Comparing hierarchical models via the marginalized deviance information criterion.

PubMed

Quintero, Adrian; Lesaffre, Emmanuel

2018-07-20

Hierarchical models are extensively used in pharmacokinetics and longitudinal studies. When the estimation is performed from a Bayesian approach, model comparison is often based on the deviance information criterion (DIC). In hierarchical models with latent variables, there are several versions of this statistic: the conditional DIC (cDIC) that incorporates the latent variables in the focus of the analysis and the marginalized DIC (mDIC) that integrates them out. Regardless of the asymptotic and coherency difficulties of cDIC, this alternative is usually used in Markov chain Monte Carlo (MCMC) methods for hierarchical models because of practical convenience. The mDIC criterion is more appropriate in most cases but requires integration of the likelihood, which is computationally demanding and not implemented in Bayesian software. Therefore, we consider a method to compute mDIC by generating replicate samples of the latent variables that need to be integrated out. This alternative can be easily conducted from the MCMC output of Bayesian packages and is widely applicable to hierarchical models in general. Additionally, we propose some approximations in order to reduce the computational complexity for large-sample situations. The method is illustrated with simulated data sets and 2 medical studies, evidencing that cDIC may be misleading whilst mDIC appears pertinent. Copyright © 2018 John Wiley & Sons, Ltd.

A Bayesian Hierarchical Model for Glacial Dynamics Based on the Shallow Ice Approximation and its Evaluation Using Analytical Solutions

NASA Astrophysics Data System (ADS)

Gopalan, Giri; Hrafnkelsson, Birgir; Aðalgeirsdóttir, Guðfinna; Jarosch, Alexander H.; Pálsson, Finnur

2018-03-01

Bayesian hierarchical modeling can assist the study of glacial dynamics and ice flow properties. This approach will allow glaciologists to make fully probabilistic predictions for the thickness of a glacier at unobserved spatio-temporal coordinates, and it will also allow for the derivation of posterior probability distributions for key physical parameters such as ice viscosity and basal sliding. The goal of this paper is to develop a proof of concept for a Bayesian hierarchical model constructed, which uses exact analytical solutions for the shallow ice approximation (SIA) introduced by Bueler et al. (2005). A suite of test simulations utilizing these exact solutions suggests that this approach is able to adequately model numerical errors and produce useful physical parameter posterior distributions and predictions. A byproduct of the development of the Bayesian hierarchical model is the derivation of a novel finite difference method for solving the SIA partial differential equation (PDE). An additional novelty of this work is the correction of numerical errors induced through a numerical solution using a statistical model. This error correcting process models numerical errors that accumulate forward in time and spatial variation of numerical errors between the dome, interior, and margin of a glacier.

Efficient Actor-Critic Algorithm with Hierarchical Model Learning and Planning

PubMed Central

Fu, QiMing

2016-01-01

To improve the convergence rate and the sample efficiency, two efficient learning methods AC-HMLP and RAC-HMLP (AC-HMLP with ℓ 2-regularization) are proposed by combining actor-critic algorithm with hierarchical model learning and planning. The hierarchical models consisting of the local and the global models, which are learned at the same time during learning of the value function and the policy, are approximated by local linear regression (LLR) and linear function approximation (LFA), respectively. Both the local model and the global model are applied to generate samples for planning; the former is used only if the state-prediction error does not surpass the threshold at each time step, while the latter is utilized at the end of each episode. The purpose of taking both models is to improve the sample efficiency and accelerate the convergence rate of the whole algorithm through fully utilizing the local and global information. Experimentally, AC-HMLP and RAC-HMLP are compared with three representative algorithms on two Reinforcement Learning (RL) benchmark problems. The results demonstrate that they perform best in terms of convergence rate and sample efficiency. PMID:27795704

Efficient Actor-Critic Algorithm with Hierarchical Model Learning and Planning.

PubMed

Zhong, Shan; Liu, Quan; Fu, QiMing

2016-01-01

To improve the convergence rate and the sample efficiency, two efficient learning methods AC-HMLP and RAC-HMLP (AC-HMLP with ℓ 2 -regularization) are proposed by combining actor-critic algorithm with hierarchical model learning and planning. The hierarchical models consisting of the local and the global models, which are learned at the same time during learning of the value function and the policy, are approximated by local linear regression (LLR) and linear function approximation (LFA), respectively. Both the local model and the global model are applied to generate samples for planning; the former is used only if the state-prediction error does not surpass the threshold at each time step, while the latter is utilized at the end of each episode. The purpose of taking both models is to improve the sample efficiency and accelerate the convergence rate of the whole algorithm through fully utilizing the local and global information. Experimentally, AC-HMLP and RAC-HMLP are compared with three representative algorithms on two Reinforcement Learning (RL) benchmark problems. The results demonstrate that they perform best in terms of convergence rate and sample efficiency.

