High-Dimensional Intrinsic Interpolation Using Gaussian Process Regression and Diffusion Maps

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

Thimmisetty, Charanraj A.; Ghanem, Roger G.; White, Joshua A.

This article considers the challenging task of estimating geologic properties of interest using a suite of proxy measurements. The current work recast this task as a manifold learning problem. In this process, this article introduces a novel regression procedure for intrinsic variables constrained onto a manifold embedded in an ambient space. The procedure is meant to sharpen high-dimensional interpolation by inferring non-linear correlations from the data being interpolated. The proposed approach augments manifold learning procedures with a Gaussian process regression. It first identifies, using diffusion maps, a low-dimensional manifold embedded in an ambient high-dimensional space associated with the data. Itmore » relies on the diffusion distance associated with this construction to define a distance function with which the data model is equipped. This distance metric function is then used to compute the correlation structure of a Gaussian process that describes the statistical dependence of quantities of interest in the high-dimensional ambient space. The proposed method is applicable to arbitrarily high-dimensional data sets. Here, it is applied to subsurface characterization using a suite of well log measurements. The predictions obtained in original, principal component, and diffusion space are compared using both qualitative and quantitative metrics. Considerable improvement in the prediction of the geological structural properties is observed with the proposed method.« less

On one-dimensional stretching functions for finite-difference calculations. [computational fluid dynamics

NASA Technical Reports Server (NTRS)

Vinokur, M.

1979-01-01

The class of one-dimensional stretching functions used in finite-difference calculations is studied. For solutions containing a highly localized region of rapid variation, simple criteria for a stretching function are derived using a truncation error analysis. These criteria are used to investigate two types of stretching functions. One is an interior stretching function, for which the location and slope of an interior clustering region are specified. The simplest such function satisfying the criteria is found to be one based on the inverse hyperbolic sine. The other type of function is a two-sided stretching function, for which the arbitrary slopes at the two ends of the one-dimensional interval are specified. The simplest such general function is found to be one based on the inverse tangent.

Variance-based interaction index measuring heteroscedasticity

NASA Astrophysics Data System (ADS)

Ito, Keiichi; Couckuyt, Ivo; Poles, Silvia; Dhaene, Tom

2016-06-01

This work is motivated by the need to deal with models with high-dimensional input spaces of real variables. One way to tackle high-dimensional problems is to identify interaction or non-interaction among input parameters. We propose a new variance-based sensitivity interaction index that can detect and quantify interactions among the input variables of mathematical functions and computer simulations. The computation is very similar to first-order sensitivity indices by Sobol'. The proposed interaction index can quantify the relative importance of input variables in interaction. Furthermore, detection of non-interaction for screening can be done with as low as 4 n + 2 function evaluations, where n is the number of input variables. Using the interaction indices based on heteroscedasticity, the original function may be decomposed into a set of lower dimensional functions which may then be analyzed separately.

A Selective Overview of Variable Selection in High Dimensional Feature Space

PubMed Central

Fan, Jianqing

2010-01-01

High dimensional statistical problems arise from diverse fields of scientific research and technological development. Variable selection plays a pivotal role in contemporary statistical learning and scientific discoveries. The traditional idea of best subset selection methods, which can be regarded as a specific form of penalized likelihood, is computationally too expensive for many modern statistical applications. Other forms of penalized likelihood methods have been successfully developed over the last decade to cope with high dimensionality. They have been widely applied for simultaneously selecting important variables and estimating their effects in high dimensional statistical inference. In this article, we present a brief account of the recent developments of theory, methods, and implementations for high dimensional variable selection. What limits of the dimensionality such methods can handle, what the role of penalty functions is, and what the statistical properties are rapidly drive the advances of the field. The properties of non-concave penalized likelihood and its roles in high dimensional statistical modeling are emphasized. We also review some recent advances in ultra-high dimensional variable selection, with emphasis on independence screening and two-scale methods. PMID:21572976

Three-Dimensional Printing: A Journey in Visualization

ERIC Educational Resources Information Center

Poetzel, Adam; Muskin, Joseph; Munroe, Anne; Russell, Craig

2012-01-01

Imagine high school students glued to computer screens--not playing video games but applying their mathematical knowledge of functions to the design of three-dimensional sculptures. Imagine these students engaging in rich discourse as they transform functions of their choosing to design unique creations. Now, imagine these students using…

Photo-Attachment of Biomolecules for Miniaturization on Wicking Si-Nanowire Platform

PubMed Central

Cheng, He; Zheng, Han; Wu, Jia Xin; Xu, Wei; Zhou, Lihan; Leong, Kam Chew; Fitzgerald, Eugene; Rajagopalan, Raj; Too, Heng Phon; Choi, Wee Kiong

2015-01-01

We demonstrated the surface functionalization of a highly three-dimensional, superhydrophilic wicking substrate using light to immobilize functional biomolecules for sensor or microarray applications. We showed here that the three-dimensional substrate was compatible with photo-attachment and the performance of functionalization was greatly improved due to both increased surface capacity and reduced substrate reflectivity. In addition, photo-attachment circumvents the problems induced by wicking effect that was typically encountered on superhydrophilic three-dimensional substrates, thus reducing the difficulty of producing miniaturized sites on such substrate. We have investigated various aspects of photo-attachment process on the nanowire substrate, including the role of different buffers, the effect of wavelength as well as how changing probe structure may affect the functionalization process. We demonstrated that substrate fabrication and functionalization can be achieved with processes compatible with microelectronics processes, hence reducing the cost of array fabrication. Such functionalization method coupled with the high capacity surface makes the substrate an ideal candidate for sensor or microarray for sensitive detection of target analytes. PMID:25689680

An adaptive ANOVA-based PCKF for high-dimensional nonlinear inverse modeling

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

Li, Weixuan, E-mail: weixuan.li@usc.edu; Lin, Guang, E-mail: guang.lin@pnnl.gov; Zhang, Dongxiao, E-mail: dxz@pku.edu.cn

2014-02-01

The probabilistic collocation-based Kalman filter (PCKF) is a recently developed approach for solving inverse problems. It resembles the ensemble Kalman filter (EnKF) in every aspect—except that it represents and propagates model uncertainty by polynomial chaos expansion (PCE) instead of an ensemble of model realizations. Previous studies have shown PCKF is a more efficient alternative to EnKF for many data assimilation problems. However, the accuracy and efficiency of PCKF depends on an appropriate truncation of the PCE series. Having more polynomial chaos basis functions in the expansion helps to capture uncertainty more accurately but increases computational cost. Selection of basis functionsmore » is particularly important for high-dimensional stochastic problems because the number of polynomial chaos basis functions required to represent model uncertainty grows dramatically as the number of input parameters (random dimensions) increases. In classic PCKF algorithms, the PCE basis functions are pre-set based on users' experience. Also, for sequential data assimilation problems, the basis functions kept in PCE expression remain unchanged in different Kalman filter loops, which could limit the accuracy and computational efficiency of classic PCKF algorithms. To address this issue, we present a new algorithm that adaptively selects PCE basis functions for different problems and automatically adjusts the number of basis functions in different Kalman filter loops. The algorithm is based on adaptive functional ANOVA (analysis of variance) decomposition, which approximates a high-dimensional function with the summation of a set of low-dimensional functions. Thus, instead of expanding the original model into PCE, we implement the PCE expansion on these low-dimensional functions, which is much less costly. We also propose a new adaptive criterion for ANOVA that is more suited for solving inverse problems. The new algorithm was tested with different examples and demonstrated great effectiveness in comparison with non-adaptive PCKF and EnKF algorithms.« less

Manifold Learning by Preserving Distance Orders.

PubMed

Ataer-Cansizoglu, Esra; Akcakaya, Murat; Orhan, Umut; Erdogmus, Deniz

2014-03-01

Nonlinear dimensionality reduction is essential for the analysis and the interpretation of high dimensional data sets. In this manuscript, we propose a distance order preserving manifold learning algorithm that extends the basic mean-squared error cost function used mainly in multidimensional scaling (MDS)-based methods. We develop a constrained optimization problem by assuming explicit constraints on the order of distances in the low-dimensional space. In this optimization problem, as a generalization of MDS, instead of forcing a linear relationship between the distances in the high-dimensional original and low-dimensional projection space, we learn a non-decreasing relation approximated by radial basis functions. We compare the proposed method with existing manifold learning algorithms using synthetic datasets based on the commonly used residual variance and proposed percentage of violated distance orders metrics. We also perform experiments on a retinal image dataset used in Retinopathy of Prematurity (ROP) diagnosis.

Modified Cheeger and Ratio Cut Methods Using the Ginzburg-Landau Functional for Classification of High-Dimensional Data

DTIC Science & Technology

2016-02-01

Modified Cheeger and Ratio Cut Methods Using the Ginzburg-Landau Functional for Classification of High-Dimensional Data Ekaterina Merkurjev*, Andrea...bertozzi@math.ucla.edu, xiaoran@isi.edu, lerman@isi.edu. Abstract Recent advances in clustering have included continuous relaxations of the Cheeger cut ...fully nonlinear Cheeger cut problem, as well as the ratio cut optimization task. Both problems are connected to total variation minimization, and the

On One-Dimensional Stretching Functions for Finite-Difference Calculations

NASA Technical Reports Server (NTRS)

Vinokur, M.

1980-01-01

The class of one dimensional stretching function used in finite difference calculations is studied. For solutions containing a highly localized region of rapid variation, simple criteria for a stretching function are derived using a truncation error analysis. These criteria are used to investigate two types of stretching functions. One is an interior stretching function, for which the location and slope of an interior clustering region are specified. The simplest such function satisfying the criteria is found to be one based on the inverse hyperbolic sine. The other type of function is a two sided stretching function, for which the arbitrary slopes at the two ends of the one dimensional interval are specified. The simplest such general function is found to be one based on the inverse tangent. The general two sided function has many applications in the construction of finite difference grids.

On one-dimensional stretching functions for finite-difference calculations. [computational fluid dynamics

NASA Technical Reports Server (NTRS)

Vinokur, M.

1983-01-01

The class of one-dimensional stretching functions used in finite-difference calculations is studied. For solutions containing a highly localized region of rapid variation, simple criteria for a stretching function are derived using a truncation error analysis. These criteria are used to investigate two types of stretching functions. One an interior stretching function, for which the location and slope of an interior clustering region are specified. The simplest such function satisfying the criteria is found to be one based on the inverse hyperbolic sine. The other type of function is a two-sided stretching function, for which the arbitrary slopes at the two ends of the one-dimensional interval are specified. The simplest such general function is found to be one based on the inverse tangent. Previously announced in STAR as N80-25055

Non-Asymptotic Oracle Inequalities for the High-Dimensional Cox Regression via Lasso.

PubMed

Kong, Shengchun; Nan, Bin

2014-01-01

We consider finite sample properties of the regularized high-dimensional Cox regression via lasso. Existing literature focuses on linear models or generalized linear models with Lipschitz loss functions, where the empirical risk functions are the summations of independent and identically distributed (iid) losses. The summands in the negative log partial likelihood function for censored survival data, however, are neither iid nor Lipschitz.We first approximate the negative log partial likelihood function by a sum of iid non-Lipschitz terms, then derive the non-asymptotic oracle inequalities for the lasso penalized Cox regression using pointwise arguments to tackle the difficulties caused by lacking iid Lipschitz losses.

Non-Asymptotic Oracle Inequalities for the High-Dimensional Cox Regression via Lasso

PubMed Central

Kong, Shengchun; Nan, Bin

2013-01-01

We consider finite sample properties of the regularized high-dimensional Cox regression via lasso. Existing literature focuses on linear models or generalized linear models with Lipschitz loss functions, where the empirical risk functions are the summations of independent and identically distributed (iid) losses. The summands in the negative log partial likelihood function for censored survival data, however, are neither iid nor Lipschitz.We first approximate the negative log partial likelihood function by a sum of iid non-Lipschitz terms, then derive the non-asymptotic oracle inequalities for the lasso penalized Cox regression using pointwise arguments to tackle the difficulties caused by lacking iid Lipschitz losses. PMID:24516328

A Hyperspherical Adaptive Sparse-Grid Method for High-Dimensional Discontinuity Detection

DOE PAGES

Zhang, Guannan; Webster, Clayton G.; Gunzburger, Max D.; ...

2015-06-24

This study proposes and analyzes a hyperspherical adaptive hierarchical sparse-grid method for detecting jump discontinuities of functions in high-dimensional spaces. The method is motivated by the theoretical and computational inefficiencies of well-known adaptive sparse-grid methods for discontinuity detection. Our novel approach constructs a function representation of the discontinuity hypersurface of an N-dimensional discontinuous quantity of interest, by virtue of a hyperspherical transformation. Then, a sparse-grid approximation of the transformed function is built in the hyperspherical coordinate system, whose value at each point is estimated by solving a one-dimensional discontinuity detection problem. Due to the smoothness of the hypersurface, the newmore » technique can identify jump discontinuities with significantly reduced computational cost, compared to existing methods. In addition, hierarchical acceleration techniques are also incorporated to further reduce the overall complexity. Rigorous complexity analyses of the new method are provided as are several numerical examples that illustrate the effectiveness of the approach.« less

A hyper-spherical adaptive sparse-grid method for high-dimensional discontinuity detection

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

Zhang, Guannan; Webster, Clayton G.; Gunzburger, Max D.

This work proposes and analyzes a hyper-spherical adaptive hierarchical sparse-grid method for detecting jump discontinuities of functions in high-dimensional spaces is proposed. The method is motivated by the theoretical and computational inefficiencies of well-known adaptive sparse-grid methods for discontinuity detection. Our novel approach constructs a function representation of the discontinuity hyper-surface of an N-dimensional dis- continuous quantity of interest, by virtue of a hyper-spherical transformation. Then, a sparse-grid approximation of the transformed function is built in the hyper-spherical coordinate system, whose value at each point is estimated by solving a one-dimensional discontinuity detection problem. Due to the smoothness of themore » hyper-surface, the new technique can identify jump discontinuities with significantly reduced computational cost, compared to existing methods. Moreover, hierarchical acceleration techniques are also incorporated to further reduce the overall complexity. Rigorous error estimates and complexity analyses of the new method are provided as are several numerical examples that illustrate the effectiveness of the approach.« less

Low-rank canonical-tensor decomposition of potential energy surfaces: application to grid-based diagrammatic vibrational Green's function theory

NASA Astrophysics Data System (ADS)

Rai, Prashant; Sargsyan, Khachik; Najm, Habib; Hermes, Matthew R.; Hirata, So

2017-09-01

A new method is proposed for a fast evaluation of high-dimensional integrals of potential energy surfaces (PES) that arise in many areas of quantum dynamics. It decomposes a PES into a canonical low-rank tensor format, reducing its integral into a relatively short sum of products of low-dimensional integrals. The decomposition is achieved by the alternating least squares (ALS) algorithm, requiring only a small number of single-point energy evaluations. Therefore, it eradicates a force-constant evaluation as the hotspot of many quantum dynamics simulations and also possibly lifts the curse of dimensionality. This general method is applied to the anharmonic vibrational zero-point and transition energy calculations of molecules using the second-order diagrammatic vibrational many-body Green's function (XVH2) theory with a harmonic-approximation reference. In this application, high dimensional PES and Green's functions are both subjected to a low-rank decomposition. Evaluating the molecular integrals over a low-rank PES and Green's functions as sums of low-dimensional integrals using the Gauss-Hermite quadrature, this canonical-tensor-decomposition-based XVH2 (CT-XVH2) achieves an accuracy of 0.1 cm-1 or higher and nearly an order of magnitude speedup as compared with the original algorithm using force constants for water and formaldehyde.

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.

New functionalities of potassium tantalate niobate deflectors enabled by the coexistence of pre-injected space charge and composition gradient

NASA Astrophysics Data System (ADS)

Zhu, Wenbin; Chao, Ju-Hung; Chen, Chang-Jiang; Campbell, Adrian L.; Henry, Michael G.; Yin, Stuart Shizhuo; Hoffman, Robert C.

2017-10-01

In most beam steering applications such as 3D printing and in vivo imaging, one of the essential challenges has been high-resolution high-speed multi-dimensional optical beam scanning. Although the pre-injected space charge controlled potassium tantalate niobate (KTN) deflectors can achieve speeds in the nanosecond regime, they deflect in only one dimension. In order to develop a high-resolution high-speed multi-dimensional KTN deflector, we studied the deflection behavior of KTN deflectors in the case of coexisting pre-injected space charge and composition gradient. We find that such coexistence can enable new functionalities of KTN crystal based electro-optic deflectors. When the direction of the composition gradient is parallel to the direction of the external electric field, the zero-deflection position can be shifted, which can reduce the internal electric field induced beam distortion, and thus enhance the resolution. When the direction of the composition gradient is perpendicular to the direction of the external electric field, two-dimensional beam scanning can be achieved by harnessing only one single piece of KTN crystal, which can result in a compact, high-speed two-dimensional deflector. Both theoretical analyses and experiments are conducted, which are consistent with each other. These new functionalities can expedite the usage of KTN deflection in many applications such as high-speed 3D printing, high-speed, high-resolution imaging, and free space broadband optical communication.

Two-dimensional mesh embedding for Galerkin B-spline methods

NASA Technical Reports Server (NTRS)

Shariff, Karim; Moser, Robert D.

1995-01-01

A number of advantages result from using B-splines as basis functions in a Galerkin method for solving partial differential equations. Among them are arbitrary order of accuracy and high resolution similar to that of compact schemes but without the aliasing error. This work develops another property, namely, the ability to treat semi-structured embedded or zonal meshes for two-dimensional geometries. This can drastically reduce the number of grid points in many applications. Both integer and non-integer refinement ratios are allowed. The report begins by developing an algorithm for choosing basis functions that yield the desired mesh resolution. These functions are suitable products of one-dimensional B-splines. Finally, test cases for linear scalar equations such as the Poisson and advection equation are presented. The scheme is conservative and has uniformly high order of accuracy throughout the domain.

Certifying an Irreducible 1024-Dimensional Photonic State Using Refined Dimension Witnesses.

PubMed

Aguilar, Edgar A; Farkas, Máté; Martínez, Daniel; Alvarado, Matías; Cariñe, Jaime; Xavier, Guilherme B; Barra, Johanna F; Cañas, Gustavo; Pawłowski, Marcin; Lima, Gustavo

2018-06-08

We report on a new class of dimension witnesses, based on quantum random access codes, which are a function of the recorded statistics and that have different bounds for all possible decompositions of a high-dimensional physical system. Thus, it certifies the dimension of the system and has the new distinct feature of identifying whether the high-dimensional system is decomposable in terms of lower dimensional subsystems. To demonstrate the practicability of this technique, we used it to experimentally certify the generation of an irreducible 1024-dimensional photonic quantum state. Therefore, certifying that the state is not multipartite or encoded using noncoupled different degrees of freedom of a single photon. Our protocol should find applications in a broad class of modern quantum information experiments addressing the generation of high-dimensional quantum systems, where quantum tomography may become intractable.

Certifying an Irreducible 1024-Dimensional Photonic State Using Refined Dimension Witnesses

NASA Astrophysics Data System (ADS)

Aguilar, Edgar A.; Farkas, Máté; Martínez, Daniel; Alvarado, Matías; Cariñe, Jaime; Xavier, Guilherme B.; Barra, Johanna F.; Cañas, Gustavo; Pawłowski, Marcin; Lima, Gustavo

2018-06-01

We report on a new class of dimension witnesses, based on quantum random access codes, which are a function of the recorded statistics and that have different bounds for all possible decompositions of a high-dimensional physical system. Thus, it certifies the dimension of the system and has the new distinct feature of identifying whether the high-dimensional system is decomposable in terms of lower dimensional subsystems. To demonstrate the practicability of this technique, we used it to experimentally certify the generation of an irreducible 1024-dimensional photonic quantum state. Therefore, certifying that the state is not multipartite or encoded using noncoupled different degrees of freedom of a single photon. Our protocol should find applications in a broad class of modern quantum information experiments addressing the generation of high-dimensional quantum systems, where quantum tomography may become intractable.

Approximating high-dimensional dynamics by barycentric coordinates with linear programming

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

Hirata, Yoshito, E-mail: yoshito@sat.t.u-tokyo.ac.jp; Aihara, Kazuyuki; Suzuki, Hideyuki

The increasing development of novel methods and techniques facilitates the measurement of high-dimensional time series but challenges our ability for accurate modeling and predictions. The use of a general mathematical model requires the inclusion of many parameters, which are difficult to be fitted for relatively short high-dimensional time series observed. Here, we propose a novel method to accurately model a high-dimensional time series. Our method extends the barycentric coordinates to high-dimensional phase space by employing linear programming, and allowing the approximation errors explicitly. The extension helps to produce free-running time-series predictions that preserve typical topological, dynamical, and/or geometric characteristics ofmore » the underlying attractors more accurately than the radial basis function model that is widely used. The method can be broadly applied, from helping to improve weather forecasting, to creating electronic instruments that sound more natural, and to comprehensively understanding complex biological data.« less

Approximating high-dimensional dynamics by barycentric coordinates with linear programming.

PubMed

Hirata, Yoshito; Shiro, Masanori; Takahashi, Nozomu; Aihara, Kazuyuki; Suzuki, Hideyuki; Mas, Paloma

2015-01-01

The increasing development of novel methods and techniques facilitates the measurement of high-dimensional time series but challenges our ability for accurate modeling and predictions. The use of a general mathematical model requires the inclusion of many parameters, which are difficult to be fitted for relatively short high-dimensional time series observed. Here, we propose a novel method to accurately model a high-dimensional time series. Our method extends the barycentric coordinates to high-dimensional phase space by employing linear programming, and allowing the approximation errors explicitly. The extension helps to produce free-running time-series predictions that preserve typical topological, dynamical, and/or geometric characteristics of the underlying attractors more accurately than the radial basis function model that is widely used. The method can be broadly applied, from helping to improve weather forecasting, to creating electronic instruments that sound more natural, and to comprehensively understanding complex biological data.

High dimensional model representation method for fuzzy structural dynamics

NASA Astrophysics Data System (ADS)

Adhikari, S.; Chowdhury, R.; Friswell, M. I.

2011-03-01

Uncertainty propagation in multi-parameter complex structures possess significant computational challenges. This paper investigates the possibility of using the High Dimensional Model Representation (HDMR) approach when uncertain system parameters are modeled using fuzzy variables. In particular, the application of HDMR is proposed for fuzzy finite element analysis of linear dynamical systems. The HDMR expansion is an efficient formulation for high-dimensional mapping in complex systems if the higher order variable correlations are weak, thereby permitting the input-output relationship behavior to be captured by the terms of low-order. The computational effort to determine the expansion functions using the α-cut method scales polynomically with the number of variables rather than exponentially. This logic is based on the fundamental assumption underlying the HDMR representation that only low-order correlations among the input variables are likely to have significant impacts upon the outputs for most high-dimensional complex systems. The proposed method is first illustrated for multi-parameter nonlinear mathematical test functions with fuzzy variables. The method is then integrated with a commercial finite element software (ADINA). Modal analysis of a simplified aircraft wing with fuzzy parameters has been used to illustrate the generality of the proposed approach. In the numerical examples, triangular membership functions have been used and the results have been validated against direct Monte Carlo simulations. It is shown that using the proposed HDMR approach, the number of finite element function calls can be reduced without significantly compromising the accuracy.

Low-rank canonical-tensor decomposition of potential energy surfaces: application to grid-based diagrammatic vibrational Green's function theory

DOE PAGES

Rai, Prashant; Sargsyan, Khachik; Najm, Habib; ...

2017-03-07

Here, a new method is proposed for a fast evaluation of high-dimensional integrals of potential energy surfaces (PES) that arise in many areas of quantum dynamics. It decomposes a PES into a canonical low-rank tensor format, reducing its integral into a relatively short sum of products of low-dimensional integrals. The decomposition is achieved by the alternating least squares (ALS) algorithm, requiring only a small number of single-point energy evaluations. Therefore, it eradicates a force-constant evaluation as the hotspot of many quantum dynamics simulations and also possibly lifts the curse of dimensionality. This general method is applied to the anharmonic vibrationalmore » zero-point and transition energy calculations of molecules using the second-order diagrammatic vibrational many-body Green's function (XVH2) theory with a harmonic-approximation reference. In this application, high dimensional PES and Green's functions are both subjected to a low-rank decomposition. Evaluating the molecular integrals over a low-rank PES and Green's functions as sums of low-dimensional integrals using the Gauss–Hermite quadrature, this canonical-tensor-decomposition-based XVH2 (CT-XVH2) achieves an accuracy of 0.1 cm -1 or higher and nearly an order of magnitude speedup as compared with the original algorithm using force constants for water and formaldehyde.« less

Low-rank canonical-tensor decomposition of potential energy surfaces: application to grid-based diagrammatic vibrational Green's function theory

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

Rai, Prashant; Sargsyan, Khachik; Najm, Habib

Here, a new method is proposed for a fast evaluation of high-dimensional integrals of potential energy surfaces (PES) that arise in many areas of quantum dynamics. It decomposes a PES into a canonical low-rank tensor format, reducing its integral into a relatively short sum of products of low-dimensional integrals. The decomposition is achieved by the alternating least squares (ALS) algorithm, requiring only a small number of single-point energy evaluations. Therefore, it eradicates a force-constant evaluation as the hotspot of many quantum dynamics simulations and also possibly lifts the curse of dimensionality. This general method is applied to the anharmonic vibrationalmore » zero-point and transition energy calculations of molecules using the second-order diagrammatic vibrational many-body Green's function (XVH2) theory with a harmonic-approximation reference. In this application, high dimensional PES and Green's functions are both subjected to a low-rank decomposition. Evaluating the molecular integrals over a low-rank PES and Green's functions as sums of low-dimensional integrals using the Gauss–Hermite quadrature, this canonical-tensor-decomposition-based XVH2 (CT-XVH2) achieves an accuracy of 0.1 cm -1 or higher and nearly an order of magnitude speedup as compared with the original algorithm using force constants for water and formaldehyde.« less

High resolution multidetector CT aided tissue analysis and quantification of lung fibrosis

NASA Astrophysics Data System (ADS)

Zavaletta, Vanessa A.; Karwoski, Ronald A.; Bartholmai, Brian; Robb, Richard A.

2006-03-01

Idiopathic pulmonary fibrosis (IPF, also known as Idiopathic Usual Interstitial Pneumontis, pathologically) is a progressive diffuse lung disease which has a median survival rate of less than four years with a prevalence of 15-20/100,000 in the United States. Global function changes are measured by pulmonary function tests and the diagnosis and extent of pulmonary structural changes are typically assessed by acquiring two-dimensional high resolution CT (HRCT) images. The acquisition and analysis of volumetric high resolution Multi-Detector CT (MDCT) images with nearly isotropic pixels offers the potential to measure both lung function and structure. This paper presents a new approach to three dimensional lung image analysis and classification of normal and abnormal structures in lungs with IPF.

Quantile Regression for Analyzing Heterogeneity in Ultra-high Dimension

PubMed Central

Wang, Lan; Wu, Yichao

2012-01-01

Ultra-high dimensional data often display heterogeneity due to either heteroscedastic variance or other forms of non-location-scale covariate effects. To accommodate heterogeneity, we advocate a more general interpretation of sparsity which assumes that only a small number of covariates influence the conditional distribution of the response variable given all candidate covariates; however, the sets of relevant covariates may differ when we consider different segments of the conditional distribution. In this framework, we investigate the methodology and theory of nonconvex penalized quantile regression in ultra-high dimension. The proposed approach has two distinctive features: (1) it enables us to explore the entire conditional distribution of the response variable given the ultra-high dimensional covariates and provides a more realistic picture of the sparsity pattern; (2) it requires substantially weaker conditions compared with alternative methods in the literature; thus, it greatly alleviates the difficulty of model checking in the ultra-high dimension. In theoretic development, it is challenging to deal with both the nonsmooth loss function and the nonconvex penalty function in ultra-high dimensional parameter space. We introduce a novel sufficient optimality condition which relies on a convex differencing representation of the penalized loss function and the subdifferential calculus. Exploring this optimality condition enables us to establish the oracle property for sparse quantile regression in the ultra-high dimension under relaxed conditions. The proposed method greatly enhances existing tools for ultra-high dimensional data analysis. Monte Carlo simulations demonstrate the usefulness of the proposed procedure. The real data example we analyzed demonstrates that the new approach reveals substantially more information compared with alternative methods. PMID:23082036

Group-theoretical analysis of two-dimensional hexagonal materials

NASA Astrophysics Data System (ADS)

Minami, Susumu; Sugita, Itaru; Tomita, Ryosuke; Oshima, Hiroyuki; Saito, Mineo

2017-10-01

Two-dimensional hexagonal materials such as graphene and silicene have highly symmetric crystal structures and Dirac cones at the K point, which induce novel electronic properties. In this report, we calculate their electronic structures by using density functional theory and analyze their band structures on the basis of the group theory. Dirac cones frequently appear when the symmetry at the K point is high; thus, two-dimensional irreducible representations are included. We discuss the relationship between symmetry and the appearance of the Dirac cone.

Functionalized sorbent for chemical separations and sequential forming process

DOEpatents

Fryxell, Glen E [Kennewick, WA; Zemanian, Thomas S [Richland, WA

2012-03-20

A highly functionalized sorbent and sequential process for making are disclosed. The sorbent includes organic short-length amino silanes and organic oligomeric polyfunctional amino silanes that are dispersed within pores of a porous support that form a 3-dimensional structure containing highly functionalized active binding sites for sorption of analytes.

Energy conservation of the scattering from one-dimensional random rough surfaces in the high-frequency limit.

PubMed

Pinel, Nicolas; Bourlier, Christophe; Saillard, Joseph

2005-08-01

Energy conservation of the scattering from one-dimensional strongly rough dielectric surfaces is investigated using the Kirchhoff approximation with single reflection and by taking the shadowing phenomenon into account, both in reflection and transmission. In addition, because no shadowing function in transmission exists in the literature, this function is presented here in detail. The model is reduced to the high-frequency limit (or geometric optics). The energy conservation criterion is investigated versus the incidence angle, the permittivity of the lower medium, and the surface rms slope.

Additivity Principle in High-Dimensional Deterministic Systems

NASA Astrophysics Data System (ADS)

Saito, Keiji; Dhar, Abhishek

2011-12-01

The additivity principle (AP), conjectured by Bodineau and Derrida [Phys. Rev. Lett. 92, 180601 (2004)PRLTAO0031-900710.1103/PhysRevLett.92.180601], is discussed for the case of heat conduction in three-dimensional disordered harmonic lattices to consider the effects of deterministic dynamics, higher dimensionality, and different transport regimes, i.e., ballistic, diffusive, and anomalous transport. The cumulant generating function (CGF) for heat transfer is accurately calculated and compared with the one given by the AP. In the diffusive regime, we find a clear agreement with the conjecture even if the system is high dimensional. Surprisingly, even in the anomalous regime the CGF is also well fitted by the AP. Lower-dimensional systems are also studied and the importance of three dimensionality for the validity is stressed.

The Fisher-Markov selector: fast selecting maximally separable feature subset for multiclass classification with applications to high-dimensional data.

PubMed

Cheng, Qiang; Zhou, Hongbo; Cheng, Jie

2011-06-01

Selecting features for multiclass classification is a critically important task for pattern recognition and machine learning applications. Especially challenging is selecting an optimal subset of features from high-dimensional data, which typically have many more variables than observations and contain significant noise, missing components, or outliers. Existing methods either cannot handle high-dimensional data efficiently or scalably, or can only obtain local optimum instead of global optimum. Toward the selection of the globally optimal subset of features efficiently, we introduce a new selector--which we call the Fisher-Markov selector--to identify those features that are the most useful in describing essential differences among the possible groups. In particular, in this paper we present a way to represent essential discriminating characteristics together with the sparsity as an optimization objective. With properly identified measures for the sparseness and discriminativeness in possibly high-dimensional settings, we take a systematic approach for optimizing the measures to choose the best feature subset. We use Markov random field optimization techniques to solve the formulated objective functions for simultaneous feature selection. Our results are noncombinatorial, and they can achieve the exact global optimum of the objective function for some special kernels. The method is fast; in particular, it can be linear in the number of features and quadratic in the number of observations. We apply our procedure to a variety of real-world data, including mid--dimensional optical handwritten digit data set and high-dimensional microarray gene expression data sets. The effectiveness of our method is confirmed by experimental results. In pattern recognition and from a model selection viewpoint, our procedure says that it is possible to select the most discriminating subset of variables by solving a very simple unconstrained objective function which in fact can be obtained with an explicit expression.

Accelerated High-Dimensional MR Imaging with Sparse Sampling Using Low-Rank Tensors

PubMed Central

He, Jingfei; Liu, Qiegen; Christodoulou, Anthony G.; Ma, Chao; Lam, Fan

2017-01-01

High-dimensional MR imaging often requires long data acquisition time, thereby limiting its practical applications. This paper presents a low-rank tensor based method for accelerated high-dimensional MR imaging using sparse sampling. This method represents high-dimensional images as low-rank tensors (or partially separable functions) and uses this mathematical structure for sparse sampling of the data space and for image reconstruction from highly undersampled data. More specifically, the proposed method acquires two datasets with complementary sampling patterns, one for subspace estimation and the other for image reconstruction; image reconstruction from highly undersampled data is accomplished by fitting the measured data with a sparsity constraint on the core tensor and a group sparsity constraint on the spatial coefficients jointly using the alternating direction method of multipliers. The usefulness of the proposed method is demonstrated in MRI applications; it may also have applications beyond MRI. PMID:27093543

Arbitrarily high-order time-stepping schemes based on the operator spectrum theory for high-dimensional nonlinear Klein-Gordon equations

NASA Astrophysics Data System (ADS)

Liu, Changying; Wu, Xinyuan

2017-07-01

In this paper we explore arbitrarily high-order Lagrange collocation-type time-stepping schemes for effectively solving high-dimensional nonlinear Klein-Gordon equations with different boundary conditions. We begin with one-dimensional periodic boundary problems and first formulate an abstract ordinary differential equation (ODE) on a suitable infinity-dimensional function space based on the operator spectrum theory. We then introduce an operator-variation-of-constants formula which is essential for the derivation of our arbitrarily high-order Lagrange collocation-type time-stepping schemes for the nonlinear abstract ODE. The nonlinear stability and convergence are rigorously analysed once the spatial differential operator is approximated by an appropriate positive semi-definite matrix under some suitable smoothness assumptions. With regard to the two dimensional Dirichlet or Neumann boundary problems, our new time-stepping schemes coupled with discrete Fast Sine / Cosine Transformation can be applied to simulate the two-dimensional nonlinear Klein-Gordon equations effectively. All essential features of the methodology are present in one-dimensional and two-dimensional cases, although the schemes to be analysed lend themselves with equal to higher-dimensional case. The numerical simulation is implemented and the numerical results clearly demonstrate the advantage and effectiveness of our new schemes in comparison with the existing numerical methods for solving nonlinear Klein-Gordon equations in the literature.

DD-HDS: A method for visualization and exploration of high-dimensional data.

PubMed

Lespinats, Sylvain; Verleysen, Michel; Giron, Alain; Fertil, Bernard

2007-09-01

Mapping high-dimensional data in a low-dimensional space, for example, for visualization, is a problem of increasingly major concern in data analysis. This paper presents data-driven high-dimensional scaling (DD-HDS), a nonlinear mapping method that follows the line of multidimensional scaling (MDS) approach, based on the preservation of distances between pairs of data. It improves the performance of existing competitors with respect to the representation of high-dimensional data, in two ways. It introduces (1) a specific weighting of distances between data taking into account the concentration of measure phenomenon and (2) a symmetric handling of short distances in the original and output spaces, avoiding false neighbor representations while still allowing some necessary tears in the original distribution. More precisely, the weighting is set according to the effective distribution of distances in the data set, with the exception of a single user-defined parameter setting the tradeoff between local neighborhood preservation and global mapping. The optimization of the stress criterion designed for the mapping is realized by "force-directed placement" (FDP). The mappings of low- and high-dimensional data sets are presented as illustrations of the features and advantages of the proposed algorithm. The weighting function specific to high-dimensional data and the symmetric handling of short distances can be easily incorporated in most distance preservation-based nonlinear dimensionality reduction methods.

Two-Dimensional Covalent Organic Frameworks for Carbon Dioxide Capture through Channel-Wall Functionalization

PubMed Central

Huang, Ning; Chen, Xiong; Krishna, Rajamani; Jiang, Donglin

2015-01-01

Ordered open channels found in two-dimensional covalent organic frameworks (2D COFs) could enable them to adsorb carbon dioxide. However, the frameworks’ dense layer architecture results in low porosity that has thus far restricted their potential for carbon dioxide adsorption. Here we report a strategy for converting a conventional 2D COF into an outstanding platform for carbon dioxide capture through channel-wall functionalization. The dense layer structure enables the dense integration of functional groups on the channel walls, creating a new version of COFs with high capacity, reusability, selectivity, and separation productivity for flue gas. These results suggest that channel-wall functional engineering could be a facile and powerful strategy to develop 2D COFs for high-performance gas storage and separation. PMID:25613010

Hyperspherical Sparse Approximation Techniques for High-Dimensional Discontinuity Detection

DOE PAGES

Zhang, Guannan; Webster, Clayton G.; Gunzburger, Max; ...

2016-08-04

This work proposes a hyperspherical sparse approximation framework for detecting jump discontinuities in functions in high-dimensional spaces. The need for a novel approach results from the theoretical and computational inefficiencies of well-known approaches, such as adaptive sparse grids, for discontinuity detection. Our approach constructs the hyperspherical coordinate representation of the discontinuity surface of a function. Then sparse approximations of the transformed function are built in the hyperspherical coordinate system, with values at each point estimated by solving a one-dimensional discontinuity detection problem. Due to the smoothness of the hypersurface, the new technique can identify jump discontinuities with significantly reduced computationalmore » cost, compared to existing methods. Several approaches are used to approximate the transformed discontinuity surface in the hyperspherical system, including adaptive sparse grid and radial basis function interpolation, discrete least squares projection, and compressed sensing approximation. Moreover, hierarchical acceleration techniques are also incorporated to further reduce the overall complexity. In conclusion, rigorous complexity analyses of the new methods are provided, as are several numerical examples that illustrate the effectiveness of our approach.« less

Computing and visualizing time-varying merge trees for high-dimensional data

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

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

2017-06-03

We introduce a new method that identifies and tracks features in arbitrary dimensions using the merge tree -- a structure for identifying topological features based on thresholding in scalar fields. This method analyzes the evolution of features of the function by tracking changes in the merge tree and relates features by matching subtrees between consecutive time steps. Using the time-varying merge tree, we present a structural visualization of the changing function that illustrates both features and their temporal evolution. We demonstrate the utility of our approach by applying it to temporal cluster analysis of high-dimensional point clouds.

Cross-entropy embedding of high-dimensional data using the neural gas model.

PubMed

Estévez, Pablo A; Figueroa, Cristián J; Saito, Kazumi

2005-01-01

A cross-entropy approach to mapping high-dimensional data into a low-dimensional space embedding is presented. The method allows to project simultaneously the input data and the codebook vectors, obtained with the Neural Gas (NG) quantizer algorithm, into a low-dimensional output space. The aim of this approach is to preserve the relationship defined by the NG neighborhood function for each pair of input and codebook vectors. A cost function based on the cross-entropy between input and output probabilities is minimized by using a Newton-Raphson method. The new approach is compared with Sammon's non-linear mapping (NLM) and the hierarchical approach of combining a vector quantizer such as the self-organizing feature map (SOM) or NG with the NLM recall algorithm. In comparison with these techniques, our method delivers a clear visualization of both data points and codebooks, and it achieves a better mapping quality in terms of the topology preservation measure q(m).

Functional inks and printing of two-dimensional materials.

PubMed

Hu, Guohua; Kang, Joohoon; Ng, Leonard W T; Zhu, Xiaoxi; Howe, Richard C T; Jones, Christopher G; Hersam, Mark C; Hasan, Tawfique

2018-05-08

Graphene and related two-dimensional materials provide an ideal platform for next generation disruptive technologies and applications. Exploiting these solution-processed two-dimensional materials in printing can accelerate this development by allowing additive patterning on both rigid and conformable substrates for flexible device design and large-scale, high-speed, cost-effective manufacturing. In this review, we summarise the current progress on ink formulation of two-dimensional materials and the printable applications enabled by them. We also present our perspectives on their research and technological future prospects.

Spectral feature design in high dimensional multispectral data

NASA Technical Reports Server (NTRS)

Chen, Chih-Chien Thomas; Landgrebe, David A.

1988-01-01

The High resolution Imaging Spectrometer (HIRIS) is designed to acquire images simultaneously in 192 spectral bands in the 0.4 to 2.5 micrometers wavelength region. It will make possible the collection of essentially continuous reflectance spectra at a spectral resolution sufficient to extract significantly enhanced amounts of information from return signals as compared to existing systems. The advantages of such high dimensional data come at a cost of increased system and data complexity. For example, since the finer the spectral resolution, the higher the data rate, it becomes impractical to design the sensor to be operated continuously. It is essential to find new ways to preprocess the data which reduce the data rate while at the same time maintaining the information content of the high dimensional signal produced. Four spectral feature design techniques are developed from the Weighted Karhunen-Loeve Transforms: (1) non-overlapping band feature selection algorithm; (2) overlapping band feature selection algorithm; (3) Walsh function approach; and (4) infinite clipped optimal function approach. The infinite clipped optimal function approach is chosen since the features are easiest to find and their classification performance is the best. After the preprocessed data has been received at the ground station, canonical analysis is further used to find the best set of features under the criterion that maximal class separability is achieved. Both 100 dimensional vegetation data and 200 dimensional soil data were used to test the spectral feature design system. It was shown that the infinite clipped versions of the first 16 optimal features had excellent classification performance. The overall probability of correct classification is over 90 percent while providing for a reduced downlink data rate by a factor of 10.

Very high order discontinuous Galerkin method in elliptic problems

NASA Astrophysics Data System (ADS)

Jaśkowiec, Jan

2017-09-01

The paper deals with high-order discontinuous Galerkin (DG) method with the approximation order that exceeds 20 and reaches 100 and even 1000 with respect to one-dimensional case. To achieve such a high order solution, the DG method with finite difference method has to be applied. The basis functions of this method are high-order orthogonal Legendre or Chebyshev polynomials. These polynomials are defined in one-dimensional space (1D), but they can be easily adapted to two-dimensional space (2D) by cross products. There are no nodes in the elements and the degrees of freedom are coefficients of linear combination of basis functions. In this sort of analysis the reference elements are needed, so the transformations of the reference element into the real one are needed as well as the transformations connected with the mesh skeleton. Due to orthogonality of the basis functions, the obtained matrices are sparse even for finite elements with more than thousands degrees of freedom. In consequence, the truncation errors are limited and very high-order analysis can be performed. The paper is illustrated with a set of benchmark examples of 1D and 2D for the elliptic problems. The example presents the great effectiveness of the method that can shorten the length of calculation over hundreds times.

Very high order discontinuous Galerkin method in elliptic problems

NASA Astrophysics Data System (ADS)

Jaśkowiec, Jan

2018-07-01

The paper deals with high-order discontinuous Galerkin (DG) method with the approximation order that exceeds 20 and reaches 100 and even 1000 with respect to one-dimensional case. To achieve such a high order solution, the DG method with finite difference method has to be applied. The basis functions of this method are high-order orthogonal Legendre or Chebyshev polynomials. These polynomials are defined in one-dimensional space (1D), but they can be easily adapted to two-dimensional space (2D) by cross products. There are no nodes in the elements and the degrees of freedom are coefficients of linear combination of basis functions. In this sort of analysis the reference elements are needed, so the transformations of the reference element into the real one are needed as well as the transformations connected with the mesh skeleton. Due to orthogonality of the basis functions, the obtained matrices are sparse even for finite elements with more than thousands degrees of freedom. In consequence, the truncation errors are limited and very high-order analysis can be performed. The paper is illustrated with a set of benchmark examples of 1D and 2D for the elliptic problems. The example presents the great effectiveness of the method that can shorten the length of calculation over hundreds times.

Resting state fMRI reveals a default mode dissociation between retrosplenial and medial prefrontal subnetworks in ASD despite motion scrubbing.

PubMed

Starck, Tuomo; Nikkinen, Juha; Rahko, Jukka; Remes, Jukka; Hurtig, Tuula; Haapsamo, Helena; Jussila, Katja; Kuusikko-Gauffin, Sanna; Mattila, Marja-Leena; Jansson-Verkasalo, Eira; Pauls, David L; Ebeling, Hanna; Moilanen, Irma; Tervonen, Osmo; Kiviniemi, Vesa J

2013-01-01

In resting state functional magnetic resonance imaging (fMRI) studies of autism spectrum disorders (ASDs) decreased frontal-posterior functional connectivity is a persistent finding. However, the picture of the default mode network (DMN) hypoconnectivity remains incomplete. In addition, the functional connectivity analyses have been shown to be susceptible even to subtle motion. DMN hypoconnectivity in ASD has been specifically called for re-evaluation with stringent motion correction, which we aimed to conduct by so-called scrubbing. A rich set of default mode subnetworks can be obtained with high dimensional group independent component analysis (ICA) which can potentially provide more detailed view of the connectivity alterations. We compared the DMN connectivity in high-functioning adolescents with ASDs to typically developing controls using ICA dual-regression with decompositions from typical to high dimensionality. Dual-regression analysis within DMN subnetworks did not reveal alterations but connectivity between anterior and posterior DMN subnetworks was decreased in ASD. The results were very similar with and without motion scrubbing thus indicating the efficacy of the conventional motion correction methods combined with ICA dual-regression. Specific dissociation between DMN subnetworks was revealed on high ICA dimensionality, where networks centered at the medial prefrontal cortex and retrosplenial cortex showed weakened coupling in adolescents with ASDs compared to typically developing control participants. Generally the results speak for disruption in the anterior-posterior DMN interplay on the network level whereas local functional connectivity in DMN seems relatively unaltered.

Regularized Embedded Multiple Kernel Dimensionality Reduction for Mine Signal Processing.

PubMed

Li, Shuang; Liu, Bing; Zhang, Chen

2016-01-01

Traditional multiple kernel dimensionality reduction models are generally based on graph embedding and manifold assumption. But such assumption might be invalid for some high-dimensional or sparse data due to the curse of dimensionality, which has a negative influence on the performance of multiple kernel learning. In addition, some models might be ill-posed if the rank of matrices in their objective functions was not high enough. To address these issues, we extend the traditional graph embedding framework and propose a novel regularized embedded multiple kernel dimensionality reduction method. Different from the conventional convex relaxation technique, the proposed algorithm directly takes advantage of a binary search and an alternative optimization scheme to obtain optimal solutions efficiently. The experimental results demonstrate the effectiveness of the proposed method for supervised, unsupervised, and semisupervised scenarios.

The assessment of function. Part II: clinical perspective of a javelin thrower with low back and groin pain

PubMed Central

Reiman, Michael P; Manske, Robert C

2012-01-01

Assessment of an individual’s functional ability can be complex. This assessment should also be individualized and adaptable to changes in functional status. In the first article of this series, we operationally defined function, discussed the construct of function, examined the evidence as it relates to assessment methods of various aspects of function, and explored the multi-dimensional nature of the concept of function. In this case report, we aim to demonstrate the utilization of a multi-dimensional assessment method (functional performance testing) as it relates to a high-level athlete presenting with pain in the low back and groin. It is our intent to demonstrate how the clinician should continually adapt their assessment dependent on the current functional abilities of the patients. PMID:23633887

Interdimensional effects in systems with quasirelativistic fermions

NASA Astrophysics Data System (ADS)

Zulkoskey, A. C.; Dick, R.; Tanaka, K.

2017-07-01

We examine the Green function and the density of states for fermions moving in three-dimensional Dirac materials with interfaces which affect the propagation properties of particles. Motivation for our research comes from interest in materials that exhibit quasirelativistic dispersion relations. By modifying Dirac-type contributions to the Hamiltonian in an interface we are able to calculate the Green function and the density of states. The density of states inside the interface exhibits interpolating behavior between two and three dimensions, with two-dimensional behavior at high energies and three-dimensional behavior at low energies, provided that the shift in the mass parameter in the interface is small. We also discuss the impact of the interpolating density of states on optical absorption in Dirac materials with a two-dimensional substructure.

Efficient statistically accurate algorithms for the Fokker-Planck equation in large dimensions

NASA Astrophysics Data System (ADS)

Chen, Nan; Majda, Andrew J.

2018-02-01

Solving the Fokker-Planck equation for high-dimensional complex turbulent dynamical systems is an important and practical issue. However, most traditional methods suffer from the curse of dimensionality and have difficulties in capturing the fat tailed highly intermittent probability density functions (PDFs) of complex systems in turbulence, neuroscience and excitable media. In this article, efficient statistically accurate algorithms are developed for solving both the transient and the equilibrium solutions of Fokker-Planck equations associated with high-dimensional nonlinear turbulent dynamical systems with conditional Gaussian structures. The algorithms involve a hybrid strategy that requires only a small number of ensembles. Here, a conditional Gaussian mixture in a high-dimensional subspace via an extremely efficient parametric method is combined with a judicious non-parametric Gaussian kernel density estimation in the remaining low-dimensional subspace. Particularly, the parametric method provides closed analytical formulae for determining the conditional Gaussian distributions in the high-dimensional subspace and is therefore computationally efficient and accurate. The full non-Gaussian PDF of the system is then given by a Gaussian mixture. Different from traditional particle methods, each conditional Gaussian distribution here covers a significant portion of the high-dimensional PDF. Therefore a small number of ensembles is sufficient to recover the full PDF, which overcomes the curse of dimensionality. Notably, the mixture distribution has significant skill in capturing the transient behavior with fat tails of the high-dimensional non-Gaussian PDFs, and this facilitates the algorithms in accurately describing the intermittency and extreme events in complex turbulent systems. It is shown in a stringent set of test problems that the method only requires an order of O (100) ensembles to successfully recover the highly non-Gaussian transient PDFs in up to 6 dimensions with only small errors.

Category inference as a function of correlational structure, category discriminability, and number of available cues.

PubMed

Lancaster, Matthew E; Shelhamer, Ryan; Homa, Donald

2013-04-01

Two experiments investigated category inference when categories were composed of correlated or uncorrelated dimensions and the categories overlapped minimally or moderately. When the categories minimally overlapped, the dimensions were strongly correlated with the category label. Following a classification learning phase, subsequent transfer required the selection of either a category label or a feature when one, two, or three features were missing. Experiments 1 and 2 differed primarily in the number of learning blocks prior to transfer. In each experiment, the inference of the category label or category feature was influenced by both dimensional and category correlations, as well as their interaction. The number of cues available at test impacted performance more when the dimensional correlations were zero and category overlap was high. However, a minimal number of cues were sufficient to produce high levels of inference when the dimensions were highly correlated; additional cues had a positive but reduced impact, even when overlap was high. Subjects were generally more accurate in inferring the category label than a category feature regardless of dimensional correlation, category overlap, or number of cues available at test. Whether the category label functioned as a special feature or not was critically dependent upon these embedded correlations, with feature inference driven more strongly by dimensional correlations.

Investigation Into Radiation-Induced Compaction of Zerodur (trademark)

NASA Technical Reports Server (NTRS)

Edwards, D. L.; Herren, K.; Hayden, M.; McDonald, K.; Sims, J. A.; Semmel, C. L.

1996-01-01

Zerodur is a low coefficient of thermal expansion glass-ceramic material. This property makes Zerodur an excellent material for high precision optical substrates. Functioning as a high precision optical substrate, a material must be dimensionally stable in the system operating environment. Published data indicate that Zerodur is dimensionally unstable when exposed to large doses of ionizing radiation. The dimensional instability is discussed as an increase in Zerodur density. This increase in density is described as a compaction. Experimental data showing proton-induced compaction of Zerodur is presented. The dependence of compaction on proton dose was determined to be a power law relationship.

Investigation Into Radiation-Induced Compaction of Zerodur (trademark)

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

Edwards, D.L.; Herren, K.; Hayden, M.

1996-03-01

Zerodur is a low coefficient of thermal expansion glass-ceramic material. This property makes Zerodur an excellent material for high precision optical substrates. Functioning as a high precision optical substrate, a material must be dimensionally stable in the system operating environment. Published data indicate that Zerodur is dimensionally unstable when exposed to large doses of ionizing radiation. The dimensional instability is discussed as an increase in Zerodur density. This increase in density is described as a compaction. Experimental data showing proton-induced compaction of Zerodur is presented. The dependence of compaction on proton dose was determined to be a power law relationship.

Using learning automata to determine proper subset size in high-dimensional spaces

NASA Astrophysics Data System (ADS)

Seyyedi, Seyyed Hossein; Minaei-Bidgoli, Behrouz

2017-03-01

In this paper, we offer a new method called FSLA (Finding the best candidate Subset using Learning Automata), which combines the filter and wrapper approaches for feature selection in high-dimensional spaces. Considering the difficulties of dimension reduction in high-dimensional spaces, FSLA's multi-objective functionality is to determine, in an efficient manner, a feature subset that leads to an appropriate tradeoff between the learning algorithm's accuracy and efficiency. First, using an existing weighting function, the feature list is sorted and selected subsets of the list of different sizes are considered. Then, a learning automaton verifies the performance of each subset when it is used as the input space of the learning algorithm and estimates its fitness upon the algorithm's accuracy and the subset size, which determines the algorithm's efficiency. Finally, FSLA introduces the fittest subset as the best choice. We tested FSLA in the framework of text classification. The results confirm its promising performance of attaining the identified goal.

A reduced-order model from high-dimensional frictional hysteresis

PubMed Central

Biswas, Saurabh; Chatterjee, Anindya

2014-01-01

Hysteresis in material behaviour includes both signum nonlinearities as well as high dimensionality. Available models for component-level hysteretic behaviour are empirical. Here, we derive a low-order model for rate-independent hysteresis from a high-dimensional massless frictional system. The original system, being given in terms of signs of velocities, is first solved incrementally using a linear complementarity problem formulation. From this numerical solution, to develop a reduced-order model, basis vectors are chosen using the singular value decomposition. The slip direction in generalized coordinates is identified as the minimizer of a dissipation-related function. That function includes terms for frictional dissipation through signum nonlinearities at many friction sites. Luckily, it allows a convenient analytical approximation. Upon solution of the approximated minimization problem, the slip direction is found. A final evolution equation for a few states is then obtained that gives a good match with the full solution. The model obtained here may lead to new insights into hysteresis as well as better empirical modelling thereof. PMID:24910522

High Content Imaging (HCI) on Miniaturized Three-Dimensional (3D) Cell Cultures

PubMed Central

Joshi, Pranav; Lee, Moo-Yeal

2015-01-01

High content imaging (HCI) is a multiplexed cell staining assay developed for better understanding of complex biological functions and mechanisms of drug action, and it has become an important tool for toxicity and efficacy screening of drug candidates. Conventional HCI assays have been carried out on two-dimensional (2D) cell monolayer cultures, which in turn limit predictability of drug toxicity/efficacy in vivo; thus, there has been an urgent need to perform HCI assays on three-dimensional (3D) cell cultures. Although 3D cell cultures better mimic in vivo microenvironments of human tissues and provide an in-depth understanding of the morphological and functional features of tissues, they are also limited by having relatively low throughput and thus are not amenable to high-throughput screening (HTS). One attempt of making 3D cell culture amenable for HTS is to utilize miniaturized cell culture platforms. This review aims to highlight miniaturized 3D cell culture platforms compatible with current HCI technology. PMID:26694477

Clustering high dimensional data using RIA

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

Aziz, Nazrina

2015-05-15

Clustering may simply represent a convenient method for organizing a large data set so that it can easily be understood and information can efficiently be retrieved. However, identifying cluster in high dimensionality data sets is a difficult task because of the curse of dimensionality. Another challenge in clustering is some traditional functions cannot capture the pattern dissimilarity among objects. In this article, we used an alternative dissimilarity measurement called Robust Influence Angle (RIA) in the partitioning method. RIA is developed using eigenstructure of the covariance matrix and robust principal component score. We notice that, it can obtain cluster easily andmore » hence avoid the curse of dimensionality. It is also manage to cluster large data sets with mixed numeric and categorical value.« less

Generalized neurofuzzy network modeling algorithms using Bézier-Bernstein polynomial functions and additive decomposition.

PubMed

Hong, X; Harris, C J

2000-01-01

This paper introduces a new neurofuzzy model construction algorithm for nonlinear dynamic systems based upon basis functions that are Bézier-Bernstein polynomial functions. This paper is generalized in that it copes with n-dimensional inputs by utilising an additive decomposition construction to overcome the curse of dimensionality associated with high n. This new construction algorithm also introduces univariate Bézier-Bernstein polynomial functions for the completeness of the generalized procedure. Like the B-spline expansion based neurofuzzy systems, Bézier-Bernstein polynomial function based neurofuzzy networks hold desirable properties such as nonnegativity of the basis functions, unity of support, and interpretability of basis function as fuzzy membership functions, moreover with the additional advantages of structural parsimony and Delaunay input space partition, essentially overcoming the curse of dimensionality associated with conventional fuzzy and RBF networks. This new modeling network is based on additive decomposition approach together with two separate basis function formation approaches for both univariate and bivariate Bézier-Bernstein polynomial functions used in model construction. The overall network weights are then learnt using conventional least squares methods. Numerical examples are included to demonstrate the effectiveness of this new data based modeling approach.

Physical Model of the Genotype-to-Phenotype Map of Proteins

NASA Astrophysics Data System (ADS)

Tlusty, Tsvi; Libchaber, Albert; Eckmann, Jean-Pierre

2017-04-01

How DNA is mapped to functional proteins is a basic question of living matter. We introduce and study a physical model of protein evolution which suggests a mechanical basis for this map. Many proteins rely on large-scale motion to function. We therefore treat protein as learning amorphous matter that evolves towards such a mechanical function: Genes are binary sequences that encode the connectivity of the amino acid network that makes a protein. The gene is evolved until the network forms a shear band across the protein, which allows for long-range, soft modes required for protein function. The evolution reduces the high-dimensional sequence space to a low-dimensional space of mechanical modes, in accord with the observed dimensional reduction between genotype and phenotype of proteins. Spectral analysis of the space of 1 06 solutions shows a strong correspondence between localization around the shear band of both mechanical modes and the sequence structure. Specifically, our model shows how mutations are correlated among amino acids whose interactions determine the functional mode.

Energy-efficient spatial-domain-based hybrid multidimensional coded-modulations enabling multi-Tb/s optical transport.

PubMed

Djordjevic, Ivan B

2011-08-15

In addition to capacity, the future high-speed optical transport networks will also be constrained by energy consumption. In order to solve the capacity and energy constraints simultaneously, in this paper we propose the use of energy-efficient hybrid D-dimensional signaling (D>4) by employing all available degrees of freedom for conveyance of the information over a single carrier including amplitude, phase, polarization and orbital angular momentum (OAM). Given the fact that the OAM eigenstates, associated with the azimuthal phase dependence of the complex electric field, are orthogonal, they can be used as basis functions for multidimensional signaling. Since the information capacity is a linear function of number of dimensions, through D-dimensional signal constellations we can significantly improve the overall optical channel capacity. The energy-efficiency problem is solved, in this paper, by properly designing the D-dimensional signal constellation such that the mutual information is maximized, while taking the energy constraint into account. We demonstrate high-potential of proposed energy-efficient hybrid D-dimensional coded-modulation scheme by Monte Carlo simulations. © 2011 Optical Society of America

An Adaptive ANOVA-based PCKF for High-Dimensional Nonlinear Inverse Modeling

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

LI, Weixuan; Lin, Guang; Zhang, Dongxiao

2014-02-01

The probabilistic collocation-based Kalman filter (PCKF) is a recently developed approach for solving inverse problems. It resembles the ensemble Kalman filter (EnKF) in every aspect—except that it represents and propagates model uncertainty by polynomial chaos expansion (PCE) instead of an ensemble of model realizations. Previous studies have shown PCKF is a more efficient alternative to EnKF for many data assimilation problems. However, the accuracy and efficiency of PCKF depends on an appropriate truncation of the PCE series. Having more polynomial chaos bases in the expansion helps to capture uncertainty more accurately but increases computational cost. Bases selection is particularly importantmore » for high-dimensional stochastic problems because the number of polynomial chaos bases required to represent model uncertainty grows dramatically as the number of input parameters (random dimensions) increases. In classic PCKF algorithms, the PCE bases are pre-set based on users’ experience. Also, for sequential data assimilation problems, the bases kept in PCE expression remain unchanged in different Kalman filter loops, which could limit the accuracy and computational efficiency of classic PCKF algorithms. To address this issue, we present a new algorithm that adaptively selects PCE bases for different problems and automatically adjusts the number of bases in different Kalman filter loops. The algorithm is based on adaptive functional ANOVA (analysis of variance) decomposition, which approximates a high-dimensional function with the summation of a set of low-dimensional functions. Thus, instead of expanding the original model into PCE, we implement the PCE expansion on these low-dimensional functions, which is much less costly. We also propose a new adaptive criterion for ANOVA that is more suited for solving inverse problems. The new algorithm is tested with different examples and demonstrated great effectiveness in comparison with non-adaptive PCKF and EnKF algorithms.« less

Regularization Methods for High-Dimensional Instrumental Variables Regression With an Application to Genetical Genomics

PubMed Central

Lin, Wei; Feng, Rui; Li, Hongzhe

2014-01-01

In genetical genomics studies, it is important to jointly analyze gene expression data and genetic variants in exploring their associations with complex traits, where the dimensionality of gene expressions and genetic variants can both be much larger than the sample size. Motivated by such modern applications, we consider the problem of variable selection and estimation in high-dimensional sparse instrumental variables models. To overcome the difficulty of high dimensionality and unknown optimal instruments, we propose a two-stage regularization framework for identifying and estimating important covariate effects while selecting and estimating optimal instruments. The methodology extends the classical two-stage least squares estimator to high dimensions by exploiting sparsity using sparsity-inducing penalty functions in both stages. The resulting procedure is efficiently implemented by coordinate descent optimization. For the representative L1 regularization and a class of concave regularization methods, we establish estimation, prediction, and model selection properties of the two-stage regularized estimators in the high-dimensional setting where the dimensionality of co-variates and instruments are both allowed to grow exponentially with the sample size. The practical performance of the proposed method is evaluated by simulation studies and its usefulness is illustrated by an analysis of mouse obesity data. Supplementary materials for this article are available online. PMID:26392642

Generalized -deformed correlation functions as spectral functions of hyperbolic geometry

NASA Astrophysics Data System (ADS)

Bonora, L.; Bytsenko, A. A.; Guimarães, M. E. X.

2014-08-01

We analyze the role of vertex operator algebra and 2d amplitudes from the point of view of the representation theory of infinite-dimensional Lie algebras, MacMahon and Ruelle functions. By definition p-dimensional MacMahon function, with , is the generating function of p-dimensional partitions of integers. These functions can be represented as amplitudes of a two-dimensional c = 1 CFT, and, as such, they can be generalized to . With some abuse of language we call the latter amplitudes generalized MacMahon functions. In this paper we show that generalized p-dimensional MacMahon functions can be rewritten in terms of Ruelle spectral functions, whose spectrum is encoded in the Patterson-Selberg function of three-dimensional hyperbolic geometry.

Weighted Distance Functions Improve Analysis of High-Dimensional Data: Application to Molecular Dynamics Simulations.

PubMed

Blöchliger, Nicolas; Caflisch, Amedeo; Vitalis, Andreas

2015-11-10

Data mining techniques depend strongly on how the data are represented and how distance between samples is measured. High-dimensional data often contain a large number of irrelevant dimensions (features) for a given query. These features act as noise and obfuscate relevant information. Unsupervised approaches to mine such data require distance measures that can account for feature relevance. Molecular dynamics simulations produce high-dimensional data sets describing molecules observed in time. Here, we propose to globally or locally weight simulation features based on effective rates. This emphasizes, in a data-driven manner, slow degrees of freedom that often report on the metastable states sampled by the molecular system. We couple this idea to several unsupervised learning protocols. Our approach unmasks slow side chain dynamics within the native state of a miniprotein and reveals additional metastable conformations of a protein. The approach can be combined with most algorithms for clustering or dimensionality reduction.

Finite-volume application of high order ENO schemes to multi-dimensional boundary-value problems

NASA Technical Reports Server (NTRS)

Casper, Jay; Dorrepaal, J. Mark

1990-01-01

The finite volume approach in developing multi-dimensional, high-order accurate essentially non-oscillatory (ENO) schemes is considered. In particular, a two dimensional extension is proposed for the Euler equation of gas dynamics. This requires a spatial reconstruction operator that attains formal high order of accuracy in two dimensions by taking account of cross gradients. Given a set of cell averages in two spatial variables, polynomial interpolation of a two dimensional primitive function is employed in order to extract high-order pointwise values on cell interfaces. These points are appropriately chosen so that correspondingly high-order flux integrals are obtained through each interface by quadrature, at each point having calculated a flux contribution in an upwind fashion. The solution-in-the-small of Riemann's initial value problem (IVP) that is required for this pointwise flux computation is achieved using Roe's approximate Riemann solver. Issues to be considered in this two dimensional extension include the implementation of boundary conditions and application to general curvilinear coordinates. Results of numerical experiments are presented for qualitative and quantitative examination. These results contain the first successful application of ENO schemes to boundary value problems with solid walls.

