., F, Na, and unexpectedly Al) distributed within the necrotic cores of DCIS, and both apatite and spheroidal whitlockite particles in invasive cancer within a matrix containing spectroscopic signatures of collagen, non-collagen proteins, cholesterol, carotenoids, and DNA. Among the three DCIS samples, we identify key similarities in MC morphology and distribution, supporting a dystrophic mineralization pathway. This multimodal methodology lays the groundwork for establishing MC heterogeneity in the context of breast cancer biology, and could dramatically improve current prognostic models. Copyright © 2017 Elsevier Inc. All rights reserved.

On the Feynman-Hellmann theorem in quantum field theory and the calculation of matrix elements

DOE PAGES

Bouchard, Chris; Chang, Chia Cheng; Kurth, Thorsten; ...

2017-07-12

In this paper, the Feynman-Hellmann theorem can be derived from the long Euclidean-time limit of correlation functions determined with functional derivatives of the partition function. Using this insight, we fully develop an improved method for computing matrix elements of external currents utilizing only two-point correlation functions. Our method applies to matrix elements of any external bilinear current, including nonzero momentum transfer, flavor-changing, and two or more current insertion matrix elements. The ability to identify and control all the systematic uncertainties in the analysis of the correlation functions stems from the unique time dependence of the ground-state matrix elements and the fact that all excited states and contact terms are Euclidean-time dependent. We demonstrate the utility of our method with a calculation of the nucleon axial charge using gradient-flowed domain-wall valence quarks on themore » $$N_f=2+1+1$$ MILC highly improved staggered quark ensemble with lattice spacing and pion mass of approximately 0.15 fm and 310 MeV respectively. We show full control over excited-state systematics with the new method and obtain a value of $$g_A = 1.213(26)$$ with a quark-mass-dependent renormalization coefficient.« less

Pre-processing ambient noise cross-correlations with equalizing the covariance matrix eigenspectrum

NASA Astrophysics Data System (ADS)

Seydoux, Léonard; de Rosny, Julien; Shapiro, Nikolai M.

2017-09-01

Passive imaging techniques from ambient seismic noise requires a nearly isotropic distribution of the noise sources in order to ensure reliable traveltime measurements between seismic stations. However, real ambient seismic noise often partially fulfils this condition. It is generated in preferential areas (in deep ocean or near continental shores), and some highly coherent pulse-like signals may be present in the data such as those generated by earthquakes. Several pre-processing techniques have been developed in order to attenuate the directional and deterministic behaviour of this real ambient noise. Most of them are applied to individual seismograms before cross-correlation computation. The most widely used techniques are the spectral whitening and temporal smoothing of the individual seismic traces. We here propose an additional pre-processing to be used together with the classical ones, which is based on the spatial analysis of the seismic wavefield. We compute the cross-spectra between all available stations pairs in spectral domain, leading to the data covariance matrix. We apply a one-bit normalization to the covariance matrix eigenspectrum before extracting the cross-correlations in the time domain. The efficiency of the method is shown with several numerical tests. We apply the method to the data collected by the USArray, when the M8.8 Maule earthquake occurred on 2010 February 27. The method shows a clear improvement compared with the classical equalization to attenuate the highly energetic and coherent waves incoming from the earthquake, and allows to perform reliable traveltime measurement even in the presence of the earthquake.

Determination of cloud liquid water content using the SSM/I

NASA Technical Reports Server (NTRS)

Alishouse, John C.; Snider, Jack B.; Westwater, Ed R.; Swift, Calvin T.; Ruf, Christopher S.

1990-01-01

As part of a calibration/validation effort for the special sensor microwave/imager (SSM/I), coincident observations of SSM/I brightness temperatures and surface-based observations of cloud liquid water were obtained. These observations were used to validate initial algorithms and to derive an improved algorithm. The initial algorithms were divided into latitudinal-, seasonal-, and surface-type zones. It was found that these initial algorithms, which were of the D-matrix type, did not yield sufficiently accurate results. The surface-based measurements of channels were investigated; however, the 85V channel was excluded because of excessive noise. It was found that there is no significant correlation between the SSM/I brightness temperatures and the surface-based cloud liquid water determination when the background surface is land or snow. A high correlation was found between brightness temperatures and ground-based measurements over the ocean.

Optimal portfolio strategy with cross-correlation matrix composed by DCCA coefficients: Evidence from the Chinese stock market

NASA Astrophysics Data System (ADS)

Sun, Xuelian; Liu, Zixian

2016-02-01

In this paper, a new estimator of correlation matrix is proposed, which is composed of the detrended cross-correlation coefficients (DCCA coefficients), to improve portfolio optimization. In contrast to Pearson's correlation coefficients (PCC), DCCA coefficients acquired by the detrended cross-correlation analysis (DCCA) method can describe the nonlinear correlation between assets, and can be decomposed in different time scales. These properties of DCCA make it possible to improve the investment effect and more valuable to investigate the scale behaviors of portfolios. The minimum variance portfolio (MVP) model and the Mean-Variance (MV) model are used to evaluate the effectiveness of this improvement. Stability analysis shows the effect of two kinds of correlation matrices on the estimation error of portfolio weights. The observed scale behaviors are significant to risk management and could be used to optimize the portfolio selection.

3D polarisation speckle as a demonstration of tensor version of the van Cittert-Zernike theorem for stochastic electromagnetic beams

NASA Astrophysics Data System (ADS)

Ma, Ning; Zhao, Juan; Hanson, Steen G.; Takeda, Mitsuo; Wang, Wei

2016-10-01

Laser speckle has received extensive studies of its basic properties and associated applications. In the majority of research on speckle phenomena, the random optical field has been treated as a scalar optical field, and the main interest has been concentrated on their statistical properties and applications of its intensity distribution. Recently, statistical properties of random electric vector fields referred to as Polarization Speckle have come to attract new interest because of their importance in a variety of areas with practical applications such as biomedical optics and optical metrology. Statistical phenomena of random electric vector fields have close relevance to the theories of speckles, polarization and coherence theory. In this paper, we investigate the correlation tensor for stochastic electromagnetic fields modulated by a depolarizer consisting of a rough-surfaced retardation plate. Under the assumption that the microstructure of the scattering surface on the depolarizer is as fine as to be unresolvable in our observation region, we have derived a relationship between the polarization matrix/coherency matrix for the modulated electric fields behind the rough-surfaced retardation plate and the coherence matrix under the free space geometry. This relation is regarded as entirely analogous to the van Cittert-Zernike theorem of classical coherence theory. Within the paraxial approximation as represented by the ABCD-matrix formalism, the three-dimensional structure of the generated polarization speckle is investigated based on the correlation tensor, indicating a typical carrot structure with a much longer axial dimension than the extent in its transverse dimension.

Measuring the Microlensing Parallax from Various Space Observatories

NASA Astrophysics Data System (ADS)

Bachelet, E.; Hinse, T. C.; Street, R.

2018-05-01

A few observational methods allow the measurement of the mass and distance of the lens-star for a microlensing event. A first estimate can be obtained by measuring the microlensing parallax effect produced by either the motion of the Earth (annual parallax) or the contemporaneous observation of the lensing event from two (or more) observatories (space or terrestrial parallax) sufficiently separated from each other. Further developing ideas originally outlined by Gould as well as Mogavero & Beaulieu, we review the possibility of measuring systematically the microlensing parallax using a telescope based on the Moon surface and other space-based observing platforms, including the upcoming WFIRST space-telescope. We first generalize the Fisher matrix formulation and present results demonstrating the advantage for each observing scenario. We conclude by outlining the limitation of the Fisher matrix analysis when submitted to a practical data modeling process. By considering a lunar-based parallax observation, we find that parameter correlations introduce a significant loss in detection efficiency of the probed lunar parallax effect.

The q-dependent detrended cross-correlation analysis of stock market

NASA Astrophysics Data System (ADS)

Zhao, Longfeng; Li, Wei; Fenu, Andrea; Podobnik, Boris; Wang, Yougui; Stanley, H. Eugene

2018-02-01

Properties of the q-dependent cross-correlation matrices of the stock market have been analyzed by using random matrix theory and complex networks. The correlation structures of the fluctuations at different magnitudes have unique properties. The cross-correlations among small fluctuations are much stronger than those among large fluctuations. The large and small fluctuations are dominated by different groups of stocks. We use complex network representation to study these q-dependent matrices and discover some new identities. By utilizing those q-dependent correlation-based networks, we are able to construct some portfolios of those more independent stocks which consistently perform better. The optimal multifractal order for portfolio optimization is around q  =  2 under the mean-variance portfolio framework, and q\\in[2, 6] under the expected shortfall criterion. These results have deepened our understanding regarding the collective behavior of the complex financial system.

Random matrix theory for analyzing the brain functional network in attention deficit hyperactivity disorder

NASA Astrophysics Data System (ADS)

Wang, Rong; Wang, Li; Yang, Yong; Li, Jiajia; Wu, Ying; Lin, Pan

2016-11-01

Attention deficit hyperactivity disorder (ADHD) is the most common childhood neuropsychiatric disorder and affects approximately 6 -7 % of children worldwide. Here, we investigate the statistical properties of undirected and directed brain functional networks in ADHD patients based on random matrix theory (RMT), in which the undirected functional connectivity is constructed based on correlation coefficient and the directed functional connectivity is measured based on cross-correlation coefficient and mutual information. We first analyze the functional connectivity and the eigenvalues of the brain functional network. We find that ADHD patients have increased undirected functional connectivity, reflecting a higher degree of linear dependence between regions, and increased directed functional connectivity, indicating stronger causality and more transmission of information among brain regions. More importantly, we explore the randomness of the undirected and directed functional networks using RMT. We find that for ADHD patients, the undirected functional network is more orderly than that for normal subjects, which indicates an abnormal increase in undirected functional connectivity. In addition, we find that the directed functional networks are more random, which reveals greater disorder in causality and more chaotic information flow among brain regions in ADHD patients. Our results not only further confirm the efficacy of RMT in characterizing the intrinsic properties of brain functional networks but also provide insights into the possibilities RMT offers for improving clinical diagnoses and treatment evaluations for ADHD patients.

Bayesian Factor Analysis When Only a Sample Covariance Matrix Is Available

ERIC Educational Resources Information Center

Hayashi, Kentaro; Arav, Marina

2006-01-01

In traditional factor analysis, the variance-covariance matrix or the correlation matrix has often been a form of inputting data. In contrast, in Bayesian factor analysis, the entire data set is typically required to compute the posterior estimates, such as Bayes factor loadings and Bayes unique variances. We propose a simple method for computing…

Anti-correlation and subsector structure in financial systems

NASA Astrophysics Data System (ADS)

Jiang, X. F.; Zheng, B.

2012-02-01

With the random matrix theory, we study the spatial structure of the Chinese stock market, the American stock market and global market indices. After taking into account the signs of the components in the eigenvectors of the cross-correlation matrix, we detect the subsector structure of the financial systems. The positive and negative subsectors are anti-correlated with respect to each other in the corresponding eigenmode. The subsector structure is strong in the Chinese stock market, while somewhat weaker in the American stock market and global market indices. Characteristics of the subsector structures in different markets are revealed.

Inference for High-dimensional Differential Correlation Matrices.

PubMed

Cai, T Tony; Zhang, Anru

2016-01-01

Motivated by differential co-expression analysis in genomics, we consider in this paper estimation and testing of high-dimensional differential correlation matrices. An adaptive thresholding procedure is introduced and theoretical guarantees are given. Minimax rate of convergence is established and the proposed estimator is shown to be adaptively rate-optimal over collections of paired correlation matrices with approximately sparse differences. Simulation results show that the procedure significantly outperforms two other natural methods that are based on separate estimation of the individual correlation matrices. The procedure is also illustrated through an analysis of a breast cancer dataset, which provides evidence at the gene co-expression level that several genes, of which a subset has been previously verified, are associated with the breast cancer. Hypothesis testing on the differential correlation matrices is also considered. A test, which is particularly well suited for testing against sparse alternatives, is introduced. In addition, other related problems, including estimation of a single sparse correlation matrix, estimation of the differential covariance matrices, and estimation of the differential cross-correlation matrices, are also discussed.

Homonuclear long-range correlation spectra from HMBC experiments by covariance processing.

PubMed

Schoefberger, Wolfgang; Smrecki, Vilko; Vikić-Topić, Drazen; Müller, Norbert

2007-07-01

We present a new application of covariance nuclear magnetic resonance processing based on 1H--13C-HMBC experiments which provides an effective way for establishing indirect 1H--1H and 13C--13C nuclear spin connectivity at natural isotope abundance. The method, which identifies correlated spin networks in terms of covariance between one-dimensional traces from a single decoupled HMBC experiment, derives 13C--13C as well as 1H--1H spin connectivity maps from the two-dimensional frequency domain heteronuclear long-range correlation data matrix. The potential and limitations of this novel covariance NMR application are demonstrated on two compounds: eugenyl-beta-D-glucopyranoside and an emodin-derivative. Copyright (c) 2007 John Wiley & Sons, Ltd.

Memory Effects and Nonequilibrium Correlations in the Dynamics of Open Quantum Systems

NASA Astrophysics Data System (ADS)

Morozov, V. G.

2018-01-01

We propose a systematic approach to the dynamics of open quantum systems in the framework of Zubarev's nonequilibrium statistical operator method. The approach is based on the relation between ensemble means of the Hubbard operators and the matrix elements of the reduced statistical operator of an open quantum system. This key relation allows deriving master equations for open systems following a scheme conceptually identical to the scheme used to derive kinetic equations for distribution functions. The advantage of the proposed formalism is that some relevant dynamical correlations between an open system and its environment can be taken into account. To illustrate the method, we derive a non-Markovian master equation containing the contribution of nonequilibrium correlations associated with energy conservation.

Three-Dimensional In Vitro Skin and Skin Cancer Models Based on Human Fibroblast-Derived Matrix.

PubMed

Berning, Manuel; Prätzel-Wunder, Silke; Bickenbach, Jackie R; Boukamp, Petra

2015-09-01

Three-dimensional in vitro skin and skin cancer models help to dissect epidermal-dermal and tumor-stroma interactions. In the model presented here, normal human dermal fibroblasts isolated from adult skin self-assembled into dermal equivalents with their specific fibroblast-derived matrix (fdmDE) over 4 weeks. The fdmDE represented a complex human extracellular matrix that was stabilized by its own heterogeneous collagen fiber meshwork, largely resembling a human dermal in vivo architecture. Complemented with normal human epidermal keratinocytes, the skin equivalent (fdmSE) thereof favored the establishment of a well-stratified and differentiated epidermis and importantly allowed epidermal regeneration in vitro for at least 24 weeks. Moreover, the fdmDE could be used to study the features of cutaneous skin cancer. Complementing fdmDE with HaCaT cells in different stages of malignancy or tumor-derived cutaneous squamous cell carcinoma cell lines, the resulting skin cancer equivalents (fdmSCEs) recapitulated the respective degree of tumorigenicity. In addition, the fdmSCE invasion phenotypes correlated with their individual degree of tissue organization, disturbance in basement membrane organization, and presence of matrix metalloproteinases. Together, fdmDE-based models are well suited for long-term regeneration of normal human epidermis and, as they recapitulate tumor-specific growth, differentiation, and invasion profiles of cutaneous skin cancer cells, also provide an excellent human in vitro skin cancer model.

Inter-class sparsity based discriminative least square regression.

PubMed

Wen, Jie; Xu, Yong; Li, Zuoyong; Ma, Zhongli; Xu, Yuanrong

2018-06-01

Least square regression is a very popular supervised classification method. However, two main issues greatly limit its performance. The first one is that it only focuses on fitting the input features to the corresponding output labels while ignoring the correlations among samples. The second one is that the used label matrix, i.e., zero-one label matrix is inappropriate for classification. To solve these problems and improve the performance, this paper presents a novel method, i.e., inter-class sparsity based discriminative least square regression (ICS_DLSR), for multi-class classification. Different from other methods, the proposed method pursues that the transformed samples have a common sparsity structure in each class. For this goal, an inter-class sparsity constraint is introduced to the least square regression model such that the margins of samples from the same class can be greatly reduced while those of samples from different classes can be enlarged. In addition, an error term with row-sparsity constraint is introduced to relax the strict zero-one label matrix, which allows the method to be more flexible in learning the discriminative transformation matrix. These factors encourage the method to learn a more compact and discriminative transformation for regression and thus has the potential to perform better than other methods. Extensive experimental results show that the proposed method achieves the best performance in comparison with other methods for multi-class classification. Copyright © 2018 Elsevier Ltd. All rights reserved.

Constructing financial network based on PMFG and threshold method

NASA Astrophysics Data System (ADS)

Nie, Chun-Xiao; Song, Fu-Tie

2018-04-01

Based on planar maximally filtered graph (PMFG) and threshold method, we introduced a correlation-based network named PMFG-based threshold network (PTN). We studied the community structure of PTN and applied ISOMAP algorithm to represent PTN in low-dimensional Euclidean space. The results show that the community corresponds well to the cluster in the Euclidean space. Further, we studied the dynamics of the community structure and constructed the normalized mutual information (NMI) matrix. Based on the real data in the market, we found that the volatility of the market can lead to dramatic changes in the community structure, and the structure is more stable during the financial crisis.

Quantitative image analysis for investigating cell-matrix interactions

NASA Astrophysics Data System (ADS)

Burkel, Brian; Notbohm, Jacob

2017-07-01

The extracellular matrix provides both chemical and physical cues that control cellular processes such as migration, division, differentiation, and cancer progression. Cells can mechanically alter the matrix by applying forces that result in matrix displacements, which in turn may localize to form dense bands along which cells may migrate. To quantify the displacements, we use confocal microscopy and fluorescent labeling to acquire high-contrast images of the fibrous material. Using a technique for quantitative image analysis called digital volume correlation, we then compute the matrix displacements. Our experimental technology offers a means to quantify matrix mechanics and cell-matrix interactions. We are now using these experimental tools to modulate mechanical properties of the matrix to study cell contraction and migration.

Assessment of the Speech Intelligibility Performance of Post Lingual Cochlear Implant Users at Different Signal-to-Noise Ratios Using the Turkish Matrix Test.

PubMed

Polat, Zahra; Bulut, Erdoğan; Ataş, Ahmet

2016-09-01

Spoken word recognition and speech perception tests in quiet are being used as a routine in assessment of the benefit which children and adult cochlear implant users receive from their devices. Cochlear implant users generally demonstrate high level performances in these test materials as they are able to achieve high level speech perception ability in quiet situations. Although these test materials provide valuable information regarding Cochlear Implant (CI) users' performances in optimal listening conditions, they do not give realistic information regarding performances in adverse listening conditions, which is the case in the everyday environment. The aim of this study was to assess the speech intelligibility performance of post lingual CI users in the presence of noise at different signal-to-noise ratio with the Matrix Test developed for Turkish language. Cross-sectional study. The thirty post lingual implant user adult subjects, who had been using implants for a minimum of one year, were evaluated with Turkish Matrix test. Subjects' speech intelligibility was measured using the adaptive and non-adaptive Matrix Test in quiet and noisy environments. The results of the study show a correlation between Pure Tone Average (PTA) values of the subjects and Matrix test Speech Reception Threshold (SRT) values in the quiet. Hence, it is possible to asses PTA values of CI users using the Matrix Test also. However, no correlations were found between Matrix SRT values in the quiet and Matrix SRT values in noise. Similarly, the correlation between PTA values and intelligibility scores in noise was also not significant. Therefore, it may not be possible to assess the intelligibility performance of CI users using test batteries performed in quiet conditions. The Matrix Test can be used to assess the benefit of CI users from their systems in everyday life, since it is possible to perform intelligibility test with the Matrix test using a material that CI users experience in their everyday life and it is possible to assess their difficulty in speech discrimination in noisy conditions they have to cope with.

Correlation and Stacking of Relative Paleointensity and Oxygen Isotope Data

NASA Astrophysics Data System (ADS)

Lurcock, P. C.; Channell, J. E.; Lee, D.

2012-12-01

The transformation of a depth-series into a time-series is routinely implemented in the geological sciences. This transformation often involves correlation of a depth-series to an astronomically calibrated time-series. Eyeball tie-points with linear interpolation are still regularly used, although these have the disadvantages of being non-repeatable and not based on firm correlation criteria. Two automated correlation methods are compared: the simulated annealing algorithm (Huybers and Wunsch, 2004) and the Match protocol (Lisiecki and Lisiecki, 2002). Simulated annealing seeks to minimize energy (cross-correlation) as "temperature" is slowly decreased. The Match protocol divides records into intervals, applies penalty functions that constrain accumulation rates, and minimizes the sum of the squares of the differences between two series while maintaining the data sequence in each series. Paired relative paleointensity (RPI) and oxygen isotope records, such as those from IODP Site U1308 and/or reference stacks such as LR04 and PISO, are warped using known warping functions, and then the un-warped and warped time-series are correlated to evaluate the efficiency of the correlation methods. Correlations are performed in tandem to simultaneously optimize RPI and oxygen isotope data. Noise spectra are introduced at differing levels to determine correlation efficiency as noise levels change. A third potential method, known as dynamic time warping, involves minimizing the sum of distances between correlated point pairs across the whole series. A "cost matrix" between the two series is analyzed to find a least-cost path through the matrix. This least-cost path is used to nonlinearly map the time/depth of one record onto the depth/time of another. Dynamic time warping can be expanded to more than two dimensions and used to stack multiple time-series. This procedure can improve on arithmetic stacks, which often lose coherent high-frequency content during the stacking process.

Comparing the structure of an emerging market with a mature one under global perturbation

NASA Astrophysics Data System (ADS)

Namaki, A.; Jafari, G. R.; Raei, R.

2011-09-01

In this paper we investigate the Tehran stock exchange (TSE) and Dow Jones Industrial Average (DJIA) in terms of perturbed correlation matrices. To perturb a stock market, there are two methods, namely local and global perturbation. In the local method, we replace a correlation coefficient of the cross-correlation matrix with one calculated from two Gaussian-distributed time series, whereas in the global method, we reconstruct the correlation matrix after replacing the original return series with Gaussian-distributed time series. The local perturbation is just a technical study. We analyze these markets through two statistical approaches, random matrix theory (RMT) and the correlation coefficient distribution. By using RMT, we find that the largest eigenvalue is an influence that is common to all stocks and this eigenvalue has a peak during financial shocks. We find there are a few correlated stocks that make the essential robustness of the stock market but we see that by replacing these return time series with Gaussian-distributed time series, the mean values of correlation coefficients, the largest eigenvalues of the stock markets and the fraction of eigenvalues that deviate from the RMT prediction fall sharply in both markets. By comparing these two markets, we can see that the DJIA is more sensitive to global perturbations. These findings are crucial for risk management and portfolio selection.

Improved Estimation and Interpretation of Correlations in Neural Circuits

PubMed Central

Yatsenko, Dimitri; Josić, Krešimir; Ecker, Alexander S.; Froudarakis, Emmanouil; Cotton, R. James; Tolias, Andreas S.

2015-01-01

Ambitious projects aim to record the activity of ever larger and denser neuronal populations in vivo. Correlations in neural activity measured in such recordings can reveal important aspects of neural circuit organization. However, estimating and interpreting large correlation matrices is statistically challenging. Estimation can be improved by regularization, i.e. by imposing a structure on the estimate. The amount of improvement depends on how closely the assumed structure represents dependencies in the data. Therefore, the selection of the most efficient correlation matrix estimator for a given neural circuit must be determined empirically. Importantly, the identity and structure of the most efficient estimator informs about the types of dominant dependencies governing the system. We sought statistically efficient estimators of neural correlation matrices in recordings from large, dense groups of cortical neurons. Using fast 3D random-access laser scanning microscopy of calcium signals, we recorded the activity of nearly every neuron in volumes 200 μm wide and 100 μm deep (150–350 cells) in mouse visual cortex. We hypothesized that in these densely sampled recordings, the correlation matrix should be best modeled as the combination of a sparse graph of pairwise partial correlations representing local interactions and a low-rank component representing common fluctuations and external inputs. Indeed, in cross-validation tests, the covariance matrix estimator with this structure consistently outperformed other regularized estimators. The sparse component of the estimate defined a graph of interactions. These interactions reflected the physical distances and orientation tuning properties of cells: The density of positive ‘excitatory’ interactions decreased rapidly with geometric distances and with differences in orientation preference whereas negative ‘inhibitory’ interactions were less selective. Because of its superior performance, this ‘sparse+latent’ estimator likely provides a more physiologically relevant representation of the functional connectivity in densely sampled recordings than the sample correlation matrix. PMID:25826696

Critical state of sand matrix soils.