Decoherence and lead-induced interdot coupling in nonequilibrium electron transport through interacting quantum dots: A hierarchical quantum master equation approach

NASA Astrophysics Data System (ADS)

Härtle, R.; Cohen, G.; Reichman, D. R.; Millis, A. J.

2013-12-01

The interplay between interference effects and electron-electron interactions in electron transport through an interacting double quantum dot system is investigated using a hierarchical quantum master equation approach which becomes exact if carried to infinite order and converges well if the temperature is not too low. Decoherence due to electron-electron interactions is found to give rise to pronounced negative differential resistance, enhanced broadening of structures in current-voltage characteristics, and an inversion of the electronic population. Dependence on gate voltage is shown to be a useful method of distinguishing decoherence-induced phenomena from effects induced by other mechanisms such as the presence of a blocking state. Comparison of results obtained by the hierarchical quantum master equation approach to those obtained from the Born-Markov approximation to the Nakajima-Zwanzig equation and from the noncrossing approximation to the nonequilibrium Green's function reveals the importance of an interdot coupling that originates from the energy dependence of the conduction bands in the leads and the need for a systematic perturbative expansion.

Three-dimensional microarchitected materials and devices using nanoparticle assembly by pointwise spatial printing.

PubMed

Saleh, Mohammad Sadeq; Hu, Chunshan; Panat, Rahul

2017-03-01

Three-dimensional (3D) hierarchical materials are important to a wide range of emerging technological applications. We report a method to synthesize complex 3D microengineered materials, such as microlattices, with nearly fully dense truss elements with a minimum diameter of approximately 20 μm and having high aspect ratios (up to 20:1) without using any templating or supporting materials. By varying the postprocessing conditions, we have also introduced an additional control over the internal porosity of the truss elements to demonstrate a hierarchical porous structure with an overall void size and feature size control of over five orders of magnitudes in length scale. The method uses direct printing of nanoparticle dispersions using the Aerosol Jet technology in 3D space without templating or supporting materials followed by binder removal and sintering. In addition to 3D microlattices, we have also demonstrated directly printed stretchable interconnects, spirals, and pillars. This assembly method could be implemented by a variety of microdroplet generation methods for fast and large-scale fabrication of the hierarchical materials for applications in tissue engineering, ultralight or multifunctional materials, microfluidics, and micro-optoelectronics.

Hierarchical modeling and robust synthesis for the preliminary design of large scale complex systems

NASA Astrophysics Data System (ADS)

Koch, Patrick Nathan

Large-scale complex systems are characterized by multiple interacting subsystems and the analysis of multiple disciplines. The design and development of such systems inevitably requires the resolution of multiple conflicting objectives. The size of complex systems, however, prohibits the development of comprehensive system models, and thus these systems must be partitioned into their constituent parts. Because simultaneous solution of individual subsystem models is often not manageable iteration is inevitable and often excessive. In this dissertation these issues are addressed through the development of a method for hierarchical robust preliminary design exploration to facilitate concurrent system and subsystem design exploration, for the concurrent generation of robust system and subsystem specifications for the preliminary design of multi-level, multi-objective, large-scale complex systems. This method is developed through the integration and expansion of current design techniques: (1) Hierarchical partitioning and modeling techniques for partitioning large-scale complex systems into more tractable parts, and allowing integration of subproblems for system synthesis, (2) Statistical experimentation and approximation techniques for increasing both the efficiency and the comprehensiveness of preliminary design exploration, and (3) Noise modeling techniques for implementing robust preliminary design when approximate models are employed. The method developed and associated approaches are illustrated through their application to the preliminary design of a commercial turbofan turbine propulsion system; the turbofan system-level problem is partitioned into engine cycle and configuration design and a compressor module is integrated for more detailed subsystem-level design exploration, improving system evaluation.

An adaptive sparse-grid high-order stochastic collocation method for Bayesian inference in groundwater reactive transport modeling

NASA Astrophysics Data System (ADS)

Zhang, Guannan; Lu, Dan; Ye, Ming; Gunzburger, Max; Webster, Clayton

2013-10-01

Bayesian analysis has become vital to uncertainty quantification in groundwater modeling, but its application has been hindered by the computational cost associated with numerous model executions required by exploring the posterior probability density function (PPDF) of model parameters. This is particularly the case when the PPDF is estimated using Markov Chain Monte Carlo (MCMC) sampling. In this study, a new approach is developed to improve the computational efficiency of Bayesian inference by constructing a surrogate of the PPDF, using an adaptive sparse-grid high-order stochastic collocation (aSG-hSC) method. Unlike previous works using first-order hierarchical basis, this paper utilizes a compactly supported higher-order hierarchical basis to construct the surrogate system, resulting in a significant reduction in the number of required model executions. In addition, using the hierarchical surplus as an error indicator allows locally adaptive refinement of sparse grids in the parameter space, which further improves computational efficiency. To efficiently build the surrogate system for the PPDF with multiple significant modes, optimization techniques are used to identify the modes, for which high-probability regions are defined and components of the aSG-hSC approximation are constructed. After the surrogate is determined, the PPDF can be evaluated by sampling the surrogate system directly without model execution, resulting in improved efficiency of the surrogate-based MCMC compared with conventional MCMC. The developed method is evaluated using two synthetic groundwater reactive transport models. The first example involves coupled linear reactions and demonstrates the accuracy of our high-order hierarchical basis approach in approximating high-dimensional posteriori distribution. The second example is highly nonlinear because of the reactions of uranium surface complexation, and demonstrates how the iterative aSG-hSC method is able to capture multimodal and non-Gaussian features of PPDF caused by model nonlinearity. Both experiments show that aSG-hSC is an effective and efficient tool for Bayesian inference.