Particle velocity distribution in a three-dimensional dusty plasma under microgravity conditions

NASA Astrophysics Data System (ADS)

Liu, Bin; Goree, J.; Pustylnik, M. Y.; Thomas, H. M.; Fortov, V. E.; Lipaev, A. M.; Usachev, A. D.; Molotkov, V. I.; Petrov, O. F.; Thoma, M. H.

2018-01-01

The velocity distribution function of dust particles immersed in a plasma was investigated under microgravity conditions. A three-dimensional (3D) cloud of polymer microspheres was suspended in a neon plasma, in the PK-4 instrument onboard the International Space Station (ISS). These dust particles were tracked using video microscopy in a cross section of the 3D dust cloud. The velocity distribution function (VDF) is found to have a non-Maxwellian shape with high-energy tails; it is fit well by a combination of low-energy Maxwellian core and a high-energy non-Gaussian Kappa-distribution halo. Similar non-Maxwellian VDFs are typically observed in space plasmas.

Two-and three-dimensional unsteady lift problems in high-speed flight

NASA Technical Reports Server (NTRS)

Lomax, Harvard; Heaslet, Max A; Fuller, Franklyn B; Sluder, Loma

1952-01-01

The problem of transient lift on two- and three-dimensional wings flying at high speeds is discussed as a boundary-value problem for the classical wave equation. Kirchoff's formula is applied so that the analysis is reduced, just as in the steady state, to an investigation of sources and doublets. The applications include the evaluation of indicial lift and pitching-moment curves for two-dimensional sinking and pitching wings flying at Mach numbers equal to 0, 0.8, 1.0, 1.2 and 2.0. Results for the sinking case are also given for a Mach number of 0.5. In addition, the indicial functions for supersonic-edged triangular wings in both forward and reverse flow are presented and compared with the two-dimensional values.

Optimization of High-Dimensional Functions through Hypercube Evaluation

PubMed Central

Abiyev, Rahib H.; Tunay, Mustafa

2015-01-01

A novel learning algorithm for solving global numerical optimization problems is proposed. The proposed learning algorithm is intense stochastic search method which is based on evaluation and optimization of a hypercube and is called the hypercube optimization (HO) algorithm. The HO algorithm comprises the initialization and evaluation process, displacement-shrink process, and searching space process. The initialization and evaluation process initializes initial solution and evaluates the solutions in given hypercube. The displacement-shrink process determines displacement and evaluates objective functions using new points, and the search area process determines next hypercube using certain rules and evaluates the new solutions. The algorithms for these processes have been designed and presented in the paper. The designed HO algorithm is tested on specific benchmark functions. The simulations of HO algorithm have been performed for optimization of functions of 1000-, 5000-, or even 10000 dimensions. The comparative simulation results with other approaches demonstrate that the proposed algorithm is a potential candidate for optimization of both low and high dimensional functions. PMID:26339237

MURI: Adaptive Waveform Design for Full Spectral Dominance

DTIC Science & Technology

2011-03-11

a three- dimensional urban tracking model, based on the nonlinear measurement model (that uses the urban multipath geometry with different types of ... the time evolution of the scattering function with a high dimensional dynamic system; a multiple particle filter technique is used to sequentially...integration of space -time coding with a fixed set of beams. It complements the

Protein Structure and Function: An Interdisciplinary Multimedia-Based Guided-Inquiry Education Module for the High School Science Classroom

ERIC Educational Resources Information Center

Bethel, Casey M.; Lieberman, Raquel L.

2014-01-01

Here we present a multidisciplinary educational unit intended for general, advanced placement, or international baccalaureate-level high school science, focused on the three-dimensional structure of proteins and their connection to function and disease. The lessons are designed within the framework of the Next Generation Science Standards to make…

Utilization of high resolution computed tomography to visualize the three dimensional structure and function of plant vasculature

USDA-ARS?s Scientific Manuscript database

High resolution x-ray computed tomography (HRCT) is a non-destructive diagnostic imaging technique with sub-micron resolution capability that is now being used to evaluate the structure and function of plant xylem network in three dimensions (3D). HRCT imaging is based on the same principles as medi...

Computational unsteady aerodynamics for lifting surfaces

NASA Technical Reports Server (NTRS)

Edwards, John W.

1988-01-01

Two dimensional problems are solved using numerical techniques. Navier-Stokes equations are studied both in the vorticity-stream function formulation which appears to be the optimal choice for two dimensional problems, using a storage approach, and in the velocity pressure formulation which minimizes the number of unknowns in three dimensional problems. Analysis shows that compact centered conservative second order schemes for the vorticity equation are the most robust for high Reynolds number flows. Serious difficulties remain in the choice of turbulent models, to keep reasonable CPU efficiency.

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

Xing, Ling-Bao; Hou, Shu-Fen; Zhou, Jin

In present work, we demonstrate an efficient and facile strategy to fabricate three-dimensional (3D) nitrogen-doped graphene aerogels (NGAs) based on melamine, which serves as reducing and functionalizing agent of graphene oxide (GO) in an aqueous medium with ammonia. Benefiting from well-defined and cross-linked 3D porous network architectures, the supercapacitor based on the NGAs exhibited a high specific capacitance of 170.5 F g{sup −1} at 0.2 A g{sup −1}, and this capacitance also showed good electrochemical stability and a high degree of reversibility in the repetitive charge/discharge cycling test. More interestingly, the prepared NGAs further exhibited high adsorption capacities and highmore » recycling performance toward several metal ions such as Pb{sup 2+}, Cu{sup 2+} and Cd{sup 2+}. Moreover, the hydrophobic carbonized nitrogen-doped graphene aerogels (CNGAs) showed outstanding adsorption and recycling performance for the removal of various oils and organic solvents. - Graphical abstract: Three-dimensional nitrogen-doped graphene aerogels were prepared by using melamine as reducing and functionalizing agent in an aqueous medium with ammonia, which showed multifunctional applications in supercapacitors and adsorption. - Highlights: • Three-dimensional nitrogen-doped graphene aerogels (NGAs) were prepared. • Melamine was used as reducing and functionalizing agent. • NGAs exhibited relatively good electrochemical properties in supercapacitor. • NGAs exhibited high adsorption performance toward several metal ions. • CNGAs showed outstanding adsorption capacities for various oils and solvents.« less

Efficient Statistically Accurate Algorithms for the Fokker-Planck Equation in Large Dimensions

NASA Astrophysics Data System (ADS)

Chen, N.; Majda, A.

2017-12-01

Solving the Fokker-Planck equation for high-dimensional complex turbulent dynamical systems is an important and practical issue. However, most traditional methods suffer from the curse of dimensionality and have difficulties in capturing the fat tailed highly intermittent probability density functions (PDFs) of complex systems in turbulence, neuroscience and excitable media. In this article, efficient statistically accurate algorithms are developed for solving both the transient and the equilibrium solutions of Fokker-Planck equations associated with high-dimensional nonlinear turbulent dynamical systems with conditional Gaussian structures. The algorithms involve a hybrid strategy that requires only a small number of ensembles. Here, a conditional Gaussian mixture in a high-dimensional subspace via an extremely efficient parametric method is combined with a judicious non-parametric Gaussian kernel density estimation in the remaining low-dimensional subspace. Particularly, the parametric method, which is based on an effective data assimilation framework, provides closed analytical formulae for determining the conditional Gaussian distributions in the high-dimensional subspace. Therefore, it is computationally efficient and accurate. The full non-Gaussian PDF of the system is then given by a Gaussian mixture. Different from the traditional particle methods, each conditional Gaussian distribution here covers a significant portion of the high-dimensional PDF. Therefore a small number of ensembles is sufficient to recover the full PDF, which overcomes the curse of dimensionality. Notably, the mixture distribution has a significant skill in capturing the transient behavior with fat tails of the high-dimensional non-Gaussian PDFs, and this facilitates the algorithms in accurately describing the intermittency and extreme events in complex turbulent systems. It is shown in a stringent set of test problems that the method only requires an order of O(100) ensembles to successfully recover the highly non-Gaussian transient PDFs in up to 6 dimensions with only small errors.

Functional Parallel Factor Analysis for Functions of One- and Two-dimensional Arguments.

PubMed

Choi, Ji Yeh; Hwang, Heungsun; Timmerman, Marieke E

2018-03-01

Parallel factor analysis (PARAFAC) is a useful multivariate method for decomposing three-way data that consist of three different types of entities simultaneously. This method estimates trilinear components, each of which is a low-dimensional representation of a set of entities, often called a mode, to explain the maximum variance of the data. Functional PARAFAC permits the entities in different modes to be smooth functions or curves, varying over a continuum, rather than a collection of unconnected responses. The existing functional PARAFAC methods handle functions of a one-dimensional argument (e.g., time) only. In this paper, we propose a new extension of functional PARAFAC for handling three-way data whose responses are sequenced along both a two-dimensional domain (e.g., a plane with x- and y-axis coordinates) and a one-dimensional argument. Technically, the proposed method combines PARAFAC with basis function expansion approximations, using a set of piecewise quadratic finite element basis functions for estimating two-dimensional smooth functions and a set of one-dimensional basis functions for estimating one-dimensional smooth functions. In a simulation study, the proposed method appeared to outperform the conventional PARAFAC. We apply the method to EEG data to demonstrate its empirical usefulness.

Development and applications of 3-dimensional integration nanotechnologies.

PubMed

Kim, Areum; Choi, Eunmi; Son, Hyungbin; Pyo, Sung Gyu

2014-02-01

Unlike conventional two-dimensional (2D) planar structures, signal or power is supplied through through-silicon via (TSV) in three-dimensional (3D) integration technology to replace wires for binding the chip/wafer. TSVs have becomes an essential technology, as they satisfy Moore's law. This 3D integration technology enables system and sensor functions at a nanoscale via the implementation of a highly integrated nano-semiconductor as well as the fabrication of a single chip with multiple functions. Thus, this technology is considered to be a new area of development for the systemization of the nano-bio area. In this review paper, the basic technology required for such 3D integration is described and methods to measure the bonding strength in order to measure the void occurring during bonding are introduced. Currently, CMOS image sensors and memory chips associated with nanotechnology are being realized on the basis of 3D integration technology. In this paper, we intend to describe the applications of high-performance nano-biosensor technology currently under development and the direction of development of a high performance lab-on-a-chip (LOC).

Big Data Toolsets to Pharmacometrics: Application of Machine Learning for Time-to-Event Analysis.

PubMed

Gong, Xiajing; Hu, Meng; Zhao, Liang

2018-05-01

Additional value can be potentially created by applying big data tools to address pharmacometric problems. The performances of machine learning (ML) methods and the Cox regression model were evaluated based on simulated time-to-event data synthesized under various preset scenarios, i.e., with linear vs. nonlinear and dependent vs. independent predictors in the proportional hazard function, or with high-dimensional data featured by a large number of predictor variables. Our results showed that ML-based methods outperformed the Cox model in prediction performance as assessed by concordance index and in identifying the preset influential variables for high-dimensional data. The prediction performances of ML-based methods are also less sensitive to data size and censoring rates than the Cox regression model. In conclusion, ML-based methods provide a powerful tool for time-to-event analysis, with a built-in capacity for high-dimensional data and better performance when the predictor variables assume nonlinear relationships in the hazard function. © 2018 The Authors. Clinical and Translational Science published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

Percolation and epidemics in a two-dimensional small world

NASA Astrophysics Data System (ADS)

Newman, M. E.; Jensen, I.; Ziff, R. M.

2002-02-01

Percolation on two-dimensional small-world networks has been proposed as a model for the spread of plant diseases. In this paper we give an analytic solution of this model using a combination of generating function methods and high-order series expansion. Our solution gives accurate predictions for quantities such as the position of the percolation threshold and the typical size of disease outbreaks as a function of the density of ``shortcuts'' in the small-world network. Our results agree with scaling hypotheses and numerical simulations for the same model.

Relaxation of a High-Energy Quasiparticle in a One-Dimensional Bose Gas

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

Tan, Shina; Glazman, Leonid I.; Pustilnik, Michael

2010-08-27

We evaluate the relaxation rate of high-energy quasiparticles in a weakly interacting one-dimensional Bose gas. Unlike in higher dimensions, the rate is a nonmonotonic function of temperature, with a maximum at the crossover to the state of suppressed density fluctuations. At the maximum, the relaxation rate may significantly exceed its zero-temperature value. We also find the dependence of the differential inelastic scattering rate on the transferred energy. This rate yields information about temperature dependence of local pair correlations.

A sparse grid based method for generative dimensionality reduction of high-dimensional data

NASA Astrophysics Data System (ADS)

Bohn, Bastian; Garcke, Jochen; Griebel, Michael

2016-03-01

Generative dimensionality reduction methods play an important role in machine learning applications because they construct an explicit mapping from a low-dimensional space to the high-dimensional data space. We discuss a general framework to describe generative dimensionality reduction methods, where the main focus lies on a regularized principal manifold learning variant. Since most generative dimensionality reduction algorithms exploit the representer theorem for reproducing kernel Hilbert spaces, their computational costs grow at least quadratically in the number n of data. Instead, we introduce a grid-based discretization approach which automatically scales just linearly in n. To circumvent the curse of dimensionality of full tensor product grids, we use the concept of sparse grids. Furthermore, in real-world applications, some embedding directions are usually more important than others and it is reasonable to refine the underlying discretization space only in these directions. To this end, we employ a dimension-adaptive algorithm which is based on the ANOVA (analysis of variance) decomposition of a function. In particular, the reconstruction error is used to measure the quality of an embedding. As an application, the study of large simulation data from an engineering application in the automotive industry (car crash simulation) is performed.

Husimi function and phase-space analysis of bilayer quantum Hall systems at ν = 2/λ

NASA Astrophysics Data System (ADS)

Calixto, M.; Peón-Nieto, C.

2018-05-01

We propose localization measures in phase space of the ground state of bilayer quantum Hall systems at fractional filling factors , to characterize the three quantum phases (shortly denoted by spin, canted and ppin) for arbitrary -isospin λ. We use a coherent state (Bargmann) representation of quantum states, as holomorphic functions in the 8-dimensional Grassmannian phase-space (a higher-dimensional generalization of the Haldane’s 2-dimensional sphere ). We quantify the localization (inverse volume) of the ground state wave function in phase-space throughout the phase diagram (i.e. as a function of Zeeman, tunneling, layer distance, etc, control parameters) with the Husimi function second moment, a kind of inverse participation ratio that behaves as an order parameter. Then we visualize the different ground state structure in phase space of the three quantum phases, the canted phase displaying a much higher delocalization (a Schrödinger cat structure) than the spin and ppin phases, where the ground state is highly coherent. We find a good agreement between analytic (variational) and numeric diagonalization results.

Bulk dynamics of Brownian hard disks: Dynamical density functional theory versus experiments on two-dimensional colloidal hard spheres

NASA Astrophysics Data System (ADS)

Stopper, Daniel; Thorneywork, Alice L.; Dullens, Roel P. A.; Roth, Roland

2018-03-01

Using dynamical density functional theory (DDFT), we theoretically study Brownian self-diffusion and structural relaxation of hard disks and compare to experimental results on quasi two-dimensional colloidal hard spheres. To this end, we calculate the self-van Hove correlation function and distinct van Hove correlation function by extending a recently proposed DDFT-approach for three-dimensional systems to two dimensions. We find that the theoretical results for both self-part and distinct part of the van Hove function are in very good quantitative agreement with the experiments up to relatively high fluid packing fractions of roughly 0.60. However, at even higher densities, deviations between the experiment and the theoretical approach become clearly visible. Upon increasing packing fraction, in experiments, the short-time self-diffusive behavior is strongly affected by hydrodynamic effects and leads to a significant decrease in the respective mean-squared displacement. By contrast, and in accordance with previous simulation studies, the present DDFT, which neglects hydrodynamic effects, shows no dependence on the particle density for this quantity.

Highly directional thermal emitter

DOEpatents

Ribaudo, Troy; Shaner, Eric A; Davids, Paul; Peters, David W

2015-03-24

A highly directional thermal emitter device comprises a two-dimensional periodic array of heavily doped semiconductor structures on a surface of a substrate. The array provides a highly directional thermal emission at a peak wavelength between 3 and 15 microns when the array is heated. For example, highly doped silicon (HDSi) with a plasma frequency in the mid-wave infrared was used to fabricate nearly perfect absorbing two-dimensional gratings structures that function as highly directional thermal radiators. The absorption and emission characteristics of the HDSi devices possessed a high degree of angular dependence for infrared absorption in the 10-12 micron range, while maintaining high reflectivity of solar radiation (.about.64%) at large incidence angles.

Three dimensional nitrogen-doped graphene aerogels functionalized with melamine for multifunctional applications in supercapacitors and adsorption

NASA Astrophysics Data System (ADS)

Xing, Ling-Bao; Hou, Shu-Fen; Zhou, Jin; Zhang, Jing-Li; Si, Weijiang; Dong, Yunhui; Zhuo, Shuping

2015-10-01

In present work, we demonstrate an efficient and facile strategy to fabricate three-dimensional (3D) nitrogen-doped graphene aerogels (NGAs) based on melamine, which serves as reducing and functionalizing agent of graphene oxide (GO) in an aqueous medium with ammonia. Benefiting from well-defined and cross-linked 3D porous network architectures, the supercapacitor based on the NGAs exhibited a high specific capacitance of 170.5 F g-1 at 0.2 A g-1, and this capacitance also showed good electrochemical stability and a high degree of reversibility in the repetitive charge/discharge cycling test. More interestingly, the prepared NGAs further exhibited high adsorption capacities and high recycling performance toward several metal ions such as Pb2+, Cu2+ and Cd2+. Moreover, the hydrophobic carbonized nitrogen-doped graphene aerogels (CNGAs) showed outstanding adsorption and recycling performance for the removal of various oils and organic solvents.

Exploring the parahippocampal cortex response to high and low spatial frequency spaces.

PubMed

Zeidman, Peter; Mullally, Sinéad L; Schwarzkopf, Dietrich Samuel; Maguire, Eleanor A

2012-05-30

The posterior parahippocampal cortex (PHC) supports a range of cognitive functions, in particular scene processing. However, it has recently been suggested that PHC engagement during functional MRI simply reflects the representation of three-dimensional local space. If so, PHC should respond to space in the absence of scenes, geometric layout, objects or contextual associations. It has also been reported that PHC activation may be influenced by low-level visual properties of stimuli such as spatial frequency. Here, we tested whether PHC was responsive to the mere sense of space in highly simplified stimuli, and whether this was affected by their spatial frequency distribution. Participants were scanned using functional MRI while viewing depictions of simple three-dimensional space, and matched control stimuli that did not depict a space. Half the stimuli were low-pass filtered to ascertain the impact of spatial frequency. We observed a significant interaction between space and spatial frequency in bilateral PHC. Specifically, stimuli depicting space (more than nonspatial stimuli) engaged the right PHC when they featured high spatial frequencies. In contrast, the interaction in the left PHC did not show a preferential response to space. We conclude that a simple depiction of three-dimensional space that is devoid of objects, scene layouts or contextual associations is sufficient to robustly engage the right PHC, at least when high spatial frequencies are present. We suggest that coding for the presence of space may be a core function of PHC, and could explain its engagement in a range of tasks, including scene processing, where space is always present.

Gaussian processes with built-in dimensionality reduction: Applications to high-dimensional uncertainty propagation

NASA Astrophysics Data System (ADS)

Tripathy, Rohit; Bilionis, Ilias; Gonzalez, Marcial

2016-09-01

Uncertainty quantification (UQ) tasks, such as model calibration, uncertainty propagation, and optimization under uncertainty, typically require several thousand evaluations of the underlying computer codes. To cope with the cost of simulations, one replaces the real response surface with a cheap surrogate based, e.g., on polynomial chaos expansions, neural networks, support vector machines, or Gaussian processes (GP). However, the number of simulations required to learn a generic multivariate response grows exponentially as the input dimension increases. This curse of dimensionality can only be addressed, if the response exhibits some special structure that can be discovered and exploited. A wide range of physical responses exhibit a special structure known as an active subspace (AS). An AS is a linear manifold of the stochastic space characterized by maximal response variation. The idea is that one should first identify this low dimensional manifold, project the high-dimensional input onto it, and then link the projection to the output. If the dimensionality of the AS is low enough, then learning the link function is a much easier problem than the original problem of learning a high-dimensional function. The classic approach to discovering the AS requires gradient information, a fact that severely limits its applicability. Furthermore, and partly because of its reliance to gradients, it is not able to handle noisy observations. The latter is an essential trait if one wants to be able to propagate uncertainty through stochastic simulators, e.g., through molecular dynamics codes. In this work, we develop a probabilistic version of AS which is gradient-free and robust to observational noise. Our approach relies on a novel Gaussian process regression with built-in dimensionality reduction. In particular, the AS is represented as an orthogonal projection matrix that serves as yet another covariance function hyper-parameter to be estimated from the data. To train the model, we design a two-step maximum likelihood optimization procedure that ensures the orthogonality of the projection matrix by exploiting recent results on the Stiefel manifold, i.e., the manifold of matrices with orthogonal columns. The additional benefit of our probabilistic formulation, is that it allows us to select the dimensionality of the AS via the Bayesian information criterion. We validate our approach by showing that it can discover the right AS in synthetic examples without gradient information using both noiseless and noisy observations. We demonstrate that our method is able to discover the same AS as the classical approach in a challenging one-hundred-dimensional problem involving an elliptic stochastic partial differential equation with random conductivity. Finally, we use our approach to study the effect of geometric and material uncertainties in the propagation of solitary waves in a one dimensional granular system.

Gaussian processes with built-in dimensionality reduction: Applications to high-dimensional uncertainty propagation

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

Tripathy, Rohit, E-mail: rtripath@purdue.edu; Bilionis, Ilias, E-mail: ibilion@purdue.edu; Gonzalez, Marcial, E-mail: marcial-gonzalez@purdue.edu

2016-09-15

Uncertainty quantification (UQ) tasks, such as model calibration, uncertainty propagation, and optimization under uncertainty, typically require several thousand evaluations of the underlying computer codes. To cope with the cost of simulations, one replaces the real response surface with a cheap surrogate based, e.g., on polynomial chaos expansions, neural networks, support vector machines, or Gaussian processes (GP). However, the number of simulations required to learn a generic multivariate response grows exponentially as the input dimension increases. This curse of dimensionality can only be addressed, if the response exhibits some special structure that can be discovered and exploited. A wide range ofmore » physical responses exhibit a special structure known as an active subspace (AS). An AS is a linear manifold of the stochastic space characterized by maximal response variation. The idea is that one should first identify this low dimensional manifold, project the high-dimensional input onto it, and then link the projection to the output. If the dimensionality of the AS is low enough, then learning the link function is a much easier problem than the original problem of learning a high-dimensional function. The classic approach to discovering the AS requires gradient information, a fact that severely limits its applicability. Furthermore, and partly because of its reliance to gradients, it is not able to handle noisy observations. The latter is an essential trait if one wants to be able to propagate uncertainty through stochastic simulators, e.g., through molecular dynamics codes. In this work, we develop a probabilistic version of AS which is gradient-free and robust to observational noise. Our approach relies on a novel Gaussian process regression with built-in dimensionality reduction. In particular, the AS is represented as an orthogonal projection matrix that serves as yet another covariance function hyper-parameter to be estimated from the data. To train the model, we design a two-step maximum likelihood optimization procedure that ensures the orthogonality of the projection matrix by exploiting recent results on the Stiefel manifold, i.e., the manifold of matrices with orthogonal columns. The additional benefit of our probabilistic formulation, is that it allows us to select the dimensionality of the AS via the Bayesian information criterion. We validate our approach by showing that it can discover the right AS in synthetic examples without gradient information using both noiseless and noisy observations. We demonstrate that our method is able to discover the same AS as the classical approach in a challenging one-hundred-dimensional problem involving an elliptic stochastic partial differential equation with random conductivity. Finally, we use our approach to study the effect of geometric and material uncertainties in the propagation of solitary waves in a one dimensional granular system.« less

Three-Dimensional Bioprinting: Toward the Era of Manufacturing Human Organs as Spare Parts for Healthcare and Medicine^.

PubMed

Mir, Tanveer Ahmad; Nakamura, Makoto

2017-06-01

Three-dimensional (3D) printing technology has been used in industrial worlds for decades. Three-dimensional bioprinting has recently received an increasing attention across the globe among researchers, academicians, students, and even the ordinary people. This emerging technique has a great potential to engineer highly organized functional bioconstructs with complex geometries and tailored components for engineering bioartificial tissues/organs for widespread applications, including transplantation, therapeutic investigation, drug development, bioassay, and disease modeling. Although many specialized 3D printers have been developed and applied to print various types of 3D tissue constructs, bioprinting technologies still have several technical challenges, including high resolution distribution of cells, controlled deposition of bioinks, suitable bioink materials, maturation of cells, and effective vascularization and innervation within engineered complex structures. In this brief review, we discuss about bioprinting approach, current limitations, and possibility of future advancements for producing engineered bioconstructs and bioartificial organs with desired functionalities.

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

ERIC Educational Resources Information Center

Kamp-Becker, Inge; Smidt, Judith; Ghahreman, Mardjan; Heinzel-Gutenbrunner, Monika; Becker, Katja; Remschmidt, Helmut

2010-01-01

There is an ongoing debate whether a differentiation of autistic subtypes, especially between Asperger Syndrome (AS) and high-functioning-autism (HFA) is possible and if so, whether it is a categorical or dimensional one. The aim of this study was to examine the possible clustering of responses in different symptom domains without making any…

Structural hierarchy in molecular films of two class II hydrophobins.

PubMed

Paananen, Arja; Vuorimaa, Elina; Torkkeli, Mika; Penttilä, Merja; Kauranen, Martti; Ikkala, Olli; Lemmetyinen, Helge; Serimaa, Ritva; Linder, Markus B

2003-05-13

Hydrophobins are highly surface-active proteins that are specific to filamentous fungi. They function as coatings on various fungal structures, enable aerial growth of hyphae, and facilitate attachment to surfaces. Little is known about their structures and structure-function relationships. In this work we show highly organized surface layers of hydrophobins, representing the most detailed structural study of hydrophobin films so far. Langmuir-Blodgett films of class II hydrophobins HFBI and HFBII from Trichoderma reesei were prepared and analyzed by atomic force microscopy. The films showed highly ordered two-dimensional crystalline structures. By combining our recent results on small-angle X-ray scattering of hydrophobin solutions, we found that the unit cells in the films have dimensions similar to those of tetrameric aggregates found in solutions. Further analysis leads to a model in which the building blocks of the two-dimensional crystals are shape-persistent supramolecules consisting of four hydrophobin molecules. The results also indicate functional and structural differences between HFBI and HFBII that help to explain differences in their properties. The possibility that the highly organized surface assemblies of hydrophobins could allow a route for manufacturing functional surfaces is suggested.

Four-dimensional MRI of renal function in the developing mouse.

PubMed

Xie, Luke; Subashi, Ergys; Qi, Yi; Knepper, Mark A; Johnson, G Allan

2014-09-01

The major roles of filtration, metabolism and high blood flow make the kidney highly vulnerable to drug-induced toxicity and other renal injuries. A method to follow kidney function is essential for the early screening of toxicity and malformations. In this study, we acquired high spatiotemporal resolution (four dimensional) datasets of normal mice to follow changes in kidney structure and function during development. The data were acquired with dynamic contrast-enhanced MRI (via keyhole imaging) and a cryogenic surface coil, allowing us to obtain a full three-dimensional image (isotropic resolution, 125 microns) every 7.7 s over a 50-min scan. This time course permitted the demonstration of both contrast enhancement and clearance. Functional changes were measured over a 17-week course (at 3, 5, 7, 9, 13 and 17 weeks). The time dimension of the MRI dataset was processed to produce unique image contrasts to segment the four regions of the kidney: cortex (CO), outer stripe (OS) of the outer medulla (OM), inner stripe (IS) of the OM and inner medulla (IM). Local volumes, time-to-peak (TTP) values and decay constants (DC) were measured in each renal region. These metrics increased significantly with age, with the exception of DC values in the IS and OS. These data will serve as a foundation for studies of normal renal physiology and future studies of renal diseases that require early detection and intervention. Copyright © 2014 John Wiley & Sons, Ltd.

Layer-by-Layer Self-Assembling Gold Nanorods and Glucose Oxidase onto Carbon Nanotubes Functionalized Sol-Gel Matrix for an Amperometric Glucose Biosensor.

PubMed

Wu, Baoyan; Hou, Shihua; Miao, Zhiying; Zhang, Cong; Ji, Yanhong

2015-09-18

A novel amperometric glucose biosensor was fabricated by layer-by-layer self-assembly of gold nanorods (AuNRs) and glucose oxidase (GOD) onto single-walled carbon nanotubes (SWCNTs)-functionalized three-dimensional sol-gel matrix. A thiolated aqueous silica sol containing SWCNTs was first assembled on the surface of a cleaned Au electrode, and then the alternate self-assembly of AuNRs and GOD were repeated to assemble multilayer films of AuNRs-GOD onto SWCNTs-functionalized silica gel for optimizing the biosensor. Among the resulting glucose biosensors, the four layers of AuNRs-GOD-modified electrode showed the best performance. The sol-SWCNTs-(AuNRs- GOD)₄/Au biosensor exhibited a good linear range of 0.01-8 mM glucose, high sensitivity of 1.08 μA/mM, and fast amperometric response within 4 s. The good performance of the proposed glucose biosensor could be mainly attributed to the advantages of the three-dimensional sol-gel matrix and stereo self-assembly films, and the natural features of one-dimensional nanostructure SWCNTs and AuNRs. This study may provide a new facile way to fabricate the enzyme-based biosensor with high performance.

Three-dimensional shape measurement system applied to superficial inspection of non-metallic pipes for the hydrocarbons transport

NASA Astrophysics Data System (ADS)

Arciniegas, Javier R.; González, Andrés. L.; Quintero, L. A.; Contreras, Carlos R.; Meneses, Jaime E.

2014-05-01

Three-dimensional shape measurement is a subject that consistently produces high scientific interest and provides information for medical, industrial and investigative applications, among others. In this paper, it is proposed to implement a three-dimensional (3D) reconstruction system for applications in superficial inspection of non-metallic pipes for the hydrocarbons transport. The system is formed by a CCD camera, a video-projector and a laptop and it is based on fringe projection technique. System functionality is evidenced by evaluating the quality of three-dimensional reconstructions obtained, which allow observing the failures and defects on the study object surface.

Rational tuning of high-energy visible light absorption for panchromatic small molecules by a two-dimensional conjugation approach

DOE PAGES

He, B.; Zherebetskyy, D.; Wang, H.; ...

2016-02-29

We have demonstrated a rational two-dimensional (2D) conjugation approach towards achieving panchromatic absorption of small molecules. Furthermore, by extending the conjugation on two orthogonal axes of an electron acceptor, namely, bay-annulated indigo (BAI), the optical absorptions could be tuned independently in both high- and low-energy regions. The unconventional modulation of the high-energy absorption is rationalized by density functional theory (DFT) calculations. Finally, we determine that a 2D tuning strategy provides novel guidelines for the design of molecular materials with tailored optoelectronic properties.

Low-dimensional carbon and MXene-based electrochemical capacitor electrodes.

PubMed

Yoon, Yeoheung; Lee, Keunsik; Lee, Hyoyoung

2016-04-29

Due to their unique structure and outstanding intrinsic physical properties such as extraordinarily high electrical conductivity, large surface area, and various chemical functionalities, low-dimension-based materials exhibit great potential for application in electrochemical capacitors (ECs). The electrical properties of electrochemical capacitors are determined by the electrode materials. Because energy charge storage is a surface process, the surface properties of the electrode materials greatly influence the electrochemical performance of the cell. Recently, graphene, a single layer of sp(2)-bonded carbon atoms arrayed into two-dimensional carbon nanomaterial, has attracted wide interest as an electrode material for electrochemical capacitor applications due to its unique properties, including a high electrical conductivity and large surface area. Several low-dimensional materials with large surface areas and high conductivity such as onion-like carbons (OLCs), carbide-derived carbons (CDCs), carbon nanotubes (CNTs), graphene, metal hydroxide, transition metal dichalcogenides (TMDs), and most recently MXene, have been developed for electrochemical capacitors. Therefore, it is useful to understand the current issues of low-dimensional materials and their device applications.

Search Techniques for Self-Organizing Systems

DTIC Science & Technology

1975-07-01

according to their associated function values. The classes need not have equal function value ranges (i.e., the . ................... "The Mucciardi- Gose ... Gose , "An Automatic Clustering Algorithm and Its !’ropertizs in High-Dimensional Spaces,’[ IFEE Trans. S s~tems, Man and Cybernetics, Vol. SMC-2

Exact traveling-wave and spatiotemporal soliton solutions to the generalized (3+1)-dimensional Schrödinger equation with polynomial nonlinearity of arbitrary order.

PubMed

Petrović, Nikola Z; Belić, Milivoj; Zhong, Wei-Ping

2011-02-01

We obtain exact traveling wave and spatiotemporal soliton solutions to the generalized (3+1)-dimensional nonlinear Schrödinger equation with variable coefficients and polynomial Kerr nonlinearity of an arbitrarily high order. Exact solutions, given in terms of Jacobi elliptic functions, are presented for the special cases of cubic-quintic and septic models. We demonstrate that the widely used method for finding exact solutions in terms of Jacobi elliptic functions is not applicable to the nonlinear Schrödinger equation with saturable nonlinearity. ©2011 American Physical Society

Phase Transitions in a Model of Y-Molecules Abstract

NASA Astrophysics Data System (ADS)

Holz, Danielle; Ruth, Donovan; Toral, Raul; Gunton, James

Immunoglobulin is a Y-shaped molecule that functions as an antibody to neutralize pathogens. In special cases where there is a high concentration of immunoglobulin molecules, self-aggregation can occur and the molecules undergo phase transitions. This prevents the molecules from completing their function. We used a simplified model of 2-Dimensional Y-molecules with three identical arms on a triangular lattice with 2-dimensional Grand Canonical Ensemble. The molecules were permitted to be placed, removed, rotated or moved on the lattice. Once phase coexistence was found, we used histogram reweighting and multicanonical sampling to calculate our phase diagram.

Two-part models with stochastic processes for modelling longitudinal semicontinuous data: Computationally efficient inference and modelling the overall marginal mean.

PubMed

Yiu, Sean; Tom, Brian Dm

2017-01-01

Several researchers have described two-part models with patient-specific stochastic processes for analysing longitudinal semicontinuous data. In theory, such models can offer greater flexibility than the standard two-part model with patient-specific random effects. However, in practice, the high dimensional integrations involved in the marginal likelihood (i.e. integrated over the stochastic processes) significantly complicates model fitting. Thus, non-standard computationally intensive procedures based on simulating the marginal likelihood have so far only been proposed. In this paper, we describe an efficient method of implementation by demonstrating how the high dimensional integrations involved in the marginal likelihood can be computed efficiently. Specifically, by using a property of the multivariate normal distribution and the standard marginal cumulative distribution function identity, we transform the marginal likelihood so that the high dimensional integrations are contained in the cumulative distribution function of a multivariate normal distribution, which can then be efficiently evaluated. Hence, maximum likelihood estimation can be used to obtain parameter estimates and asymptotic standard errors (from the observed information matrix) of model parameters. We describe our proposed efficient implementation procedure for the standard two-part model parameterisation and when it is of interest to directly model the overall marginal mean. The methodology is applied on a psoriatic arthritis data set concerning functional disability.

Study of genetic direct search algorithms for function optimization

NASA Technical Reports Server (NTRS)

Zeigler, B. P.

1974-01-01

The results are presented of a study to determine the performance of genetic direct search algorithms in solving function optimization problems arising in the optimal and adaptive control areas. The findings indicate that: (1) genetic algorithms can outperform standard algorithms in multimodal and/or noisy optimization situations, but suffer from lack of gradient exploitation facilities when gradient information can be utilized to guide the search. (2) For large populations, or low dimensional function spaces, mutation is a sufficient operator. However for small populations or high dimensional functions, crossover applied in about equal frequency with mutation is an optimum combination. (3) Complexity, in terms of storage space and running time, is significantly increased when population size is increased or the inversion operator, or the second level adaptation routine is added to the basic structure.

AucPR: an AUC-based approach using penalized regression for disease prediction with high-dimensional omics data.

PubMed

Yu, Wenbao; Park, Taesung

2014-01-01

It is common to get an optimal combination of markers for disease classification and prediction when multiple markers are available. Many approaches based on the area under the receiver operating characteristic curve (AUC) have been proposed. Existing works based on AUC in a high-dimensional context depend mainly on a non-parametric, smooth approximation of AUC, with no work using a parametric AUC-based approach, for high-dimensional data. We propose an AUC-based approach using penalized regression (AucPR), which is a parametric method used for obtaining a linear combination for maximizing the AUC. To obtain the AUC maximizer in a high-dimensional context, we transform a classical parametric AUC maximizer, which is used in a low-dimensional context, into a regression framework and thus, apply the penalization regression approach directly. Two kinds of penalization, lasso and elastic net, are considered. The parametric approach can avoid some of the difficulties of a conventional non-parametric AUC-based approach, such as the lack of an appropriate concave objective function and a prudent choice of the smoothing parameter. We apply the proposed AucPR for gene selection and classification using four real microarray and synthetic data. Through numerical studies, AucPR is shown to perform better than the penalized logistic regression and the nonparametric AUC-based method, in the sense of AUC and sensitivity for a given specificity, particularly when there are many correlated genes. We propose a powerful parametric and easily-implementable linear classifier AucPR, for gene selection and disease prediction for high-dimensional data. AucPR is recommended for its good prediction performance. Beside gene expression microarray data, AucPR can be applied to other types of high-dimensional omics data, such as miRNA and protein data.

Nonlinear dimensionality reduction of CT histogram based feature space for predicting recurrence-free survival in non-small-cell lung cancer

NASA Astrophysics Data System (ADS)

Kawata, Y.; Niki, N.; Ohmatsu, H.; Aokage, K.; Kusumoto, M.; Tsuchida, T.; Eguchi, K.; Kaneko, M.

2015-03-01

Advantages of CT scanners with high resolution have allowed the improved detection of lung cancers. In the recent release of positive results from the National Lung Screening Trial (NLST) in the US showing that CT screening does in fact have a positive impact on the reduction of lung cancer related mortality. While this study does show the efficacy of CT based screening, physicians often face the problems of deciding appropriate management strategies for maximizing patient survival and for preserving lung function. Several key manifold-learning approaches efficiently reveal intrinsic low-dimensional structures latent in high-dimensional data spaces. This study was performed to investigate whether the dimensionality reduction can identify embedded structures from the CT histogram feature of non-small-cell lung cancer (NSCLC) space to improve the performance in predicting the likelihood of RFS for patients with NSCLC.

Pseudocapacitive organic catechol derivative-functionalized three-dimensional graphene aerogel hybrid electrodes for high-performance supercapacitors

NASA Astrophysics Data System (ADS)

Choi, Jaewon; Yang, MinHo; Kim, Sung-Kon

2017-11-01

Bio-inspired and environmentally friendly chemical functionalization is a successful way to a new class of hybrid electrode materials for applications in energy storage. Quinone (Q)-hydroquinone (QH2) couples, a prototypical example of organic redox systems, provide fast and reversible proton-coupled electron-transfer reactions which lead to increased capacity. To achieve high capacitance and rate performance, constructing three-dimensional (3D) continuous porous structure is highly desirable. Here we report the hybrid electrodes (GA-C) consisting of 3D graphene aerogel (GA) functionalized with organic redox-active material, catechol derivative, for application to high-performance supercapacitors. The catechol derivative is adsorbed on the surface of GA through non-covalent interactions and promotes fast and reversible Q/QH2 faradaic reactions, providing large specific capacitance of 188 F g-1 at a current of 1 A g-1 and a specific energy of ∼25 Wh kg-1 at a specific power of ∼18,000 W kg-1. 3D continuous porous structure of GA electrode facilitates ion and electron transports, resulting in high rate performance (∼140 F g-1 at a current of 10 A g-1).

OBSERVER RATING VERSUS THREE-DIMENSIONAL MOTION ANALYSIS OF LOWER EXTREMITY KINEMATICS DURING FUNCTIONAL SCREENING TESTS: A SYSTEMATIC REVIEW.

PubMed

Maclachlan, Liam; White, Steven G; Reid, Duncan

2015-08-01

Functional assessments are conducted in both clinical and athletic settings in an attempt to identify those individuals who exhibit movement patterns that may increase their risk of non-contact injury. In place of highly sophisticated three-dimensional motion analysis, functional testing can be completed through observation. To evaluate the validity of movement observation assessments by summarizing the results of articles comparing human observation in real-time or video play-back and three-dimensional motion analysis of lower extremity kinematics during functional screening tests. Systematic review. A computerized systematic search was conducted through Medline, SPORTSdiscus, Scopus, Cinhal, and Cochrane health databases between February and April of 2014. Validity studies comparing human observation (real-time or video play-back) to three-dimensional motion analysis of functional tasks were selected. Only studies comprising uninjured, healthy subjects conducting lower extremity functional assessments were appropriate for review. Eligible observers were certified health practitioners or qualified members of sports and athletic training teams that conduct athlete screening. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) was used to appraise the literature. Results are presented in terms of functional tasks. Six studies met the inclusion criteria. Across these studies, two-legged squats, single-leg squats, drop-jumps, and running and cutting manoeuvres were the functional tasks analysed. When compared to three-dimensional motion analysis, observer ratings of lower extremity kinematics, such as knee position in relation to the foot, demonstrated mixed results. Single-leg squats achieved target sensitivity values (≥ 80%) but not specificity values (≥ 50%>%). Drop-jump task agreement ranged from poor (< 50%) to excellent (> 80%). Two-legged squats achieved 88% sensitivity and 85% specificity. Mean underestimations as large as 198 (peak knee flexion) were found in the results of those assessing running and side-step cutting manoeuvres. Variables such as the speed of movement, the methods of rating, the profiles of participants and the experience levels of observers may have influenced the outcomes of functional testing. The small number of studies used limits generalizability. Furthermore, this review used two dimensional video-playback for the majority of observations. If the movements had been rated in real-time three dimensional video, the results may have been different. Slower, speed controlled movements using dichotomous ratings reach target sensitivity and demonstrate higher overall levels of agreement. As a result, their utilization in functional screening is advocated. 1A.

Unsteady three-dimensional thermal field prediction in turbine blades using nonlinear BEM

NASA Technical Reports Server (NTRS)

Martin, Thomas J.; Dulikravich, George S.

1993-01-01

A time-and-space accurate and computationally efficient fully three dimensional unsteady temperature field analysis computer code has been developed for truly arbitrary configurations. It uses boundary element method (BEM) formulation based on an unsteady Green's function approach, multi-point Gaussian quadrature spatial integration on each panel, and a highly clustered time-step integration. The code accepts either temperatures or heat fluxes as boundary conditions that can vary in time on a point-by-point basis. Comparisons of the BEM numerical results and known analytical unsteady results for simple shapes demonstrate very high accuracy and reliability of the algorithm. An example of computed three dimensional temperature and heat flux fields in a realistically shaped internally cooled turbine blade is also discussed.

A functional video-based anthropometric measuring system

NASA Technical Reports Server (NTRS)

Nixon, J. H.; Cater, J. P.

1982-01-01

A high-speed anthropometric three dimensional measurement system using the Selcom Selspot motion tracking instrument for visual data acquisition is discussed. A three-dimensional scanning system was created which collects video, audio, and performance data on a single standard video cassette recorder. Recording rates of 1 megabit per second for periods of up to two hours are possible with the system design. A high-speed off-the-shelf motion analysis system for collecting optical information as used. The video recording adapter (VRA) is interfaced to the Selspot data acquisition system.

Nearly Interactive Parabolized Navier-Stokes Solver for High Speed Forebody and Inlet Flows

NASA Technical Reports Server (NTRS)

Benson, Thomas J.; Liou, May-Fun; Jones, William H.; Trefny, Charles J.

2009-01-01

A system of computer programs is being developed for the preliminary design of high speed inlets and forebodies. The system comprises four functions: geometry definition, flow grid generation, flow solver, and graphics post-processor. The system runs on a dedicated personal computer using the Windows operating system and is controlled by graphical user interfaces written in MATLAB (The Mathworks, Inc.). The flow solver uses the Parabolized Navier-Stokes equations to compute millions of mesh points in several minutes. Sample two-dimensional and three-dimensional calculations are demonstrated in the paper.

Impedance Eduction in Sound Fields With Peripherally Varying Liners and Flow

NASA Technical Reports Server (NTRS)

Watson, W. R.; Jones, M. G.

2015-01-01

A two-dimensional impedance eduction theory is extended to three-dimensional sound fields and peripherally varying duct liners. The approach is to first measure the acoustic pressure field at a series of flush-mounted wall microphones located around the periphery of the flow duct. The numerical solution for the acoustic pressure field at these microphones is also obtained by solving the three-dimensional convected Helmholtz equation using the finite element method. A quadratic objective function based on the difference between the measured and finite element solution is constructed and the unknown impedance function is obtained by minimizing this objective function. Impedance spectra educed for two uniform-structure liners (a wire-mesh and a conventional liner) and a hard-soft-hard peripherally varying liner (for which the soft segment is that of the conventional liner) are presented. Results are presented at three mean flow Mach numbers and fourteen sound source frequencies. The impedance spectra of the uniform-structure liners are also computed using a two-dimensional impedance eduction theory. The primary conclusions of the study are: 1) when measured data is used with the uniform-structure liners, the three-dimensional theory reproduces the same impedance spectra as the two-dimensional theory except for frequencies corresponding to very low or very high liner attenuation; and 2) good agreement between the educed impedance spectra of the uniform structure conventional liner and the soft segment of the peripherally varying liner is obtained.

TH-A-9A-04: Incorporating Liver Functionality in Radiation Therapy Treatment Planning

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

Wu, V; Epelman, M; Feng, M

2014-06-15

Purpose: Liver SBRT patients have both variable pretreatment liver function (e.g., due to degree of cirrhosis and/or prior treatments) and sensitivity to radiation, leading to high variability in potential liver toxicity with similar doses. This work aims to explicitly incorporate liver perfusion into treatment planning to redistribute dose to preserve well-functioning areas without compromising target coverage. Methods: Voxel-based liver perfusion, a measure of functionality, was computed from dynamic contrast-enhanced MRI. Two optimization models with different cost functions subject to the same dose constraints (e.g., minimum target EUD and maximum critical structure EUDs) were compared. The cost functions minimized were EUDmore » (standard model) and functionality-weighted EUD (functional model) to the liver. The resulting treatment plans delivering the same target EUD were compared with respect to their DVHs, their dose wash difference, the average dose delivered to voxels of a particular perfusion level, and change in number of high-/low-functioning voxels receiving a particular dose. Two-dimensional synthetic and three-dimensional clinical examples were studied. Results: The DVHs of all structures of plans from each model were comparable. In contrast, in plans obtained with the functional model, the average dose delivered to high-/low-functioning voxels was lower/higher than in plans obtained with its standard counterpart. The number of high-/low-functioning voxels receiving high/low dose was lower in the plans that considered perfusion in the cost function than in the plans that did not. Redistribution of dose can be observed in the dose wash differences. Conclusion: Liver perfusion can be used during treatment planning potentially to minimize the risk of toxicity during liver SBRT, resulting in better global liver function. The functional model redistributes dose in the standard model from higher to lower functioning voxels, while achieving the same target EUD and satisfying dose limits to critical structures. This project is funded by MCubed and grant R01-CA132834.« less

Elitist Binary Wolf Search Algorithm for Heuristic Feature Selection in High-Dimensional Bioinformatics Datasets.

PubMed

Li, Jinyan; Fong, Simon; Wong, Raymond K; Millham, Richard; Wong, Kelvin K L

2017-06-28

Due to the high-dimensional characteristics of dataset, we propose a new method based on the Wolf Search Algorithm (WSA) for optimising the feature selection problem. The proposed approach uses the natural strategy established by Charles Darwin; that is, 'It is not the strongest of the species that survives, but the most adaptable'. This means that in the evolution of a swarm, the elitists are motivated to quickly obtain more and better resources. The memory function helps the proposed method to avoid repeat searches for the worst position in order to enhance the effectiveness of the search, while the binary strategy simplifies the feature selection problem into a similar problem of function optimisation. Furthermore, the wrapper strategy gathers these strengthened wolves with the classifier of extreme learning machine to find a sub-dataset with a reasonable number of features that offers the maximum correctness of global classification models. The experimental results from the six public high-dimensional bioinformatics datasets tested demonstrate that the proposed method can best some of the conventional feature selection methods up to 29% in classification accuracy, and outperform previous WSAs by up to 99.81% in computational time.

Terahertz emission from the intrinsic Josephson junctions of high-symmetry thermally-managed Bi2Sr2CaCu2O8+δ microstrip antennas

NASA Astrophysics Data System (ADS)

Klemm, Richard A.; Davis, Andrew E.; Wang, Qing X.; Yamamoto, Takashi; Cerkoney, Daniel P.; Reid, Candy; Koopman, Maximiliaan L.; Minami, Hidetoshi; Kashiwagi, Takanari; Rain, Joseph R.; Doty, Constance M.; Sedlack, Michael A.; Morales, Manuel A.; Watanabe, Chiharu; Tsujimoto, Manabu; Delfanazari, Kaveh; Kadowaki, Kazuo

2017-12-01

We show for high-symmetry disk, square, or equilateral triangular thin microstrip antennas of any composition respectively obeying C ∞v , C 4v , and C 3v point group symmetries, that the transverse magnetic electromagnetic cavity mode wave functions are restricted in form to those that are one-dimensional representations of those point groups. Plots of the common nodal points of the ten lowest-energy non-radiating two-dimensional representations of each of these three symmetries are presented. For comparison with symmetry-broken disk intrinsic Josephson junction microstrip antennas constructed from the highly anisotropic layered superconductor Bi2Sr2CaCu2O8+δ (BSCCO), we present plots of the ten lowest frequency orthonormal wave functions and of their emission power angular distributions. These results are compared with previous results for square and equilateral triangular thin microstrip antennas.

Three-dimensional desirability spaces for quality-by-design-based HPLC development.

PubMed

Mokhtar, Hatem I; Abdel-Salam, Randa A; Hadad, Ghada M

2015-04-01

In this study, three-dimensional desirability spaces were introduced as a graphical representation method of design space. This was illustrated in the context of application of quality-by-design concepts on development of a stability indicating gradient reversed-phase high-performance liquid chromatography method for the determination of vinpocetine and α-tocopheryl acetate in a capsule dosage form. A mechanistic retention model to optimize gradient time, initial organic solvent concentration and ternary solvent ratio was constructed for each compound from six experimental runs. Then, desirability function of each optimized criterion and subsequently the global desirability function were calculated throughout the knowledge space. The three-dimensional desirability spaces were plotted as zones exceeding a threshold value of desirability index in space defined by the three optimized method parameters. Probabilistic mapping of desirability index aided selection of design space within the potential desirability subspaces. Three-dimensional desirability spaces offered better visualization and potential design spaces for the method as a function of three method parameters with ability to assign priorities to this critical quality as compared with the corresponding resolution spaces. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Evaluation of training nurses to perform semi-automated three-dimensional left ventricular ejection fraction using a customised workstation-based training protocol.

PubMed

Guppy-Coles, Kristyan B; Prasad, Sandhir B; Smith, Kym C; Hillier, Samuel; Lo, Ada; Atherton, John J

2015-06-01

We aimed to determine the feasibility of training cardiac nurses to evaluate left ventricular function utilising a semi-automated, workstation-based protocol on three dimensional echocardiography images. Assessment of left ventricular function by nurses is an attractive concept. Recent developments in three dimensional echocardiography coupled with border detection assistance have reduced inter- and intra-observer variability and analysis time. This could allow abbreviated training of nurses to assess cardiac function. A comparative, diagnostic accuracy study evaluating left ventricular ejection fraction assessment utilising a semi-automated, workstation-based protocol performed by echocardiography-naïve nurses on previously acquired three dimensional echocardiography images. Nine cardiac nurses underwent two brief lectures about cardiac anatomy, physiology and three dimensional left ventricular ejection fraction assessment, before a hands-on demonstration in 20 cases. We then selected 50 cases from our three dimensional echocardiography library based on optimal image quality with a broad range of left ventricular ejection fractions, which was quantified by two experienced sonographers and the average used as the comparator for the nurses. Nurses independently measured three dimensional left ventricular ejection fraction using the Auto lvq package with semi-automated border detection. The left ventricular ejection fraction range was 25-72% (70% with a left ventricular ejection fraction <55%). All nurses showed excellent agreement with the sonographers. Minimal intra-observer variability was noted on both short-term (same day) and long-term (>2 weeks later) retest. It is feasible to train nurses to measure left ventricular ejection fraction utilising a semi-automated, workstation-based protocol on previously acquired three dimensional echocardiography images. Further study is needed to determine the feasibility of training nurses to acquire three dimensional echocardiography images on real-world patients to measure left ventricular ejection fraction. Nurse-performed evaluation of left ventricular function could facilitate the broader application of echocardiography to allow cost-effective screening and monitoring for left ventricular dysfunction in high-risk populations. © 2014 John Wiley & Sons Ltd.

Some applications of the multi-dimensional fractional order for the Riemann-Liouville derivative

NASA Astrophysics Data System (ADS)

Ahmood, Wasan Ajeel; Kiliçman, Adem

2017-01-01

In this paper, the aim of this work is to study theorem for the one-dimensional space-time fractional deriative, generalize some function for the one-dimensional fractional by table represents the fractional Laplace transforms of some elementary functions to be valid for the multi-dimensional fractional Laplace transform and give the definition of the multi-dimensional fractional Laplace transform. This study includes that, dedicate the one-dimensional fractional Laplace transform for functions of only one independent variable and develop of the one-dimensional fractional Laplace transform to multi-dimensional fractional Laplace transform based on the modified Riemann-Liouville derivative.

Robust hashing with local models for approximate similarity search.

PubMed

Song, Jingkuan; Yang, Yi; Li, Xuelong; Huang, Zi; Yang, Yang

2014-07-01

Similarity search plays an important role in many applications involving high-dimensional data. Due to the known dimensionality curse, the performance of most existing indexing structures degrades quickly as the feature dimensionality increases. Hashing methods, such as locality sensitive hashing (LSH) and its variants, have been widely used to achieve fast approximate similarity search by trading search quality for efficiency. However, most existing hashing methods make use of randomized algorithms to generate hash codes without considering the specific structural information in the data. In this paper, we propose a novel hashing method, namely, robust hashing with local models (RHLM), which learns a set of robust hash functions to map the high-dimensional data points into binary hash codes by effectively utilizing local structural information. In RHLM, for each individual data point in the training dataset, a local hashing model is learned and used to predict the hash codes of its neighboring data points. The local models from all the data points are globally aligned so that an optimal hash code can be assigned to each data point. After obtaining the hash codes of all the training data points, we design a robust method by employing l2,1 -norm minimization on the loss function to learn effective hash functions, which are then used to map each database point into its hash code. Given a query data point, the search process first maps it into the query hash code by the hash functions and then explores the buckets, which have similar hash codes to the query hash code. Extensive experimental results conducted on real-life datasets show that the proposed RHLM outperforms the state-of-the-art methods in terms of search quality and efficiency.

Vena contracta analysis by color Doppler three-dimensional transesophageal echocardiography shows geometrical differences between prolapse and pseudoprolapse in eccentric mitral regurgitation.

PubMed

Berdejo, Javier; Shiota, Maiko; Mihara, Hirotsugu; Itabashi, Yuji; Utsunomiya, Hiroto; Shiota, Takahiro

2017-05-01

Evaluation of eccentric mitral regurgitation (MR) remains extremely difficult and the role played by its etiology, functional or degenerative, is not well understood. This study aimed to demonstrate the value of three-dimensional transesophageal echocardiography (3DTEE) in the evaluation of eccentric MR identifying geometric differences in the vena contracta area between functional and degenerative etiologies. We studied 61 patients with eccentric MR (30 functional and 31 degenerative). Regurgitant orifice area was determined by the two-dimensional proximal isovelocity surface area (2DPISA) and the 3DTEE methods. The ratio between maximum and minimum lengths of the vena contracta was calculated in each patient. Effective regurgitant orifice area by the 2DPISA method was smaller than that estimated by 3DTEE (0.56±0.21 vs 0.72±0.25 cm 2 ). A better correlation between both methods was seen in degenerative mitral regurgitation (DMR; r=.83), with a mean underestimation of 8.2% by the 2DPISA method. A much worse correlation was found in functional mitral regurgitation (FMR; r=.39), where a mean underestimation by the 2DPISA method of 29.1% was observed. There was a more elongated and curved vena contracta in FMR compared to that in DMR (length ratio: 3.4±1.0 vs 2.2±0.7, P<.0001). Three-dimensional transesophageal echocardiography identifies a more elongated regurgitant orifice in eccentric FMR compared to that in eccentric DMR. This difference may explain the greater underestimation of effective regurgitant orifice area by the 2DPISA method in FMR. High-quality 3DTEE analysis of vena contracta area would be a highly recommended alternative. © 2017, Wiley Periodicals, Inc.

A Multivariate Granger Causality Concept towards Full Brain Functional Connectivity.

PubMed

Schmidt, Christoph; Pester, Britta; Schmid-Hertel, Nicole; Witte, Herbert; Wismüller, Axel; Leistritz, Lutz

2016-01-01

Detecting changes of spatially high-resolution functional connectivity patterns in the brain is crucial for improving the fundamental understanding of brain function in both health and disease, yet still poses one of the biggest challenges in computational neuroscience. Currently, classical multivariate Granger Causality analyses of directed interactions between single process components in coupled systems are commonly restricted to spatially low- dimensional data, which requires a pre-selection or aggregation of time series as a preprocessing step. In this paper we propose a new fully multivariate Granger Causality approach with embedded dimension reduction that makes it possible to obtain a representation of functional connectivity for spatially high-dimensional data. The resulting functional connectivity networks may consist of several thousand vertices and thus contain more detailed information compared to connectivity networks obtained from approaches based on particular regions of interest. Our large scale Granger Causality approach is applied to synthetic and resting state fMRI data with a focus on how well network community structure, which represents a functional segmentation of the network, is preserved. It is demonstrated that a number of different community detection algorithms, which utilize a variety of algorithmic strategies and exploit topological features differently, reveal meaningful information on the underlying network module structure.

Simulation Of Wave Function And Probability Density Of Modified Poschl Teller Potential Derived Using Supersymmetric Quantum Mechanics

NASA Astrophysics Data System (ADS)

Angraini, Lily Maysari; Suparmi, Variani, Viska Inda

2010-12-01

SUSY quantum mechanics can be applied to solve Schrodinger equation for high dimensional system that can be reduced into one dimensional system and represented in lowering and raising operators. Lowering and raising operators can be obtained using relationship between original Hamiltonian equation and the (super) potential equation. In this paper SUSY quantum mechanics is used as a method to obtain the wave function and the energy level of the Modified Poschl Teller potential. The graph of wave function equation and probability density is simulated by using Delphi 7.0 programming language. Finally, the expectation value of quantum mechanics operator could be calculated analytically using integral form or probability density graph resulted by the programming.

Layer-by-Layer Self-Assembling Gold Nanorods and Glucose Oxidase onto Carbon Nanotubes Functionalized Sol-Gel Matrix for an Amperometric Glucose Biosensor

PubMed Central

Wu, Baoyan; Hou, Shihua; Miao, Zhiying; Zhang, Cong; Ji, Yanhong

2015-01-01

A novel amperometric glucose biosensor was fabricated by layer-by-layer self-assembly of gold nanorods (AuNRs) and glucose oxidase (GOD) onto single-walled carbon nanotubes (SWCNTs)-functionalized three-dimensional sol-gel matrix. A thiolated aqueous silica sol containing SWCNTs was first assembled on the surface of a cleaned Au electrode, and then the alternate self-assembly of AuNRs and GOD were repeated to assemble multilayer films of AuNRs-GOD onto SWCNTs-functionalized silica gel for optimizing the biosensor. Among the resulting glucose biosensors, the four layers of AuNRs-GOD-modified electrode showed the best performance. The sol-SWCNTs-(AuNRs-GOD)4/Au biosensor exhibited a good linear range of 0.01–8 mM glucose, high sensitivity of 1.08 μA/mM, and fast amperometric response within 4 s. The good performance of the proposed glucose biosensor could be mainly attributed to the advantages of the three-dimensional sol-gel matrix and stereo self-assembly films, and the natural features of one-dimensional nanostructure SWCNTs and AuNRs. This study may provide a new facile way to fabricate the enzyme-based biosensor with high performance. PMID:28347080

Two-dimensional analytic weighting functions for limb scattering

NASA Astrophysics Data System (ADS)

Zawada, D. J.; Bourassa, A. E.; Degenstein, D. A.

2017-10-01

Through the inversion of limb scatter measurements it is possible to obtain vertical profiles of trace species in the atmosphere. Many of these inversion methods require what is often referred to as weighting functions, or derivatives of the radiance with respect to concentrations of trace species in the atmosphere. Several radiative transfer models have implemented analytic methods to calculate weighting functions, alleviating the computational burden of traditional numerical perturbation methods. Here we describe the implementation of analytic two-dimensional weighting functions, where derivatives are calculated relative to atmospheric constituents in a two-dimensional grid of altitude and angle along the line of sight direction, in the SASKTRAN-HR radiative transfer model. Two-dimensional weighting functions are required for two-dimensional inversions of limb scatter measurements. Examples are presented where the analytic two-dimensional weighting functions are calculated with an underlying one-dimensional atmosphere. It is shown that the analytic weighting functions are more accurate than ones calculated with a single scatter approximation, and are orders of magnitude faster than a typical perturbation method. Evidence is presented that weighting functions for stratospheric aerosols calculated under a single scatter approximation may not be suitable for use in retrieval algorithms under solar backscatter conditions.

Three-dimensional fine structure of the organization of microtubules in neurite varicosities by ultra-high voltage electron microscope tomography.

PubMed

Nishida, Tomoki; Yoshimura, Ryoichi; Endo, Yasuhisa

2017-09-01

Neurite varicosities are highly specialized compartments that are involved in neurotransmitter/ neuromodulator release and provide a physiological platform for neural functions. However, it remains unclear how microtubule organization contributes to the form of varicosity. Here, we examine the three-dimensional structure of microtubules in varicosities of a differentiated PC12 neural cell line using ultra-high voltage electron microscope tomography. Three-dimensional imaging showed that a part of the varicosities contained an accumulation of organelles that were separated from parallel microtubule arrays. Further detailed analysis using serial sections and whole-mount tomography revealed microtubules running in a spindle shape of swelling in some other types of varicosities. These electron tomographic results showed that the structural diversity and heterogeneity of microtubule organization supported the form of varicosities, suggesting that a different distribution pattern of microtubules in varicosities is crucial to the regulation of varicosities development.

High-resolution neutron diffraction study of CuNCN: New evidence of structure anomalies at low temperature

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

Jacobs, Philipp; Houben, Andreas; Dronskowski, Richard, E-mail: drons@HAL9000.ac.rwth-aachen.de

Copper carbodiimide (CuNCN) is the nitrogen-containing analogue of cupric oxide. Based on high-resolution neutron-diffraction data, CuNCN's lattice parameters are derived as a function of the temperature. In accordance with a recent synchrotron study, a clear trend in the cell parameter a is observed accompanying the changing magnetic behavior. With decreasing temperature, a slowly decreases to a minimum at ∼100 K after which it rises again. The same trend—albeit more pronounced—is observed for the c lattice parameter at ∼35 K. The herein presented neutron powder-diffraction data also support the conjectured sequence of transitions from the high-temperature one-dimensional resonating valence-bond (RVB) statemore » to a transient two-dimensional RVB state and eventually, at lowest temperatures, into another two-dimensional RVB state, presumably the ground state.« less

Ultra-high resolution computed tomography imaging

DOEpatents

Paulus, Michael J.; Sari-Sarraf, Hamed; Tobin, Jr., Kenneth William; Gleason, Shaun S.; Thomas, Jr., Clarence E.

2002-01-01

A method for ultra-high resolution computed tomography imaging, comprising the steps of: focusing a high energy particle beam, for example x-rays or gamma-rays, onto a target object; acquiring a 2-dimensional projection data set representative of the target object; generating a corrected projection data set by applying a deconvolution algorithm, having an experimentally determined a transfer function, to the 2-dimensional data set; storing the corrected projection data set; incrementally rotating the target object through an angle of approximately 180.degree., and after each the incremental rotation, repeating the radiating, acquiring, generating and storing steps; and, after the rotating step, applying a cone-beam algorithm, for example a modified tomographic reconstruction algorithm, to the corrected projection data sets to generate a 3-dimensional image. The size of the spot focus of the beam is reduced to not greater than approximately 1 micron, and even to not greater than approximately 0.5 microns.