PubMed

Marto, Aminaton; Tan, Choy Soon; Makhtar, Ahmad Mahir; Kung Leong, Tiong

2014-01-01

The Critical State Soil Mechanic (CSSM) is a globally recognised framework while the critical states for sand and clay are both well established. Nevertheless, the development of the critical state of sand matrix soils is lacking. This paper discusses the development of critical state lines and corresponding critical state parameters for the investigated material, sand matrix soils using sand-kaolin mixtures. The output of this paper can be used as an interpretation framework for the research on liquefaction susceptibility of sand matrix soils in the future. The strain controlled triaxial test apparatus was used to provide the monotonic loading onto the reconstituted soil specimens. All tested soils were subjected to isotropic consolidation and sheared under undrained condition until critical state was ascertain. Based on the results of 32 test specimens, the critical state lines for eight different sand matrix soils were developed together with the corresponding values of critical state parameters, M, λ, and Γ. The range of the value of M, λ, and Γ is 0.803-0.998, 0.144-0.248, and 1.727-2.279, respectively. These values are comparable to the critical state parameters of river sand and kaolin clay. However, the relationship between fines percentages and these critical state parameters is too scattered to be correlated.

Distance descending ordering method: An O(n) algorithm for inverting the mass matrix in simulation of macromolecules with long branches

NASA Astrophysics Data System (ADS)

Xu, Xiankun; Li, Peiwen

2017-11-01

Fixman's work in 1974 and the follow-up studies have developed a method that can factorize the inverse of mass matrix into an arithmetic combination of three sparse matrices-one of them is positive definite and needs to be further factorized by using the Cholesky decomposition or similar methods. When the molecule subjected to study is of serial chain structure, this method can achieve O (n) time complexity. However, for molecules with long branches, Cholesky decomposition about the corresponding positive definite matrix will introduce massive fill-in due to its nonzero structure. Although there are several methods can be used to reduce the number of fill-in, none of them could strictly guarantee for zero fill-in for all molecules according to our test, and thus cannot obtain O (n) time complexity by using these traditional methods. In this paper we present a new method that can guarantee for no fill-in in doing the Cholesky decomposition, which was developed based on the correlations between the mass matrix and the geometrical structure of molecules. As a result, the inverting of mass matrix will remain the O (n) time complexity, no matter the molecule structure has long branches or not.

Critical State of Sand Matrix Soils

PubMed Central

Marto, Aminaton; Tan, Choy Soon; Makhtar, Ahmad Mahir; Kung Leong, Tiong

2014-01-01

The Critical State Soil Mechanic (CSSM) is a globally recognised framework while the critical states for sand and clay are both well established. Nevertheless, the development of the critical state of sand matrix soils is lacking. This paper discusses the development of critical state lines and corresponding critical state parameters for the investigated material, sand matrix soils using sand-kaolin mixtures. The output of this paper can be used as an interpretation framework for the research on liquefaction susceptibility of sand matrix soils in the future. The strain controlled triaxial test apparatus was used to provide the monotonic loading onto the reconstituted soil specimens. All tested soils were subjected to isotropic consolidation and sheared under undrained condition until critical state was ascertain. Based on the results of 32 test specimens, the critical state lines for eight different sand matrix soils were developed together with the corresponding values of critical state parameters, M, λ, and Γ. The range of the value of M, λ, and Γ is 0.803–0.998, 0.144–0.248, and 1.727–2.279, respectively. These values are comparable to the critical state parameters of river sand and kaolin clay. However, the relationship between fines percentages and these critical state parameters is too scattered to be correlated. PMID:24757417

Failure prediction in ceramic composites using acoustic emission and digital image correlation

NASA Astrophysics Data System (ADS)

Whitlow, Travis; Jones, Eric; Przybyla, Craig

2016-02-01

The objective of the work performed here was to develop a methodology for linking in-situ detection of localized matrix cracking to the final failure location in continuous fiber reinforced CMCs. First, the initiation and growth of matrix cracking are measured and triangulated via acoustic emission (AE) detection. High amplitude events at relatively low static loads can be associated with initiation of large matrix cracks. When there is a localization of high amplitude events, a measurable effect on the strain field can be observed. Full field surface strain measurements were obtained using digital image correlation (DIC). An analysis using the combination of the AE and DIC data was able to predict the final failure location.

Enhanced Constraints for Accurate Lower Bounds on Many-Electron Quantum Energies from Variational Two-Electron Reduced Density Matrix Theory.

PubMed

Mazziotti, David A

2016-10-07

A central challenge of physics is the computation of strongly correlated quantum systems. The past ten years have witnessed the development and application of the variational calculation of the two-electron reduced density matrix (2-RDM) without the wave function. In this Letter we present an orders-of-magnitude improvement in the accuracy of 2-RDM calculations without an increase in their computational cost. The advance is based on a low-rank, dual formulation of an important constraint on the 2-RDM, the T2 condition. Calculations are presented for metallic chains and a cadmium-selenide dimer. The low-scaling T2 condition will have significant applications in atomic and molecular, condensed-matter, and nuclear physics.

Enhanced Constraints for Accurate Lower Bounds on Many-Electron Quantum Energies from Variational Two-Electron Reduced Density Matrix Theory

NASA Astrophysics Data System (ADS)

Mazziotti, David A.

2016-10-01

A central challenge of physics is the computation of strongly correlated quantum systems. The past ten years have witnessed the development and application of the variational calculation of the two-electron reduced density matrix (2-RDM) without the wave function. In this Letter we present an orders-of-magnitude improvement in the accuracy of 2-RDM calculations without an increase in their computational cost. The advance is based on a low-rank, dual formulation of an important constraint on the 2-RDM, the T 2 condition. Calculations are presented for metallic chains and a cadmium-selenide dimer. The low-scaling T 2 condition will have significant applications in atomic and molecular, condensed-matter, and nuclear physics.

Framework influence of erbium doped oxyfluoride glasses on their optical properties

NASA Astrophysics Data System (ADS)

Środa, Marcin; Cholewa-Kowalska, Katarzyna; Różański, Marek; Nocuń, Marek

2011-01-01

Glasses of different matrix (phosphate, borate, silicate and lead-silicate) were studied for their optical properties. The effect of Er dopant on transmittance and luminescence properties was presented. The significant “red shift” and “blue shift” of UV edge absorption were discussed based on the changes in the framework of the borate and phosphate glasses, respectively. It was showed that the integral intensity of the two main optical absorption transitions monotonically increases with the order: phosphate < borate < silicate < lead-silicate. Ellipsometric measurement was applied to obtain the refractive index of the glasses. The correlation between the shift of edge absorption and the change of refractive index was presented. Effect of glassy matrix on luminescence of Er3+ was discussed.

Geometry of modified release formulations during dissolution--influence on performance of dosage forms with diclofenac sodium.

PubMed

Dorożyński, Przemysław; Kulinowski, Piotr; Jamróz, Witold; Juszczyk, Ewelina

2014-12-30

The objectives of the work included: presentation of magnetic resonance imaging (MRI) and fractal analysis based approach to comparison of dosage forms of different composition, structure, and assessment of the influence of the compositional factors i.e., matrix type, excipients etc., on properties and performance of the dosage form during drug dissolution. The work presents the first attempt to compare MRI data obtained for tablet formulations of different composition and characterized by distinct differences in hydration and drug dissolution mechanisms. The main difficulty, in such a case stems from differences in hydration behavior and tablet's geometry i.e., swelling, cracking, capping etc. A novel approach to characterization of matrix systems i.e., quantification of changes of geometrical complexity of the matrix shape during drug dissolution has been developed. Using three chosen commercial modified release tablet formulations with diclofenac sodium we present the method of parameterization of their geometrical complexity on the base of fractal analysis. The main result of the study is the correlation between the hydrating tablet behavior and drug dissolution - the increase of geometrical complexity expressed as fractal dimension relates to the increased variability of drug dissolution results. Copyright © 2014 Elsevier B.V. All rights reserved.

A computational model of in vitro angiogenesis based on extracellular matrix fibre orientation.

PubMed

Edgar, Lowell T; Sibole, Scott C; Underwood, Clayton J; Guilkey, James E; Weiss, Jeffrey A

2013-01-01

Recent interest in the process of vascularisation within the biomedical community has motivated numerous new research efforts focusing on the process of angiogenesis. Although the role of chemical factors during angiogenesis has been well documented, the role of mechanical factors, such as the interaction between angiogenic vessels and the extracellular matrix, remains poorly understood. In vitro methods for studying angiogenesis exist; however, measurements available using such techniques often suffer from limited spatial and temporal resolutions. For this reason, computational models have been extensively employed to investigate various aspects of angiogenesis. This paper outlines the formulation and validation of a simple and robust computational model developed to accurately simulate angiogenesis based on length, branching and orientation morphometrics collected from vascularised tissue constructs. Microvessels were represented as a series of connected line segments. The morphology of the vessels was determined by a linear combination of the collagen fibre orientation, the vessel density gradient and a random walk component. Excellent agreement was observed between computational and experimental morphometric data over time. Computational predictions of microvessel orientation within an anisotropic matrix correlated well with experimental data. The accuracy of this modelling approach makes it a valuable platform for investigating the role of mechanical interactions during angiogenesis.

Coherent Activity in Bilateral Parieto-Occipital Cortices during P300-BCI Operation.

PubMed

Takano, Kouji; Ora, Hiroki; Sekihara, Kensuke; Iwaki, Sunao; Kansaku, Kenji

2014-01-01

The visual P300 brain-computer interface (BCI), a popular system for electroencephalography (EEG)-based BCI, uses the P300 event-related potential to select an icon arranged in a flicker matrix. In earlier studies, we used green/blue (GB) luminance and chromatic changes in the P300-BCI system and reported that this luminance and chromatic flicker matrix was associated with better performance and greater subject comfort compared with the conventional white/gray (WG) luminance flicker matrix. To highlight areas involved in improved P300-BCI performance, we used simultaneous EEG-fMRI recordings and showed enhanced activities in bilateral and right lateralized parieto-occipital areas. Here, to capture coherent activities of the areas during P300-BCI, we collected whole-head 306-channel magnetoencephalography data. When comparing functional connectivity between the right and left parieto-occipital channels, significantly greater functional connectivity in the alpha band was observed under the GB flicker matrix condition than under the WG flicker matrix condition. Current sources were estimated with a narrow-band adaptive spatial filter, and mean imaginary coherence was computed in the alpha band. Significantly greater coherence was observed in the right posterior parietal cortex under the GB than under the WG condition. Re-analysis of previous EEG-based P300-BCI data showed significant correlations between the power of the coherence of the bilateral parieto-occipital cortices and their performance accuracy. These results suggest that coherent activity in the bilateral parieto-occipital cortices plays a significant role in effectively driving the P300-BCI.

Innovative Structural Materials and Sections with Strain Hardening Cementitious Composites

NASA Astrophysics Data System (ADS)

Dey, Vikram

The motivation of this work is based on development of new construction products with strain hardening cementitious composites (SHCC) geared towards sustainable residential applications. The proposed research has three main objectives: automation of existing manufacturing systems for SHCC laminates; multi-level characterization of mechanical properties of fiber, matrix, interface and composites phases using servo-hydraulic and digital image correlation techniques. Structural behavior of these systems were predicted using ductility based design procedures using classical laminate theory and structural mechanics. SHCC sections are made up of thin sections of matrix with Portland cement based binder and fine aggregates impregnating continuous one-dimensional fibers in individual or bundle form or two/three dimensional woven, bonded or knitted textiles. Traditional fiber reinforced concrete (FRC) use random dispersed chopped fibers in the matrix at a low volume fractions, typically 1-2% to avoid to avoid fiber agglomeration and balling. In conventional FRC, fracture localization occurs immediately after the first crack, resulting in only minor improvement in toughness and tensile strength. However in SHCC systems, distribution of cracking throughout the specimen is facilitated by the fiber bridging mechanism. Influence of material properties of yarn, composition, geometry and weave patterns of textile in the behavior of laminated SHCC skin composites were investigated. Contribution of the cementitious matrix in the early age and long-term performance of laminated composites was studied with supplementary cementitious materials such as fly ash, silica fume, and wollastonite. A closed form model with classical laminate theory and ply discount method, coupled with a damage evolution model was utilized to simulate the non-linear tensile response of these composite materials. A constitutive material model developed earlier in the group was utilized to characterize and correlate the behavior of these structural composites under uniaxial tension and flexural loading responses. Development and use of analytical models enables optimal design for application of these materials in structural applications. Another area of immediate focus is the development of new construction products from SHCC laminates such as angles, channels, hat sections, closed sections with optimized cross sections. Sandwich composites with stress skin-cellular core concept were also developed to utilize strength and ductility of fabric reinforced skin in addition to thickness, ductility, and thermal benefits of cellular core materials. The proposed structurally efficient and durable sections promise to compete with wood and light gage steel based sections for lightweight construction and panel application.

Plate Deformation Behavior of Polymer Matrix Composite-Ti Honeycomb-Metal Sandwiches for Pressurized Propulsion Component Applications

NASA Technical Reports Server (NTRS)

Bertelsen, William D.; Shin, E. eugene; Thesken, John C.; Sutter, James K.; Martin, Rich

2004-01-01

THe objectives are: 1. To experimentally validate bi-axial plate flexural performance of PMC-Ti H/C-A286 sandwich panels for the internally pressurized RBCC combustion chamber support structure. 2. To explore ASTM 2-D plate flexure test (D 6416) to simulate the internal pressure loading and to correlate the results with analytical and FE modeling based on 2-D flexure properties.

THE MEASUREMENT OF BONE QUALITY USING GRAY LEVEL CO-OCCURRENCE MATRIX TEXTURAL FEATURES.

PubMed

Shirvaikar, Mukul; Huang, Ning; Dong, Xuanliang Neil

2016-10-01

In this paper, statistical methods for the estimation of bone quality to predict the risk of fracture are reported. Bone mineral density and bone architecture properties are the main contributors of bone quality. Dual-energy X-ray Absorptiometry (DXA) is the traditional clinical measurement technique for bone mineral density, but does not include architectural information to enhance the prediction of bone fragility. Other modalities are not practical due to cost and access considerations. This study investigates statistical parameters based on the Gray Level Co-occurrence Matrix (GLCM) extracted from two-dimensional projection images and explores links with architectural properties and bone mechanics. Data analysis was conducted on Micro-CT images of 13 trabecular bones (with an in-plane spatial resolution of about 50μm). Ground truth data for bone volume fraction (BV/TV), bone strength and modulus were available based on complex 3D analysis and mechanical tests. Correlation between the statistical parameters and biomechanical test results was studied using regression analysis. The results showed Cluster-Shade was strongly correlated with the microarchitecture of the trabecular bone and related to mechanical properties. Once the principle thesis of utilizing second-order statistics is established, it can be extended to other modalities, providing cost and convenience advantages for patients and doctors.

THE MEASUREMENT OF BONE QUALITY USING GRAY LEVEL CO-OCCURRENCE MATRIX TEXTURAL FEATURES

PubMed Central

Shirvaikar, Mukul; Huang, Ning; Dong, Xuanliang Neil

2016-01-01

In this paper, statistical methods for the estimation of bone quality to predict the risk of fracture are reported. Bone mineral density and bone architecture properties are the main contributors of bone quality. Dual-energy X-ray Absorptiometry (DXA) is the traditional clinical measurement technique for bone mineral density, but does not include architectural information to enhance the prediction of bone fragility. Other modalities are not practical due to cost and access considerations. This study investigates statistical parameters based on the Gray Level Co-occurrence Matrix (GLCM) extracted from two-dimensional projection images and explores links with architectural properties and bone mechanics. Data analysis was conducted on Micro-CT images of 13 trabecular bones (with an in-plane spatial resolution of about 50μm). Ground truth data for bone volume fraction (BV/TV), bone strength and modulus were available based on complex 3D analysis and mechanical tests. Correlation between the statistical parameters and biomechanical test results was studied using regression analysis. The results showed Cluster-Shade was strongly correlated with the microarchitecture of the trabecular bone and related to mechanical properties. Once the principle thesis of utilizing second-order statistics is established, it can be extended to other modalities, providing cost and convenience advantages for patients and doctors. PMID:28042512

Burst Ductility of Zirconium Clads: The Defining Role of Residual Stress

NASA Astrophysics Data System (ADS)

Kumar, Gulshan; Kanjarla, A. K.; Lodh, Arijit; Singh, Jaiveer; Singh, Ramesh; Srivastava, D.; Dey, G. K.; Saibaba, N.; Doherty, R. D.; Samajdar, Indradev

2016-08-01

Closed end burst tests, using room temperature water as pressurizing medium, were performed on a number of industrially produced zirconium (Zr) clads. A total of 31 samples were selected based on observed differences in burst ductility. The latter was represented as total circumferential elongation or TCE. The selected samples, with a range of TCE values (5 to 35 pct), did not show any correlation with mechanical properties along axial direction, microstructural parameters, crystallographic textures, and outer tube-surface normal ( σ 11) and shear ( τ 13) components of the residual stress matrix. TCEs, however, had a clear correlation with hydrostatic residual stress ( P h), as estimated from tri-axial stress analysis on the outer tube surface. Estimated P h also scaled with measured normal stress ( σ 33) at the tube cross section. An elastic-plastic finite element model with ductile damage failure criterion was developed to understand the burst mechanism of zirconium clads. Experimentally measured P h gradients were imposed on a solid element continuum finite element (FE) simulation to mimic the residual stresses present prior to pressurization. Trends in experimental TCEs were also brought out with computationally efficient shell element-based FE simulations imposing the outer tube-surface P h values. Suitable components of the residual stress matrix thus determined the burst performance of the Zr clads.

The study of Thai stock market across the 2008 financial crisis

NASA Astrophysics Data System (ADS)

Kanjamapornkul, K.; Pinčák, Richard; Bartoš, Erik

2016-11-01

The cohomology theory for financial market can allow us to deform Kolmogorov space of time series data over time period with the explicit definition of eight market states in grand unified theory. The anti-de Sitter space induced from a coupling behavior field among traders in case of a financial market crash acts like gravitational field in financial market spacetime. Under this hybrid mathematical superstructure, we redefine a behavior matrix by using Pauli matrix and modified Wilson loop for time series data. We use it to detect the 2008 financial market crash by using a degree of cohomology group of sphere over tensor field in correlation matrix over all possible dominated stocks underlying Thai SET50 Index Futures. The empirical analysis of financial tensor network was performed with the help of empirical mode decomposition and intrinsic time scale decomposition of correlation matrix and the calculation of closeness centrality of planar graph.

LANDSAT-D Thematic Mapper image dimensionality reduction and geometric correction accuracy. [Walnut Creek Watershed, Texas

NASA Technical Reports Server (NTRS)

Ford, G. E. (Principal Investigator)

1984-01-01

Principal components transformations was applied to a Walnut Creek, Texas subscene to reduce the dimensionality of the multispectral sensor data. This transformation was also applied to a LANDSAT 3 MSS subscene of the same area acquired in a different season and year. Results of both procedures are tabulated and allow for comparisons between TM and MSS data. The TM correlation matrix shows that visible bands 1 to 3 exhibit a high degree of correlation in the range 0.92 to 0.96. Correlation for bands 5 to 7 is 0.93. Band 4 is not highly correlated with any other band, with corrections in the range 0.13 to 0.52. The thermal band (6) is not highly correlated with other bands in the range 0.13 to 0.46. The MSS correlation matrix shows that bands 4 and 5 are highly correlated (0.96) as are bands 6 and 7 with a correlation of 0.92.

Quasiparticle random phase approximation uncertainties and their correlations in the analysis of 0{nu}{beta}{beta} decay

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

Faessler, Amand; Rodin, V.; Fogli, G. L.

2009-03-01

The variances and covariances associated to the nuclear matrix elements of neutrinoless double beta decay (0{nu}{beta}{beta}) are estimated within the quasiparticle random phase approximation. It is shown that correlated nuclear matrix elements uncertainties play an important role in the comparison of 0{nu}{beta}{beta} decay rates for different nuclei, and that they are degenerate with the uncertainty in the reconstructed Majorana neutrino mass.

Aortic Wall Extracellular Matrix Proteins Correlate with Syntax Score in Patients Undergoing Coronary Artery Bypass Surgery

PubMed Central

Chiong, Terri; Cheow, Esther S. H.; Woo, Chin C.; Lin, Xiao Y.; Khin, Lay W.; Lee, Chuen N.; Hartman, Mikael; Sze, Siu K.; Sorokin, Vitaly A.

2016-01-01

Aims: The SYNTAX score correlate with major cardiovascular events post-revascularization, although the histopathological basis is unclear. We aim to evaluate the association between syntax score and extracellular matrix histological characteristics of aortic punch tissue obtained during coronary artery bypass surgery (CABG). This analysis compares coronary artery bypass surgery patients with High and Low syntax score which were followed up for one year period. Methods and Results: Patients with High (score ≥ 33, (n=77)) and Low Syntax Scores (score ≤ 22, (n=71)) undergoing elective CABG were recruited prospectively. Baseline clinical characteristics and surgical risks were well matched. At 1 year, EMACCE (Sum of cardiovascular death, stroke, congestive cardiac failure, and limb, gut and myocardial ischemia) was significantly elevated in the High syntax group (P=0.022). Mass spectrometry (MS)-based quantitative iTRAQ proteomic results validated on independent cohort by immunohistochemistry (IHC) revealed that the High syntax group had significantly upraised Collagen I (P<0.0001) and Elastin (P<0.0001) content in ascending aortic wall. Conclusion: This study shows that aortic extracellular matrix (ECM) differ between High and Low syntax groups with up-regulation of Collagen I and Elastin level in High Syntax Score group. This identifies aortic punches collected during CABG as another biomarker source related with atherosclerosis severity and possible clinical outcome. PMID:27347220

Evolutions of fluctuation modes and inner structures of global stock markets

NASA Astrophysics Data System (ADS)

Yan, Yan; Wang, Lei; Liu, Maoxin; Chen, Xiaosong

2016-09-01

The paper uses empirical data, including 42 globally main stock indices in the period 1996-2014, to systematically study the evolution of fluctuation modes and inner structures of global stock markets. The data are large in scale considering both time and space. A covariance matrix-based principle fluctuation mode analysis (PFMA) is used to explore the properties of the global stock markets. It has been ignored by previous studies that covariance matrix is more suitable than the correlation matrix to be the basis of PFMA. It is found that the principle fluctuation modes of global stock markets are in the same directions, and global stock markets are divided into three clusters, which are found to be closely related to the countries’ locations with exceptions of China, Russia and Czech Republic. A time-stable correlation network constructing method is proposed to solve the problem of high-level statistical uncertainty when the estimated periods are very short, and the complex dynamic network (CDN) is constructed to investigate the evolution of inner structures. The results show when the clusters emerge and how long the clusters exist. When the 2008 financial crisis broke out, the indices form one cluster. After these crises, only the European cluster still exists. These findings complement the previous studies, and can help investors and regulators to understand the global stock markets.

Role of short-range correlation in facilitation of wave propagation in a long-range ladder chain

NASA Astrophysics Data System (ADS)

Farzadian, O.; Niry, M. D.

2018-09-01

We extend a new method for generating a random chain, which has a kind of short-range correlation induced by a repeated sequence while retaining long-range correlation. Three distinct methods are considered to study the localization-delocalization transition of mechanical waves in one-dimensional disordered media with simultaneous existence of short and long-range correlation. First, a transfer-matrix method was used to calculate numerically the localization length of a wave in a binary chain. We found that the existence of short-range correlation in a long-range correlated chain can increase the localization length at the resonance frequency Ωc. Then, we carried out an analytical study of the delocalization properties of the waves in correlated disordered media around Ωc. Finally, we apply a dynamical method based on the direct numerical simulation of the wave equation to study the propagation of waves in the correlated chain. Imposing short-range correlation on the long-range background will lead the propagation to super-diffusive transport. The results obtained with all three methods are in agreement with each other.

3D Progressive Damage Modeling for Laminated Composite Based on Crack Band Theory and Continuum Damage Mechanics

NASA Technical Reports Server (NTRS)

Wang, John T.; Pineda, Evan J.; Ranatunga, Vipul; Smeltzer, Stanley S.

2015-01-01

A simple continuum damage mechanics (CDM) based 3D progressive damage analysis (PDA) tool for laminated composites was developed and implemented as a user defined material subroutine to link with a commercially available explicit finite element code. This PDA tool uses linear lamina properties from standard tests, predicts damage initiation with an easy-to-implement Hashin-Rotem failure criteria, and in the damage evolution phase, evaluates the degradation of material properties based on the crack band theory and traction-separation cohesive laws. It follows Matzenmiller et al.'s formulation to incorporate the degrading material properties into the damaged stiffness matrix. Since nonlinear shear and matrix stress-strain relations are not implemented, correction factors are used for slowing the reduction of the damaged shear stiffness terms to reflect the effect of these nonlinearities on the laminate strength predictions. This CDM based PDA tool is implemented as a user defined material (VUMAT) to link with the Abaqus/Explicit code. Strength predictions obtained, using this VUMAT, are correlated with test data for a set of notched specimens under tension and compression loads.