The construction of a two-dimensional reproducing kernel function and its application in a biomedical model.

PubMed

Guo, Qi; Shen, Shu-Ting

2016-04-29

There are two major classes of cardiac tissue models: the ionic model and the FitzHugh-Nagumo model. During computer simulation, each model entails solving a system of complex ordinary differential equations and a partial differential equation with non-flux boundary conditions. The reproducing kernel method possesses significant applications in solving partial differential equations. The derivative of the reproducing kernel function is a wavelet function, which has local properties and sensitivities to singularity. Therefore, study on the application of reproducing kernel would be advantageous. Applying new mathematical theory to the numerical solution of the ventricular muscle model so as to improve its precision in comparison with other methods at present. A two-dimensional reproducing kernel function inspace is constructed and applied in computing the solution of two-dimensional cardiac tissue model by means of the difference method through time and the reproducing kernel method through space. Compared with other methods, this method holds several advantages such as high accuracy in computing solutions, insensitivity to different time steps and a slow propagation speed of error. It is suitable for disorderly scattered node systems without meshing, and can arbitrarily change the location and density of the solution on different time layers. The reproducing kernel method has higher solution accuracy and stability in the solutions of the two-dimensional cardiac tissue model.

A unique case of "double-orifice aortic valve"-comprehensive assessment by 2-, 3-dimensional, and color Doppler echocardiography.

PubMed

Stirrup, James E; Cowburn, Peter J; Pousios, Dimitrios; Ohri, Sunil K; Shah, Benoy N

2016-09-01

Transesophageal echocardiography (TEE) is a powerful imaging tool for the comprehensive assessment of valvular structure and function. TEE may be of added benefit when anatomy is difficult to delineate accurately by transthoracic echocardiography. In this article, we present 2-, 3-dimensional, and color Doppler TEE images from a male patient with aortic stenosis. A highly unusual and complex pattern of valvular calcification created a functionally "double-orifice" valve. Such an abnormality may have implications for the accuracy of continuous-wave Doppler echocardiography, which assumes a single orifice valve in native aortic valves. © 2016, Wiley Periodicals, Inc.

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

Tipireddy, R.; Stinis, P.; Tartakovsky, A. M.

In this paper, we present a novel approach for solving steady-state stochastic partial differential equations (PDEs) with high-dimensional random parameter space. The proposed approach combines spatial domain decomposition with basis adaptation for each subdomain. The basis adaptation is used to address the curse of dimensionality by constructing an accurate low-dimensional representation of the stochastic PDE solution (probability density function and/or its leading statistical moments) in each subdomain. Restricting the basis adaptation to a specific subdomain affords finding a locally accurate solution. Then, the solutions from all of the subdomains are stitched together to provide a global solution. We support ourmore » construction with numerical experiments for a steady-state diffusion equation with a random spatially dependent coefficient. Lastly, our results show that highly accurate global solutions can be obtained with significantly reduced computational costs.« less

Constrained optimization by radial basis function interpolation for high-dimensional expensive black-box problems with infeasible initial points

NASA Astrophysics Data System (ADS)

Regis, Rommel G.

2014-02-01

This article develops two new algorithms for constrained expensive black-box optimization that use radial basis function surrogates for the objective and constraint functions. These algorithms are called COBRA and Extended ConstrLMSRBF and, unlike previous surrogate-based approaches, they can be used for high-dimensional problems where all initial points are infeasible. They both follow a two-phase approach where the first phase finds a feasible point while the second phase improves this feasible point. COBRA and Extended ConstrLMSRBF are compared with alternative methods on 20 test problems and on the MOPTA08 benchmark automotive problem (D.R. Jones, Presented at MOPTA 2008), which has 124 decision variables and 68 black-box inequality constraints. The alternatives include a sequential penalty derivative-free algorithm, a direct search method with kriging surrogates, and two multistart methods. Numerical results show that COBRA algorithms are competitive with Extended ConstrLMSRBF and they generally outperform the alternatives on the MOPTA08 problem and most of the test problems.

SEMIPARAMETRIC QUANTILE REGRESSION WITH HIGH-DIMENSIONAL COVARIATES

PubMed Central

Zhu, Liping; Huang, Mian; Li, Runze

2012-01-01

This paper is concerned with quantile regression for a semiparametric regression model, in which both the conditional mean and conditional variance function of the response given the covariates admit a single-index structure. This semiparametric regression model enables us to reduce the dimension of the covariates and simultaneously retains the flexibility of nonparametric regression. Under mild conditions, we show that the simple linear quantile regression offers a consistent estimate of the index parameter vector. This is a surprising and interesting result because the single-index model is possibly misspecified under the linear quantile regression. With a root-n consistent estimate of the index vector, one may employ a local polynomial regression technique to estimate the conditional quantile function. This procedure is computationally efficient, which is very appealing in high-dimensional data analysis. We show that the resulting estimator of the quantile function performs asymptotically as efficiently as if the true value of the index vector were known. The methodologies are demonstrated through comprehensive simulation studies and an application to a real dataset. PMID:24501536

Spherical hashing: binary code embedding with hyperspheres.

PubMed

Heo, Jae-Pil; Lee, Youngwoon; He, Junfeng; Chang, Shih-Fu; Yoon, Sung-Eui

2015-11-01

Many binary code embedding schemes have been actively studied recently, since they can provide efficient similarity search, and compact data representations suitable for handling large scale image databases. Existing binary code embedding techniques encode high-dimensional data by using hyperplane-based hashing functions. In this paper we propose a novel hypersphere-based hashing function, spherical hashing, to map more spatially coherent data points into a binary code compared to hyperplane-based hashing functions. We also propose a new binary code distance function, spherical Hamming distance, tailored for our hypersphere-based binary coding scheme, and design an efficient iterative optimization process to achieve both balanced partitioning for each hash function and independence between hashing functions. Furthermore, we generalize spherical hashing to support various similarity measures defined by kernel functions. Our extensive experiments show that our spherical hashing technique significantly outperforms state-of-the-art techniques based on hyperplanes across various benchmarks with sizes ranging from one to 75 million of GIST, BoW and VLAD descriptors. The performance gains are consistent and large, up to 100 percent improvements over the second best method among tested methods. These results confirm the unique merits of using hyperspheres to encode proximity regions in high-dimensional spaces. Finally, our method is intuitive and easy to implement.

Recursive search method for the image elements of functionally defined surfaces

NASA Astrophysics Data System (ADS)

Vyatkin, S. I.

2017-05-01

This paper touches upon the synthesis of high-quality images in real time and the technique for specifying three-dimensional objects on the basis of perturbation functions. The recursive search method for the image elements of functionally defined objects with the use of graphics processing units is proposed. The advantages of such an approach over the frame-buffer visualization method are shown.

Self-assembled three-dimensional and compressible interdigitated thin-film supercapacitors and batteries

PubMed Central

Nyström, Gustav; Marais, Andrew; Karabulut, Erdem; Wågberg, Lars; Cui, Yi; Hamedi, Mahiar M.

2015-01-01

Traditional thin-film energy-storage devices consist of stacked layers of active films on two-dimensional substrates and do not exploit the third dimension. Fully three-dimensional thin-film devices would allow energy storage in bulk materials with arbitrary form factors and with mechanical properties unique to bulk materials such as compressibility. Here we show three-dimensional energy-storage devices based on layer-by-layer self-assembly of interdigitated thin films on the surface of an open-cell aerogel substrate. We demonstrate a reversibly compressible three-dimensional supercapacitor with carbon nanotube electrodes and a three-dimensional hybrid battery with a copper hexacyanoferrate ion intercalating cathode and a carbon nanotube anode. The three-dimensional supercapacitor shows stable operation over 400 cycles with a capacitance of 25 F g−1 and is fully functional even at compressions up to 75%. Our results demonstrate that layer-by-layer self-assembly inside aerogels is a rapid, precise and scalable route for building high-surface-area 3D thin-film devices. PMID:26021485

Two dimensional thermal and charge mapping of power thyristors

NASA Technical Reports Server (NTRS)

Hu, S. P.; Rabinovici, B. M.

1975-01-01

The two dimensional static and dynamic current density distributions within the junction of semiconductor power switching devices and in particular the thyristors were obtained. A method for mapping the thermal profile of the device junctions with fine resolution using an infrared beam and measuring the attenuation through the device as a function of temperature were developed. The results obtained are useful in the design and quality control of high power semiconductor switching devices.

Mineralized three-dimensional bone constructs

NASA Technical Reports Server (NTRS)

Pellis, Neal R. (Inventor); Clarke, Mark S. F. (Inventor); Sundaresan, Alamelu (Inventor)

2011-01-01

The present disclosure provides ex vivo-derived mineralized three-dimensional bone constructs. The bone constructs are obtained by culturing osteoblasts and osteoclast precursors under randomized gravity vector conditions. Preferably, the randomized gravity vector conditions are obtained using a low shear stress rotating bioreactor, such as a High Aspect Ratio Vessel (HARV) culture system. The bone constructs of the disclosure have utility in physiological studies of bone formation and bone function, in drug discovery, and in orthopedics.

Mineralized Three-Dimensional Bone Constructs

NASA Technical Reports Server (NTRS)

Clarke, Mark S. F. (Inventor); Sundaresan, Alamelu (Inventor); Pellis, Neal R. (Inventor)

2013-01-01

The present disclosure provides ex vivo-derived mineralized three-dimensional bone constructs. The bone constructs are obtained by culturing osteoblasts and osteoclast precursors under randomized gravity vector conditions. Preferably, the randomized gravity vector conditions are obtained using a low shear stress rotating bioreactor, such as a High Aspect Ratio Vessel (HARV) culture system. The bone constructs of the disclosure have utility in physiological studies of bone formation and bone function, in drug discovery, and in orthopedics.

Preparation and Characterization of Single Ion Conductors from High Surface Area Fumed Silica

NASA Technical Reports Server (NTRS)

Zhang, H.; Maitra, P.; Liu, B.; Wunder, S. L.; Lin, H.-P.; Salomon, M.; Hagedorn, Norman H. (Technical Monitor)

2002-01-01

Anions that can form dissociative salts with Li(+) have been prepared and covalently attached to high surface area fumed silica. When blended with polyethylene oxide (PEO), the functionalized fumed silica suppresses the crystallization of the PEO, provides dimensional stability, and serves as a single ion conductor. Since functionalized fumed silica is easily dispersed in common polar solvents, it can be incorporated in both the polymer electrolyte and the electrodes.

Transfer printing techniques for materials assembly and micro/nanodevice fabrication.

PubMed

Carlson, Andrew; Bowen, Audrey M; Huang, Yonggang; Nuzzo, Ralph G; Rogers, John A

2012-10-09

Transfer printing represents a set of techniques for deterministic assembly of micro-and nanomaterials into spatially organized, functional arrangements with two and three-dimensional layouts. Such processes provide versatile routes not only to test structures and vehicles for scientific studies but also to high-performance, heterogeneously integrated functional systems, including those in flexible electronics, three-dimensional and/or curvilinear optoelectronics, and bio-integrated sensing and therapeutic devices. This article summarizes recent advances in a variety of transfer printing techniques, ranging from the mechanics and materials aspects that govern their operation to engineering features of their use in systems with varying levels of complexity. A concluding section presents perspectives on opportunities for basic and applied research, and on emerging use of these methods in high throughput, industrial-scale manufacturing. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Locating landmarks on high-dimensional free energy surfaces

PubMed Central

Chen, Ming; Yu, Tang-Qing; Tuckerman, Mark E.

2015-01-01

Coarse graining of complex systems possessing many degrees of freedom can often be a useful approach for analyzing and understanding key features of these systems in terms of just a few variables. The relevant energy landscape in a coarse-grained description is the free energy surface as a function of the coarse-grained variables, which, despite the dimensional reduction, can still be an object of high dimension. Consequently, navigating and exploring this high-dimensional free energy surface is a nontrivial task. In this paper, we use techniques from multiscale modeling, stochastic optimization, and machine learning to devise a strategy for locating minima and saddle points (termed “landmarks”) on a high-dimensional free energy surface “on the fly” and without requiring prior knowledge of or an explicit form for the surface. In addition, we propose a compact graph representation of the landmarks and connections between them, and we show that the graph nodes can be subsequently analyzed and clustered based on key attributes that elucidate important properties of the system. Finally, we show that knowledge of landmark locations allows for the efficient determination of their relative free energies via enhanced sampling techniques. PMID:25737545

Stable long-time semiclassical description of zero-point energy in high-dimensional molecular systems.

PubMed

Garashchuk, Sophya; Rassolov, Vitaly A

2008-07-14

Semiclassical implementation of the quantum trajectory formalism [J. Chem. Phys. 120, 1181 (2004)] is further developed to give a stable long-time description of zero-point energy in anharmonic systems of high dimensionality. The method is based on a numerically cheap linearized quantum force approach; stabilizing terms compensating for the linearization errors are added into the time-evolution equations for the classical and nonclassical components of the momentum operator. The wave function normalization and energy are rigorously conserved. Numerical tests are performed for model systems of up to 40 degrees of freedom.

Acceleration and stability of a high-current ion beam in induction fields

NASA Astrophysics Data System (ADS)

Karas', V. I.; Manuilenko, O. V.; Tarakanov, V. P.; Federovskaya, O. V.

2013-03-01

A one-dimensional nonlinear analytic theory of the filamentation instability of a high-current ion beam is formulated. The results of 2.5-dimensional numerical particle-in-cell simulations of acceleration and stability of an annular compensated ion beam (CIB) in a linear induction particle accelerator are presented. It is shown that additional transverse injection of electron beams in magnetically insulated gaps (cusps) improves the quality of the ion-beam distribution function and provides uniform beam acceleration along the accelerator. The CIB filamentation instability in both the presence and the absence of an external magnetic field is considered.

STILTS Plotting Tools

NASA Astrophysics Data System (ADS)

Taylor, M. B.

2009-09-01

The new plotting functionality in version 2.0 of STILTS is described. STILTS is a mature and powerful package for all kinds of table manipulation, and this version adds facilities for generating plots from one or more tables to its existing wide range of non-graphical capabilities. 2- and 3-dimensional scatter plots and 1-dimensional histograms may be generated using highly configurable style parameters. Features include multiple dataset overplotting, variable transparency, 1-, 2- or 3-dimensional symmetric or asymmetric error bars, higher-dimensional visualization using color, and textual point labeling. Vector and bitmapped output formats are supported. The plotting options provide enough flexibility to perform meaningful visualization on datasets from a few points up to tens of millions. Arbitrarily large datasets can be plotted without heavy memory usage.

High frequency estimation of 2-dimensional cavity scattering

NASA Astrophysics Data System (ADS)

Dering, R. S.

1984-12-01

This thesis develops a simple ray tracing approximation for the high frequency scattering from a two-dimensional cavity. Whereas many other cavity scattering algorithms are very time consuming, this method is very swift. The analytical development of the ray tracing approach is performed in great detail, and it is shown how the radar cross section (RCS) depends on the cavity's length and width along with the radar wave's angle of incidence. This explains why the cavity's RCS oscillates as a function of incident angle. The RCS of a two dimensional cavity was measured experimentally, and these results were compared to computer calculations based on the high frequency ray tracing theory. The comparison was favorable in the sense that angular RCS minima and maxima were exactly predicted even though accuracy of the RCS magnitude decreased for incident angles far off-axis. Overall, once this method is extended to three dimensions, the technique shows promise as a fast first approximation of high frequency cavity scattering.

A computer program for uncertainty analysis integrating regression and Bayesian methods

USGS Publications Warehouse

Lu, Dan; Ye, Ming; Hill, Mary C.; Poeter, Eileen P.; Curtis, Gary

2014-01-01

This work develops a new functionality in UCODE_2014 to evaluate Bayesian credible intervals using the Markov Chain Monte Carlo (MCMC) method. The MCMC capability in UCODE_2014 is based on the FORTRAN version of the differential evolution adaptive Metropolis (DREAM) algorithm of Vrugt et al. (2009), which estimates the posterior probability density function of model parameters in high-dimensional and multimodal sampling problems. The UCODE MCMC capability provides eleven prior probability distributions and three ways to initialize the sampling process. It evaluates parametric and predictive uncertainties and it has parallel computing capability based on multiple chains to accelerate the sampling process. This paper tests and demonstrates the MCMC capability using a 10-dimensional multimodal mathematical function, a 100-dimensional Gaussian function, and a groundwater reactive transport model. The use of the MCMC capability is made straightforward and flexible by adopting the JUPITER API protocol. With the new MCMC capability, UCODE_2014 can be used to calculate three types of uncertainty intervals, which all can account for prior information: (1) linear confidence intervals which require linearity and Gaussian error assumptions and typically 10s–100s of highly parallelizable model runs after optimization, (2) nonlinear confidence intervals which require a smooth objective function surface and Gaussian observation error assumptions and typically 100s–1,000s of partially parallelizable model runs after optimization, and (3) MCMC Bayesian credible intervals which require few assumptions and commonly 10,000s–100,000s or more partially parallelizable model runs. Ready access allows users to select methods best suited to their work, and to compare methods in many circumstances.

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

PubMed

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

2018-01-11

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

Adjoint-Based, Three-Dimensional Error Prediction and Grid Adaptation

NASA Technical Reports Server (NTRS)

Park, Michael A.

2002-01-01

Engineering computational fluid dynamics (CFD) analysis and design applications focus on output functions (e.g., lift, drag). Errors in these output functions are generally unknown and conservatively accurate solutions may be computed. Computable error estimates can offer the possibility to minimize computational work for a prescribed error tolerance. Such an estimate can be computed by solving the flow equations and the linear adjoint problem for the functional of interest. The computational mesh can be modified to minimize the uncertainty of a computed error estimate. This robust mesh-adaptation procedure automatically terminates when the simulation is within a user specified error tolerance. This procedure for estimating and adapting to error in a functional is demonstrated for three-dimensional Euler problems. An adaptive mesh procedure that links to a Computer Aided Design (CAD) surface representation is demonstrated for wing, wing-body, and extruded high lift airfoil configurations. The error estimation and adaptation procedure yielded corrected functions that are as accurate as functions calculated on uniformly refined grids with ten times as many grid points.

Conjugate-gradient optimization method for orbital-free density functional calculations.

PubMed

Jiang, Hong; Yang, Weitao

2004-08-01

Orbital-free density functional theory as an extension of traditional Thomas-Fermi theory has attracted a lot of interest in the past decade because of developments in both more accurate kinetic energy functionals and highly efficient numerical methodology. In this paper, we developed a conjugate-gradient method for the numerical solution of spin-dependent extended Thomas-Fermi equation by incorporating techniques previously used in Kohn-Sham calculations. The key ingredient of the method is an approximate line-search scheme and a collective treatment of two spin densities in the case of spin-dependent extended Thomas-Fermi problem. Test calculations for a quartic two-dimensional quantum dot system and a three-dimensional sodium cluster Na216 with a local pseudopotential demonstrate that the method is accurate and efficient. (c) 2004 American Institute of Physics.

A comprehensive analysis of earthquake damage patterns using high dimensional model representation feature selection

NASA Astrophysics Data System (ADS)

Taşkin Kaya, Gülşen

2013-10-01

Recently, earthquake damage assessment using satellite images has been a very popular ongoing research direction. Especially with the availability of very high resolution (VHR) satellite images, a quite detailed damage map based on building scale has been produced, and various studies have also been conducted in the literature. As the spatial resolution of satellite images increases, distinguishability of damage patterns becomes more cruel especially in case of using only the spectral information during classification. In order to overcome this difficulty, textural information needs to be involved to the classification to improve the visual quality and reliability of damage map. There are many kinds of textural information which can be derived from VHR satellite images depending on the algorithm used. However, extraction of textural information and evaluation of them have been generally a time consuming process especially for the large areas affected from the earthquake due to the size of VHR image. Therefore, in order to provide a quick damage map, the most useful features describing damage patterns needs to be known in advance as well as the redundant features. In this study, a very high resolution satellite image after Iran, Bam earthquake was used to identify the earthquake damage. Not only the spectral information, textural information was also used during the classification. For textural information, second order Haralick features were extracted from the panchromatic image for the area of interest using gray level co-occurrence matrix with different size of windows and directions. In addition to using spatial features in classification, the most useful features representing the damage characteristic were selected with a novel feature selection method based on high dimensional model representation (HDMR) giving sensitivity of each feature during classification. The method called HDMR was recently proposed as an efficient tool to capture the input-output relationships in high-dimensional systems for many problems in science and engineering. The HDMR method is developed to improve the efficiency of the deducing high dimensional behaviors. The method is formed by a particular organization of low dimensional component functions, in which each function is the contribution of one or more input variables to the output variables.

Incremental online learning in high dimensions.

PubMed

Vijayakumar, Sethu; D'Souza, Aaron; Schaal, Stefan

2005-12-01

Locally weighted projection regression (LWPR) is a new algorithm for incremental nonlinear function approximation in high-dimensional spaces with redundant and irrelevant input dimensions. At its core, it employs nonparametric regression with locally linear models. In order to stay computationally efficient and numerically robust, each local model performs the regression analysis with a small number of univariate regressions in selected directions in input space in the spirit of partial least squares regression. We discuss when and how local learning techniques can successfully work in high-dimensional spaces and review the various techniques for local dimensionality reduction before finally deriving the LWPR algorithm. The properties of LWPR are that it (1) learns rapidly with second-order learning methods based on incremental training, (2) uses statistically sound stochastic leave-one-out cross validation for learning without the need to memorize training data, (3) adjusts its weighting kernels based on only local information in order to minimize the danger of negative interference of incremental learning, (4) has a computational complexity that is linear in the number of inputs, and (5) can deal with a large number of-possibly redundant-inputs, as shown in various empirical evaluations with up to 90 dimensional data sets. For a probabilistic interpretation, predictive variance and confidence intervals are derived. To our knowledge, LWPR is the first truly incremental spatially localized learning method that can successfully and efficiently operate in very high-dimensional spaces.

Mapping morphological shape as a high-dimensional functional curve

PubMed Central

Fu, Guifang; Huang, Mian; Bo, Wenhao; Hao, Han; Wu, Rongling

2018-01-01

Abstract Detecting how genes regulate biological shape has become a multidisciplinary research interest because of its wide application in many disciplines. Despite its fundamental importance, the challenges of accurately extracting information from an image, statistically modeling the high-dimensional shape and meticulously locating shape quantitative trait loci (QTL) affect the progress of this research. In this article, we propose a novel integrated framework that incorporates shape analysis, statistical curve modeling and genetic mapping to detect significant QTLs regulating variation of biological shape traits. After quantifying morphological shape via a radius centroid contour approach, each shape, as a phenotype, was characterized as a high-dimensional curve, varying as angle θ runs clockwise with the first point starting from angle zero. We then modeled the dynamic trajectories of three mean curves and variation patterns as functions of θ. Our framework led to the detection of a few significant QTLs regulating the variation of leaf shape collected from a natural population of poplar, Populus szechuanica var tibetica. This population, distributed at altitudes 2000–4500 m above sea level, is an evolutionarily important plant species. This is the first work in the quantitative genetic shape mapping area that emphasizes a sense of ‘function’ instead of decomposing the shape into a few discrete principal components, as the majority of shape studies do. PMID:28062411

Density functional theory versus quantum Monte Carlo simulations of Fermi gases in the optical-lattice arena★

NASA Astrophysics Data System (ADS)

Pilati, Sebastiano; Zintchenko, Ilia; Troyer, Matthias; Ancilotto, Francesco

2018-04-01

We benchmark the ground state energies and the density profiles of atomic repulsive Fermi gases in optical lattices (OLs) computed via density functional theory (DFT) against the results of diffusion Monte Carlo (DMC) simulations. The main focus is on a half-filled one-dimensional OLs, for which the DMC simulations performed within the fixed-node approach provide unbiased results. This allows us to demonstrate that the local spin-density approximation (LSDA) to the exchange-correlation functional of DFT is very accurate in the weak and intermediate interactions regime, and also to underline its limitations close to the strongly-interacting Tonks-Girardeau limit and in very deep OLs. We also consider a three-dimensional OL at quarter filling, showing also in this case the high accuracy of the LSDA in the moderate interaction regime. The one-dimensional data provided in this study may represent a useful benchmark to further develop DFT methods beyond the LSDA and they will hopefully motivate experimental studies to accurately measure the equation of state of Fermi gases in higher-dimensional geometries. Supplementary material in the form of one pdf file available from the Journal web page at http://https://doi.org/10.1140/epjb/e2018-90021-1.

Uniform electron gases. III. Low-density gases on three-dimensional spheres.

PubMed

Agboola, Davids; Knol, Anneke L; Gill, Peter M W; Loos, Pierre-François

2015-08-28

By combining variational Monte Carlo (VMC) and complete-basis-set limit Hartree-Fock (HF) calculations, we have obtained near-exact correlation energies for low-density same-spin electrons on a three-dimensional sphere (3-sphere), i.e., the surface of a four-dimensional ball. In the VMC calculations, we compare the efficacies of two types of one-electron basis functions for these strongly correlated systems and analyze the energy convergence with respect to the quality of the Jastrow factor. The HF calculations employ spherical Gaussian functions (SGFs) which are the curved-space analogs of Cartesian Gaussian functions. At low densities, the electrons become relatively localized into Wigner crystals, and the natural SGF centers are found by solving the Thomson problem (i.e., the minimum-energy arrangement of n point charges) on the 3-sphere for various values of n. We have found 11 special values of n whose Thomson sites are equivalent. Three of these are the vertices of four-dimensional Platonic solids - the hyper-tetrahedron (n = 5), the hyper-octahedron (n = 8), and the 24-cell (n = 24) - and a fourth is a highly symmetric structure (n = 13) which has not previously been reported. By calculating the harmonic frequencies of the electrons around their equilibrium positions, we also find the first-order vibrational corrections to the Thomson energy.

Three-dimensional in vitro cancer spheroid models for Photodynamic Therapy: Strengths and Opportunities

NASA Astrophysics Data System (ADS)

Evans, Conor

2015-03-01

Three dimensional, in vitro spheroid cultures offer considerable utility for the development and testing of anticancer photodynamic therapy regimens. More complex than monolayer cultures, three-dimensional spheroid systems replicate many of the important cell-cell and cell-matrix interactions that modulate treatment response in vivo. Simple enough to be grown by the thousands and small enough to be optically interrogated, spheroid cultures lend themselves to high-content and high-throughput imaging approaches. These advantages have enabled studies investigating photosensitizer uptake, spatiotemporal patterns of therapeutic response, alterations in oxygen diffusion and consumption during therapy, and the exploration of mechanisms that underlie therapeutic synergy. The use of quantitative imaging methods, in particular, has accelerated the pace of three-dimensional in vitro photodynamic therapy studies, enabling the rapid compilation of multiple treatment response parameters in a single experiment. Improvements in model cultures, the creation of new molecular probes of cell state and function, and innovations in imaging toolkits will be important for the advancement of spheroid culture systems for future photodynamic therapy studies.

Nanostructuring one-dimensional and amorphous lithium peroxide for high round-trip efficiency in lithium-oxygen batteries.

PubMed

Dutta, Arghya; Wong, Raymond A; Park, Woonghyeon; Yamanaka, Keisuke; Ohta, Toshiaki; Jung, Yousung; Byon, Hye Ryung

2018-02-14

The major challenge facing lithium-oxygen batteries is the insulating and bulk lithium peroxide discharge product, which causes sluggish decomposition and increasing overpotential during recharge. Here, we demonstrate an improved round-trip efficiency of ~80% by means of a mesoporous carbon electrode, which directs the growth of one-dimensional and amorphous lithium peroxide. Morphologically, the one-dimensional nanostructures with small volume and high surface show improved charge transport and promote delithiation (lithium ion dissolution) during recharge and thus plays a critical role in the facile decomposition of lithium peroxide. Thermodynamically, density functional calculations reveal that disordered geometric arrangements of the surface atoms in the amorphous structure lead to weaker binding of the key reaction intermediate lithium superoxide, yielding smaller oxygen reduction and evolution overpotentials compared to the crystalline surface. This study suggests a strategy to enhance the decomposition rate of lithium peroxide by exploiting the size and shape of one-dimensional nanostructured lithium peroxide.

Solution of D dimensional Dirac equation for hyperbolic tangent potential using NU method and its application in material properties

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

Suparmi, A., E-mail: soeparmi@staff.uns.ac.id; Cari, C., E-mail: cari@staff.uns.ac.id; Pratiwi, B. N., E-mail: namakubetanurpratiwi@gmail.com

2016-02-08

The analytical solution of D-dimensional Dirac equation for hyperbolic tangent potential is investigated using Nikiforov-Uvarov method. In the case of spin symmetry the D dimensional Dirac equation reduces to the D dimensional Schrodinger equation. The D dimensional relativistic energy spectra are obtained from D dimensional relativistic energy eigen value equation by using Mat Lab software. The corresponding D dimensional radial wave functions are formulated in the form of generalized Jacobi polynomials. The thermodynamically properties of materials are generated from the non-relativistic energy eigen-values in the classical limit. In the non-relativistic limit, the relativistic energy equation reduces to the non-relativistic energy.more » The thermal quantities of the system, partition function and specific heat, are expressed in terms of error function and imaginary error function which are numerically calculated using Mat Lab software.« less

Polyallylamine-Rh nanosheet nanoassemblies-carbon nanotubes organic-inorganic nanohybrids: A electrocatalyst superior to Pt for the hydrogen evolution reaction

NASA Astrophysics Data System (ADS)

Bai, Juan; Xing, Shi-Hui; Zhu, Ying-Ying; Jiang, Jia-Xing; Zeng, Jing-Hui; Chen, Yu

2018-05-01

Rationally tailoring the surface/interface structures of noble metal nanostructures emerges as a highly efficient method for improving their electrocatalytic activity, selectivity, and long-term stability. Recently, hydrogen evolution reaction is attracting more and more attention due to the energy crisis and environment pollution. Herein, we successfully synthesize polyallylamine-functionalized rhodium nanosheet nanoassemblies-carbon nanotube nanohybrids via a facile one-pot hydrothermal method. Three-dimensionally branched rhodium nanosheet nanoassemblies are consisted of two dimensionally atomically thick ultrathin rhodium nanosheets. The as-prepared polyallylamine-functionalized rhodium nanosheet nanoassemblies-carbon nanotube nanohybrids show the excellent electrocatalytic activity for the hydrogen evolution reaction in acidic media, with a low onset reduction potential of -1 mV, a small overpotential of 5 mV at 10 mA cm-2, which is much superior to commercial platinum nanocrystals. Two dimensionally ultrathin morphology of rhodium nanosheet, particular rhodium-polyallylamine interface, and three-dimensionally networks induced by carbon nanotube are the key factors for the excellent hydrogen evolution reaction activity in acidic media.

A Multi-Resolution Nonlinear Mapping Technique for Design and Analysis Applications

NASA Technical Reports Server (NTRS)

Phan, Minh Q.

1998-01-01

This report describes a nonlinear mapping technique where the unknown static or dynamic system is approximated by a sum of dimensionally increasing functions (one-dimensional curves, two-dimensional surfaces, etc.). These lower dimensional functions are synthesized from a set of multi-resolution basis functions, where the resolutions specify the level of details at which the nonlinear system is approximated. The basis functions also cause the parameter estimation step to become linear. This feature is taken advantage of to derive a systematic procedure to determine and eliminate basis functions that are less significant for the particular system under identification. The number of unknown parameters that must be estimated is thus reduced and compact models obtained. The lower dimensional functions (identified curves and surfaces) permit a kind of "visualization" into the complexity of the nonlinearity itself.

A Multi-Resolution Nonlinear Mapping Technique for Design and Analysis Application

NASA Technical Reports Server (NTRS)

Phan, Minh Q.

1997-01-01

This report describes a nonlinear mapping technique where the unknown static or dynamic system is approximated by a sum of dimensionally increasing functions (one-dimensional curves, two-dimensional surfaces, etc.). These lower dimensional functions are synthesized from a set of multi-resolution basis functions, where the resolutions specify the level of details at which the nonlinear system is approximated. The basis functions also cause the parameter estimation step to become linear. This feature is taken advantage of to derive a systematic procedure to determine and eliminate basis functions that are less significant for the particular system under identification. The number of unknown parameters that must be estimated is thus reduced and compact models obtained. The lower dimensional functions (identified curves and surfaces) permit a kind of "visualization" into the complexity of the nonlinearity itself.

Estimating the functional dimensionality of neural representations.

PubMed

Ahlheim, Christiane; Love, Bradley C

2018-06-07

Recent advances in multivariate fMRI analysis stress the importance of information inherent to voxel patterns. Key to interpreting these patterns is estimating the underlying dimensionality of neural representations. Dimensions may correspond to psychological dimensions, such as length and orientation, or involve other coding schemes. Unfortunately, the noise structure of fMRI data inflates dimensionality estimates and thus makes it difficult to assess the true underlying dimensionality of a pattern. To address this challenge, we developed a novel approach to identify brain regions that carry reliable task-modulated signal and to derive an estimate of the signal's functional dimensionality. We combined singular value decomposition with cross-validation to find the best low-dimensional projection of a pattern of voxel-responses at a single-subject level. Goodness of the low-dimensional reconstruction is measured as Pearson correlation with a test set, which allows to test for significance of the low-dimensional reconstruction across participants. Using hierarchical Bayesian modeling, we derive the best estimate and associated uncertainty of underlying dimensionality across participants. We validated our method on simulated data of varying underlying dimensionality, showing that recovered dimensionalities match closely true dimensionalities. We then applied our method to three published fMRI data sets all involving processing of visual stimuli. The results highlight three possible applications of estimating the functional dimensionality of neural data. Firstly, it can aid evaluation of model-based analyses by revealing which areas express reliable, task-modulated signal that could be missed by specific models. Secondly, it can reveal functional differences across brain regions. Thirdly, knowing the functional dimensionality allows assessing task-related differences in the complexity of neural patterns. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Three-Dimensional Mapping of Atmospheric Boundary Layer Structure and Winds with a High Performance Lidar

DTIC Science & Technology

1991-04-01

convective plumes which originate from the surface through solar heating. Unlike tower, sodar, and airplane measurements, lidar can provide essentially...average spacing between these bands is given by Xmax. The determination of Xma is complicated by the fact that the two-dimensional spectral power...eddy which results depends critically on the choice of the indicator function (Marht and Frank, 1988). This is due to the fact that the measurement

Beta Hebbian Learning as a New Method for Exploratory Projection Pursuit.

PubMed

Quintián, Héctor; Corchado, Emilio

2017-09-01

In this research, a novel family of learning rules called Beta Hebbian Learning (BHL) is thoroughly investigated to extract information from high-dimensional datasets by projecting the data onto low-dimensional (typically two dimensional) subspaces, improving the existing exploratory methods by providing a clear representation of data's internal structure. BHL applies a family of learning rules derived from the Probability Density Function (PDF) of the residual based on the beta distribution. This family of rules may be called Hebbian in that all use a simple multiplication of the output of the neural network with some function of the residuals after feedback. The derived learning rules can be linked to an adaptive form of Exploratory Projection Pursuit and with artificial distributions, the networks perform as the theory suggests they should: the use of different learning rules derived from different PDFs allows the identification of "interesting" dimensions (as far from the Gaussian distribution as possible) in high-dimensional datasets. This novel algorithm, BHL, has been tested over seven artificial datasets to study the behavior of BHL parameters, and was later applied successfully over four real datasets, comparing its results, in terms of performance, with other well-known Exploratory and projection models such as Maximum Likelihood Hebbian Learning (MLHL), Locally-Linear Embedding (LLE), Curvilinear Component Analysis (CCA), Isomap and Neural Principal Component Analysis (Neural PCA).

A tensorial approach to access cognitive workload related to mental arithmetic from EEG functional connectivity estimates.

PubMed

Dimitriadis, S I; Sun, Yu; Kwok, K; Laskaris, N A; Bezerianos, A

2013-01-01

The association of functional connectivity patterns with particular cognitive tasks has long been a topic of interest in neuroscience, e.g., studies of functional connectivity have demonstrated its potential use for decoding various brain states. However, the high-dimensionality of the pairwise functional connectivity limits its usefulness in some real-time applications. In the present study, the methodology of tensor subspace analysis (TSA) is used to reduce the initial high-dimensionality of the pairwise coupling in the original functional connectivity network to a space of condensed descriptive power, which would significantly decrease the computational cost and facilitate the differentiation of brain states. We assess the feasibility of the proposed method on EEG recordings when the subject was performing mental arithmetic task which differ only in the difficulty level (easy: 1-digit addition v.s. 3-digit additions). Two different cortical connective networks were detected, and by comparing the functional connectivity networks in different work states, it was found that the task-difficulty is best reflected in the connectivity structure of sub-graphs extending over parietooccipital sites. Incorporating this data-driven information within original TSA methodology, we succeeded in predicting the difficulty level from connectivity patterns in an efficient way that can be implemented so as to work in real-time.

Quantifying uncertainty in partially specified biological models: how can optimal control theory help us?

PubMed

Adamson, M W; Morozov, A Y; Kuzenkov, O A

2016-09-01

Mathematical models in biology are highly simplified representations of a complex underlying reality and there is always a high degree of uncertainty with regards to model function specification. This uncertainty becomes critical for models in which the use of different functions fitting the same dataset can yield substantially different predictions-a property known as structural sensitivity. Thus, even if the model is purely deterministic, then the uncertainty in the model functions carries through into uncertainty in model predictions, and new frameworks are required to tackle this fundamental problem. Here, we consider a framework that uses partially specified models in which some functions are not represented by a specific form. The main idea is to project infinite dimensional function space into a low-dimensional space taking into account biological constraints. The key question of how to carry out this projection has so far remained a serious mathematical challenge and hindered the use of partially specified models. Here, we propose and demonstrate a potentially powerful technique to perform such a projection by using optimal control theory to construct functions with the specified global properties. This approach opens up the prospect of a flexible and easy to use method to fulfil uncertainty analysis of biological models.

Shot noise at high temperatures

NASA Astrophysics Data System (ADS)

Gutman, D. B.; Gefen, Yuval

2003-07-01

We consider the possibility of measuring nonequilibrium properties of the current correlation functions at high temperatures (and small bias). Through the example of the third cumulant of the current (S3) we demonstrate that odd-order correlation functions represent nonequilibrium physics even at small external bias and high temperatures. We calculate S3=y(eV/T)e2I for a quasi-one-dimensional diffusive constriction. We calculate the scaling function y in two regimes: when the scattering processes are purely elastic and when the inelastic electron-electron scattering is strong. In both cases we find that y interpolates between two constants. In the low- (high-) temperature limit y is strongly (weakly) enhanced (suppressed) by the electron-electron scattering.

MHz gravitational waves from short-term anisotropic inflation

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

Ito, Asuka; Soda, Jiro

2016-04-18

We reveal the universality of short-term anisotropic inflation. As a demonstration, we study inflation with an exponential type gauge kinetic function which is ubiquitous in models obtained by dimensional reduction from higher dimensional fundamental theory. It turns out that an anisotropic inflation universally takes place in the later stage of conventional inflation. Remarkably, we find that primordial gravitational waves with a peak amplitude around 10{sup −26}∼10{sup −27} are copiously produced in high-frequency bands 10 MHz∼100 MHz. If we could detect such gravitational waves in future, we would be able to probe higher dimensional fundamental theory.

Explorations on High Dimensional Landscapes: Spin Glasses and Deep Learning

NASA Astrophysics Data System (ADS)

Sagun, Levent

This thesis deals with understanding the structure of high-dimensional and non-convex energy landscapes. In particular, its focus is on the optimization of two classes of functions: homogeneous polynomials and loss functions that arise in machine learning. In the first part, the notion of complexity of a smooth, real-valued function is studied through its critical points. Existing theoretical results predict that certain random functions that are defined on high dimensional domains have a narrow band of values whose pre-image contains the bulk of its critical points. This section provides empirical evidence for convergence of gradient descent to local minima whose energies are near the predicted threshold justifying the existing asymptotic theory. Moreover, it is empirically shown that a similar phenomenon may hold for deep learning loss functions. Furthermore, there is a comparative analysis of gradient descent and its stochastic version showing that in high dimensional regimes the latter is a mere speedup. The next study focuses on the halting time of an algorithm at a given stopping condition. Given an algorithm, the normalized fluctuations of the halting time follow a distribution that remains unchanged even when the input data is sampled from a new distribution. Two qualitative classes are observed: a Gumbel-like distribution that appears in Google searches, human decision times, and spin glasses and a Gaussian-like distribution that appears in conjugate gradient method, deep learning with MNIST and random input data. Following the universality phenomenon, the Hessian of the loss functions of deep learning is studied. The spectrum is seen to be composed of two parts, the bulk which is concentrated around zero, and the edges which are scattered away from zero. Empirical evidence is presented for the bulk indicating how over-parametrized the system is, and for the edges that depend on the input data. Furthermore, an algorithm is proposed such that it would explore such large dimensional, degenerate landscapes to locate a solution with decent generalization properties. Finally, a demonstration of how the new method can explain the empirical success of some of the recent methods that have been proposed for distributed deep learning. In the second part, two applied machine learning problems are studied that are complementary to the machine learning problems of part I. First, US asylum applications cases are studied using random forests on the data of past twenty years. Using only features up to when the case opens, the algorithm can predict the outcome of the case with 80% accuracy. Next, a particular question and answer system has been studied. The questions are collected from Jeopardy! show and they fed to Google, then the results are parsed into a recurrent neural network to come up with a system that would outcome the answer to the original question. Close to 50% accuracy is achieved where human level benchmark is just a little above 60%.

Observation of Phase-Filling Singularities in the Optical Dielectric Function of Highly Doped n-Type Ge.

PubMed

Xu, Chi; Fernando, Nalin S; Zollner, Stefan; Kouvetakis, John; Menéndez, José

2017-06-30

Phase-filling singularities in the optical response function of highly doped (>10^{19}  cm^{-3}) germanium are theoretically predicted and experimentally confirmed using spectroscopic ellipsometry. Contrary to direct-gap semiconductors, which display the well-known Burstein-Moss phenomenology upon doping, the critical point in the joint density of electronic states associated with the partially filled conduction band in n-Ge corresponds to the so-called E_{1} and E_{1}+Δ_{1} transitions, which are two-dimensional in character. As a result of this reduced dimensionality, there is no edge shift induced by Pauli blocking. Instead, one observes the "original" critical point (shifted only by band gap renormalization) and an additional feature associated with the level occupation discontinuity at the Fermi level. The experimental observation of this feature is made possible by the recent development of low-temperature, in situ doping techniques that allow the fabrication of highly doped films with exceptionally flat doping profiles.

Discontinuous Galerkin algorithms for fully kinetic plasmas

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

Juno, J.; Hakim, A.; TenBarge, J.

Here, we present a new algorithm for the discretization of the non-relativistic Vlasov–Maxwell system of equations for the study of plasmas in the kinetic regime. Using the discontinuous Galerkin finite element method for the spatial discretization, we obtain a high order accurate solution for the plasma's distribution function. Time stepping for the distribution function is done explicitly with a third order strong-stability preserving Runge–Kutta method. Since the Vlasov equation in the Vlasov–Maxwell system is a high dimensional transport equation, up to six dimensions plus time, we take special care to note various features we have implemented to reduce the costmore » while maintaining the integrity of the solution, including the use of a reduced high-order basis set. A series of benchmarks, from simple wave and shock calculations, to a five dimensional turbulence simulation, are presented to verify the efficacy of our set of numerical methods, as well as demonstrate the power of the implemented features.« less

Discontinuous Galerkin algorithms for fully kinetic plasmas

DOE PAGES

Juno, J.; Hakim, A.; TenBarge, J.; ...

2017-10-10

Here, we present a new algorithm for the discretization of the non-relativistic Vlasov–Maxwell system of equations for the study of plasmas in the kinetic regime. Using the discontinuous Galerkin finite element method for the spatial discretization, we obtain a high order accurate solution for the plasma's distribution function. Time stepping for the distribution function is done explicitly with a third order strong-stability preserving Runge–Kutta method. Since the Vlasov equation in the Vlasov–Maxwell system is a high dimensional transport equation, up to six dimensions plus time, we take special care to note various features we have implemented to reduce the costmore » while maintaining the integrity of the solution, including the use of a reduced high-order basis set. A series of benchmarks, from simple wave and shock calculations, to a five dimensional turbulence simulation, are presented to verify the efficacy of our set of numerical methods, as well as demonstrate the power of the implemented features.« less

Reduced order surrogate modelling (ROSM) of high dimensional deterministic simulations

NASA Astrophysics Data System (ADS)

Mitry, Mina

Often, computationally expensive engineering simulations can prohibit the engineering design process. As a result, designers may turn to a less computationally demanding approximate, or surrogate, model to facilitate their design process. However, owing to the the curse of dimensionality, classical surrogate models become too computationally expensive for high dimensional data. To address this limitation of classical methods, we develop linear and non-linear Reduced Order Surrogate Modelling (ROSM) techniques. Two algorithms are presented, which are based on a combination of linear/kernel principal component analysis and radial basis functions. These algorithms are applied to subsonic and transonic aerodynamic data, as well as a model for a chemical spill in a channel. The results of this thesis show that ROSM can provide a significant computational benefit over classical surrogate modelling, sometimes at the expense of a minor loss in accuracy.

High-performance neuroprosthetic control by an individual with tetraplegia.

PubMed

Collinger, Jennifer L; Wodlinger, Brian; Downey, John E; Wang, Wei; Tyler-Kabara, Elizabeth C; Weber, Douglas J; McMorland, Angus J C; Velliste, Meel; Boninger, Michael L; Schwartz, Andrew B

2013-02-16

Paralysis or amputation of an arm results in the loss of the ability to orient the hand and grasp, manipulate, and carry objects, functions that are essential for activities of daily living. Brain-machine interfaces could provide a solution to restoring many of these lost functions. We therefore tested whether an individual with tetraplegia could rapidly achieve neurological control of a high-performance prosthetic limb using this type of an interface. We implanted two 96-channel intracortical microelectrodes in the motor cortex of a 52-year-old individual with tetraplegia. Brain-machine-interface training was done for 13 weeks with the goal of controlling an anthropomorphic prosthetic limb with seven degrees of freedom (three-dimensional translation, three-dimensional orientation, one-dimensional grasping). The participant's ability to control the prosthetic limb was assessed with clinical measures of upper limb function. This study is registered with ClinicalTrials.gov, NCT01364480. The participant was able to move the prosthetic limb freely in the three-dimensional workspace on the second day of training. After 13 weeks, robust seven-dimensional movements were performed routinely. Mean success rate on target-based reaching tasks was 91·6% (SD 4·4) versus median chance level 6·2% (95% CI 2·0-15·3). Improvements were seen in completion time (decreased from a mean of 148 s [SD 60] to 112 s [6]) and path efficiency (increased from 0·30 [0·04] to 0·38 [0·02]). The participant was also able to use the prosthetic limb to do skilful and coordinated reach and grasp movements that resulted in clinically significant gains in tests of upper limb function. No adverse events were reported. With continued development of neuroprosthetic limbs, individuals with long-term paralysis could recover the natural and intuitive command signals for hand placement, orientation, and reaching, allowing them to perform activities of daily living. Defense Advanced Research Projects Agency, National Institutes of Health, Department of Veterans Affairs, and UPMC Rehabilitation Institute. Copyright © 2013 Elsevier Ltd. All rights reserved.

A second-order accurate kinetic-theory-based method for inviscid compressible flows

NASA Technical Reports Server (NTRS)

Deshpande, Suresh M.

1986-01-01

An upwind method for the numerical solution of the Euler equations is presented. This method, called the kinetic numerical method (KNM), is based on the fact that the Euler equations are moments of the Boltzmann equation of the kinetic theory of gases when the distribution function is Maxwellian. The KNM consists of two phases, the convection phase and the collision phase. The method is unconditionally stable and explicit. It is highly vectorizable and can be easily made total variation diminishing for the distribution function by a suitable choice of the interpolation strategy. The method is applied to a one-dimensional shock-propagation problem and to a two-dimensional shock-reflection problem.

Multiview alignment hashing for efficient image search.

PubMed

Liu, Li; Yu, Mengyang; Shao, Ling

2015-03-01

Hashing is a popular and efficient method for nearest neighbor search in large-scale data spaces by embedding high-dimensional feature descriptors into a similarity preserving Hamming space with a low dimension. For most hashing methods, the performance of retrieval heavily depends on the choice of the high-dimensional feature descriptor. Furthermore, a single type of feature cannot be descriptive enough for different images when it is used for hashing. Thus, how to combine multiple representations for learning effective hashing functions is an imminent task. In this paper, we present a novel unsupervised multiview alignment hashing approach based on regularized kernel nonnegative matrix factorization, which can find a compact representation uncovering the hidden semantics and simultaneously respecting the joint probability distribution of data. In particular, we aim to seek a matrix factorization to effectively fuse the multiple information sources meanwhile discarding the feature redundancy. Since the raised problem is regarded as nonconvex and discrete, our objective function is then optimized via an alternate way with relaxation and converges to a locally optimal solution. After finding the low-dimensional representation, the hashing functions are finally obtained through multivariable logistic regression. The proposed method is systematically evaluated on three data sets: 1) Caltech-256; 2) CIFAR-10; and 3) CIFAR-20, and the results show that our method significantly outperforms the state-of-the-art multiview hashing techniques.

Using maximum topology matching to explore differences in species distribution models

USGS Publications Warehouse

Poco, Jorge; Doraiswamy, Harish; Talbert, Marian; Morisette, Jeffrey; Silva, Claudio

2015-01-01

Species distribution models (SDM) are used to help understand what drives the distribution of various plant and animal species. These models are typically high dimensional scalar functions, where the dimensions of the domain correspond to predictor variables of the model algorithm. Understanding and exploring the differences between models help ecologists understand areas where their data or understanding of the system is incomplete and will help guide further investigation in these regions. These differences can also indicate an important source of model to model uncertainty. However, it is cumbersome and often impractical to perform this analysis using existing tools, which allows for manual exploration of the models usually as 1-dimensional curves. In this paper, we propose a topology-based framework to help ecologists explore the differences in various SDMs directly in the high dimensional domain. In order to accomplish this, we introduce the concept of maximum topology matching that computes a locality-aware correspondence between similar extrema of two scalar functions. The matching is then used to compute the similarity between two functions. We also design a visualization interface that allows ecologists to explore SDMs using their topological features and to study the differences between pairs of models found using maximum topological matching. We demonstrate the utility of the proposed framework through several use cases using different data sets and report the feedback obtained from ecologists.

Conformational Sampling in Template-Free Protein Loop Structure Modeling: An Overview

PubMed Central

Li, Yaohang

2013-01-01

Accurately modeling protein loops is an important step to predict three-dimensional structures as well as to understand functions of many proteins. Because of their high flexibility, modeling the three-dimensional structures of loops is difficult and is usually treated as a “mini protein folding problem” under geometric constraints. In the past decade, there has been remarkable progress in template-free loop structure modeling due to advances of computational methods as well as stably increasing number of known structures available in PDB. This mini review provides an overview on the recent computational approaches for loop structure modeling. In particular, we focus on the approaches of sampling loop conformation space, which is a critical step to obtain high resolution models in template-free methods. We review the potential energy functions for loop modeling, loop buildup mechanisms to satisfy geometric constraints, and loop conformation sampling algorithms. The recent loop modeling results are also summarized. PMID:24688696

Conformational sampling in template-free protein loop structure modeling: an overview.

PubMed

Li, Yaohang

2013-01-01

Accurately modeling protein loops is an important step to predict three-dimensional structures as well as to understand functions of many proteins. Because of their high flexibility, modeling the three-dimensional structures of loops is difficult and is usually treated as a "mini protein folding problem" under geometric constraints. In the past decade, there has been remarkable progress in template-free loop structure modeling due to advances of computational methods as well as stably increasing number of known structures available in PDB. This mini review provides an overview on the recent computational approaches for loop structure modeling. In particular, we focus on the approaches of sampling loop conformation space, which is a critical step to obtain high resolution models in template-free methods. We review the potential energy functions for loop modeling, loop buildup mechanisms to satisfy geometric constraints, and loop conformation sampling algorithms. The recent loop modeling results are also summarized.

Numerical solution to the glancing sidewall oblique shock wave/turbulent boundary layer interaction in three dimension

NASA Technical Reports Server (NTRS)

Anderson, B. H.; Benson, T. J.

1983-01-01

A supersonic three-dimensional viscous forward-marching computer design code called PEPSIS is used to obtain a numerical solution of the three-dimensional problem of the interaction of a glancing sidewall oblique shock wave and a turbulent boundary layer. Very good results are obtained for a test case that was run to investigate the use of the wall-function boundary-condition approximation for a highly complex three-dimensional shock-boundary layer interaction. Two additional test cases (coarse mesh and medium mesh) are run to examine the question of near-wall resolution when no-slip boundary conditions are applied. A comparison with experimental data shows that the PEPSIS code gives excellent results in general and is practical for three-dimensional supersonic inlet calculations.

Numerical solution to the glancing sidewall oblique shock wave/turbulent boundary layer interaction in three-dimension

NASA Technical Reports Server (NTRS)

Anderson, B. H.; Benson, T. J.

1983-01-01

A supersonic three-dimensional viscous forward-marching computer design code called PEPSIS is used to obtain a numerical solution of the three-dimensional problem of the interaction of a glancing sidewall oblique shock wave and a turbulent boundary layer. Very good results are obtained for a test case that was run to investigate the use of the wall-function boundary-condition approximation for a highly complex three-dimensional shock-boundary layer interaction. Two additional test cases (coarse mesh and medium mesh) are run to examine the question of near-wall resolution when no-slip boundary conditions are applied. A comparison with experimental data shows that the PEPSIS code gives excellent results in general and is practical for three-dimensional supersonic inlet calculations.

Atomically-thick two-dimensional crystals: electronic structure regulation and energy device construction.

PubMed

Sun, Yongfu; Gao, Shan; Xie, Yi

2014-01-21

Atomically-thick two-dimensional crystals can provide promising opportunities to satisfy people's requirement of next-generation flexible and transparent nanodevices. However, the characterization of these low-dimensional structures and the understanding of their clear structure-property relationship encounter many great difficulties, owing to the lack of long-range order in the third dimensionality. In this review, we survey the recent progress in fine structure characterization by X-ray absorption fine structure spectroscopy and also overview electronic structure modulation by density-functional calculations in the ultrathin two-dimensional crystals. In addition, we highlight their structure-property relationship, transparent and flexible device construction as well as wide applications in photoelectrochemical water splitting, photodetectors, thermoelectric conversion, touchless moisture sensing, supercapacitors and lithium ion batteries. Finally, we outline the major challenges and opportunities that face the atomically-thick two-dimensional crystals. It is anticipated that the present review will deepen people's understanding of this field and hence contribute to guide the future design of high-efficiency energy-related devices.

Two-dimensional vocal tracts with three-dimensional behavior in the numerical generation of vowels.

PubMed

Arnela, Marc; Guasch, Oriol

2014-01-01

Two-dimensional (2D) numerical simulations of vocal tract acoustics may provide a good balance between the high quality of three-dimensional (3D) finite element approaches and the low computational cost of one-dimensional (1D) techniques. However, 2D models are usually generated by considering the 2D vocal tract as a midsagittal cut of a 3D version, i.e., using the same radius function, wall impedance, glottal flow, and radiation losses as in 3D, which leads to strong discrepancies in the resulting vocal tract transfer functions. In this work, a four step methodology is proposed to match the behavior of 2D simulations with that of 3D vocal tracts with circular cross-sections. First, the 2D vocal tract profile becomes modified to tune the formant locations. Second, the 2D wall impedance is adjusted to fit the formant bandwidths. Third, the 2D glottal flow gets scaled to recover 3D pressure levels. Fourth and last, the 2D radiation model is tuned to match the 3D model following an optimization process. The procedure is tested for vowels /a/, /i/, and /u/ and the obtained results are compared with those of a full 3D simulation, a conventional 2D approach, and a 1D chain matrix model.

Dynamic correlations in the highly dilute 2D electron liquid: Loss function, critical wave vector and analytic plasmon dispersion

NASA Astrophysics Data System (ADS)

Drachta, Jürgen T.; Kreil, Dominik; Hobbiger, Raphael; Böhm, Helga M.

2018-03-01

Correlations, highly important in low-dimensional systems, are known to decrease the plasmon dispersion of two-dimensional electron liquids. Here we calculate the plasmon properties, applying the 'Dynamic Many-Body Theory', accounting for correlated two-particle-two-hole fluctuations. These dynamic correlations are found to significantly lower the plasmon's energy. For the data obtained numerically, we provide an analytic expression that is valid across a wide range both of densities and of wave vectors. Finally, we demonstrate how this can be invoked in determining the actual electron densities from measurements on an AlGaAs quantum well.

Two-dimensional fluorescence spectroscopy of uranium isotopes in femtosecond laser ablation plumes

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

Phillips, Mark C.; Brumfield, Brian E.; LaHaye, Nicole

Here, we demonstrate measurement of uranium isotopes in femtosecond laser ablation plumes using two-dimensional fluorescence spectroscopy (2DFS). The high-resolution, tunable CW-laser spectroscopy technique clearly distinguishes atomic absorption from 235U and 238U in natural and highly enriched uranium metal samples. We present analysis of spectral resolution and analytical performance of 2DFS as a function of ambient pressure. Simultaneous measurement using time-resolved absorption spectroscopy provides information on temporal dynamics of the laser ablation plume and saturation behavior of fluorescence signals. The rapid, non-contact measurement is promising for in-field, standoff measurements of uranium enrichment for nuclear safety and security.

Two-dimensional fluorescence spectroscopy of uranium isotopes in femtosecond laser ablation plumes

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

Phillips, Mark C.; Brumfield, Brian E.; LaHaye, Nicole L.

We demonstrate measurement of uranium isotopes in femtosecond laser ablation plumes using two-dimensional fluorescence spectroscopy (2DFS). The high-resolution, tunable CW-laser spectroscopy technique clearly distinguishes atomic absorption from 235U and 238U in natural and highly enriched uranium metal samples. We present analysis of spectral resolution and analytical performance of 2DFS as a function of ambient pressure. Simultaneous measurement using time-resolved absorption spectroscopy provides information on temporal dynamics of the laser ablation plume and saturation behavior of fluorescence signals. The rapid, non-contact measurement is promising for in-field, standoff measurements of uranium enrichment for nuclear safety and security applications.

Two-dimensional fluorescence spectroscopy of uranium isotopes in femtosecond laser ablation plumes

DOE PAGES

Phillips, Mark C.; Brumfield, Brian E.; LaHaye, Nicole; ...

2017-06-19

Here, we demonstrate measurement of uranium isotopes in femtosecond laser ablation plumes using two-dimensional fluorescence spectroscopy (2DFS). The high-resolution, tunable CW-laser spectroscopy technique clearly distinguishes atomic absorption from 235U and 238U in natural and highly enriched uranium metal samples. We present analysis of spectral resolution and analytical performance of 2DFS as a function of ambient pressure. Simultaneous measurement using time-resolved absorption spectroscopy provides information on temporal dynamics of the laser ablation plume and saturation behavior of fluorescence signals. The rapid, non-contact measurement is promising for in-field, standoff measurements of uranium enrichment for nuclear safety and security.

Rational Protein Engineering Guided by Deep Mutational Scanning

PubMed Central

Shin, HyeonSeok; Cho, Byung-Kwan

2015-01-01

Sequence–function relationship in a protein is commonly determined by the three-dimensional protein structure followed by various biochemical experiments. However, with the explosive increase in the number of genome sequences, facilitated by recent advances in sequencing technology, the gap between protein sequences available and three-dimensional structures is rapidly widening. A recently developed method termed deep mutational scanning explores the functional phenotype of thousands of mutants via massive sequencing. Coupled with a highly efficient screening system, this approach assesses the phenotypic changes made by the substitution of each amino acid sequence that constitutes a protein. Such an informational resource provides the functional role of each amino acid sequence, thereby providing sufficient rationale for selecting target residues for protein engineering. Here, we discuss the current applications of deep mutational scanning and consider experimental design. PMID:26404267

Field theory of the inverse cascade in two-dimensional turbulence

NASA Astrophysics Data System (ADS)

Mayo, Jackson R.

2005-11-01

A two-dimensional fluid, stirred at high wave numbers and damped by both viscosity and linear friction, is modeled by a statistical field theory. The fluid’s long-distance behavior is studied using renormalization-group (RG) methods, as begun by Forster, Nelson, and Stephen [Phys. Rev. A 16, 732 (1977)]. With friction, which dissipates energy at low wave numbers, one expects a stationary inverse energy cascade for strong enough stirring. While such developed turbulence is beyond the quantitative reach of perturbation theory, a combination of exact and perturbative results suggests a coherent picture of the inverse cascade. The zero-friction fluctuation-dissipation theorem (FDT) is derived from a generalized time-reversal symmetry and implies zero anomalous dimension for the velocity even when friction is present. Thus the Kolmogorov scaling of the inverse cascade cannot be explained by any RG fixed point. The β function for the dimensionless coupling ĝ is computed through two loops; the ĝ3 term is positive, as already known, but the ĝ5 term is negative. An ideal cascade requires a linear β function for large ĝ , consistent with a Padé approximant to the Borel transform. The conjecture that the Kolmogorov spectrum arises from an RG flow through large ĝ is compatible with other results, but the accurate k-5/3 scaling is not explained and the Kolmogorov constant is not estimated. The lack of scale invariance should produce intermittency in high-order structure functions, as observed in some but not all numerical simulations of the inverse cascade. When analogous RG methods are applied to the one-dimensional Burgers equation using an FDT-preserving dimensional continuation, equipartition is obtained instead of a cascade—in agreement with simulations.

A Unified Approach to Functional Principal Component Analysis and Functional Multiple-Set Canonical Correlation.

PubMed

Choi, Ji Yeh; Hwang, Heungsun; Yamamoto, Michio; Jung, Kwanghee; Woodward, Todd S

2017-06-01

Functional principal component analysis (FPCA) and functional multiple-set canonical correlation analysis (FMCCA) are data reduction techniques for functional data that are collected in the form of smooth curves or functions over a continuum such as time or space. In FPCA, low-dimensional components are extracted from a single functional dataset such that they explain the most variance of the dataset, whereas in FMCCA, low-dimensional components are obtained from each of multiple functional datasets in such a way that the associations among the components are maximized across the different sets. In this paper, we propose a unified approach to FPCA and FMCCA. The proposed approach subsumes both techniques as special cases. Furthermore, it permits a compromise between the techniques, such that components are obtained from each set of functional data to maximize their associations across different datasets, while accounting for the variance of the data well. We propose a single optimization criterion for the proposed approach, and develop an alternating regularized least squares algorithm to minimize the criterion in combination with basis function approximations to functions. We conduct a simulation study to investigate the performance of the proposed approach based on synthetic data. We also apply the approach for the analysis of multiple-subject functional magnetic resonance imaging data to obtain low-dimensional components of blood-oxygen level-dependent signal changes of the brain over time, which are highly correlated across the subjects as well as representative of the data. The extracted components are used to identify networks of neural activity that are commonly activated across the subjects while carrying out a working memory task.

Channel microband electrode arrays for mechanistic electrochemistry. Two-dimensional voltammetry:  transport-limited currents.

PubMed

Alden, J A; Feldman, M A; Hill, E; Prieto, F; Oyama, M; Coles, B A; Compton, R G; Dobson, P J; Leigh, P A

1998-05-01

A channel electrode array, with electrodes ranging in size from the millimeter to the submicrometer scale, is used for the amperometric interrogation of mechanistically complex electrode processes. In this way, the transport-limited current, measured as a function of both electrode size and electrolyte flow rate (convection), is shown to provide a highly sensitive probe of mechanism and kinetics. The application of "two-dimensional voltammetry" to diverse electrode processes, including E, ECE, ECEE, EC', and DISP2 reactions, is reported.

Quality Function Deployment for Large Systems

NASA Technical Reports Server (NTRS)

Dean, Edwin B.

1992-01-01

Quality Function Deployment (QFD) is typically applied to small subsystems. This paper describes efforts to extend QFD to large scale systems. It links QFD to the system engineering process, the concurrent engineering process, the robust design process, and the costing process. The effect is to generate a tightly linked project management process of high dimensionality which flushes out issues early to provide a high quality, low cost, and, hence, competitive product. A pre-QFD matrix linking customers to customer desires is described.

Improved Tandem Measurement Techniques for Aerosol Particle Analysis

NASA Astrophysics Data System (ADS)

Rawat, Vivek Kumar

Non-spherical, chemically inhomogeneous (complex) nanoparticles are encountered in a number of natural and engineered environments, including combustion systems (which produces highly non-spherical aggregates), reactors used in gas-phase materials synthesis of doped or multicomponent materials, and in ambient air. These nanoparticles are often highly diverse in size, composition and shape, and hence require determination of property distribution functions for accurate characterization. This thesis focuses on development of tandem mobility-mass measurement techniques coupled with appropriate data inversion routines to facilitate measurement of two dimensional size-mass distribution functions while correcting for the non-idealities of the instruments. Chapter 1 provides the detailed background and motivation for the studies performed in this thesis. In chapter 2, the development of an inversion routine is described which is employed to determine two dimensional size-mass distribution functions from Differential Mobility Analyzer-Aerosol Particle Mass analyzer tandem measurements. Chapter 3 demonstrates the application of the two dimensional distribution function to compute cumulative mass distribution function and also evaluates the validity of this technique by comparing the calculated total mass concentrations to measured values for a variety of aerosols. In Chapter 4, this tandem measurement technique with the inversion routine is employed to analyze colloidal suspensions. Chapter 5 focuses on application of a transverse modulation ion mobility spectrometer coupled with a mass spectrometer to study the effect of vapor dopants on the mobility shifts of sub 2 nm peptide ion clusters. These mobility shifts are then compared to models based on vapor uptake theories. Finally, in Chapter 6, a conclusion of all the studies performed in this thesis is provided and future avenues of research are discussed.

Real-time generation of the Wigner distribution of complex functions using phase conjugation in photorefractive materials.

PubMed

Sun, P C; Fainman, Y

1990-09-01

An optical processor for real-time generation of the Wigner distribution of complex amplitude functions is introduced. The phase conjugation of the input signal is accomplished by a highly efficient self-pumped phase conjugator based on a 45 degrees -cut barium titanate photorefractive crystal. Experimental results on the real-time generation of Wigner distribution slices for complex amplitude two-dimensional optical functions are presented and discussed.

Thermoelectric and phonon transport properties of two-dimensional IV-VI compounds.

PubMed

Shafique, Aamir; Shin, Young-Han

2017-03-30

We explore the thermoelectric and phonon transport properties of two-dimensional monochalcogenides (SnSe, SnS, GeSe, and GeS) using density functional theory combined with Boltzmann transport theory. We studied the electronic structures, Seebeck coefficients, electrical conductivities, lattice thermal conductivities, and figures of merit of these two-dimensional materials, which showed that the thermoelectric performance of monolayer of these compounds is improved in comparison compared to their bulk phases. High figures of merit (ZT) are predicted for SnSe (ZT = 2.63, 2.46), SnS (ZT = 1.75, 1.88), GeSe (ZT = 1.99, 1.73), and GeS (ZT = 1.85, 1.29) at 700 K along armchair and zigzag directions, respectively. Phonon dispersion calculations confirm the dynamical stability of these compounds. The calculated lattice thermal conductivities are low while the electrical conductivities and Seebeck coefficients are high. Thus, the properties of the monolayers show high potential toward thermoelectric applications.

Dilepton production from the quark-gluon plasma using (3 +1 )-dimensional anisotropic dissipative hydrodynamics

NASA Astrophysics Data System (ADS)

Ryblewski, Radoslaw; Strickland, Michael

2015-07-01

We compute dilepton production from the deconfined phase of the quark-gluon plasma using leading-order (3 +1 )-dimensional anisotropic hydrodynamics. The anisotropic hydrodynamics equations employed describe the full spatiotemporal evolution of the transverse temperature, spheroidal momentum-space anisotropy parameter, and the associated three-dimensional collective flow of the matter. The momentum-space anisotropy is also taken into account in the computation of the dilepton production rate, allowing for a self-consistent description of dilepton production from the quark-gluon plasma. For our final results, we present predictions for high-energy dilepton yields as a function of invariant mass, transverse momentum, and pair rapidity. We demonstrate that high-energy dilepton production is extremely sensitive to the assumed level of initial momentum-space anisotropy of the quark-gluon plasma. As a result, it may be possible to experimentally constrain the early-time momentum-space anisotropy of the quark-gluon plasma generated in relativistic heavy-ion collisions using high-energy dilepton yields.

One-Dimensional and Two-Dimensional Analytical Solutions for Functionally Graded Beams with Different Moduli in Tension and Compression

PubMed Central

Li, Xue; Dong, Jiao

2018-01-01

The material considered in this study not only has a functionally graded characteristic but also exhibits different tensile and compressive moduli of elasticity. One-dimensional and two-dimensional mechanical models for a functionally graded beam with a bimodular effect were established first. By taking the grade function as an exponential expression, the analytical solutions of a bimodular functionally graded beam under pure bending and lateral-force bending were obtained. The regression from a two-dimensional solution to a one-dimensional solution is verified. The physical quantities in a bimodular functionally graded beam are compared with their counterparts in a classical problem and a functionally graded beam without a bimodular effect. The validity of the plane section assumption under pure bending and lateral-force bending is analyzed. Three typical cases that the tensile modulus is greater than, equal to, or less than the compressive modulus are discussed. The result indicates that due to the introduction of the bimodular functionally graded effect of the materials, the maximum tensile and compressive bending stresses may not take place at the bottom and top of the beam. The real location at which the maximum bending stress takes place is determined via the extreme condition for the analytical solution. PMID:29772835

Certain approximation problems for functions on the infinite-dimensional torus: Lipschitz spaces

NASA Astrophysics Data System (ADS)

Platonov, S. S.

2018-02-01

We consider some questions about the approximation of functions on the infinite-dimensional torus by trigonometric polynomials. Our main results are analogues of the direct and inverse theorems in the classical theory of approximation of periodic functions and a description of the Lipschitz spaces on the infinite-dimensional torus in terms of the best approximation.

A low-frequency wave motion mechanism enables efficient energy transport in carbon nanotubes at high heat fluxes.

PubMed

Zhang, Xiaoliang; Hu, Ming; Poulikakos, Dimos

2012-07-11

The great majority of investigations of thermal transport in carbon nanotubes (CNTs) in the open literature focus on low heat fluxes, that is, in the regime of validity of the Fourier heat conduction law. In this paper, by performing nonequilibrium molecular dynamics simulations we investigated thermal transport in a single-walled CNT bridging two Si slabs under constant high heat flux. An anomalous wave-like kinetic energy profile was observed, and a previously unexplored, wave-dominated energy transport mechanism is identified for high heat fluxes in CNTs, originated from excited low frequency transverse acoustic waves. The transported energy, in terms of a one-dimensional low frequency mechanical wave, is quantified as a function of the total heat flux applied and is compared to the energy transported by traditional Fourier heat conduction. The results show that the low frequency wave actually overtakes traditional Fourier heat conduction and efficiently transports the energy at high heat flux. Our findings reveal an important new mechanism for high heat flux energy transport in low-dimensional nanostructures, such as one-dimensional (1-D) nanotubes and nanowires, which could be very relevant to high heat flux dissipation such as in micro/nanoelectronics applications.

Marginally specified priors for non-parametric Bayesian estimation

PubMed Central

Kessler, David C.; Hoff, Peter D.; Dunson, David B.

2014-01-01

Summary Prior specification for non-parametric Bayesian inference involves the difficult task of quantifying prior knowledge about a parameter of high, often infinite, dimension. A statistician is unlikely to have informed opinions about all aspects of such a parameter but will have real information about functionals of the parameter, such as the population mean or variance. The paper proposes a new framework for non-parametric Bayes inference in which the prior distribution for a possibly infinite dimensional parameter is decomposed into two parts: an informative prior on a finite set of functionals, and a non-parametric conditional prior for the parameter given the functionals. Such priors can be easily constructed from standard non-parametric prior distributions in common use and inherit the large support of the standard priors on which they are based. Additionally, posterior approximations under these informative priors can generally be made via minor adjustments to existing Markov chain approximation algorithms for standard non-parametric prior distributions. We illustrate the use of such priors in the context of multivariate density estimation using Dirichlet process mixture models, and in the modelling of high dimensional sparse contingency tables. PMID:25663813

Windowed Green function method for the Helmholtz equation in the presence of multiply layered media

NASA Astrophysics Data System (ADS)

Bruno, O. P.; Pérez-Arancibia, C.

2017-06-01

This paper presents a new methodology for the solution of problems of two- and three-dimensional acoustic scattering (and, in particular, two-dimensional electromagnetic scattering) by obstacles and defects in the presence of an arbitrary number of penetrable layers. Relying on the use of certain slow-rise windowing functions, the proposed windowed Green function approach efficiently evaluates oscillatory integrals over unbounded domains, with high accuracy, without recourse to the highly expensive Sommerfeld integrals that have typically been used to account for the effect of underlying planar multilayer structures. The proposed methodology, whose theoretical basis was presented in the recent contribution (Bruno et al. 2016 SIAM J. Appl. Math. 76, 1871-1898. (doi:10.1137/15M1033782)), is fast, accurate, flexible and easy to implement. Our numerical experiments demonstrate that the numerical errors resulting from the proposed approach decrease faster than any negative power of the window size. In a number of examples considered in this paper, the proposed method is up to thousands of times faster, for a given accuracy, than corresponding methods based on the use of Sommerfeld integrals.

Windowed Green function method for the Helmholtz equation in the presence of multiply layered media.

PubMed

Bruno, O P; Pérez-Arancibia, C

2017-06-01

This paper presents a new methodology for the solution of problems of two- and three-dimensional acoustic scattering (and, in particular, two-dimensional electromagnetic scattering) by obstacles and defects in the presence of an arbitrary number of penetrable layers. Relying on the use of certain slow-rise windowing functions, the proposed windowed Green function approach efficiently evaluates oscillatory integrals over unbounded domains, with high accuracy, without recourse to the highly expensive Sommerfeld integrals that have typically been used to account for the effect of underlying planar multilayer structures. The proposed methodology, whose theoretical basis was presented in the recent contribution (Bruno et al. 2016 SIAM J. Appl. Math. 76 , 1871-1898. (doi:10.1137/15M1033782)), is fast, accurate, flexible and easy to implement. Our numerical experiments demonstrate that the numerical errors resulting from the proposed approach decrease faster than any negative power of the window size. In a number of examples considered in this paper, the proposed method is up to thousands of times faster, for a given accuracy, than corresponding methods based on the use of Sommerfeld integrals.

Visualization of Two Dimensional to Three Dimensional Transformations--Exploration through Technology

ERIC Educational Resources Information Center

Costa, G. B.; Gorak, M.; Melendez, B. S.

2006-01-01

A small class of functions is described that easily lend themselves to two-dimensional and three-dimensional visualizations at the basic calculus level. The intended audience is those educators involved in the instruction of elementary calculus. This note is an educational piece that begins with the question: "What happens if a function defined on…

Basis adaptation and domain decomposition for steady partial differential equations with random coefficients

DOE PAGES

Tipireddy, R.; Stinis, P.; Tartakovsky, A. M.

2017-09-04

In this paper, we present a novel approach for solving steady-state stochastic partial differential equations (PDEs) with high-dimensional random parameter space. The proposed approach combines spatial domain decomposition with basis adaptation for each subdomain. The basis adaptation is used to address the curse of dimensionality by constructing an accurate low-dimensional representation of the stochastic PDE solution (probability density function and/or its leading statistical moments) in each subdomain. Restricting the basis adaptation to a specific subdomain affords finding a locally accurate solution. Then, the solutions from all of the subdomains are stitched together to provide a global solution. We support ourmore » construction with numerical experiments for a steady-state diffusion equation with a random spatially dependent coefficient. Lastly, our results show that highly accurate global solutions can be obtained with significantly reduced computational costs.« less

Basis adaptation and domain decomposition for steady-state partial differential equations with random coefficients

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

Tipireddy, R.; Stinis, P.; Tartakovsky, A. M.

We present a novel approach for solving steady-state stochastic partial differential equations (PDEs) with high-dimensional random parameter space. The proposed approach combines spatial domain decomposition with basis adaptation for each subdomain. The basis adaptation is used to address the curse of dimensionality by constructing an accurate low-dimensional representation of the stochastic PDE solution (probability density function and/or its leading statistical moments) in each subdomain. Restricting the basis adaptation to a specific subdomain affords finding a locally accurate solution. Then, the solutions from all of the subdomains are stitched together to provide a global solution. We support our construction with numericalmore » experiments for a steady-state diffusion equation with a random spatially dependent coefficient. Our results show that highly accurate global solutions can be obtained with significantly reduced computational costs.« less

Retention in porous layer pillar array planar separation platforms

DOE PAGES

Lincoln, Danielle R.; Lavrik, Nickolay V.; Kravchenko, Ivan I.; ...

2016-08-11

Here, this work presents the retention capabilities and surface area enhancement of highly ordered, high-aspect-ratio, open-platform, two-dimensional (2D) pillar arrays when coated with a thin layer of porous silicon oxide (PSO). Photolithographically prepared pillar arrays were coated with 50–250 nm of PSO via plasma-enhanced chemical vapor deposition and then functionalized with either octadecyltrichlorosilane or n-butyldimethylchlorosilane. Theoretical calculations indicate that a 50 nm layer of PSO increases the surface area of a pillar nearly 120-fold. Retention capabilities were tested by observing capillary-action-driven development under various conditions, as well as by running one-dimensional separations on varying thicknesses of PSO. Increasing the thicknessmore » of PSO on an array clearly resulted in greater retention of the analyte(s) in question in both experiments. In culmination, a two-dimensional separation of fluorescently derivatized amines was performed to further demonstrate the capabilities of these fabricated platforms.« less

Retention in porous layer pillar array planar separation platforms

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

Lincoln, Danielle R.; Lavrik, Nickolay V.; Kravchenko, Ivan I.

Here, this work presents the retention capabilities and surface area enhancement of highly ordered, high-aspect-ratio, open-platform, two-dimensional (2D) pillar arrays when coated with a thin layer of porous silicon oxide (PSO). Photolithographically prepared pillar arrays were coated with 50–250 nm of PSO via plasma-enhanced chemical vapor deposition and then functionalized with either octadecyltrichlorosilane or n-butyldimethylchlorosilane. Theoretical calculations indicate that a 50 nm layer of PSO increases the surface area of a pillar nearly 120-fold. Retention capabilities were tested by observing capillary-action-driven development under various conditions, as well as by running one-dimensional separations on varying thicknesses of PSO. Increasing the thicknessmore » of PSO on an array clearly resulted in greater retention of the analyte(s) in question in both experiments. In culmination, a two-dimensional separation of fluorescently derivatized amines was performed to further demonstrate the capabilities of these fabricated platforms.« less

Human brain lesion-deficit inference remapped.

PubMed

Mah, Yee-Haur; Husain, Masud; Rees, Geraint; Nachev, Parashkev

2014-09-01

Our knowledge of the anatomical organization of the human brain in health and disease draws heavily on the study of patients with focal brain lesions. Historically the first method of mapping brain function, it is still potentially the most powerful, establishing the necessity of any putative neural substrate for a given function or deficit. Great inferential power, however, carries a crucial vulnerability: without stronger alternatives any consistent error cannot be easily detected. A hitherto unexamined source of such error is the structure of the high-dimensional distribution of patterns of focal damage, especially in ischaemic injury-the commonest aetiology in lesion-deficit studies-where the anatomy is naturally shaped by the architecture of the vascular tree. This distribution is so complex that analysis of lesion data sets of conventional size cannot illuminate its structure, leaving us in the dark about the presence or absence of such error. To examine this crucial question we assembled the largest known set of focal brain lesions (n = 581), derived from unselected patients with acute ischaemic injury (mean age = 62.3 years, standard deviation = 17.8, male:female ratio = 0.547), visualized with diffusion-weighted magnetic resonance imaging, and processed with validated automated lesion segmentation routines. High-dimensional analysis of this data revealed a hidden bias within the multivariate patterns of damage that will consistently distort lesion-deficit maps, displacing inferred critical regions from their true locations, in a manner opaque to replication. Quantifying the size of this mislocalization demonstrates that past lesion-deficit relationships estimated with conventional inferential methodology are likely to be significantly displaced, by a magnitude dependent on the unknown underlying lesion-deficit relationship itself. Past studies therefore cannot be retrospectively corrected, except by new knowledge that would render them redundant. Positively, we show that novel machine learning techniques employing high-dimensional inference can nonetheless accurately converge on the true locus. We conclude that current inferences about human brain function and deficits based on lesion mapping must be re-evaluated with methodology that adequately captures the high-dimensional structure of lesion data. © The Author (2014). Published by Oxford University Press on behalf of the Guarantors of Brain.

Three-dimensional CTOA and constraint effects during stable tearing in a thin-sheet material

NASA Technical Reports Server (NTRS)

Dawicke, D. S.; Newman, J. C., Jr.; Bigelow, C. A.

1995-01-01

A small strain theory, three-dimensional elastic-plastic finite element analysis was used to simulate fracture in thin sheet 2024-T3 aluminum alloy in the T-L orientation. Both straight and tunneled cracks were modeled. The tunneled crack front shapes as a function of applied stress were obtained from the fracture surface of tested specimens. The stable crack growth behavior was measured at the specimen surface as a function of applied stress. The fracture simulation modeled the crack tunneling and extension as a function of applied stress. The results indicated that the global constraint factor, alpha(sub g), initially dropped during stable crack growth. After peak applied stress was achieved, alpha(sub g) began to increase slightly. The effect of crack front shape on alpha(sub g) was small, but the crack front shape did greatly influence the local constraint and through-thickness crack-tip opening angle (CTOA) behavior. The surface values of CTOA for the tunneled crack front model agreed well with experimental measurements, showing the same initial decrease from high values during the initial 3mm of crack growth at the specimen's surface. At the same time, the interior CTOA values increased from low angles. After the initial stable tearing region, the CTOA was constant through the thickness. The three-dimensional analysis appears to confirm the potential of CTOA as a two-dimensional fracture criterion.

ICA model order selection of task co-activation networks.

PubMed

Ray, Kimberly L; McKay, D Reese; Fox, Peter M; Riedel, Michael C; Uecker, Angela M; Beckmann, Christian F; Smith, Stephen M; Fox, Peter T; Laird, Angela R

2013-01-01

Independent component analysis (ICA) has become a widely used method for extracting functional networks in the brain during rest and task. Historically, preferred ICA dimensionality has widely varied within the neuroimaging community, but typically varies between 20 and 100 components. This can be problematic when comparing results across multiple studies because of the impact ICA dimensionality has on the topology of its resultant components. Recent studies have demonstrated that ICA can be applied to peak activation coordinates archived in a large neuroimaging database (i.e., BrainMap Database) to yield whole-brain task-based co-activation networks. A strength of applying ICA to BrainMap data is that the vast amount of metadata in BrainMap can be used to quantitatively assess tasks and cognitive processes contributing to each component. In this study, we investigated the effect of model order on the distribution of functional properties across networks as a method for identifying the most informative decompositions of BrainMap-based ICA components. Our findings suggest dimensionality of 20 for low model order ICA to examine large-scale brain networks, and dimensionality of 70 to provide insight into how large-scale networks fractionate into sub-networks. We also provide a functional and organizational assessment of visual, motor, emotion, and interoceptive task co-activation networks as they fractionate from low to high model-orders.

ICA model order selection of task co-activation networks

PubMed Central

Ray, Kimberly L.; McKay, D. Reese; Fox, Peter M.; Riedel, Michael C.; Uecker, Angela M.; Beckmann, Christian F.; Smith, Stephen M.; Fox, Peter T.; Laird, Angela R.

2013-01-01

Independent component analysis (ICA) has become a widely used method for extracting functional networks in the brain during rest and task. Historically, preferred ICA dimensionality has widely varied within the neuroimaging community, but typically varies between 20 and 100 components. This can be problematic when comparing results across multiple studies because of the impact ICA dimensionality has on the topology of its resultant components. Recent studies have demonstrated that ICA can be applied to peak activation coordinates archived in a large neuroimaging database (i.e., BrainMap Database) to yield whole-brain task-based co-activation networks. A strength of applying ICA to BrainMap data is that the vast amount of metadata in BrainMap can be used to quantitatively assess tasks and cognitive processes contributing to each component. In this study, we investigated the effect of model order on the distribution of functional properties across networks as a method for identifying the most informative decompositions of BrainMap-based ICA components. Our findings suggest dimensionality of 20 for low model order ICA to examine large-scale brain networks, and dimensionality of 70 to provide insight into how large-scale networks fractionate into sub-networks. We also provide a functional and organizational assessment of visual, motor, emotion, and interoceptive task co-activation networks as they fractionate from low to high model-orders. PMID:24339802

Uniform electron gases. III. Low-density gases on three-dimensional spheres

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

Agboola, Davids; Knol, Anneke L.; Gill, Peter M. W., E-mail: peter.gill@anu.edu.au

2015-08-28

By combining variational Monte Carlo (VMC) and complete-basis-set limit Hartree-Fock (HF) calculations, we have obtained near-exact correlation energies for low-density same-spin electrons on a three-dimensional sphere (3-sphere), i.e., the surface of a four-dimensional ball. In the VMC calculations, we compare the efficacies of two types of one-electron basis functions for these strongly correlated systems and analyze the energy convergence with respect to the quality of the Jastrow factor. The HF calculations employ spherical Gaussian functions (SGFs) which are the curved-space analogs of Cartesian Gaussian functions. At low densities, the electrons become relatively localized into Wigner crystals, and the natural SGFmore » centers are found by solving the Thomson problem (i.e., the minimum-energy arrangement of n point charges) on the 3-sphere for various values of n. We have found 11 special values of n whose Thomson sites are equivalent. Three of these are the vertices of four-dimensional Platonic solids — the hyper-tetrahedron (n = 5), the hyper-octahedron (n = 8), and the 24-cell (n = 24) — and a fourth is a highly symmetric structure (n = 13) which has not previously been reported. By calculating the harmonic frequencies of the electrons around their equilibrium positions, we also find the first-order vibrational corrections to the Thomson energy.« less

Dimensions of personality structure among patients with substance use disorders and co-occurring personality disorders: a comparison with psychiatric outpatients and healthy controls.

PubMed

Di Pierro, Rossella; Preti, Emanuele; Vurro, Nicoletta; Madeddu, Fabio

2014-08-01

Although dual diagnosis has been a topic of great scientific interest for a long time, few studies have investigated the personality traits that characterize patients suffering from substance use disorders and co-occurring personality disorders through a dimensional approach. The present study aimed to evaluate structural personality profiles among dual-diagnosis inpatients to identify specific personality impairments associated with dual diagnosis. The present study involved 97 participants divided into three groups: 37 dual-diagnosis inpatients, 30 psychiatric outpatients and 30 nonclinical controls. Dimensions of personality functioning were assessed and differences between groups were tested using Kernberg's dimensional model of personality. Results showed that dual diagnosis was associated with the presence of difficulties in three main dimensions of personality functioning. Dual-diagnosis inpatients reported a poorly integrated identity with difficulties in the capacity to invest, poorly integrated moral values, and high levels of self-direct and other-direct aggression. The present study highlighted that a dimensional approach to the study of dual diagnosis may clarify the personality functioning of patients suffering from this pathological condition. The use of the dimensional approach could help to advance research on dual diagnosis, and it could have important implications on clinical treatment programs for dual-diagnosis inpatients. Copyright © 2014 Elsevier Inc. All rights reserved.

Entangled singularity patterns of photons in Ince-Gauss modes

NASA Astrophysics Data System (ADS)

Krenn, Mario; Fickler, Robert; Huber, Marcus; Lapkiewicz, Radek; Plick, William; Ramelow, Sven; Zeilinger, Anton

2013-01-01

Photons with complex spatial mode structures open up possibilities for new fundamental high-dimensional quantum experiments and for novel quantum information tasks. Here we show entanglement of photons with complex vortex and singularity patterns called Ince-Gauss modes. In these modes, the position and number of singularities vary depending on the mode parameters. We verify two-dimensional and three-dimensional entanglement of Ince-Gauss modes. By measuring one photon and thereby defining its singularity pattern, we nonlocally steer the singularity structure of its entangled partner, while the initial singularity structure of the photons is undefined. In addition we measure an Ince-Gauss specific quantum-correlation function with possible use in future quantum communication protocols.

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

Mikheev, Evgeny; Himmetoglu, Burak; Kajdos, Adam P.

We analyze and compare the temperature dependence of the electron mobility of two- and three-dimensional electron liquids in SrTiO{sub 3}. The contributions of electron-electron scattering must be taken into account to accurately describe the mobility in both cases. For uniformly doped, three-dimensional electron liquids, the room temperature mobility crosses over from longitudinal optical (LO) phonon-scattering-limited to electron-electron-scattering-limited as a function of carrier density. In high-density, two-dimensional electron liquids, LO phonon scattering is completely screened and the mobility is dominated by electron-electron scattering up to room temperature. The possible origins of the observed behavior and the consequences for approaches to improvemore » the mobility are discussed.« less

HCl dissociating on a rigid Au(111) surface: A six-dimensional quantum mechanical study on a new potential energy surface based on the RPBE functional.

PubMed

Liu, Tianhui; Fu, Bina; Zhang, Dong H

2017-04-28

The dissociative chemisorption of HCl on the Au(111) surface has recently been an interesting and important subject, regarding the discrepancy between the theoretical dissociation probabilities and the experimental sticking probabilities. We here constructed an accurate full-dimensional (six-dimensional (6D)) potential energy surface (PES) based on the density functional theory (DFT) with the revised Perdew-Burke-Ernzerhof (RPBE) functional, and performed 6D quantum mechanical (QM) calculations for HCl dissociating on a rigid Au(111) surface. The effects of vibrational excitations, rotational orientations, and site-averaging approximation on the present RPBE PES are investigated. Due to the much higher barrier height obtained on the RPBE PES than on the PW91 PES, the agreement between the present theoretical and experimental results is greatly improved. In particular, at the very low kinetic energy, the QM-RPBE dissociation probability agrees well with the experimental data. However, the computed QM-RPBE reaction probabilities are still markedly different from the experimental values at most of the energy regions. In addition, the QM-RPBE results achieve good agreement with the recent ab initio molecular dynamics calculations based on the RPBE functional at high kinetic energies.

A scalable method to improve gray matter segmentation at ultra high field MRI.

PubMed

Gulban, Omer Faruk; Schneider, Marian; Marquardt, Ingo; Haast, Roy A M; De Martino, Federico

2018-01-01

High-resolution (functional) magnetic resonance imaging (MRI) at ultra high magnetic fields (7 Tesla and above) enables researchers to study how anatomical and functional properties change within the cortical ribbon, along surfaces and across cortical depths. These studies require an accurate delineation of the gray matter ribbon, which often suffers from inclusion of blood vessels, dura mater and other non-brain tissue. Residual segmentation errors are commonly corrected by browsing the data slice-by-slice and manually changing labels. This task becomes increasingly laborious and prone to error at higher resolutions since both work and error scale with the number of voxels. Here we show that many mislabeled, non-brain voxels can be corrected more efficiently and semi-automatically by representing three-dimensional anatomical images using two-dimensional histograms. We propose both a uni-modal (based on first spatial derivative) and multi-modal (based on compositional data analysis) approach to this representation and quantify the benefits in 7 Tesla MRI data of nine volunteers. We present an openly accessible Python implementation of these approaches and demonstrate that editing cortical segmentations using two-dimensional histogram representations as an additional post-processing step aids existing algorithms and yields improved gray matter borders. By making our data and corresponding expert (ground truth) segmentations openly available, we facilitate future efforts to develop and test segmentation algorithms on this challenging type of data.

A scalable method to improve gray matter segmentation at ultra high field MRI

PubMed Central

De Martino, Federico

2018-01-01

High-resolution (functional) magnetic resonance imaging (MRI) at ultra high magnetic fields (7 Tesla and above) enables researchers to study how anatomical and functional properties change within the cortical ribbon, along surfaces and across cortical depths. These studies require an accurate delineation of the gray matter ribbon, which often suffers from inclusion of blood vessels, dura mater and other non-brain tissue. Residual segmentation errors are commonly corrected by browsing the data slice-by-slice and manually changing labels. This task becomes increasingly laborious and prone to error at higher resolutions since both work and error scale with the number of voxels. Here we show that many mislabeled, non-brain voxels can be corrected more efficiently and semi-automatically by representing three-dimensional anatomical images using two-dimensional histograms. We propose both a uni-modal (based on first spatial derivative) and multi-modal (based on compositional data analysis) approach to this representation and quantify the benefits in 7 Tesla MRI data of nine volunteers. We present an openly accessible Python implementation of these approaches and demonstrate that editing cortical segmentations using two-dimensional histogram representations as an additional post-processing step aids existing algorithms and yields improved gray matter borders. By making our data and corresponding expert (ground truth) segmentations openly available, we facilitate future efforts to develop and test segmentation algorithms on this challenging type of data. PMID:29874295

High transparent shape memory gel

NASA Astrophysics Data System (ADS)

Gong, Jin; Arai, Masanori; Kabir, M. H.; Makino, Masato; Furukawa, Hidemitsu

2014-03-01

Gels are a new material having three-dimensional network structures of macromolecules. They possess excellent properties as swellability, high permeability and biocompatibility, and have been applied in various fields of daily life, food, medicine, architecture, and chemistry. In this study, we tried to prepare new multi-functional and high-strength gels by using Meso-Decoration (Meso-Deco), one new method of structure design at intermediate mesoscale. High-performance rigid-rod aromatic polymorphic crystals, and the functional groups of thermoreversible Diels-Alder reaction were introduced into soft gels as crosslinkable pendent chains. The functionalization and strengthening of gels can be realized by meso-decorating the gels' structure using high-performance polymorphic crystals and thermoreversible pendent chains. New gels with good mechanical properties, novel optical properties and thermal properties are expected to be developed.

A primary exploration to quasi-two-dimensional rare-earth ferromagnetic particles: holmium-doped MoS2 sheet as room-temperature magnetic semiconductor

NASA Astrophysics Data System (ADS)

Chen, Xi; Lin, Zheng-Zhe

2018-05-01

Recently, two-dimensional materials and nanoparticles with robust ferromagnetism are even of great interest to explore basic physics in nanoscale spintronics. More importantly, room-temperature magnetic semiconducting materials with high Curie temperature is essential for developing next-generation spintronic and quantum computing devices. Here, we develop a theoretical model on the basis of density functional theory calculations and the Ruderman-Kittel-Kasuya-Yoshida theory to predict the thermal stability of two-dimensional magnetic materials. Compared with other rare-earth (dysprosium (Dy) and erbium (Er)) and 3 d (copper (Cu)) impurities, holmium-doped (Ho-doped) single-layer 1H-MoS2 is proposed as promising semiconductor with robust magnetism. The calculations at the level of hybrid HSE06 functional predict a Curie temperature much higher than room temperature. Ho-doped MoS2 sheet possesses fully spin-polarized valence and conduction bands, which is a prerequisite for flexible spintronic applications.

Convolutionless Nakajima-Zwanzig equations for stochastic analysis in nonlinear dynamical systems.

PubMed

Venturi, D; Karniadakis, G E

2014-06-08

Determining the statistical properties of stochastic nonlinear systems is of major interest across many disciplines. Currently, there are no general efficient methods to deal with this challenging problem that involves high dimensionality, low regularity and random frequencies. We propose a framework for stochastic analysis in nonlinear dynamical systems based on goal-oriented probability density function (PDF) methods. The key idea stems from techniques of irreversible statistical mechanics, and it relies on deriving evolution equations for the PDF of quantities of interest, e.g. functionals of the solution to systems of stochastic ordinary and partial differential equations. Such quantities could be low-dimensional objects in infinite dimensional phase spaces. We develop the goal-oriented PDF method in the context of the time-convolutionless Nakajima-Zwanzig-Mori formalism. We address the question of approximation of reduced-order density equations by multi-level coarse graining, perturbation series and operator cumulant resummation. Numerical examples are presented for stochastic resonance and stochastic advection-reaction problems.

Robust and Efficient Biomolecular Clustering of Tumor Based on ${p}$ -Norm Singular Value Decomposition.

PubMed

Kong, Xiang-Zhen; Liu, Jin-Xing; Zheng, Chun-Hou; Hou, Mi-Xiao; Wang, Juan

2017-07-01

High dimensionality has become a typical feature of biomolecular data. In this paper, a novel dimension reduction method named p-norm singular value decomposition (PSVD) is proposed to seek the low-rank approximation matrix to the biomolecular data. To enhance the robustness to outliers, the Lp-norm is taken as the error function and the Schatten p-norm is used as the regularization function in the optimization model. To evaluate the performance of PSVD, the Kmeans clustering method is then employed for tumor clustering based on the low-rank approximation matrix. Extensive experiments are carried out on five gene expression data sets including two benchmark data sets and three higher dimensional data sets from the cancer genome atlas. The experimental results demonstrate that the PSVD-based method outperforms many existing methods. Especially, it is experimentally proved that the proposed method is more efficient for processing higher dimensional data with good robustness, stability, and superior time performance.

One dimensional metallic edges in atomically thin WSe2 induced by air exposure

NASA Astrophysics Data System (ADS)

Addou, Rafik; Smyth, Christopher M.; Noh, Ji-Young; Lin, Yu-Chuan; Pan, Yi; Eichfeld, Sarah M.; Fölsch, Stefan; Robinson, Joshua A.; Cho, Kyeongjae; Feenstra, Randall M.; Wallace, Robert M.

2018-04-01

Transition metal dichalcogenides are a unique class of layered two-dimensional (2D) crystals with extensive promising applications. Tuning the electronic properties of low-dimensional materials is vital for engineering new functionalities. Surface oxidation is of particular interest because it is a relatively simple method of functionalization. By means of scanning probe microscopy and x-ray photoelectron spectroscopy, we report the observation of metallic edges in atomically thin WSe2 monolayers grown by chemical vapor deposition on epitaxial graphene. Scanning tunneling microscopy shows structural details of WSe2 edges and scanning tunneling spectroscopy reveals the metallic nature of the oxidized edges. Photoemission demonstrates that the formation of metallic sub-stoichiometric tungsten oxide (WO2.7) is responsible for the high conductivity measured along the edges. Ab initio calculations validate the susceptibility of WSe2 nanoribbon edges to oxidation. The zigzag terminated edge exhibits metallic behavior prior the air-exposure and remains metallic after oxidation. Comprehending and exploiting this property opens a new opportunity for application in advanced electronic devices.

Calculation of flow about posts and powerhead model. [space shuttle main engine

NASA Technical Reports Server (NTRS)

Anderson, P. G.; Farmer, R. C.

1985-01-01

A three dimensional analysis of the non-uniform flow around the liquid oxygen (LOX) posts in the Space Shuttle Main Engine (SSME) powerhead was performed to determine possible factors contributing to the failure of the posts. Also performed was three dimensional numerical fluid flow analysis of the high pressure fuel turbopump (HPFTP) exhaust system, consisting of the turnaround duct (TAD), two-duct hot gas manifold (HGM), and the Version B transfer ducts. The analysis was conducted in the following manner: (1) modeling the flow around a single and small clusters (2 to 10) of posts; (2) modeling the velocity field in the cross plane; and (3) modeling the entire flow region with a three dimensional network type model. Shear stress functions which will permit viscous analysis without requiring excessive numbers of computational grid points were developed. These wall functions, laminar and turbulent, have been compared to standard Blasius solutions and are directly applicable to the cylinder in cross flow class of problems to which the LOX post problem belongs.

Convolutionless Nakajima–Zwanzig equations for stochastic analysis in nonlinear dynamical systems

PubMed Central

Venturi, D.; Karniadakis, G. E.

2014-01-01

Determining the statistical properties of stochastic nonlinear systems is of major interest across many disciplines. Currently, there are no general efficient methods to deal with this challenging problem that involves high dimensionality, low regularity and random frequencies. We propose a framework for stochastic analysis in nonlinear dynamical systems based on goal-oriented probability density function (PDF) methods. The key idea stems from techniques of irreversible statistical mechanics, and it relies on deriving evolution equations for the PDF of quantities of interest, e.g. functionals of the solution to systems of stochastic ordinary and partial differential equations. Such quantities could be low-dimensional objects in infinite dimensional phase spaces. We develop the goal-oriented PDF method in the context of the time-convolutionless Nakajima–Zwanzig–Mori formalism. We address the question of approximation of reduced-order density equations by multi-level coarse graining, perturbation series and operator cumulant resummation. Numerical examples are presented for stochastic resonance and stochastic advection–reaction problems. PMID:24910519

Coherence and dimensionality of intense spatiospectral twin beams

NASA Astrophysics Data System (ADS)

Peřina, Jan

2015-07-01

Spatiospectral properties of twin beams at their transition from low to high intensities are analyzed in parametric and paraxial approximations using decomposition into paired spatial and spectral modes. Intensity auto- and cross-correlation functions are determined and compared in the spectral and temporal domains as well as the transverse wave-vector and crystal output planes. Whereas the spectral, temporal, and transverse wave-vector coherence increases with the increasing pump intensity, coherence in the crystal output plane is almost independent of the pump intensity owing to the mode structure in this plane. The corresponding auto- and cross-correlation functions approach each other for larger pump intensities. The entanglement dimensionality of a twin beam is determined with a comparison of several approaches.

Transcending the slow bimolecular recombination in lead-halide perovskites for electroluminescence

PubMed Central

Xing, Guichuan; Wu, Bo; Wu, Xiangyang; Li, Mingjie; Du, Bin; Wei, Qi; Guo, Jia; Yeow, Edwin K. L.; Sum, Tze Chien; Huang, Wei

2017-01-01

The slow bimolecular recombination that drives three-dimensional lead-halide perovskites' outstanding photovoltaic performance is conversely a fundamental limitation for electroluminescence. Under electroluminescence working conditions with typical charge densities lower than 1015 cm−3, defect-states trapping in three-dimensional perovskites competes effectively with the bimolecular radiative recombination. Herein, we overcome this limitation using van-der-Waals-coupled Ruddlesden-Popper perovskite multi-quantum-wells. Injected charge carriers are rapidly localized from adjacent thin few layer (n≤4) multi-quantum-wells to the thick (n≥5) multi-quantum-wells with extremely high efficiency (over 85%) through quantum coupling. Light emission originates from excitonic recombination in the thick multi-quantum-wells at much higher decay rate and efficiency than bimolecular recombination in three-dimensional perovskites. These multi-quantum-wells retain the simple solution processability and high charge carrier mobility of two-dimensional lead-halide perovskites. Importantly, these Ruddlesden-Popper perovskites offer new functionalities unavailable in single phase constituents, permitting the transcendence of the slow bimolecular recombination bottleneck in lead-halide perovskites for efficient electroluminescence. PMID:28239146

Transcending the slow bimolecular recombination in lead-halide perovskites for electroluminescence.

PubMed

Xing, Guichuan; Wu, Bo; Wu, Xiangyang; Li, Mingjie; Du, Bin; Wei, Qi; Guo, Jia; Yeow, Edwin K L; Sum, Tze Chien; Huang, Wei

2017-02-27

The slow bimolecular recombination that drives three-dimensional lead-halide perovskites' outstanding photovoltaic performance is conversely a fundamental limitation for electroluminescence. Under electroluminescence working conditions with typical charge densities lower than 10 15  cm -3 , defect-states trapping in three-dimensional perovskites competes effectively with the bimolecular radiative recombination. Herein, we overcome this limitation using van-der-Waals-coupled Ruddlesden-Popper perovskite multi-quantum-wells. Injected charge carriers are rapidly localized from adjacent thin few layer (n≤4) multi-quantum-wells to the thick (n≥5) multi-quantum-wells with extremely high efficiency (over 85%) through quantum coupling. Light emission originates from excitonic recombination in the thick multi-quantum-wells at much higher decay rate and efficiency than bimolecular recombination in three-dimensional perovskites. These multi-quantum-wells retain the simple solution processability and high charge carrier mobility of two-dimensional lead-halide perovskites. Importantly, these Ruddlesden-Popper perovskites offer new functionalities unavailable in single phase constituents, permitting the transcendence of the slow bimolecular recombination bottleneck in lead-halide perovskites for efficient electroluminescence.

Ferroelectricity in Covalently functionalized Two-dimensional Materials: Integration of High-mobility Semiconductors and Nonvolatile Memory.

PubMed

Wu, Menghao; Dong, Shuai; Yao, Kailun; Liu, Junming; Zeng, Xiao Cheng

2016-11-09

Realization of ferroelectric semiconductors by conjoining ferroelectricity with semiconductors remains a challenging task because most present-day ferroelectric materials are unsuitable for such a combination due to their wide bandgaps. Herein, we show first-principles evidence toward the realization of a new class of two-dimensional (2D) ferroelectric semiconductors through covalent functionalization of many prevailing 2D materials. Members in this new class of 2D ferroelectric semiconductors include covalently functionalized germanene, and stanene (Nat. Commun. 2014, 5, 3389), as well as MoS 2 monolayer (Nat. Chem. 2015, 7, 45), covalent functionalization of the surface of bulk semiconductors such as silicon (111) (J. Phys. Chem. B 2006, 110 , 23898), and the substrates of oxides such as silica with self-assembly monolayers (Nano Lett. 2014, 14, 1354). The newly predicted 2D ferroelectric semiconductors possess high mobility, modest bandgaps, and distinct ferroelectricity that can be exploited for developing various heterostructural devices with desired functionalities. For example, we propose applications of the 2D materials as 2D ferroelectric field-effect transistors with ultrahigh on/off ratio, topological transistors with Dirac Fermions switchable between holes and electrons, ferroelectric junctions with ultrahigh electro-resistance, and multiferroic junctions for controlling spin by electric fields. All these heterostructural devices take advantage of the combination of high-mobility semiconductors with fast writing and nondestructive reading capability of nonvolatile memory, thereby holding great potential for the development of future multifunctional devices.

Challenges in Cardiac Tissue Engineering

PubMed Central

Tandon, Nina; Godier, Amandine; Maidhof, Robert; Marsano, Anna; Martens, Timothy P.; Radisic, Milica

2010-01-01

Cardiac tissue engineering aims to create functional tissue constructs that can reestablish the structure and function of injured myocardium. Engineered constructs can also serve as high-fidelity models for studies of cardiac development and disease. In a general case, the biological potential of the cell—the actual “tissue engineer”—is mobilized by providing highly controllable three-dimensional environments that can mediate cell differentiation and functional assembly. For cardiac regeneration, some of the key requirements that need to be met are the selection of a human cell source, establishment of cardiac tissue matrix, electromechanical cell coupling, robust and stable contractile function, and functional vascularization. We review here the potential and challenges of cardiac tissue engineering for developing therapies that could prevent or reverse heart failure. PMID:19698068

Inference of Vohradský's Models of Genetic Networks by Solving Two-Dimensional Function Optimization Problems

PubMed Central

Kimura, Shuhei; Sato, Masanao; Okada-Hatakeyama, Mariko

2013-01-01

The inference of a genetic network is a problem in which mutual interactions among genes are inferred from time-series of gene expression levels. While a number of models have been proposed to describe genetic networks, this study focuses on a mathematical model proposed by Vohradský. Because of its advantageous features, several researchers have proposed the inference methods based on Vohradský's model. When trying to analyze large-scale networks consisting of dozens of genes, however, these methods must solve high-dimensional non-linear function optimization problems. In order to resolve the difficulty of estimating the parameters of the Vohradský's model, this study proposes a new method that defines the problem as several two-dimensional function optimization problems. Through numerical experiments on artificial genetic network inference problems, we showed that, although the computation time of the proposed method is not the shortest, the method has the ability to estimate parameters of Vohradský's models more effectively with sufficiently short computation times. This study then applied the proposed method to an actual inference problem of the bacterial SOS DNA repair system, and succeeded in finding several reasonable regulations. PMID:24386175

Printing three-dimensional tissue analogues with decellularized extracellular matrix bioink

PubMed Central

Pati, Falguni; Jang, Jinah; Ha, Dong-Heon; Won Kim, Sung; Rhie, Jong-Won; Shim, Jin-Hyung; Kim, Deok-Ho; Cho, Dong-Woo

2014-01-01

The ability to print and pattern all the components that make up a tissue (cells and matrix materials) in three dimensions to generate structures similar to tissues is an exciting prospect of bioprinting. However, the majority of the matrix materials used so far for bioprinting cannot represent the complexity of natural extracellular matrix (ECM) and thus are unable to reconstitute the intrinsic cellular morphologies and functions. Here, we develop a method for the bioprinting of cell-laden constructs with novel decellularized extracellular matrix (dECM) bioink capable of providing an optimized microenvironment conducive to the growth of three-dimensional structured tissue. We show the versatility and flexibility of the developed bioprinting process using tissue-specific dECM bioinks, including adipose, cartilage and heart tissues, capable of providing crucial cues for cells engraftment, survival and long-term function. We achieve high cell viability and functionality of the printed dECM structures using our bioprinting method. PMID:24887553

Three-dimensional Organization of Layered Apical Cytoskeletal Networks Associated with Mouse Airway Tissue Development

NASA Astrophysics Data System (ADS)

Tateishi, Kazuhiro; Nishida, Tomoki; Inoue, Kanako; Tsukita, Sachiko

2017-03-01

The cytoskeleton is an essential cellular component that enables various sophisticated functions of epithelial cells by forming specialized subcellular compartments. However, the functional and structural roles of cytoskeletons in subcellular compartmentalization are still not fully understood. Here we identified a novel network structure consisting of actin filaments, intermediate filaments, and microtubules directly beneath the apical membrane in mouse airway multiciliated cells and in cultured epithelial cells. Three-dimensional imaging by ultra-high voltage electron microscopy and immunofluorescence revealed that the morphological features of each network depended on the cell type and were spatiotemporally integrated in association with tissue development. Detailed analyses using Odf2 mutant mice, which lack ciliary basal feet and apical microtubules, suggested a novel contribution of the intermediate filaments to coordinated ciliary beating. These findings provide a new perspective for viewing epithelial cell differentiation and tissue morphogenesis through the structure and function of apical cytoskeletal networks.

Printing three-dimensional tissue analogues with decellularized extracellular matrix bioink

NASA Astrophysics Data System (ADS)

Pati, Falguni; Jang, Jinah; Ha, Dong-Heon; Won Kim, Sung; Rhie, Jong-Won; Shim, Jin-Hyung; Kim, Deok-Ho; Cho, Dong-Woo

2014-06-01

The ability to print and pattern all the components that make up a tissue (cells and matrix materials) in three dimensions to generate structures similar to tissues is an exciting prospect of bioprinting. However, the majority of the matrix materials used so far for bioprinting cannot represent the complexity of natural extracellular matrix (ECM) and thus are unable to reconstitute the intrinsic cellular morphologies and functions. Here, we develop a method for the bioprinting of cell-laden constructs with novel decellularized extracellular matrix (dECM) bioink capable of providing an optimized microenvironment conducive to the growth of three-dimensional structured tissue. We show the versatility and flexibility of the developed bioprinting process using tissue-specific dECM bioinks, including adipose, cartilage and heart tissues, capable of providing crucial cues for cells engraftment, survival and long-term function. We achieve high cell viability and functionality of the printed dECM structures using our bioprinting method.

Two-dimensional dielectric nanosheets: novel nanoelectronics from nanocrystal building blocks.

PubMed

Osada, Minoru; Sasaki, Takayoshi

2012-01-10

Two-dimensional (2D) nanosheets, which possess atomic or molecular thickness and infinite planar lengths, are regarded as the thinnest functional nanomaterials. The recent development of methods for manipulating graphene (carbon nanosheet) has provided new possibilities and applications for 2D systems; many amazing functionalities such as high electron mobility and quantum Hall effects have been discovered. However, graphene is a conductor, and electronic technology also requires insulators, which are essential for many devices such as memories, capacitors, and gate dielectrics. Along with graphene, inorganic nanosheets have thus increasingly attracted fundamental research interest because they have the potential to be used as dielectric alternatives in next-generation nanoelectronics. Here, we review the progress made in the properties of dielectric nanosheets, highlighting emerging functionalities in electronic applications. We also present a perspective on the advantages offered by this class of materials for future nanoelectronics. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Functional Connectivity among Spikes in Low Dimensional Space during Working Memory Task in Rat

PubMed Central

Tian, Xin

2014-01-01

Working memory (WM) is critically important in cognitive tasks. The functional connectivity has been a powerful tool for understanding the mechanism underlying the information processing during WM tasks. The aim of this study is to investigate how to effectively characterize the dynamic variations of the functional connectivity in low dimensional space among the principal components (PCs) which were extracted from the instantaneous firing rate series. Spikes were obtained from medial prefrontal cortex (mPFC) of rats with implanted microelectrode array and then transformed into continuous series via instantaneous firing rate method. Granger causality method is proposed to study the functional connectivity. Then three scalar metrics were applied to identify the changes of the reduced dimensionality functional network during working memory tasks: functional connectivity (GC), global efficiency (E) and casual density (CD). As a comparison, GC, E and CD were also calculated to describe the functional connectivity in the original space. The results showed that these network characteristics dynamically changed during the correct WM tasks. The measure values increased to maximum, and then decreased both in the original and in the reduced dimensionality. Besides, the feature values of the reduced dimensionality were significantly higher during the WM tasks than they were in the original space. These findings suggested that functional connectivity among the spikes varied dynamically during the WM tasks and could be described effectively in the low dimensional space. PMID:24658291

Dimension of quantum phase space measured by photon correlations

NASA Astrophysics Data System (ADS)

Leuchs, Gerd; Glauber, Roy J.; Schleich, Wolfgang P.

2015-06-01

We show that the different values 1, 2 and 3 of the normalized second-order correlation function {g}(2)(0) corresponding to a coherent state, a thermal state and a highly squeezed vacuum originate from the different dimensionality of these states in phase space. In particular, we derive an exact expression for {g}(2)(0) in terms of the ratio of the moments of the classical energy evaluated with the Wigner function of the quantum state of interest and corrections proportional to the reciprocal of powers of the average number of photons. In this way we establish a direct link between {g}(2)(0) and the shape of the state in phase space. Moreover, we illuminate this connection by demonstrating that in the semi-classical limit the familiar photon statistics of a thermal state arise from an area in phase space weighted by a two-dimensional Gaussian, whereas those of a highly squeezed state are governed by a line-integral of a one-dimensional Gaussian. We dedicate this article to Margarita and Vladimir Man’ko on the occasion of their birthdays. The topic of our contribution is deeply rooted in and motivated by their love for non-classical light, quantum mechanical phase space distribution functions and orthogonal polynomials. Indeed, through their articles, talks and most importantly by many stimulating discussions and intensive collaborations with us they have contributed much to our understanding of physics. Happy birthday to you both!

Superalgebraically convergent smoothly windowed lattice sums for doubly periodic Green functions in three-dimensional space

PubMed Central

Bruno, Oscar P.; Turc, Catalin; Venakides, Stephanos

2016-01-01

This work, part I in a two-part series, presents: (i) a simple and highly efficient algorithm for evaluation of quasi-periodic Green functions, as well as (ii) an associated boundary-integral equation method for the numerical solution of problems of scattering of waves by doubly periodic arrays of scatterers in three-dimensional space. Except for certain ‘Wood frequencies’ at which the quasi-periodic Green function ceases to exist, the proposed approach, which is based on smooth windowing functions, gives rise to tapered lattice sums which converge superalgebraically fast to the Green function—that is, faster than any power of the number of terms used. This is in sharp contrast to the extremely slow convergence exhibited by the lattice sums in the absence of smooth windowing. (The Wood-frequency problem is treated in part II.) This paper establishes rigorously the superalgebraic convergence of the windowed lattice sums. A variety of numerical results demonstrate the practical efficiency of the proposed approach. PMID:27493573

Immunoactive two-dimensional self-assembly of monoclonal antibodies in aqueous solution revealed by atomic force microscopy

NASA Astrophysics Data System (ADS)

Ido, Shinichiro; Kimiya, Hirokazu; Kobayashi, Kei; Kominami, Hiroaki; Matsushige, Kazumi; Yamada, Hirofumi

2014-03-01

The conformational flexibility of antibodies in solution directly affects their immune function. Namely, the flexible hinge regions of immunoglobulin G (IgG) antibodies are essential in epitope-specific antigen recognition and biological effector function. The antibody structure, which is strongly related to its functions, has been partially revealed by electron microscopy and X-ray crystallography, but only under non-physiological conditions. Here we observed monoclonal IgG antibodies in aqueous solution by high-resolution frequency modulation atomic force microscopy (FM-AFM). We found that monoclonal antibodies self-assemble into hexamers, which form two-dimensional crystals in aqueous solution. Furthermore, by directly observing antibody-antigen interactions using FM-AFM, we revealed that IgG molecules in the crystal retain immunoactivity. As the self-assembled monolayer crystal of antibodies retains immunoactivity at a neutral pH and is functionally stable at a wide range of pH and temperature, the antibody crystal is applicable to new biotechnological platforms for biosensors or bioassays.

Semiclassical propagation of Wigner functions.

PubMed

Dittrich, T; Gómez, E A; Pachón, L A

2010-06-07

We present a comprehensive study of semiclassical phase-space propagation in the Wigner representation, emphasizing numerical applications, in particular as an initial-value representation. Two semiclassical approximation schemes are discussed. The propagator of the Wigner function based on van Vleck's approximation replaces the Liouville propagator by a quantum spot with an oscillatory pattern reflecting the interference between pairs of classical trajectories. Employing phase-space path integration instead, caustics in the quantum spot are resolved in terms of Airy functions. We apply both to two benchmark models of nonlinear molecular potentials, the Morse oscillator and the quartic double well, to test them in standard tasks such as computing autocorrelation functions and propagating coherent states. The performance of semiclassical Wigner propagation is very good even in the presence of marked quantum effects, e.g., in coherent tunneling and in propagating Schrodinger cat states, and of classical chaos in four-dimensional phase space. We suggest options for an effective numerical implementation of our method and for integrating it in Monte-Carlo-Metropolis algorithms suitable for high-dimensional systems.

Three-dimensional habitat structure and landscape genetics: a step forward in estimating functional connectivity.

PubMed

Milanesi, P; Holderegger, R; Bollmann, K; Gugerli, F; Zellweger, F

2017-02-01

Estimating connectivity among fragmented habitat patches is crucial for evaluating the functionality of ecological networks. However, current estimates of landscape resistance to animal movement and dispersal lack landscape-level data on local habitat structure. Here, we used a landscape genetics approach to show that high-fidelity habitat structure maps derived from Light Detection and Ranging (LiDAR) data critically improve functional connectivity estimates compared to conventional land cover data. We related pairwise genetic distances of 128 Capercaillie (Tetrao urogallus) genotypes to least-cost path distances at multiple scales derived from land cover data. Resulting β values of linear mixed effects models ranged from 0.372 to 0.495, while those derived from LiDAR ranged from 0.558 to 0.758. The identification and conservation of functional ecological networks suffering from habitat fragmentation and homogenization will thus benefit from the growing availability of detailed and contiguous data on three-dimensional habitat structure and associated habitat quality. © 2016 by the Ecological Society of America.

Neural networks with multiple general neuron models: a hybrid computational intelligence approach using Genetic Programming.

PubMed

Barton, Alan J; Valdés, Julio J; Orchard, Robert

2009-01-01

Classical neural networks are composed of neurons whose nature is determined by a certain function (the neuron model), usually pre-specified. In this paper, a type of neural network (NN-GP) is presented in which: (i) each neuron may have its own neuron model in the form of a general function, (ii) any layout (i.e network interconnection) is possible, and (iii) no bias nodes or weights are associated to the connections, neurons or layers. The general functions associated to a neuron are learned by searching a function space. They are not provided a priori, but are rather built as part of an Evolutionary Computation process based on Genetic Programming. The resulting network solutions are evaluated based on a fitness measure, which may, for example, be based on classification or regression errors. Two real-world examples are presented to illustrate the promising behaviour on classification problems via construction of a low-dimensional representation of a high-dimensional parameter space associated to the set of all network solutions.

Highly Nitrogen-Doped Three-Dimensional Carbon Fibers Network with Superior Sodium Storage Capacity.

PubMed

Lei, Wen; Xiao, Weiping; Li, Jingde; Li, Gaoran; Wu, Zexing; Xuan, Cuijuan; Luo, Dan; Deng, Ya-Ping; Wang, Deli; Chen, Zhongwei

2017-08-30

Inspired by the excellent absorption capability of spongelike bacterial cellulose (BC), three-dimensional hierarchical porous carbon fibers doped with an ultrahigh content of N (21.2 atom %) (i.e., nitrogen-doped carbon fibers, NDCFs) were synthesized by an adsorption-swelling strategy using BC as the carbonaceous material. When used as anode materials for sodium-ion batteries, the NDCFs deliver a high reversible capacity of 86.2 mAh g -1 even after 2000 cycles at a high current density of 10.0 A g -1 . It is proposed that the excellent Na + storage performance is mainly due to the defective surface of the NDCFs created by the high content of N dopant. Density functional theory (DFT) calculations show that the defect sites created by N doping can strongly "host" Na + and therefore contribute to the enhanced storage capacity.

A high density two-dimensional electron gas in an oxide heterostructure on Si (001)

NASA Astrophysics Data System (ADS)

Jin, E. N.; Kornblum, L.; Kumah, D. P.; Zou, K.; Broadbridge, C. C.; Ngai, J. H.; Ahn, C. H.; Walker, F. J.

2014-11-01

We present the growth and characterization of layered heterostructures comprised of LaTiO3 and SrTiO3 epitaxially grown on Si (001). Magnetotransport measurements show that the sheet carrier densities of the heterostructures scale with the number of LaTiO3/SrTiO3 interfaces, consistent with the presence of an interfacial 2-dimensional electron gas (2DEG) at each interface. Sheet carrier densities of 8.9 × 1014 cm-2 per interface are observed. Integration of such high density oxide 2DEGs on silicon provides a bridge between the exceptional properties and functionalities of oxide 2DEGs and microelectronic technologies.

Three-Dimensional Right Ventricular Strain Versus Volume Quantification in Heart Transplant Recipients in Relation to Pulmonary Artery Pressure.

PubMed

Sade, Leyla Elif; Kozan, Hatice; Eroglu, Serpil; Pirat, Bahar; Aydinalp, Alp; Sezgin, Atilla; Muderrisoglu, Haldun

2017-02-01

Residual pulmonary hypertension challenges the right ventricular function and worsens the prognosis in heart transplant recipients. The complex geometry of the right ventricle complicates estimation of its function with conventional transthoracic echocardiography. We evaluated right ventricular function in heart transplant recipients with the use of 3-dimensional echocardiography in relation to systolic pulmonary artery pressure. We performed 32 studies in 26 heart transplant patients, with 6 patients having 2 studies at different time points with different pressures and thus included. Right atrial volume, tricuspid annular plane systolic excursion, peak systolic annular velocity, fractional area change, and 2-dimensional speckle tracking longitudinal strain were obtained by 2-dimensional and tissue Doppler imaging. Three-dimensional right ventricular volumes, ejection fraction, and 3-dimensional right ventricular strain were obtained from the 3-dimensional data set by echocardiographers. Systolic pulmonary artery pressure was obtained during right heart catheterization. Overall mean systolic pulmonary artery pressure was 26 ± 7 mm Hg (range, 14-44 mmHg). Three-dimensional end-diastolic (r = 0.75; P < .001) and end-systolic volumes (r = 0.55; P = .001)correlated well with systolic pulmonary artery pressure. Right ventricular ejection fraction and right atrium volume also significantly correlated with systolic pulmonary artery pressure (r = 0.49 and P = .01 for both). However, right ventricular 2- and 3-dimensional strain, tricuspid annular plane systolic excursion, and tricuspid annular velocity did not. The effects of pulmonary hemodynamic burden on right ventricular function are better estimated by a 3-dimensional volume evaluation than with 3-dimensional longitudinal strain and other 2-dimensional and tissue Doppler measurements. These results suggest that the peculiar anatomy of the right ventricle necessitates 3-dimensional volume quantification in heart transplant recipients in relation to residual pulmonary hypertension.

Multilocality and fusion rules on the generalized structure functions in two-dimensional and three-dimensional Navier-Stokes turbulence.

PubMed

Gkioulekas, Eleftherios

2016-09-01

Using the fusion-rules hypothesis for three-dimensional and two-dimensional Navier-Stokes turbulence, we generalize a previous nonperturbative locality proof to multiple applications of the nonlinear interactions operator on generalized structure functions of velocity differences. We call this generalization of nonperturbative locality to multiple applications of the nonlinear interactions operator "multilocality." The resulting cross terms pose a new challenge requiring a new argument and the introduction of a new fusion rule that takes advantage of rotational symmetry. Our main result is that the fusion-rules hypothesis implies both locality and multilocality in both the IR and UV limits for the downscale energy cascade of three-dimensional Navier-Stokes turbulence and the downscale enstrophy cascade and inverse energy cascade of two-dimensional Navier-Stokes turbulence. We stress that these claims relate to nonperturbative locality of generalized structure functions on all orders and not the term-by-term perturbative locality of diagrammatic theories or closure models that involve only two-point correlation and response functions.

Columnar organization of orientation domains in V1

NASA Astrophysics Data System (ADS)

Liedtke, Joscha; Wolf, Fred

In the primary visual cortex (V1) of primates and carnivores, the functional architecture of basic stimulus selectivities appears similar across cortical layers (Hubel & Wiesel, 1962) justifying the use of two-dimensional cortical models and disregarding organization in the third dimension. Here we show theoretically that already small deviations from an exact columnar organization lead to non-trivial three-dimensional functional structures. We extend two-dimensional random field models (Schnabel et al., 2007) to a three-dimensional cortex by keeping a typical scale in each layer and introducing a correlation length in the third, columnar dimension. We examine in detail the three-dimensional functional architecture for different cortical geometries with different columnar correlation lengths. We find that (i) topological defect lines are generally curved and (ii) for large cortical curvatures closed loops and reconnecting topological defect lines appear. This theory extends the class of random field models by introducing a columnar dimension and provides a systematic statistical assessment of the three-dimensional functional architecture of V1 (see also (Tanaka et al., 2011)).

Depressurization and two-phase flow of water containing high levels of dissolved nitrogen gas

NASA Technical Reports Server (NTRS)

Simoneau, R. J.

1981-01-01

Depressurization of water containing various concentrations of dissolved nitrogen gas was studied. In a nonflow depressurization experiment, water with very high nitrogen content was depressurized at rates from 0.09 to 0.50 MPa per second and a metastable behavior which was a strong function of the depressurization rate was observed. Flow experiments were performed in an axisymmetric, converging diverging nozzle, a two dimensional, converging nozzle with glass sidewalls, and a sharp edge orifice. The converging diverging nozzle exhibited choked flow behavior even at nitrogen concentration levels as low as 4 percent of the saturation level. The flow rates were independent of concentration level. Flow in the two dimensional, converging, visual nozzle appeared to have a sufficient pressure drop at the throat to cause nitrogen to come out of solution, but choking occurred further downstream. The orifice flow motion pictures showed considerable oscillation downstream of the orifice and parallel to the flow. Nitrogen bubbles appeared in the flow at back pressures as high as 3.28 MPa, and the level at which bubbles were no longer visible was a function of nitrogen concentration.

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

PubMed

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

2018-05-21

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

Spatial mapping and statistical reproducibility of an array of 256 one-dimensional quantum wires

NASA Astrophysics Data System (ADS)

Al-Taie, H.; Smith, L. W.; Lesage, A. A. J.; See, P.; Griffiths, J. P.; Beere, H. E.; Jones, G. A. C.; Ritchie, D. A.; Kelly, M. J.; Smith, C. G.

2015-08-01

We utilize a multiplexing architecture to measure the conductance properties of an array of 256 split gates. We investigate the reproducibility of the pinch off and one-dimensional definition voltage as a function of spatial location on two different cooldowns, and after illuminating the device. The reproducibility of both these properties on the two cooldowns is high, the result of the density of the two-dimensional electron gas returning to a similar state after thermal cycling. The spatial variation of the pinch-off voltage reduces after illumination; however, the variation of the one-dimensional definition voltage increases due to an anomalous feature in the center of the array. A technique which quantifies the homogeneity of split-gate properties across the array is developed which captures the experimentally observed trends. In addition, the one-dimensional definition voltage is used to probe the density of the wafer at each split gate in the array on a micron scale using a capacitive model.

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

PubMed

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

2018-01-01

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

Highly luminescent colloidal nanoplates of perovskite cesium lead halide and their oriented assemblies

DOE PAGES

Bekenstein, Yehonadav; Koscher, Brent A.; Eaton, Samuel W.; ...

2015-12-15

Anisotropic colloidal quasi-two-dimensional nanoplates (NPLs) hold great promise as functional materials due to their combination of low dimensional optoelectronic properties and versatility through colloidal synthesis. Recently, lead-halide perovskites have emerged as important optoelectronic materials with excellent efficiencies in photovoltaic and light-emitting applications. Here we report the synthesis of quantum confined all inorganic cesium lead halide nanoplates in the perovskite crystal structure that are also highly luminescent (PLQY 84%). The controllable self-assembly of nanoplates either into stacked columnar phases or crystallographic-oriented thin-sheet structures is demonstrated. Furthermore, the broad accessible emission range, high native quantum yields, and ease of self-assembly make perovskitemore » NPLs an ideal platform for fundamental optoelectronic studies and the investigation of future devices.« less

Resolution extension by image summing in serial femtosecond crystallography of two-dimensional membrane-protein crystals

DOE PAGES

Casadei, Cecilia M.; Tsai, Ching-Ju; Barty, Anton; ...