Holographic hierarchy in the Gaussian matrix model via the fuzzy sphere

NASA Astrophysics Data System (ADS)

Garner, David; Ramgoolam, Sanjaye

2013-10-01

The Gaussian Hermitian matrix model was recently proposed to have a dual string description with worldsheets mapping to a sphere target space. The correlators were written as sums over holomorphic (Belyi) maps from worldsheets to the two-dimensional sphere, branched over three points. We express the matrix model correlators by using the fuzzy sphere construction of matrix algebras, which can be interpreted as a string field theory description of the Belyi strings. This gives the correlators in terms of trivalent ribbon graphs that represent the couplings of irreducible representations of su(2), which can be evaluated in terms of 3j and 6j symbols. The Gaussian model perturbed by a cubic potential is then recognised as a generating function for Ponzano-Regge partition functions for 3-manifolds having the worldsheet as boundary, and equipped with boundary data determined by the ribbon graphs. This can be viewed as a holographic extension of the Belyi string worldsheets to membrane worldvolumes, forming part of a holographic hierarchy linking, via the large N expansion, the zero-dimensional QFT of the Matrix model to 2D strings and 3D membranes. Note that if, after removing the white vertices, the graph contains a blue edge connecting to the same black vertex at both ends, then the triangulation generated from the black edges will contain faces that resemble cut discs. These faces are triangles with two of the edges identified.

Serum levels of matrix metalloproteinases: implications in clinical neurology.

PubMed

Romi, Fredrik; Helgeland, Geir; Gilhus, Nils Erik

2012-01-01

Matrix metalloproteinases (MMPs) are zinc-dependent enzymes involved in remodeling extracellular matrix and cell-matrix interactions. A pathogenic role of MMPs in neurological disorders is likely. This paper focuses on serological clinical aspects only. In multiple sclerosis, higher serum MMP-3 is seen during relapses. Lower serum MMP-8 and -9 levels correlate with fewer contrast-enhanced T(2)-weighted MRI lesions, and serum MMP-9 can be used in monitoring treatment. In myasthenia gravis, serum MMP-2, -3, and -9 levels are elevated in both generalized and ocular diseases. A proportion of the patients have markedly increased serum MMP-3. In acute stroke, higher serum MMP-9 correlates with larger infarct volume, stroke severity, and worse functional outcome, and serum MMP-3 is significantly lower than in several other neurological disorders and healthy controls. In amyotrophic lateral sclerosis, serum MMP-2 correlates with disease progression, and both serum MMP-1 and -2 are elevated. In Alzheimer's disease, serum MMP-3, -9, and -10 are elevated. In migraine, serum MMP-2 is elevated, and also MMP-9 in those patients with migraine without aura. MMP-9 is implicated in the pathogenesis of experimental epilepsy. A pathogenic role of MMPs in these conditions could be related to their ability to degrade extracellular matrix. MMPs may also facilitate autoimmunity. Copyright © 2012 S. Karger AG, Basel.

Modeling and possible implementation of self-learning equivalence-convolutional neural structures for auto-encoding-decoding and clusterization of images

NASA Astrophysics Data System (ADS)

Krasilenko, Vladimir G.; Lazarev, Alexander A.; Nikitovich, Diana V.

2017-08-01

Self-learning equivalent-convolutional neural structures (SLECNS) for auto-coding-decoding and image clustering are discussed. The SLECNS architectures and their spatially invariant equivalent models (SI EMs) using the corresponding matrix-matrix procedures with basic operations of continuous logic and non-linear processing are proposed. These SI EMs have several advantages, such as the ability to recognize image fragments with better efficiency and strong cross correlation. The proposed clustering method of fragments with regard to their structural features is suitable not only for binary, but also color images and combines self-learning and the formation of weight clustered matrix-patterns. Its model is constructed and designed on the basis of recursively processing algorithms and to k-average method. The experimental results confirmed that larger images and 2D binary fragments with a large numbers of elements may be clustered. For the first time the possibility of generalization of these models for space invariant case is shown. The experiment for an image with dimension of 256x256 (a reference array) and fragments with dimensions of 7x7 and 21x21 for clustering is carried out. The experiments, using the software environment Mathcad, showed that the proposed method is universal, has a significant convergence, the small number of iterations is easily, displayed on the matrix structure, and confirmed its prospects. Thus, to understand the mechanisms of self-learning equivalence-convolutional clustering, accompanying her to the competitive processes in neurons, and the neural auto-encoding-decoding and recognition principles with the use of self-learning cluster patterns is very important which used the algorithm and the principles of non-linear processing of two-dimensional spatial functions of images comparison. These SIEMs can simply describe the signals processing during the all training and recognition stages and they are suitable for unipolar-coding multilevel signals. We show that the implementation of SLECNS based on known equivalentors or traditional correlators is possible if they are based on proposed equivalental two-dimensional functions of image similarity. The clustering efficiency in such models and their implementation depends on the discriminant properties of neural elements of hidden layers. Therefore, the main models and architecture parameters and characteristics depends on the applied types of non-linear processing and function used for image comparison or for adaptive-equivalental weighing of input patterns. Real model experiments in Mathcad are demonstrated, which confirm that non-linear processing on equivalent functions allows you to determine the neuron winners and adjust the weight matrix. Experimental results have shown that such models can be successfully used for auto- and hetero-associative recognition. They can also be used to explain some mechanisms known as "focus" and "competing gain-inhibition concept". The SLECNS architecture and hardware implementations of its basic nodes based on multi-channel convolvers and correlators with time integration are proposed. The parameters and performance of such architectures are estimated.

Two-Way Gene Interaction From Microarray Data Based on Correlation Methods

PubMed Central

Alavi Majd, Hamid; Talebi, Atefeh; Gilany, Kambiz; Khayyer, Nasibeh

2016-01-01

Background Gene networks have generated a massive explosion in the development of high-throughput techniques for monitoring various aspects of gene activity. Networks offer a natural way to model interactions between genes, and extracting gene network information from high-throughput genomic data is an important and difficult task. Objectives The purpose of this study is to construct a two-way gene network based on parametric and nonparametric correlation coefficients. The first step in constructing a Gene Co-expression Network is to score all pairs of gene vectors. The second step is to select a score threshold and connect all gene pairs whose scores exceed this value. Materials and Methods In the foundation-application study, we constructed two-way gene networks using nonparametric methods, such as Spearman’s rank correlation coefficient and Blomqvist’s measure, and compared them with Pearson’s correlation coefficient. We surveyed six genes of venous thrombosis disease, made a matrix entry representing the score for the corresponding gene pair, and obtained two-way interactions using Pearson’s correlation, Spearman’s rank correlation, and Blomqvist’s coefficient. Finally, these methods were compared with Cytoscape, based on BIND, and Gene Ontology, based on molecular function visual methods; R software version 3.2 and Bioconductor were used to perform these methods. Results Based on the Pearson and Spearman correlations, the results were the same and were confirmed by Cytoscape and GO visual methods; however, Blomqvist’s coefficient was not confirmed by visual methods. Conclusions Some results of the correlation coefficients are not the same with visualization. The reason may be due to the small number of data. PMID:27621916

Uncarboxylated matrix Gla protein (ucMGP) is associated with coronary artery calcification in haemodialysis patients.

PubMed

Cranenburg, Ellen C M; Brandenburg, Vincent M; Vermeer, Cees; Stenger, Melanie; Mühlenbruch, Georg; Mahnken, Andreas H; Gladziwa, Ulrich; Ketteler, Markus; Schurgers, Leon J

2009-02-01

Matrix gamma-carboxyglutamate (Gla) protein (MGP) is a potent local inhibitor of cardiovascular calcification and accumulates at areas of calcification in its uncarboxylated form (ucMGP). We previously found significantly lower circulating ucMGP levels in patients with a high vascular calcification burden. Here we report on the potential of circulating ucMGP to serve as a biomarker for vascular calcification in haemodialysis (HD) patients. Circulating ucMGP levels were measured with an ELISA-based assay in 40 HD patients who underwent multi-slice computed tomography (MSCT) scanning to quantify the extent of coronary artery calcification (CAC). The mean ucMGP level in HD patients (193 +/- 65 nM) was significantly lower as compared to apparently healthy subjects of the same age (441 +/- 97 nM; p < 0.001) and patients with rheumatoid arthritis (RA) without CAC (560 +/- 140 nM; p < 0.001). Additionally, ucMGP levels correlated inversely with CAC scores (r = -0.41; p = 0.009), and this correlation persisted after adjustment for age, dialysis vintage and high-sensitivity C-reactive protein (hs-CRP). Since circulating ucMGP levels are significantly and inversely correlated with the extent of CAC in HD patients, ucMGP may become a tool for identifying HD patients with a high probability of cardiovascular calcification.

gRINN: a tool for calculation of residue interaction energies and protein energy network analysis of molecular dynamics simulations.

PubMed

Serçinoglu, Onur; Ozbek, Pemra

2018-05-25

Atomistic molecular dynamics (MD) simulations generate a wealth of information related to the dynamics of proteins. If properly analyzed, this information can lead to new insights regarding protein function and assist wet-lab experiments. Aiming to identify interactions between individual amino acid residues and the role played by each in the context of MD simulations, we present a stand-alone software called gRINN (get Residue Interaction eNergies and Networks). gRINN features graphical user interfaces (GUIs) and a command-line interface for generating and analyzing pairwise residue interaction energies and energy correlations from protein MD simulation trajectories. gRINN utilizes the features of NAMD or GROMACS MD simulation packages and automatizes the steps necessary to extract residue-residue interaction energies from user-supplied simulation trajectories, greatly simplifying the analysis for the end-user. A GUI, including an embedded molecular viewer, is provided for visualization of interaction energy time-series, distributions, an interaction energy matrix, interaction energy correlations and a residue correlation matrix. gRINN additionally offers construction and analysis of Protein Energy Networks, providing residue-based metrics such as degrees, betweenness-centralities, closeness centralities as well as shortest path analysis. gRINN is free and open to all users without login requirement at http://grinn.readthedocs.io.

Multivariate analysis of scale-dependent associations between bats and landscape structure

USGS Publications Warehouse

Gorresen, P.M.; Willig, M.R.; Strauss, R.E.

2005-01-01

The assessment of biotic responses to habitat disturbance and fragmentation generally has been limited to analyses at a single spatial scale. Furthermore, methods to compare responses between scales have lacked the ability to discriminate among patterns related to the identity, strength, or direction of associations of biotic variables with landscape attributes. We present an examination of the relationship of population- and community-level characteristics of phyllostomid bats with habitat features that were measured at multiple spatial scales in Atlantic rain forest of eastern Paraguay. We used a matrix of partial correlations between each biotic response variable (i.e., species abundance, species richness, and evenness) and a suite of landscape characteristics to represent the multifaceted associations of bats with spatial structure. Correlation matrices can correspond based on either the strength (i.e., magnitude) or direction (i.e., sign) of association. Therefore, a simulation model independently evaluated correspondence in the magnitude and sign of correlations among scales, and results were combined via a meta-analysis to provide an overall test of significance. Our approach detected both species-specific differences in response to landscape structure and scale dependence in those responses. This matrix-simulation approach has broad applicability to ecological situations in which multiple intercorrelated factors contribute to patterns in space or time. ?? 2005 by the Ecological Society of America.

Active contour-based visual tracking by integrating colors, shapes, and motions.

PubMed

Hu, Weiming; Zhou, Xue; Li, Wei; Luo, Wenhan; Zhang, Xiaoqin; Maybank, Stephen

2013-05-01

In this paper, we present a framework for active contour-based visual tracking using level sets. The main components of our framework include contour-based tracking initialization, color-based contour evolution, adaptive shape-based contour evolution for non-periodic motions, dynamic shape-based contour evolution for periodic motions, and the handling of abrupt motions. For the initialization of contour-based tracking, we develop an optical flow-based algorithm for automatically initializing contours at the first frame. For the color-based contour evolution, Markov random field theory is used to measure correlations between values of neighboring pixels for posterior probability estimation. For adaptive shape-based contour evolution, the global shape information and the local color information are combined to hierarchically evolve the contour, and a flexible shape updating model is constructed. For the dynamic shape-based contour evolution, a shape mode transition matrix is learnt to characterize the temporal correlations of object shapes. For the handling of abrupt motions, particle swarm optimization is adopted to capture the global motion which is applied to the contour in the current frame to produce an initial contour in the next frame.

A METHOD FOR IN-SITU CHARACTERIZATION OF RF HEATING IN PARALLEL TRANSMIT MRI

PubMed Central

Alon, Leeor; Deniz, Cem Murat; Brown, Ryan; Sodickson, Daniel K.; Zhu, Yudong

2012-01-01

In ultra high field magnetic resonance imaging, parallel radio-frequency (RF) transmission presents both opportunities and challenges for specific absorption rate (SAR) management. On one hand, parallel transmission provides flexibility in tailoring electric fields in the body while facilitating magnetization profile control. On the other hand, it increases the complexity of energy deposition as well as possibly exacerbating local SAR by improper design or delivery of RF pulses. This study shows that the information needed to characterize RF heating in parallel transmission is contained within a local power correlation matrix. Building upon a calibration scheme involving a finite number of magnetic resonance thermometry measurements, the present work establishes a way of estimating the local power correlation matrix. Determination of this matrix allows prediction of temperature change for an arbitrary parallel transmit RF pulse. In the case of a three transmit coil MR experiment in a phantom, determination and validation of the power correlation matrix was conducted in less than 200 minutes with induced temperature changes of <4 degrees C. Further optimization and adaptation are possible, and simulations evaluating potential feasibility for in vivo use are presented. The method allows general characteristics indicative of RF coil/pulse safety determined in situ. PMID:22714806

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

Bouchard, Chris; Chang, Chia Cheng; Kurth, Thorsten

In this paper, the Feynman-Hellmann theorem can be derived from the long Euclidean-time limit of correlation functions determined with functional derivatives of the partition function. Using this insight, we fully develop an improved method for computing matrix elements of external currents utilizing only two-point correlation functions. Our method applies to matrix elements of any external bilinear current, including nonzero momentum transfer, flavor-changing, and two or more current insertion matrix elements. The ability to identify and control all the systematic uncertainties in the analysis of the correlation functions stems from the unique time dependence of the ground-state matrix elements and the fact that all excited states and contact terms are Euclidean-time dependent. We demonstrate the utility of our method with a calculation of the nucleon axial charge using gradient-flowed domain-wall valence quarks on themore » $$N_f=2+1+1$$ MILC highly improved staggered quark ensemble with lattice spacing and pion mass of approximately 0.15 fm and 310 MeV respectively. We show full control over excited-state systematics with the new method and obtain a value of $$g_A = 1.213(26)$$ with a quark-mass-dependent renormalization coefficient.« less

Synthesis and Characterisation of ETS-10/Acetate-based Ionic Liquid/Chitosan Mixed Matrix Membranes for CO2/N2 Permeation.

PubMed

Casado-Coterillo, Clara; Del Mar López-Guerrero, María; Irabien, Angel

2014-06-19

Mixed matrix membranes (MMMs) were prepared by incorporating organic surfactant-free hydrothermally synthesised ETS-10 and 1-ethyl-3-methylimidazolium acetate ionic liquid (IL) to chitosan (CS) polymer matrix. The membrane material characteristics and permselectivity performance of the two-component membranes were compared with the three-component membrane and the pure CS membrane. The addition of IL increased CO2 solubility of the polymer, and, thus, the CO2 affinity was maintained for the MMMs, which can be correlated with the crystallinity, measured by FT-IR, and void fraction calculations from differences between theoretical and experimental densities. The mechanical resistance was enhanced by the ETS-10 nanoparticles, and flexibility decreased in the two-component ETS-10/CS MMMs, but the flexibility imparted by the IL remained in three-component ETS-10/IL/CS MMMs. The results of this work provide insight into another way of facing the adhesion challenge in MMMs and obtain CO2 selective MMMs from renewable or green chemistry materials.

Acid-Base and the Skeleton

NASA Astrophysics Data System (ADS)

Bushinsky, David A.

2008-09-01

Chronic metabolic acidosis increases urine calcium (Ca) excretion in the absence of a concomitant increase in intestinal Ca absorption resulting in a net loss of total body. The source of this additional urine Ca is almost certainly the skeleton, the primary reservoir of body Ca. In vitro metabolic acidosis, modeled as a primary reduction in medium bicarbonate concentration, acutely (<24 h) stimulates Ca efflux primarily through physicochemical mineral dissolution while at later time periods (>24 h) cell-mediated mechanisms predominate. In cultured neonatal mouse calvariae, acidosis-induced, cell-mediated Ca efflux is mediated by effects on both osteoblasts and osteoclasts. Metabolic acidosis inhibits extracellular matrix production by osteoblasts, as determined by measurement of collagen levels and levels for the non-collagenous matrix proteins osteopontin and matrix gla protein. Metabolic acidosis upregulates osteoblastic expression of RANKL (Receptor Activator of NFκB Ligand), an important osteoclastogenic and osteoclast-activating factor. Acidosis also increases osteoclastic activity as measured by release of β-glucuronidase, an enzyme whose secretion correlates with osteoclast-mediated bone resorption.

EvolQG - An R package for evolutionary quantitative genetics

PubMed Central

Melo, Diogo; Garcia, Guilherme; Hubbe, Alex; Assis, Ana Paula; Marroig, Gabriel

2016-01-01

We present an open source package for performing evolutionary quantitative genetics analyses in the R environment for statistical computing. Evolutionary theory shows that evolution depends critically on the available variation in a given population. When dealing with many quantitative traits this variation is expressed in the form of a covariance matrix, particularly the additive genetic covariance matrix or sometimes the phenotypic matrix, when the genetic matrix is unavailable and there is evidence the phenotypic matrix is sufficiently similar to the genetic matrix. Given this mathematical representation of available variation, the \\textbf{EvolQG} package provides functions for calculation of relevant evolutionary statistics; estimation of sampling error; corrections for this error; matrix comparison via correlations, distances and matrix decomposition; analysis of modularity patterns; and functions for testing evolutionary hypotheses on taxa diversification. PMID:27785352

Effect of flaw size and temperature on the matrix cracking behavior of a brittle ceramic matrix composite

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

Anandakumar, U.; Webb, J.E.; Singh, R.N.

The matrix cracking behavior of a zircon matrix - uniaxial SCS 6 fiber composite was studied as a function of initial flaw size and temperature. The composites were fabricated by a tape casting and hot pressing technique. Surface flaws of controlled size were introduced using a vicker`s indenter. The composite samples were tested in three point flexure at three different temperatures to study the non steady state and steady state matrix cracking behavior. The composite samples exhibited steady state and non steady matrix cracking behavior at all temperatures. The steady state matrix cracking stress and steady state crack size increasedmore » with increasing temperature. The results of the study correlated well with the results predicted by the matrix cracking models.« less

Characterization of the energy-dependent uncertainty and correlation in silicon neutron displacement damage metrics

NASA Astrophysics Data System (ADS)

Griffin, Patrick; Rochman, Dimitri; Koning, Arjan

2017-09-01

A rigorous treatment of the uncertainty in the underlying nuclear data on silicon displacement damage metrics is presented. The uncertainty in the cross sections and recoil atom spectra are propagated into the energy-dependent uncertainty contribution in the silicon displacement kerma and damage energy using a Total Monte Carlo treatment. An energy-dependent covariance matrix is used to characterize the resulting uncertainty. A strong correlation between different reaction channels is observed in the high energy neutron contributions to the displacement damage metrics which supports the necessity of using a Monte Carlo based method to address the nonlinear nature of the uncertainty propagation.

Effect of long-range correlation on the metal-insulator transition in a disordered molecular crystal

NASA Astrophysics Data System (ADS)

Unge, Mikael; Stafström, Sven

2006-12-01

Localization lengths of the electronic states in a disordered two-dimensional system, resembling highly anisotropic molecular crystals such as pentacene, have been calculated numerically using the transfer matrix method. The disorder is based on a model with small random fluctuations of induced molecular dipole moments which give rise to long-range correlated disorder in the on-site energies as well as a coupling between the on-site energies and the intermolecular interactions. Our calculations show that molecular crystals such as pentacene can exhibit states with very long localization lengths with a possibility to reach a truly metallic state.

Rheological properties of isotropic magnetorheological elastomers featuring an epoxidized natural rubber

NASA Astrophysics Data System (ADS)

Azhani Yunus, Nurul; Amri Mazlan, Saiful; Ubaidillah; Choi, Seung-Bok; Imaduddin, Fitrian; Aziz, Siti Aishah Abdul; Khairi, Muntaz Hana Ahmad

2016-10-01

This study presents principal field-dependent rheological properties of magnetorheological elastomers (MREs) in which an epoxidized natural rubber (ENR) is adopted as a matrix (in short, we call it ENR-based MREs). The isotropic ENR-based MRE samples are fabricated by mixing the ENR compound with carbonyl iron particles (CIPs) with different weight percentages. The morphological properties of the samples are firstly analysed using the microstructure assessment. The influences of the magnetic field on the viscoelastic properties of ENR-based MREs are then examined through the dynamic test under various excitation frequencies. The microstructure of MRE samples exhibits a homogeneous distribution of CIPs in the ENR matrix. The dramatic increment of storage modulus, loss modulus and loss tangent of the ENR-based MREs are also observed from the field-dependent rheological test. This directly demonstrates that the stiffness and damping properties of the samples can be adjusted by the magnetic field. It is also seen that the CIP content, exciting frequency and the magnetic field essentially influence the dynamic properties of the ENR-based MREs. The strong correlation between the magnetization and the magneto-induced storage modulus could be used as a useful guidance in synthesizing the ENR-based MREs for certain applications.

Temporal evolution of financial-market correlations.

PubMed

Fenn, Daniel J; Porter, Mason A; Williams, Stacy; McDonald, Mark; Johnson, Neil F; Jones, Nick S

2011-08-01

We investigate financial market correlations using random matrix theory and principal component analysis. We use random matrix theory to demonstrate that correlation matrices of asset price changes contain structure that is incompatible with uncorrelated random price changes. We then identify the principal components of these correlation matrices and demonstrate that a small number of components accounts for a large proportion of the variability of the markets that we consider. We characterize the time-evolving relationships between the different assets by investigating the correlations between the asset price time series and principal components. Using this approach, we uncover notable changes that occurred in financial markets and identify the assets that were significantly affected by these changes. We show in particular that there was an increase in the strength of the relationships between several different markets following the 2007-2008 credit and liquidity crisis.

Temporal evolution of financial-market correlations

NASA Astrophysics Data System (ADS)

Fenn, Daniel J.; Porter, Mason A.; Williams, Stacy; McDonald, Mark; Johnson, Neil F.; Jones, Nick S.

2011-08-01

We investigate financial market correlations using random matrix theory and principal component analysis. We use random matrix theory to demonstrate that correlation matrices of asset price changes contain structure that is incompatible with uncorrelated random price changes. We then identify the principal components of these correlation matrices and demonstrate that a small number of components accounts for a large proportion of the variability of the markets that we consider. We characterize the time-evolving relationships between the different assets by investigating the correlations between the asset price time series and principal components. Using this approach, we uncover notable changes that occurred in financial markets and identify the assets that were significantly affected by these changes. We show in particular that there was an increase in the strength of the relationships between several different markets following the 2007-2008 credit and liquidity crisis.

Fast Metabolite Identification in Nuclear Magnetic Resonance Metabolomic Studies: Statistical Peak Sorting and Peak Overlap Detection for More Reliable Database Queries.

PubMed

Hoijemberg, Pablo A; Pelczer, István

2018-01-05

A lot of time is spent by researchers in the identification of metabolites in NMR-based metabolomic studies. The usual metabolite identification starts employing public or commercial databases to match chemical shifts thought to belong to a given compound. Statistical total correlation spectroscopy (STOCSY), in use for more than a decade, speeds the process by finding statistical correlations among peaks, being able to create a better peak list as input for the database query. However, the (normally not automated) analysis becomes challenging due to the intrinsic issue of peak overlap, where correlations of more than one compound appear in the STOCSY trace. Here we present a fully automated methodology that analyzes all STOCSY traces at once (every peak is chosen as driver peak) and overcomes the peak overlap obstacle. Peak overlap detection by clustering analysis and sorting of traces (POD-CAST) first creates an overlap matrix from the STOCSY traces, then clusters the overlap traces based on their similarity and finally calculates a cumulative overlap index (COI) to account for both strong and intermediate correlations. This information is gathered in one plot to help the user identify the groups of peaks that would belong to a single molecule and perform a more reliable database query. The simultaneous examination of all traces reduces the time of analysis, compared to viewing STOCSY traces by pairs or small groups, and condenses the redundant information in the 2D STOCSY matrix into bands containing similar traces. The COI helps in the detection of overlapping peaks, which can be added to the peak list from another cross-correlated band. POD-CAST overcomes the generally overlooked and underestimated presence of overlapping peaks and it detects them to include them in the search of all compounds contributing to the peak overlap, enabling the user to accelerate the metabolite identification process with more successful database queries and searching all tentative compounds in the sample set.

Metamorphosis in the Porosity of Recycled Concretes Through the Use of a Recycled Polyethylene Terephthalate (PET) Additive. Correlations between the Porous Network and Concrete Properties

PubMed Central

Mendivil-Escalante, José Miguel; Gómez-Soberón, José Manuel; Almaral-Sánchez, Jorge Luis; Cabrera-Covarrubias, Francisca Guadalupe

2017-01-01

In the field of construction, sustainable building materials are currently undergoing a process of technological development. This study aims to contribute to understanding the behavior of the fundamental properties of concretes prepared with recycled coarse aggregates that incorporate a polyethylene terephthalate (PET)-based additive in their matrix (produced by synthesis and glycolysis of recycled PET bottles) in an attempt to reduce their high porosity. Techniques to measure the gas adsorption, water porosity, Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) were used to evaluate the effect of the additive on the physical, mechanical and microstructural properties of these concretes. Porosity reductions of up to 30.60% are achieved with the addition of 1%, 3%, 4%, 5%, 7% and 9% of the additive, defining a new state in the behavioral model of the additive (the overdosage point) in the concrete matrix; in addition, the porous network of these concretes and their correlation with other physical and mechanical properties are also explained. PMID:28772540

Metamorphosis in the Porosity of Recycled Concretes Through the Use of a Recycled Polyethylene Terephthalate (PET) Additive. Correlations between the Porous Network and Concrete Properties.

PubMed

Mendivil-Escalante, José Miguel; Gómez-Soberón, José Manuel; Almaral-Sánchez, Jorge Luis; Cabrera-Covarrubias, Francisca Guadalupe

2017-02-14

In the field of construction, sustainable building materials are currently undergoing a process of technological development. This study aims to contribute to understanding the behavior of the fundamental properties of concretes prepared with recycled coarse aggregates that incorporate a polyethylene terephthalate (PET)-based additive in their matrix (produced by synthesis and glycolysis of recycled PET bottles) in an attempt to reduce their high porosity. Techniques to measure the gas adsorption, water porosity, Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) were used to evaluate the effect of the additive on the physical, mechanical and microstructural properties of these concretes. Porosity reductions of up to 30.60% are achieved with the addition of 1%, 3%, 4%, 5%, 7% and 9% of the additive, defining a new state in the behavioral model of the additive (the overdosage point) in the concrete matrix; in addition, the porous network of these concretes and their correlation with other physical and mechanical properties are also explained.

Random Matrix Theory and Econophysics

NASA Astrophysics Data System (ADS)

Rosenow, Bernd

2000-03-01

Random Matrix Theory (RMT) [1] is used in many branches of physics as a ``zero information hypothesis''. It describes generic behavior of different classes of systems, while deviations from its universal predictions allow to identify system specific properties. We use methods of RMT to analyze the cross-correlation matrix C of stock price changes [2] of the largest 1000 US companies. In addition to its scientific interest, the study of correlations between the returns of different stocks is also of practical relevance in quantifying the risk of a given stock portfolio. We find [3,4] that the statistics of most of the eigenvalues of the spectrum of C agree with the predictions of RMT, while there are deviations for some of the largest eigenvalues. We interpret these deviations as a system specific property, e.g. containing genuine information about correlations in the stock market. We demonstrate that C shares universal properties with the Gaussian orthogonal ensemble of random matrices. Furthermore, we analyze the eigenvectors of C through their inverse participation ratio and find eigenvectors with large ratios at both edges of the eigenvalue spectrum - a situation reminiscent of localization theory results. This work was done in collaboration with V. Plerou, P. Gopikrishnan, T. Guhr, L.A.N. Amaral, and H.E Stanley and is related to recent work of Laloux et al.. 1. T. Guhr, A. Müller Groeling, and H.A. Weidenmüller, ``Random Matrix Theories in Quantum Physics: Common Concepts'', Phys. Rep. 299, 190 (1998). 2. See, e.g. R.N. Mantegna and H.E. Stanley, Econophysics: Correlations and Complexity in Finance (Cambridge University Press, Cambridge, England, 1999). 3. V. Plerou, P. Gopikrishnan, B. Rosenow, L.A.N. Amaral, and H.E. Stanley, ``Universal and Nonuniversal Properties of Cross Correlations in Financial Time Series'', Phys. Rev. Lett. 83, 1471 (1999). 4. V. Plerou, P. Gopikrishnan, T. Guhr, B. Rosenow, L.A.N. Amaral, and H.E. Stanley, ``Random Matrix Theory Analysis of Diffusion in Stock Price Dynamics, preprint

Covariance structure in the skull of Catarrhini: a case of pattern stasis and magnitude evolution.

PubMed

de Oliveira, Felipe Bandoni; Porto, Arthur; Marroig, Gabriel

2009-04-01

The study of the genetic variance/covariance matrix (G-matrix) is a recent and fruitful approach in evolutionary biology, providing a window of investigating for the evolution of complex characters. Although G-matrix studies were originally conducted for microevolutionary timescales, they could be extrapolated to macroevolution as long as the G-matrix remains relatively constant, or proportional, along the period of interest. A promising approach to investigating the constancy of G-matrices is to compare their phenotypic counterparts (P-matrices) in a large group of related species; if significant similarity is found among several taxa, it is very likely that the underlying G-matrices are also equivalent. Here we study the similarity of covariance and correlation structure in a broad sample of Old World monkeys and apes (Catarrhini). We made phylogenetically structured comparisons of correlation and covariance matrices derived from 39 skull traits, ranging from between species to the superfamily level. We also compared the overall magnitude of integration between skull traits (r2) for all Catarrhini genera. Our results show that P-matrices were not strictly constant among catarrhines, but the amount of divergence observed among taxa was generally low. There was significant and positive correlation between the amount of divergence in correlation and covariance patterns among the 30 genera and their phylogenetic distances derived from a recently proposed phylogenetic hypothesis. Our data demonstrate that the P-matrices remained relatively similar along the evolutionary history of catarrhines, and comparisons with the G-matrix available for a New World monkey genus (Saguinus) suggests that the same holds for all anthropoids. The magnitude of integration, in contrast, varied considerably among genera, indicating that evolution of the magnitude, rather than the pattern of inter-trait correlations, might have played an important role in the diversification of the catarrhine skull.

A technique for plasma velocity-space cross-correlation

NASA Astrophysics Data System (ADS)

Mattingly, Sean; Skiff, Fred

2018-05-01

An advance in experimental plasma diagnostics is presented and used to make the first measurement of a plasma velocity-space cross-correlation matrix. The velocity space correlation function can detect collective fluctuations of plasmas through a localized measurement. An empirical decomposition, singular value decomposition, is applied to this Hermitian matrix in order to obtain the plasma fluctuation eigenmode structure on the ion distribution function. A basic theory is introduced and compared to the modes obtained by the experiment. A full characterization of these modes is left for future work, but an outline of this endeavor is provided. Finally, the requirements for this experimental technique in other plasma regimes are discussed.

Correlation of pretreatment 18F-FDG PET tumor textural features with gene expression in pharyngeal cancer and implications for radiotherapy-based treatment outcomes.

PubMed

Chen, Shang-Wen; Shen, Wei-Chih; Lin, Ying-Chun; Chen, Rui-Yun; Hsieh, Te-Chun; Yen, Kuo-Yang; Kao, Chia-Hung

2017-04-01

This study investigated the correlation of the matrix heterogeneity of tumors on 18 F-fluorodeoxyglucose positron emission tomography-computed tomography (PET-CT) with gene-expression profiling in patients with pharyngeal cancer and determined the prognostic factors for radiotherapy-based treatment outcomes. We retrospectively reviewed the records of 57 patients with stage III-IV oropharyngeal or hypopharyngeal cancer who had completed definitive therapy. Four groups of the textural features as well as 31 indices were studied in addition to maximum standard uptake value, metastatic tumor volume, and total lesion glycolysis. Immunohistochemical data from pretreatment biopsy specimens (Glut1, CAIX, VEGF, HIF-1α, EGFR, Ki-67, Bcl-2, CLAUDIN-4, YAP-1, c-Met, and p16) were analyzed. The relationships between the indices and genomic expression were studied, and the robustness of various textural features relative to cause-specific survival and primary relapse-free survival was analyzed. The overexpression of VEGF was positively associated with the increased values of the matrix heterogeneity obtained using gray-level nonuniformity for zone (GLNUz) and run-length nonuniformity (RLNU). Advanced T stage (p = 0.01, hazard ratio [HR] = 3.38), a VEGF immunoreactive score of >2 (p = 0.03, HR = 2.79), and a higher GLNUz value (p = 0.04, HR = 2.51) were prognostic factors for low cause-specific survival, whereas advanced T stage, a HIF-1α staining percentage of ≥80%, and a higher GLNUz value were prognostic factors for low primary-relapse free survival. The overexpression of VEGF was associated with the increased matrix index of GLNUz and RLNU. For patients with pharyngeal cancer requiring radiotherapy, the treatment outcome can be stratified according to the textural features, T stage, and biomarkers.

Genomic and protein expression profiling identifies CDK6 as novel independent prognostic marker in medulloblastoma.

PubMed

Mendrzyk, Frank; Radlwimmer, Bernhard; Joos, Stefan; Kokocinski, Felix; Benner, Axel; Stange, Daniel E; Neben, Kai; Fiegler, Heike; Carter, Nigel P; Reifenberger, Guido; Korshunov, Andrey; Lichter, Peter

2005-12-01

Medulloblastoma is the most common malignant brain tumor in children. Despite multimodal aggressive treatment, nearly half of the patients die as a result of this tumor. Identification of molecular markers for prognosis and development of novel pathogenesis-based therapies depends crucially on a better understanding of medulloblastoma pathomechanisms. We performed genome-wide analysis of DNA copy number imbalances in 47 medulloblastomas using comparative genomic hybridization to large insert DNA microarrays (matrix-CGH). The expression of selected candidate genes identified by matrix-CGH was analyzed immunohistochemically on tissue microarrays representing medulloblastomas from 189 clinically well-documented patients. To identify novel prognostic markers, genomic findings and protein expression data were correlated to patient survival. Matrix-CGH analysis revealed frequent DNA copy number alterations of several novel candidate regions. Among these, gains at 17q23.2-qter (P < .01) and losses at 17p13.1 to 17p13.3 (P = .04) were significantly correlated to poor prognosis. Within 17q23.2-qter and 7q21.2, two of the most frequently gained chromosomal regions, confined amplicons were identified that contained the PPM1D and CDK6 genes, respectively. Immunohistochemistry revealed strong expression of PPM1D in 148 (88%) of 168 and CDK6 in 50 (30%) of 169 medulloblastomas. Overexpression of CDK6 correlated significantly with poor prognosis (P < .01) and represented an independent prognostic marker of overall survival on multivariate analysis (P = .02). We identified CDK6 as a novel molecular marker that can be determined by immunohistochemistry on routinely processed tissue specimens and may facilitate the prognostic assessment of medulloblastoma patients. Furthermore, increased protein-levels of PPM1D and CDK6 may link the TP53 and RB1 tumor suppressor pathways to medulloblastoma pathomechanisms.

Identifying the structure of group correlation in the Korean financial market

NASA Astrophysics Data System (ADS)

Ahn, Sanghyun; Choi, Jaewon; Lim, Gyuchang; Cha, Kil Young; Kim, Sooyong; Kim, Kyungsik

2011-06-01

We investigate the structure of the cross-correlation in the Korean stock market. We analyze daily cross-correlations between price fluctuations of 586 different Korean stock entities for the 6-year time period from 2003 to 2008. The main purpose is to investigate the structure of group correlation and its stability by undressing the market-wide effect using the Markowitz multi-factor model and the network-based approach. We find the explicit list of significant firms in the few largest eigenvectors from the undressed correlation matrix. We also observe that each contributor is involved in the same business sectors. The structure of group correlation can not remain constant during each 1-year time period with different starting points, whereas only two largest eigenvectors are stable for 6 years 8-9 eigenvectors remain stable for half-year. The structure of group correlation in the Korean financial market is disturbed during a sufficiently short time period even though the group correlation exists as an ensemble for the 6-year time period in the evolution of the system. We verify the structure of group correlation by applying a network-based approach. In addition, we examine relations between market capitalization and businesses. The Korean stock market shows a different behavior compared to mature markets, implying that the KOSPI is a target for short-positioned investors.

Correlation between the viscoelastic heterogeneity and the domain wall motion of Fe-based metallic glass

NASA Astrophysics Data System (ADS)

Ouyang, S.; Song, L. J.; Liu, Y. H.; Huo, J. T.; Wang, J. Q.; Xu, W.; Li, J. L.; Wang, C. T.; Wang, X. M.; Li, R. W.

2018-06-01

The soft magnetic properties of Fe-based metallic glasses are reduced significantly by external and residual stresses, e.g., the susceptibility decreases and coercivity increases, which limits their application severely. Unraveling the micromechanism of how the stress influences the soft magnetic properties is of great help for enhancing the performance of Fe-based metallic glasses. In this work, we investigate the effect of viscoelastic heterogeneity on the motion of magnetic domain wall surrounding nanoindentations. Compared to the matrix, dissipation of the viscoelastic heterogeneity increases toward the nanoindentation. Meanwhile, the motion of domain wall under external magnetic field becomes more difficult toward the nanoindentations. A correlation between the viscoelastic dissipation and the moving ability of magnetic domain walls is observed, which can be well fitted using magnetoelastic coupling theory. This suggests that manipulating the microscale viscoelastic heterogeneity is probably a helpful strategy for enhancing the soft magnetic properties of metallic glasses.

Truncated feature representation for automatic target detection using transformed data-based decomposition

NASA Astrophysics Data System (ADS)

Riasati, Vahid R.

2016-05-01

In this work, the data covariance matrix is diagonalized to provide an orthogonal bases set using the eigen vectors of the data. The eigen-vector decomposition of the data is transformed and filtered in the transform domain to truncate the data for robust features related to a specified set of targets. These truncated eigen features are then combined and reconstructed to utilize in a composite filter and consequently utilized for the automatic target detection of the same class of targets. The results associated with the testing of the current technique are evaluated using the peak-correlation and peak-correlation energy metrics and are presented in this work. The inverse transformed eigen-bases of the current technique may be thought of as an injected sparsity to minimize data in representing the skeletal data structure information associated with the set of targets under consideration.

Detrended cross-correlation coefficient: Application to predict apoptosis protein subcellular localization.

PubMed

Liang, Yunyun; Liu, Sanyang; Zhang, Shengli

2016-12-01

Apoptosis, or programed cell death, plays a central role in the development and homeostasis of an organism. Obtaining information on subcellular location of apoptosis proteins is very helpful for understanding the apoptosis mechanism. The prediction of subcellular localization of an apoptosis protein is still a challenging task, and existing methods mainly based on protein primary sequences. In this paper, we introduce a new position-specific scoring matrix (PSSM)-based method by using detrended cross-correlation (DCCA) coefficient of non-overlapping windows. Then a 190-dimensional (190D) feature vector is constructed on two widely used datasets: CL317 and ZD98, and support vector machine is adopted as classifier. To evaluate the proposed method, objective and rigorous jackknife cross-validation tests are performed on the two datasets. The results show that our approach offers a novel and reliable PSSM-based tool for prediction of apoptosis protein subcellular localization. Copyright © 2016 Elsevier Inc. All rights reserved.

Role of the Euclidean signature in lattice calculations of quasidistributions and other nonlocal matrix elements

NASA Astrophysics Data System (ADS)

Briceño, Raúl A.; Hansen, Maxwell T.; Monahan, Christopher J.

2017-07-01

Lattice quantum chromodynamics (QCD) provides the only known systematic, nonperturbative method for first-principles calculations of nucleon structure. However, for quantities such as light-front parton distribution functions (PDFs) and generalized parton distributions (GPDs), the restriction to Euclidean time prevents direct calculation of the desired observable. Recently, progress has been made in relating these quantities to matrix elements of spatially nonlocal, zero-time operators, referred to as quasidistributions. Still, even for these time-independent matrix elements, potential subtleties have been identified in the role of the Euclidean signature. In this work, we investigate the analytic behavior of spatially nonlocal correlation functions and demonstrate that the matrix elements obtained from Euclidean lattice QCD are identical to those obtained using the Lehmann-Symanzik-Zimmermann reduction formula in Minkowski space. After arguing the equivalence on general grounds, we also show that it holds in a perturbative calculation, where special care is needed to identify the lattice prediction. Finally we present a proof of the uniqueness of the matrix elements obtained from Minkowski and Euclidean correlation functions to all order in perturbation theory.

Role of the Euclidean signature in lattice calculations of quasidistributions and other nonlocal matrix elements

DOE PAGES

Briceno, Raul A.; Hansen, Maxwell T.; Monahan, Christopher J.

2017-07-11

Lattice quantum chromodynamics (QCD) provides the only known systematic, nonperturbative method for first-principles calculations of nucleon structure. However, for quantities such as light-front parton distribution functions (PDFs) and generalized parton distributions (GPDs), the restriction to Euclidean time prevents direct calculation of the desired observable. Recently, progress has been made in relating these quantities to matrix elements of spatially nonlocal, zero-time operators, referred to as quasidistributions. Still, even for these time-independent matrix elements, potential subtleties have been identified in the role of the Euclidean signature. In this work, we investigate the analytic behavior of spatially nonlocal correlation functions and demonstrate thatmore » the matrix elements obtained from Euclidean lattice QCD are identical to those obtained using the Lehmann-Symanzik-Zimmermann reduction formula in Minkowski space. After arguing the equivalence on general grounds, we also show that it holds in a perturbative calculation, where special care is needed to identify the lattice prediction. Lastly, we present a proof of the uniqueness of the matrix elements obtained from Minkowski and Euclidean correlation functions to all order in perturbation theory.« less

Strain Rate Dependent Deformation and Strength Modeling of a Polymer Matrix Composite Utilizing a Micromechanics Approach. Degree awarded by Cincinnati Univ.

NASA Technical Reports Server (NTRS)

Goldberg, Robert K.

1999-01-01

Potential gas turbine applications will expose polymer matrix composites to very high strain rate loading conditions, requiring an ability to understand and predict the material behavior under extreme conditions. Specifically, analytical methods designed for these applications must have the capability of properly capturing the strain rate sensitivities and nonlinearities that are present in the material response. The Ramaswamy-Stouffer constitutive equations, originally developed to analyze the viscoplastic deformation of metals, have been modified to simulate the nonlinear deformation response of ductile, crystalline polymers. The constitutive model is characterized and correlated for two representative ductile polymers. Fiberite 977-2 and PEEK, and the computed results correlate well with experimental values. The polymer constitutive equations are implemented in a mechanics of materials based composite micromechanics model to predict the nonlinear, rate dependent deformation response of a composite ply. Uniform stress and uniform strain assumptions are applied to compute the effective stresses of a composite unit cell from the applied strains. The micromechanics equations are successfully verified for two polymer matrix composites. IM7/977-2 and AS4/PEEK. The ultimate strength of a composite ply is predicted with the Hashin failure criteria that were implemented in the composite micromechanics model. The failure stresses of the two composite material systems are accurately predicted for a variety of fiber orientations and strain rates. The composite deformation model is implemented in LS-DYNA, a commercially available transient dynamic explicit finite element code. The matrix constitutive equations are converted into an incremental form, and the model is implemented into LS-DYNA through the use of a user defined material subroutine. The deformation response of a bulk polymer and a polymer matrix composite are predicted by finite element analyses. The results compare reasonably well to experimental values, with some discrepancies. The discrepancies are at least partially caused by the method used to integrate the rate equations in the polymer constitutive model.

Protein structure recognition: From eigenvector analysis to structural threading method

NASA Astrophysics Data System (ADS)

Cao, Haibo

In this work, we try to understand the protein folding problem using pair-wise hydrophobic interaction as the dominant interaction for the protein folding process. We found a strong correlation between amino acid sequence and the corresponding native structure of the protein. Some applications of this correlation were discussed in this dissertation include the domain partition and a new structural threading method as well as the performance of this method in the CASP5 competition. In the first part, we give a brief introduction to the protein folding problem. Some essential knowledge and progress from other research groups was discussed. This part include discussions of interactions among amino acids residues, lattice HP model, and the designablity principle. In the second part, we try to establish the correlation between amino acid sequence and the corresponding native structure of the protein. This correlation was observed in our eigenvector study of protein contact matrix. We believe the correlation is universal, thus it can be used in automatic partition of protein structures into folding domains. In the third part, we discuss a threading method based on the correlation between amino acid sequence and ominant eigenvector of the structure contact-matrix. A mathematically straightforward iteration scheme provides a self-consistent optimum global sequence-structure alignment. The computational efficiency of this method makes it possible to search whole protein structure databases for structural homology without relying on sequence similarity. The sensitivity and specificity of this method is discussed, along with a case of blind test prediction. In the appendix, we list the overall performance of this threading method in CASP5 blind test in comparison with other existing approaches.

Extending the range of real time density matrix renormalization group simulations

NASA Astrophysics Data System (ADS)

Kennes, D. M.; Karrasch, C.

2016-03-01

We discuss a few simple modifications to time-dependent density matrix renormalization group (DMRG) algorithms which allow to access larger time scales. We specifically aim at beginners and present practical aspects of how to implement these modifications within any standard matrix product state (MPS) based formulation of the method. Most importantly, we show how to 'combine' the Schrödinger and Heisenberg time evolutions of arbitrary pure states | ψ 〉 and operators A in the evaluation of 〈A〉ψ(t) = 〈 ψ | A(t) | ψ 〉 . This includes quantum quenches. The generalization to (non-)thermal mixed state dynamics 〈A〉ρ(t) =Tr [ ρA(t) ] induced by an initial density matrix ρ is straightforward. In the context of linear response (ground state or finite temperature T > 0) correlation functions, one can extend the simulation time by a factor of two by 'exploiting time translation invariance', which is efficiently implementable within MPS DMRG. We present a simple analytic argument for why a recently-introduced disentangler succeeds in reducing the effort of time-dependent simulations at T > 0. Finally, we advocate the python programming language as an elegant option for beginners to set up a DMRG code.

Incorporation of Mean Stress Effects into the Micromechanical Analysis of the High Strain Rate Response of Polymer Matrix Composites

NASA Technical Reports Server (NTRS)

Goldberg, Robert K.; Roberts, Gary D.; Gilat, Amos

2002-01-01

The results presented here are part of an ongoing research program, to develop strain rate dependent deformation and failure models for the analysis of polymer matrix composites subject to high strain rate impact loads. A micromechanics approach is employed in this work, in which state variable constitutive equations originally developed for metals have been modified to model the deformation of the polymer matrix, and a strength of materials based micromechanics method is used to predict the effective response of the composite. In the analysis of the inelastic deformation of the polymer matrix, the definitions of the effective stress and effective inelastic strain have been modified in order to account for the effect of hydrostatic stresses, which are significant in polymers. Two representative polymers, a toughened epoxy and a brittle epoxy, are characterized through the use of data from tensile and shear tests across a variety of strain rates. Results computed by using the developed constitutive equations correlate well with data generated via experiments. The procedure used to incorporate the constitutive equations within a micromechanics method is presented, and sample calculations of the deformation response of a composite for various fiber orientations and strain rates are discussed.

Modeling the two-way feedback between contractility and matrix realignment reveals a nonlinear mode of cancer cell invasion

PubMed Central

Ahmadzadeh, Hossein; Webster, Marie R.; Behera, Reeti; Jimenez Valencia, Angela M.; Wirtz, Denis; Weeraratna, Ashani T.; Shenoy, Vivek B.

2017-01-01

Cancer cell invasion from primary tumors is mediated by a complex interplay between cellular adhesions, actomyosin-driven contractility, and the physical characteristics of the extracellular matrix (ECM). Here, we incorporate a mechanochemical free-energy–based approach to elucidate how the two-way feedback loop between cell contractility (induced by the activity of chemomechanical interactions such as Ca2+ and Rho signaling pathways) and matrix fiber realignment and strain stiffening enables the cells to polarize and develop contractile forces to break free from the tumor spheroids and invade into the ECM. Interestingly, through this computational model, we are able to identify a critical stiffness that is required by the matrix to break intercellular adhesions and initiate cell invasion. Also, by considering the kinetics of the cell movement, our model predicts a biphasic invasiveness with respect to the stiffness of the matrix. These predictions are validated by analyzing the invasion of melanoma cells in collagen matrices of varying concentration. Our model also predicts a positive correlation between the elongated morphology of the invading cells and the alignment of fibers in the matrix, suggesting that cell polarization is directly proportional to the stiffness and alignment of the matrix. In contrast, cells in nonfibrous matrices are found to be rounded and not polarized, underscoring the key role played by the nonlinear mechanics of fibrous matrices. Importantly, our model shows that mechanical principles mediated by the contractility of the cells and the nonlinearity of the ECM behavior play a crucial role in determining the phenotype of the cell invasion. PMID:28196892

Silicone Polymer Composites for Thermal Protection System: Fiber Reinforcements and Microstructures

DTIC Science & Technology

2010-01-01

angles were tested. Detailed microstructural, mass loss, and peak erosion analyses were conducted on the phenolic -based matrix composite (control) and...silicone-based matrix composites to understand their protective mechanisms. Keywords silicone polymer matrix composites, phenolic polymer matrix...erosion analyses were conducted on the phenolic -based matrix composite (control) and silicone-based matrix composites to understand their protective

Statistics of time delay and scattering correlation functions in chaotic systems. II. Semiclassical approximation

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

Novaes, Marcel

2015-06-15

We consider S-matrix correlation functions for a chaotic cavity having M open channels, in the absence of time-reversal invariance. Relying on a semiclassical approximation, we compute the average over E of the quantities Tr[S{sup †}(E − ϵ) S(E + ϵ)]{sup n}, for general positive integer n. Our result is an infinite series in ϵ, whose coefficients are rational functions of M. From this, we extract moments of the time delay matrix Q = − iħS{sup †}dS/dE and check that the first 8 of them agree with the random matrix theory prediction from our previous paper [M. Novaes, J. Math. Phys.more » 56, 062110 (2015)].« less

Inhomogeneity induced and appropriately parameterized semilocal exchange and correlation energy functionals in two-dimensions.