2018-01-01

Previous proof-of-concept measurements on single-layer two-dimensional membrane-protein crystals performed at X-ray free-electron lasers (FELs) have demonstrated that the collection of meaningful diffraction patterns, which is not possible at synchrotrons because of radiation-damage issues, is feasible. Here, the results obtained from the analysis of a thousand single-shot, room-temperature X-ray FEL diffraction images from two-dimensional crystals of a bacteriorhodopsin mutant are reported in detail. The high redundancy in the measurements boosts the intensity signal-to-noise ratio, so that the values of the diffracted intensities can be reliably determined down to the detector-edge resolution of 4 Å. The results show that two-dimensional serial crystallography atmore » X-ray FELs is a suitable method to study membrane proteins to near-atomic length scales at ambient temperature. The method presented here can be extended to pump–probe studies of optically triggered structural changes on submillisecond timescales in two-dimensional crystals, which allow functionally relevant large-scale motions that may be quenched in three-dimensional crystals.« less

Resolution extension by image summing in serial femtosecond crystallography of two-dimensional membrane-protein crystals

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

Casadei, Cecilia M.; Tsai, Ching-Ju; Barty, Anton

Previous proof-of-concept measurements on single-layer two-dimensional membrane-protein crystals performed at X-ray free-electron lasers (FELs) have demonstrated that the collection of meaningful diffraction patterns, which is not possible at synchrotrons because of radiation-damage issues, is feasible. Here, the results obtained from the analysis of a thousand single-shot, room-temperature X-ray FEL diffraction images from two-dimensional crystals of a bacteriorhodopsin mutant are reported in detail. The high redundancy in the measurements boosts the intensity signal-to-noise ratio, so that the values of the diffracted intensities can be reliably determined down to the detector-edge resolution of 4 Å. The results show that two-dimensional serial crystallography atmore » X-ray FELs is a suitable method to study membrane proteins to near-atomic length scales at ambient temperature. The method presented here can be extended to pump–probe studies of optically triggered structural changes on submillisecond timescales in two-dimensional crystals, which allow functionally relevant large-scale motions that may be quenched in three-dimensional crystals.« less

Exploring excited eigenstates of many-body systems using the functional renormalization group

NASA Astrophysics Data System (ADS)

Klöckner, Christian; Kennes, Dante Marvin; Karrasch, Christoph

2018-05-01

We introduce approximate, functional renormalization group based schemes to obtain correlation functions in pure excited eigenstates of large fermionic many-body systems at arbitrary energies. The algorithms are thoroughly benchmarked and their strengths and shortcomings are documented using a one-dimensional interacting tight-binding chain as a prototypical testbed. We study two "toy applications" from the world of Luttinger liquid physics: the survival of power laws in lowly excited states as well as the spectral function of high-energy "block" excitations, which feature several single-particle Fermi edges.

Unveiling the mechanism of the promising two-dimensional photoswitch - Hemithioindigo

NASA Astrophysics Data System (ADS)

Li, Donglin; Yang, Yonggang; Li, Chaozheng; Liu, Yufang

2018-07-01

The control of internal molecular motions by outside stimuli is a decisive task in the construction of functional molecules and molecular machines. Light-induced intramolecular rotations of photoswitches have attracted increasing research interests because of the high stability and high reversibility of photoswitches. Recently, Henry et al. reported an unprecedented two-dimensional controlled photoswitch, the hemithioindigo (HTI) derivative Z1, whose single bond rotation in dimethyl sulphoxide (DMSO) solvent and double bond rotation in cyclohexane solvent can be induced by visible light (J. Am. Chem. Soc. 2016, 138, 12,219). Here we investigate the intramolecular rotations of the HTI and Z1 in different polar solvents by time-dependent density functional theory (TDDFT) and Nonadiabatic dynamic simulations. Due to the steric hindrance between methyl and thioindigo fragment, the rotations of Z1 in the excited state are obstructed. Interestingly, the HTI exhibits two distinct rotation paths in DMSO and cyclohexane solvents at about 50 fs. The intermolecular hydrogen bonds between HTI and DMSO play an important role in the rotation of HTI in DMSO solvent. Therefore, the HTI is a more promising two-dimensional photoswitch compared with the Z1. Our finding is thus of fundamental importance to understand the mechanisms of this class of photoswitches and design complex molecular behavior.

Early identification of amyloid heart disease by technetium-99m-pyrophosphate scintigraphy: a study with familial amyloid polyneuropathy

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

Hongo, M.; Hirayama, J.; Fujii, T.

1987-03-01

To determine whether technetium-99m-pyrophosphate (Tc-99m-PYP) scanning or two-dimensional echocardiography can detect amyloid heart disease in an earlier stage of familial amyloid polyneuropathy, 15 patients were examined. Although 10 of the 15 patients had no clinical evidence of congestive heart failure, as well as normal ventricular wall thickness and normal values for left ventricular systolic function, five (50%) of them showed mild or moderate myocardial uptake. On the other hand, none had characteristic highly refractile myocardial echoes on the two-dimensional echocardiographic images (p less than 0.01), and values for diastolic function were reduced in four of the five and normal inmore » the remaining one. In 85 control subjects, diffuse positive pyrophosphate scans of the heart were found in four (5%) of them (three with dilated cardiomyopathy and one with sarcoidosis), and highly refractile granular sparkling echoes were observed in nine (11%) (five with hypertrophic cardiomyopathy, three with aortic stenosis, and one with hypereosinophilic syndrome). We conclude that Tc-99m-PYP scanning is a more sensitive and specific method and may have the potential ability to detect amyloid heart disease in the earlier stage of familial amyloid polyneuropathy than two-dimensional echocardiography.« less

Iteration and superposition encryption scheme for image sequences based on multi-dimensional keys

NASA Astrophysics Data System (ADS)

Han, Chao; Shen, Yuzhen; Ma, Wenlin

2017-12-01

An iteration and superposition encryption scheme for image sequences based on multi-dimensional keys is proposed for high security, big capacity and low noise information transmission. Multiple images to be encrypted are transformed into phase-only images with the iterative algorithm and then are encrypted by different random phase, respectively. The encrypted phase-only images are performed by inverse Fourier transform, respectively, thus new object functions are generated. The new functions are located in different blocks and padded zero for a sparse distribution, then they propagate to a specific region at different distances by angular spectrum diffraction, respectively and are superposed in order to form a single image. The single image is multiplied with a random phase in the frequency domain and then the phase part of the frequency spectrums is truncated and the amplitude information is reserved. The random phase, propagation distances, truncated phase information in frequency domain are employed as multiple dimensional keys. The iteration processing and sparse distribution greatly reduce the crosstalk among the multiple encryption images. The superposition of image sequences greatly improves the capacity of encrypted information. Several numerical experiments based on a designed optical system demonstrate that the proposed scheme can enhance encrypted information capacity and make image transmission at a highly desired security level.

Three-Dimensional Unstained Live-Cell Imaging Using Stimulated Parametric Emission Microscopy

NASA Astrophysics Data System (ADS)

Dang, Hieu M.; Kawasumi, Takehito; Omura, Gen; Umano, Toshiyuki; Kajiyama, Shin'ichiro; Ozeki, Yasuyuki; Itoh, Kazuyoshi; Fukui, Kiichi

2009-09-01

The ability to perform high-resolution unstained live imaging is very important to in vivo study of cell structures and functions. Stimulated parametric emission (SPE) microscopy is a nonlinear-optical microscopy based on ultra-fast electronic nonlinear-optical responses. For the first time, we have successfully applied this technique to archive three-dimensional (3D) images of unstained sub-cellular structures, such as, microtubules, nuclei, nucleoli, etc. in live cells. Observation of a complete cell division confirms the ability of SPE microscopy for long time-scale imaging.

Self-assembled three-dimensional chiral colloidal architecture

NASA Astrophysics Data System (ADS)

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

2017-11-01

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

Simulations of High-Gain Shock-Ignited Inertial-Confinement-Fusion Implosions Using Less Than 1 MJ of Direct KrF Laser Energy

DTIC Science & Technology

2009-05-01

transport, and thermonuclear burn. Using FAST, three classes of shock-ignited targets were designed that achieve one-dimensional fusion - energy gains in the...MJ) G a in Figure 1: Results of one-dimensional simulations showing the fusion energy gain as a function of KrF laser energy for three classes of...rises smoothly (according to a double power (a) Spike width: 160 ps (b) Spike power: 1530 TW Figure 4: Examples of fusion - energy gain contours for a shock

Exciton Polaritons in a Two-Dimensional Lieb Lattice with Spin-Orbit Coupling

NASA Astrophysics Data System (ADS)

Whittaker, C. E.; Cancellieri, E.; Walker, P. M.; Gulevich, D. R.; Schomerus, H.; Vaitiekus, D.; Royall, B.; Whittaker, D. M.; Clarke, E.; Iorsh, I. V.; Shelykh, I. A.; Skolnick, M. S.; Krizhanovskii, D. N.

2018-03-01

We study exciton polaritons in a two-dimensional Lieb lattice of micropillars. The energy spectrum of the system features two flat bands formed from S and Px ,y photonic orbitals, into which we trigger bosonic condensation under high power excitation. The symmetry of the orbital wave functions combined with photonic spin-orbit coupling gives rise to emission patterns with pseudospin texture in the flat band condensates. Our Letter shows the potential of polariton lattices for emulating flat band Hamiltonians with spin-orbit coupling, orbital degrees of freedom, and interactions.

Exciton Polaritons in a Two-Dimensional Lieb Lattice with Spin-Orbit Coupling.

PubMed

Whittaker, C E; Cancellieri, E; Walker, P M; Gulevich, D R; Schomerus, H; Vaitiekus, D; Royall, B; Whittaker, D M; Clarke, E; Iorsh, I V; Shelykh, I A; Skolnick, M S; Krizhanovskii, D N

2018-03-02

We study exciton polaritons in a two-dimensional Lieb lattice of micropillars. The energy spectrum of the system features two flat bands formed from S and P_{x,y} photonic orbitals, into which we trigger bosonic condensation under high power excitation. The symmetry of the orbital wave functions combined with photonic spin-orbit coupling gives rise to emission patterns with pseudospin texture in the flat band condensates. Our Letter shows the potential of polariton lattices for emulating flat band Hamiltonians with spin-orbit coupling, orbital degrees of freedom, and interactions.

New Funding Opportunity from the Human Biomolecular Atlas Program (HuBMAP)! | Informatics Technology for Cancer Research (ITCR)

Cancer.gov

The NIH Common Fund Human Biomolecular Atlas Program (HuBMAP) aims to develop a framework for functional mapping the human body with cellular resolution to enhance our understanding of cellular organization-function. HuBMAP will accelerate the development of the next generation of tools and techniques to generate 3D tissue maps using validated high-content, high-throughput imaging and omics assays, and establish an open data platform for integrating, visualizing data to build multi-dimensional maps.

Meso-decorated self-healing gels: network structure and properties

NASA Astrophysics Data System (ADS)

Gong, Jin; Sawamura, Kensuke; Igarashi, Susumu; Furukawa, Hidemitsu

2013-04-01

Gels are a new material having three-dimensional network structures of macromolecules. They possess excellent properties as swellability, high permeability and biocompatibility, and have been applied in various fields of daily life, food, medicine, architecture, and chemistry. In this study, we tried to prepare new multi-functional and high-strength gels by using Meso-Decoration (Meso-Deco), one new method of structure design at intermediate mesoscale. High-performance rigid-rod aromatic polymorphic crystals, and the functional groups of thermoreversible Diels-Alder reaction were introduced into soft gels as crosslinkable pendent chains. The functionalization and strengthening of gels can be realized by meso-decorating the gels' structure using high-performance polymorphic crystals and thermoreversible pendent chains. New gels with good mechanical properties, novel optical properties and thermal properties are expected to be developed.

Two-dimensional artificial light-harvesting antennae with predesigned high-order structure and robust photosensitising activity

PubMed Central

Feng, Xiao; Ding, Xuesong; Chen, Long; Wu, Yang; Liu, Lili; Addicoat, Matthew; Irle, Stephan; Dong, Yuping; Jiang, Donglin

2016-01-01

Highly ordered discrete assemblies of chlorophylls that are found in natural light-harvesting antennae are key to photosynthesis, which converts light energy to chemical energy and is the principal producer of organic matter on Earth. Porphyrins and phthalocyanines, which are analogues of chlorophylls, exhibit a strong absorbance of visible and near-infrared light, respectively. A highly ordered porphyrin-co-phthalocyanine antennae would harvest photons over the entire solar spectrum for chemical transformation. However, such a robust antennae has not yet been synthesised. Herein, we report a strategy that merges covalent bonds and noncovalent forces to produce highly ordered two-dimensional porphyrin-co-phthalocyanine antennae. This methodology enables control over the stoichiometry and order of the porphyrin and phthalocyanine units; more importantly, this approach is compatible with various metalloporphyrin and metallophthalocyanine derivatives and thus may lead to the generation of a broad structural diversity of two-dimensional artificial antennae. These ordered porphyrin-co-phthalocyanine two-dimensional antennae exhibit unique optical properties and catalytic functions that are not available with single-component or non-structured materials. These 2D artificial antennae exhibit exceptional light-harvesting capacity over the entire solar spectrum as a result of a synergistic light-absorption effect. In addition, they exhibit outstanding photosensitising activities in using both visible and near-infrared photons for producing singlet oxygen. PMID:27622274

Systematic analysis of Ca2+ homeostasis in Saccharomyces cerevisiae based on chemical-genetic interaction profiles

PubMed Central

Ghanegolmohammadi, Farzan; Yoshida, Mitsunori; Ohnuki, Shinsuke; Sukegawa, Yuko; Okada, Hiroki; Obara, Keisuke; Kihara, Akio; Suzuki, Kuninori; Kojima, Tetsuya; Yachie, Nozomu; Hirata, Dai; Ohya, Yoshikazu

2017-01-01

We investigated the global landscape of Ca2+ homeostasis in budding yeast based on high-dimensional chemical-genetic interaction profiles. The morphological responses of 62 Ca2+-sensitive (cls) mutants were quantitatively analyzed with the image processing program CalMorph after exposure to a high concentration of Ca2+. After a generalized linear model was applied, an analysis of covariance model was used to detect significant Ca2+–cls interactions. We found that high-dimensional, morphological Ca2+–cls interactions were mixed with positive (86%) and negative (14%) chemical-genetic interactions, whereas one-dimensional fitness Ca2+–cls interactions were all negative in principle. Clustering analysis with the interaction profiles revealed nine distinct gene groups, six of which were functionally associated. In addition, characterization of Ca2+–cls interactions revealed that morphology-based negative interactions are unique signatures of sensitized cellular processes and pathways. Principal component analysis was used to discriminate between suppression and enhancement of the Ca2+-sensitive phenotypes triggered by inactivation of calcineurin, a Ca2+-dependent phosphatase. Finally, similarity of the interaction profiles was used to reveal a connected network among the Ca2+ homeostasis units acting in different cellular compartments. Our analyses of high-dimensional chemical-genetic interaction profiles provide novel insights into the intracellular network of yeast Ca2+ homeostasis. PMID:28566553

Numbers and functions in quantum field theory

NASA Astrophysics Data System (ADS)

Schnetz, Oliver

2018-04-01

We review recent results in the theory of numbers and single-valued functions on the complex plane which arise in quantum field theory. These results are the basis for a new approach to high-loop-order calculations. As concrete examples, we provide scheme-independent counterterms of primitive log-divergent graphs in ϕ4 theory up to eight loops and the renormalization functions β , γ , γm of dimensionally regularized ϕ4 theory in the minimal subtraction scheme up to seven loops.

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

Williams, Brian J.; Marcy, Peter W.

We will investigate the use of derivative information in complex computer model emulation when the correlation function is of the compactly supported Bohman class. To this end, a Gaussian process model similar to that used by Kaufman et al. (2011) is extended to a situation where first partial derivatives in each dimension are calculated at each input site (i.e. using gradients). A simulation study in the ten-dimensional case is conducted to assess the utility of the Bohman correlation function against strictly positive correlation functions when a high degree of sparsity is induced.

Integral formulae of the canonical correlation functions for the one dimensional transverse Ising model

NASA Astrophysics Data System (ADS)

Inoue, Makoto

2017-12-01

Some new formulae of the canonical correlation functions for the one dimensional quantum transverse Ising model are found by the ST-transformation method using a Morita's sum rule and its extensions for the two dimensional classical Ising model. As a consequence we obtain a time-independent term of the dynamical correlation functions. Differences of quantum version and classical version of these formulae are also discussed.

Laser interference fringe tomography: a novel 3D imaging technique for pathology

NASA Astrophysics Data System (ADS)

Kazemzadeh, Farnoud; Haylock, Thomas M.; Chifman, Lev M.; Hajian, Arsen R.; Behr, Bradford B.; Cenko, Andrew T.; Meade, Jeff T.; Hendrikse, Jan

2011-03-01

Laser interference fringe tomography (LIFT) is within the class of optical imaging devices designed for in vivo and ex vivo medical imaging applications. LIFT is a very simple and cost-effective three-dimensional imaging device with performance rivaling some of the leading three-dimensional imaging devices used for histology. Like optical coherence tomography (OCT), it measures the reflectivity as a function of depth within a sample and is capable of producing three-dimensional images from optically scattering media. LIFT has the potential capability to produce high spectral resolution, full-color images. The optical design of LIFT along with the planned iterations for improvements and miniaturization are presented and discussed in addition to the theoretical concepts and preliminary imaging results of the device.

Stability enhancement and electronic tunability of two-dimensional SbIV compounds via surface functionalization

NASA Astrophysics Data System (ADS)

Zhou, Wenhan; Guo, Shiying; Liu, Xuhai; Cai, Bo; Song, Xiufeng; Zhu, Zhen; Zhang, Shengli

2018-01-01

We propose a family of hydrogenated- and halogenated-SbIV (SbIVX-2) materials that simultaneously have two-dimensional (2D) structures, high stability and appealing electronic properties. Based on first-principles total-energy and vibrational-spectra calculations, SbIVX-2 monolayers are found both thermally and dynamically stable. Varying IV and X elements can rationally tune the electronic properties of SbIVX-2 monolayers, effectively modulating the band gap from 0 to 3.42 eV. Regarding such superior stability and broad band-gap range, SbIVX-2 monolayers are expected to be synthesized in experiments and taken as promising candidates for low-dimensional electronic and optoelectronic devices, such as blue-to-ultraviolet light-emitting diodes (LED) and photodetectors.

A cubic spline approximation for problems in fluid mechanics

NASA Technical Reports Server (NTRS)

Rubin, S. G.; Graves, R. A., Jr.

1975-01-01

A cubic spline approximation is presented which is suited for many fluid-mechanics problems. This procedure provides a high degree of accuracy, even with a nonuniform mesh, and leads to an accurate treatment of derivative boundary conditions. The truncation errors and stability limitations of several implicit and explicit integration schemes are presented. For two-dimensional flows, a spline-alternating-direction-implicit method is evaluated. The spline procedure is assessed, and results are presented for the one-dimensional nonlinear Burgers' equation, as well as the two-dimensional diffusion equation and the vorticity-stream function system describing the viscous flow in a driven cavity. Comparisons are made with analytic solutions for the first two problems and with finite-difference calculations for the cavity flow.

A new redox-active coordination polymer with cobalticinium dicarboxylate.

PubMed

Kondo, Mitsuru; Hayakawa, Yuri; Miyazawa, Makoto; Oyama, Aiko; Unoura, Kei; Kawaguchi, Hiroyuki; Naito, Tetsuyoshi; Maeda, Kenji; Uchida, Fumio

2004-09-20

A new two-dimensional coordination polymer with cobalticinium 1,1'-dicarboxylate (ccdc) incorporated in the framework has been prepared, the ccdc functioning as unique monoanionic dicarboxylate ligands. The compound shows a high redox activity based on the ccdc units. Copyright 2004 American Chemical Society

Normed kernel function-based fuzzy possibilistic C-means (NKFPCM) algorithm for high-dimensional breast cancer database classification with feature selection is based on Laplacian Score

NASA Astrophysics Data System (ADS)

Lestari, A. W.; Rustam, Z.

2017-07-01

In the last decade, breast cancer has become the focus of world attention as this disease is one of the primary leading cause of death for women. Therefore, it is necessary to have the correct precautions and treatment. In previous studies, Fuzzy Kennel K-Medoid algorithm has been used for multi-class data. This paper proposes an algorithm to classify the high dimensional data of breast cancer using Fuzzy Possibilistic C-means (FPCM) and a new method based on clustering analysis using Normed Kernel Function-Based Fuzzy Possibilistic C-Means (NKFPCM). The objective of this paper is to obtain the best accuracy in classification of breast cancer data. In order to improve the accuracy of the two methods, the features candidates are evaluated using feature selection, where Laplacian Score is used. The results show the comparison accuracy and running time of FPCM and NKFPCM with and without feature selection.

Tissue matrix arrays for high throughput screening and systems analysis of cell function

PubMed Central

Beachley, Vince Z.; Wolf, Matthew T.; Sadtler, Kaitlyn; Manda, Srikanth S.; Jacobs, Heather; Blatchley, Michael; Bader, Joel S.; Pandey, Akhilesh; Pardoll, Drew; Elisseeff, Jennifer H.

2015-01-01

Cell and protein arrays have demonstrated remarkable utility in the high-throughput evaluation of biological responses; however, they lack the complexity of native tissue and organs. Here, we describe tissue extracellular matrix (ECM) arrays for screening biological outputs and systems analysis. We spotted processed tissue ECM particles as two-dimensional arrays or incorporated them with cells to generate three-dimensional cell-matrix microtissue arrays. We then investigated the response of human stem, cancer, and immune cells to tissue ECM arrays originating from 11 different tissues, and validated the 2D and 3D arrays as representative of the in vivo microenvironment through quantitative analysis of tissue-specific cellular responses, including matrix production, adhesion and proliferation, and morphological changes following culture. The biological outputs correlated with tissue proteomics, and network analysis identified several proteins linked to cell function. Our methodology enables broad screening of ECMs to connect tissue-specific composition with biological activity, providing a new resource for biomaterials research and translation. PMID:26480475

Compressive light field imaging

NASA Astrophysics Data System (ADS)

Ashok, Amit; Neifeld, Mark A.

2010-04-01

Light field imagers such as the plenoptic and the integral imagers inherently measure projections of the four dimensional (4D) light field scalar function onto a two dimensional sensor and therefore, suffer from a spatial vs. angular resolution trade-off. Programmable light field imagers, proposed recently, overcome this spatioangular resolution trade-off and allow high-resolution capture of the (4D) light field function with multiple measurements at the cost of a longer exposure time. However, these light field imagers do not exploit the spatio-angular correlations inherent in the light fields of natural scenes and thus result in photon-inefficient measurements. Here, we describe two architectures for compressive light field imaging that require relatively few photon-efficient measurements to obtain a high-resolution estimate of the light field while reducing the overall exposure time. Our simulation study shows that, compressive light field imagers using the principal component (PC) measurement basis require four times fewer measurements and three times shorter exposure time compared to a conventional light field imager in order to achieve an equivalent light field reconstruction quality.

Melt dispersion and electrospinning of non-functionalized multiwalled carbon nanotubes in thermoplastic polyurethane.

PubMed

Hunley, Matthew T; Pötschke, Petra; Long, Timothy E

2009-12-16

Nanoscale fibers with embedded, aligned, and percolated non-functionalized multiwalled carbon nanotubes (MWCNTs) were fabricated through electrospinning dispersions based on melt-compounded thermoplastic polyurethane/MWCNT nanocomposite, with up to 10 wt.-% MWCNTs. Transmission electron microscopy indicated that the nanotubes were highly oriented and percolated throughout the fibers, even at high MWCNT concentrations. The coupling of efficient melt compounding with electrospinning eliminated the need for intensive surface functionalization or sonication of the MWCNTs, and the high aspect ratio as well as the electrical and mechanical properties of the nanotubes were retained. This method provides a more efficient technique to generate one-dimensional nanofibers with aligned MWCNTs. Copyright © 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Local matrix learning in clustering and applications for manifold visualization.

PubMed

Arnonkijpanich, Banchar; Hasenfuss, Alexander; Hammer, Barbara

2010-05-01

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

Zero-dimensional to three-dimensional nanojoining: current status and potential applications

DOE PAGES

Ma, Ying; Li, Hong; Bridges, Denzel; ...

2016-08-01

We report that the continuing miniaturization of microelectronics is pushing advanced manufacturing into nanomanufacturing. Nanojoining is a bottom-up assembly technique that enables functional nanodevice fabrication with dissimilar nanoscopic building blocks and/or molecular components. Various conventional joining techniques have been modified and re-invented for joining nanomaterials. Our review surveys recent progress in nanojoining methods, as compared to conventional joining processes. Examples of nanojoining are given and classified by the dimensionality of the joining materials. At each classification, nanojoining is reviewed and discussed according to materials specialties, low dimensional processing features, energy input mechanisms and potential applications. The preparation of new intermetallicmore » materials by reactive nanoscale multilayer foils based on self-propagating high-temperature synthesis is highlighted. This review will provide insight into nanojoining fundamentals and innovative applications in power electronics packaging, plasmonic devices, nanosoldering for printable electronics, 3D printing and space manufacturing.« less

Two dimensional eye tracking: Sampling rate of forcing function

NASA Technical Reports Server (NTRS)

Hornseth, J. P.; Monk, D. L.; Porterfield, J. L.; Mcmurry, R. L.

1978-01-01

A study was conducted to determine the minimum update rate of a forcing function display required for the operator to approximate the tracking performance obtained on a continuous display. In this study, frequency analysis was used to determine whether there was an associated change in the transfer function characteristics of the operator. It was expected that as the forcing function display update rate was reduced, from 120 to 15 samples per second, the operator's response to the high frequency components of the forcing function would show a decrease in gain, an increase in phase lag, and a decrease in coherence.

Executive function in middle childhood and the relationship with theory of mind.

PubMed

Wilson, Jennifer; Andrews, Glenda; Hogan, Christy; Wang, Si; Shum, David H K

2018-01-01

A group of 126 typically developing children (aged 5-12 years) completed three cool executive function tasks (spatial working memory, stop signal, intra-extra dimensional shift), two hot executive function tasks (gambling, delay of gratification), one advanced theory of mind task (strange stories with high versus low affective tone), and a vocabulary test. Older children performed better than younger children, consistent with the protracted development of hot and cool executive functions and theory of mind. Multiple regression analyses showed that hot and cool executive functions were correlated but they predicted theory of mind in different ways.

Discovering biclusters in gene expression data based on high-dimensional linear geometries

PubMed Central

Gan, Xiangchao; Liew, Alan Wee-Chung; Yan, Hong

2008-01-01

Background In DNA microarray experiments, discovering groups of genes that share similar transcriptional characteristics is instrumental in functional annotation, tissue classification and motif identification. However, in many situations a subset of genes only exhibits consistent pattern over a subset of conditions. Conventional clustering algorithms that deal with the entire row or column in an expression matrix would therefore fail to detect these useful patterns in the data. Recently, biclustering has been proposed to detect a subset of genes exhibiting consistent pattern over a subset of conditions. However, most existing biclustering algorithms are based on searching for sub-matrices within a data matrix by optimizing certain heuristically defined merit functions. Moreover, most of these algorithms can only detect a restricted set of bicluster patterns. Results In this paper, we present a novel geometric perspective for the biclustering problem. The biclustering process is interpreted as the detection of linear geometries in a high dimensional data space. Such a new perspective views biclusters with different patterns as hyperplanes in a high dimensional space, and allows us to handle different types of linear patterns simultaneously by matching a specific set of linear geometries. This geometric viewpoint also inspires us to propose a generic bicluster pattern, i.e. the linear coherent model that unifies the seemingly incompatible additive and multiplicative bicluster models. As a particular realization of our framework, we have implemented a Hough transform-based hyperplane detection algorithm. The experimental results on human lymphoma gene expression dataset show that our algorithm can find biologically significant subsets of genes. Conclusion We have proposed a novel geometric interpretation of the biclustering problem. We have shown that many common types of bicluster are just different spatial arrangements of hyperplanes in a high dimensional data space. An implementation of the geometric framework using the Fast Hough transform for hyperplane detection can be used to discover biologically significant subsets of genes under subsets of conditions for microarray data analysis. PMID:18433477

EPS-LASSO: Test for High-Dimensional Regression Under Extreme Phenotype Sampling of Continuous Traits.

PubMed

Xu, Chao; Fang, Jian; Shen, Hui; Wang, Yu-Ping; Deng, Hong-Wen

2018-01-25

Extreme phenotype sampling (EPS) is a broadly-used design to identify candidate genetic factors contributing to the variation of quantitative traits. By enriching the signals in extreme phenotypic samples, EPS can boost the association power compared to random sampling. Most existing statistical methods for EPS examine the genetic factors individually, despite many quantitative traits have multiple genetic factors underlying their variation. It is desirable to model the joint effects of genetic factors, which may increase the power and identify novel quantitative trait loci under EPS. The joint analysis of genetic data in high-dimensional situations requires specialized techniques, e.g., the least absolute shrinkage and selection operator (LASSO). Although there are extensive research and application related to LASSO, the statistical inference and testing for the sparse model under EPS remain unknown. We propose a novel sparse model (EPS-LASSO) with hypothesis test for high-dimensional regression under EPS based on a decorrelated score function. The comprehensive simulation shows EPS-LASSO outperforms existing methods with stable type I error and FDR control. EPS-LASSO can provide a consistent power for both low- and high-dimensional situations compared with the other methods dealing with high-dimensional situations. The power of EPS-LASSO is close to other low-dimensional methods when the causal effect sizes are small and is superior when the effects are large. Applying EPS-LASSO to a transcriptome-wide gene expression study for obesity reveals 10 significant body mass index associated genes. Our results indicate that EPS-LASSO is an effective method for EPS data analysis, which can account for correlated predictors. The source code is available at https://github.com/xu1912/EPSLASSO. hdeng2@tulane.edu. Supplementary data are available at Bioinformatics online. © The Author (2018). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Three-Dimensional Audio Client Library

NASA Technical Reports Server (NTRS)

Rizzi, Stephen A.

2005-01-01

The Three-Dimensional Audio Client Library (3DAudio library) is a group of software routines written to facilitate development of both stand-alone (audio only) and immersive virtual-reality application programs that utilize three-dimensional audio displays. The library is intended to enable the development of three-dimensional audio client application programs by use of a code base common to multiple audio server computers. The 3DAudio library calls vendor-specific audio client libraries and currently supports the AuSIM Gold-Server and Lake Huron audio servers. 3DAudio library routines contain common functions for (1) initiation and termination of a client/audio server session, (2) configuration-file input, (3) positioning functions, (4) coordinate transformations, (5) audio transport functions, (6) rendering functions, (7) debugging functions, and (8) event-list-sequencing functions. The 3DAudio software is written in the C++ programming language and currently operates under the Linux, IRIX, and Windows operating systems.

Changes in Dimensionality and Fractal Scaling Suggest Soft-Assembled Dynamics in Human EEG

PubMed Central

Wiltshire, Travis J.; Euler, Matthew J.; McKinney, Ty L.; Butner, Jonathan E.

2017-01-01

Humans are high-dimensional, complex systems consisting of many components that must coordinate in order to perform even the simplest of activities. Many behavioral studies, especially in the movement sciences, have advanced the notion of soft-assembly to describe how systems with many components coordinate to perform specific functions while also exhibiting the potential to re-structure and then perform other functions as task demands change. Consistent with this notion, within cognitive neuroscience it is increasingly accepted that the brain flexibly coordinates the networks needed to cope with changing task demands. However, evaluation of various indices of soft-assembly has so far been absent from neurophysiological research. To begin addressing this gap, we investigated task-related changes in two distinct indices of soft-assembly using the established phenomenon of EEG repetition suppression. In a repetition priming task, we assessed evidence for changes in the correlation dimension and fractal scaling exponents during stimulus-locked event-related potentials, as a function of stimulus onset and familiarity, and relative to spontaneous non-task-related activity. Consistent with predictions derived from soft-assembly, results indicated decreases in dimensionality and increases in fractal scaling exponents from resting to pre-stimulus states and following stimulus onset. However, contrary to predictions, familiarity tended to increase dimensionality estimates. Overall, the findings support the view from soft-assembly that neural dynamics should become increasingly ordered as external task demands increase, and support the broader application of soft-assembly logic in understanding human behavior and electrophysiology. PMID:28919862

The solid-state signaling pathway from extracellular matrix to nuclear matrix: The critical role of three-dimensional architecture for functional differentiation

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

Lelievre, S.; Bissell, M.J.

Breast cells are useful experimental subjects for cell biologists because the mammary gland is one of the few tissues that undergoes dramatic changes in form and function after adulthood. Recently, the study in our laboratory of a human breast tumor progression series has allowed for the analysis of changes in cellular architecture (including nuclear architecture) when phenotypically normal cells become tumorigenic. This research aims to participate in the battle against breast cancer by helping to understand tumor progression and to identify new therapeutic markers for cancer treatment. This article explores the advantages and challenges of using high resolution X-ray computedmore » microtomography for the study of 3-dimensional organization of breast tissue architecture.« less

Electronic fitness function for screening semiconductors as thermoelectric materials

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

Xing, Guangzong; Sun, Jifeng; Li, Yuwei

Here, we introduce a simple but efficient electronic fitness function (EFF) that describes the electronic aspect of the thermoelectric performance. This EFF finds materials that overcome the inverse relationship between σ and S based on the complexity of the electronic structures regardless of specific origin (e.g., isosurface corrugation, valley degeneracy, heavy-light bands mixture, valley anisotropy or reduced dimensionality). This function is well suited for application in high throughput screening. We applied this function to 75 different thermoelectric and potential thermoelectric materials including full- and half-Heuslers, binary semiconductors, and Zintl phases. We find an efficient screening using this transport function. Themore » EFF identifies known high-performance p- and n-type Zintl phases and half-Heuslers. In addition, we find some previously unstudied phases with superior EFF.« less

Electronic fitness function for screening semiconductors as thermoelectric materials

DOE PAGES

Xing, Guangzong; Sun, Jifeng; Li, Yuwei; ...

2017-11-17

Here, we introduce a simple but efficient electronic fitness function (EFF) that describes the electronic aspect of the thermoelectric performance. This EFF finds materials that overcome the inverse relationship between σ and S based on the complexity of the electronic structures regardless of specific origin (e.g., isosurface corrugation, valley degeneracy, heavy-light bands mixture, valley anisotropy or reduced dimensionality). This function is well suited for application in high throughput screening. We applied this function to 75 different thermoelectric and potential thermoelectric materials including full- and half-Heuslers, binary semiconductors, and Zintl phases. We find an efficient screening using this transport function. Themore » EFF identifies known high-performance p- and n-type Zintl phases and half-Heuslers. In addition, we find some previously unstudied phases with superior EFF.« less

Three Dimensional Neuronal Cell Cultures More Accurately Model Voltage Gated Calcium Channel Functionality in Freshly Dissected Nerve Tissue

PubMed Central

Kisaalita, William

2012-01-01

It has been demonstrated that neuronal cells cultured on traditional flat surfaces may exhibit exaggerated voltage gated calcium channel (VGCC) functionality. To gain a better understanding of this phenomenon, primary neuronal cells harvested from mice superior cervical ganglion (SCG) were cultured on two dimensional (2D) flat surfaces and in three dimensional (3D) synthetic poly-L-lactic acid (PLLA) and polystyrene (PS) polymer scaffolds. These 2D- and 3D-cultured cells were compared to cells in freshly dissected SCG tissues, with respect to intracellular calcium increase in response to high K+ depolarization. The calcium increases were identical for 3D-cultured and freshly dissected, but significantly higher for 2D-cultured cells. This finding established the physiological relevance of 3D-cultured cells. To shed light on the mechanism behind the exaggerated 2D-cultured cells’ functionality, transcriptase expression and related membrane protein distributions (caveolin-1) were obtained. Our results support the view that exaggerated VGCC functionality from 2D cultured SCG cells is possibly due to differences in membrane architecture, characterized by uniquely organized caveolar lipid rafts. The practical implication of use of 3D-cultured cells in preclinical drug discovery studies is that such platforms would be more effective in eliminating false positive hits and as such improve the overall yield from screening campaigns. PMID:23049767

Three-Dimensional Printable High-Temperature and High-Rate Heaters.

PubMed

Yao, Yonggang; Fu, Kun Kelvin; Yan, Chaoyi; Dai, Jiaqi; Chen, Yanan; Wang, Yibo; Zhang, Bilun; Hitz, Emily; Hu, Liangbing

2016-05-24

High temperature heaters are ubiquitously used in materials synthesis and device processing. In this work, we developed three-dimensional (3D) printed reduced graphene oxide (RGO)-based heaters to function as high-performance thermal supply with high temperature and ultrafast heating rate. Compared with other heating sources, such as furnace, laser, and infrared radiation, the 3D printed heaters demonstrated in this work have the following distinct advantages: (1) the RGO based heater can operate at high temperature up to 3000 K because of using the high temperature-sustainable carbon material; (2) the heater temperature can be ramped up and down with extremely fast rates, up to ∼20 000 K/second; (3) heaters with different shapes can be directly printed with small sizes and onto different substrates to enable heating anywhere. The 3D printable RGO heaters can be applied to a wide range of nanomanufacturing when precise temperature control in time, placement, and the ramping rate are important.

Seizure-Onset Mapping Based on Time-Variant Multivariate Functional Connectivity Analysis of High-Dimensional Intracranial EEG: A Kalman Filter Approach.

PubMed

Lie, Octavian V; van Mierlo, Pieter

2017-01-01

The visual interpretation of intracranial EEG (iEEG) is the standard method used in complex epilepsy surgery cases to map the regions of seizure onset targeted for resection. Still, visual iEEG analysis is labor-intensive and biased due to interpreter dependency. Multivariate parametric functional connectivity measures using adaptive autoregressive (AR) modeling of the iEEG signals based on the Kalman filter algorithm have been used successfully to localize the electrographic seizure onsets. Due to their high computational cost, these methods have been applied to a limited number of iEEG time-series (<60). The aim of this study was to test two Kalman filter implementations, a well-known multivariate adaptive AR model (Arnold et al. 1998) and a simplified, computationally efficient derivation of it, for their potential application to connectivity analysis of high-dimensional (up to 192 channels) iEEG data. When used on simulated seizures together with a multivariate connectivity estimator, the partial directed coherence, the two AR models were compared for their ability to reconstitute the designed seizure signal connections from noisy data. Next, focal seizures from iEEG recordings (73-113 channels) in three patients rendered seizure-free after surgery were mapped with the outdegree, a graph-theory index of outward directed connectivity. Simulation results indicated high levels of mapping accuracy for the two models in the presence of low-to-moderate noise cross-correlation. Accordingly, both AR models correctly mapped the real seizure onset to the resection volume. This study supports the possibility of conducting fully data-driven multivariate connectivity estimations on high-dimensional iEEG datasets using the Kalman filter approach.

Multi-Material Closure Model for High-Order Finite Element Lagrangian Hydrodynamics

DOE PAGES

Dobrev, V. A.; Kolev, T. V.; Rieben, R. N.; ...

2016-04-27

We present a new closure model for single fluid, multi-material Lagrangian hydrodynamics and its application to high-order finite element discretizations of these equations [1]. The model is general with respect to the number of materials, dimension and space and time discretizations. Knowledge about exact material interfaces is not required. Material indicator functions are evolved by a closure computation at each quadrature point of mixed cells, which can be viewed as a high-order variational generalization of the method of Tipton [2]. This computation is defined by the notion of partial non-instantaneous pressure equilibration, while the full pressure equilibration is achieved bymore » both the closure model and the hydrodynamic motion. Exchange of internal energy between materials is derived through entropy considerations, that is, every material produces positive entropy, and the total entropy production is maximized in compression and minimized in expansion. Results are presented for standard one-dimensional two-material problems, followed by two-dimensional and three-dimensional multi-material high-velocity impact arbitrary Lagrangian–Eulerian calculations. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.« less

Multi-Material Closure Model for High-Order Finite Element Lagrangian Hydrodynamics

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

Dobrev, V. A.; Kolev, T. V.; Rieben, R. N.

We present a new closure model for single fluid, multi-material Lagrangian hydrodynamics and its application to high-order finite element discretizations of these equations [1]. The model is general with respect to the number of materials, dimension and space and time discretizations. Knowledge about exact material interfaces is not required. Material indicator functions are evolved by a closure computation at each quadrature point of mixed cells, which can be viewed as a high-order variational generalization of the method of Tipton [2]. This computation is defined by the notion of partial non-instantaneous pressure equilibration, while the full pressure equilibration is achieved bymore » both the closure model and the hydrodynamic motion. Exchange of internal energy between materials is derived through entropy considerations, that is, every material produces positive entropy, and the total entropy production is maximized in compression and minimized in expansion. Results are presented for standard one-dimensional two-material problems, followed by two-dimensional and three-dimensional multi-material high-velocity impact arbitrary Lagrangian–Eulerian calculations. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.« less

Interaction phenomenon to dimensionally reduced p-gBKP equation

NASA Astrophysics Data System (ADS)

Zhang, Runfa; Bilige, Sudao; Bai, Yuexing; Lü, Jianqing; Gao, Xiaoqing

2018-02-01

Based on searching the combining of quadratic function and exponential (or hyperbolic cosine) function from the Hirota bilinear form of the dimensionally reduced p-gBKP equation, eight class of interaction solutions are derived via symbolic computation with Mathematica. The submergence phenomenon, presented to illustrate the dynamical features concerning these obtained solutions, is observed by three-dimensional plots and density plots with particular choices of the involved parameters between the exponential (or hyperbolic cosine) function and the quadratic function. It is proved that the interference between the two solitary waves is inelastic.

Color image encryption by using Yang-Gu mixture amplitude-phase retrieval algorithm in gyrator transform domain and two-dimensional Sine logistic modulation map

NASA Astrophysics Data System (ADS)

Sui, Liansheng; Liu, Benqing; Wang, Qiang; Li, Ye; Liang, Junli

2015-12-01

A color image encryption scheme is proposed based on Yang-Gu mixture amplitude-phase retrieval algorithm and two-coupled logistic map in gyrator transform domain. First, the color plaintext image is decomposed into red, green and blue components, which are scrambled individually by three random sequences generated by using the two-dimensional Sine logistic modulation map. Second, each scrambled component is encrypted into a real-valued function with stationary white noise distribution in the iterative amplitude-phase retrieval process in the gyrator transform domain, and then three obtained functions are considered as red, green and blue channels to form the color ciphertext image. Obviously, the ciphertext image is real-valued function and more convenient for storing and transmitting. In the encryption and decryption processes, the chaotic random phase mask generated based on logistic map is employed as the phase key, which means that only the initial values are used as private key and the cryptosystem has high convenience on key management. Meanwhile, the security of the cryptosystem is enhanced greatly because of high sensitivity of the private keys. Simulation results are presented to prove the security and robustness of the proposed scheme.

Biomimetic Graphene-Based 3D Scaffold for Long-Term Cell Culture and Real-Time Electrochemical Monitoring.

PubMed

Hu, Xue-Bo; Liu, Yan-Ling; Wang, Wen-Jie; Zhang, Hai-Wei; Qin, Yu; Guo, Shan; Zhang, Xin-Wei; Fu, Lei; Huang, Wei-Hua

2018-01-16

Current achievements on electrochemical monitoring of cells are often gained on two-dimensional (2D) substrates, which fail in mimicking the cellular environments and accurately reproducing the cellular functions within a three-dimensional (3D) tissue. In this regard, 3D scaffold concurrently integrated with the function of cell culture and electrochemical sensing is conceivably a promising platform to monitor cells in real time under their in vivo-like 3D microenvironments. However, it is particularly challenging to construct such a multifunctional scaffold platform. Herein, we developed a 3-aminophenylboronic acid (APBA) functionalized graphene foam (GF) network, which combines the biomimetic property of APBA with the mechanical and electrochemical properties of GF. Hence, the GF network can serve as a 3D scaffold to culture cells for a long period with high viability and simultaneously as an electrode for highly sensitive electrochemical sensing. This allows monitoring of gaseous messengers H 2 S released from the cells cultured on the 3D scaffold in real time. This work represents considerable progress in fabricating 3D cell culture scaffold with electrochemical properties, thereby facilitating future studies of physiologically relevant processes.

Chemically doped three-dimensional porous graphene monoliths for high-performance flexible field emitters.

PubMed

Kim, Ho Young; Jeong, Sooyeon; Jeong, Seung Yol; Baeg, Kang-Jun; Han, Joong Tark; Jeong, Mun Seok; Lee, Geon-Woong; Jeong, Hee Jin

2015-03-12

Despite the recent progress in the fabrication of field emitters based on graphene nanosheets, their morphological and electrical properties, which affect their degree of field enhancement as well as the electron tunnelling barrier height, should be controlled to allow for better field-emission properties. Here we report a method that allows the synthesis of graphene-based emitters with a high field-enhancement factor and a low work function. The method involves forming monolithic three-dimensional (3D) graphene structures by freeze-drying of a highly concentrated graphene paste and subsequent work-function engineering by chemical doping. Graphene structures with vertically aligned edges were successfully fabricated by the freeze-drying process. Furthermore, their number density could be controlled by varying the composition of the graphene paste. Al- and Au-doped 3D graphene emitters were fabricated by introducing the corresponding dopant solutions into the graphene sheets. The resulting field-emission characteristics of the resulting emitters are discussed. The synthesized 3D graphene emitters were highly flexible, maintaining their field-emission properties even when bent at large angles. This is attributed to the high crystallinity and emitter density and good chemical stability of the 3D graphene emitters, as well as to the strong interactions between the 3D graphene emitters and the substrate.

From free energy to expected energy: Improving energy-based value function approximation in reinforcement learning.

PubMed

Elfwing, Stefan; Uchibe, Eiji; Doya, Kenji

2016-12-01

Free-energy based reinforcement learning (FERL) was proposed for learning in high-dimensional state and action spaces. However, the FERL method does only really work well with binary, or close to binary, state input, where the number of active states is fewer than the number of non-active states. In the FERL method, the value function is approximated by the negative free energy of a restricted Boltzmann machine (RBM). In our earlier study, we demonstrated that the performance and the robustness of the FERL method can be improved by scaling the free energy by a constant that is related to the size of network. In this study, we propose that RBM function approximation can be further improved by approximating the value function by the negative expected energy (EERL), instead of the negative free energy, as well as being able to handle continuous state input. We validate our proposed method by demonstrating that EERL: (1) outperforms FERL, as well as standard neural network and linear function approximation, for three versions of a gridworld task with high-dimensional image state input; (2) achieves new state-of-the-art results in stochastic SZ-Tetris in both model-free and model-based learning settings; and (3) significantly outperforms FERL and standard neural network function approximation for a robot navigation task with raw and noisy RGB images as state input and a large number of actions. Copyright © 2016 The Author(s). Published by Elsevier Ltd.. All rights reserved.

An accurate and efficient method for piezoelectric coated functional devices based on the two-dimensional Green’s function for a normal line force and line charge

NASA Astrophysics Data System (ADS)

Hou, Peng-Fei; Zhang, Yang

2017-09-01

Because most piezoelectric functional devices, including sensors, actuators and energy harvesters, are in the form of a piezoelectric coated structure, it is valuable to present an accurate and efficient method for obtaining the electro-mechanical coupling fields of this coated structure under mechanical and electrical loads. With this aim, the two-dimensional Green’s function for a normal line force and line charge on the surface of coated structure, which is a combination of an orthotropic piezoelectric coating and orthotropic elastic substrate, is presented in the form of elementary functions based on the general solution method. The corresponding electro-mechanical coupling fields of this coated structure under arbitrary mechanical and electrical loads can then be obtained by the superposition principle and Gauss integration. Numerical results show that the presented method has high computational precision, efficiency and stability. It can be used to design the best coating thickness in functional devices, improve the sensitivity of sensors, and improve the efficiency of actuators and energy harvesters. This method could be an efficient tool for engineers in engineering applications.

SQUID picovoltometry of single crystal Bi2Sr2CaCu2O(8+delta) - Observation of the crossover from high-temperature Arrhenius to low-temperature vortex-glass behavior

NASA Astrophysics Data System (ADS)

Safar, H.; Gammel, P. L.; Bishop, D. J.; Mitzi, D. B.; Kapitulnik, A.

1992-04-01

A SQUID voltmeter has been used to measure current-voltage curves in untwinned crystals of Bi2Sr2CaCu2O(8+delta) as a function of temperature and magnetic field. The data show a clear crossover from high-temperature Arrhenius behavior to a critical region associated with the low-temperature three-dimensional vortex-glass phase transition. The critical exponents v(z - 1) = 7 +/- 1 in this system are in accord with theoretical models and previous measurements in YBa2Cu3O7. The width of the critical region collapses below 2 T, reflecting the changing role of dimensionality with field.

Superconductivity in electron-doped arsenene

NASA Astrophysics Data System (ADS)

Kong, Xin; Gao, Miao; Yan, Xun-Wang; Lu, Zhong-Yi; Xiang, Tao

2018-04-01

Based on the first-principles density functional theory electronic structure calculation, we investigate the possible phonon-mediated superconductivity in arsenene, a two-dimensional buckled arsenic atomic sheet, under electron doping. We find that the strong superconducting pairing interaction results mainly from the $p_z$-like electrons of arsenic atoms and the $A_1$ phonon mode around the $K$ point, and the superconducting transition temperature can be as high as 30.8 K in the arsenene with 0.2 doped electrons per unit cell and 12\\% applied biaxial tensile strain. This transition temperature is about ten times higher than that in the bulk arsenic under high pressure. It is also the highest transition temperature that is predicted for electron-doped two-dimensional elemental superconductors, including graphene, silicene, phosphorene, and borophene.

Robust check loss-based variable selection of high-dimensional single-index varying-coefficient model

NASA Astrophysics Data System (ADS)

Song, Yunquan; Lin, Lu; Jian, Ling

2016-07-01

Single-index varying-coefficient model is an important mathematical modeling method to model nonlinear phenomena in science and engineering. In this paper, we develop a variable selection method for high-dimensional single-index varying-coefficient models using a shrinkage idea. The proposed procedure can simultaneously select significant nonparametric components and parametric components. Under defined regularity conditions, with appropriate selection of tuning parameters, the consistency of the variable selection procedure and the oracle property of the estimators are established. Moreover, due to the robustness of the check loss function to outliers in the finite samples, our proposed variable selection method is more robust than the ones based on the least squares criterion. Finally, the method is illustrated with numerical simulations.

Two-dimensional numerical model for the high electron mobility transistor

NASA Astrophysics Data System (ADS)

Loret, Dany

1987-11-01

A two-dimensional numerical drift-diffusion model for the High Electron Mobility Transistor (HEMT) is presented. Special attention is paid to the modeling of the current flow over the heterojunction. A finite difference scheme is used to solve the equations, and a variable mesh spacing was implemented to cope with the strong variations of functions near the heterojunction. Simulation results are compared to experimental data for a 0.7 μm gate length device. Small-signal transconductances and cut-off frequency obtained from the 2-D model agree well with the experimental values from S-parameter measurements. It is shown that the numerical models give good insight into device behaviour, including important parasitic effects such as electron injection into the bulk GaAs.

Quantum correlation of high dimensional system in a dephasing environment

NASA Astrophysics Data System (ADS)

Ji, Yinghua; Ke, Qiang; Hu, Juju

2018-05-01

For a high dimensional spin-S system embedded in a dephasing environment, we theoretically analyze the time evolutions of quantum correlation and entanglement via Frobenius norm and negativity. The quantum correlation dynamics can be considered as a function of the decoherence parameters, including the ratio between the system oscillator frequency ω0 and the reservoir cutoff frequency ωc , and the different environment temperature. It is shown that the quantum correlation can not only measure nonclassical correlation of the considered system, but also perform a better robustness against the dissipation. In addition, the decoherence presents the non-Markovian features and the quantum correlation freeze phenomenon. The former is much weaker than that in the sub-Ohmic or Ohmic thermal reservoir environment.

Multi-Dimensional High Order Essentially Non-Oscillatory Finite Difference Methods in Generalized Coordinates

NASA Technical Reports Server (NTRS)

Shu, Chi-Wang

1998-01-01

This project is about the development of high order, non-oscillatory type schemes for computational fluid dynamics. Algorithm analysis, implementation, and applications are performed. Collaborations with NASA scientists have been carried out to ensure that the research is relevant to NASA objectives. The combination of ENO finite difference method with spectral method in two space dimension is considered, jointly with Cai [3]. The resulting scheme behaves nicely for the two dimensional test problems with or without shocks. Jointly with Cai and Gottlieb, we have also considered one-sided filters for spectral approximations to discontinuous functions [2]. We proved theoretically the existence of filters to recover spectral accuracy up to the discontinuity. We also constructed such filters for practical calculations.

Robust learning for optimal treatment decision with NP-dimensionality

PubMed Central

Shi, Chengchun; Song, Rui; Lu, Wenbin

2016-01-01

In order to identify important variables that are involved in making optimal treatment decision, Lu, Zhang and Zeng (2013) proposed a penalized least squared regression framework for a fixed number of predictors, which is robust against the misspecification of the conditional mean model. Two problems arise: (i) in a world of explosively big data, effective methods are needed to handle ultra-high dimensional data set, for example, with the dimension of predictors is of the non-polynomial (NP) order of the sample size; (ii) both the propensity score and conditional mean models need to be estimated from data under NP dimensionality. In this paper, we propose a robust procedure for estimating the optimal treatment regime under NP dimensionality. In both steps, penalized regressions are employed with the non-concave penalty function, where the conditional mean model of the response given predictors may be misspecified. The asymptotic properties, such as weak oracle properties, selection consistency and oracle distributions, of the proposed estimators are investigated. In addition, we study the limiting distribution of the estimated value function for the obtained optimal treatment regime. The empirical performance of the proposed estimation method is evaluated by simulations and an application to a depression dataset from the STAR*D study. PMID:28781717

GATE: software for the analysis and visualization of high-dimensional time series expression data.

PubMed

MacArthur, Ben D; Lachmann, Alexander; Lemischka, Ihor R; Ma'ayan, Avi

2010-01-01

We present Grid Analysis of Time series Expression (GATE), an integrated computational software platform for the analysis and visualization of high-dimensional biomolecular time series. GATE uses a correlation-based clustering algorithm to arrange molecular time series on a two-dimensional hexagonal array and dynamically colors individual hexagons according to the expression level of the molecular component to which they are assigned, to create animated movies of systems-level molecular regulatory dynamics. In order to infer potential regulatory control mechanisms from patterns of correlation, GATE also allows interactive interroga-tion of movies against a wide variety of prior knowledge datasets. GATE movies can be paused and are interactive, allowing users to reconstruct networks and perform functional enrichment analyses. Movies created with GATE can be saved in Flash format and can be inserted directly into PDF manuscript files as interactive figures. GATE is available for download and is free for academic use from http://amp.pharm.mssm.edu/maayan-lab/gate.htm

Electron-phonon interaction in efficient perovskite blue emitters

NASA Astrophysics Data System (ADS)

Gong, Xiwen; Voznyy, Oleksandr; Jain, Ankit; Liu, Wenjia; Sabatini, Randy; Piontkowski, Zachary; Walters, Grant; Bappi, Golam; Nokhrin, Sergiy; Bushuyev, Oleksandr; Yuan, Mingjian; Comin, Riccardo; McCamant, David; Kelley, Shana O.; Sargent, Edward H.

2018-06-01

Low-dimensional perovskites have—in view of their high radiative recombination rates—shown great promise in achieving high luminescence brightness and colour saturation. Here we investigate the effect of electron-phonon interactions on the luminescence of single crystals of two-dimensional perovskites, showing that reducing these interactions can lead to bright blue emission in two-dimensional perovskites. Resonance Raman spectra and deformation potential analysis show that strong electron-phonon interactions result in fast non-radiative decay, and that this lowers the photoluminescence quantum yield (PLQY). Neutron scattering, solid-state NMR measurements of spin-lattice relaxation, density functional theory simulations and experimental atomic displacement measurements reveal that molecular motion is slowest, and rigidity greatest, in the brightest emitter. By varying the molecular configuration of the ligands, we show that a PLQY up to 79% and linewidth of 20 nm can be reached by controlling crystal rigidity and electron-phonon interactions. Designing crystal structures with electron-phonon interactions in mind offers a previously underexplored avenue to improve optoelectronic materials' performance.

Nonlinear intrinsic variables and state reconstruction in multiscale simulations

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

Dsilva, Carmeline J., E-mail: cdsilva@princeton.edu; Talmon, Ronen, E-mail: ronen.talmon@yale.edu; Coifman, Ronald R., E-mail: coifman@math.yale.edu

2013-11-14

Finding informative low-dimensional descriptions of high-dimensional simulation data (like the ones arising in molecular dynamics or kinetic Monte Carlo simulations of physical and chemical processes) is crucial to understanding physical phenomena, and can also dramatically assist in accelerating the simulations themselves. In this paper, we discuss and illustrate the use of nonlinear intrinsic variables (NIV) in the mining of high-dimensional multiscale simulation data. In particular, we focus on the way NIV allows us to functionally merge different simulation ensembles, and different partial observations of these ensembles, as well as to infer variables not explicitly measured. The approach relies on certainmore » simple features of the underlying process variability to filter out measurement noise and systematically recover a unique reference coordinate frame. We illustrate the approach through two distinct sets of atomistic simulations: a stochastic simulation of an enzyme reaction network exhibiting both fast and slow time scales, and a molecular dynamics simulation of alanine dipeptide in explicit water.« less

Nonlinear intrinsic variables and state reconstruction in multiscale simulations

NASA Astrophysics Data System (ADS)

Dsilva, Carmeline J.; Talmon, Ronen; Rabin, Neta; Coifman, Ronald R.; Kevrekidis, Ioannis G.

2013-11-01

Finding informative low-dimensional descriptions of high-dimensional simulation data (like the ones arising in molecular dynamics or kinetic Monte Carlo simulations of physical and chemical processes) is crucial to understanding physical phenomena, and can also dramatically assist in accelerating the simulations themselves. In this paper, we discuss and illustrate the use of nonlinear intrinsic variables (NIV) in the mining of high-dimensional multiscale simulation data. In particular, we focus on the way NIV allows us to functionally merge different simulation ensembles, and different partial observations of these ensembles, as well as to infer variables not explicitly measured. The approach relies on certain simple features of the underlying process variability to filter out measurement noise and systematically recover a unique reference coordinate frame. We illustrate the approach through two distinct sets of atomistic simulations: a stochastic simulation of an enzyme reaction network exhibiting both fast and slow time scales, and a molecular dynamics simulation of alanine dipeptide in explicit water.

[Three-dimensional reconstruction of functional brain images].

PubMed

Inoue, M; Shoji, K; Kojima, H; Hirano, S; Naito, Y; Honjo, I

1999-08-01

We consider PET (positron emission tomography) measurement with SPM (Statistical Parametric Mapping) analysis to be one of the most useful methods to identify activated areas of the brain involved in language processing. SPM is an effective analytical method that detects markedly activated areas over the whole brain. However, with the conventional presentations of these functional brain images, such as horizontal slices, three directional projection, or brain surface coloring, makes understanding and interpreting the positional relationships among various brain areas difficult. Therefore, we developed three-dimensionally reconstructed images from these functional brain images to improve the interpretation. The subjects were 12 normal volunteers. The following three types of images were constructed: 1) routine images by SPM, 2) three-dimensional static images, and 3) three-dimensional dynamic images, after PET images were analyzed by SPM during daily dialog listening. The creation of images of both the three-dimensional static and dynamic types employed the volume rendering method by VTK (The Visualization Toolkit). Since the functional brain images did not include original brain images, we synthesized SPM and MRI brain images by self-made C++ programs. The three-dimensional dynamic images were made by sequencing static images with available software. Images of both the three-dimensional static and dynamic types were processed by a personal computer system. Our newly created images showed clearer positional relationships among activated brain areas compared to the conventional method. To date, functional brain images have been employed in fields such as neurology or neurosurgery, however, these images may be useful even in the field of otorhinolaryngology, to assess hearing and speech. Exact three-dimensional images based on functional brain images are important for exact and intuitive interpretation, and may lead to new developments in brain science. Currently, the surface model is the most common method of three-dimensional display. However, the volume rendering method may be more effective for imaging regions such as the brain.

Beating the curse of dimension with accurate statistics for the Fokker-Planck equation in complex turbulent systems.

PubMed

Chen, Nan; Majda, Andrew J

2017-12-05

Solving the Fokker-Planck equation for high-dimensional complex dynamical systems is an important issue. Recently, the authors developed efficient statistically accurate algorithms for solving the Fokker-Planck equations associated with high-dimensional nonlinear turbulent dynamical systems with conditional Gaussian structures, which contain many strong non-Gaussian features such as intermittency and fat-tailed probability density functions (PDFs). The algorithms involve a hybrid strategy with a small number of samples [Formula: see text], where a conditional Gaussian mixture in a high-dimensional subspace via an extremely efficient parametric method is combined with a judicious Gaussian kernel density estimation in the remaining low-dimensional subspace. In this article, two effective strategies are developed and incorporated into these algorithms. The first strategy involves a judicious block decomposition of the conditional covariance matrix such that the evolutions of different blocks have no interactions, which allows an extremely efficient parallel computation due to the small size of each individual block. The second strategy exploits statistical symmetry for a further reduction of [Formula: see text] The resulting algorithms can efficiently solve the Fokker-Planck equation with strongly non-Gaussian PDFs in much higher dimensions even with orders in the millions and thus beat the curse of dimension. The algorithms are applied to a [Formula: see text]-dimensional stochastic coupled FitzHugh-Nagumo model for excitable media. An accurate recovery of both the transient and equilibrium non-Gaussian PDFs requires only [Formula: see text] samples! In addition, the block decomposition facilitates the algorithms to efficiently capture the distinct non-Gaussian features at different locations in a [Formula: see text]-dimensional two-layer inhomogeneous Lorenz 96 model, using only [Formula: see text] samples. Copyright © 2017 the Author(s). Published by PNAS.

Nanoassembled dynamic optical waveguides and sensors based on zeolite L nanocontainers

NASA Astrophysics Data System (ADS)

Barroso, Álvaro; Dieckmann, Katrin; Alpmann, Christina; Buscher, Tim; Studer, Armido; Denz, Cornelia

2015-03-01

Although optical functional devices as waveguides and sensors are of utmost importance for metrology on the nano scale, the micro-and nano-assembly by optical means of functional materials to create such optical elements has yet not been considered. In the last years, an elegant strategy based on holographic optical tweezers (HOT) has been developed to design and fabricate permanent and dynamic three-dimensional micro- and nanostructures based on functional nanocontainers as building blocks. Nanocontainers that exhibit stable and ordered voids to hierarchically organize guest materials are especially attractive. Zeolite L are a type of porous micro-sized crystals which features a high number of strictly one-dimensional, parallel aligned nanochannels. They are highly interesting as building blocks of functional nano-and microsystems due to their potential as nanocontainers to accommodate various different guest molecules and to assemble them in specific configurations. For instance, based on zeolite L crystals, microscopic polarization sensors and chains of several microcrystals for hierarchical supramolecular organization have been realized. Here, we demonstrate the ability of nanocontainers in general, and zeolite L crystals in particular to represent the basic constituent of optical functional microsystems. We show that the capability of HOT to manipulate multitude of non-spherical microparticles in three dimensions can be exploited for the investigation of zeolite L nanocontainers as dynamic optical waveguides. Moreover, we implement as additional elements dye-loaded zeolite L to sense the guiding features of these novel waveguides with high spatial precision and microspheres to enhance the light coupling into the zeolite L waveguides. With this elaborated approach of using nanocontainers as tailored building blocks for functional optical systems a new era of bricking optical components in a lego-like style becomes feasible.

Phthalo-carbonitride: an ab initio prediction of a stable two-dimensional material

NASA Astrophysics Data System (ADS)

Tsetseris, Leonidas

2016-06-01

Using density-functional theory calculations, we identify a stable two-dimensional carbonitride polymorph which resembles the core of phthalocyanine molecules. This so-called phthalo-carbonitride is found to be the lowest-energy polymer made of tetracyanoethylene molecules. It is a two-dimensional metal in its pristine form. Functionalization of the phthalo-cores with copper or iron atoms retains the metallic character of the material, but also adds magnetization to the system. Based on these properties and the established use of phthalocyanine molecules in various applications, the growth of phthalo-carbonitride sheets can add another multi-functional building block to the research and technology of two-dimensional materials.