PubMed

Patra, Abhilash; Jana, Subrata; Samal, Prasanjit

2018-04-07

The construction of meta generalized gradient approximations based on the density matrix expansion (DME) is considered as one of the most accurate techniques to design semilocal exchange energy functionals in two-dimensional density functional formalism. The exchange holes modeled using DME possess unique features that make it a superior entity. Parameterized semilocal exchange energy functionals based on the DME are proposed. The use of different forms of the momentum and flexible parameters is to subsume the non-uniform effects of the density in the newly constructed semilocal functionals. In addition to the exchange functionals, a suitable correlation functional is also constructed by working upon the local correlation functional developed for 2D homogeneous electron gas. The non-local effects are induced into the correlation functional by a parametric form of one of the newly constructed exchange energy functionals. The proposed functionals are applied to the parabolic quantum dots with a varying number of confined electrons and the confinement strength. The results obtained with the aforementioned functionals are quite satisfactory, which indicates why these are suitable for two-dimensional quantum systems.

Inhomogeneity induced and appropriately parameterized semilocal exchange and correlation energy functionals in two-dimensions

NASA Astrophysics Data System (ADS)

Patra, Abhilash; Jana, Subrata; Samal, Prasanjit

2018-04-01

The construction of meta generalized gradient approximations based on the density matrix expansion (DME) is considered as one of the most accurate techniques to design semilocal exchange energy functionals in two-dimensional density functional formalism. The exchange holes modeled using DME possess unique features that make it a superior entity. Parameterized semilocal exchange energy functionals based on the DME are proposed. The use of different forms of the momentum and flexible parameters is to subsume the non-uniform effects of the density in the newly constructed semilocal functionals. In addition to the exchange functionals, a suitable correlation functional is also constructed by working upon the local correlation functional developed for 2D homogeneous electron gas. The non-local effects are induced into the correlation functional by a parametric form of one of the newly constructed exchange energy functionals. The proposed functionals are applied to the parabolic quantum dots with a varying number of confined electrons and the confinement strength. The results obtained with the aforementioned functionals are quite satisfactory, which indicates why these are suitable for two-dimensional quantum systems.

Random matrix theory and cross-correlations in global financial indices and local stock market indices

NASA Astrophysics Data System (ADS)

Nobi, Ashadun; Maeng, Seong Eun; Ha, Gyeong Gyun; Lee, Jae Woo

2013-02-01

We analyzed cross-correlations between price fluctuations of global financial indices (20 daily stock indices over the world) and local indices (daily indices of 200 companies in the Korean stock market) by using random matrix theory (RMT). We compared eigenvalues and components of the largest and the second largest eigenvectors of the cross-correlation matrix before, during, and after the global financial the crisis in the year 2008. We find that the majority of its eigenvalues fall within the RMT bounds [ λ -, λ +], where λ - and λ + are the lower and the upper bounds of the eigenvalues of random correlation matrices. The components of the eigenvectors for the largest positive eigenvalues indicate the identical financial market mode dominating the global and local indices. On the other hand, the components of the eigenvector corresponding to the second largest eigenvalue are positive and negative values alternatively. The components before the crisis change sign during the crisis, and those during the crisis change sign after the crisis. The largest inverse participation ratio (IPR) corresponding to the smallest eigenvector is higher after the crisis than during any other periods in the global and local indices. During the global financial the crisis, the correlations among the global indices and among the local stock indices are perturbed significantly. However, the correlations between indices quickly recover the trends before the crisis.

On Digital Simulation of Multicorrelated Random Processes and Its Applications. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Sinha, A. K.

1973-01-01

Two methods are described to simulate, on a digital computer, a set of correlated, stationary, and Gaussian time series with zero mean from the given matrix of power spectral densities and cross spectral densities. The first method is based upon trigonometric series with random amplitudes and deterministic phase angles. The random amplitudes are generated by using a standard random number generator subroutine. An example is given which corresponds to three components of wind velocities at two different spatial locations for a total of six correlated time series. In the second method, the whole process is carried out using the Fast Fourier Transform approach. This method gives more accurate results and works about twenty times faster for a set of six correlated time series.

Complex correlation approach for high frequency financial data

NASA Astrophysics Data System (ADS)

Wilinski, Mateusz; Ikeda, Yuichi; Aoyama, Hideaki

2018-02-01

We propose a novel approach that allows the calculation of a Hilbert transform based complex correlation for unevenly spaced data. This method is especially suitable for high frequency trading data, which are of a particular interest in finance. Its most important feature is the ability to take into account lead-lag relations on different scales, without knowing them in advance. We also present results obtained with this approach while working on Tokyo Stock Exchange intraday quotations. We show that individual sectors and subsectors tend to form important market components which may follow each other with small but significant delays. These components may be recognized by analysing eigenvectors of complex correlation matrix for Nikkei 225 stocks. Interestingly, sectorial components are also found in eigenvectors corresponding to the bulk eigenvalues, traditionally treated as noise.

Two-point correlators revisited: fast and slow scales in multifield models of inflation

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

Ghersi, José T. Gálvez; Frolov, Andrei V., E-mail: joseg@sfu.ca, E-mail: frolov@sfu.ca

2017-05-01

We study the structure of two-point correlators of the inflationary field fluctuations in order to improve the accuracy and efficiency of the existing methods to calculate primordial spectra. We present a description motivated by the separation of the fast and slow evolving components of the spectrum which is based on Cholesky decomposition of the field correlator matrix. Our purpose is to rewrite all the relevant equations of motion in terms of slowly varying quantities. This is important in order to consider the contribution from high-frequency modes to the spectrum without affecting computational performance. The slow-roll approximation is not required tomore » reproduce the main distinctive features in the power spectrum for each specific model of inflation.« less

Excitation energies from range-separated time-dependent density and density matrix functional theory.

PubMed

Pernal, Katarzyna

2012-05-14

Time-dependent density functional theory (TD-DFT) in the adiabatic formulation exhibits known failures when applied to predicting excitation energies. One of them is the lack of the doubly excited configurations. On the other hand, the time-dependent theory based on a one-electron reduced density matrix functional (time-dependent density matrix functional theory, TD-DMFT) has proven accurate in determining single and double excitations of H(2) molecule if the exact functional is employed in the adiabatic approximation. We propose a new approach for computing excited state energies that relies on functionals of electron density and one-electron reduced density matrix, where the latter is applied in the long-range region of electron-electron interactions. A similar approach has been recently successfully employed in predicting ground state potential energy curves of diatomic molecules even in the dissociation limit, where static correlation effects are dominating. In the paper, a time-dependent functional theory based on the range-separation of electronic interaction operator is rigorously formulated. To turn the approach into a practical scheme the adiabatic approximation is proposed for the short- and long-range components of the coupling matrix present in the linear response equations. In the end, the problem of finding excitation energies is turned into an eigenproblem for a symmetric matrix. Assignment of obtained excitations is discussed and it is shown how to identify double excitations from the analysis of approximate transition density matrix elements. The proposed method used with the short-range local density approximation (srLDA) and the long-range Buijse-Baerends density matrix functional (lrBB) is applied to H(2) molecule (at equilibrium geometry and in the dissociation limit) and to Be atom. The method accounts for double excitations in the investigated systems but, unfortunately, the accuracy of some of them is poor. The quality of the other excitations is in general much better than that offered by TD-DFT-LDA or TD-DMFT-BB approximations if the range-separation parameter is properly chosen. The latter remains an open problem.

A Gaussian random field model for similarity-based smoothing in Bayesian disease mapping.

PubMed

Baptista, Helena; Mendes, Jorge M; MacNab, Ying C; Xavier, Miguel; Caldas-de-Almeida, José

2016-08-01

Conditionally specified Gaussian Markov random field (GMRF) models with adjacency-based neighbourhood weight matrix, commonly known as neighbourhood-based GMRF models, have been the mainstream approach to spatial smoothing in Bayesian disease mapping. In the present paper, we propose a conditionally specified Gaussian random field (GRF) model with a similarity-based non-spatial weight matrix to facilitate non-spatial smoothing in Bayesian disease mapping. The model, named similarity-based GRF, is motivated for modelling disease mapping data in situations where the underlying small area relative risks and the associated determinant factors do not vary systematically in space, and the similarity is defined by "similarity" with respect to the associated disease determinant factors. The neighbourhood-based GMRF and the similarity-based GRF are compared and accessed via a simulation study and by two case studies, using new data on alcohol abuse in Portugal collected by the World Mental Health Survey Initiative and the well-known lip cancer data in Scotland. In the presence of disease data with no evidence of positive spatial correlation, the simulation study showed a consistent gain in efficiency from the similarity-based GRF, compared with the adjacency-based GMRF with the determinant risk factors as covariate. This new approach broadens the scope of the existing conditional autocorrelation models. © The Author(s) 2016.

On the thermal stability of physical vapor deposited oxide-hardened nanocrystalline gold thin films

DOE PAGES

Argibay, Nicolas; Mogonye, J. E.; Michael, Joseph R.; ...

2015-04-08

We describe a correlation between electrical resistivity and grain size for PVD synthesized polycrystalline oxide-hardened metal-matrix thin films in oxide-dilute (<5 vol. % oxide phase) compositions. The correlation is based on the Mayadas-Shatzkes (M-S) electron scattering model, predictive of grain size evolution as a function of composition in the oxide-dilute regime for 2 μm thick Au-ZnO films. We describe a technique to investigate grain boundary (GB) mobility and the thermal stability of GBs based on in situelectrical resistivity measurements during annealing experiments, interpreted using a combination of the M-S model and the Michels et al. model describing solute drag stabilizedmore » grain growth kinetics. Using this technique, activation energy and pre-exponential Arrhenius parameter values of E a = 21.6 kJ/mol and A o = 2.3 × 10 -17 m 2/s for Au-1 vol. % ZnO and E a =12.7 kJ/mol and A o = 3.1 × 10 -18 m 2/s for Au-2 vol.% ZnO were determined. In the oxide-dilute regime, the grain size reduction of the Au matrix yielded a maximum hardness of 2.6 GPa for 5 vol. % ZnO. A combined model including percolation behavior and grain refinement is presented that accurately describes the composition dependent change in electrical resistivity throughout the entire composition range for Au-ZnO thin films. As a result, the proposed correlations are supported by microstructural characterization using transmission electron microscopy and electron diffraction mapping for grain size determination.« less

Kernel-aligned multi-view canonical correlation analysis for image recognition

NASA Astrophysics Data System (ADS)

Su, Shuzhi; Ge, Hongwei; Yuan, Yun-Hao

2016-09-01

Existing kernel-based correlation analysis methods mainly adopt a single kernel in each view. However, only a single kernel is usually insufficient to characterize nonlinear distribution information of a view. To solve the problem, we transform each original feature vector into a 2-dimensional feature matrix by means of kernel alignment, and then propose a novel kernel-aligned multi-view canonical correlation analysis (KAMCCA) method on the basis of the feature matrices. Our proposed method can simultaneously employ multiple kernels to better capture the nonlinear distribution information of each view, so that correlation features learned by KAMCCA can have well discriminating power in real-world image recognition. Extensive experiments are designed on five real-world image datasets, including NIR face images, thermal face images, visible face images, handwritten digit images, and object images. Promising experimental results on the datasets have manifested the effectiveness of our proposed method.

Computationally Efficient 2D DOA Estimation with Uniform Rectangular Array in Low-Grazing Angle.

PubMed

Shi, Junpeng; Hu, Guoping; Zhang, Xiaofei; Sun, Fenggang; Xiao, Yu

2017-02-26

In this paper, we propose a computationally efficient spatial differencing matrix set (SDMS) method for two-dimensional direction of arrival (2D DOA) estimation with uniform rectangular arrays (URAs) in a low-grazing angle (LGA) condition. By rearranging the auto-correlation and cross-correlation matrices in turn among different subarrays, the SDMS method can estimate the two parameters independently with one-dimensional (1D) subspace-based estimation techniques, where we only perform difference for auto-correlation matrices and the cross-correlation matrices are kept completely. Then, the pair-matching of two parameters is achieved by extracting the diagonal elements of URA. Thus, the proposed method can decrease the computational complexity, suppress the effect of additive noise and also have little information loss. Simulation results show that, in LGA, compared to other methods, the proposed methods can achieve performance improvement in the white or colored noise conditions.

Computationally Efficient 2D DOA Estimation with Uniform Rectangular Array in Low-Grazing Angle

PubMed Central

Shi, Junpeng; Hu, Guoping; Zhang, Xiaofei; Sun, Fenggang; Xiao, Yu

2017-01-01

In this paper, we propose a computationally efficient spatial differencing matrix set (SDMS) method for two-dimensional direction of arrival (2D DOA) estimation with uniform rectangular arrays (URAs) in a low-grazing angle (LGA) condition. By rearranging the auto-correlation and cross-correlation matrices in turn among different subarrays, the SDMS method can estimate the two parameters independently with one-dimensional (1D) subspace-based estimation techniques, where we only perform difference for auto-correlation matrices and the cross-correlation matrices are kept completely. Then, the pair-matching of two parameters is achieved by extracting the diagonal elements of URA. Thus, the proposed method can decrease the computational complexity, suppress the effect of additive noise and also have little information loss. Simulation results show that, in LGA, compared to other methods, the proposed methods can achieve performance improvement in the white or colored noise conditions. PMID:28245634

Quantumness and the role of locality on quantum correlations

NASA Astrophysics Data System (ADS)

Bellomo, G.; Plastino, A.; Plastino, A. R.

2016-06-01

Quantum correlations in a physical system are usually studied with respect to a unique and fixed decomposition of the system into subsystems, without fully exploiting the rich structure of the state space. Here, we show several examples in which the consideration of different ways to decompose a physical system enhances the quantum resources and accounts for a more flexible definition of quantumness measures. Furthermore, we give a different perspective regarding how to reassess the fact that local operations play a key role in general quantumness measures that go beyond entanglement—as discordlike ones. We propose a family of measures to quantify the maximum quantumness of a given state. For the discord-based case, we present some analytical results for 2 ×d -dimensional states. Applying our definition to low-dimensional bipartite states, we show that different behaviors can be reported for separable and entangled states vis-à-vis those corresponding to the usual measures of quantum correlations. We show that there is a close link between our proposal and the criterion to witness quantum correlations based on the rank of the correlation matrix, proposed by Dakić, Vedral, and Brukner [Phys. Rev. Lett. 105, 190502 (2010), 10.1103/PhysRevLett.105.190502].

Treating Sample Covariances for Use in Strongly Coupled Atmosphere-Ocean Data Assimilation

NASA Astrophysics Data System (ADS)

Smith, Polly J.; Lawless, Amos S.; Nichols, Nancy K.

2018-01-01

Strongly coupled data assimilation requires cross-domain forecast error covariances; information from ensembles can be used, but limited sampling means that ensemble derived error covariances are routinely rank deficient and/or ill-conditioned and marred by noise. Thus, they require modification before they can be incorporated into a standard assimilation framework. Here we compare methods for improving the rank and conditioning of multivariate sample error covariance matrices for coupled atmosphere-ocean data assimilation. The first method, reconditioning, alters the matrix eigenvalues directly; this preserves the correlation structures but does not remove sampling noise. We show that it is better to recondition the correlation matrix rather than the covariance matrix as this prevents small but dynamically important modes from being lost. The second method, model state-space localization via the Schur product, effectively removes sample noise but can dampen small cross-correlation signals. A combination that exploits the merits of each is found to offer an effective alternative.

Understanding the Relationship between Red Wine Matrix, Tannin Activity, and Sensory Properties.

PubMed

Watrelot, Aude A; Byrnes, Nadia K; Heymann, Hildegarde; Kennedy, James A

2016-11-30

One major red wine mouthfeel characteristic, astringency, is derived from grape-extracted tannins and is considered to be a result of interaction with salivary proteins and the oral mucosa. To improve our understanding of the role that the enthalpy of interaction of tannin with a hydrophobic surface (tannin activity) has in astringency perception, a chromatographic method was used to determine the tannin concentration and activity of 34 Cabernet Sauvignon wines, as well as sensory analysis done on 13 of those wines. In addition, astringency-relevant matrix parameters (pH, titratable acidity, ethanol, glucose, and fructose) were measured across all wines. Tannin activity was not significantly correlated with any matrix variables, and the perception of drying and grippy was not correlated with tannin concentration and activity. However, ethanol content was well related to mouthfeel attributes and appeared to drive perceived drying. Although fructose and glucose content were well correlated, they did not drive the perception of sweetness, which is explained by the well-known mixture suppression effect.

Examination of the Chayes-Kruskal procedure for testing correlations between proportions

USGS Publications Warehouse

Kork, J.O.

1977-01-01

The Chayes-Kruskal procedure for testing correlations between proportions uses a linear approximation to the actual closure transformation to provide a null value, pij, against which an observed closed correlation coefficient, rij, can be tested. It has been suggested that a significant difference between pij and rij would indicate a nonzero covariance relationship between the ith and jth open variables. In this paper, the linear approximation to the closure transformation is described in terms of a matrix equation. Examination of the solution set of this equation shows that estimation of, or even the identification of, significant nonzero open correlations is essentially impossible even if the number of variables and the sample size are large. The method of solving the matrix equation is described in the appendix. ?? 1977 Plenum Publishing Corporation.

Quantum correlation properties in Matrix Product States of finite-number spin rings

NASA Astrophysics Data System (ADS)

Zhu, Jing-Min; He, Qi-Kai

2018-02-01

The organization and structure of quantum correlation (QC) of quantum spin-chains are very rich and complex. Hence the depiction and measures about the QC of finite-number spin rings deserved to be investigated intensively by using Matrix Product States(MPSs) in addition to the case with infinite-number. Here the dependencies of the geometric quantum discord(GQD) of two spin blocks on the total spin number, the spacing spin number and the environment parameter are presented in detail. We also compare the GQD with the total correlation(TC) and the classical correlation(CC) and illustrate its characteristics. Predictably, our findings may provide the potential of designing the optimal QC experimental detection proposals and pave the way for the designation of optimal quantum information processing schemes.

Calibrating a population-based job-exposure matrix using inspection measurements to estimate historical occupational exposure to lead for a population-based cohort in Shanghai, China

PubMed Central

Koh, Dong-Hee; Bhatti, Parveen; Coble, Joseph B.; Stewart, Patricia A; Lu, Wei; Shu, Xiao-Ou; Ji, Bu-Tian; Xue, Shouzheng; Locke, Sarah J.; Portengen, Lutzen; Yang, Gong; Chow, Wong-Ho; Gao, Yu-Tang; Rothman, Nathaniel; Vermeulen, Roel; Friesen, Melissa C.

2012-01-01

The epidemiologic evidence for the carcinogenicity of lead is inconsistent and requires improved exposure assessment to estimate risk. We evaluated historical occupational lead exposure for a population-based cohort of women (n=74,942) by calibrating a job-exposure matrix (JEM) with lead fume (n=20,084) and lead dust (n=5,383) measurements collected over four decades in Shanghai, China. Using mixed-effect models, we calibrated intensity JEM ratings to the measurements using fixed-effects terms for year and JEM rating. We developed job/industry-specific estimates from the random-effects terms for job and industry. The model estimates were applied to subjects’ jobs when the JEM probability rating was high for either job or industry; remaining jobs were considered unexposed. The models predicted that exposure increased monotonically with JEM intensity rating and decreased 20–50-fold over time. The cumulative calibrated JEM estimates and job/industry-specific estimates were highly correlated (Pearson correlation=0.79–0.84). Overall, 5% of the person-years and 8% of the women were exposed to lead fume; 2% of the person-years and 4% of the women were exposed to lead dust. The most common lead-exposed jobs were manufacturing electronic equipment. These historical lead estimates should enhance our ability to detect associations between lead exposure and cancer risk in future epidemiologic analyses. PMID:22910004

Project Effectiveness and the Balance of Power in Matrix Organizations: An Exploratory Study.

DTIC Science & Technology

1986-09-01

Vasconcellos , Eduardo . "A Model for a Better Understanding of the Matrix Structure." IEEE Transactions on Engineering Management, EM-26: 56-65 (August...coercive power correlated negatively with degree of support (39:219-220). Vasconcellos recognized the five common power sources referenced above and...effect. The second variable identified by Vasconcellos was used to differentiate matrix structures. He felt that it was necessary to differentiate

Procollagen III N-terminal Propeptide and Desmosine are Released by Matrix Destruction in Pulmonary Tuberculosis

PubMed Central

Seddon, Jo; Kasprowicz, Victoria; Walker, Naomi F.; Yuen, Ho Ming; Sunpath, Henry; Tezera, Liku; Meintjes, Graeme; Wilkinson, Robert J.; Bishai, William R.; Friedland, Jon S.; Elkington, Paul T.

2013-01-01

Background. Tuberculosis is transmitted by patients with pulmonary disease. Matrix metalloproteinases (MMPs) drive lung destruction in tuberculosis but the resulting matrix degradation products (MDPs) have not been studied. We investigate the hypothesis that MMP activity generates matrix turnover products as correlates of lung pathology. Methods. Induced sputum and plasma were collected prospectively from human immunodeficiency virus (HIV) positive and negative patients with pulmonary tuberculosis and controls. Concentrations of MDPs and MMPs were analyzed by ELISA and Luminex array in 2 patient cohorts. Results. Procollagen III N-terminal propeptide (PIIINP) was 3.8-fold higher in induced sputum of HIV-uninfected tuberculosis patients compared to controls and desmosine, released during elastin degradation, was 2.4-fold higher. PIIINP was elevated in plasma of tuberculosis patients. Plasma PIIINP correlated with induced sputum MMP-1 concentrations and radiological scores, demonstrating that circulating MDPs reflect lung destruction. In a second patient cohort of mixed HIV seroprevalence, plasma PIIINP concentration was increased 3.0-fold above controls (P < .001). Plasma matrix metalloproteinase-8 concentrations were also higher in tuberculosis patients (P = .001). Receiver operating characteristic analysis utilizing these 2 variables demonstrated an area under the curve of 0.832 (P < .001). Conclusions. In pulmonary tuberculosis, MMP-driven immunopathology generates matrix degradation products. PMID:23922364

Energy and energy gradient matrix elements with N-particle explicitly correlated complex Gaussian basis functions with L =1

NASA Astrophysics Data System (ADS)

Bubin, Sergiy; Adamowicz, Ludwik

2008-03-01

In this work we consider explicitly correlated complex Gaussian basis functions for expanding the wave function of an N-particle system with the L =1 total orbital angular momentum. We derive analytical expressions for various matrix elements with these basis functions including the overlap, kinetic energy, and potential energy (Coulomb interaction) matrix elements, as well as matrix elements of other quantities. The derivatives of the overlap, kinetic, and potential energy integrals with respect to the Gaussian exponential parameters are also derived and used to calculate the energy gradient. All the derivations are performed using the formalism of the matrix differential calculus that facilitates a way of expressing the integrals in an elegant matrix form, which is convenient for the theoretical analysis and the computer implementation. The new method is tested in calculations of two systems: the lowest P state of the beryllium atom and the bound P state of the positronium molecule (with the negative parity). Both calculations yielded new, lowest-to-date, variational upper bounds, while the number of basis functions used was significantly smaller than in previous studies. It was possible to accomplish this due to the use of the analytic energy gradient in the minimization of the variational energy.

Energy and energy gradient matrix elements with N-particle explicitly correlated complex Gaussian basis functions with L=1.

PubMed

Bubin, Sergiy; Adamowicz, Ludwik

2008-03-21

In this work we consider explicitly correlated complex Gaussian basis functions for expanding the wave function of an N-particle system with the L=1 total orbital angular momentum. We derive analytical expressions for various matrix elements with these basis functions including the overlap, kinetic energy, and potential energy (Coulomb interaction) matrix elements, as well as matrix elements of other quantities. The derivatives of the overlap, kinetic, and potential energy integrals with respect to the Gaussian exponential parameters are also derived and used to calculate the energy gradient. All the derivations are performed using the formalism of the matrix differential calculus that facilitates a way of expressing the integrals in an elegant matrix form, which is convenient for the theoretical analysis and the computer implementation. The new method is tested in calculations of two systems: the lowest P state of the beryllium atom and the bound P state of the positronium molecule (with the negative parity). Both calculations yielded new, lowest-to-date, variational upper bounds, while the number of basis functions used was significantly smaller than in previous studies. It was possible to accomplish this due to the use of the analytic energy gradient in the minimization of the variational energy.

Modeling and life prediction methodology for Titanium Matrix Composites subjected to mission profiles

NASA Technical Reports Server (NTRS)

Mirdamadi, M.; Johnson, W. S.

1994-01-01

Titanium matrix composites (TMC) are being evaluated as structural materials for elevated temperature applications in future generation hypersonic vehicles. In such applications, TMC components are subjected to complex thermomechanical loading profiles at various elevated temperatures. Therefore, thermomechanical fatigue (TMF) testing, using a simulated mission profile, is essential for evaluation and development of life prediction methodologies. The objective of the research presented in this paper was to evaluate the TMF response of the (0/90)2s SCS-6/Timetal-21S subjected to a generic hypersonic flight profile and its portions with a temperature ranging from -130 C to 816 C. It was found that the composite modulus, prior to rapid degradation, had consistent values for all the profiles tested. A micromechanics based analysis was used to predict the stress-strain response of the laminate and of the constituents in each ply during thermomechanical loading conditions by using only constituent properties as input. The fiber was modeled as elastic with transverse orthotropic and temperature dependent properties. The matrix was modeled using a thermoviscoplastic constitutive relation. In the analysis, the composite modulus degradation was assumed to result from matrix cracking and was modeled by reducing the matrix modulus. Fatigue lives of the composite subjected to the complex generic hypersonic flight profile were well correlated using the predicted stress in 0 degree fibers.

Inference for High-dimensional Differential Correlation Matrices *

PubMed Central

Cai, T. Tony; Zhang, Anru

2015-01-01

Motivated by differential co-expression analysis in genomics, we consider in this paper estimation and testing of high-dimensional differential correlation matrices. An adaptive thresholding procedure is introduced and theoretical guarantees are given. Minimax rate of convergence is established and the proposed estimator is shown to be adaptively rate-optimal over collections of paired correlation matrices with approximately sparse differences. Simulation results show that the procedure significantly outperforms two other natural methods that are based on separate estimation of the individual correlation matrices. The procedure is also illustrated through an analysis of a breast cancer dataset, which provides evidence at the gene co-expression level that several genes, of which a subset has been previously verified, are associated with the breast cancer. Hypothesis testing on the differential correlation matrices is also considered. A test, which is particularly well suited for testing against sparse alternatives, is introduced. In addition, other related problems, including estimation of a single sparse correlation matrix, estimation of the differential covariance matrices, and estimation of the differential cross-correlation matrices, are also discussed. PMID:26500380

Community Detection for Correlation Matrices

NASA Astrophysics Data System (ADS)

MacMahon, Mel; Garlaschelli, Diego

2015-04-01

A challenging problem in the study of complex systems is that of resolving, without prior information, the emergent, mesoscopic organization determined by groups of units whose dynamical activity is more strongly correlated internally than with the rest of the system. The existing techniques to filter correlations are not explicitly oriented towards identifying such modules and can suffer from an unavoidable information loss. A promising alternative is that of employing community detection techniques developed in network theory. Unfortunately, this approach has focused predominantly on replacing network data with correlation matrices, a procedure that we show to be intrinsically biased because of its inconsistency with the null hypotheses underlying the existing algorithms. Here, we introduce, via a consistent redefinition of null models based on random matrix theory, the appropriate correlation-based counterparts of the most popular community detection techniques. Our methods can filter out both unit-specific noise and system-wide dependencies, and the resulting communities are internally correlated and mutually anticorrelated. We also implement multiresolution and multifrequency approaches revealing hierarchically nested subcommunities with "hard" cores and "soft" peripheries. We apply our techniques to several financial time series and identify mesoscopic groups of stocks which are irreducible to a standard, sectorial taxonomy; detect "soft stocks" that alternate between communities; and discuss implications for portfolio optimization and risk management.

Portfolio optimization and the random magnet problem

NASA Astrophysics Data System (ADS)

Rosenow, B.; Plerou, V.; Gopikrishnan, P.; Stanley, H. E.

2002-08-01

Diversification of an investment into independently fluctuating assets reduces its risk. In reality, movements of assets are mutually correlated and therefore knowledge of cross-correlations among asset price movements are of great importance. Our results support the possibility that the problem of finding an investment in stocks which exposes invested funds to a minimum level of risk is analogous to the problem of finding the magnetization of a random magnet. The interactions for this "random magnet problem" are given by the cross-correlation matrix C of stock returns. We find that random matrix theory allows us to make an estimate for C which outperforms the standard estimate in terms of constructing an investment which carries a minimum level of risk.

Statistical Analysis of Big Data on Pharmacogenomics

PubMed Central

Fan, Jianqing; Liu, Han

2013-01-01

This paper discusses statistical methods for estimating complex correlation structure from large pharmacogenomic datasets. We selectively review several prominent statistical methods for estimating large covariance matrix for understanding correlation structure, inverse covariance matrix for network modeling, large-scale simultaneous tests for selecting significantly differently expressed genes and proteins and genetic markers for complex diseases, and high dimensional variable selection for identifying important molecules for understanding molecule mechanisms in pharmacogenomics. Their applications to gene network estimation and biomarker selection are used to illustrate the methodological power. Several new challenges of Big data analysis, including complex data distribution, missing data, measurement error, spurious correlation, endogeneity, and the need for robust statistical methods, are also discussed. PMID:23602905

Reliability, validity and feasibility of nail ultrasonography in psoriatic arthritis.

PubMed

Arbault, Anaïs; Devilliers, Hervé; Laroche, Davy; Cayot, Audrey; Vabres, Pierre; Maillefert, Jean-Francis; Ornetti, Paul

2016-10-01

To determine the feasibility, reliability and validity of nails ultrasonography in psoriatic arthritis as an outcome measure. Pilot prospective single-centre study of eight ultrasonography parameters in B mode and power Doppler concerning the distal interphalangeal (DIP) joint, the matrix, the bed and nail plate. Intra-observer and inter-observer reliability was evaluated for the seven quantitative parameters (ICC and kappa). Correlations between ultrasonography and clinical variables were searched to assess external validity. Feasibility was assessed by the time to carry out the examination and the percentage of missing data. Twenty-seven patients with psoriatic arthritis (age 55.0±16.2 years, disease duration 13.4±9.4 years) were included. Of these, 67% presented nail involvement on ultrasonography vs 37% on physical examination (P<0.05). Reliability was good (ICC and weighted kappa>0.75) for the seven quantitative parameters, except for synovitis of the DIP joint in B mode. The synovitis of the DIP joint revealed by ultrasonography correlated with the total number of clinical synovitis and Doppler US of the nail (matrix and bed). Doppler US of the matrix correlated with VAS pain but not with the ASDAS-CRP or with clinical enthesitis. No significant correlation was found with US nail thickness. The feasibility and reliability of ultrasonography of the nail in psoriatic arthritis appear to be satisfactory. Among the eight parameters evaluated, power Doppler of the matrix which correlated with local inflammation (DIP joint and bed) and with VAS pain could become an interesting outcome measure, provided that it is also sensitive to change. Copyright © 2015 Société française de rhumatologie. Published by Elsevier SAS. All rights reserved.

Increased expression of matrix metalloproteinase-10, nerve growth factor and substance P in the painful degenerate intervertebral disc

PubMed Central

Richardson, Stephen M; Doyle, Paul; Minogue, Ben M; Gnanalingham, Kanna; Hoyland, Judith A

2009-01-01

Introduction Matrix metalloproteinases (MMPs) are known to be involved in the degradation of the nucleus pulposus (NP) during intervertebral disc (IVD) degeneration. This study investigated MMP-10 (stromelysin-2) expression in the NP during IVD degeneration and correlated its expression with pro-inflammatory cytokines and molecules involved in innervation and nociception during degeneration which results in low back pain (LBP). Methods Human NP tissue was obtained at postmortem (PM) from patients without a history of back pain and graded as histologically normal or degenerate. Symptomatic degenerate NP samples were also obtained at surgery for LBP. Expression of MMP-10 mRNA and protein was analysed using real-time polymerase chain reaction and immunohistochemistry. Gene expression for pro-inflammatory cytokines interleukin-1 (IL-1) and tumour necrosis factor-alpha (TNF-α), nerve growth factor (NGF) and the pain-associated neuropeptide substance P were also analysed. Correlations between MMP-10 and IL-1, TNF-α and NGF were assessed along with NGF with substance P. Results MMP-10 mRNA was significantly increased in surgical degenerate NP when compared to PM normal and PM degenerate samples. MMP-10 protein was also significantly higher in degenerate surgical NP samples compared to PM normal. IL-1 and MMP-10 mRNA demonstrated a significant correlation in surgical degenerate samples, while TNF-α was not correlated with MMP-10 mRNA. NGF was significantly correlated with both MMP-10 and substance P mRNA in surgical degenerate NP samples. Conclusions MMP-10 expression is increased in the symptomatic degenerate IVD, where it may contribute to matrix degradation and initiation of nociception. Importantly, this study suggests differences in the pathways involved in matrix degradation between painful and pain-free IVD degeneration. PMID:19695094

Increased expression of matrix metalloproteinase-10, nerve growth factor and substance P in the painful degenerate intervertebral disc.

PubMed

Richardson, Stephen M; Doyle, Paul; Minogue, Ben M; Gnanalingham, Kanna; Hoyland, Judith A

2009-01-01

Matrix metalloproteinases (MMPs) are known to be involved in the degradation of the nucleus pulposus (NP) during intervertebral disc (IVD) degeneration. This study investigated MMP-10 (stromelysin-2) expression in the NP during IVD degeneration and correlated its expression with pro-inflammatory cytokines and molecules involved in innervation and nociception during degeneration which results in low back pain (LBP). Human NP tissue was obtained at postmortem (PM) from patients without a history of back pain and graded as histologically normal or degenerate. Symptomatic degenerate NP samples were also obtained at surgery for LBP. Expression of MMP-10 mRNA and protein was analysed using real-time polymerase chain reaction and immunohistochemistry. Gene expression for pro-inflammatory cytokines interleukin-1 (IL-1) and tumour necrosis factor-alpha (TNF-alpha), nerve growth factor (NGF) and the pain-associated neuropeptide substance P were also analysed. Correlations between MMP-10 and IL-1, TNF-alpha and NGF were assessed along with NGF with substance P. MMP-10 mRNA was significantly increased in surgical degenerate NP when compared to PM normal and PM degenerate samples. MMP-10 protein was also significantly higher in degenerate surgical NP samples compared to PM normal. IL-1 and MMP-10 mRNA demonstrated a significant correlation in surgical degenerate samples, while TNF-alpha was not correlated with MMP-10 mRNA. NGF was significantly correlated with both MMP-10 and substance P mRNA in surgical degenerate NP samples. MMP-10 expression is increased in the symptomatic degenerate IVD, where it may contribute to matrix degradation and initiation of nociception. Importantly, this study suggests differences in the pathways involved in matrix degradation between painful and pain-free IVD degeneration.

Tenascin-C levels in synovial fluid are elevated after injury to the human and canine joint and correlate with markers of inflammation and matrix degradation.

PubMed

Chockalingam, P S; Glasson, S S; Lohmander, L S

2013-02-01

We have previously shown the capacity of tenascin-C (TN-C) to induce inflammatory mediators and matrix degradation in vitro in human articular cartilage. The objective of the present study was to follow TN-C release into knee synovial fluid after acute joint injury or in joint disease, and to correlate TN-C levels with markers of cartilage matrix degradation and inflammation. Human knee synovial fluid samples (n = 164) were from a cross-sectional convenience cohort. Diagnostic groups were knee healthy reference, knee anterior cruciate ligament rupture, with or without concomitant meniscus lesions, isolated knee meniscus injury, acute inflammatory arthritis (AIA) and knee osteoarthritis (OA). TN-C was measured in synovial fluid samples using an enzyme-linked immunosorbent assay (ELISA) and results correlated to other cartilage markers. TN-C release was also monitored in joints of dogs that underwent knee instability surgery. Statistically significantly higher levels of TN-C compared to reference subjects were observed in the joint fluid of all human disease groups and in the dogs that underwent knee instability surgery. Statistically significant correlations were observed between the TN-C levels in the synovial fluid of the human patients and the levels of aggrecanase-dependent Ala-Arg-Gly-aggrecan (ARG-aggrecan) fragments and matrix metalloproteinases 1 and 3. We find highly elevated levels of TN-C in human knee joints after injury, AIA or OA that correlated with markers of cartilage degradation and inflammation. TN-C in synovial fluid may serve dual roles as a marker of joint damage and a stimulant of further joint degradation. Copyright © 2012 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.

A simple method to determine evaporation and compensate for liquid losses in small-scale cell culture systems.

PubMed

Wiegmann, Vincent; Martinez, Cristina Bernal; Baganz, Frank

2018-04-24

Establish a method to indirectly measure evaporation in microwell-based cell culture systems and show that the proposed method allows compensating for liquid losses in fed-batch processes. A correlation between evaporation and the concentration of Na + was found (R 2  = 0.95) when using the 24-well-based miniature bioreactor system (micro-Matrix) for a batch culture with GS-CHO. Based on these results, a method was developed to counteract evaporation with periodic water additions based on measurements of the Na + concentration. Implementation of this method resulted in a reduction of the relative liquid loss after 15 days of a fed-batch cultivation from 36.7 ± 6.7% without volume corrections to 6.9 ± 6.5% with volume corrections. A procedure was established to indirectly measure evaporation through a correlation with the level of Na + ions in solution and deriving a simple formula to account for liquid losses.

Resistance Curves in the Tensile and Compressive Longitudinal Failure of Composites

NASA Technical Reports Server (NTRS)

Camanho, Pedro P.; Catalanotti, Giuseppe; Davila, Carlos G.; Lopes, Claudio S.; Bessa, Miguel A.; Xavier, Jose C.

2010-01-01

This paper presents a new methodology to measure the crack resistance curves associated with fiber-dominated failure modes in polymer-matrix composites. These crack resistance curves not only characterize the fracture toughness of the material, but are also the basis for the identification of the parameters of the softening laws used in the analytical and numerical simulation of fracture in composite materials. The method proposed is based on the identification of the crack tip location by the use of Digital Image Correlation and the calculation of the J-integral directly from the test data using a simple expression derived for cross-ply composite laminates. It is shown that the results obtained using the proposed methodology yield crack resistance curves similar to those obtained using FEM-based methods in compact tension carbon-epoxy specimens. However, it is also shown that the Digital Image Correlation based technique can be used to extract crack resistance curves in compact compression tests for which FEM-based techniques are inadequate.

An Intelligent Architecture Based on Field Programmable Gate Arrays Designed to Detect Moving Objects by Using Principal Component Analysis

PubMed Central

Bravo, Ignacio; Mazo, Manuel; Lázaro, José L.; Gardel, Alfredo; Jiménez, Pedro; Pizarro, Daniel

2010-01-01

This paper presents a complete implementation of the Principal Component Analysis (PCA) algorithm in Field Programmable Gate Array (FPGA) devices applied to high rate background segmentation of images. The classical sequential execution of different parts of the PCA algorithm has been parallelized. This parallelization has led to the specific development and implementation in hardware of the different stages of PCA, such as computation of the correlation matrix, matrix diagonalization using the Jacobi method and subspace projections of images. On the application side, the paper presents a motion detection algorithm, also entirely implemented on the FPGA, and based on the developed PCA core. This consists of dynamically thresholding the differences between the input image and the one obtained by expressing the input image using the PCA linear subspace previously obtained as a background model. The proposal achieves a high ratio of processed images (up to 120 frames per second) and high quality segmentation results, with a completely embedded and reliable hardware architecture based on commercial CMOS sensors and FPGA devices. PMID:22163406

Anomaly-specified virtual dimensionality

NASA Astrophysics Data System (ADS)

Chen, Shih-Yu; Paylor, Drew; Chang, Chein-I.

2013-09-01

Virtual dimensionality (VD) has received considerable interest where VD is used to estimate the number of spectral distinct signatures, denoted by p. Unfortunately, no specific definition is provided by VD for what a spectrally distinct signature is. As a result, various types of spectral distinct signatures determine different values of VD. There is no one value-fit-all for VD. In order to address this issue this paper presents a new concept, referred to as anomaly-specified VD (AS-VD) which determines the number of anomalies of interest present in the data. Specifically, two types of anomaly detection algorithms are of particular interest, sample covariance matrix K-based anomaly detector developed by Reed and Yu, referred to as K-RXD and sample correlation matrix R-based RXD, referred to as R-RXD. Since K-RXD is only determined by 2nd order statistics compared to R-RXD which is specified by statistics of the first two orders including sample mean as the first order statistics, the values determined by K-RXD and R-RXD will be different. Experiments are conducted in comparison with widely used eigen-based approaches.

An intelligent architecture based on Field Programmable Gate Arrays designed to detect moving objects by using Principal Component Analysis.

PubMed

Bravo, Ignacio; Mazo, Manuel; Lázaro, José L; Gardel, Alfredo; Jiménez, Pedro; Pizarro, Daniel

2010-01-01

This paper presents a complete implementation of the Principal Component Analysis (PCA) algorithm in Field Programmable Gate Array (FPGA) devices applied to high rate background segmentation of images. The classical sequential execution of different parts of the PCA algorithm has been parallelized. This parallelization has led to the specific development and implementation in hardware of the different stages of PCA, such as computation of the correlation matrix, matrix diagonalization using the Jacobi method and subspace projections of images. On the application side, the paper presents a motion detection algorithm, also entirely implemented on the FPGA, and based on the developed PCA core. This consists of dynamically thresholding the differences between the input image and the one obtained by expressing the input image using the PCA linear subspace previously obtained as a background model. The proposal achieves a high ratio of processed images (up to 120 frames per second) and high quality segmentation results, with a completely embedded and reliable hardware architecture based on commercial CMOS sensors and FPGA devices.

Dynamic deformation image de-blurring and image processing for digital imaging correlation measurement

NASA Astrophysics Data System (ADS)

Guo, X.; Li, Y.; Suo, T.; Liu, H.; Zhang, C.

2017-11-01

This paper proposes a method for de-blurring of images captured in the dynamic deformation of materials. De-blurring is achieved based on the dynamic-based approach, which is used to estimate the Point Spread Function (PSF) during the camera exposure window. The deconvolution process involving iterative matrix calculations of pixels, is then performed on the GPU to decrease the time cost. Compared to the Gauss method and the Lucy-Richardson method, it has the best result of the image restoration. The proposed method has been evaluated by using the Hopkinson bar loading system. In comparison to the blurry image, the proposed method has successfully restored the image. It is also demonstrated from image processing applications that the de-blurring method can improve the accuracy and the stability of the digital imaging correlation measurement.

SU-E-E-16: The Application of Texture Analysis for Differentiation of Central Cancer From Atelectasis

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

Gao, M; Fan, T; Duan, J

2015-06-15

Purpose: Prospectively assess the potential utility of texture analysis for differentiation of central cancer from atelectasis. Methods: 0 consecutive central lung cancer patients who were referred for CT imaging and PET-CT were enrolled. Radiotherapy doctor delineate the tumor and atelectasis according to the fusion imaging based on CT image and PET-CT image. The texture parameters (such as energy, correlation, sum average, difference average, difference entropy), were obtained respectively to quantitatively discriminate tumor and atelectasis based on gray level co-occurrence matrix (GLCM) Results: The texture analysis results showed that the parameters of correlation and sum average had an obviously statistical significance(P<0.05).more » Conclusion: the results of this study indicate that texture analysis may be useful for the differentiation of central lung cancer and atelectasis.« less

The large-N Yang-Mills S matrix is ultraviolet finite, but the large-N QCD S matrix is only renormalizable

NASA Astrophysics Data System (ADS)

Bochicchio, Marco

2017-03-01

Yang-Mills (YM) theory and QCD are known to be renormalizable, but not ultraviolet (UV) finite, order by order, in perturbation theory. It is a fundamental question whether YM theory or QCD is UV finite, or only renormalizable, order by order, in the large-N 't Hooft or Veneziano expansions. We demonstrate that the renormalization group (RG) and asymptotic freedom imply that in 't Hooft large-N expansion the S matrix in YM theory is UV finite, while in both 't Hooft and Veneziano large-N expansions, the S matrix in confining massless QCD is renormalizable but not UV finite. By the same argument, the large-N N =1 supersymmetry (SUSY) YM S matrix is UV finite as well. Besides, we demonstrate that, in both 't Hooft and Veneziano large-N expansions, the correlators of local gauge-invariant operators, as opposed to the S matrix, are renormalizable but, in general, not UV finite, either in YM theory and N =1 SUSY YM theory or a fortiori in massless QCD. Moreover, we compute explicitly the counterterms that arise from renormalizing the 't Hooft and Veneziano expansions by deriving in confining massless QCD-like theories a low-energy theorem of the Novikov-Shifman-Vainshtein-Zakharov type that relates the log derivative with respect to the gauge coupling of a k -point correlator, or the log derivative with respect to the RG-invariant scale, to a (k +1 )-point correlator with the insertion of Tr F2 at zero momentum. Finally, we argue that similar results hold in the large-N limit of a vast class of confining massive QCD-like theories, provided a renormalization scheme exists—as, for example, MS ¯ —in which the beta function is not dependent on the masses. Specifically, in both 't Hooft and Veneziano large-N expansions, the S matrix in confining massive QCD and massive N =1 SUSY QCD is renormalizable but not UV finite.

A Generalized Method of Image Analysis from an Intercorrelation Matrix which May Be Singular.

ERIC Educational Resources Information Center

Yanai, Haruo; Mukherjee, Bishwa Nath

1987-01-01

This generalized image analysis method is applicable to singular and non-singular correlation matrices (CMs). Using the orthogonal projector and a weaker generalized inverse matrix, image and anti-image covariance matrices can be derived from a singular CM. (SLD)

Maintenance of neural progenitor cell stemness in 3D hydrogels requires matrix remodelling

NASA Astrophysics Data System (ADS)

Madl, Christopher M.; Lesavage, Bauer L.; Dewi, Ruby E.; Dinh, Cong B.; Stowers, Ryan S.; Khariton, Margarita; Lampe, Kyle J.; Nguyen, Duong; Chaudhuri, Ovijit; Enejder, Annika; Heilshorn, Sarah C.

2017-12-01

Neural progenitor cell (NPC) culture within three-dimensional (3D) hydrogels is an attractive strategy for expanding a therapeutically relevant number of stem cells. However, relatively little is known about how 3D material properties such as stiffness and degradability affect the maintenance of NPC stemness in the absence of differentiation factors. Over a physiologically relevant range of stiffness from ~0.5 to 50 kPa, stemness maintenance did not correlate with initial hydrogel stiffness. In contrast, hydrogel degradation was both correlated with, and necessary for, maintenance of NPC stemness. This requirement for degradation was independent of cytoskeletal tension generation and presentation of engineered adhesive ligands, instead relying on matrix remodelling to facilitate cadherin-mediated cell-cell contact and promote β-catenin signalling. In two additional hydrogel systems, permitting NPC-mediated matrix remodelling proved to be a generalizable strategy for stemness maintenance in 3D. Our findings have identified matrix remodelling, in the absence of cytoskeletal tension generation, as a previously unknown strategy to maintain stemness in 3D.

Maintenance of Neural Progenitor Cell Stemness in 3D Hydrogels Requires Matrix Remodeling

PubMed Central

Madl, Christopher M.; LeSavage, Bauer L.; Dewi, Ruby E.; Dinh, Cong B.; Stowers, Ryan S.; Khariton, Margarita; Lampe, Kyle J.; Nguyen, Duong; Chaudhuri, Ovijit; Enejder, Annika; Heilshorn, Sarah C.

2017-01-01

Neural progenitor cell (NPC) culture within 3D hydrogels is an attractive strategy for expanding a therapeutically-relevant number of stem cells. However, relatively little is known about how 3D material properties such as stiffness and degradability affect the maintenance of NPC stemness in the absence of differentiation factors. Over a physiologically-relevant range of stiffness from ~0.5–50 kPa, stemness maintenance did not correlate with initial hydrogel stiffness. In contrast, hydrogel degradation was both correlated with, and necessary for, maintenance of NPC stemness. This requirement for degradation was independent of cytoskeletal tension generation and presentation of engineered adhesive ligands, instead relying on matrix remodeling to facilitate cadherin-mediated cell-cell contact and promote β-catenin signaling. In two additional hydrogel systems, permitting NPC-mediated matrix remodeling proved to be a generalizable strategy for stemness maintenance in 3D. Our findings have identified matrix remodeling, in the absence of cytoskeletal tension generation, as a previously unknown strategy to maintain stemness in 3D. PMID:29115291

Identification of Serine Conformers by Matrix-Isolation IR Spectroscopy Aided by Near-Infrared Laser Induced Conformational Change, 2D Correlation Analysis, and Quantum Mechanical Anharmonic Computations

PubMed Central

Najbauer, Eszter E.; Bazsó, Gábor; Apóstolo, Rui; Fausto, Rui; Biczysko, Malgorzata; Barone, Vincenzo; Tarczay, György

2018-01-01

The conformers of α-serine were investigated by matrix-isolation IR spectroscopy combined with NIR laser irradiation. This method, aided by 2D correlation analysis, enabled unambiguously grouping the spectral lines to individual conformers. On the basis of comparison of at least nine experimentally observed vibrational transitions of each conformer with empirically scaled (SQM) and anharmonic (GVPT2) computed IR spectra, 6 conformers were identified. In addition, the presence of at least one more conformer in Ar matrix was proved, and a short-lived conformer with a half-live of (3.7±0.5)·103 s in N2 matrix was generated by NIR irradiation. The analysis of the NIR laser induced conversions revealed that the excitation of the stretching overtone of both the side-chain and the carboxylic OH groups can effectively promote conformational changes, but remarkably different paths were observed for the two kinds of excitations. PMID:26201050

More efficient parameter estimates for factor analysis of ordinal variables by ridge generalized least squares.