A one-pot gold seed-assisted synthesis of gold/platinum wire nanoassemblies and their enhanced electrocatalytic activity for the oxidation of oxalic acid

NASA Astrophysics Data System (ADS)

Bai, Juan; Fang, Chun-Long; Liu, Zong-Huai; Chen, Yu

2016-01-01

Three-dimensional (3D) noble metal nanoassemblies composed of one-dimensional (1D) nanowires have been attracting much interest due to the unique physical and chemical properties of 1D nanowires as well as the particular interconnected open-pore structure of 3D nanoassemblies. In this work, well-defined Au/Pt wire nanoassemblies were synthesized by using a facile NaBH4 reduction method in the presence of a branched form of polyethyleneimine (PEI). A study of the growth mechanism indicated the morphology of the final product to be highly related to the molecular structure of the polymeric amine. Also, the preferred Pt-on-Pt deposition contributed to the formation of the 1D Pt nanowires. The Au/Pt wire nanoassemblies were functionalized with PEI at the same time that these nanoassemblies were synthesized due to the strong N-Pt bond. The chemically functionalized Au/Pt wire nanoassemblies exhibited better electrocatalytic activity for the electro-oxidation of oxalic acid than did commercial Pt black.Three-dimensional (3D) noble metal nanoassemblies composed of one-dimensional (1D) nanowires have been attracting much interest due to the unique physical and chemical properties of 1D nanowires as well as the particular interconnected open-pore structure of 3D nanoassemblies. In this work, well-defined Au/Pt wire nanoassemblies were synthesized by using a facile NaBH4 reduction method in the presence of a branched form of polyethyleneimine (PEI). A study of the growth mechanism indicated the morphology of the final product to be highly related to the molecular structure of the polymeric amine. Also, the preferred Pt-on-Pt deposition contributed to the formation of the 1D Pt nanowires. The Au/Pt wire nanoassemblies were functionalized with PEI at the same time that these nanoassemblies were synthesized due to the strong N-Pt bond. The chemically functionalized Au/Pt wire nanoassemblies exhibited better electrocatalytic activity for the electro-oxidation of oxalic acid than did commercial Pt black. Electronic supplementary information (ESI) available: Experimental details and additional physical characterization. See DOI: 10.1039/c5nr08150e

Experimental witness of genuine high-dimensional entanglement

NASA Astrophysics Data System (ADS)

Guo, Yu; Hu, Xiao-Min; Liu, Bi-Heng; Huang, Yun-Feng; Li, Chuan-Feng; Guo, Guang-Can

2018-06-01

Growing interest has been invested in exploring high-dimensional quantum systems, for their promising perspectives in certain quantum tasks. How to characterize a high-dimensional entanglement structure is one of the basic questions to take full advantage of it. However, it is not easy for us to catch the key feature of high-dimensional entanglement, for the correlations derived from high-dimensional entangled states can be possibly simulated with copies of lower-dimensional systems. Here, we follow the work of Kraft et al. [Phys. Rev. Lett. 120, 060502 (2018), 10.1103/PhysRevLett.120.060502], and present the experimental realizing of creation and detection, by the normalized witness operation, of the notion of genuine high-dimensional entanglement, which cannot be decomposed into lower-dimensional Hilbert space and thus form the entanglement structures existing in high-dimensional systems only. Our experiment leads to further exploration of high-dimensional quantum systems.

Entanglement Entropy of the Six-Dimensional Horowitz-Strominger Black Hole

NASA Astrophysics Data System (ADS)

Li, Huai-Fan; Zhang, Sheng-Li; Wu, Yue-Qin; Ren, Zhao

By using the entanglement entropy method, the statistical entropy of the Bose and Fermi fields in a thin film is calculated and the Bekenstein-Hawking entropy of six-dimensional Horowitz-Strominger black hole is obtained. Here, the Bose and Fermi fields are entangled with the quantum states in six-dimensional Horowitz-Strominger black hole and the fields are outside of the horizon. The divergence of brick-wall model is avoided without any cutoff by the new equation of state density obtained with the generalized uncertainty principle. The calculation implies that the high density quantum states near the event horizon are strongly correlated with the quantum states in black hole. The black hole entropy is a quantum effect. It is an intrinsic characteristic of space-time. The ultraviolet cutoff in the brick-wall model is unreasonable. The generalized uncertainty principle should be considered in the high energy quantum field near the event horizon. Using the quantum statistical method, we directly calculate the partition function of the Bose and Fermi fields under the background of the six-dimensional black hole. The difficulty in solving the wave equations of various particles is overcome.

Hand-held optoacoustic probe for three-dimensional imaging of human morphology and function

NASA Astrophysics Data System (ADS)

Deán-Ben, X. Luís.; Razansky, Daniel

2014-03-01

We report on a hand-held imaging probe for real-time optoacoustic visualization of deep tissues in three dimensions. The proposed solution incorporates a two-dimensional array of ultrasonic sensors densely distributed on a spherical surface, whereas illumination is performed coaxially through a cylindrical cavity in the array. Visualization of three-dimensional tomographic data at a frame rate of 10 images per second is enabled by parallel recording of 256 time-resolved signals for each individual laser pulse along with a highly efficient GPUbased real-time reconstruction. A liquid coupling medium (water), enclosed in a transparent membrane, is used to guarantee transmission of the optoacoustically generated waves to the ultrasonic detectors. Excitation at multiple wavelengths further allows imaging spectrally distinctive tissue chromophores such as oxygenated and deoxygenated haemoglobin. The performance is showcased by video-rate tracking of deep tissue vasculature and three-dimensional measurements of blood oxygenenation in a healthy human volunteer. The flexibility provided by the hand-held hardware design, combined with the real-time operation, makes the developed platform highly usable for both small animal research and clinical imaging in multiple indications, including cancer, inflammation, skin and cardiovascular diseases, diagnostics of lymphatic system and breast

The three-dimensional structure of aquaporin-1

NASA Astrophysics Data System (ADS)

Walz, Thomas; Hirai, Teruhisa; Murata, Kazuyoshi; Heymann, J. Bernard; Mitsuoka, Kaoru; Fujiyoshi, Yoshinori; Smith, Barbara L.; Agre, Peter; Engel, Andreas

1997-06-01

The entry and exit of water from cells is a fundamental process of life. Recognition of the high water permeability of red blood cells led to the proposal that specialized water pores exist in the plasma membrane. Expression in Xenopus oocytes and functional studies of an erythrocyte integral membrane protein of relative molecular mass 28,000, identified it as the mercury-sensitive water channel, aquaporin-1 (AQP1). Many related proteins, all belonging to the major intrinsic protein (MIP) family, are found throughout nature. AQP1 is a homotetramer containing four independent aqueous channels. When reconstituted into lipid bilayers, the protein forms two-dimensional lattices with a unit cell containing two tetramers in opposite orientation. Here we present the three-dimensional structure of AQP1 determined at 6Å resolution by cryo-electron microscopy. Each AQP1 monomer has six tilted, bilayer-spanning α-helices which form a right-handed bundle surrounding a central density. These results, together with functional studies, provide a model that identifies the aqueous pore in the AQP1 molecule and indicates the organization of the tetrameric complex in the membrane.

Single shot, three-dimensional fluorescence microscopy with a spatially rotating point spread function

PubMed Central

Wang, Zhaojun; Cai, Yanan; Liang, Yansheng; Zhou, Xing; Yan, Shaohui; Dan, Dan; Bianco, Piero R.; Lei, Ming; Yao, Baoli

2017-01-01

A wide-field fluorescence microscope with a double-helix point spread function (PSF) is constructed to obtain the specimen’s three-dimensional distribution with a single snapshot. Spiral-phase-based computer-generated holograms (CGHs) are adopted to make the depth-of-field of the microscope adjustable. The impact of system aberrations on the double-helix PSF at high numerical aperture is analyzed to reveal the necessity of the aberration correction. A modified cepstrum-based reconstruction scheme is promoted in accordance with properties of the new double-helix PSF. The extended depth-of-field images and the corresponding depth maps for both a simulated sample and a tilted section slice of bovine pulmonary artery endothelial (BPAE) cells are recovered, respectively, verifying that the depth-of-field is properly extended and the depth of the specimen can be estimated at a precision of 23.4nm. This three-dimensional fluorescence microscope with a framerate-rank time resolution is suitable for studying the fast developing process of thin and sparsely distributed micron-scale cells in extended depth-of-field. PMID:29296483

Nonclassical models of the theory of plates and shells

NASA Astrophysics Data System (ADS)

Annin, Boris D.; Volchkov, Yuri M.

2017-11-01

Publications dealing with the study of methods of reducing a three-dimensional problem of the elasticity theory to a two-dimensional problem of the theory of plates and shells are reviewed. Two approaches are considered: the use of kinematic and force hypotheses and expansion of solutions of the three-dimensional elasticity theory in terms of the complete system of functions. Papers where a three-dimensional problem is reduced to a two-dimensional problem with the use of several approximations of each of the unknown functions (stresses and displacements) by segments of the Legendre polynomials are also reviewed.

Pathophysiology of Degenerative Mitral Regurgitation: New 3-Dimensional Imaging Insights.

PubMed

Antoine, Clemence; Mantovani, Francesca; Benfari, Giovanni; Mankad, Sunil V; Maalouf, Joseph F; Michelena, Hector I; Enriquez-Sarano, Maurice

2018-01-01

Despite its high prevalence, little is known about mechanisms of mitral regurgitation in degenerative mitral valve disease apart from the leaflet prolapse itself. Mitral valve is a complex structure, including mitral annulus, mitral leaflets, papillary muscles, chords, and left ventricular walls. All these structures are involved in physiological and pathological functioning of this valvuloventricular complex but up to now were difficult to analyze because of inherent limitations of 2-dimensional imaging. The advent of 3-dimensional echocardiography, computed tomography, and cardiac magnetic resonance imaging overcoming these limitations provides new insights into mechanistic analysis of degenerative mitral regurgitation. This review will detail the contribution of quantitative and qualitative dynamic analysis of mitral annulus and mitral leaflets by new imaging methods in the understanding of degenerative mitral regurgitation pathophysiology. © 2018 American Heart Association, Inc.

Study of guided modes in three-dimensional composites

NASA Astrophysics Data System (ADS)

Baste, S.; Gerard, A.

The propagation of elastic waves in a three-dimensional carbon-carbon composite is modeled with a mixed variational method, using the Bloch or Floquet theories and the Hellinger-Reissner function for two independent fields. The model of the equivalent homogeneous material only exists below a cut-off frequency of about 600 kHz. The existence below the cut-off frequency of two guided waves can account for the presence of a slow guided wave on either side of the cut-off frequency. Optical modes are generated at low frequencies, and can attain high velocites (rapid guided modes of 15,000 m/sec).

Protein Simulation Data in the Relational Model.

PubMed

Simms, Andrew M; Daggett, Valerie

2012-10-01

High performance computing is leading to unprecedented volumes of data. Relational databases offer a robust and scalable model for storing and analyzing scientific data. However, these features do not come without a cost-significant design effort is required to build a functional and efficient repository. Modeling protein simulation data in a relational database presents several challenges: the data captured from individual simulations are large, multi-dimensional, and must integrate with both simulation software and external data sites. Here we present the dimensional design and relational implementation of a comprehensive data warehouse for storing and analyzing molecular dynamics simulations using SQL Server.

Protein Simulation Data in the Relational Model

PubMed Central

Simms, Andrew M.; Daggett, Valerie

2011-01-01

High performance computing is leading to unprecedented volumes of data. Relational databases offer a robust and scalable model for storing and analyzing scientific data. However, these features do not come without a cost—significant design effort is required to build a functional and efficient repository. Modeling protein simulation data in a relational database presents several challenges: the data captured from individual simulations are large, multi-dimensional, and must integrate with both simulation software and external data sites. Here we present the dimensional design and relational implementation of a comprehensive data warehouse for storing and analyzing molecular dynamics simulations using SQL Server. PMID:23204646

On-surface formation of one-dimensional polyphenylene through Bergman cyclization.

PubMed

Sun, Qiang; Zhang, Chi; Li, Zhiwen; Kong, Huihui; Tan, Qinggang; Hu, Aiguo; Xu, Wei

2013-06-12

On-surface fabrication of covalently interlinked conjugated nanostructures has attracted significant attention, mainly because of the high stability and efficient electron transport ability of these structures. Here, from the interplay of scanning tunneling microscopy imaging and density functional theory calculations, we report for the first time on-surface formation of one-dimensional polyphenylene chains through Bergman cyclization followed by radical polymerization on Cu(110). The formed surface nanostructures were further corroborated by the results for the ex situ-synthesized molecular product after Bergman cyclization. These findings are of particular interest and importance for the construction of molecular electronic nanodevices on surfaces.

Micromachined integrated quantum circuit containing a superconducting qubit

NASA Astrophysics Data System (ADS)

Brecht, Teresa; Chu, Yiwen; Axline, Christopher; Pfaff, Wolfgang; Blumoff, Jacob; Chou, Kevin; Krayzman, Lev; Frunzio, Luigi; Schoelkopf, Robert

We demonstrate a functional multilayer microwave integrated quantum circuit (MMIQC). This novel hardware architecture combines the high coherence and isolation of three-dimensional structures with the advantages of integrated circuits made with lithographic techniques. We present fabrication and measurement of a two-cavity/one-qubit prototype, including a transmon coupled to a three-dimensional microwave cavity micromachined in a silicon wafer. It comprises a simple MMIQC with competitive lifetimes and the ability to perform circuit QED operations in the strong dispersive regime. Furthermore, the design and fabrication techniques that we have developed are extensible to more complex quantum information processing devices.

Observation of a two-dimensional Fermi surface and Dirac dispersion in YbMnSb2

NASA Astrophysics Data System (ADS)

Kealhofer, Robert; Jang, Sooyoung; Griffin, Sinéad M.; John, Caolan; Benavides, Katherine A.; Doyle, Spencer; Helm, T.; Moll, Philip J. W.; Neaton, Jeffrey B.; Chan, Julia Y.; Denlinger, J. D.; Analytis, James G.

2018-01-01

We present the crystal structure, electronic structure, and transport properties of the material YbMnSb2, a candidate system for the investigation of Dirac physics in the presence of magnetic order. Our measurements reveal that this system is a low-carrier-density semimetal with a two-dimensional Fermi surface arising from a Dirac dispersion, consistent with the predictions of density-functional-theory calculations of the antiferromagnetic system. The low temperature resistivity is very large, suggesting that scattering in this system is highly efficient at dissipating momentum despite its Dirac-like nature.

The dimension split element-free Galerkin method for three-dimensional potential problems

NASA Astrophysics Data System (ADS)

Meng, Z. J.; Cheng, H.; Ma, L. D.; Cheng, Y. M.

2018-06-01

This paper presents the dimension split element-free Galerkin (DSEFG) method for three-dimensional potential problems, and the corresponding formulae are obtained. The main idea of the DSEFG method is that a three-dimensional potential problem can be transformed into a series of two-dimensional problems. For these two-dimensional problems, the improved moving least-squares (IMLS) approximation is applied to construct the shape function, which uses an orthogonal function system with a weight function as the basis functions. The Galerkin weak form is applied to obtain a discretized system equation, and the penalty method is employed to impose the essential boundary condition. The finite difference method is selected in the splitting direction. For the purposes of demonstration, some selected numerical examples are solved using the DSEFG method. The convergence study and error analysis of the DSEFG method are presented. The numerical examples show that the DSEFG method has greater computational precision and computational efficiency than the IEFG method.

Hypersonic Combustor Model Inlet CFD Simulations and Experimental Comparisons

NASA Technical Reports Server (NTRS)

Venkatapathy, E.; TokarcikPolsky, S.; Deiwert, G. S.; Edwards, Thomas A. (Technical Monitor)

1995-01-01

Numerous two-and three-dimensional computational simulations were performed for the inlet associated with the combustor model for the hypersonic propulsion experiment in the NASA Ames 16-Inch Shock Tunnel. The inlet was designed to produce a combustor-inlet flow that is nearly two-dimensional and of sufficient mass flow rate for large scale combustor testing. The three-dimensional simulations demonstrated that the inlet design met all the design objectives and that the inlet produced a very nearly two-dimensional combustor inflow profile. Numerous two-dimensional simulations were performed with various levels of approximations such as in the choice of chemical and physical models, as well as numerical approximations. Parametric studies were conducted to better understand and to characterize the inlet flow. Results from the two-and three-dimensional simulations were used to predict the mass flux entering the combustor and a mass flux correlation as a function of facility stagnation pressure was developed. Surface heat flux and pressure measurements were compared with the computed results and good agreement was found. The computational simulations helped determine the inlet low characteristics in the high enthalpy environment, the important parameters that affect the combustor-inlet flow, and the sensitivity of the inlet flow to various modeling assumptions.

Monolayer group-III monochalcogenides by oxygen functionalization: a promising class of two-dimensional topological insulators

NASA Astrophysics Data System (ADS)

Zhou, Si; Liu, Cheng-Cheng; Zhao, Jijun; Yao, Yugui

2018-03-01

Monolayer group-III monochalcogenides (MX, M = Ga, In; X = S, Se, Te), an emerging category of two-dimensional (2D) semiconductors, hold great promise for electronics, optoelectronics and catalysts. By first-principles calculations, we show that the phonon dispersion and Raman spectra, as well as the electronic and topological properties of monolayer MX can be tuned by oxygen functionalization. Chemisorption of oxygen atoms on one side or both sides of the MX sheet narrows or even closes the band gap, enlarges work function, and significantly reduces the carrier effective mass. More excitingly, InS, InSe, and InTe monolayers with double-side oxygen functionalization are 2D topological insulators with sizeable bulk gap up to 0.21 eV. Their low-energy bands near the Fermi level are dominated by the px and py orbitals of atoms, allowing band engineering via in-plane strains. Our studies provide viable strategy for realizing quantum spin Hall effect in monolayer group-III monochalcogenides at room temperature, and utilizing these novel 2D materials for high-speed and dissipationless transport devices.

Nonlinear Conservation Laws and Finite Volume Methods

NASA Astrophysics Data System (ADS)

Leveque, Randall J.

Introduction Software Notation Classification of Differential Equations Derivation of Conservation Laws The Euler Equations of Gas Dynamics Dissipative Fluxes Source Terms Radiative Transfer and Isothermal Equations Multi-dimensional Conservation Laws The Shock Tube Problem Mathematical Theory of Hyperbolic Systems Scalar Equations Linear Hyperbolic Systems Nonlinear Systems The Riemann Problem for the Euler Equations Numerical Methods in One Dimension Finite Difference Theory Finite Volume Methods Importance of Conservation Form - Incorrect Shock Speeds Numerical Flux Functions Godunov's Method Approximate Riemann Solvers High-Resolution Methods Other Approaches Boundary Conditions Source Terms and Fractional Steps Unsplit Methods Fractional Step Methods General Formulation of Fractional Step Methods Stiff Source Terms Quasi-stationary Flow and Gravity Multi-dimensional Problems Dimensional Splitting Multi-dimensional Finite Volume Methods Grids and Adaptive Refinement Computational Difficulties Low-Density Flows Discrete Shocks and Viscous Profiles Start-Up Errors Wall Heating Slow-Moving Shocks Grid Orientation Effects Grid-Aligned Shocks Magnetohydrodynamics The MHD Equations One-Dimensional MHD Solving the Riemann Problem Nonstrict Hyperbolicity Stiffness The Divergence of B Riemann Problems in Multi-dimensional MHD Staggered Grids The 8-Wave Riemann Solver Relativistic Hydrodynamics Conservation Laws in Spacetime The Continuity Equation The 4-Momentum of a Particle The Stress-Energy Tensor Finite Volume Methods Multi-dimensional Relativistic Flow Gravitation and General Relativity References

A multifunctional chemical sensor based on a three-dimensional lanthanide metal-organic framework

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

Du, Pei-Yao; Liao, Sheng-Yun; Gu, Wen, E-mail: guwen68@nankai.edu.cn

2016-12-15

A 3D lanthanide MOF with formula [Sm{sub 2}(abtc){sub 1.5}(H{sub 2}O){sub 3}(DMA)]·H{sub 2}O·DMA (1) has been successfully synthesized via solvothermal method. Luminescence studies reveal that 1 exhibits dual functional detection benzyl alcohol and benzaldehyde among different aromatic molecules. In addition, 1 displays a turn-on luminescence sensing with respect to ethanol among different alcohol molecules, which suggests that 1 is also a promising luminescent probe for high selective sensing of ethanol. - Highlights: • A three-dimensional lanthanide metal-organic framework has been synthesized. • Complex 1 exhibits dual functional detection benzyl alcohol and benzaldehyde among different aromatic molecules. • Complex 1 displays amore » turn-on luminescence sensing with respect to ethanol among different alcohol molecules.« less

Visualizing the BEC-BCS crossover in a two-dimensional Fermi gas: Pairing gaps and dynamical response functions from ab initio computations

NASA Astrophysics Data System (ADS)

Vitali, Ettore; Shi, Hao; Qin, Mingpu; Zhang, Shiwei

2017-12-01

Experiments with ultracold atoms provide a highly controllable laboratory setting with many unique opportunities for precision exploration of quantum many-body phenomena. The nature of such systems, with strong interaction and quantum entanglement, makes reliable theoretical calculations challenging. Especially difficult are excitation and dynamical properties, which are often the most directly relevant to experiment. We carry out exact numerical calculations, by Monte Carlo sampling of imaginary-time propagation of Slater determinants, to compute the pairing gap in the two-dimensional Fermi gas from first principles. Applying state-of-the-art analytic continuation techniques, we obtain the spectral function and the density and spin structure factors providing unique tools to visualize the BEC-BCS crossover. These quantities will allow for a direct comparison with experiments.

Hierarchical nanoporous metals as a path toward the ultimate three-dimensional functionality.

PubMed

Fujita, Takeshi

2017-01-01

Nanoporous metals prepared via dealloying or selective leaching of solid solution alloys and compounds represent an emerging class of materials. They possess a three-dimensional (3D) structure of randomly interpenetrating ligaments/nanopores with sizes between 5 nm and several tens of micrometers, which can be tuned by varying their preparation conditions (such as dealloying time and temperature) or additional thermal coarsening. As compared to other nanostructured materials, nanoporous metals have many advantages, including their bicontinuous structure, tunable pore sizes, bulk form, good electrical conductivity, and high structural stability. Therefore, nanoporous metals represent ideal 3D materials with versatile functionality, which can be utilized in various fields. In this review, we describe the recent applications of nanoporous metals in molecular detection, catalysis, 3D graphene synthesis, hierarchical pore formation, and additive manufacturing (3D printing) together with our own achievements in these areas. Finally, we discuss possible ways of realizing the ultimate 3D functionality beyond the scope of nanoporous metals.

Hierarchical nanoporous metals as a path toward the ultimate three-dimensional functionality

PubMed Central

Fujita, Takeshi

2017-01-01

Abstract Nanoporous metals prepared via dealloying or selective leaching of solid solution alloys and compounds represent an emerging class of materials. They possess a three-dimensional (3D) structure of randomly interpenetrating ligaments/nanopores with sizes between 5 nm and several tens of micrometers, which can be tuned by varying their preparation conditions (such as dealloying time and temperature) or additional thermal coarsening. As compared to other nanostructured materials, nanoporous metals have many advantages, including their bicontinuous structure, tunable pore sizes, bulk form, good electrical conductivity, and high structural stability. Therefore, nanoporous metals represent ideal 3D materials with versatile functionality, which can be utilized in various fields. In this review, we describe the recent applications of nanoporous metals in molecular detection, catalysis, 3D graphene synthesis, hierarchical pore formation, and additive manufacturing (3D printing) together with our own achievements in these areas. Finally, we discuss possible ways of realizing the ultimate 3D functionality beyond the scope of nanoporous metals. PMID:29057026

Hierarchical nanoporous metals as a path toward the ultimate three-dimensional functionality

NASA Astrophysics Data System (ADS)

Fujita, Takeshi

2017-12-01

Nanoporous metals prepared via dealloying or selective leaching of solid solution alloys and compounds represent an emerging class of materials. They possess a three-dimensional (3D) structure of randomly interpenetrating ligaments/nanopores with sizes between 5 nm and several tens of micrometers, which can be tuned by varying their preparation conditions (such as dealloying time and temperature) or additional thermal coarsening. As compared to other nanostructured materials, nanoporous metals have many advantages, including their bicontinuous structure, tunable pore sizes, bulk form, good electrical conductivity, and high structural stability. Therefore, nanoporous metals represent ideal 3D materials with versatile functionality, which can be utilized in various fields. In this review, we describe the recent applications of nanoporous metals in molecular detection, catalysis, 3D graphene synthesis, hierarchical pore formation, and additive manufacturing (3D printing) together with our own achievements in these areas. Finally, we discuss possible ways of realizing the ultimate 3D functionality beyond the scope of nanoporous metals.

Enhanced Exciton and Photon Confinement in Ruddlesden-Popper Perovskite Microplatelets for Highly Stable Low-Threshold Polarized Lasing.

PubMed

Li, Mingjie; Wei, Qi; Muduli, Subas Kumar; Yantara, Natalia; Xu, Qiang; Mathews, Nripan; Mhaisalkar, Subodh G; Xing, Guichuan; Sum, Tze Chien

2018-06-01

At the heart of electrically driven semiconductors lasers lies their gain medium that typically comprises epitaxially grown double heterostuctures or multiple quantum wells. The simultaneous spatial confinement of charge carriers and photons afforded by the smaller bandgaps and higher refractive index of the active layers as compared to the cladding layers in these structures is essential for the optical-gain enhancement favorable for device operation. Emulating these inorganic gain media, superb properties of highly stable low-threshold (as low as ≈8 µJ cm -2 ) linearly polarized lasing from solution-processed Ruddlesden-Popper (RP) perovskite microplatelets are realized. Detailed investigations using microarea transient spectroscopies together with finite-difference time-domain simulations validate that the mixed lower-dimensional RP perovskites (functioning as cladding layers) within the microplatelets provide both enhanced exciton and photon confinement for the higher-dimensional RP perovskites (functioning as the active gain media). Furthermore, structure-lasing-threshold relationship (i.e., correlating the content of lower-dimensional RP perovskites in a single microplatelet) vital for design and performance optimization is established. Dual-wavelength lasing from these quasi-2D RP perovskite microplatelets can also be achieved. These unique properties distinguish RP perovskite microplatelets as a new family of self-assembled multilayer planar waveguide gain media favorable for developing efficient lasers. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

AGT/ℤ2

NASA Astrophysics Data System (ADS)

Le Floch, Bruno; Turiaci, Gustavo J.

2017-12-01

We relate Liouville/Toda CFT correlators on Riemann surfaces with boundaries and cross-cap states to supersymmetric observables in four-dimensional N=2 gauge theories. Our construction naturally involves four-dimensional theories with fields defined on different ℤ2 quotients of the sphere (hemisphere and projective space) but nevertheless interacting with each other. The six-dimensional origin is a ℤ2 quotient of the setup giving rise to the usual AGT correspondence. To test the correspondence, we work out the ℝℙ4 partition function of four-dimensional N=2 theories by combining a 3d lens space and a 4d hemisphere partition functions. The same technique reproduces known ℝℙ2 partition functions in a form that lets us easily check two-dimensional Seiberg-like dualities on this nonorientable space. As a bonus we work out boundary and cross-cap wavefunctions in Toda CFT.

A new procedure for investigating three-dimensional stress fields in a thin plate with a through-the-thickness crack

NASA Astrophysics Data System (ADS)

Yi, Dake; Wang, TzuChiang

2018-06-01

In the paper, a new procedure is proposed to investigate three-dimensional fracture problems of a thin elastic plate with a long through-the-thickness crack under remote uniform tensile loading. The new procedure includes a new analytical method and high accurate finite element simulations. In the part of theoretical analysis, three-dimensional Maxwell stress functions are employed in order to derive three-dimensional crack tip fields. Based on the theoretical analysis, an equation which can describe the relationship among the three-dimensional J-integral J( z), the stress intensity factor K( z) and the tri-axial stress constraint level T z ( z) is derived first. In the part of finite element simulations, a fine mesh including 153360 elements is constructed to compute the stress field near the crack front, J( z) and T z ( z). Numerical results show that in the plane very close to the free surface, the K field solution is still valid for in-plane stresses. Comparison with the numerical results shows that the analytical results are valid.

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

PubMed Central

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

2018-01-01

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

Force dependent internalization of magnetic nanoparticles results in highly loaded endothelial cells for use as potential therapy delivery vectors.

PubMed

MacDonald, Cristin; Barbee, Kenneth; Polyak, Boris

2012-05-01

To investigate the kinetics, mechanism and extent of MNP loading into endothelial cells and the effect of this loading on cell function. MNP uptake was examined under field on/off conditions, utilizing varying magnetite concentration MNPs. MNP-loaded cell viability and functional integrity was assessed using metabolic respiration, cell proliferation and migration assays. MNP uptake in endothelial cells significantly increased under the influence of a magnetic field versus non-magnetic conditions. Larger magnetite density of the MNPs led to a higher MNP internalization by cells under application of a magnetic field without compromising cellular respiration activity. Two-dimensional migration assays at no field showed that higher magnetite loading resulted in greater cell migration rates. In a three-dimensional migration assay under magnetic field, the migration rate of MNP-loaded cells was more than twice that of unloaded cells and was comparable to migration stimulated by a serum gradient. Our results suggest that endothelial cell uptake of MNPs is a force dependent process. The in vitro assays determined that cell health is not adversely affected by high MNP loadings, allowing these highly magnetically responsive cells to be potentially beneficial therapy (gene, drug or cell) delivery systems.

Estimating the expected value of partial perfect information in health economic evaluations using integrated nested Laplace approximation.

PubMed

Heath, Anna; Manolopoulou, Ioanna; Baio, Gianluca

2016-10-15

The Expected Value of Perfect Partial Information (EVPPI) is a decision-theoretic measure of the 'cost' of parametric uncertainty in decision making used principally in health economic decision making. Despite this decision-theoretic grounding, the uptake of EVPPI calculations in practice has been slow. This is in part due to the prohibitive computational time required to estimate the EVPPI via Monte Carlo simulations. However, recent developments have demonstrated that the EVPPI can be estimated by non-parametric regression methods, which have significantly decreased the computation time required to approximate the EVPPI. Under certain circumstances, high-dimensional Gaussian Process (GP) regression is suggested, but this can still be prohibitively expensive. Applying fast computation methods developed in spatial statistics using Integrated Nested Laplace Approximations (INLA) and projecting from a high-dimensional into a low-dimensional input space allows us to decrease the computation time for fitting these high-dimensional GP, often substantially. We demonstrate that the EVPPI calculated using our method for GP regression is in line with the standard GP regression method and that despite the apparent methodological complexity of this new method, R functions are available in the package BCEA to implement it simply and efficiently. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Handy elementary algebraic properties of the geometry of entanglement

NASA Astrophysics Data System (ADS)

Blair, Howard A.; Alsing, Paul M.

2013-05-01

The space of separable states of a quantum system is a hyperbolic surface in a high dimensional linear space, which we call the separation surface, within the exponentially high dimensional linear space containing the quantum states of an n component multipartite quantum system. A vector in the linear space is representable as an n-dimensional hypermatrix with respect to bases of the component linear spaces. A vector will be on the separation surface iff every determinant of every 2-dimensional, 2-by-2 submatrix of the hypermatrix vanishes. This highly rigid constraint can be tested merely in time asymptotically proportional to d, where d is the dimension of the state space of the system due to the extreme interdependence of the 2-by-2 submatrices. The constraint on 2-by-2 determinants entails an elementary closed formformula for a parametric characterization of the entire separation surface with d-1 parameters in the char- acterization. The state of a factor of a partially separable state can be calculated in time asymptotically proportional to the dimension of the state space of the component. If all components of the system have approximately the same dimension, the time complexity of calculating a component state as a function of the parameters is asymptotically pro- portional to the time required to sort the basis. Metric-based entanglement measures of pure states are characterized in terms of the separation hypersurface.

Functionalized carbon micro/nanostructures for biomolecular detection

NASA Astrophysics Data System (ADS)

Penmatsa, Varun

Advancements in the micro-and nano-scale fabrication techniques have opened up new avenues for the development of portable, scalable and easier-to-use biosensors. Over the last few years, electrodes made of carbon have been widely used as sensing units in biosensors due to their attractive physiochemical properties. The aim of this research is to investigate different strategies to develop functionalized high surface carbon micro/nano-structures for electrochemical and biosensing devices. High aspect ratio three-dimensional carbon microarrays were fabricated via carbon microelectromechanical systems (C-MEMS) technique, which is based on pyrolyzing pre-patterned organic photoresist polymers. To further increase the surface area of the carbon microstructures, surface porosity was introduced by two strategies, i.e. (i) using F127 as porogen and (ii) oxygen reactive ion etch (RIE) treatment. Electrochemical characterization showed that porous carbon thin film electrodes prepared by using F127 as porogen had an effective surface area (Aeff 185%) compared to the conventional carbon electrode. To achieve enhanced electrochemical sensitivity for C-MEMS based functional devices, graphene was conformally coated onto high aspect ratio three-dimensional (3D) carbon micropillar arrays using electrostatic spray deposition (ESD) technique. The amperometric response of graphene/carbon micropillar electrode arrays exhibited higher electrochemical activity, improved charge transfer and a linear response towards H2O2 detection between 250μM to 5.5mM. Furthermore, carbon structures with dimensions from 50 nano-to micrometer level have been fabricated by pyrolyzing photo-nanoimprint lithography patterned organic resist polymer. Microstructure, elemental composition and resistivity characterization of the carbon nanostructures produced by this process were very similar to conventional photoresist derived carbon. Surface functionalization of the carbon nanostructures was performed using direct amination technique. Considering the need for requisite functional groups to covalently attach bioreceptors on the carbon surface for biomolecule detection, different oxidation techniques were compared to study the types of carbon-oxygen groups formed on the surface and their percentages with respect to different oxidation pretreatment times. Finally, a label-free detection strategy using signaling aptamer/protein binding complex for platelet-derived growth factor oncoprotein detection on functionalized three-dimensional carbon microarrays platform was demonstrated. The sensor showed near linear relationship between the relative fluorescence difference and protein concentration even in the sub-nanomolar range with an excellent detection limit of 5 pmol.

Coherent diffraction imaging of nanoscale strain evolution in a single crystal under high pressure

PubMed Central

Yang, Wenge; Huang, Xiaojing; Harder, Ross; Clark, Jesse N.; Robinson, Ian K.; Mao, Ho-kwang

2013-01-01

The evolution of morphology and internal strain under high pressure fundamentally alters the physical property, structural stability, phase transition and deformation mechanism of materials. Until now, only averaged strain distributions have been studied. Bragg coherent X-ray diffraction imaging is highly sensitive to the internal strain distribution of individual crystals but requires coherent illumination, which can be compromised by the complex high-pressure sample environment. Here we report the successful de-convolution of these effects with the recently developed mutual coherent function method to reveal the three-dimensional strain distribution inside a 400 nm gold single crystal during compression within a diamond-anvil cell. The three-dimensional morphology and evolution of the strain under pressures up to 6.4 GPa were obtained with better than 30 nm spatial resolution. In addition to providing a new approach for high-pressure nanotechnology and rheology studies, we draw fundamental conclusions about the origin of the anomalous compressibility of nanocrystals. PMID:23575684

Coherent diffraction imaging of nanoscale strain evolution in a single crystal under high pressure.

PubMed

Yang, Wenge; Huang, Xiaojing; Harder, Ross; Clark, Jesse N; Robinson, Ian K; Mao, Ho-kwang

2013-01-01

The evolution of morphology and internal strain under high pressure fundamentally alters the physical property, structural stability, phase transition and deformation mechanism of materials. Until now, only averaged strain distributions have been studied. Bragg coherent X-ray diffraction imaging is highly sensitive to the internal strain distribution of individual crystals but requires coherent illumination, which can be compromised by the complex high-pressure sample environment. Here we report the successful de-convolution of these effects with the recently developed mutual coherent function method to reveal the three-dimensional strain distribution inside a 400 nm gold single crystal during compression within a diamond-anvil cell. The three-dimensional morphology and evolution of the strain under pressures up to 6.4 GPa were obtained with better than 30 nm spatial resolution. In addition to providing a new approach for high-pressure nanotechnology and rheology studies, we draw fundamental conclusions about the origin of the anomalous compressibility of nanocrystals.

Mining High-Dimensional Data

NASA Astrophysics Data System (ADS)

Wang, Wei; Yang, Jiong

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

HYPOTHESIS TESTING FOR HIGH-DIMENSIONAL SPARSE BINARY REGRESSION

PubMed Central

Mukherjee, Rajarshi; Pillai, Natesh S.; Lin, Xihong

2015-01-01

In this paper, we study the detection boundary for minimax hypothesis testing in the context of high-dimensional, sparse binary regression models. Motivated by genetic sequencing association studies for rare variant effects, we investigate the complexity of the hypothesis testing problem when the design matrix is sparse. We observe a new phenomenon in the behavior of detection boundary which does not occur in the case of Gaussian linear regression. We derive the detection boundary as a function of two components: a design matrix sparsity index and signal strength, each of which is a function of the sparsity of the alternative. For any alternative, if the design matrix sparsity index is too high, any test is asymptotically powerless irrespective of the magnitude of signal strength. For binary design matrices with the sparsity index that is not too high, our results are parallel to those in the Gaussian case. In this context, we derive detection boundaries for both dense and sparse regimes. For the dense regime, we show that the generalized likelihood ratio is rate optimal; for the sparse regime, we propose an extended Higher Criticism Test and show it is rate optimal and sharp. We illustrate the finite sample properties of the theoretical results using simulation studies. PMID:26246645

Three-dimensional femtosecond laser processing for lab-on-a-chip applications

NASA Astrophysics Data System (ADS)

Sima, Felix; Sugioka, Koji; Vázquez, Rebeca Martínez; Osellame, Roberto; Kelemen, Lóránd; Ormos, Pal

2018-02-01

The extremely high peak intensity associated with ultrashort pulse width of femtosecond laser allows us to induce nonlinear interaction such as multiphoton absorption and tunneling ionization with materials that are transparent to the laser wavelength. More importantly, focusing the femtosecond laser beam inside the transparent materials confines the nonlinear interaction only within the focal volume, enabling three-dimensional (3D) micro- and nanofabrication. This 3D capability offers three different schemes, which involve undeformative, subtractive, and additive processing. The undeformative processing preforms internal refractive index modification to construct optical microcomponents including optical waveguides. Subtractive processing can realize the direct fabrication of 3D microfluidics, micromechanics, microelectronics, and photonic microcomponents in glass. Additive processing represented by two-photon polymerization enables the fabrication of 3D polymer micro- and nanostructures for photonic and microfluidic devices. These different schemes can be integrated to realize more functional microdevices including lab-on-a-chip devices, which are miniaturized laboratories that can perform reaction, detection, analysis, separation, and synthesis of biochemical materials with high efficiency, high speed, high sensitivity, low reagent consumption, and low waste production. This review paper describes the principles and applications of femtosecond laser 3D micro- and nanofabrication for lab-on-a-chip applications. A hybrid technique that promises to enhance functionality of lab-on-a-chip devices is also introduced.

Simulating the effect of high column density absorbers on the one-dimensional Lyman α forest flux power spectrum

NASA Astrophysics Data System (ADS)

Rogers, Keir K.; Bird, Simeon; Peiris, Hiranya V.; Pontzen, Andrew; Font-Ribera, Andreu; Leistedt, Boris

2018-03-01

We measure the effect of high column density absorbing systems of neutral hydrogen (H I) on the one-dimensional (1D) Lyman α forest flux power spectrum using cosmological hydrodynamical simulations from the Illustris project. High column density absorbers (which we define to be those with H I column densities N(H I) > 1.6 × 10^{17} atoms cm^{-2}) cause broadened absorption lines with characteristic damping wings. These damping wings bias the 1D Lyman α forest flux power spectrum by causing absorption in quasar spectra away from the location of the absorber itself. We investigate the effect of high column density absorbers on the Lyman α forest using hydrodynamical simulations for the first time. We provide templates as a function of column density and redshift, allowing the flexibility to accurately model residual contamination, i.e. if an analysis selectively clips out the largest damping wings. This flexibility will improve cosmological parameter estimation, for example, allowing more accurate measurement of the shape of the power spectrum, with implications for cosmological models containing massive neutrinos or a running of the spectral index. We provide fitting functions to reproduce these results so that they can be incorporated straightforwardly into a data analysis pipeline.

Three-dimensional inversion recovery manganese-enhanced MRI of mouse brain using super-resolution reconstruction to visualize nuclei involved in higher brain function.

PubMed

Poole, Dana S; Plenge, Esben; Poot, Dirk H J; Lakke, Egbert A J F; Niessen, Wiro J; Meijering, Erik; van der Weerd, Louise

2014-07-01

The visualization of activity in mouse brain using inversion recovery spin echo (IR-SE) manganese-enhanced MRI (MEMRI) provides unique contrast, but suffers from poor resolution in the slice-encoding direction. Super-resolution reconstruction (SRR) is a resolution-enhancing post-processing technique in which multiple low-resolution slice stacks are combined into a single volume of high isotropic resolution using computational methods. In this study, we investigated, first, whether SRR can improve the three-dimensional resolution of IR-SE MEMRI in the slice selection direction, whilst maintaining or improving the contrast-to-noise ratio of the two-dimensional slice stacks. Second, the contrast-to-noise ratio of SRR IR-SE MEMRI was compared with a conventional three-dimensional gradient echo (GE) acquisition. Quantitative experiments were performed on a phantom containing compartments of various manganese concentrations. The results showed that, with comparable scan times, the signal-to-noise ratio of three-dimensional GE acquisition is higher than that of SRR IR-SE MEMRI. However, the contrast-to-noise ratio between different compartments can be superior with SRR IR-SE MEMRI, depending on the chosen inversion time. In vivo experiments were performed in mice receiving manganese using an implanted osmotic pump. The results showed that SRR works well as a resolution-enhancing technique in IR-SE MEMRI experiments. In addition, the SRR image also shows a number of brain structures that are more clearly discernible from the surrounding tissues than in three-dimensional GE acquisition, including a number of nuclei with specific higher brain functions, such as memory, stress, anxiety and reward behavior. Copyright © 2014 John Wiley & Sons, Ltd.

Level set methods for detonation shock dynamics using high-order finite elements

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

Dobrev, V. A.; Grogan, F. C.; Kolev, T. V.

Level set methods are a popular approach to modeling evolving interfaces. We present a level set ad- vection solver in two and three dimensions using the discontinuous Galerkin method with high-order nite elements. During evolution, the level set function is reinitialized to a signed distance function to maintain ac- curacy. Our approach leads to stable front propagation and convergence on high-order, curved, unstructured meshes. The ability of the solver to implicitly track moving fronts lends itself to a number of applications; in particular, we highlight applications to high-explosive (HE) burn and detonation shock dynamics (DSD). We provide results for two-more » and three-dimensional benchmark problems as well as applications to DSD.« less

High Resolution WENO Simulation of 3D Detonation Waves

DTIC Science & Technology

2012-02-27

pocket behind the detonation front was not observed in their results because the rotating transverse detonation completely consumed the unburned gas. Dou...three-dimensional detonations We add source terms (functions of x, y, z and t) to the PDE system so that the following functions are exact solutions to... detonation rotates counter-clockwise, opposite to that in [48]. It can be seen that, the triple lines and transverse waves collide with the walls, and strong

Synergism of Dewetting and Self-Wrinkling To Create Two-Dimensional Ordered Arrays of Functional Microspheres.

PubMed

Han, Xue; Hou, Jing; Xie, Jixun; Yin, Jian; Tong, Yi; Lu, Conghua; Möhwald, Helmuth

2016-06-29

Here we report a simple, novel, yet robust nonlithographic method for the controlled fabrication of two-dimensional (2-D) ordered arrays of polyethylene glycol (PEG) microspheres. It is based on the synergistic combination of two bottom-up processes enabling periodic structure formation for the first time: dewetting and the mechanical wrinkle formation. The deterministic dewetting results from the hydrophilic polymer PEG on an incompatible polystyrene (PS) film bound to a polydimethylsiloxane (PDMS) substrate, which is directed both by a wrinkled template and by the template-directed in-situ self-wrinkling PS/PDMS substrate. Two strategies have been introduced to achieve synergism to enhance the 2-D ordering, i.e., employing 2-D in-situ self-wrinkling substrates and boundary conditions. As a result, we achieve highly ordered 2-D arrays of PEG microspheres with desired self-organized microstructures, such as the array location (e.g., selectively on the crest/in the valley of the wrinkles), diameter, spacing of the microspheres, and array direction. Additionally, the coordination of PEG with HAuCl4 is utilized to fabricate 2-D ordered arrays of functional PEG-HAuCl4 composite microspheres, which are further converted into different Au nanoparticle arrays. This simple versatile combined strategy could be extended to fabricate highly ordered 2-D arrays of other functional materials and achieve desirable properties and functionalities.

Inference of Functionally-Relevant N-acetyltransferase Residues Based on Statistical Correlations.

PubMed

Neuwald, Andrew F; Altschul, Stephen F

2016-12-01

Over evolutionary time, members of a superfamily of homologous proteins sharing a common structural core diverge into subgroups filling various functional niches. At the sequence level, such divergence appears as correlations that arise from residue patterns distinct to each subgroup. Such a superfamily may be viewed as a population of sequences corresponding to a complex, high-dimensional probability distribution. Here we model this distribution as hierarchical interrelated hidden Markov models (hiHMMs), which describe these sequence correlations implicitly. By characterizing such correlations one may hope to obtain information regarding functionally-relevant properties that have thus far evaded detection. To do so, we infer a hiHMM distribution from sequence data using Bayes' theorem and Markov chain Monte Carlo (MCMC) sampling, which is widely recognized as the most effective approach for characterizing a complex, high dimensional distribution. Other routines then map correlated residue patterns to available structures with a view to hypothesis generation. When applied to N-acetyltransferases, this reveals sequence and structural features indicative of functionally important, yet generally unknown biochemical properties. Even for sets of proteins for which nothing is known beyond unannotated sequences and structures, this can lead to helpful insights. We describe, for example, a putative coenzyme-A-induced-fit substrate binding mechanism mediated by arginine residue switching between salt bridge and π-π stacking interactions. A suite of programs implementing this approach is available (psed.igs.umaryland.edu).

Ab initio simulation of diffractometer instrumental function for high-resolution X-ray diffraction1

PubMed Central

Mikhalychev, Alexander; Benediktovitch, Andrei; Ulyanenkova, Tatjana; Ulyanenkov, Alex

2015-01-01

Modeling of the X-ray diffractometer instrumental function for a given optics configuration is important both for planning experiments and for the analysis of measured data. A fast and universal method for instrumental function simulation, suitable for fully automated computer realization and describing both coplanar and noncoplanar measurement geometries for any combination of X-ray optical elements, is proposed. The method can be identified as semi-analytical backward ray tracing and is based on the calculation of a detected signal as an integral of X-ray intensities for all the rays reaching the detector. The high speed of calculation is provided by the expressions for analytical integration over the spatial coordinates that describe the detection point. Consideration of the three-dimensional propagation of rays without restriction to the diffraction plane provides the applicability of the method for noncoplanar geometry and the accuracy for characterization of the signal from a two-dimensional detector. The correctness of the simulation algorithm is checked in the following two ways: by verifying the consistency of the calculated data with the patterns expected for certain simple limiting cases and by comparing measured reciprocal-space maps with the corresponding maps simulated by the proposed method for the same diffractometer configurations. Both kinds of tests demonstrate the agreement of the simulated instrumental function shape with the measured data. PMID:26089760

A new method for automated high-dimensional lesion segmentation evaluated in vascular injury and applied to the human occipital lobe.

PubMed

Mah, Yee-Haur; Jager, Rolf; Kennard, Christopher; Husain, Masud; Nachev, Parashkev

2014-07-01

Making robust inferences about the functional neuroanatomy of the brain is critically dependent on experimental techniques that examine the consequences of focal loss of brain function. Unfortunately, the use of the most comprehensive such technique-lesion-function mapping-is complicated by the need for time-consuming and subjective manual delineation of the lesions, greatly limiting the practicability of the approach. Here we exploit a recently-described general measure of statistical anomaly, zeta, to devise a fully-automated, high-dimensional algorithm for identifying the parameters of lesions within a brain image given a reference set of normal brain images. We proceed to evaluate such an algorithm in the context of diffusion-weighted imaging of the commonest type of lesion used in neuroanatomical research: ischaemic damage. Summary performance metrics exceed those previously published for diffusion-weighted imaging and approach the current gold standard-manual segmentation-sufficiently closely for fully-automated lesion-mapping studies to become a possibility. We apply the new method to 435 unselected images of patients with ischaemic stroke to derive a probabilistic map of the pattern of damage in lesions involving the occipital lobe, demonstrating the variation of anatomical resolvability of occipital areas so as to guide future lesion-function studies of the region. Copyright © 2012 Elsevier Ltd. All rights reserved.

Technical note: a landmark-based approach to the study of the ear ossicles using ultra-high-resolution X-ray computed tomography data.

PubMed

Schmidt, Jodi L; Cole, Theodore M; Silcox, Mary T

2011-08-01

Previous study of the ear ossicles in Primates has demonstrated that they vary on both functional and phylogenetic bases. Such studies have generally employed two-dimensional linear measurements rather than three-dimensional data. The availability of Ultra- high-resolution X-ray computed tomography (UhrCT) has made it possible to accurately image the ossicles so that broadly accepted methodologies for acquiring and studying morphometric data can be applied. Using UhrCT data also allows for the ossicular chain to be studied in anatomical position, so that it is possible to consider the spatial and size relationships of all three bones. One issue impeding the morphometric study of the ear ossicles is a lack of broadly recognized landmarks. Distinguishing landmarks on the ossicles is difficult in part because there are only two areas of articulation in the ossicular chain, one of which (the malleus/incus articulation) has a complex three-dimensional form. A measurement error study is presented demonstrating that a suite of 16 landmarks can be precisely located on reconstructions of the ossicles from UhrCT data. Estimates of measurement error showed that most landmarks were highly replicable, with an average CV for associated interlandmark distances of less than 3%. The positions of these landmarks are chosen to reflect not only the overall shape of the bones in the chain and their relative positions, but also functional parameters. This study should provide a basis for further examination of the smallest bones in the body in three dimensions. Copyright © 2011 Wiley-Liss, Inc.

A Zero-Dimensional Organic Seesaw-Shaped Tin Bromide with Highly Efficient Strongly Stokes-Shifted Deep-Red Emission

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

Zhou, Chenkun; Lin, Haoran; Shi, Hongliang

The synthesis and characterization is reported of (C 9NH 20) 2SnBr 4, a novel organic metal halide hybrid with a zero-dimensional (0D) structure, in which individual seesaw-shaped tin (II) bromide anions (SnBr 4 2-) are co-crystallized with 1-butyl-1-methylpyrrolidinium cations (C 9NH 20 +). Upon photoexcitation, the bulk crystals exhibit a highly efficient broadband deep-red emission peaked at 695 nm, with a large Stokes shift of 332 nm and a high quantum efficiency of around 46 %. Furthermore, the unique photophysical properties of this hybrid material are attributed to two major factors: 1) the 0D structure allowing the bulk crystals tomore » exhibit the intrinsic properties of individual SnBr 4 2- species, and 2) the seesaw structure then enables a pronounced excited state structural deformation as confirmed by density functional theory (DFT) calculations.« less

Pressure-dependent surface viscosity and its surprising consequences in interfacial lubrication flows

NASA Astrophysics Data System (ADS)

Manikantan, Harishankar; Squires, Todd M.

2017-02-01

The surface shear rheology of many insoluble surfactants depends strongly on the surface pressure (or concentration) of that surfactant. Here we highlight the dramatic consequences that surface-pressure-dependent surface viscosities have on interfacially dominant flows, by considering lubrication-style geometries within high Boussinesq (Bo) number flows. As with three-dimensional lubrication, high-Bo surfactant flows through thin gaps give high surface pressures, which in turn increase the local surface viscosity, further amplifying lubrication stresses and surface pressures. Despite their strong nonlinearity, the governing equations are separable, so that results from two-dimensional Newtonian lubrication analyses may be immediately adapted to treat surfactant monolayers with a general functional form of ηs(Π ) . Three paradigmatic systems are analyzed to reveal qualitatively new features: a maximum, self-limiting value for surfactant fluxes and particle migration velocities appears for Π -thickening surfactants, and kinematic reversibility is broken for the journal bearing and for suspensions more generally.

A weighted ℓ{sub 1}-minimization approach for sparse polynomial chaos expansions

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

Peng, Ji; Hampton, Jerrad; Doostan, Alireza, E-mail: alireza.doostan@colorado.edu

2014-06-15

This work proposes a method for sparse polynomial chaos (PC) approximation of high-dimensional stochastic functions based on non-adapted random sampling. We modify the standard ℓ{sub 1}-minimization algorithm, originally proposed in the context of compressive sampling, using a priori information about the decay of the PC coefficients, when available, and refer to the resulting algorithm as weightedℓ{sub 1}-minimization. We provide conditions under which we may guarantee recovery using this weighted scheme. Numerical tests are used to compare the weighted and non-weighted methods for the recovery of solutions to two differential equations with high-dimensional random inputs: a boundary value problem with amore » random elliptic operator and a 2-D thermally driven cavity flow with random boundary condition.« less

Control-surface hinge-moment calculations for a high-aspect-ratio supercritical wing

NASA Technical Reports Server (NTRS)

Perry, B., III

1978-01-01

The hinge moments, at selected flight conditions, resulting from deflecting two trailing edge control surfaces (one inboard and one midspan) on a high aspect ratio, swept, fuel conservative wing with a supercritical airfoil are estimated. Hinge moment results obtained from procedures which employ a recently developed transonic analysis are given. In this procedure a three dimensional inviscid transonic aerodynamics computer program is combined with a two dimensional turbulent boundary layer program in order to obtain an interacted solution. These results indicate that trends of the estimated hinge moment as a function of deflection angle are similar to those from experimental hinge moment measurements made on wind tunnel models with swept supercritical wings tested at similar values of free stream Mach number and angle of attack.

Control-surface hinge-moment calculations for a high-aspect-ratio supercritical wing

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

Perry, B.I.

1978-09-01

The hinge moments, at selected flight conditions, resulting from deflecting two trailing edge control surfaces (one inboard and one midspan) on a high aspect ratio, swept, fuel conservative wing with a supercritical airfoil are estimated. Hinge moment results obtained from procedures which employ a recently developed transonic analysis are given. In this procedure a three dimensional inviscid transonic aerodynamics computer program is combined with a two dimensional turbulent boundary layer program in order to obtain an interacted solution. These results indicate that trends of the estimated hinge moment as a function of deflection angle are similar to those from experimentalmore » hinge moment measurements made on wind tunnel models with swept supercritical wings tested at similar values of free stream Mach number and angle of attack.« less

A two-state hysteresis model from high-dimensional friction

PubMed Central

Biswas, Saurabh; Chatterjee, Anindya

2015-01-01

In prior work (Biswas & Chatterjee 2014 Proc. R. Soc. A 470, 20130817 (doi:10.1098/rspa.2013.0817)), we developed a six-state hysteresis model from a high-dimensional frictional system. Here, we use a more intuitively appealing frictional system that resembles one studied earlier by Iwan. The basis functions now have simple analytical description. The number of states required decreases further, from six to the theoretical minimum of two. The number of fitted parameters is reduced by an order of magnitude, to just six. An explicit and faster numerical solution method is developed. Parameter fitting to match different specified hysteresis loops is demonstrated. In summary, a new two-state model of hysteresis is presented that is ready for practical implementation. Essential Matlab code is provided. PMID:26587279

High Frequency Ground Motion from Finite Fault Rupture Simulations

NASA Astrophysics Data System (ADS)

Crempien, Jorge G. F.

There are many tectonically active regions on earth with little or no recorded ground motions. The Eastern United States is a typical example of regions with active faults, but with low to medium seismicity that has prevented sufficient ground motion recordings. Because of this, it is necessary to use synthetic ground motion methods in order to estimate the earthquake hazard a region might have. Ground motion prediction equations for spectral acceleration typically have geometric attenuation proportional to the inverse of distance away from the fault. Earthquakes simulated with one-dimensional layered earth models have larger geometric attenuation than the observed ground motion recordings. We show that as incident angles of rays increase at welded boundaries between homogeneous flat layers, the transmitted rays decrease in amplitude dramatically. As the receiver distance increases away from the source, the angle of incidence of up-going rays increases, producing negligible transmitted ray amplitude, thus increasing the geometrical attenuation. To work around this problem we propose a model in which we separate wave propagation for low and high frequencies at a crossover frequency, typically 1Hz. The high-frequency portion of strong ground motion is computed with a homogeneous half-space and amplified with the available and more complex one- or three-dimensional crustal models using the quarter wavelength method. We also make use of seismic coda energy density observations as scattering impulse response functions. We incorporate scattering impulse response functions into our Green's functions by convolving the high-frequency homogeneous half-space Green's functions with normalized synthetic scatterograms to reproduce scattering physical effects in recorded seismograms. This method was validated against ground motion for earthquakes recorded in California and Japan, yielding results that capture the duration and spectral response of strong ground motion.

Quantitative 3D reconstruction of airway and pulmonary vascular trees using HRCT

NASA Astrophysics Data System (ADS)

Wood, Susan A.; Hoford, John D.; Hoffman, Eric A.; Zerhouni, Elias A.; Mitzner, Wayne A.

1993-07-01

Accurate quantitative measurements of airway and vascular dimensions are essential to evaluate function in the normal and diseased lung. In this report, a novel method is described for three-dimensional extraction and analysis of pulmonary tree structures using data from High Resolution Computed Tomography (HRCT). Serially scanned two-dimensional slices of the lower left lobe of isolated dog lungs were stacked to create a volume of data. Airway and vascular trees were three-dimensionally extracted using a three dimensional seeded region growing algorithm based on difference in CT number between wall and lumen. To obtain quantitative data, we reduced each tree to its central axis. From the central axis, branch length is measured as the distance between two successive branch points, branch angle is measured as the angle produced by two daughter branches, and cross sectional area is measured from a plane perpendicular to the central axis point. Data derived from these methods can be used to localize and quantify structural differences both during changing physiologic conditions and in pathologic lungs.

Observations of two-dimensional monolayer zinc oxide

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

Sahoo, Trilochan, E-mail: trilochansahoo@gmail.com; Nayak, Sanjeev K.; Chelliah, Pandian

2016-03-15

Highlights: • Synthesis of planer ZnO nanostructure. • Observation of multilayered and monolayer ZnO. • DFT calculation of (10-10), (11-20) and (0 0 0 1) planes of ZnO. • Stability of non-polar (10-10) and (11-20) planes of ZnO. - Abstract: This letter reports the observations of planar two-dimensional ZnO synthesized using the hydrothermal growth technique. High-resolution transmission electron microscopy revealed the formation of a two-dimensional honeycomb lattice and aggregated structures of layered ZnO. The nonpolar (10-10) and (11-20) planes were present in the X-ray diffraction patterns, but the characteristic (0 0 0 1) peak of bulk ZnO was absent. Themore » study found that nonpolar freestanding ZnO structures composed of a single or few layers may be more stable and may have a higher probability of formation than their polar counterparts. The stability of the nonpolar two-dimensional hexagonal ZnO slabs is supported by density functional theory studies.« less

Visualization of Potential Energy Function Using an Isoenergy Approach and 3D Prototyping

ERIC Educational Resources Information Center

Teplukhin, Alexander; Babikov, Dmitri

2015-01-01

In our three-dimensional world, one can plot, see, and comprehend a function of two variables at most, V(x,y). One cannot plot a function of three or more variables. For this reason, visualization of the potential energy function in its full dimensionality is impossible even for the smallest polyatomic molecules, such as triatomics. This creates…

Functional imaging of the human brain using a modular, fibre-less, high-density diffuse optical tomography system.

PubMed

Chitnis, Danial; Cooper, Robert J; Dempsey, Laura; Powell, Samuel; Quaggia, Simone; Highton, David; Elwell, Clare; Hebden, Jeremy C; Everdell, Nicholas L

2016-10-01

We present the first three-dimensional, functional images of the human brain to be obtained using a fibre-less, high-density diffuse optical tomography system. Our technology consists of independent, miniaturized, silicone-encapsulated DOT modules that can be placed directly on the scalp. Four of these modules were arranged to provide up to 128, dual-wavelength measurement channels over a scalp area of approximately 60 × 65 mm 2 . Using a series of motor-cortex stimulation experiments, we demonstrate that this system can obtain high-quality, continuous-wave measurements at source-detector separations ranging from 14 to 55 mm in adults, in the presence of hair. We identify robust haemodynamic response functions in 5 out of 5 subjects, and present diffuse optical tomography images that depict functional haemodynamic responses that are well-localized in all three dimensions at both the individual and group levels. This prototype modular system paves the way for a new generation of wearable, wireless, high-density optical neuroimaging technologies.

3D Printed Bionic Nanodevices.

PubMed

Kong, Yong Lin; Gupta, Maneesh K; Johnson, Blake N; McAlpine, Michael C

2016-06-01

The ability to three-dimensionally interweave biological and functional materials could enable the creation of bionic devices possessing unique and compelling geometries, properties, and functionalities. Indeed, interfacing high performance active devices with biology could impact a variety of fields, including regenerative bioelectronic medicines, smart prosthetics, medical robotics, and human-machine interfaces. Biology, from the molecular scale of DNA and proteins, to the macroscopic scale of tissues and organs, is three-dimensional, often soft and stretchable, and temperature sensitive. This renders most biological platforms incompatible with the fabrication and materials processing methods that have been developed and optimized for functional electronics, which are typically planar, rigid and brittle. A number of strategies have been developed to overcome these dichotomies. One particularly novel approach is the use of extrusion-based multi-material 3D printing, which is an additive manufacturing technology that offers a freeform fabrication strategy. This approach addresses the dichotomies presented above by (1) using 3D printing and imaging for customized, hierarchical, and interwoven device architectures; (2) employing nanotechnology as an enabling route for introducing high performance materials, with the potential for exhibiting properties not found in the bulk; and (3) 3D printing a range of soft and nanoscale materials to enable the integration of a diverse palette of high quality functional nanomaterials with biology. Further, 3D printing is a multi-scale platform, allowing for the incorporation of functional nanoscale inks, the printing of microscale features, and ultimately the creation of macroscale devices. This blending of 3D printing, novel nanomaterial properties, and 'living' platforms may enable next-generation bionic systems. In this review, we highlight this synergistic integration of the unique properties of nanomaterials with the versatility of extrusion-based 3D printing technologies to interweave nanomaterials and fabricate novel bionic devices.

3D Printed Bionic Nanodevices

PubMed Central

Kong, Yong Lin; Gupta, Maneesh K.; Johnson, Blake N.; McAlpine, Michael C.

2016-01-01

Summary The ability to three-dimensionally interweave biological and functional materials could enable the creation of bionic devices possessing unique and compelling geometries, properties, and functionalities. Indeed, interfacing high performance active devices with biology could impact a variety of fields, including regenerative bioelectronic medicines, smart prosthetics, medical robotics, and human-machine interfaces. Biology, from the molecular scale of DNA and proteins, to the macroscopic scale of tissues and organs, is three-dimensional, often soft and stretchable, and temperature sensitive. This renders most biological platforms incompatible with the fabrication and materials processing methods that have been developed and optimized for functional electronics, which are typically planar, rigid and brittle. A number of strategies have been developed to overcome these dichotomies. One particularly novel approach is the use of extrusion-based multi-material 3D printing, which is an additive manufacturing technology that offers a freeform fabrication strategy. This approach addresses the dichotomies presented above by (1) using 3D printing and imaging for customized, hierarchical, and interwoven device architectures; (2) employing nanotechnology as an enabling route for introducing high performance materials, with the potential for exhibiting properties not found in the bulk; and (3) 3D printing a range of soft and nanoscale materials to enable the integration of a diverse palette of high quality functional nanomaterials with biology. Further, 3D printing is a multi-scale platform, allowing for the incorporation of functional nanoscale inks, the printing of microscale features, and ultimately the creation of macroscale devices. This blending of 3D printing, novel nanomaterial properties, and ‘living’ platforms may enable next-generation bionic systems. In this review, we highlight this synergistic integration of the unique properties of nanomaterials with the versatility of extrusion-based 3D printing technologies to interweave nanomaterials and fabricate novel bionic devices. PMID:27617026

Three-dimensional thermal analysis of a high-level waste repository

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

Altenbach, T.J.

1979-04-01

The analysis used the TRUMP computer code to evaluate the thermal fields for six repository scenarios that studied the effects of room ventilation, room backfill, and repository thermal diffusivity. The results for selected nodes are presented as plots showing the effect of temperature as a function of time. 15 figures, 6 tables.

Probing Carrier Transport and Structure-Property Relationship of Highly Ordered Organic Semiconductors at the Two-Dimensional Limit.

PubMed

Zhang, Yuhan; Qiao, Jingsi; Gao, Si; Hu, Fengrui; He, Daowei; Wu, Bing; Yang, Ziyi; Xu, Bingchen; Li, Yun; Shi, Yi; Ji, Wei; Wang, Peng; Wang, Xiaoyong; Xiao, Min; Xu, Hangxun; Xu, Jian-Bin; Wang, Xinran

2016-01-08

One of the basic assumptions in organic field-effect transistors, the most fundamental device unit in organic electronics, is that charge transport occurs two dimensionally in the first few molecular layers near the dielectric interface. Although the mobility of bulk organic semiconductors has increased dramatically, direct probing of intrinsic charge transport in the two-dimensional limit has not been possible due to excessive disorders and traps in ultrathin organic thin films. Here, highly ordered single-crystalline mono- to tetralayer pentacene crystals are realized by van der Waals (vdW) epitaxy on hexagonal BN. We find that the charge transport is dominated by hopping in the first conductive layer, but transforms to bandlike in subsequent layers. Such an abrupt phase transition is attributed to strong modulation of the molecular packing by interfacial vdW interactions, as corroborated by quantitative structural characterization and density functional theory calculations. The structural modulation becomes negligible beyond the second conductive layer, leading to a mobility saturation thickness of only ∼3  nm. Highly ordered organic ultrathin films provide a platform for new physics and device structures (such as heterostructures and quantum wells) that are not possible in conventional bulk crystals.