PubMed

Yuan, Ke-Hai; Jiang, Ge; Cheng, Ying

2017-11-01

Data in psychology are often collected using Likert-type scales, and it has been shown that factor analysis of Likert-type data is better performed on the polychoric correlation matrix than on the product-moment covariance matrix, especially when the distributions of the observed variables are skewed. In theory, factor analysis of the polychoric correlation matrix is best conducted using generalized least squares with an asymptotically correct weight matrix (AGLS). However, simulation studies showed that both least squares (LS) and diagonally weighted least squares (DWLS) perform better than AGLS, and thus LS or DWLS is routinely used in practice. In either LS or DWLS, the associations among the polychoric correlation coefficients are completely ignored. To mend such a gap between statistical theory and empirical work, this paper proposes new methods, called ridge GLS, for factor analysis of ordinal data. Monte Carlo results show that, for a wide range of sample sizes, ridge GLS methods yield uniformly more accurate parameter estimates than existing methods (LS, DWLS, AGLS). A real-data example indicates that estimates by ridge GLS are 9-20% more efficient than those by existing methods. Rescaled and adjusted test statistics as well as sandwich-type standard errors following the ridge GLS methods also perform reasonably well. © 2017 The British Psychological Society.

Network-Based Detection and Classification of Seismovolcanic Tremors: Example From the Klyuchevskoy Volcanic Group in Kamchatka

NASA Astrophysics Data System (ADS)

Soubestre, Jean; Shapiro, Nikolai M.; Seydoux, Léonard; de Rosny, Julien; Droznin, Dmitry V.; Droznina, Svetlana Ya.; Senyukov, Sergey L.; Gordeev, Evgeniy I.

2018-01-01

We develop a network-based method for detecting and classifying seismovolcanic tremors. The proposed approach exploits the coherence of tremor signals across the network that is estimated from the array covariance matrix. The method is applied to four and a half years of continuous seismic data recorded by 19 permanent seismic stations in the vicinity of the Klyuchevskoy volcanic group in Kamchatka (Russia), where five volcanoes were erupting during the considered time period. We compute and analyze daily covariance matrices together with their eigenvalues and eigenvectors. As a first step, most coherent signals corresponding to dominating tremor sources are detected based on the width of the covariance matrix eigenvalues distribution. Thus, volcanic tremors of the two volcanoes known as most active during the considered period, Klyuchevskoy and Tolbachik, are efficiently detected. As a next step, we consider the daily array covariance matrix's first eigenvector. Our main hypothesis is that these eigenvectors represent the principal components of the daily seismic wavefield and, for days with tremor activity, characterize dominant tremor sources. Those daily first eigenvectors, which can be used as network-based fingerprints of tremor sources, are then grouped into clusters using correlation coefficient as a measure of the vector similarity. As a result, we identify seven clusters associated with different periods of activity of four volcanoes: Tolbachik, Klyuchevskoy, Shiveluch, and Kizimen. The developed method does not require a priori knowledge and is fully automatic; and the database of the network-based tremor fingerprints can be continuously enriched with newly available data.

Matrix metalloproteinase 14 modulates diabetes and Alzheimer's disease cross-talk: a meta-analysis.

PubMed

Cheng, Jack; Liu, Hsin-Ping; Lee, Cheng-Chun; Chen, Mei-Ying; Lin, Wei-Yong; Tsai, Fuu-Jen

2018-02-01

Diabetes mellitus is associated with dementia, but whether diabetes is associated with Alzheimer's disease remains controversial. Alzheimer's disease is characterized by amyloid beta aggregation. We hypothesized that genes, involved in amyloid beta degradation, may be altered due to diabetes and thus participate in progression of Alzheimer's disease. Expression profiling of amyloid beta-degrading enzymes in streptozotocin-induced diabetic mice and their correlation with expression of amyloid precursor protein in hippocampus of Alzheimer's disease patients were accessed. We found that matrix metalloproteinase 14 decreased in brain but not in other tissues of streptozotocin-induced diabetic mice, and was negatively correlated with expression of amyloid precursor protein in hippocampus of Alzheimer's disease patients. These findings suggested matrix metalloproteinase 14 may link insulin-deficient diabetes to Alzheimer's disease.

Multidimensional Compressed Sensing MRI Using Tensor Decomposition-Based Sparsifying Transform

PubMed Central

Yu, Yeyang; Jin, Jin; Liu, Feng; Crozier, Stuart

2014-01-01

Compressed Sensing (CS) has been applied in dynamic Magnetic Resonance Imaging (MRI) to accelerate the data acquisition without noticeably degrading the spatial-temporal resolution. A suitable sparsity basis is one of the key components to successful CS applications. Conventionally, a multidimensional dataset in dynamic MRI is treated as a series of two-dimensional matrices, and then various matrix/vector transforms are used to explore the image sparsity. Traditional methods typically sparsify the spatial and temporal information independently. In this work, we propose a novel concept of tensor sparsity for the application of CS in dynamic MRI, and present the Higher-order Singular Value Decomposition (HOSVD) as a practical example. Applications presented in the three- and four-dimensional MRI data demonstrate that HOSVD simultaneously exploited the correlations within spatial and temporal dimensions. Validations based on cardiac datasets indicate that the proposed method achieved comparable reconstruction accuracy with the low-rank matrix recovery methods and, outperformed the conventional sparse recovery methods. PMID:24901331

Density matrix-based time-dependent configuration interaction approach to ultrafast spin-flip dynamics

NASA Astrophysics Data System (ADS)

Wang, Huihui; Bokarev, Sergey I.; Aziz, Saadullah G.; Kühn, Oliver

2017-08-01

Recent developments in attosecond spectroscopy yield access to the correlated motion of electrons on their intrinsic timescales. Spin-flip dynamics is usually considered in the context of valence electronic states, where spin-orbit coupling is weak and processes related to the electron spin are usually driven by nuclear motion. However, for core-excited states, where the core-hole has a nonzero angular momentum, spin-orbit coupling is strong enough to drive spin-flips on a much shorter timescale. Using density matrix-based time-dependent restricted active space configuration interaction including spin-orbit coupling, we address an unprecedentedly short spin-crossover for the example of L-edge (2p→3d) excited states of a prototypical Fe(II) complex. This process occurs on a timescale, which is faster than that of Auger decay (∼4 fs) treated here explicitly. Modest variations of carrier frequency and pulse duration can lead to substantial changes in the spin-state yield, suggesting its control by soft X-ray light.

Experimental investigation of the ordering pathway in a Ni-33 at.%Cr alloy

DOE PAGES

Gwalani, B.; Alam, T.; Miller, C.; ...

2016-06-17

The present study involves a detailed experimental investigation of the concurrent compositional clustering and long-range ordering tendencies in a Ni-33 at.%Cr alloy, carried out by coupling synchrotron-based X-ray diffraction (XRD), transmission electron microscopy (TEM), and atom probe tomography (APT). Synchrotron-based XRD results clearly exhibited progressively increasing lattice contraction in the matrix with increasing isothermal aging time, at 475 degrees C, eventually leading to the development of long-range ordering (LRO) of the Pt2Mo-type. Detailed TEM and APT investigations revealed that this LRO in the matrix is manifested in the form of nanometer-scale ordered domains, and the spatial distribution, size, morphology andmore » compositional evolution of these domains have been carefully investigated. Here, the APT results also revealed the early stages of compositional clustering prior to the onset of long-range ordering in this alloy and such compositional clustering can potentially be correlated to the lattice contraction and previously proposed short-range ordering tendencies.« less

Synchronization and desynchronization in a network of locally coupled Wilson-Cowan oscillators.

PubMed

Campbell, S; Wang, D

1996-01-01

A network of Wilson-Cowan (WC) oscillators is constructed, and its emergent properties of synchronization and desynchronization are investigated by both computer simulation and formal analysis. The network is a 2D matrix, where each oscillator is coupled only to its neighbors. We show analytically that a chain of locally coupled oscillators (the piecewise linear approximation to the WC oscillator) synchronizes, and we present a technique to rapidly entrain finite numbers of oscillators. The coupling strengths change on a fast time scale based on a Hebbian rule. A global separator is introduced which receives input from and sends feedback to each oscillator in the matrix. The global separator is used to desynchronize different oscillator groups. Unlike many other models, the properties of this network emerge from local connections that preserve spatial relationships among components and are critical for encoding Gestalt principles of feature grouping. The ability to synchronize and desynchronize oscillator groups within this network offers a promising approach for pattern segmentation and figure/ground segregation based on oscillatory correlation.

A Noise Removal Method for Uniform Circular Arrays in Complex Underwater Noise Environments with Low SNR

PubMed Central

Xia, Huijun; Yang, Kunde; Ma, Yuanliang; Wang, Yong; Liu, Yaxiong

2017-01-01

Generally, many beamforming methods are derived under the assumption of white noise. In practice, the actual underwater ambient noise is complex. As a result, the noise removal capacity of the beamforming method may be deteriorated considerably. Furthermore, in underwater environment with extremely low signal-to-noise ratio (SNR), the performances of the beamforming method may be deteriorated. To tackle these problems, a noise removal method for uniform circular array (UCA) is proposed to remove the received noise and improve the SNR in complex noise environments with low SNR. First, the symmetrical noise sources are defined and the spatial correlation of the symmetrical noise sources is calculated. Then, based on the preceding results, the noise covariance matrix is decomposed into symmetrical and asymmetrical components. Analysis indicates that the symmetrical component only affect the real part of the noise covariance matrix. Consequently, the delay-and-sum (DAS) beamforming is performed by using the imaginary part of the covariance matrix to remove the symmetrical component. However, the noise removal method causes two problems. First, the proposed method produces a false target. Second, the proposed method would seriously suppress the signal when it is located in some directions. To solve the first problem, two methods to reconstruct the signal covariance matrix are presented: based on the estimation of signal variance and based on the constrained optimization algorithm. To solve the second problem, we can design the array configuration and select the suitable working frequency. Theoretical analysis and experimental results are included to demonstrate that the proposed methods are particularly effective in complex noise environments with low SNR. The proposed method can be extended to any array. PMID:28598386

Black phosphorus-assisted laser desorption ionization mass spectrometry for the determination of low-molecular-weight compounds in biofluids.

PubMed

He, Xiao-Mei; Ding, Jun; Yu, Lei; Hussain, Dilshad; Feng, Yu-Qi

2016-09-01

Quantitative analysis of small molecules by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has been a challenging task due to matrix-derived interferences in low m/z region and poor reproducibility of MS signal response. In this study, we developed an approach by applying black phosphorus (BP) as a matrix-assisted laser desorption ionization (MALDI) matrix for the quantitative analysis of small molecules for the first time. Black phosphorus-assisted laser desorption/ionization mass spectrometry (BP/ALDI-MS) showed clear background and exhibited superior detection sensitivity toward quaternary ammonium compounds compared to carbon-based materials. By combining stable isotope labeling (SIL) strategy with BP/ALDI-MS (SIL-BP/ALDI-MS), a variety of analytes labeled with quaternary ammonium group were sensitively detected. Moreover, the isotope-labeled forms of analytes also served as internal standards, which broadened the analyte coverage of BP/ALDI-MS and improved the reproducibility of MS signals. Based on these advantages, a reliable method for quantitative analysis of aldehydes from complex biological samples (saliva, urine, and serum) was successfully established. Good linearities were obtained for five aldehydes in the range of 0.1-20.0 μM with correlation coefficients (R (2)) larger than 0.9928. The LODs were found to be 20 to 100 nM. Reproducibility of the method was obtained with intra-day and inter-day relative standard deviations (RSDs) less than 10.4 %, and the recoveries in saliva samples ranged from 91.4 to 117.1 %. Taken together, the proposed SIL-BP/ALDI-MS strategy has proved to be a reliable tool for quantitative analysis of aldehydes from complex samples. Graphical Abstract An approach for the determination of small molecules was developed by using black phosphorus (BP) as a matrix-assisted laser desorption ionization (MALDI) matrix.

Surface Plasmon Resonance imaging-based sensing for anti-bovine immunoglobulins detection in human milk and serum.

PubMed

Scarano, S; Scuffi, C; Mascini, M; Minunni, M

2011-11-30

Only few papers deal with Surface Plasmon Resonance imaging (SPRi) direct detection on complex matrices, limiting the biosensor application to real analytical problems. In this work a SPRi biosensor for anti-bovine IgG detection in untreated human bodily fluids, i.e. diluted human serum and milk, was developed. Enhanced levels of cow's milk antibodies in children's serum are suspected for their possible correlation with Type 1 diabetes during childhood and their detection in real samples was up to now performed by classical immunoassays based on indirect detection. The biosensor was optimised in standard samples and then in untreated human milk for anti-bovine IgG direct detection. The key novelty of the work is the evaluation of matrix effect by applying to real samples an experimental and ex ante method previously developed for SPRi signal sampling in standard solutions, called "Data Analyzer"; it punctually visualises and analyses the behaviour of receptor spots of the array, to select only spot areas with the best specific vs. unspecific signal values. In this way, benefits provide by SPRi image analysis are exploited here to quantify and minimise drawbacks due to the matrix effect, allowing to by-pass every matrix pre-treatment except dilution. Copyright © 2011 Elsevier B.V. All rights reserved.

Correlated matrix-fluctuation-mediated activated transport of dilute penetrants in glass-forming liquids and suspensions

NASA Astrophysics Data System (ADS)

Zhang, Rui; Schweizer, Kenneth S.

2017-05-01

We formulate a microscopic, force-level statistical mechanical theory for the activated diffusion of dilute penetrants in dense liquids, colloidal suspensions, and glasses. The approach explicitly and self-consistently accounts for coupling between penetrant hopping and matrix dynamic displacements that actively facilitate the hopping event. The key new ideas involve two mechanistically (at a stochastic trajectory level) coupled dynamic free energy functions for the matrix and spherical penetrant particles. A single dynamic coupling parameter quantifies how much the matrix displaces relative to the penetrant when the latter reaches its transition state which is determined via the enforcement of a temporal causality or coincidence condition. The theory is implemented for dilute penetrants smaller than the matrix particles, with or without penetrant-matrix attractive forces. Model calculations reveal a rich dependence of the penetrant diffusion constant and degree of dynamic coupling on size ratio, volume fraction, and attraction strength. In the absence of attractions, a near exponential decrease of penetrant diffusivity with size ratio over an intermediate range is predicted, in contrast to the much steeper, non-exponential variation if one assumes local matrix dynamical fluctuations are not correlated with penetrant motion. For sticky penetrants, the relative and absolute influence of caging versus physical bond formation is studied. The conditions for a dynamic crossover from the case where a time scale separation between penetrant and matrix activated hopping exists to a "slaved" or "constraint release" fully coupled regime are determined. The particle mixture model is mapped to treat experimental thermal systems and applied to make predictions for the diffusivity of water, toluene, methanol, and oxygen in polyvinylacetate liquids and glasses. The theory agrees well with experiment with values of the penetrant-matrix size ratio close to their chemically intuitive values.

Correlated matrix-fluctuation-mediated activated transport of dilute penetrants in glass-forming liquids and suspensions

PubMed Central

Schweizer, Kenneth S.

2017-01-01

We formulate a microscopic, force-level statistical mechanical theory for the activated diffusion of dilute penetrants in dense liquids, colloidal suspensions, and glasses. The approach explicitly and self-consistently accounts for coupling between penetrant hopping and matrix dynamic displacements that actively facilitate the hopping event. The key new ideas involve two mechanistically (at a stochastic trajectory level) coupled dynamic free energy functions for the matrix and spherical penetrant particles. A single dynamic coupling parameter quantifies how much the matrix displaces relative to the penetrant when the latter reaches its transition state which is determined via the enforcement of a temporal causality or coincidence condition. The theory is implemented for dilute penetrants smaller than the matrix particles, with or without penetrant-matrix attractive forces. Model calculations reveal a rich dependence of the penetrant diffusion constant and degree of dynamic coupling on size ratio, volume fraction, and attraction strength. In the absence of attractions, a near exponential decrease of penetrant diffusivity with size ratio over an intermediate range is predicted, in contrast to the much steeper, non-exponential variation if one assumes local matrix dynamical fluctuations are not correlated with penetrant motion. For sticky penetrants, the relative and absolute influence of caging versus physical bond formation is studied. The conditions for a dynamic crossover from the case where a time scale separation between penetrant and matrix activated hopping exists to a “slaved” or “constraint release” fully coupled regime are determined. The particle mixture model is mapped to treat experimental thermal systems and applied to make predictions for the diffusivity of water, toluene, methanol, and oxygen in polyvinylacetate liquids and glasses. The theory agrees well with experiment with values of the penetrant-matrix size ratio close to their chemically intuitive values. PMID:28527449

Distance matrix-based approach to protein structure prediction.

PubMed

Kloczkowski, Andrzej; Jernigan, Robert L; Wu, Zhijun; Song, Guang; Yang, Lei; Kolinski, Andrzej; Pokarowski, Piotr

2009-03-01

Much structural information is encoded in the internal distances; a distance matrix-based approach can be used to predict protein structure and dynamics, and for structural refinement. Our approach is based on the square distance matrix D = [r(ij)(2)] containing all square distances between residues in proteins. This distance matrix contains more information than the contact matrix C, that has elements of either 0 or 1 depending on whether the distance r (ij) is greater or less than a cutoff value r (cutoff). We have performed spectral decomposition of the distance matrices D = sigma lambda(k)V(k)V(kT), in terms of eigenvalues lambda kappa and the corresponding eigenvectors v kappa and found that it contains at most five nonzero terms. A dominant eigenvector is proportional to r (2)--the square distance of points from the center of mass, with the next three being the principal components of the system of points. By predicting r (2) from the sequence we can approximate a distance matrix of a protein with an expected RMSD value of about 7.3 A, and by combining it with the prediction of the first principal component we can improve this approximation to 4.0 A. We can also explain the role of hydrophobic interactions for the protein structure, because r is highly correlated with the hydrophobic profile of the sequence. Moreover, r is highly correlated with several sequence profiles which are useful in protein structure prediction, such as contact number, the residue-wise contact order (RWCO) or mean square fluctuations (i.e. crystallographic temperature factors). We have also shown that the next three components are related to spatial directionality of the secondary structure elements, and they may be also predicted from the sequence, improving overall structure prediction. We have also shown that the large number of available HIV-1 protease structures provides a remarkable sampling of conformations, which can be viewed as direct structural information about the dynamics. After structure matching, we apply principal component analysis (PCA) to obtain the important apparent motions for both bound and unbound structures. There are significant similarities between the first few key motions and the first few low-frequency normal modes calculated from a static representative structure with an elastic network model (ENM) that is based on the contact matrix C (related to D), strongly suggesting that the variations among the observed structures and the corresponding conformational changes are facilitated by the low-frequency, global motions intrinsic to the structure. Similarities are also found when the approach is applied to an NMR ensemble, as well as to atomic molecular dynamics (MD) trajectories. Thus, a sufficiently large number of experimental structures can directly provide important information about protein dynamics, but ENM can also provide a similar sampling of conformations. Finally, we use distance constraints from databases of known protein structures for structure refinement. We use the distributions of distances of various types in known protein structures to obtain the most probable ranges or the mean-force potentials for the distances. We then impose these constraints on structures to be refined or include the mean-force potentials directly in the energy minimization so that more plausible structural models can be built. This approach has been successfully used by us in 2006 in the CASPR structure refinement (http://predictioncenter.org/caspR).

The Variability and Interpretation of Earthquake Source Mechanisms in The Geysers Geothermal Field From a Bayesian Standpoint Based on the Choice of a Noise Model

NASA Astrophysics Data System (ADS)

Mustać, Marija; Tkalčić, Hrvoje; Burky, Alexander L.

2018-01-01

Moment tensor (MT) inversion studies of events in The Geysers geothermal field mostly focused on microseismicity and found a large number of earthquakes with significant non-double-couple (non-DC) seismic radiation. Here we concentrate on the largest events in the area in recent years using a hierarchical Bayesian MT inversion. Initially, we show that the non-DC components of the MT can be reliably retrieved using regional waveform data from a small number of stations. Subsequently, we present results for a number of events and show that accounting for noise correlations can lead to retrieval of a lower isotropic (ISO) component and significantly different focal mechanisms. We compute the Bayesian evidence to compare solutions obtained with different assumptions of the noise covariance matrix. Although a diagonal covariance matrix produces a better waveform fit, inversions that account for noise correlations via an empirically estimated noise covariance matrix account for interdependences of data errors and are preferred from a Bayesian point of view. This implies that improper treatment of data noise in waveform inversions can result in fitting the noise and misinterpreting the non-DC components. Finally, one of the analyzed events is characterized as predominantly DC, while the others still have significant non-DC components, probably as a result of crack opening, which is a reasonable hypothesis for The Geysers geothermal field geological setting.

Linear-response time-dependent density-functional theory with pairing fields.

PubMed

Peng, Degao; van Aggelen, Helen; Yang, Yang; Yang, Weitao

2014-05-14

Recent development in particle-particle random phase approximation (pp-RPA) broadens the perspective on ground state correlation energies [H. van Aggelen, Y. Yang, and W. Yang, Phys. Rev. A 88, 030501 (2013), Y. Yang, H. van Aggelen, S. N. Steinmann, D. Peng, and W. Yang, J. Chem. Phys. 139, 174110 (2013); D. Peng, S. N. Steinmann, H. van Aggelen, and W. Yang, J. Chem. Phys. 139, 104112 (2013)] and N ± 2 excitation energies [Y. Yang, H. van Aggelen, and W. Yang, J. Chem. Phys. 139, 224105 (2013)]. So far Hartree-Fock and approximated density-functional orbitals have been utilized to evaluate the pp-RPA equation. In this paper, to further explore the fundamentals and the potential use of pairing matrix dependent functionals, we present the linear-response time-dependent density-functional theory with pairing fields with both adiabatic and frequency-dependent kernels. This theory is related to the density-functional theory and time-dependent density-functional theory for superconductors, but is applied to normal non-superconducting systems for our purpose. Due to the lack of the proof of the one-to-one mapping between the pairing matrix and the pairing field for time-dependent systems, the linear-response theory is established based on the representability assumption of the pairing matrix. The linear response theory justifies the use of approximated density-functionals in the pp-RPA equation. This work sets the fundamentals for future density-functional development to enhance the description of ground state correlation energies and N ± 2 excitation energies.

A Model-Based Systems Engineering Methodology for Employing Architecture In System Analysis: Developing Simulation Models Using Systems Modeling Language Products to Link Architecture and Analysis

DTIC Science & Technology

2016-06-01

characteristics, experimental design techniques, and analysis methodologies that distinguish each phase of the MBSE MEASA. To ensure consistency... methodology . Experimental design selection, simulation analysis, and trade space analysis support the final two stages. Figure 27 segments the MBSE MEASA...rounding has the potential to increase the correlation between columns of the experimental design matrix. The design methodology presented in Vieira

Genetic diversity and gene differentiation among ten species of Zingiberaceae from Eastern India.

PubMed

Mohanty, Sujata; Panda, Manoj Kumar; Acharya, Laxmikanta; Nayak, Sanghamitra

2014-08-01

In the present study, genetic fingerprints of ten species of Zingiberaceae from eastern India were developed using PCR-based markers. 19 RAPD (Rapid Amplified polymorphic DNA), 8 ISSR (Inter Simple Sequence Repeats) and 8 SSR (Simple Sequence Repeats) primers were used to elucidate genetic diversity important for utilization, management and conservation. These primers produced 789 loci, out of which 773 loci were polymorphic (including 220 unique loci) and 16 monomorphic loci. Highest number of bands amplified (263) in Curcuma caesia whereas lowest (209) in Zingiber cassumunar. Though all the markers discriminated the species effectively, analysis of combined data of all markers resulted in better distinction of individual species. Highest number of loci was amplified with SSR primers with resolving power in a range of 17.4-39. Dendrogram based on three molecular data using unweighted pair group method with arithmetic mean classified all the species into two clusters. Mantle matrix correspondence test revealed high matrix correlation in all the cases. Correlation values for RAPD, ISSR and SSR were 0.797, 0.84 and 0.8, respectively, with combined data. In both the genera wild and cultivated species were completely separated from each other at genomic level. It also revealed distinct genetic identity between species of Curcuma and Zingiber. High genetic diversity documented in the present study provides a baseline data for optimization of conservation and breeding programme of the studied zingiberacious species.

Dimension-six matrix elements for meson mixing and lifetimes from sum rules

NASA Astrophysics Data System (ADS)

Kirk, M.; Lenz, A.; Rauh, T.

2017-12-01

The hadronic matrix elements of dimension-six Δ F = 0, 2 operators are crucial inputs for the theory predictions of mixing observables and lifetime ratios in the B and D system. We determine them using HQET sum rules for three-point correlators. The results of the required three-loop computation of the correlators and the one-loop computation of the QCD-HQET matching are given in analytic form. For mixing matrix elements we find very good agreement with recent lattice results and comparable theoretical uncertainties. For lifetime matrix elements we present the first ever determination in the D meson sector and the first determination of Δ B = 0 matrix elements with uncertainties under control — superseeding preliminary lattice studies stemming from 2001 and earlier. With our state-of-the-art determination of the bag parameters we predict: τ( B +)/ τ( B d 0 ) = 1.082 - 0.026 + 0.022 , τ( B s 0 )/ τ( B d 0 ) = 0.9994 ± 0.0025, τ( D +)/ τ( D 0) = 2. 7 - 0.8 + 0.7 and the mixing-observables in the B s and B d system, in good agreement with the most recent experimental averages.

Micromechanical Modeling of Woven Metal Matrix Composites

NASA Technical Reports Server (NTRS)

Bednarcyk, Brett A.; Pindera, Marek-Jerzy

1997-01-01

This report presents the results of an extensive micromechanical modeling effort for woven metal matrix composites. The model is employed to predict the mechanical response of 8-harness (8H) satin weave carbon/copper (C/Cu) composites. Experimental mechanical results for this novel high thermal conductivity material were recently reported by Bednarcyk et al. along with preliminary model results. The micromechanics model developed herein is based on an embedded approach. A micromechanics model for the local (micro-scale) behavior of the woven composite, the original method of cells (Aboudi), is embedded in a global (macro-scale) micromechanics model (the three-dimensional generalized method of cells (GMC-3D) (Aboudi). This approach allows representation of true repeating unit cells for woven metal matrix composites via GMC-3D, and representation of local effects, such as matrix plasticity, yarn porosity, and imperfect fiber-matrix bonding. In addition, the equations of GMC-3D were reformulated to significantly reduce the number of unknown quantities that characterize the deformation fields at the microlevel in order to make possible the analysis of actual microstructures of woven composites. The resulting micromechanical model (WCGMC) provides an intermediate level of geometric representation, versatility, and computational efficiency with respect to previous analytical and numerical models for woven composites, but surpasses all previous modeling work by allowing the mechanical response of a woven metal matrix composite, with an elastoplastic matrix, to be examined for the first time. WCGMC is employed to examine the effects of composite microstructure, porosity, residual stresses, and imperfect fiber-matrix bonding on the predicted mechanical response of 8H satin C/Cu. The previously reported experimental results are summarized, and the model predictions are compared to monotonic and cyclic tensile and shear test data. By considering appropriate levels of porosity, residual stresses, and imperfect fiber-matrix debonding, reasonably good qualitative and quantitative correlation is achieved between model and experiment.

Analysis of network clustering behavior of the Chinese stock market

NASA Astrophysics Data System (ADS)

Chen, Huan; Mai, Yong; Li, Sai-Ping

2014-11-01

Random Matrix Theory (RMT) and the decomposition of correlation matrix method are employed to analyze spatial structure of stocks interactions and collective behavior in the Shanghai and Shenzhen stock markets in China. The result shows that there exists prominent sector structures, with subsectors including the Real Estate (RE), Commercial Banks (CB), Pharmaceuticals (PH), Distillers&Vintners (DV) and Steel (ST) industries. Furthermore, the RE and CB subsectors are mostly anti-correlated. We further study the temporal behavior of the dataset and find that while the sector structures are relatively stable from 2007 through 2013, the correlation between the real estate and commercial bank stocks shows large variations. By employing the ensemble empirical mode decomposition (EEMD) method, we show that this anti-correlation behavior is closely related to the monetary and austerity policies of the Chinese government during the period of study.

Development and Preliminary Validation of the Strategic Thinking Mindset Test (STMT)

DTIC Science & Technology

2017-06-01

reliability. The test’s three subscales (intellectual flexibility, inclusiveness, and humility) each correlated significantly with alternative measures of...34 TABLE 9. STAGE 4 SAMPLE DEMOGRAPHICS ................................................................ 35 TABLE 10. INTERITEM CORRELATION ...MATRIX (ALL ITEMS) ...................................... 39 TABLE 11. ITEM-SCALE AND VALIDITY CORRELATIONS (ALL ITEMS) .................... 40

Intelligence and Semen Quality Are Positively Correlated

ERIC Educational Resources Information Center

Arden, Rosalind; Gottfredson, Linda S.; Miller, Geoffrey; Pierce, Arand

2009-01-01

Human cognitive abilities inter-correlate to form a positive matrix, from which a large first factor, called "Spearman's g" or general intelligence, can be extracted. General intelligence itself is correlated with many important health outcomes including cardio-vascular function and longevity. However, the important evolutionary question of…

MRL and SuperFine+MRL: new supertree methods

PubMed Central

2012-01-01

Background Supertree methods combine trees on subsets of the full taxon set together to produce a tree on the entire set of taxa. Of the many supertree methods, the most popular is MRP (Matrix Representation with Parsimony), a method that operates by first encoding the input set of source trees by a large matrix (the "MRP matrix") over {0,1, ?}, and then running maximum parsimony heuristics on the MRP matrix. Experimental studies evaluating MRP in comparison to other supertree methods have established that for large datasets, MRP generally produces trees of equal or greater accuracy than other methods, and can run on larger datasets. A recent development in supertree methods is SuperFine+MRP, a method that combines MRP with a divide-and-conquer approach, and produces more accurate trees in less time than MRP. In this paper we consider a new approach for supertree estimation, called MRL (Matrix Representation with Likelihood). MRL begins with the same MRP matrix, but then analyzes the MRP matrix using heuristics (such as RAxML) for 2-state Maximum Likelihood. Results We compared MRP and SuperFine+MRP with MRL and SuperFine+MRL on simulated and biological datasets. We examined the MRP and MRL scores of each method on a wide range of datasets, as well as the resulting topological accuracy of the trees. Our experimental results show that MRL, coupled with a very good ML heuristic such as RAxML, produced more accurate trees than MRP, and MRL scores were more strongly correlated with topological accuracy than MRP scores. Conclusions SuperFine+MRP, when based upon a good MP heuristic, such as TNT, produces among the best scores for both MRP and MRL, and is generally faster and more topologically accurate than other supertree methods we tested. PMID:22280525

Orthogonal bases of invariants in tensor models

NASA Astrophysics Data System (ADS)

Diaz, Pablo; Rey, Soo-Jong

2018-02-01

Representation theory provides an efficient framework to count and classify invariants in tensor models of (gauge) symmetry G d = U( N 1) ⊗ · · · ⊗ U( N d ) . We show that there are two natural ways of counting invariants, one for arbitrary G d and another valid for large rank of G d . We construct basis of invariant operators based on the counting, and compute correlators of their elements. The basis associated with finite rank of G d diagonalizes two-point function. It is analogous to the restricted Schur basis used in matrix models. We comment on future directions for investigation.

SYNCSA--R tool for analysis of metacommunities based on functional traits and phylogeny of the community components.

PubMed

Debastiani, Vanderlei J; Pillar, Valério D

2012-08-01

SYNCSA is an R package for the analysis of metacommunities based on functional traits and phylogeny of the community components. It offers tools to calculate several matrix correlations that express trait-convergence assembly patterns, trait-divergence assembly patterns and phylogenetic signal in functional traits at the species pool level and at the metacommunity level. SYNCSA is a package for the R environment, under a GPL-2 open-source license and freely available on CRAN official web server for R (http://cran.r-project.org). vanderleidebastiani@yahoo.com.br.

Second-order perturbation theory with a density matrix renormalization group self-consistent field reference function: theory and application to the study of chromium dimer.

PubMed

Kurashige, Yuki; Yanai, Takeshi

2011-09-07

We present a second-order perturbation theory based on a density matrix renormalization group self-consistent field (DMRG-SCF) reference function. The method reproduces the solution of the complete active space with second-order perturbation theory (CASPT2) when the DMRG reference function is represented by a sufficiently large number of renormalized many-body basis, thereby being named DMRG-CASPT2 method. The DMRG-SCF is able to describe non-dynamical correlation with large active space that is insurmountable to the conventional CASSCF method, while the second-order perturbation theory provides an efficient description of dynamical correlation effects. The capability of our implementation is demonstrated for an application to the potential energy curve of the chromium dimer, which is one of the most demanding multireference systems that require best electronic structure treatment for non-dynamical and dynamical correlation as well as large basis sets. The DMRG-CASPT2/cc-pwCV5Z calculations were performed with a large (3d double-shell) active space consisting of 28 orbitals. Our approach using large-size DMRG reference addressed the problems of why the dissociation energy is largely overestimated by CASPT2 with the small active space consisting of 12 orbitals (3d4s), and also is oversensitive to the choice of the zeroth-order Hamiltonian. © 2011 American Institute of Physics

Different matrix micro-environments in colon cancer and diverticular disease.

PubMed

Klinge, U; Rosch, R; Junge, K; Krones, C J; Stumpf, M; Lynen-Jansen, P; Mertens, P R; Schumpelick, V

2007-05-01

The extracellular matrix and the interactive signalling between its components are thought to play a pivotal role for tumour development and metastasis formation. An altered matrix composition as potential underlying pathology for the development of colorectal cancer was hypothesized. In a retrospective study of patients with colon cancer, the extracellular matrix in tumour-free bowel specimen was investigated in comparison with non-infected bowel specimen from patients operated on for colonic diverticulosis. The following matrix parameters with known associations to tumour formation, cell proliferation, invasion and metastasis were analysed by immunohistochemistry and quantified by a scoring system: VEGF, TGF-beta, ESDN, CD117, c-erb-2, cyclin D1, p53, p27, COX-2, YB-1, collagen I/III, MMP-13, PAI and uPAR. Expression profiles and correlations were calculated. The comparison of the two groups revealed a significantly decreased immunostaining for CD117 and TGF-beta in the cancer group (8.5+/-2.6 vs 10.3+/-2,1 and 4.9+/-1.5 vs 8.1+/-3, respectively), whereas PAI scores were significantly higher than in patients with diverticular disease (8.1+/-1.6 vs 6.2+/-0.9). Overall correlation patterns of matrix parameters indicated pronounced differences between tumour-free tissue in cancer patients compared with patients with diverticular disease. Our results indicate distinct differences in the colonic tissue architecture between cancer patients and patients with diverticulitis that support the notion of an altered matrix composition predisposing to the development of colon cancer.

Measuring order in disordered systems and disorder in ordered systems: Random matrix theory for isotropic and nematic liquid crystals and its perspective on pseudo-nematic domains

NASA Astrophysics Data System (ADS)

Zhao, Yan; Stratt, Richard M.

2018-05-01

Surprisingly long-ranged intermolecular correlations begin to appear in isotropic (orientationally disordered) phases of liquid crystal forming molecules when the temperature or density starts to close in on the boundary with the nematic (ordered) phase. Indeed, the presence of slowly relaxing, strongly orientationally correlated, sets of molecules under putatively disordered conditions ("pseudo-nematic domains") has been apparent for some time from light-scattering and optical-Kerr experiments. Still, a fully microscopic characterization of these domains has been lacking. We illustrate in this paper how pseudo-nematic domains can be studied in even relatively small computer simulations by looking for order-parameter tensor fluctuations much larger than one would expect from random matrix theory. To develop this idea, we show that random matrix theory offers an exact description of how the probability distribution for liquid-crystal order parameter tensors converges to its macroscopic-system limit. We then illustrate how domain properties can be inferred from finite-size-induced deviations from these random matrix predictions. A straightforward generalization of time-independent random matrix theory also allows us to prove that the analogous random matrix predictions for the time dependence of the order-parameter tensor are similarly exact in the macroscopic limit, and that relaxation behavior of the domains can be seen in the breakdown of the finite-size scaling required by that random-matrix theory.

Evidence for spin correlation in tt production

DOE PAGES

Abazov, Victor Mukhamedovich

2012-01-19

We present a measurement of the ratio of events with correlated t and t spins to the total number of tt events. This ratio f is evaluated using a matrix-element-based approach in 729 tt candidate events with a single lepton ℓ (electron or muon) and at least four jets. The analyzed pp collisions data correspond to an integrated luminosity of 5.3 fb -1 and were collected with the D0 detector at the Fermilab Tevatron collider operating at a center-of-mass energy \\(\\sqrt{s}=1.96\\) TeV. Combining this result with a recent measurement of f in dileptonic final states, we find f in agreementmore » with the standard model. In addition, the combination provides evidence for the presence of spin correlation in tt events with a significance of more than 3 standard deviations.« less

Spatio-temporal Reconstruction of Neural Sources Using Indirect Dominant Mode Rejection.

PubMed

Jafadideh, Alireza Talesh; Asl, Babak Mohammadzadeh

2018-04-27

Adaptive minimum variance based beamformers (MVB) have been successfully applied to magnetoencephalogram (MEG) and electroencephalogram (EEG) data to localize brain activities. However, the performance of these beamformers falls down in situations where correlated or interference sources exist. To overcome this problem, we propose indirect dominant mode rejection (iDMR) beamformer application in brain source localization. This method by modifying measurement covariance matrix makes MVB applicable in source localization in the presence of correlated and interference sources. Numerical results on both EEG and MEG data demonstrate that presented approach accurately reconstructs time courses of active sources and localizes those sources with high spatial resolution. In addition, the results of real AEF data show the good performance of iDMR in empirical situations. Hence, iDMR can be reliably used for brain source localization especially when there are correlated and interference sources.

Family Reintegration Experiences of Soldiers with Mild Traumatic Brain Injury

DTIC Science & Technology

2014-02-26

depression scores in the spouse. Weak within-couple correlation were indicated on the other measures. Table 3 presents the Spearman correlation matrix...separately. Table 2: Spearman Correlation Coefficients for Couples Spouse MAT Spouse Depression Spouse...Anxiety Soldier MAT -0.06 Soldier Depression -0.61 Soldier Anxiety -0.12 Table 3: Spearman Correlation Coefficients for Soldiers and

Effect of proton transfer on the electronic coupling in DNA

NASA Astrophysics Data System (ADS)

Rak, Janusz; Makowska, Joanna; Voityuk, Alexander A.

2006-06-01

The effects of single and double proton transfer within Watson-Crick base pairs on donor-acceptor electronic couplings, Vda, in DNA are studied on the bases of quantum chemical calculations. Four dimers [AT,AT], [GC,GC], [GC,AT] and [GC,TA)] are considered. Three techniques - the generalized Mulliken-Hush scheme, the fragment charge method and the diabatic states method - are employed to estimate Vda for hole transfer between base pairs. We show that both single- and double proton transfer (PT) reactions may substantially affect the electronic coupling in DNA. The electronic coupling in [AT,AT] is predicted to be most sensitive to PT. Single PT within the first base pair in the dimer leads to increase in the hole transfer efficiency by a factor of 4, while proton transfer within the second pair should substantially, by 2.7 times, decrease the rate of charge transfer. Thus, directional asymmetry of the PT effects on the electronic coupling is predicted. The changes in the Vda matrix elements correlate with the topological properties of orbitals of donor and acceptor and can be qualitatively rationalized in terms of resonance structures of donor and acceptor. Atomic pair contributions to the Vda matrix elements are also analyzed.

Multireference second order perturbation theory with a simplified treatment of dynamical correlation.

PubMed

Xu, Enhua; Zhao, Dongbo; Li, Shuhua

2015-10-13

A multireference second order perturbation theory based on a complete active space configuration interaction (CASCI) function or density matrix renormalized group (DMRG) function has been proposed. This method may be considered as an approximation to the CAS/A approach with the same reference, in which the dynamical correlation is simplified with blocked correlated second order perturbation theory based on the generalized valence bond (GVB) reference (GVB-BCPT2). This method, denoted as CASCI-BCPT2/GVB or DMRG-BCPT2/GVB, is size consistent and has a similar computational cost as the conventional second order perturbation theory (MP2). We have applied it to investigate a number of problems of chemical interest. These problems include bond-breaking potential energy surfaces in four molecules, the spectroscopic constants of six diatomic molecules, the reaction barrier for the automerization of cyclobutadiene, and the energy difference between the monocyclic and bicyclic forms of 2,6-pyridyne. Our test applications demonstrate that CASCI-BCPT2/GVB can provide comparable results with CASPT2 (second order perturbation theory based on the complete active space self-consistent-field wave function) for systems under study. Furthermore, the DMRG-BCPT2/GVB method is applicable to treat strongly correlated systems with large active spaces, which are beyond the capability of CASPT2.

Characterizing the Joint Effect of Diverse Test-Statistic Correlation Structures and Effect Size on False Discovery Rates in a Multiple-Comparison Study of Many Outcome Measures

NASA Technical Reports Server (NTRS)

Feiveson, Alan H.; Ploutz-Snyder, Robert; Fiedler, James

2011-01-01

In their 2009 Annals of Statistics paper, Gavrilov, Benjamini, and Sarkar report the results of a simulation assessing the robustness of their adaptive step-down procedure (GBS) for controlling the false discovery rate (FDR) when normally distributed test statistics are serially correlated. In this study we extend the investigation to the case of multiple comparisons involving correlated non-central t-statistics, in particular when several treatments or time periods are being compared to a control in a repeated-measures design with many dependent outcome measures. In addition, we consider several dependence structures other than serial correlation and illustrate how the FDR depends on the interaction between effect size and the type of correlation structure as indexed by Foerstner s distance metric from an identity. The relationship between the correlation matrix R of the original dependent variables and R, the correlation matrix of associated t-statistics is also studied. In general R depends not only on R, but also on sample size and the signed effect sizes for the multiple comparisons.

Damage Characterization in SiC/SiC Composites using Electrical Resistance

NASA Technical Reports Server (NTRS)

Smith, Craig E.; Xia, Zhenhai

2011-01-01

SiC/SiC ceramic matrix composites (CMCs) under creep-rupture loading accumulate damage by means of local matrix cracks that typically form near a stress concentration, such as a 90o fiber tow or large matrix pore, and grow over time. Such damage is difficult to detect through conventional techniques. Electrical resistance changes can be correlated with matrix cracking to provide a means of damage detection. Sylramic-iBN fiber-reinforced SiC composites with both melt infiltrated (MI) and chemical vapor infiltrated (CVI) matrix types are compared here. Results for both systems exhibit an increase in resistance prior to fracture, which can be detected either in situ or post-damage.

QCD dirac operator at nonzero chemical potential: lattice data and matrix model.

PubMed

Akemann, Gernot; Wettig, Tilo

2004-03-12

Recently, a non-Hermitian chiral random matrix model was proposed to describe the eigenvalues of the QCD Dirac operator at nonzero chemical potential. This matrix model can be constructed from QCD by mapping it to an equivalent matrix model which has the same symmetries as QCD with chemical potential. Its microscopic spectral correlations are conjectured to be identical to those of the QCD Dirac operator. We investigate this conjecture by comparing large ensembles of Dirac eigenvalues in quenched SU(3) lattice QCD at a nonzero chemical potential to the analytical predictions of the matrix model. Excellent agreement is found in the two regimes of weak and strong non-Hermiticity, for several different lattice volumes.

Bose-Einstein correlations: A study of an invariance group

NASA Astrophysics Data System (ADS)

Bialas, A.; Zalewski, K.

2005-08-01

A group of transformations changing the phases of the elements of the single-particle density matrix, but leaving unchanged the predictions for identical particles concerning the momentum distributions, momentum correlations etc., is identified. Its implications for the determinations of the interaction regions from studies of Bose-Einstein correlations are discussed.

Plasma Levels of Matrix Metalloproteinase (MMP)-2, MMP-9 and Tumor Necrosis Factor-α in Chronic Hepatitis C Virus Patients.

PubMed

Abdel-Latif, Mohamed S

2015-01-01

In chronic HCV infection, pathological accumulation of the extracellular matrix is the main feature of liver fibrosis; that indicates the imbalanced rate of increased matrix synthesis to decreased breakdown of connective tissue proteins. Matrix metalloproteinases (MMPs) play a crucial role in remodeling of extracellular matrix. It is known that expression of MMPs is regulated by Tumor necrosis factor (TNF)-α. Also, levels of TNF-α in liver and serum are increased in chronic HCV patient. Accordingly, this study aimed to correlate the plasma levels of MMP-2, MMP-9 and TNF-α in chronic HCV patients with the pathogenesis of the liver. The current study was conducted on 15 fibrotic liver cases with detectable HCV RNA, 10 HCV cirrhotic liver cases, and 15 control subjects of matched age and sex. Plasma MMP-2, MMP-9 and TNF-α were measured by ELISA. Data revealed that the MMP2, MMP9 and TNF-α levels showed a significant elevation in chronic HCV patients compared to control group (p= 0.001). But, no significant correlation was observed in levels of MMP-2, MMP-9, and TNF-α between fibrotic and cirrhotic cases. MMP-2, MMP-9 and TNF-α showed high reproducibility to differentiate chronic HCV patients from control group. On the contrary, MMP-2, MMP-9 and TNF-α were not able to differentiate fibrotic from cirrhotic liver cases. Thus, MMP-2, MMP-9 and TNF-α could not be correlated with the progression of liver disease. Rather they could be used as prognostic markers of liver fibrosis.

Optoelectronic associative memory

NASA Technical Reports Server (NTRS)

Chao, Tien-Hsin (Inventor)

1993-01-01

An associative optical memory including an input spatial light modulator (SLM) in the form of an edge enhanced liquid crystal light valve (LCLV) and a pair of memory SLM's in the form of liquid crystal televisions (LCTV's) forms a matrix array of an input image which is cross correlated with a matrix array of stored images. The correlation product is detected and nonlinearly amplified to illuminate a replica of the stored image array to select the stored image correlating with the input image. The LCLV is edge enhanced by reducing the bias frequency and voltage and rotating its orientation. The edge enhancement and nonlinearity of the photodetection improves the orthogonality of the stored image. The illumination of the replicate stored image provides a clean stored image, uncontaminated by the image comparison process.

Simultaneous infrared and UV-visible absorption spectra of matrix-isolated carbon vapor

NASA Technical Reports Server (NTRS)

Kurtz, Joe; Huffman, Donald R.

1989-01-01

Carbon molecules were suggested as possible carriers of the diffuse interstellar bands. In particular, it was proposed that the 443 nm diffuse interstellar band is due to the same molecule which gives rise to the 447 nm absorption feature in argon matrix-isolated carbon vapor. If so, then an associated C-C stretching mode should be seen in the IR. By doing spectroscopy in both the IR and UV-visible regions on the same sample, the present work provides evidence for correlating UV-visible absorption features with those found in the IR. Early data indicates no correlation between the strongest IR feature (1997/cm) and the 447 nm band. Correlation with weaker IR features is being investigated.

A note on implementation of decaying product correlation structures for quasi-least squares.

PubMed

Shults, Justine; Guerra, Matthew W

2014-08-30

This note implements an unstructured decaying product matrix via the quasi-least squares approach for estimation of the correlation parameters in the framework of generalized estimating equations. The structure we consider is fairly general without requiring the large number of parameters that are involved in a fully unstructured matrix. It is straightforward to show that the quasi-least squares estimators of the correlation parameters yield feasible values for the unstructured decaying product structure. Furthermore, subject to conditions that are easily checked, the quasi-least squares estimators are valid for longitudinal Bernoulli data. We demonstrate implementation of the structure in a longitudinal clinical trial with both a continuous and binary outcome variable. Copyright © 2014 John Wiley & Sons, Ltd.

Rapid simulated gastric fluid digestion of in-seed/grain proteins expressed in genetically engineered crops.

PubMed

Schafer, Barry W; Embrey, Shawna K; Herman, Rod A

2016-11-01

The speed of simulated gastric digestion of proteins expressed in genetically engineered (GE) crops is commonly used to inform the allergenicity risk assessment. However, persistence of purified proteins in simulated gastric fluid (SGF) is poorly correlated with the allergenic status of proteins. It has been proposed that the plant or food matrix may affect the digestion of proteins and should be considered in interpreting digestion results. Here the SGF digestion of several GE proteins both as purified preparations and in soybean, corn, and cotton seed/grain extracts (in-matrix) are compared. Cry1F, Cry1Ac, phosphinothricin acetyltransferase (PAT), aryloxyalkanoate dioxygenase-1 (AAD-1), aryloxyalkanoate dioxygenase-12 (AAD-12), and double mutant 5-enol pyruvylshikimate-3-phosphate synthase (2mEPSPS) were all found to rapidly digest both as purified protein preparations and in seed/grain extracts from GE crops expressing these proteins. Based on these results, purified protein from microbial sources is a suitable surrogate for proteins in-matrix when conducting SGF digestion studies. Copyright © 2016 Elsevier Inc. All rights reserved.