Shape component analysis: structure-preserving dimension reduction on biological shape spaces.

PubMed

Lee, Hao-Chih; Liao, Tao; Zhang, Yongjie Jessica; Yang, Ge

2016-03-01

Quantitative shape analysis is required by a wide range of biological studies across diverse scales, ranging from molecules to cells and organisms. In particular, high-throughput and systems-level studies of biological structures and functions have started to produce large volumes of complex high-dimensional shape data. Analysis and understanding of high-dimensional biological shape data require dimension-reduction techniques. We have developed a technique for non-linear dimension reduction of 2D and 3D biological shape representations on their Riemannian spaces. A key feature of this technique is that it preserves distances between different shapes in an embedded low-dimensional shape space. We demonstrate an application of this technique by combining it with non-linear mean-shift clustering on the Riemannian spaces for unsupervised clustering of shapes of cellular organelles and proteins. Source code and data for reproducing results of this article are freely available at https://github.com/ccdlcmu/shape_component_analysis_Matlab The implementation was made in MATLAB and supported on MS Windows, Linux and Mac OS. geyang@andrew.cmu.edu. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Probing Carrier Transport and Structure-Property Relationship of Highly Ordered Organic Semiconductors at the Two-Dimensional Limit

NASA Astrophysics Data System (ADS)

Zhang, Yuhan; Qiao, Jingsi; Gao, Si; Hu, Fengrui; He, Daowei; Wu, Bing; Yang, Ziyi; Xu, Bingchen; Li, Yun; Shi, Yi; Ji, Wei; Wang, Peng; Wang, Xiaoyong; Xiao, Min; Xu, Hangxun; Xu, Jian-Bin; Wang, Xinran

2016-01-01

One of the basic assumptions in organic field-effect transistors, the most fundamental device unit in organic electronics, is that charge transport occurs two dimensionally in the first few molecular layers near the dielectric interface. Although the mobility of bulk organic semiconductors has increased dramatically, direct probing of intrinsic charge transport in the two-dimensional limit has not been possible due to excessive disorders and traps in ultrathin organic thin films. Here, highly ordered single-crystalline mono- to tetralayer pentacene crystals are realized by van der Waals (vdW) epitaxy on hexagonal BN. We find that the charge transport is dominated by hopping in the first conductive layer, but transforms to bandlike in subsequent layers. Such an abrupt phase transition is attributed to strong modulation of the molecular packing by interfacial vdW interactions, as corroborated by quantitative structural characterization and density functional theory calculations. The structural modulation becomes negligible beyond the second conductive layer, leading to a mobility saturation thickness of only ˜3 nm . Highly ordered organic ultrathin films provide a platform for new physics and device structures (such as heterostructures and quantum wells) that are not possible in conventional bulk crystals.

Addressing Curse of Dimensionality in Sensitivity Analysis: How Can We Handle High-Dimensional Problems?

NASA Astrophysics Data System (ADS)

Safaei, S.; Haghnegahdar, A.; Razavi, S.

2016-12-01

Complex environmental models are now the primary tool to inform decision makers for the current or future management of environmental resources under the climate and environmental changes. These complex models often contain a large number of parameters that need to be determined by a computationally intensive calibration procedure. Sensitivity analysis (SA) is a very useful tool that not only allows for understanding the model behavior, but also helps in reducing the number of calibration parameters by identifying unimportant ones. The issue is that most global sensitivity techniques are highly computationally demanding themselves for generating robust and stable sensitivity metrics over the entire model response surface. Recently, a novel global sensitivity analysis method, Variogram Analysis of Response Surfaces (VARS), is introduced that can efficiently provide a comprehensive assessment of global sensitivity using the Variogram concept. In this work, we aim to evaluate the effectiveness of this highly efficient GSA method in saving computational burden, when applied to systems with extra-large number of input factors ( 100). We use a test function and a hydrological modelling case study to demonstrate the capability of VARS method in reducing problem dimensionality by identifying important vs unimportant input factors.

Tunable electronic and magnetic properties of two-dimensional materials and their one-dimensional derivatives.

PubMed

Zhang, Zhuhua; Liu, Xiaofei; Yu, Jin; Hang, Yang; Li, Yao; Guo, Yufeng; Xu, Ying; Sun, Xu; Zhou, Jianxin; Guo, Wanlin

2016-01-01

Low-dimensional materials exhibit many exceptional properties and functionalities which can be efficiently tuned by externally applied force or fields. Here we review the current status of research on tuning the electronic and magnetic properties of low-dimensional carbon, boron nitride, metal-dichalcogenides, phosphorene nanomaterials by applied engineering strain, external electric field and interaction with substrates, etc, with particular focus on the progress of computational methods and studies. We highlight the similarities and differences of the property modulation among one- and two-dimensional nanomaterials. Recent breakthroughs in experimental demonstration of the tunable functionalities in typical nanostructures are also presented. Finally, prospective and challenges for applying the tunable properties into functional devices are discussed. WIREs Comput Mol Sci 2016, 6:324-350. doi: 10.1002/wcms.1251 For further resources related to this article, please visit the WIREs website. The authors have declared no conflicts of interest for this article.

[New method of mixed gas infrared spectrum analysis based on SVM].

PubMed

Bai, Peng; Xie, Wen-Jun; Liu, Jun-Hua

2007-07-01

A new method of infrared spectrum analysis based on support vector machine (SVM) for mixture gas was proposed. The kernel function in SVM was used to map the seriously overlapping absorption spectrum into high-dimensional space, and after transformation, the high-dimensional data could be processed in the original space, so the regression calibration model was established, then the regression calibration model with was applied to analyze the concentration of component gas. Meanwhile it was proved that the regression calibration model with SVM also could be used for component recognition of mixture gas. The method was applied to the analysis of different data samples. Some factors such as scan interval, range of the wavelength, kernel function and penalty coefficient C that affect the model were discussed. Experimental results show that the component concentration maximal Mean AE is 0.132%, and the component recognition accuracy is higher than 94%. The problems of overlapping absorption spectrum, using the same method for qualitative and quantitative analysis, and limit number of training sample, were solved. The method could be used in other mixture gas infrared spectrum analyses, promising theoretic and application values.

Novel carbazole derivatives with quinoline ring: synthesis, electronic transition, and two-photon absorption three-dimensional optical data storage.

PubMed

Li, Liang; Wang, Ping; Hu, Yanlei; Lin, Geng; Wu, Yiqun; Huang, Wenhao; Zhao, Quanzhong

2015-03-15

We designed carbazole unit with an extended π conjugation by employing Vilsmeier formylation reaction and Knoevenagel condensation to facilitate the functional groups of quinoline from 3- or 3,6-position of carbazole. Two compounds doped with poly(methyl methacrylate) (PMMA) films were prepared. To explore the electronic transition properties of these compounds, one-photon absorption properties were experimentally measured and theoretically calculated by using the time-dependent density functional theory. We surveyed these films by using an 800 nm Ti:sapphire 120-fs laser with two-photon absorption (TPA) fluorescence emission properties and TPA coefficients to obtain the TPA cross sections. A three-dimensional optical data storage experiment was conducted by using a TPA photoreaction with an 800 nm-fs laser on the film to obtain a seven-layer optical data storage. The experiment proves that these carbazole derivatives are well suited for two-photon 3D optical storage, thus laying the foundation for the research of multilayer high-density and ultra-high-density optical information storage materials. Copyright © 2014 Elsevier B.V. All rights reserved.

Phase diagram of two-dimensional hard rods from fundamental mixed measure density functional theory

NASA Astrophysics Data System (ADS)

Wittmann, René; Sitta, Christoph E.; Smallenburg, Frank; Löwen, Hartmut

2017-10-01

A density functional theory for the bulk phase diagram of two-dimensional orientable hard rods is proposed and tested against Monte Carlo computer simulation data. In detail, an explicit density functional is derived from fundamental mixed measure theory and freely minimized numerically for hard discorectangles. The phase diagram, which involves stable isotropic, nematic, smectic, and crystalline phases, is obtained and shows good agreement with the simulation data. Our functional is valid for a multicomponent mixture of hard particles with arbitrary convex shapes and provides a reliable starting point to explore various inhomogeneous situations of two-dimensional hard rods and their Brownian dynamics.

Thin film mechanics

NASA Astrophysics Data System (ADS)

Cooper, Ryan C.

This doctoral thesis details the methods of determining mechanical properties of two classes of novel thin films suspended two-dimensional crystals and electron beam irradiated microfilms of polydimethylsiloxane (PDMS). Thin films are used in a variety of surface coatings to alter the opto-electronic properties or increase the wear or corrosion resistance and are ideal for micro- and nanoelectromechanical system fabrication. One of the challenges in fabricating thin films is the introduction of strains which can arise due to application techniques, geometrical conformation, or other spurious conditions. Chapters 2-4 focus on two dimensional materials. This is the intrinsic limit of thin films-being constrained to one atomic or molecular unit of thickness. These materials have mechanical, electrical, and optical properties ideal for micro- and nanoelectromechanical systems with truly novel device functionality. As such, the breadth of applications that can benefit from a treatise on two dimensional film mechanics is reason enough for exploration. This study explores the anomylously high strength of two dimensional materials. Furthermore, this work also aims to bridge four main gaps in the understanding of material science: bridging the gap between ab initio calculations and finite element analysis, bridging the gap between ab initio calculations and experimental results, nanoscale to microscale, and microscale to mesoscale. A nonlinear elasticity model is used to determine the necessary elastic constants to define the strain-energy density function for finite strain. Then, ab initio calculations-density functional theory-is used to calculate the nonlinear elastic response. Chapter 2 focuses on validating this methodology with atomic force microscope nanoindentation on molybdenum disulfide. Chapter 3 explores the convergence criteria of three density functional theory solvers to further verify the numerical calculations. Chapter 4 then uses this model to investigate the role of grain boundaries on the strength of chemical vapor deposited graphene. The results from these studies suggest that two dimensional films have remarkably high strength-reaching the intrinsic limit of molecular bonds. Chapter 5 explores the viscoelastic properties of heterogeneous polydimethylsiloxane (PDMS) microfilms through dynamic nanoindentation. PDMS microfilms are irradiated with an electron beam creating a 3 m-thick film with an increased cross-link density. The change in mechanical properties of PDMS due to thermal history and accelerator have been explored by a variety of tests, but the effect of electron beam irradiation is still unknown. The resulting structure is a stiff microfilm embedded in a soft rubber with some transformational strain induced by the cross-linking volume changes. Chapter 5 employs a combination of dynamic nanoindentation and finite element analysis to determine the change in stiffness as a function of electron beam irradiation. The experimental results are compared to the literature. The results of these experimental and numerical techniques provide exciting opportu- nities in future research. Two dimensional materials and flexible thin films are exciting materials for novel applications with new form factors, such as flexible electronics and microfluidic devices. The results herein indicate that you can accurately model the strength of two dimsensional materials and that these materials are robust against nanoscale defects. The results also reveal local variation of mechanical properties in PDMS microfilms. This allows one to design substrates that flex with varying amounts of strain on the surface. Combining the mechanics of two dimensional materials with that of a locally irradiated PDMS film could achieve a new class of flexible microelectromechanical systems. Large-scale growth of two dimensional materials will be structurally robust-even in the presence of nanostructural defects-and PDMS microfilms can be irradiated to vary strain of the electromechanical systems. These systems could be designed to investigate electromechanical coupling in two dimensional films or for a substitute to traditional silicon microdevices. (Abstract shortened by UMI.)

The representation of visual depth perception based on the plenoptic function in the retina and its neural computation in visual cortex V1.

PubMed

Songnian, Zhao; Qi, Zou; Chang, Liu; Xuemin, Liu; Shousi, Sun; Jun, Qiu

2014-04-23

How it is possible to "faithfully" represent a three-dimensional stereoscopic scene using Cartesian coordinates on a plane, and how three-dimensional perceptions differ between an actual scene and an image of the same scene are questions that have not yet been explored in depth. They seem like commonplace phenomena, but in fact, they are important and difficult issues for visual information processing, neural computation, physics, psychology, cognitive psychology, and neuroscience. The results of this study show that the use of plenoptic (or all-optical) functions and their dual plane parameterizations can not only explain the nature of information processing from the retina to the primary visual cortex and, in particular, the characteristics of the visual pathway's optical system and its affine transformation, but they can also clarify the reason why the vanishing point and line exist in a visual image. In addition, they can better explain the reasons why a three-dimensional Cartesian coordinate system can be introduced into the two-dimensional plane to express a real three-dimensional scene. 1. We introduce two different mathematical expressions of the plenoptic functions, Pw and Pv that can describe the objective world. We also analyze the differences between these two functions when describing visual depth perception, that is, the difference between how these two functions obtain the depth information of an external scene.2. The main results include a basic method for introducing a three-dimensional Cartesian coordinate system into a two-dimensional plane to express the depth of a scene, its constraints, and algorithmic implementation. In particular, we include a method to separate the plenoptic function and proceed with the corresponding transformation in the retina and visual cortex.3. We propose that size constancy, the vanishing point, and vanishing line form the basis of visual perception of the outside world, and that the introduction of a three-dimensional Cartesian coordinate system into a two dimensional plane reveals a corresponding mapping between a retinal image and the vanishing point and line.

The representation of visual depth perception based on the plenoptic function in the retina and its neural computation in visual cortex V1

PubMed Central

2014-01-01

Background How it is possible to “faithfully” represent a three-dimensional stereoscopic scene using Cartesian coordinates on a plane, and how three-dimensional perceptions differ between an actual scene and an image of the same scene are questions that have not yet been explored in depth. They seem like commonplace phenomena, but in fact, they are important and difficult issues for visual information processing, neural computation, physics, psychology, cognitive psychology, and neuroscience. Results The results of this study show that the use of plenoptic (or all-optical) functions and their dual plane parameterizations can not only explain the nature of information processing from the retina to the primary visual cortex and, in particular, the characteristics of the visual pathway’s optical system and its affine transformation, but they can also clarify the reason why the vanishing point and line exist in a visual image. In addition, they can better explain the reasons why a three-dimensional Cartesian coordinate system can be introduced into the two-dimensional plane to express a real three-dimensional scene. Conclusions 1. We introduce two different mathematical expressions of the plenoptic functions, P w and P v that can describe the objective world. We also analyze the differences between these two functions when describing visual depth perception, that is, the difference between how these two functions obtain the depth information of an external scene. 2. The main results include a basic method for introducing a three-dimensional Cartesian coordinate system into a two-dimensional plane to express the depth of a scene, its constraints, and algorithmic implementation. In particular, we include a method to separate the plenoptic function and proceed with the corresponding transformation in the retina and visual cortex. 3. We propose that size constancy, the vanishing point, and vanishing line form the basis of visual perception of the outside world, and that the introduction of a three-dimensional Cartesian coordinate system into a two dimensional plane reveals a corresponding mapping between a retinal image and the vanishing point and line. PMID:24755246

Three-dimensional instability of standing waves

NASA Astrophysics Data System (ADS)

Zhu, Qiang; Liu, Yuming; Yue, Dick K. P.

2003-12-01

We investigate the three-dimensional instability of finite-amplitude standing surface waves under the influence of gravity. The analysis employs the transition matrix (TM) approach and uses a new high-order spectral element (HOSE) method for computation of the nonlinear wave dynamics. HOSE is an extension of the original high-order spectral method (HOS) wherein nonlinear wave wave and wave body interactions are retained up to high order in wave steepness. Instead of global basis functions in HOS, however, HOSE employs spectral elements to allow for complex free-surface geometries and surface-piercing bodies. Exponential convergence of HOS with respect to the total number of spectral modes (for a fixed number of elements) and interaction order is retained in HOSE. In this study, we use TM-HOSE to obtain the stability of general three-dimensional perturbations (on a two-dimensional surface) on two classes of standing waves: plane standing waves in a rectangular tank; and radial/azimuthal standing waves in a circular basin. For plane standing waves, we confirm the known result of two-dimensional side-bandlike instability. In addition, we find a novel three-dimensional instability for base flow of any amplitude. The dominant component of the unstable disturbance is an oblique (standing) wave oriented at an arbitrary angle whose frequency is close to the (nonlinear) frequency of the original standing wave. This finding is confirmed by direct long-time simulations using HOSE which show that the nonlinear evolution leads to classical Fermi Pasta Ulam recurrence. For the circular basin, we find that, beyond a threshold wave steepness, a standing wave (of nonlinear frequency Omega) is unstable to three-dimensional perturbations. The unstable perturbation contains two dominant (standing-wave) components, the sum of whose frequencies is close to 2Omega. From the cases we consider, the critical wave steepness is found to generally decrease/increase with increasing radial/azimuthal mode number of the base standing wave. Finally, we show that the instability we find for both two- and three-dimensional standing waves is a result of third-order (quartet) resonance.

Towards effective interactive three-dimensional colour postprocessing

NASA Technical Reports Server (NTRS)

Bailey, B. C.; Hajjar, J. F.; Abel, J. F.

1986-01-01

Recommendations for the development of effective three-dimensional, graphical color postprocessing are made. First, the evaluation of large, complex numerical models demands that a postprocessor be highly interactive. A menu of available functions should be provided and these operations should be performed quickly so that a sense of continuity and spontaneity exists during the post-processing session. Second, an agenda for three-dimensional color postprocessing is proposed. A postprocessor must be versatile with respect to application and basic algorithms must be designed so that they are flexible. A complete selection of tools is necessary to allow arbitrary specification of views, extraction of qualitative information, and access to detailed quantitative and problem information. Finally, full use of advanced display hardware is necessary if interactivity is to be maximized and effective postprocessing of today's numerical simulations is to be achieved.

Cellular Responses to Mechanical Stress Selected Contribution: A Three-Dimensional Model for Assessment of in Vitro Toxicity in Balaena Mysticetus Renal Tissue

NASA Technical Reports Server (NTRS)

Goodwin, T. J.; Coate-Li, L.; Linnehan, R. M.; Hammond, T. G.

2000-01-01

This study established two- and three-dimensional renal proximal tubular cell cultures of the endangered species bowhead whale (Balaena mysticetus), developed SV40-transfected cultures, and cloned the 61-amino acid open reading frame for the metallothionein protein, the primary binding site for heavy metal contamination in mammals. Microgravity research, modulations in mechanical culture conditions (modeled microgravity), and shear stress have spawned innovative approaches to understanding the dynamics of cellular interactions, gene expression, and differentiation in several cellular systems. These investigations have led to the creation of ex vivo tissue models capable of serving as physiological research analogs for three-dimensional cellular interactions. These models are enabling studies in immune function, tissue modeling for basic research, and neoplasia. Three-dimensional cellular models emulate aspects of in vivo cellular architecture and physiology and may facilitate environmental toxicological studies aimed at elucidating biological functions and responses at the cellular level. Marine mammals occupy a significant ecological niche (72% of the Earth's surface is water) in terms of the potential for information on bioaccumulation and transport of terrestrial and marine environmental toxins in high-order vertebrates. Few ex vivo models of marine mammal physiology exist in vitro to accomplish the aforementioned studies. Techniques developed in this investigation, based on previous tissue modeling successes, may serve to facilitate similar research in other marine mammals.

Selected contribution: a three-dimensional model for assessment of in vitro toxicity in balaena mysticetus renal tissue

NASA Technical Reports Server (NTRS)

Goodwin, T. J.; Coate-Li, L.; Linnehan, R. M.; Hammond, T. G.

2000-01-01

This study established two- and three-dimensional renal proximal tubular cell cultures of the endangered species bowhead whale (Balaena mysticetus), developed SV40-transfected cultures, and cloned the 61-amino acid open reading frame for the metallothionein protein, the primary binding site for heavy metal contamination in mammals. Microgravity research, modulations in mechanical culture conditions (modeled microgravity), and shear stress have spawned innovative approaches to understanding the dynamics of cellular interactions, gene expression, and differentiation in several cellular systems. These investigations have led to the creation of ex vivo tissue models capable of serving as physiological research analogs for three-dimensional cellular interactions. These models are enabling studies in immune function, tissue modeling for basic research, and neoplasia. Three-dimensional cellular models emulate aspects of in vivo cellular architecture and physiology and may facilitate environmental toxicological studies aimed at elucidating biological functions and responses at the cellular level. Marine mammals occupy a significant ecological niche (72% of the Earth's surface is water) in terms of the potential for information on bioaccumulation and transport of terrestrial and marine environmental toxins in high-order vertebrates. Few ex vivo models of marine mammal physiology exist in vitro to accomplish the aforementioned studies. Techniques developed in this investigation, based on previous tissue modeling successes, may serve to facilitate similar research in other marine mammals.

Functionalized graphene and other two-dimensional materials for photovoltaic devices: device design and processing.

PubMed

Liu, Zhike; Lau, Shu Ping; Yan, Feng

2015-08-07

Graphene is the thinnest two-dimensional (2D) carbon material and has many advantages including high carrier mobilities and conductivity, high optical transparency, excellent mechanical flexibility and chemical stability, which make graphene an ideal material for various optoelectronic devices. The major applications of graphene in photovoltaic devices are for transparent electrodes and charge transport layers. Several other 2D materials have also shown advantages in charge transport and light absorption over traditional semiconductor materials used in photovoltaic devices. Great achievements in the applications of 2D materials in photovoltaic devices have been reported, yet numerous challenges still remain. For practical applications, the device performance should be further improved by optimizing the 2D material synthesis, film transfer, surface functionalization and chemical/physical doping processes. In this review, we will focus on the recent advances in the applications of graphene and other 2D materials in various photovoltaic devices, including organic solar cells, Schottky junction solar cells, dye-sensitized solar cells, quantum dot-sensitized solar cells, other inorganic solar cells, and perovskite solar cells, in terms of the functionalization techniques of the materials, the device design and the device performance. Finally, conclusions and an outlook for the future development of this field will be addressed.

Classification of Microarray Data Using Kernel Fuzzy Inference System

PubMed Central

Kumar Rath, Santanu

2014-01-01

The DNA microarray classification technique has gained more popularity in both research and practice. In real data analysis, such as microarray data, the dataset contains a huge number of insignificant and irrelevant features that tend to lose useful information. Classes with high relevance and feature sets with high significance are generally referred for the selected features, which determine the samples classification into their respective classes. In this paper, kernel fuzzy inference system (K-FIS) algorithm is applied to classify the microarray data (leukemia) using t-test as a feature selection method. Kernel functions are used to map original data points into a higher-dimensional (possibly infinite-dimensional) feature space defined by a (usually nonlinear) function ϕ through a mathematical process called the kernel trick. This paper also presents a comparative study for classification using K-FIS along with support vector machine (SVM) for different set of features (genes). Performance parameters available in the literature such as precision, recall, specificity, F-measure, ROC curve, and accuracy are considered to analyze the efficiency of the classification model. From the proposed approach, it is apparent that K-FIS model obtains similar results when compared with SVM model. This is an indication that the proposed approach relies on kernel function. PMID:27433543

Inflation in Flatland

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

Hinterbichler, Kurt; Joyce, Austin; Khoury, Justin, E-mail: kurt.hinterbichler@case.edu, E-mail: austin.joyce@columbia.edu, E-mail: jkhoury@sas.upenn.edu

We investigate the symmetry structure of inflation in 2+1 dimensions. In particular, we show that the asymptotic symmetries of three-dimensional de Sitter space are in one-to-one correspondence with cosmological adiabatic modes for the curvature perturbation. In 2+1 dimensions, the asymptotic symmetry algebra is infinite-dimensional, given by two copies of the Virasoro algebra, and can be traced to the conformal symmetries of the two-dimensional spatial slices of de Sitter. We study the consequences of this infinite-dimensional symmetry for inflationary correlation functions, finding new soft theorems that hold only in 2+1 dimensions. Expanding the correlation functions as a power series in themore » soft momentum q , these relations constrain the traceless part of the tensorial coefficient at each order in q in terms of a lower-point function. As a check, we verify that the O( q {sup 2}) identity is satisfied by inflationary correlation functions in the limit of small sound speed.« less

Delamination Analysis of a Multilayered Two-Dimensional Functionally Graded Cantilever Beam

NASA Astrophysics Data System (ADS)

Rizov, V.

2017-11-01

Delamination fracture behaviour of a multilayered two-dimensional functionally graded cantilever beam is analyzed in terms of the strain energy release rate. The beam is made of an arbitrary number of layers. Perfect adhesion is assumed between layers. Each layer has individual thickness and material properties. Besides, the material is two-dimensional functionally graded in the cross-section of each layer. There is a delamination crack located arbitrary between layers. The beam is loaded by a bending moment applied at the free end of the lower crack arm. The upper crack arm is free of stresses. The solution to strain energy release rate derived is applied to investigate the influence of the crack location and the material gradient on the delamination fracture. The results obtained can be used to optimize the multilayered two-dimensional functionally graded beam structure with respect to the delamination fracture behaviour.

SAR Processing Based On Two-Dimensional Transfer Function

NASA Technical Reports Server (NTRS)

Chang, Chi-Yung; Jin, Michael Y.; Curlander, John C.

1994-01-01

Exact transfer function, ETF, is two-dimensional transfer function that constitutes basis of improved frequency-domain-convolution algorithm for processing synthetic-aperture-radar, SAR data. ETF incorporates terms that account for Doppler effect of motion of radar relative to scanned ground area and for antenna squint angle. Algorithm based on ETF outperforms others.

Teleportation of a 3-dimensional GHZ State

NASA Astrophysics Data System (ADS)

Cao, Hai-Jing; Wang, Huai-Sheng; Li, Peng-Fei; Song, He-Shan

2012-05-01

The process of teleportation of a completely unknown 3-dimensional GHZ state is considered. Three maximally entangled 3-dimensional Bell states function as quantum channel in the scheme. This teleportation scheme can be directly generalized to teleport an unknown d-dimensional GHZ state.

A Multi-Dimensional Functional Principal Components Analysis of EEG Data

PubMed Central

Hasenstab, Kyle; Scheffler, Aaron; Telesca, Donatello; Sugar, Catherine A.; Jeste, Shafali; DiStefano, Charlotte; Şentürk, Damla

2017-01-01

Summary The electroencephalography (EEG) data created in event-related potential (ERP) experiments have a complex high-dimensional structure. Each stimulus presentation, or trial, generates an ERP waveform which is an instance of functional data. The experiments are made up of sequences of multiple trials, resulting in longitudinal functional data and moreover, responses are recorded at multiple electrodes on the scalp, adding an electrode dimension. Traditional EEG analyses involve multiple simplifications of this structure to increase the signal-to-noise ratio, effectively collapsing the functional and longitudinal components by identifying key features of the ERPs and averaging them across trials. Motivated by an implicit learning paradigm used in autism research in which the functional, longitudinal and electrode components all have critical interpretations, we propose a multidimensional functional principal components analysis (MD-FPCA) technique which does not collapse any of the dimensions of the ERP data. The proposed decomposition is based on separation of the total variation into subject and subunit level variation which are further decomposed in a two-stage functional principal components analysis. The proposed methodology is shown to be useful for modeling longitudinal trends in the ERP functions, leading to novel insights into the learning patterns of children with Autism Spectrum Disorder (ASD) and their typically developing peers as well as comparisons between the two groups. Finite sample properties of MD-FPCA are further studied via extensive simulations. PMID:28072468

A multi-dimensional functional principal components analysis of EEG data.

PubMed

Hasenstab, Kyle; Scheffler, Aaron; Telesca, Donatello; Sugar, Catherine A; Jeste, Shafali; DiStefano, Charlotte; Şentürk, Damla

2017-09-01

The electroencephalography (EEG) data created in event-related potential (ERP) experiments have a complex high-dimensional structure. Each stimulus presentation, or trial, generates an ERP waveform which is an instance of functional data. The experiments are made up of sequences of multiple trials, resulting in longitudinal functional data and moreover, responses are recorded at multiple electrodes on the scalp, adding an electrode dimension. Traditional EEG analyses involve multiple simplifications of this structure to increase the signal-to-noise ratio, effectively collapsing the functional and longitudinal components by identifying key features of the ERPs and averaging them across trials. Motivated by an implicit learning paradigm used in autism research in which the functional, longitudinal, and electrode components all have critical interpretations, we propose a multidimensional functional principal components analysis (MD-FPCA) technique which does not collapse any of the dimensions of the ERP data. The proposed decomposition is based on separation of the total variation into subject and subunit level variation which are further decomposed in a two-stage functional principal components analysis. The proposed methodology is shown to be useful for modeling longitudinal trends in the ERP functions, leading to novel insights into the learning patterns of children with Autism Spectrum Disorder (ASD) and their typically developing peers as well as comparisons between the two groups. Finite sample properties of MD-FPCA are further studied via extensive simulations. © 2017, The International Biometric Society.

SAMBA: Sparse Approximation of Moment-Based Arbitrary Polynomial Chaos

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

Ahlfeld, R., E-mail: r.ahlfeld14@imperial.ac.uk; Belkouchi, B.; Montomoli, F.

2016-09-01

A new arbitrary Polynomial Chaos (aPC) method is presented for moderately high-dimensional problems characterised by limited input data availability. The proposed methodology improves the algorithm of aPC and extends the method, that was previously only introduced as tensor product expansion, to moderately high-dimensional stochastic problems. The fundamental idea of aPC is to use the statistical moments of the input random variables to develop the polynomial chaos expansion. This approach provides the possibility to propagate continuous or discrete probability density functions and also histograms (data sets) as long as their moments exist, are finite and the determinant of the moment matrixmore » is strictly positive. For cases with limited data availability, this approach avoids bias and fitting errors caused by wrong assumptions. In this work, an alternative way to calculate the aPC is suggested, which provides the optimal polynomials, Gaussian quadrature collocation points and weights from the moments using only a handful of matrix operations on the Hankel matrix of moments. It can therefore be implemented without requiring prior knowledge about statistical data analysis or a detailed understanding of the mathematics of polynomial chaos expansions. The extension to more input variables suggested in this work, is an anisotropic and adaptive version of Smolyak's algorithm that is solely based on the moments of the input probability distributions. It is referred to as SAMBA (PC), which is short for Sparse Approximation of Moment-Based Arbitrary Polynomial Chaos. It is illustrated that for moderately high-dimensional problems (up to 20 different input variables or histograms) SAMBA can significantly simplify the calculation of sparse Gaussian quadrature rules. SAMBA's efficiency for multivariate functions with regard to data availability is further demonstrated by analysing higher order convergence and accuracy for a set of nonlinear test functions with 2, 5 and 10 different input distributions or histograms.« less

SAMBA: Sparse Approximation of Moment-Based Arbitrary Polynomial Chaos

NASA Astrophysics Data System (ADS)

Ahlfeld, R.; Belkouchi, B.; Montomoli, F.

2016-09-01

A new arbitrary Polynomial Chaos (aPC) method is presented for moderately high-dimensional problems characterised by limited input data availability. The proposed methodology improves the algorithm of aPC and extends the method, that was previously only introduced as tensor product expansion, to moderately high-dimensional stochastic problems. The fundamental idea of aPC is to use the statistical moments of the input random variables to develop the polynomial chaos expansion. This approach provides the possibility to propagate continuous or discrete probability density functions and also histograms (data sets) as long as their moments exist, are finite and the determinant of the moment matrix is strictly positive. For cases with limited data availability, this approach avoids bias and fitting errors caused by wrong assumptions. In this work, an alternative way to calculate the aPC is suggested, which provides the optimal polynomials, Gaussian quadrature collocation points and weights from the moments using only a handful of matrix operations on the Hankel matrix of moments. It can therefore be implemented without requiring prior knowledge about statistical data analysis or a detailed understanding of the mathematics of polynomial chaos expansions. The extension to more input variables suggested in this work, is an anisotropic and adaptive version of Smolyak's algorithm that is solely based on the moments of the input probability distributions. It is referred to as SAMBA (PC), which is short for Sparse Approximation of Moment-Based Arbitrary Polynomial Chaos. It is illustrated that for moderately high-dimensional problems (up to 20 different input variables or histograms) SAMBA can significantly simplify the calculation of sparse Gaussian quadrature rules. SAMBA's efficiency for multivariate functions with regard to data availability is further demonstrated by analysing higher order convergence and accuracy for a set of nonlinear test functions with 2, 5 and 10 different input distributions or histograms.

The impact of tics, obsessive-compulsive symptoms, and impulsivity on global functioning in Tourette syndrome.

PubMed

Kano, Yukiko; Kono, Toshiaki; Matsuda, Natsumi; Nonaka, Maiko; Kuwabara, Hitoshi; Shimada, Takafumi; Shishikura, Kurie; Konno, Chizue; Ohta, Masataka

2015-03-30

This study investigated the relationships between tics, obsessive-compulsive symptoms (OCS), and impulsivity, and their effects on global functioning in Japanese patients with Tourette syndrome (TS), using the dimensional approach for OCS. Fifty-three TS patients were assessed using the Yale Global Tic Severity Scale, the Dimensional Yale-Brown Obsessive-Compulsive Scale, the Impulsivity Rating Scale, and the Global Assessment of Functioning Scale. Although tic severity scores were significantly and positively correlated with OCS severity scores, impulsivity severity scores were not significantly correlated with either. The global functioning score was significantly and negatively correlated with tic and OCS severity scores. Of the 6 dimensional OCS scores, only aggression scores had a significant negative correlation with global functioning scores. A stepwise multiple regression analysis showed that only OCS severity scores were significantly associated with global functioning scores. Despite a moderate correlation between tic severity and OCS severity, the impact of OCS on global functioning was greater than that of tics. Of the OCS dimensions, only aggression had a significant impact on global functioning. Our findings suggest that it is important to examine OCS using a dimensional approach when analyzing global functioning in TS patients. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Structure and Growth Control of Organic-Inorganic Halide Perovskites for Optoelectronics: From Polycrystalline Films to Single Crystals.

PubMed

Chen, Yani; He, Minhong; Peng, Jiajun; Sun, Yong; Liang, Ziqi

2016-04-01

Recently, organic-inorganic halide perovskites have sparked tremendous research interest because of their ground-breaking photovoltaic performance. The crystallization process and crystal shape of perovskites have striking impacts on their optoelectronic properties. Polycrystalline films and single crystals are two main forms of perovskites. Currently, perovskite thin films have been under intensive investigation while studies of perovskite single crystals are just in their infancy. This review article is concentrated upon the control of perovskite structures and growth, which are intimately correlated for improvements of not only solar cells but also light-emitting diodes, lasers, and photodetectors. We begin with the survey of the film formation process of perovskites including deposition methods and morphological optimization avenues. Strategies such as the use of additives, thermal annealing, solvent annealing, atmospheric control, and solvent engineering have been successfully employed to yield high-quality perovskite films. Next, we turn to summarize the shape evolution of perovskites single crystals from three-dimensional large sized single crystals, two-dimensional nanoplates, one-dimensional nanowires, to zero-dimensional quantum dots. Siginificant functions of perovskites single crystals are highlighted, which benefit fundamental studies of intrinsic photophysics. Then, the growth mechanisms of the previously mentioned perovskite crystals are unveiled. Lastly, perspectives for structure and growth control of perovskites are outlined towards high-performance (opto)electronic devices.

A unified wall function for compressible turbulence modelling

NASA Astrophysics Data System (ADS)

Ong, K. C.; Chan, A.

2018-05-01

Turbulence modelling near the wall often requires a high mesh density clustered around the wall and the first cells adjacent to the wall to be placed in the viscous sublayer. As a result, the numerical stability is constrained by the smallest cell size and hence requires high computational overhead. In the present study, a unified wall function is developed which is valid for viscous sublayer, buffer sublayer and inertial sublayer, as well as including effects of compressibility, heat transfer and pressure gradient. The resulting wall function applies to compressible turbulence modelling for both isothermal and adiabatic wall boundary conditions with the non-zero pressure gradient. Two simple wall function algorithms are implemented for practical computation of isothermal and adiabatic wall boundary conditions. The numerical results show that the wall function evaluates the wall shear stress and turbulent quantities of wall adjacent cells at wide range of non-dimensional wall distance and alleviate the number and size of cells required.

Three-dimensional temporomandibular joint modeling and animation.

PubMed

Cascone, Piero; Rinaldi, Fabrizio; Pagnoni, Mario; Marianetti, Tito Matteo; Tedaldi, Massimiliano

2008-11-01

The three-dimensional (3D) temporomandibular joint (TMJ) model derives from a study of the cranium by 3D virtual reality and mandibular function animation. The starting point of the project is high-fidelity digital acquisition of a human dry skull. The cooperation between the maxillofacial surgeon and the cartoonist enables the reconstruction of the fibroconnective components of the TMJ that are the keystone for comprehension of the anatomic and functional features of the mandible. The skeletal model is customized with the apposition of the temporomandibular ligament, the articular disk, the retrodiskal tissue, and the medial and the lateral ligament of the disk. The simulation of TMJ movement is the result of the integration of up-to-date data on the biomechanical restrictions. The 3D TMJ model is an easy-to-use application that may be run on a personal computer for the study of the TMJ and its biomechanics.

Prediction of clinical depression scores and detection of changes in whole-brain using resting-state functional MRI data with partial least squares regression

PubMed Central

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

2017-01-01

In diagnostic applications of statistical machine learning methods to brain imaging data, common problems include data high-dimensionality and co-linearity, which often cause over-fitting and instability. To overcome these problems, we applied partial least squares (PLS) regression to resting-state functional magnetic resonance imaging (rs-fMRI) data, creating a low-dimensional representation that relates symptoms to brain activity and that predicts clinical measures. Our experimental results, based upon data from clinically depressed patients and healthy controls, demonstrated that PLS and its kernel variants provided significantly better prediction of clinical measures than ordinary linear regression. Subsequent classification using predicted clinical scores distinguished depressed patients from healthy controls with 80% accuracy. Moreover, loading vectors for latent variables enabled us to identify brain regions relevant to depression, including the default mode network, the right superior frontal gyrus, and the superior motor area. PMID:28700672

Experimental demonstration of two-dimensional hybrid waveguide-integrated plasmonic crystals on silicon-on-insulator platform

NASA Astrophysics Data System (ADS)

Ren, Guanghui; Yudistira, Didit; Nguyen, Thach G.; Khodasevych, Iryna; Schoenhardt, Steffen; Berean, Kyle J.; Hamm, Joachim M.; Hess, Ortwin; Mitchell, Arnan

2017-07-01

Nanoscale plasmonic structures can offer unique functionality due to extreme sub-wavelength optical confinement, but the realization of complex plasmonic circuits is hampered by high propagation losses. Hybrid approaches can potentially overcome this limitation, but only few practical approaches based on either single or few element arrays of nanoantennas on dielectric nanowire have been experimentally demonstrated. In this paper, we demonstrate a two dimensional hybrid photonic plasmonic crystal interfaced with a standard silicon photonic platform. Off resonance, we observe low loss propagation through our structure, while on resonance we observe strong propagation suppression and intense concentration of light into a dense lattice of nanoscale hot-spots on the surface providing clear evidence of a hybrid photonic plasmonic crystal bandgap. This fully integrated approach is compatible with established silicon-on-insulator (SOI) fabrication techniques and constitutes a significant step toward harnessing plasmonic functionality within SOI photonic circuits.

Generative Topographic Mapping (GTM): Universal Tool for Data Visualization, Structure-Activity Modeling and Dataset Comparison.

PubMed

Kireeva, N; Baskin, I I; Gaspar, H A; Horvath, D; Marcou, G; Varnek, A

2012-04-01

Here, the utility of Generative Topographic Maps (GTM) for data visualization, structure-activity modeling and database comparison is evaluated, on hand of subsets of the Database of Useful Decoys (DUD). Unlike other popular dimensionality reduction approaches like Principal Component Analysis, Sammon Mapping or Self-Organizing Maps, the great advantage of GTMs is providing data probability distribution functions (PDF), both in the high-dimensional space defined by molecular descriptors and in 2D latent space. PDFs for the molecules of different activity classes were successfully used to build classification models in the framework of the Bayesian approach. Because PDFs are represented by a mixture of Gaussian functions, the Bhattacharyya kernel has been proposed as a measure of the overlap of datasets, which leads to an elegant method of global comparison of chemical libraries. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Joint sparse learning for 3-D facial expression generation.

PubMed

Song, Mingli; Tao, Dacheng; Sun, Shengpeng; Chen, Chun; Bu, Jiajun

2013-08-01

3-D facial expression generation, including synthesis and retargeting, has received intensive attentions in recent years, because it is important to produce realistic 3-D faces with specific expressions in modern film production and computer games. In this paper, we present joint sparse learning (JSL) to learn mapping functions and their respective inverses to model the relationship between the high-dimensional 3-D faces (of different expressions and identities) and their corresponding low-dimensional representations. Based on JSL, we can effectively and efficiently generate various expressions of a 3-D face by either synthesizing or retargeting. Furthermore, JSL is able to restore 3-D faces with holes by learning a mapping function between incomplete and intact data. Experimental results on a wide range of 3-D faces demonstrate the effectiveness of the proposed approach by comparing with representative ones in terms of quality, time cost, and robustness.

Design and synthesis of diverse functional kinked nanowire structures for nanoelectronic bioprobes.

PubMed

Xu, Lin; Jiang, Zhe; Qing, Quan; Mai, Liqiang; Zhang, Qingjie; Lieber, Charles M

2013-02-13

Functional kinked nanowires (KNWs) represent a new class of nanowire building blocks, in which functional devices, for example, nanoscale field-effect transistors (nanoFETs), are encoded in geometrically controlled nanowire superstructures during synthesis. The bottom-up control of both structure and function of KNWs enables construction of spatially isolated point-like nanoelectronic probes that are especially useful for monitoring biological systems where finely tuned feature size and structure are highly desired. Here we present three new types of functional KNWs including (1) the zero-degree KNW structures with two parallel heavily doped arms of U-shaped structures with a nanoFET at the tip of the "U", (2) series multiplexed functional KNW integrating multi-nanoFETs along the arm and at the tips of V-shaped structures, and (3) parallel multiplexed KNWs integrating nanoFETs at the two tips of W-shaped structures. First, U-shaped KNWs were synthesized with separations as small as 650 nm between the parallel arms and used to fabricate three-dimensional nanoFET probes at least 3 times smaller than previous V-shaped designs. In addition, multiple nanoFETs were encoded during synthesis in one of the arms/tip of V-shaped and distinct arms/tips of W-shaped KNWs. These new multiplexed KNW structures were structurally verified by optical and electron microscopy of dopant-selective etched samples and electrically characterized using scanning gate microscopy and transport measurements. The facile design and bottom-up synthesis of these diverse functional KNWs provides a growing toolbox of building blocks for fabricating highly compact and multiplexed three-dimensional nanoprobes for applications in life sciences, including intracellular and deep tissue/cell recordings.

High-Responsivity Graphene-Boron Nitride Photodetector and Autocorrelator in a Silicon Photonic Integrated Circuit.

PubMed

Shiue, Ren-Jye; Gao, Yuanda; Wang, Yifei; Peng, Cheng; Robertson, Alexander D; Efetov, Dmitri K; Assefa, Solomon; Koppens, Frank H L; Hone, James; Englund, Dirk

2015-11-11

Graphene and other two-dimensional (2D) materials have emerged as promising materials for broadband and ultrafast photodetection and optical modulation. These optoelectronic capabilities can augment complementary metal-oxide-semiconductor (CMOS) devices for high-speed and low-power optical interconnects. Here, we demonstrate an on-chip ultrafast photodetector based on a two-dimensional heterostructure consisting of high-quality graphene encapsulated in hexagonal boron nitride. Coupled to the optical mode of a silicon waveguide, this 2D heterostructure-based photodetector exhibits a maximum responsivity of 0.36 A/W and high-speed operation with a 3 dB cutoff at 42 GHz. From photocurrent measurements as a function of the top-gate and source-drain voltages, we conclude that the photoresponse is consistent with hot electron mediated effects. At moderate peak powers above 50 mW, we observe a saturating photocurrent consistent with the mechanisms of electron-phonon supercollision cooling. This nonlinear photoresponse enables optical on-chip autocorrelation measurements with picosecond-scale timing resolution and exceptionally low peak powers.

A hypersonic lift mechanism with decoupled lift and drag surfaces

NASA Astrophysics Data System (ADS)

Xu, YiZhe; Xu, ZhiQi; Li, ShaoGuang; Li, Juan; Bai, ChenYuan; Wu, ZiNiu

2013-05-01

In the present study, we propose a novel lift mechanism for which the lifting surface produces only lift. This is achieved by mounting a two-dimensional shock-shock interaction generator below the lifting surface. The shock-shock interaction theory in conjunction with a three dimensional correction and checked with computational fluid dynamics (CFD) is used to analyze the lift and drag forces as function of the geometrical parameters and inflow Mach number. Through this study, though limited to only inviscid flow, we conclude that it is possible to obtain a high lift to drag ratio by suitably arranging the shock interaction generator.

Thermal conductivity in large - J two-dimensional antiferromagnets: Role of phonon scattering

DOE PAGES

Chernyshev, A. L.; Brenig, Wolfram

2015-08-05

Different types of relaxation processes for magnon heat current are discussed, with a particular focus on coupling to three-dimensional phonons. There is thermal conductivity by these in-plane magnetic excitations using two distinct techniques: Boltzmann formalism within the relaxation-time approximation and memory-function approach. Also considered are the scattering of magnons by both acoustic and optical branches of phonons. We demonstrate an accord between the two methods, regarding the asymptotic behavior of the effective relaxation rates. It is strongly suggested that scattering from optical or zone-boundary phonons is important for magnon heat current relaxation in a high-temperature window of ΘD≲T<< J.

Functional Supramolecular Polymers*

PubMed Central

Aida, T.; Meijer, E.W.; Stupp, S.I.

2012-01-01

Supramolecular polymers can be random and entangled coils with the mechanical properties of plastics and elastomers, but with great capacity for processability, recycling, and self-healing due to their reversible monomer-to-polymer transitions. At the other extreme, supramolecular polymers can be formed by self-assembly among designed subunits to yield shape-persistent and highly ordered filaments. The use of strong and directional interactions among molecular subunits can achieve not only rich dynamic behavior but also high degrees of internal order that are not known in ordinary polymers. They can resemble, for example, the ordered and dynamic one-dimensional supramolecular assemblies of the cell cytoskeleton, and possess useful biological and electronic functions. PMID:22344437

Probing the atomic structure of metallic nanoclusters with the tip of a scanning tunneling microscope.

PubMed

Schouteden, Koen; Lauwaet, Koen; Janssens, Ewald; Barcaro, Giovanni; Fortunelli, Alessandro; Van Haesendonck, Chris; Lievens, Peter

2014-02-21

Preformed Co clusters with an average diameter of 2.5 nm are produced in the gas phase and are deposited under controlled ultra-high vacuum conditions onto a thin insulating NaCl film on Au(111). Relying on a combined experimental and theoretical investigation, we demonstrate visualization of the three-dimensional atomic structure of the Co clusters by high-resolution scanning tunneling microscopy (STM) using a Cl functionalized STM tip that can be obtained on the NaCl surface. More generally, use of a functionalized STM tip may allow for systematic atomic structure determination with STM of nanoparticles that are deposited on metal surfaces.

Effects of nasal septum perforation repair surgery on three-dimensional airflow: an evaluation using computational fluid dynamics.

PubMed

Nomura, Tsutomu; Ushio, Munetaka; Kondo, Kenji; Yamasoba, Tatsuya

2015-11-01

The purpose of this research is to determine the cause of nasal perforation symptoms and to predict post-operative function after nasal perforation repair surgery. A realistic three-dimensional (3D) model of the nose with a septal perforation was reconstructed using a computed tomography (CT) scan from a patient with nasal septal defect. The numerical simulation was carried out using ANSYS CFX V13.0. Pre- and post-operative models were compared by their velocity, pressure gradient (PG), wall shear (WS), shear strain rate (SSR) and turbulence kinetic energy in three plains. In the post-operative state, the crossflows had disappeared, and stream lines bound to the olfactory cleft area had appeared. After surgery, almost all of high-shear stress areas were disappeared comparing pre-operative model. In conclusion, the effects of surgery to correct nasal septal perforation were evaluated using a three-dimensional airflow evaluation. Following the surgery, crossflows disappeared, and WS, PG and SSR rate were decreased. A high WS.PG and SSR were suspected as causes of nasal perforation symptoms.

Wavepacket dynamics and the multi-configurational time-dependent Hartree approach

NASA Astrophysics Data System (ADS)

Manthe, Uwe

2017-06-01

Multi-configurational time-dependent Hartree (MCTDH) based approaches are efficient, accurate, and versatile methods for high-dimensional quantum dynamics simulations. Applications range from detailed investigations of polyatomic reaction processes in the gas phase to high-dimensional simulations studying the dynamics of condensed phase systems described by typical solid state physics model Hamiltonians. The present article presents an overview of the different areas of application and provides a comprehensive review of the underlying theory. The concepts and guiding ideas underlying the MCTDH approach and its multi-mode and multi-layer extensions are discussed in detail. The general structure of the equations of motion is highlighted. The representation of the Hamiltonian and the correlated discrete variable representation (CDVR), which provides an efficient multi-dimensional quadrature in MCTDH calculations, are discussed. Methods which facilitate the calculation of eigenstates, the evaluation of correlation functions, and the efficient representation of thermal ensembles in MCTDH calculations are described. Different schemes for the treatment of indistinguishable particles in MCTDH calculations and recent developments towards a unified multi-layer MCTDH theory for systems including bosons and fermions are discussed.

Modelling and prediction for chaotic fir laser attractor using rational function neural network.

PubMed

Cho, S

2001-02-01

Many real-world systems such as irregular ECG signal, volatility of currency exchange rate and heated fluid reaction exhibit highly complex nonlinear characteristic known as chaos. These chaotic systems cannot be retreated satisfactorily using linear system theory due to its high dimensionality and irregularity. This research focuses on prediction and modelling of chaotic FIR (Far InfraRed) laser system for which the underlying equations are not given. This paper proposed a method for prediction and modelling a chaotic FIR laser time series using rational function neural network. Three network architectures, TDNN (Time Delayed Neural Network), RBF (radial basis function) network and the RF (rational function) network, are also presented. Comparisons between these networks performance show the improvements introduced by the RF network in terms of a decrement in network complexity and better ability of predictability.

Quantitative secondary electron imaging for work function extraction at atomic level and layer identification of graphene

PubMed Central

Zhou, Yangbo; Fox, Daniel S; Maguire, Pierce; O’Connell, Robert; Masters, Robert; Rodenburg, Cornelia; Wu, Hanchun; Dapor, Maurizio; Chen, Ying; Zhang, Hongzhou

2016-01-01

Two-dimensional (2D) materials usually have a layer-dependent work function, which require fast and accurate detection for the evaluation of their device performance. A detection technique with high throughput and high spatial resolution has not yet been explored. Using a scanning electron microscope, we have developed and implemented a quantitative analytical technique which allows effective extraction of the work function of graphene. This technique uses the secondary electron contrast and has nanometre-resolved layer information. The measurement of few-layer graphene flakes shows the variation of work function between graphene layers with a precision of less than 10 meV. It is expected that this technique will prove extremely useful for researchers in a broad range of fields due to its revolutionary throughput and accuracy. PMID:26878907

An insight into morphometric descriptors of cell shape that pertain to regenerative medicine.

PubMed

Lobo, Joana; See, Eugene Yong-Shun; Biggs, Manus; Pandit, Abhay

2016-07-01

Cellular morphology has recently been indicated as a powerful indicator of cellular function. The analysis of cell shape has evolved from rudimentary forms of microscopic visual inspection to more advanced methodologies that utilize high-resolution microscopy coupled with sophisticated computer hardware and software for data analysis. Despite this progress, there is still a lack of standardization in quantification of morphometric parameters. In addition, uncertainty remains as to which methodologies and parameters of cell morphology will yield meaningful data, which methods should be utilized to categorize cell shape, and the extent of reliability of measurements and the interpretation of the resulting analysis. A large range of descriptors has been employed to objectively assess the cellular morphology in two-dimensional and three-dimensional domains. Intuitively, simple and applicable morphometric descriptors are preferable and standardized protocols for cell shape analysis can be achieved with the help of computerized tools. In this review, cellular morphology is discussed as a descriptor of cellular function and the current morphometric parameters that are used quantitatively in two- and three-dimensional environments are described. Furthermore, the current problems associated with these morphometric measurements are addressed. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Collective charge excitations of the two-dimensional electride Ca2N

NASA Astrophysics Data System (ADS)

Cudazzo, Pierluigi; Gatti, Matteo

2017-09-01

Ca2N is a layered material that has been recently identified as a two-dimensional (2D) electride, an unusual ionic compound in which electrons serve as anions. The electronic properties of 2D electrides attract considerable interest as the anionic electrons, which form a 2D layer sandwiched between atomic planes, are highly mobile as they are not attached to any ion. Here, on the basis of first-principles time-dependent density-functional theory calculations, we investigate the collective excitations of the electrons—i.e., the plasmons—in Ca2N as a function of wave vector q . Our calculations reveal an intrinsic negative in-plane dispersion of the anionic plasmon, in striking contrast with the homogeneous electron gas. Moreover, for wave vectors q normal to the planes, we find a long-lived plasmon that continues to exist well beyond the first Brillouin zone. This is a mark of the electronic inhomogeneities in the charge response that Ca2N shares with other layered materials like transition-metal dichalcogenides and MgB2. Finally, we compare the plasmon properties of Ca2N in its bulk and monolayer forms, which shows the effect of the different electronic structures and dimensionalities.

Defect-Repairable Latent Feature Extraction of Driving Behavior via a Deep Sparse Autoencoder

PubMed Central

Taniguchi, Tadahiro; Takenaka, Kazuhito; Bando, Takashi

2018-01-01

Data representing driving behavior, as measured by various sensors installed in a vehicle, are collected as multi-dimensional sensor time-series data. These data often include redundant information, e.g., both the speed of wheels and the engine speed represent the velocity of the vehicle. Redundant information can be expected to complicate the data analysis, e.g., more factors need to be analyzed; even varying the levels of redundancy can influence the results of the analysis. We assume that the measured multi-dimensional sensor time-series data of driving behavior are generated from low-dimensional data shared by the many types of one-dimensional data of which multi-dimensional time-series data are composed. Meanwhile, sensor time-series data may be defective because of sensor failure. Therefore, another important function is to reduce the negative effect of defective data when extracting low-dimensional time-series data. This study proposes a defect-repairable feature extraction method based on a deep sparse autoencoder (DSAE) to extract low-dimensional time-series data. In the experiments, we show that DSAE provides high-performance latent feature extraction for driving behavior, even for defective sensor time-series data. In addition, we show that the negative effect of defects on the driving behavior segmentation task could be reduced using the latent features extracted by DSAE. PMID:29462931

On the Importance of Both Dimensional and Discrete Models of Emotion.

PubMed

Harmon-Jones, Eddie; Harmon-Jones, Cindy; Summerell, Elizabeth

2017-09-29

We review research on the structure and functions of emotions that has benefitted from a serious consideration of both discrete and dimensional perspectives on emotion. To illustrate this point, we review research that demonstrates: (1) how affective valence within discrete emotions differs as a function of individuals and situations, and how these differences relate to various functions; (2) that anger (and other emotional states) should be considered as a discrete emotion but there are dimensions around and within anger; (3) that similarities exist between approach-related positive and negative discrete emotions and they have unique motivational functions; (4) that discrete emotions and broad dimensions of emotions both have unique functions; and (5) evidence that a "new" discrete emotion with discrete functions exists within a broader emotion family. We hope that this consideration of both discrete and dimensional perspectives on emotion will assist in understanding the functions of emotions.

On the Importance of Both Dimensional and Discrete Models of Emotion

PubMed Central

Harmon-Jones, Eddie

2017-01-01

We review research on the structure and functions of emotions that has benefitted from a serious consideration of both discrete and dimensional perspectives on emotion. To illustrate this point, we review research that demonstrates: (1) how affective valence within discrete emotions differs as a function of individuals and situations, and how these differences relate to various functions; (2) that anger (and other emotional states) should be considered as a discrete emotion but there are dimensions around and within anger; (3) that similarities exist between approach-related positive and negative discrete emotions and they have unique motivational functions; (4) that discrete emotions and broad dimensions of emotions both have unique functions; and (5) evidence that a “new” discrete emotion with discrete functions exists within a broader emotion family. We hope that this consideration of both discrete and dimensional perspectives on emotion will assist in understanding the functions of emotions. PMID:28961185

Regularization by Functions of Bounded Variation and Applications to Image Enhancement

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

Casas, E.; Kunisch, K.; Pola, C.

1999-09-15

Optimization problems regularized by bounded variation seminorms are analyzed. The optimality system is obtained and finite-dimensional approximations of bounded variation function spaces as well as of the optimization problems are studied. It is demonstrated that the choice of the vector norm in the definition of the bounded variation seminorm is of special importance for approximating subspaces consisting of piecewise constant functions. Algorithms based on a primal-dual framework that exploit the structure of these nondifferentiable optimization problems are proposed. Numerical examples are given for denoising of blocky images with very high noise.

One-Dimensional Assembly of Conductive and Capacitive Metal Oxide Electrodes for High-Performance Asymmetric Supercapacitors.

PubMed

Harilal, Midhun; Vidyadharan, Baiju; Misnon, Izan Izwan; Anilkumar, Gopinathan M; Lowe, Adrian; Ismail, Jamil; Yusoff, Mashitah M; Jose, Rajan

2017-03-29

A one-dimensional morphology comprising nanograins of two metal oxides, one with higher electrical conductivity (CuO) and the other with higher charge storability (Co 3 O 4 ), is developed by electrospinning technique. The CuO-Co 3 O 4 nanocomposite nanowires thus formed show high specific capacitance, high rate capability, and high cycling stability compared to their single-component nanowire counterparts when used as a supercapacitor electrode. Practical symmetric (SSCs) and asymmetric (ASCs) supercapacitors are fabricated using commercial activated carbon, CuO, Co 3 O 4 , and CuO-Co 3 O 4 composite nanowires, and their properties are compared. A high energy density of ∼44 Wh kg -1 at a power density of 14 kW kg -1 is achieved in CuO-Co 3 O 4 ASCs employing aqueous alkaline electrolytes, enabling them to store high energy at a faster rate. The current methodology of hybrid nanowires of various functional materials could be applied to extend the performance limit of diverse electrical and electrochemical devices.

Yielding physically-interpretable emulators - A Sparse PCA approach

NASA Astrophysics Data System (ADS)

Galelli, S.; Alsahaf, A.; Giuliani, M.; Castelletti, A.

2015-12-01

Projection-based techniques, such as Principal Orthogonal Decomposition (POD), are a common approach to surrogate high-fidelity process-based models by lower order dynamic emulators. With POD, the dimensionality reduction is achieved by using observations, or 'snapshots' - generated with the high-fidelity model -, to project the entire set of input and state variables of this model onto a smaller set of basis functions that account for most of the variability in the data. While reduction efficiency and variance control of POD techniques are usually very high, the resulting emulators are structurally highly complex and can hardly be given a physically meaningful interpretation as each basis is a projection of the entire set of inputs and states. In this work, we propose a novel approach based on Sparse Principal Component Analysis (SPCA) that combines the several assets of POD methods with the potential for ex-post interpretation of the emulator structure. SPCA reduces the number of non-zero coefficients in the basis functions by identifying a sparse matrix of coefficients. While the resulting set of basis functions may retain less variance of the snapshots, the presence of a few non-zero coefficients assists in the interpretation of the underlying physical processes. The SPCA approach is tested on the reduction of a 1D hydro-ecological model (DYRESM-CAEDYM) used to describe the main ecological and hydrodynamic processes in Tono Dam, Japan. An experimental comparison against a standard POD approach shows that SPCA achieves the same accuracy in emulating a given output variable - for the same level of dimensionality reduction - while yielding better insights of the main process dynamics.

Optical Spatial integration methods for ambiguity function generation

NASA Technical Reports Server (NTRS)

Tamura, P. N.; Rebholz, J. J.; Daehlin, O. T.; Lee, T. C.

1981-01-01

A coherent optical spatial integration approach to ambiguity function generation is described. It uses one dimensional acousto-optic Bragg cells as input tranducers in conjunction with a space variant linear phase shifter, a passive optical element, to generate the two dimensional ambiguity function in one exposure. Results of a real time implementation of this system are shown.

KAM Tori for 1D Nonlinear Wave Equationswith Periodic Boundary Conditions

NASA Astrophysics Data System (ADS)

Chierchia, Luigi; You, Jiangong

In this paper, one-dimensional (1D) nonlinear wave equations with periodic boundary conditions are considered; V is a periodic smooth or analytic function and the nonlinearity f is an analytic function vanishing together with its derivative at u≡0. It is proved that for ``most'' potentials V(x), the above equation admits small-amplitude periodic or quasi-periodic solutions corresponding to finite dimensional invariant tori for an associated infinite dimensional dynamical system. The proof is based on an infinite dimensional KAM theorem which allows for multiple normal frequencies.

2D Kac-Moody symmetry of 4D Yang-Mills theory

DOE PAGES

He, Temple; Mitra, Prahar; Strominger, Andrew

2016-10-25

Scattering amplitudes of any four-dimensional theory with nonabelian gauge group G may be recast as two-dimensional correlation functions on the asymptotic twosphere at null in nity. The soft gluon theorem is shown, for massless theories at the semiclassical level, to be the Ward identity of a holomorphic two-dimensional G-Kac-Moody symmetry acting on these correlation functions. Holomorphic Kac-Moody current insertions are positive helicity soft gluon insertions. Furthermore, the Kac-Moody transformations are a CPT invariant subgroup of gauge transformations which act nontrivially at null in nity and comprise the four-dimensional asymptotic symmetry group.

Beta functions in Chirally deformed supersymmetric sigma models in two dimensions

NASA Astrophysics Data System (ADS)

Vainshtein, Arkady

2016-10-01

We study two-dimensional sigma models where the chiral deformation diminished the original 𝒩 = (2, 2) supersymmetry to the chiral one, 𝒩 = (0, 2). Such heterotic models were discovered previously on the world sheet of non-Abelian stringy solitons supported by certain four-dimensional 𝒩 = 1 theories. We study geometric aspects and holomorphic properties of these models, and derive a number of exact expressions for the β functions in terms of the anomalous dimensions analogous to the NSVZ β function in four-dimensional Yang-Mills. Instanton calculus provides a straightforward method for the derivation.

Beta Functions in Chirally Deformed Supersymmetric Sigma Models in Two Dimensions

NASA Astrophysics Data System (ADS)

Vainshtein, Arkady

We study two-dimensional sigma models where the chiral deformation diminished the original 𝒩 =(2, 2) supersymmetry to the chiral one, 𝒩 =(0, 2). Such heterotic models were discovered previously on the world sheet of non-Abelian stringy solitons supported by certain four-dimensional 𝒩 = 1 theories. We study geometric aspects and holomorphic properties of these models, and derive a number of exact expressions for the β functions in terms of the anomalous dimensions analogous to the NSVZ β function in four-dimensional Yang-Mills. Instanton calculus provides a straightforward method for the derivation.

Multicellular tumor spheroids as an in vivo-like tumor model for three-dimensional imaging of chemotherapeutic and nano material cellular penetration.

PubMed

Ma, Hui-li; Jiang, Qiao; Han, Siyuan; Wu, Yan; Cui Tomshine, Jin; Wang, Dongliang; Gan, Yaling; Zou, Guozhang; Liang, Xing-Jie

2012-01-01

We present a flexible and highly reproducible method using three-dimensional (3D) multicellular tumor spheroids to quantify chemotherapeutic and nanoparticle penetration properties in vitro. We generated HeLa cell-derived spheroids using the liquid overlay method. To properly characterize HeLa spheroids, scanning electron microscopy, transmission electron microscopy, and multiphoton microscopy were used to obtain high-resolution 3D images of HeLa spheroids. Next, pairing high-resolution optical characterization techniques with flow cytometry, we quantitatively compared the penetration of doxorubicin, quantum dots, and synthetic micelles into 3D HeLa spheroid versus HeLa cells grown in a traditional two-dimensional culturing system. Our data revealed that 3D cultured HeLa cells acquired several clinically relevant morphologic and cellular characteristics (such as resistance to chemotherapeutics) often found in human solid tumors. These characteristic, however, could not be captured using conventional two-dimensional cell culture techniques. This study demonstrated the remarkable versatility of HeLa spheroid 3D imaging. In addition, our results revealed the capability of HeLa spheroids to function as a screening tool for nanoparticles or synthetic micelles that, due to their inherent size, charge, and hydrophobicity, can penetrate into solid tumors and act as delivery vehicles for chemotherapeutics. The development of this image-based, reproducible, and quantifiable in vitro HeLa spheroid screening tool will greatly aid future exploration of chemotherapeutics and nanoparticle delivery into solid tumors.

Sufficient Forecasting Using Factor Models

PubMed Central

Fan, Jianqing; Xue, Lingzhou; Yao, Jiawei

2017-01-01

We consider forecasting a single time series when there is a large number of predictors and a possible nonlinear effect. The dimensionality was first reduced via a high-dimensional (approximate) factor model implemented by the principal component analysis. Using the extracted factors, we develop a novel forecasting method called the sufficient forecasting, which provides a set of sufficient predictive indices, inferred from high-dimensional predictors, to deliver additional predictive power. The projected principal component analysis will be employed to enhance the accuracy of inferred factors when a semi-parametric (approximate) factor model is assumed. Our method is also applicable to cross-sectional sufficient regression using extracted factors. The connection between the sufficient forecasting and the deep learning architecture is explicitly stated. The sufficient forecasting correctly estimates projection indices of the underlying factors even in the presence of a nonparametric forecasting function. The proposed method extends the sufficient dimension reduction to high-dimensional regimes by condensing the cross-sectional information through factor models. We derive asymptotic properties for the estimate of the central subspace spanned by these projection directions as well as the estimates of the sufficient predictive indices. We further show that the natural method of running multiple regression of target on estimated factors yields a linear estimate that actually falls into this central subspace. Our method and theory allow the number of predictors to be larger than the number of observations. We finally demonstrate that the sufficient forecasting improves upon the linear forecasting in both simulation studies and an empirical study of forecasting macroeconomic variables. PMID:29731537

Functional ultrasound imaging of intrinsic connectivity in the living rat brain with high spatiotemporal resolution

PubMed Central

Osmanski, Bruno-Félix; Pezet, Sophie; Ricobaraza, Ana; Lenkei, Zsolt; Tanter, Mickael

2014-01-01

Long-range coherences in spontaneous brain activity reflect functional connectivity. Here we propose a novel, highly resolved connectivity mapping approach, using ultrafast functional ultrasound (fUS), which enables imaging of cerebral microvascular haemodynamics deep in the anaesthetized rodent brain, through a large thinned-skull cranial window, with pixel dimensions of 100 μm × 100 μm in-plane. The millisecond-range temporal resolution allows unambiguous cancellation of low-frequency cardio-respiratory noise. Both seed-based and singular value decomposition analysis of spatial coherences in the low-frequency (<0.1 Hz) spontaneous fUS signal fluctuations reproducibly report, at different coronal planes, overlapping high-contrast, intrinsic functional connectivity patterns. These patterns are similar to major functional networks described in humans by resting-state fMRI, such as the lateral task-dependent network putatively anticorrelated with the midline default-mode network. These results introduce fUS as a powerful novel neuroimaging method, which could be extended to portable systems for three-dimensional functional connectivity imaging in awake and freely moving rodents. PMID:25277668

Psychometric Properties of the Chinese Version of the Beck Depression Inventory-II Using the Rasch Model

ERIC Educational Resources Information Center

Wu, Pei-Chen; Chang, Lily

2008-01-01

The authors investigated the Chinese version of the Beck Depression Inventory-II (BDI-II-C; Chinese Behavioral Science Corporation, 2000) within the Rasch framework in terms of dimensionality, item difficulty, and category functioning. Two underlying scale dimensions, relatively high item difficulties, and a need for collapsing 2 response…

Functional Characterization of Resting and Adenovirus-Induced Reactive Astrocytes in Three-Dimensional Culture

PubMed Central

Woo, Junsung; Im, Sun-Kyoung; Chun, Heejung; Jung, Soon-Young; Oh, Soo-Jin; Choi, Nakwon

2017-01-01

Brain is a rich environment where neurons and glia interact with neighboring cells as well as extracellular matrix in three-dimensional (3D) space. Astrocytes, which are the most abundant cells in the mammalian brain, reside in 3D space and extend highly branched processes that form microdomains and contact synapses. It has been suggested that astrocytes cultured in 3D might be maintained in a less reactive state as compared to those growing in a traditional, two-dimensional (2D) monolayer culture. However, the functional characterization of the astrocytes in 3D culture has been lacking. Here we cocultured neurons and astrocytes in 3D and examined the morphological, molecular biological, and electrophysiological properties of the 3D-cultured hippocampal astrocytes. In our 3D neuron-astrocyte coculture, astrocytes showed a typical morphology of a small soma with many branches and exhibited a unique membrane property of passive conductance, more closely resembling their native in vivo counterparts. Moreover, we also induced reactive astrocytosis in culture by infecting with high-titer adenovirus to mimic pathophysiological conditions in vivo. Adenoviral infection induced morphological changes in astrocytes, increased passive conductance, and increased GABA content as well as tonic GABA release, which are characteristics of reactive gliosis. Together, our study presents a powerful in vitro model resembling both physiological and pathophysiological conditions in vivo, and thereby provides a versatile experimental tool for studying various neurological diseases that accompany reactive astrocytes. PMID:28680301

Two-dimensional longitudinal strain assessment in the presence of myocardial contrast agents is only feasible with speckle-tracking after microbubble destruction.

PubMed

Cavalcante, João L; Collier, Patrick; Plana, Juan C; Agler, Deborah; Thomas, James D; Marwick, Thomas H

2012-12-01

Longitudinal strain (LS) imaging is an important tool for the quantification of left ventricular function and deformation, but its assessment is challenging in the presence of echocardiographic contrast agents (CAs). The aim of this study was to test the hypothesis that destruction of microbubbles using high mechanical index (MI) could allow the measurement of LS. LS was measured using speckle strain (speckle-tracking LS [STLS]) and Velocity Vector Imaging (VVI) before and after CA administration in 30 consecutive patients. Low MI was used for left ventricular opacification and three-dimensional high MI for microbubble destruction. Four different settings were tested over 60 sec: (1) baseline LS without contrast, (2) LS after CA administration with low MI (0.3), (3) LS after CA administration with high MI (0.9), and (4) LS after microbubble destruction with high MI and three-dimensional imaging. Baseline feasibility of LS assessment (99.3% and 98.2% with STLS and VVI, respectively) was reduced after CA administration using STLS at low (69%, P < .0001) and high (95.4%, P = .0002) MI as well as with VVI (93.8%, P = .004, and 84.7%, P < .0001, respectively). STLS assessment was feasible with high MI after microbubble destruction (1.7% of uninterpretable segments vs 0.7%, P = .26) but not using VVI (7.2% vs 1.8%, P < .001). Regardless of which microbubbles or image settings were used, VVI was associated with significant variability and overestimation of global LS (for low MI, +4.7%, P < .01; for high MI, +3.3%, P < .001; for high MI after microbubble destruction, +1.3%, P = .04). LS assessment is most feasible without contrast. If a CA is necessary, the calculation of LS is feasible using the speckle-tracking method, if three-dimensional imaging is used as a tool for microbubble destruction 1 min after CA administration. Copyright © 2012. Published by Mosby, Inc.

RNA FRABASE 2.0: an advanced web-accessible database with the capacity to search the three-dimensional fragments within RNA structures

PubMed Central

2010-01-01

Background Recent discoveries concerning novel functions of RNA, such as RNA interference, have contributed towards the growing importance of the field. In this respect, a deeper knowledge of complex three-dimensional RNA structures is essential to understand their new biological functions. A number of bioinformatic tools have been proposed to explore two major structural databases (PDB, NDB) in order to analyze various aspects of RNA tertiary structures. One of these tools is RNA FRABASE 1.0, the first web-accessible database with an engine for automatic search of 3D fragments within PDB-derived RNA structures. This search is based upon the user-defined RNA secondary structure pattern. In this paper, we present and discuss RNA FRABASE 2.0. This second version of the system represents a major extension of this tool in terms of providing new data and a wide spectrum of novel functionalities. An intuitionally operated web server platform enables very fast user-tailored search of three-dimensional RNA fragments, their multi-parameter conformational analysis and visualization. Description RNA FRABASE 2.0 has stored information on 1565 PDB-deposited RNA structures, including all NMR models. The RNA FRABASE 2.0 search engine algorithms operate on the database of the RNA sequences and the new library of RNA secondary structures, coded in the dot-bracket format extended to hold multi-stranded structures and to cover residues whose coordinates are missing in the PDB files. The library of RNA secondary structures (and their graphics) is made available. A high level of efficiency of the 3D search has been achieved by introducing novel tools to formulate advanced searching patterns and to screen highly populated tertiary structure elements. RNA FRABASE 2.0 also stores data and conformational parameters in order to provide "on the spot" structural filters to explore the three-dimensional RNA structures. An instant visualization of the 3D RNA structures is provided. RNA FRABASE 2.0 is freely available at http://rnafrabase.cs.put.poznan.pl. Conclusions RNA FRABASE 2.0 provides a novel database and powerful search engine which is equipped with new data and functionalities that are unavailable elsewhere. Our intention is that this advanced version of the RNA FRABASE will be of interest to all researchers working in the RNA field. PMID:20459631

RNA FRABASE 2.0: an advanced web-accessible database with the capacity to search the three-dimensional fragments within RNA structures.

PubMed

Popenda, Mariusz; Szachniuk, Marta; Blazewicz, Marek; Wasik, Szymon; Burke, Edmund K; Blazewicz, Jacek; Adamiak, Ryszard W

2010-05-06

Recent discoveries concerning novel functions of RNA, such as RNA interference, have contributed towards the growing importance of the field. In this respect, a deeper knowledge of complex three-dimensional RNA structures is essential to understand their new biological functions. A number of bioinformatic tools have been proposed to explore two major structural databases (PDB, NDB) in order to analyze various aspects of RNA tertiary structures. One of these tools is RNA FRABASE 1.0, the first web-accessible database with an engine for automatic search of 3D fragments within PDB-derived RNA structures. This search is based upon the user-defined RNA secondary structure pattern. In this paper, we present and discuss RNA FRABASE 2.0. This second version of the system represents a major extension of this tool in terms of providing new data and a wide spectrum of novel functionalities. An intuitionally operated web server platform enables very fast user-tailored search of three-dimensional RNA fragments, their multi-parameter conformational analysis and visualization. RNA FRABASE 2.0 has stored information on 1565 PDB-deposited RNA structures, including all NMR models. The RNA FRABASE 2.0 search engine algorithms operate on the database of the RNA sequences and the new library of RNA secondary structures, coded in the dot-bracket format extended to hold multi-stranded structures and to cover residues whose coordinates are missing in the PDB files. The library of RNA secondary structures (and their graphics) is made available. A high level of efficiency of the 3D search has been achieved by introducing novel tools to formulate advanced searching patterns and to screen highly populated tertiary structure elements. RNA FRABASE 2.0 also stores data and conformational parameters in order to provide "on the spot" structural filters to explore the three-dimensional RNA structures. An instant visualization of the 3D RNA structures is provided. RNA FRABASE 2.0 is freely available at http://rnafrabase.cs.put.poznan.pl. RNA FRABASE 2.0 provides a novel database and powerful search engine which is equipped with new data and functionalities that are unavailable elsewhere. Our intention is that this advanced version of the RNA FRABASE will be of interest to all researchers working in the RNA field.

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

PubMed

Ji, Shuiwang

2013-07-11

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

Construction of high-dimensional universal quantum logic gates using a Λ system coupled with a whispering-gallery-mode microresonator.

PubMed

He, Ling Yan; Wang, Tie-Jun; Wang, Chuan

2016-07-11

High-dimensional quantum system provides a higher capacity of quantum channel, which exhibits potential applications in quantum information processing. However, high-dimensional universal quantum logic gates is difficult to achieve directly with only high-dimensional interaction between two quantum systems and requires a large number of two-dimensional gates to build even a small high-dimensional quantum circuits. In this paper, we propose a scheme to implement a general controlled-flip (CF) gate where the high-dimensional single photon serve as the target qudit and stationary qubits work as the control logic qudit, by employing a three-level Λ-type system coupled with a whispering-gallery-mode microresonator. In our scheme, the required number of interaction times between the photon and solid state system reduce greatly compared with the traditional method which decomposes the high-dimensional Hilbert space into 2-dimensional quantum space, and it is on a shorter temporal scale for the experimental realization. Moreover, we discuss the performance and feasibility of our hybrid CF gate, concluding that it can be easily extended to a 2n-dimensional case and it is feasible with current technology.

Identification of aerodynamic models for maneuvering aircraft

NASA Technical Reports Server (NTRS)

Lan, C. Edward; Hu, C. C.

1992-01-01

A Fourier analysis method was developed to analyze harmonic forced-oscillation data at high angles of attack as functions of the angle of attack and its time rate of change. The resulting aerodynamic responses at different frequencies are used to build up the aerodynamic models involving time integrals of the indicial type. An efficient numerical method was also developed to evaluate these time integrals for arbitrary motions based on a concept of equivalent harmonic motion. The method was verified by first using results from two-dimensional and three-dimensional linear theories. The developed models for C sub L, C sub D, and C sub M based on high-alpha data for a 70 deg delta wing in harmonic motions showed accurate results in reproducing hysteresis. The aerodynamic models are further verified by comparing with test data using ramp-type motions.

From experimental imaging techniques to virtual embryology.

PubMed

Weninger, Wolfgang J; Tassy, Olivier; Darras, Sébastien; Geyer, Stefan H; Thieffry, Denis

2004-01-01

Modern embryology increasingly relies on descriptive and functional three dimensional (3D) and four dimensional (4D) analysis of physically, optically, or virtually sectioned specimens. To cope with the technical requirements, new methods for high detailed in vivo imaging, as well as the generation of high resolution digital volume data sets for the accurate visualisation of transgene activity and gene product presence, in the context of embryo morphology, were recently developed and are under construction. These methods profoundly change the scientific applicability, appearance and style of modern embryo representations. In this paper, we present an overview of the emerging techniques to create, visualise and administrate embryo representations (databases, digital data sets, 3-4D embryo reconstructions, models, etc.), and discuss the implications of these new methods on the work of modern embryologists, including, research, teaching, the selection of specific model organisms, and potential collaborators.

Three-Dimensional Blood-Brain Barrier Model for in vitro Studies of Neurovascular Pathology

NASA Astrophysics Data System (ADS)

Cho, Hansang; Seo, Ji Hae; Wong, Keith H. K.; Terasaki, Yasukazu; Park, Joseph; Bong, Kiwan; Arai, Ken; Lo, Eng H.; Irimia, Daniel

2015-10-01

Blood-brain barrier (BBB) pathology leads to neurovascular disorders and is an important target for therapies. However, the study of BBB pathology is difficult in the absence of models that are simple and relevant. In vivo animal models are highly relevant, however they are hampered by complex, multi-cellular interactions that are difficult to decouple. In vitro models of BBB are simpler, however they have limited functionality and relevance to disease processes. To address these limitations, we developed a 3-dimensional (3D) model of BBB on a microfluidic platform. We verified the tightness of the BBB by showing its ability to reduce the leakage of dyes and to block the transmigration of immune cells towards chemoattractants. Moreover, we verified the localization at endothelial cell boundaries of ZO-1 and VE-Cadherin, two components of tight and adherens junctions. To validate the functionality of the BBB model, we probed its disruption by neuro-inflammation mediators and ischemic conditions and measured the protective function of antioxidant and ROCK-inhibitor treatments. Overall, our 3D BBB model provides a robust platform, adequate for detailed functional studies of BBB and for the screening of BBB-targeting drugs in neurological diseases.

Approximation of Optimal Infinite Dimensional Compensators for Flexible Structures

NASA Technical Reports Server (NTRS)

Gibson, J. S.; Mingori, D. L.; Adamian, A.; Jabbari, F.

1985-01-01

The infinite dimensional compensator for a large class of flexible structures, modeled as distributed systems are discussed, as well as an approximation scheme for designing finite dimensional compensators to approximate the infinite dimensional compensator. The approximation scheme is applied to develop a compensator for a space antenna model based on wrap-rib antennas being built currently. While the present model has been simplified, it retains the salient features of rigid body modes and several distributed components of different characteristics. The control and estimator gains are represented by functional gains, which provide graphical representations of the control and estimator laws. These functional gains also indicate the convergence of the finite dimensional compensators and show which modes the optimal compensator ignores.

An improved exceedance theory for combined random stresses

NASA Technical Reports Server (NTRS)

Lester, H. C.

1974-01-01

An extension is presented of Rice's classic solution for the exceedances of a constant level by a single random process to its counterpart for an n-dimensional vector process. An interaction boundary, analogous to the constant level considered by Rice for the one-dimensional case, is assumed in the form of a hypersurface. The theory for the numbers of boundary exceedances is developed by using a joint statistical approach which fully accounts for all cross-correlation effects. An exact expression is derived for the n-dimensional exceedance density function, which is valid for an arbitrary interaction boundary. For application to biaxial states of combined random stress, the general theory is reduced to the two-dimensional case. An elliptical stress interaction boundary is assumed and the exact expression for the density function is presented. The equations are expressed in a format which facilitates calculating the exceedances by numerically evaluating a line integral. The behavior of the density function for the two-dimensional case is briefly discussed.

Tunable electronic and magnetic properties of two‐dimensional materials and their one‐dimensional derivatives

PubMed Central

Zhang, Zhuhua; Liu, Xiaofei; Yu, Jin; Hang, Yang; Li, Yao; Guo, Yufeng; Xu, Ying; Sun, Xu; Zhou, Jianxin

2016-01-01

Low‐dimensional materials exhibit many exceptional properties and functionalities which can be efficiently tuned by externally applied force or fields. Here we review the current status of research on tuning the electronic and magnetic properties of low‐dimensional carbon, boron nitride, metal‐dichalcogenides, phosphorene nanomaterials by applied engineering strain, external electric field and interaction with substrates, etc, with particular focus on the progress of computational methods and studies. We highlight the similarities and differences of the property modulation among one‐ and two‐dimensional nanomaterials. Recent breakthroughs in experimental demonstration of the tunable functionalities in typical nanostructures are also presented. Finally, prospective and challenges for applying the tunable properties into functional devices are discussed. WIREs Comput Mol Sci 2016, 6:324–350. doi: 10.1002/wcms.1251 For further resources related to this article, please visit the WIREs website. Conflict of interest: The authors have declared no conflicts of interest for this article. PMID:27818710

Self-Assembled Polysaccharide Nanotubes Generated from β-1,3-Glucan Polysaccharides

NASA Astrophysics Data System (ADS)

Numata, Munenori; Shinkai, Seiji

β-1,3-Glucans act as unique natural nanotubes, the features of which are greatly different from other natural or synthetic helical polymers. The origin mostly stems from their strong helix-forming nature and reversible interconversion between single-strand random coil and triple-strand helix. During this interconversion process, they can accept functional polymers, molecular assemblies and nanoparticles in an induced-fit manner to create water-soluble one-dimensional nanocomposites, where individual conjugated polymers or molecular assemblies can be incorporated into the one-dimensional hollow constructed by the helical superstructure of β-1,3-glucans. The advantageous point of the β-1,3-glucan hosting system is that the selective modification of β-1,3-glucans leads to the creation of various functional one-dimensional nanocomposites in a supramolecular manner, being applicable toward fundamental nanomaterials such as sensors or circuits. Furthermore, the composites with functional surfaces can act as one-dimensional building blocks toward further hierarchical self-assemblies, leading to the creation of two- or three-dimensional nanoarchitectures.

van der Waals epitaxial two-dimensional CdSxSe(1-x) semiconductor alloys with tunable-composition and application to flexible optoelectronics.

PubMed

Xia, Jing; Zhao, Yun-Xuan; Wang, Lei; Li, Xuan-Ze; Gu, Yi-Yi; Cheng, Hua-Qiu; Meng, Xiang-Min

2017-09-21

Despite the substantial progress in the development of two-dimensional (2D) materials from conventional layered crystals, it still remains particularly challenging to produce high-quality 2D non-layered semiconductor alloys which may bring in some unique properties and new functions. In this work, the synthesis of well-oriented 2D non-layered CdS x Se (1-x) semiconductor alloy flakes with tunable compositions and optical properties is established. Structural analysis reveals that the 2D non-layered alloys follow an incommensurate van der Waals epitaxial growth pattern. Photoluminescence measurements show that the 2D alloys have composition-dependent direct bandgaps with the emission peak varying from 1.8 eV to 2.3 eV, coinciding well with the density functional theory calculations. Furthermore, photodetectors based on the CdS x Se (1-x) flakes exhibit a high photoresponsivity of 703 A W -1 with an external quantum efficiency of 1.94 × 10 3 and a response time of 39 ms. Flexible devices fabricated on a thin mica substrate display good mechanical stability upon repeated bending. This work suggests a facile and general method to produce high-quality 2D non-layered semiconductor alloys for next-generation optoelectronic devices.

Force Dependent Internalization of Magnetic Nanoparticles Results in Highly Loaded Endothelial Cells for Use as Potential Therapy Delivery Vectors

PubMed Central

MacDonald, Cristin; Barbee, Kenneth

2015-01-01

Purpose To investigate the kinetics, mechanism and extent of MNP loading into endothelial cells and the effect of this loading on cell function. Methods MNP uptake was examined under field on/off conditions, utilizing varying magnetite concentration MNPs. MNP-loaded cell viability and functional integrity was assessed using metabolic respiration, cell proliferation and migration assays. Results MNP uptake in endothelial cells significantly increased under the influence of a magnetic field versus non-magnetic conditions. Larger magnetite density of the MNPs led to a higher MNP internalization by cells under application of a magnetic field without compromising cellular respiration activity. Two-dimensional migration assays at no field showed that higher magnetite loading resulted in greater cell migration rates. In a three-dimensional migration assay under magnetic field, the migration rate of MNP-loaded cells was more than twice that of unloaded cells and was comparable to migration stimulated by a serum gradient. Conclusions Our results suggest that endothelial cell uptake of MNPs is a force dependent process. The in vitro assays determined that cell health is not adversely affected by high MNP loadings, allowing these highly magnetically responsive cells to be potentially beneficial therapy (gene, drug or cell) delivery systems. PMID:22234617

Self-Assembled Polymeric Ionic Liquid-Functionalized Cellulose Nano-crystals: Constructing 3D Ion-conducting Channels Within Ionic Liquid-based Composite Polymer Electrolytes.

PubMed

Shi, Qing Xuan; Xia, Qing; Xiang, Xiao; Ye, Yun Sheng; Peng, Hai Yan; Xue, Zhi Gang; Xie, Xiao Lin; Mai, Yiu-Wing

2017-09-04

Composite polymeric and ionic liquid (IL) electrolytes are some of the most promising electrolyte systems for safer battery technology. Although much effort has been directed towards enhancing the transport properties of polymer electrolytes (PEs) through nanoscopic modification by incorporating nano-fillers, it is still difficult to construct ideal ion conducting networks. Here, a novel class of three-dimensional self-assembled polymeric ionic liquid (PIL)-functionalized cellulose nano-crystals (CNC) confining ILs in surface-grafted PIL polymer chains, able to form colloidal crystal polymer electrolytes (CCPE), is reported. The high-strength CNC nano-fibers, decorated with PIL polymer chains, can spontaneously form three-dimensional interpenetrating nano-network scaffolds capable of supporting electrolytes with continuously connected ion conducting networks with IL being concentrated in conducting domains. These new CCPE have exceptional ionic conductivities, low activation energies (close to bulk IL electrolyte with dissolved Li salt), high Li + transport numbers, low interface resistances and improved interface compatibilities. Furthermore, the CCPE displays good electrochemical properties and a good battery performance. This approach offers a route to leak-free, non-flammable and high ionic conductivity solid-state PE in energy conversion devices. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

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

Masih Das, Paul; Danda, Gopinath; Cupo, Andrew

Black phosphorus (BP) is a highly anisotropic allotrope of phosphorus with high promise for fast functional electronics and optoelectronics. We demonstrate that high-resolution and controlled structural modification of few-layer BP along arbitrary crystal direction can be achieved with nanometer-scale precision on a few-minute timescales leading to the formation of sub-nm wide armchair and zigzag BP nanoribbons. The nanoribbons are assembled, along with nanopores and nanogaps, using a combination of mechanical-liquid exfoliation and in situ transmission electron microscope (TEM) and scanning TEM nanosculpting. Here we report time-dependent structural properties of the one-dimensional systems under electron irradiation and probe their oxidation propertiesmore » with electron energy-loss spectroscopy (EELS). Finally, we demonstrate the use of STEM to controllably narrow and thin the nanoribbons until they break into nanogaps. The observations are rationalized using density functional theory for transition state calculations and electronic band-structure evolution for the various stages of the narrowing procedure. In particular, we predict that the sub- and few-nm wide BP nanoribbons realized experimentally possess clear one-dimensional quantum confinement, even when the systems are made up of a few layers. We find the demonstration of this procedure is key for the development of BP-based electronic, optoelectronic, thermoelectric, and other applications in reduced dimensions.« less

Controlled Sculpture of Black Phosphorus Nanoribbons

DOE PAGES

Masih Das, Paul; Danda, Gopinath; Cupo, Andrew; ...

2016-05-18

Black phosphorus (BP) is a highly anisotropic allotrope of phosphorus with high promise for fast functional electronics and optoelectronics. We demonstrate that high-resolution and controlled structural modification of few-layer BP along arbitrary crystal direction can be achieved with nanometer-scale precision on a few-minute timescales leading to the formation of sub-nm wide armchair and zigzag BP nanoribbons. The nanoribbons are assembled, along with nanopores and nanogaps, using a combination of mechanical-liquid exfoliation and in situ transmission electron microscope (TEM) and scanning TEM nanosculpting. Here we report time-dependent structural properties of the one-dimensional systems under electron irradiation and probe their oxidation propertiesmore » with electron energy-loss spectroscopy (EELS). Finally, we demonstrate the use of STEM to controllably narrow and thin the nanoribbons until they break into nanogaps. The observations are rationalized using density functional theory for transition state calculations and electronic band-structure evolution for the various stages of the narrowing procedure. In particular, we predict that the sub- and few-nm wide BP nanoribbons realized experimentally possess clear one-dimensional quantum confinement, even when the systems are made up of a few layers. We find the demonstration of this procedure is key for the development of BP-based electronic, optoelectronic, thermoelectric, and other applications in reduced dimensions.« less

A framework for optimal kernel-based manifold embedding of medical image data.

PubMed

Zimmer, Veronika A; Lekadir, Karim; Hoogendoorn, Corné; Frangi, Alejandro F; Piella, Gemma

2015-04-01

Kernel-based dimensionality reduction is a widely used technique in medical image analysis. To fully unravel the underlying nonlinear manifold the selection of an adequate kernel function and of its free parameters is critical. In practice, however, the kernel function is generally chosen as Gaussian or polynomial and such standard kernels might not always be optimal for a given image dataset or application. In this paper, we present a study on the effect of the kernel functions in nonlinear manifold embedding of medical image data. To this end, we first carry out a literature review on existing advanced kernels developed in the statistics, machine learning, and signal processing communities. In addition, we implement kernel-based formulations of well-known nonlinear dimensional reduction techniques such as Isomap and Locally Linear Embedding, thus obtaining a unified framework for manifold embedding using kernels. Subsequently, we present a method to automatically choose a kernel function and its associated parameters from a pool of kernel candidates, with the aim to generate the most optimal manifold embeddings. Furthermore, we show how the calculated selection measures can be extended to take into account the spatial relationships in images, or used to combine several kernels to further improve the embedding results. Experiments are then carried out on various synthetic and phantom datasets for numerical assessment of the methods. Furthermore, the workflow is applied to real data that include brain manifolds and multispectral images to demonstrate the importance of the kernel selection in the analysis of high-dimensional medical images. Copyright © 2014 Elsevier Ltd. All rights reserved.

Exact Recovery of Chaotic Systems from Highly Corrupted Data

DTIC Science & Technology

2016-08-01

dimension to reconstruct a state space which preserves the topological properties of the original system. In [CM87, RS92], the authors use the singular...in high dimensional nonlinear functional spaces [Spr94, SL00, LCC04]. In this work, we bring together connections between compressed sensing, splitting... compact , connected attractor Λ and the flow admits a unique so-called “physical" measure µ with supp(µ) = Λ. An invariant probability measure µ for a flow

Measurement of Detonation Velocity for a Nonideal Heterogeneous Explosive in Axisymmetric and Two-Dimensional Geometries

NASA Astrophysics Data System (ADS)

Higgins, Andrew

2009-12-01

Detonation in a heterogeneous explosive with a relatively sparse concentration of reaction centers ("hot spots") is investigated experimentally. The explosive system considered is nitromethane gelled with PMMA and with glass microballoons (GMB's) in suspension. The detonation velocity is measured as a function of the characteristic charge dimension (diameter or thickness) in both axisymmetric and two-dimensional geometries. The use of a unique, annular charge geometry (with the diameter of the annulus much greater than the annular gap thickness) permits quasi-two-dimensional detonations to be observed without undesirable lateral rarefactions that result from a finite aspect ratio. The results confirm the prior findings of Gois et al. (1996) which show that, for a low concentration of GMB's, detonation propagation does not exhibit the expected 2:1 scaling from axisymmetric to planar geometries. This reinforces the idea that detonation in highly nonideal explosives is not governed exclusively by global front curvature.

Three-dimensional localization of nanoscale battery reactions using soft X-ray tomography.

PubMed

Yu, Young-Sang; Farmand, Maryam; Kim, Chunjoong; Liu, Yijin; Grey, Clare P; Strobridge, Fiona C; Tyliszczak, Tolek; Celestre, Rich; Denes, Peter; Joseph, John; Krishnan, Harinarayan; Maia, Filipe R N C; Kilcoyne, A L David; Marchesini, Stefano; Leite, Talita Perciano Costa; Warwick, Tony; Padmore, Howard; Cabana, Jordi; Shapiro, David A

2018-03-02

Battery function is determined by the efficiency and reversibility of the electrochemical phase transformations at solid electrodes. The microscopic tools available to study the chemical states of matter with the required spatial resolution and chemical specificity are intrinsically limited when studying complex architectures by their reliance on two-dimensional projections of thick material. Here, we report the development of soft X-ray ptychographic tomography, which resolves chemical states in three dimensions at 11 nm spatial resolution. We study an ensemble of nano-plates of lithium iron phosphate extracted from a battery electrode at 50% state of charge. Using a set of nanoscale tomograms, we quantify the electrochemical state and resolve phase boundaries throughout the volume of individual nanoparticles. These observations reveal multiple reaction points, intra-particle heterogeneity, and size effects that highlight the importance of multi-dimensional analytical tools in providing novel insight to the design of the next generation of high-performance devices.

Laser absorption spectroscopy for measurement of He metastable atoms of a microhollow cathode plasma

NASA Astrophysics Data System (ADS)

Ueno, Keisuke; Kamebuchi, Kenta; Kakutani, Jiro; Matsuoka, Leo; Namba, Shinichi; Fujii, Keisuke; Shikama, Taiichi; Hasuo, Masahiro

2018-01-01

We generated a 0.3-mm-diameter DC, hollow-cathode helium discharge in a gas pressure range of 10-80 kPa. In discharge plasmas, we measured position-dependent laser absorption spectra for helium 23S1-23P0 transition with a spatial resolution of 55 µm. From the results of the analysis of the measured spectra using Voigt functions and including both the Doppler and collision broadening, we produced two-dimensional maps of the metastable 23S1 atomic densities and gas temperatures of the plasmas. We found that, at all pressures, the gas temperatures were approximately uniform in space with values in the range of 400-1500 K and the 23S1 atomic densities were ˜1019 m-3. We also found that the two-dimensional density distribution profiles became ring-shaped at high gas pressures, which is qualitatively consistent with the two-dimensional fluid simulation results.

A Score of the Ability of a Three-Dimensional Protein Model to Retrieve Its Own Sequence as a Quantitative Measure of Its Quality and Appropriateness

PubMed Central

Martínez-Castilla, León P.; Rodríguez-Sotres, Rogelio

2010-01-01

Background Despite the remarkable progress of bioinformatics, how the primary structure of a protein leads to a three-dimensional fold, and in turn determines its function remains an elusive question. Alignments of sequences with known function can be used to identify proteins with the same or similar function with high success. However, identification of function-related and structure-related amino acid positions is only possible after a detailed study of every protein. Folding pattern diversity seems to be much narrower than sequence diversity, and the amino acid sequences of natural proteins have evolved under a selective pressure comprising structural and functional requirements acting in parallel. Principal Findings The approach described in this work begins by generating a large number of amino acid sequences using ROSETTA [Dantas G et al. (2003) J Mol Biol 332:449–460], a program with notable robustness in the assignment of amino acids to a known three-dimensional structure. The resulting sequence-sets showed no conservation of amino acids at active sites, or protein-protein interfaces. Hidden Markov models built from the resulting sequence sets were used to search sequence databases. Surprisingly, the models retrieved from the database sequences belonged to proteins with the same or a very similar function. Given an appropriate cutoff, the rate of false positives was zero. According to our results, this protocol, here referred to as Rd.HMM, detects fine structural details on the folding patterns, that seem to be tightly linked to the fitness of a structural framework for a specific biological function. Conclusion Because the sequence of the native protein used to create the Rd.HMM model was always amongst the top hits, the procedure is a reliable tool to score, very accurately, the quality and appropriateness of computer-modeled 3D-structures, without the need for spectroscopy data. However, Rd.HMM is very sensitive to the conformational features of the models' backbone. PMID:20830209

Three-dimensional autoradiographic localization of quench-corrected glycine receptor specific activity in the mouse brain using sup 3 H-strychnine as the ligand

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

White, W.F.; O'Gorman, S.; Roe, A.W.

1990-03-01

The autoradiographic analysis of neurotransmitter receptor distribution is a powerful technique that provides extensive information on the localization of neurotransmitter systems. Computer methodologies are described for the analysis of autoradiographic material which include quench correction, 3-dimensional display, and quantification based on anatomical boundaries determined from the tissue sections. These methodologies are applied to the problem of the distribution of glycine receptors measured by 3H-strychnine binding in the mouse CNS. The most distinctive feature of this distribution is its marked caudorostral gradient. The highest densities of binding sites within this gradient were seen in somatic motor and sensory areas; high densitiesmore » of binding were seen in branchial efferent and special sensory areas. Moderate levels were seen in nuclei related to visceral function. Densities within the reticular formation paralleled the overall gradient with high to moderate levels of binding. The colliculi had low and the diencephalon had very low levels of binding. No binding was seen in the cerebellum or the telencephalon with the exception of the amygdala, which had very low levels of specific binding. This distribution of glycine receptors correlates well with the known functional distribution of glycine synaptic function. These data are illustrated in 3 dimensions and discussed in terms of the significance of the analysis techniques on this type of data as well as the functional significance of the distribution of glycine receptors.« less

Markerless human motion tracking using hierarchical multi-swarm cooperative particle swarm optimization.

PubMed

Saini, Sanjay; Zakaria, Nordin; Rambli, Dayang Rohaya Awang; Sulaiman, Suziah

2015-01-01

The high-dimensional search space involved in markerless full-body articulated human motion tracking from multiple-views video sequences has led to a number of solutions based on metaheuristics, the most recent form of which is Particle Swarm Optimization (PSO). However, the classical PSO suffers from premature convergence and it is trapped easily into local optima, significantly affecting the tracking accuracy. To overcome these drawbacks, we have developed a method for the problem based on Hierarchical Multi-Swarm Cooperative Particle Swarm Optimization (H-MCPSO). The tracking problem is formulated as a non-linear 34-dimensional function optimization problem where the fitness function quantifies the difference between the observed image and a projection of the model configuration. Both the silhouette and edge likelihoods are used in the fitness function. Experiments using Brown and HumanEva-II dataset demonstrated that H-MCPSO performance is better than two leading alternative approaches-Annealed Particle Filter (APF) and Hierarchical Particle Swarm Optimization (HPSO). Further, the proposed tracking method is capable of automatic initialization and self-recovery from temporary tracking failures. Comprehensive experimental results are presented to support the claims.

Secondary Psychometric Examination of the Dimensional Obsessive-Compulsive Scale: Classical Testing, Item Response Theory, and Differential Item Functioning.

PubMed

Thibodeau, Michel A; Leonard, Rachel C; Abramowitz, Jonathan S; Riemann, Bradley C

2015-12-01

The Dimensional Obsessive-Compulsive Scale (DOCS) is a promising measure of obsessive-compulsive disorder (OCD) symptoms but has received minimal psychometric attention. We evaluated the utility and reliability of DOCS scores. The study included 832 students and 300 patients with OCD. Confirmatory factor analysis supported the originally proposed four-factor structure. DOCS total and subscale scores exhibited good to excellent internal consistency in both samples (α = .82 to α = .96). Patient DOCS total scores reduced substantially during treatment (t = 16.01, d = 1.02). DOCS total scores discriminated between students and patients (sensitivity = 0.76, 1 - specificity = 0.23). The measure did not exhibit gender-based differential item functioning as tested by Mantel-Haenszel chi-square tests. Expected response options for each item were plotted as a function of item response theory and demonstrated that DOCS scores incrementally discriminate OCD symptoms ranging from low to extremely high severity. Incremental differences in DOCS scores appear to represent unbiased and reliable differences in true OCD symptom severity. © The Author(s) 2014.

Intramolecular Hydrogen Bonding in Benzoxazines: When Structural Design Becomes Functional.

PubMed

Froimowicz, Pablo; Zhang, Kan; Ishida, Hatsuo

2016-02-18

The future evolution of benzoxazines and polybenzoxazines as advanced molecular, structural, functional, engineering, and newly commercial materials depends to a great extent on a deeper and more fundamental understanding at the molecular level. In this contribution, the field of benzoxazines is briefly introduced along with a more detailed review of ortho-amide-functional benzoxazines, which are the main subjects of this article. Provided in this article are the detailed and solid scientific evidences of intramolecular five-membered-ring hydrogen bonding, which is supposed to be responsible for the unique and characteristic features exhibited by this ever-growing family of ortho-functionalized benzoxazines. One-dimensional (1D) (1)H NMR spectroscopy was used to study various concentrations of benzoxazines in various solvents with different hydrogen-bonding capability and at various temperatures to investigate in detail the nature of hydrogen bonding in both ortho-amide-functionalized benzoxazine and its para counterpart. These materials were further investigated by two-dimensional (2D) (1)H-(1)H nuclear Overhauser effect spectroscopy (NOESY) to verify and support the conclusions derived during the 1D (1)H NMR experiments. Only highly purified single-crystal benzoxazine samples have been used for this study to avoid additional interactions caused by any impurities. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Controlled Interactions between Two Dimensional Layered Inorganic Nanosheets and Polymers

DTIC Science & Technology

2016-06-15

with end-functionalized polymers . First, an end-functionalized polymer with conjugated end-molecule, pyrene, is successfully employed to boron... polymers . First, an end-functionalized polymer with conjugated end-molecule, pyrenes, is successfully employed to boron nitride nanosheets (BNNS), and...AFRL-AFOSR-JP-TR-2016-0071 Controlled Interactions between Two Dimensional Layered Inorganic Nanosheets and Polymers Cheolmin Park YONSEI UNIVERSITY

Machine-Learning Classifier for Patients with Major Depressive Disorder: Multifeature Approach Based on a High-Order Minimum Spanning Tree Functional Brain Network.

PubMed

Guo, Hao; Qin, Mengna; Chen, Junjie; Xu, Yong; Xiang, Jie

2017-01-01

High-order functional connectivity networks are rich in time information that can reflect dynamic changes in functional connectivity between brain regions. Accordingly, such networks are widely used to classify brain diseases. However, traditional methods for processing high-order functional connectivity networks generally include the clustering method, which reduces data dimensionality. As a result, such networks cannot be effectively interpreted in the context of neurology. Additionally, due to the large scale of high-order functional connectivity networks, it can be computationally very expensive to use complex network or graph theory to calculate certain topological properties. Here, we propose a novel method of generating a high-order minimum spanning tree functional connectivity network. This method increases the neurological significance of the high-order functional connectivity network, reduces network computing consumption, and produces a network scale that is conducive to subsequent network analysis. To ensure the quality of the topological information in the network structure, we used frequent subgraph mining technology to capture the discriminative subnetworks as features and combined this with quantifiable local network features. Then we applied a multikernel learning technique to the corresponding selected features to obtain the final classification results. We evaluated our proposed method using a data set containing 38 patients with major depressive disorder and 28 healthy controls. The experimental results showed a classification accuracy of up to 97.54%.

Machine-Learning Classifier for Patients with Major Depressive Disorder: Multifeature Approach Based on a High-Order Minimum Spanning Tree Functional Brain Network

PubMed Central

Qin, Mengna; Chen, Junjie; Xu, Yong; Xiang, Jie

2017-01-01

High-order functional connectivity networks are rich in time information that can reflect dynamic changes in functional connectivity between brain regions. Accordingly, such networks are widely used to classify brain diseases. However, traditional methods for processing high-order functional connectivity networks generally include the clustering method, which reduces data dimensionality. As a result, such networks cannot be effectively interpreted in the context of neurology. Additionally, due to the large scale of high-order functional connectivity networks, it can be computationally very expensive to use complex network or graph theory to calculate certain topological properties. Here, we propose a novel method of generating a high-order minimum spanning tree functional connectivity network. This method increases the neurological significance of the high-order functional connectivity network, reduces network computing consumption, and produces a network scale that is conducive to subsequent network analysis. To ensure the quality of the topological information in the network structure, we used frequent subgraph mining technology to capture the discriminative subnetworks as features and combined this with quantifiable local network features. Then we applied a multikernel learning technique to the corresponding selected features to obtain the final classification results. We evaluated our proposed method using a data set containing 38 patients with major depressive disorder and 28 healthy controls. The experimental results showed a classification accuracy of up to 97.54%. PMID:29387141

Microchip-Based Single-Cell Functional Proteomics for Biomedical Applications

PubMed Central

Lu, Yao; Yang, Liu; Wei, Wei; Shi, Qihui

2017-01-01

Cellular heterogeneity has been widely recognized but only recently have single cell tools become available that allow characterizing heterogeneity at the genomic and proteomic levels. We review the technological advances in microchip-based toolkits for single-cell functional proteomics. Each of these tools has distinct advantages and limitations, and a few have advanced toward being applied to address biological or clinical problems that fail to be addressed by traditional population-based methods. High-throughput single-cell proteomic assays generate high-dimensional data sets that contain new information and thus require developing new analytical framework to extract new biology. In this review article, we highlight a few biological and clinical applications in which the microchip-based single-cell proteomic tools provide unique advantages. The examples include resolving functional heterogeneity and dynamics of immune cells, dissecting cell-cell interaction by creating well-contolled on-chip microenvironment, capturing high-resolution snapshots of immune system functions in patients for better immunotherapy and elucidating phosphoprotein signaling networks in cancer cells for guiding effective molecularly targeted therapies. PMID:28280819

Functional CAR models for large spatially correlated functional datasets.

PubMed

Zhang, Lin; Baladandayuthapani, Veerabhadran; Zhu, Hongxiao; Baggerly, Keith A; Majewski, Tadeusz; Czerniak, Bogdan A; Morris, Jeffrey S

2016-01-01

We develop a functional conditional autoregressive (CAR) model for spatially correlated data for which functions are collected on areal units of a lattice. Our model performs functional response regression while accounting for spatial correlations with potentially nonseparable and nonstationary covariance structure, in both the space and functional domains. We show theoretically that our construction leads to a CAR model at each functional location, with spatial covariance parameters varying and borrowing strength across the functional domain. Using basis transformation strategies, the nonseparable spatial-functional model is computationally scalable to enormous functional datasets, generalizable to different basis functions, and can be used on functions defined on higher dimensional domains such as images. Through simulation studies, we demonstrate that accounting for the spatial correlation in our modeling leads to improved functional regression performance. Applied to a high-throughput spatially correlated copy number dataset, the model identifies genetic markers not identified by comparable methods that ignore spatial correlations.

Ab Initio Structural Modeling of and Experimental Validation for Chlamydia trachomatis Protein CT296 Reveal Structural Similarity to Fe(II) 2-Oxoglutarate-Dependent Enzymes▿

PubMed Central

Kemege, Kyle E.; Hickey, John M.; Lovell, Scott; Battaile, Kevin P.; Zhang, Yang; Hefty, P. Scott

2011-01-01

Chlamydia trachomatis is a medically important pathogen that encodes a relatively high percentage of proteins with unknown function. The three-dimensional structure of a protein can be very informative regarding the protein's functional characteristics; however, determining protein structures experimentally can be very challenging. Computational methods that model protein structures with sufficient accuracy to facilitate functional studies have had notable successes. To evaluate the accuracy and potential impact of computational protein structure modeling of hypothetical proteins encoded by Chlamydia, a successful computational method termed I-TASSER was utilized to model the three-dimensional structure of a hypothetical protein encoded by open reading frame (ORF) CT296. CT296 has been reported to exhibit functional properties of a divalent cation transcription repressor (DcrA), with similarity to the Escherichia coli iron-responsive transcriptional repressor, Fur. Unexpectedly, the I-TASSER model of CT296 exhibited no structural similarity to any DNA-interacting proteins or motifs. To validate the I-TASSER-generated model, the structure of CT296 was solved experimentally using X-ray crystallography. Impressively, the ab initio I-TASSER-generated model closely matched (2.72-Å Cα root mean square deviation [RMSD]) the high-resolution (1.8-Å) crystal structure of CT296. Modeled and experimentally determined structures of CT296 share structural characteristics of non-heme Fe(II) 2-oxoglutarate-dependent enzymes, although key enzymatic residues are not conserved, suggesting a unique biochemical process is likely associated with CT296 function. Additionally, functional analyses did not support prior reports that CT296 has properties shared with divalent cation repressors such as Fur. PMID:21965559

Ab initio structural modeling of and experimental validation for Chlamydia trachomatis protein CT296 reveal structural similarity to Fe(II) 2-oxoglutarate-dependent enzymes

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

Kemege, Kyle E.; Hickey, John M.; Lovell, Scott

2012-02-13

Chlamydia trachomatis is a medically important pathogen that encodes a relatively high percentage of proteins with unknown function. The three-dimensional structure of a protein can be very informative regarding the protein's functional characteristics; however, determining protein structures experimentally can be very challenging. Computational methods that model protein structures with sufficient accuracy to facilitate functional studies have had notable successes. To evaluate the accuracy and potential impact of computational protein structure modeling of hypothetical proteins encoded by Chlamydia, a successful computational method termed I-TASSER was utilized to model the three-dimensional structure of a hypothetical protein encoded by open reading frame (ORF)more » CT296. CT296 has been reported to exhibit functional properties of a divalent cation transcription repressor (DcrA), with similarity to the Escherichia coli iron-responsive transcriptional repressor, Fur. Unexpectedly, the I-TASSER model of CT296 exhibited no structural similarity to any DNA-interacting proteins or motifs. To validate the I-TASSER-generated model, the structure of CT296 was solved experimentally using X-ray crystallography. Impressively, the ab initio I-TASSER-generated model closely matched (2.72-{angstrom} C{alpha} root mean square deviation [RMSD]) the high-resolution (1.8-{angstrom}) crystal structure of CT296. Modeled and experimentally determined structures of CT296 share structural characteristics of non-heme Fe(II) 2-oxoglutarate-dependent enzymes, although key enzymatic residues are not conserved, suggesting a unique biochemical process is likely associated with CT296 function. Additionally, functional analyses did not support prior reports that CT296 has properties shared with divalent cation repressors such as Fur.« less

Improving Mixed Variable Optimization of Computational and Model Parameters Using Multiple Surrogate Functions

DTIC Science & Technology

2008-03-01

multiplicative corrections as well as space mapping transformations for models defined over a lower dimensional space. A corrected surrogate model for the...correction functions used in [72]. If the low fidelity model g(x̃) is defined over a lower dimensional space then a space mapping transformation is...required. As defined in [21, 72], space mapping is a method of mapping between models of different dimensionality or fidelity. Let P denote the space

Recurrence relations in one-dimensional Ising models.

PubMed

da Conceição, C M Silva; Maia, R N P

2017-09-01

The exact finite-size partition function for the nonhomogeneous one-dimensional (1D) Ising model is found through an approach using algebra operators. Specifically, in this paper we show that the partition function can be computed through a trace from a linear second-order recurrence relation with nonconstant coefficients in matrix form. A relation between the finite-size partition function and the generalized Lucas polynomials is found for the simple homogeneous model, thus establishing a recursive formula for the partition function. This is an important property and it might indicate the possible existence of recurrence relations in higher-dimensional Ising models. Moreover, assuming quenched disorder for the interactions within the model, the quenched averaged magnetic susceptibility displays a nontrivial behavior due to changes in the ferromagnetic concentration probability.

Three-particle correlation functions of quasi-two-dimensional one-component and binary colloid suspensions.

PubMed

Ho, Hau My; Lin, Binhua; Rice, Stuart A

2006-11-14

We report the results of experimental determinations of the triplet correlation functions of quasi-two-dimensional one-component and binary colloid suspensions in which the colloid-colloid interaction is short ranged. The suspensions studied range in density from modestly dilute to solid. The triplet correlation function of the one-component colloid system reveals extensive ordering deep in the liquid phase. At the same density the ordering of the larger diameter component in a binary colloid system is greatly diminished by a very small amount of the smaller diameter component. The possible utilization of information contained in the triplet correlation function in the theory of melting of a quasi-two-dimensional system is briefly discussed.

Three-Dimensional-Moldable Nanofiber-Reinforced Transparent Composites with a Hierarchically Self-Assembled "Reverse" Nacre-like Architecture.

PubMed

Biswas, Subir K; Sano, Hironari; Shams, Md Iftekhar; Yano, Hiroyuki

2017-09-06

Achieving a structural hierarchy and a uniform nanofiller dispersion simultaneously remains highly challenging for obtaining a robust polymer nanocomposite of immiscible components. In this study, a remarkably facile Pickering emulsification approach is developed to fabricate hierarchical composites of immiscible acrylic polymer and native cellulose nanofibers by taking advantage of the dual role of the nanofibers as both emulsion stabilizer and polymer reinforcement. The composites feature a unique "reverse" nacre-like microstructure reinforced with a well-dispersed two-tier hierarchical nanofiber network, leading to a synergistic high strength, modulus, and toughness (20, 50, and 53 times that of neat polymer, respectively), high optical transparency (89%), high flexibility, and a drastically low thermal expansion (13 ppm K -1 , 1/15th of the neat polymer). The nanocomposites have a three-dimensional-shape moldability, also their surface can be patterned with micro/nanoscale features with high fidelity by in situ compression molding, making them attractive as the substrate for flexible displays, smart contact lens devices, and photovoltaics. The Pickering emulsification approach should be broadly applicable for the fabrication of novel functional materials of various immiscible components.

High-Level Heteroatom Doped Two-Dimensional Carbon Architectures for Highly Efficient Lithium-Ion Storage.

PubMed

Wang, Zhijie; Wang, Yanyan; Wang, Wenhui; Yu, Xiaoliang; Lv, Wei; Xiang, Bin; He, Yan-Bing

2018-01-01

In this work, high-level heteroatom doped two-dimensional hierarchical carbon architectures (H-2D-HCA) are developed for highly efficient Li-ion storage applications. The achieved H-2D-HCA possesses a hierarchical 2D morphology consisting of tiny carbon nanosheets vertically grown on carbon nanoplates and containing a hierarchical porosity with multiscale pore size. More importantly, the H-2D-HCA shows abundant heteroatom functionality, with sulfur (S) doping of 0.9% and nitrogen (N) doping of as high as 15.5%, in which the electrochemically active N accounts for 84% of total N heteroatoms. In addition, the H-2D-HCA also has an expanded interlayer distance of 0.368 nm. When used as lithium-ion battery anodes, it shows excellent Li-ion storage performance. Even at a high current density of 5 A g -1 , it still delivers a high discharge capacity of 329 mA h g -1 after 1,000 cycles. First principle calculations verifies that such unique microstructure characteristics and high-level heteroatom doping nature can enhance Li adsorption stability, electronic conductivity and Li diffusion mobility of carbon nanomaterials. Therefore, the H-2D-HCA could be promising candidates for next-generation LIB anodes.

High-Level Heteroatom Doped Two-Dimensional Carbon Architectures for Highly Efficient Lithium-Ion Storage

NASA Astrophysics Data System (ADS)

Wang, Zhijie; Wang, Yanyan; Wang, Wenhui; Yu, Xiaoliang; Lv, Wei; Xiang, Bin; He, Yan-Bing

2018-04-01

In this work, high-level heteroatom doped two-dimensional hierarchical carbon architectures (H-2D-HCA) are developed for highly efficient Li-ion storage applications. The achieved H-2D-HCA possesses a hierarchical 2D morphology consisting of tiny carbon nanosheets vertically grown on carbon nanoplates and containing a hierarchical porosity with multiscale pore size. More importantly, the H-2D-HCA shows abundant heteroatom functionality, with sulfur (S) doping of 0.9 % and nitrogen (N) doping of as high as 15.5 %, in which the electrochemically active N accounts for 84 % of total N heteroatoms. In addition, the H-2D-HCA also has an expanded interlayer distance of 0.368 nm. When used as lithium-ion battery anodes, it shows excellent Li-ion storage performance. Even at a high current density of 5 A g-1, it still delivered a high discharge capacity of 329 mA h g-1 after 1000 cycles. First principle calculations verified that such unique microstructure characteristics and high-level heteroatom doping nature can enhance Li adsorption stability, electronic conductivity and Li diffusion mobility of carbon nanomaterials. Therefore, the H-2D-HCA could be promising candidates for next-generation LIB anodes.

[Functional diversity characteristics of canopy tree species of Jianfengling tropical montane rainforest on Hainan Island, China.

PubMed

Xu, Ge Xi; Shi, Zuo Min; Tang, Jing Chao; Liu, Shun; Ma, Fan Qiang; Xu, Han; Liu, Shi Rong; Li, Yi de

2016-11-18

Based on three 1-hm 2 plots of Jianfengling tropical montane rainforest on Hainan Island, 11 commom used functional traits of canopy trees were measured. After combining with topographical factors and trees census data of these three plots, we compared the impacts of weighted species abundance on two functional dispersion indices, mean pairwise distance (MPD) and mean nearest taxon distance (MNTD), by using single- and multi-dimensional traits, respectively. The relationship between functional richness of the forest canopies and species abundance was analyzed. We used a null model approach to explore the variations in standardized size effects of MPD and MNTD, which were weighted by species abundance and eliminated the influences of species richness diffe-rences among communities, and assessed functional diversity patterns of the forest canopies and their responses to local habitat heterogeneity at community's level. The results showed that variation in MPD was greatly dependent on the dimensionalities of functional traits as well as species abundance. The correlations between weighted and non-weighted MPD based on different dimensional traits were relatively weak (R=0.359-0.628). On the contrary, functional traits and species abundance had relatively weak effects on MNTD, which brought stronger correlations between weighted and non-weighted MNTD based on different dimensional traits (R=0.746-0.820). Functional dispersion of the forest canopies were generally overestimated when using non-weighted MPD and MNTD. Functional richness of the forest canopies showed an exponential relationship with species abundance (F=128.20; R 2 =0.632; AIC=97.72; P＜0.001), which might exist a species abundance threshold value. Patterns of functional diversity of the forest canopies based on different dimensional functional traits and their habitat responses showed variations in some degree. Forest canopies in the valley usually had relatively stronger biological competition, and functional diversity was higher than expected functional diversity randomized by null model, which indicated dispersed distribution of functional traits among canopy tree species in this habitat. However, the functional diversity of the forest canopies tended to be close or lower than randomization in the other habitat types, which demonstrated random or clustered distribution of the functional traits among canopy tree species.

Distortion control in 20MnCr5 bevel gears after liquid nitriding process to maintain precision dimensions

NASA Astrophysics Data System (ADS)

Mahendiran, M.; Kavitha, M.

2018-02-01

Robotic and automotive gears are generally very high precision components with limitations in tolerances. Bevel gears are more widely used and dimensionally very close tolerance components that need stability without any backlash or distortion for smooth and trouble free functions. Nitriding is carried out to enhance wear resistance of the surface. The aim of this paper is to reduce the distortion in liquid nitriding process, though plasma nitriding is preferred for high precision components. Various trials were conducted to optimize the process parameters, considering pre dimensional setting for nominal nitriding layer growth. Surface cleaning, suitable fixtures and stress relieving operations were also done to optimize the process. Micro structural analysis and Vickers hardness testing were carried out for analyzing the phase changes, variation in surface hardness and case depth. CNC gear testing machine was used for determining the distortion level. The presence of white layer was found for about 10-15μm in the case depth of 250± 3.5μm showing an average surface hardness of 670 HV. Hence the economical liquid nitriding process was successfully used for producing high hardness and wear resistant coating over 20MnCr5 material with less distortion and reduced secondary grinding process for dimensional control.

ESTIMATION OF FUNCTIONALS OF SPARSE COVARIANCE MATRICES.

PubMed

Fan, Jianqing; Rigollet, Philippe; Wang, Weichen

High-dimensional statistical tests often ignore correlations to gain simplicity and stability leading to null distributions that depend on functionals of correlation matrices such as their Frobenius norm and other ℓ r norms. Motivated by the computation of critical values of such tests, we investigate the difficulty of estimation the functionals of sparse correlation matrices. Specifically, we show that simple plug-in procedures based on thresholded estimators of correlation matrices are sparsity-adaptive and minimax optimal over a large class of correlation matrices. Akin to previous results on functional estimation, the minimax rates exhibit an elbow phenomenon. Our results are further illustrated in simulated data as well as an empirical study of data arising in financial econometrics.

ESTIMATION OF FUNCTIONALS OF SPARSE COVARIANCE MATRICES

PubMed Central

Fan, Jianqing; Rigollet, Philippe; Wang, Weichen

2016-01-01

High-dimensional statistical tests often ignore correlations to gain simplicity and stability leading to null distributions that depend on functionals of correlation matrices such as their Frobenius norm and other ℓr norms. Motivated by the computation of critical values of such tests, we investigate the difficulty of estimation the functionals of sparse correlation matrices. Specifically, we show that simple plug-in procedures based on thresholded estimators of correlation matrices are sparsity-adaptive and minimax optimal over a large class of correlation matrices. Akin to previous results on functional estimation, the minimax rates exhibit an elbow phenomenon. Our results are further illustrated in simulated data as well as an empirical study of data arising in financial econometrics. PMID:26806986

Organization of the Macaque Extrastriate Visual Cortex Re-Examined Using the Principle of Spatial Continuity of Function

PubMed Central

Aflalo, T. N.

2011-01-01

How is the macaque monkey extrastriate cortex organized? Is vision divisible into separate tasks, such as object recognition and spatial processing, each emphasized in a different anatomical stream? If so, how many streams exist? What are the hierarchical relationships among areas? The present study approached the organization of the extrastriate cortex in a novel manner. A principled relationship exists between cortical function and cortical topography. Similar functions tend to be located near each other, within the constraints of mapping a highly dimensional space of functions onto the two-dimensional space of the cortex. We used this principle to re-examine the functional organization of the extrastriate cortex given current knowledge about its topographic organization. The goal of the study was to obtain a model of the functional relationships among the visual areas, including the number of functional streams into which they are grouped, the pattern of informational overlap among the streams, and the hierarchical relationships among areas. To test each functional description, we mapped it to a model cortex according to the principle of optimal continuity and assessed whether it accurately reconstructed a version of the extrastriate topography. Of the models tested, the one that best reconstructed the topography included four functional streams rather than two, six levels of hierarchy per stream, and a specific pattern of informational overlap among streams and areas. A specific mixture of functions was predicted for each visual area. This description matched findings in the physiological literature, and provided predictions of functional relationships that have yet to be tested physiologically. PMID:21068269

Modeling multivariate time series on manifolds with skew radial basis functions.

PubMed

Jamshidi, Arta A; Kirby, Michael J

2011-01-01

We present an approach for constructing nonlinear empirical mappings from high-dimensional domains to multivariate ranges. We employ radial basis functions and skew radial basis functions for constructing a model using data that are potentially scattered or sparse. The algorithm progresses iteratively, adding a new function at each step to refine the model. The placement of the functions is driven by a statistical hypothesis test that accounts for correlation in the multivariate range variables. The test is applied on training and validation data and reveals nonstatistical or geometric structure when it fails. At each step, the added function is fit to data contained in a spatiotemporally defined local region to determine the parameters--in particular, the scale of the local model. The scale of the function is determined by the zero crossings of the autocorrelation function of the residuals. The model parameters and the number of basis functions are determined automatically from the given data, and there is no need to initialize any ad hoc parameters save for the selection of the skew radial basis functions. Compactly supported skew radial basis functions are employed to improve model accuracy, order, and convergence properties. The extension of the algorithm to higher-dimensional ranges produces reduced-order models by exploiting the existence of correlation in the range variable data. Structure is tested not just in a single time series but between all pairs of time series. We illustrate the new methodologies using several illustrative problems, including modeling data on manifolds and the prediction of chaotic time series.

LETTER TO THE EDITOR: Fractal diffusion coefficient from dynamical zeta functions

NASA Astrophysics Data System (ADS)

Cristadoro, Giampaolo

2006-03-01

Dynamical zeta functions provide a powerful method to analyse low-dimensional dynamical systems when the underlying symbolic dynamics is under control. On the other hand, even simple one-dimensional maps can show an intricate structure of the grammar rules that may lead to a non-smooth dependence of global observables on parameters changes. A paradigmatic example is the fractal diffusion coefficient arising in a simple piecewise linear one-dimensional map of the real line. Using the Baladi-Ruelle generalization of the Milnor-Thurnston kneading determinant, we provide the exact dynamical zeta function for such a map and compute the diffusion coefficient from its smallest zero.

Solution of D dimensional Dirac equation for coulombic potential using NU method and its thermodynamics properties

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

Cari, C., E-mail: cari@staff.uns.ac.id; Suparmi, A., E-mail: soeparmi@staff.uns.ac.id; Yunianto, M., E-mail: muhtaryunianto@staff.uns.ac.id

2016-02-08

The analytical solution of Ddimensional Dirac equation for Coulombic potential is investigated using Nikiforov-Uvarov method. The D dimensional relativistic energy spectra are obtained from relativistic energy eigenvalue equation by using Mat Lab software.The corresponding D dimensional radial wave functions are formulated in the form of generalized Jacobi and Laguerre Polynomials. In the non-relativistic limit, the relativistic energy equation reduces to the non-relativistic energy which will be applied to determine some thermodynamical properties of the system. The thermodynamical properties of the system are expressed in terms of error function and imaginary error function.

Stochastic modeling and simulation of reaction-diffusion system with Hill function dynamics.

PubMed

Chen, Minghan; Li, Fei; Wang, Shuo; Cao, Young

2017-03-14

Stochastic simulation of reaction-diffusion systems presents great challenges for spatiotemporal biological modeling and simulation. One widely used framework for stochastic simulation of reaction-diffusion systems is reaction diffusion master equation (RDME). Previous studies have discovered that for the RDME, when discretization size approaches zero, reaction time for bimolecular reactions in high dimensional domains tends to infinity. In this paper, we demonstrate that in the 1D domain, highly nonlinear reaction dynamics given by Hill function may also have dramatic change when discretization size is smaller than a critical value. Moreover, we discuss methods to avoid this problem: smoothing over space, fixed length smoothing over space and a hybrid method. Our analysis reveals that the switch-like Hill dynamics reduces to a linear function of discretization size when the discretization size is small enough. The three proposed methods could correctly (under certain precision) simulate Hill function dynamics in the microscopic RDME system.

Feature extraction with deep neural networks by a generalized discriminant analysis.

PubMed

Stuhlsatz, André; Lippel, Jens; Zielke, Thomas

2012-04-01

We present an approach to feature extraction that is a generalization of the classical linear discriminant analysis (LDA) on the basis of deep neural networks (DNNs). As for LDA, discriminative features generated from independent Gaussian class conditionals are assumed. This modeling has the advantages that the intrinsic dimensionality of the feature space is bounded by the number of classes and that the optimal discriminant function is linear. Unfortunately, linear transformations are insufficient to extract optimal discriminative features from arbitrarily distributed raw measurements. The generalized discriminant analysis (GerDA) proposed in this paper uses nonlinear transformations that are learnt by DNNs in a semisupervised fashion. We show that the feature extraction based on our approach displays excellent performance on real-world recognition and detection tasks, such as handwritten digit recognition and face detection. In a series of experiments, we evaluate GerDA features with respect to dimensionality reduction, visualization, classification, and detection. Moreover, we show that GerDA DNNs can preprocess truly high-dimensional input data to low-dimensional representations that facilitate accurate predictions even if simple linear predictors or measures of similarity are used.

A General Exponential Framework for Dimensionality Reduction.

PubMed

Wang, Su-Jing; Yan, Shuicheng; Yang, Jian; Zhou, Chun-Guang; Fu, Xiaolan

2014-02-01

As a general framework, Laplacian embedding, based on a pairwise similarity matrix, infers low dimensional representations from high dimensional data. However, it generally suffers from three issues: 1) algorithmic performance is sensitive to the size of neighbors; 2) the algorithm encounters the well known small sample size (SSS) problem; and 3) the algorithm de-emphasizes small distance pairs. To address these issues, here we propose exponential embedding using matrix exponential and provide a general framework for dimensionality reduction. In the framework, the matrix exponential can be roughly interpreted by the random walk over the feature similarity matrix, and thus is more robust. The positive definite property of matrix exponential deals with the SSS problem. The behavior of the decay function of exponential embedding is more significant in emphasizing small distance pairs. Under this framework, we apply matrix exponential to extend many popular Laplacian embedding algorithms, e.g., locality preserving projections, unsupervised discriminant projections, and marginal fisher analysis. Experiments conducted on the synthesized data, UCI, and the Georgia Tech face database show that the proposed new framework can well address the issues mentioned above.