Hierarchical Time-Lagged Independent Component Analysis: Computing Slow Modes and Reaction Coordinates for Large Molecular Systems.

PubMed

Pérez-Hernández, Guillermo; Noé, Frank

2016-12-13

Analysis of molecular dynamics, for example using Markov models, often requires the identification of order parameters that are good indicators of the rare events, i.e. good reaction coordinates. Recently, it has been shown that the time-lagged independent component analysis (TICA) finds the linear combinations of input coordinates that optimally represent the slow kinetic modes and may serve in order to define reaction coordinates between the metastable states of the molecular system. A limitation of the method is that both computing time and memory requirements scale with the square of the number of input features. For large protein systems, this exacerbates the use of extensive feature sets such as the distances between all pairs of residues or even heavy atoms. Here we derive a hierarchical TICA (hTICA) method that approximates the full TICA solution by a hierarchical, divide-and-conquer calculation. By using hTICA on distances between heavy atoms we identify previously unknown relaxation processes in the bovine pancreatic trypsin inhibitor.

Local Approximation and Hierarchical Methods for Stochastic Optimization

NASA Astrophysics Data System (ADS)

Cheng, Bolong

In this thesis, we present local and hierarchical approximation methods for two classes of stochastic optimization problems: optimal learning and Markov decision processes. For the optimal learning problem class, we introduce a locally linear model with radial basis function for estimating the posterior mean of the unknown objective function. The method uses a compact representation of the function which avoids storing the entire history, as is typically required by nonparametric methods. We derive a knowledge gradient policy with the locally parametric model, which maximizes the expected value of information. We show the policy is asymptotically optimal in theory, and experimental works suggests that the method can reliably find the optimal solution on a range of test functions. For the Markov decision processes problem class, we are motivated by an application where we want to co-optimize a battery for multiple revenue, in particular energy arbitrage and frequency regulation. The nature of this problem requires the battery to make charging and discharging decisions at different time scales while accounting for the stochastic information such as load demand, electricity prices, and regulation signals. Computing the exact optimal policy becomes intractable due to the large state space and the number of time steps. We propose two methods to circumvent the computation bottleneck. First, we propose a nested MDP model that structure the co-optimization problem into smaller sub-problems with reduced state space. This new model allows us to understand how the battery behaves down to the two-second dynamics (that of the frequency regulation market). Second, we introduce a low-rank value function approximation for backward dynamic programming. This new method only requires computing the exact value function for a small subset of the state space and approximate the entire value function via low-rank matrix completion. We test these methods on historical price data from the PJM Interconnect and show that it outperforms the baseline approach used in the industry.

A structure adapted multipole method for electrostatic interactions in protein dynamics

NASA Astrophysics Data System (ADS)

Niedermeier, Christoph; Tavan, Paul

1994-07-01

We present an algorithm for rapid approximate evaluation of electrostatic interactions in molecular dynamics simulations of proteins. Traditional algorithms require computational work of the order O(N2) for a system of N particles. Truncation methods which try to avoid that effort entail untolerably large errors in forces, energies and other observables. Hierarchical multipole expansion algorithms, which can account for the electrostatics to numerical accuracy, scale with O(N log N) or even with O(N) if they become augmented by a sophisticated scheme for summing up forces. To further reduce the computational effort we propose an algorithm that also uses a hierarchical multipole scheme but considers only the first two multipole moments (i.e., charges and dipoles). Our strategy is based on the consideration that numerical accuracy may not be necessary to reproduce protein dynamics with sufficient correctness. As opposed to previous methods, our scheme for hierarchical decomposition is adjusted to structural and dynamical features of the particular protein considered rather than chosen rigidly as a cubic grid. As compared to truncation methods we manage to reduce errors in the computation of electrostatic forces by a factor of 10 with only marginal additional effort.

Hierarchic Extensions in the Static and Dynamic Analysis of Elastic Beams. Ph.D. Thesis, 1990 Final Report, May 1990

NASA Technical Reports Server (NTRS)

Watson, Robert A.

1991-01-01

Approximate solutions of static and dynamic beam problems by the p-version of the finite element method are investigated. Within a hierarchy of engineering beam idealizations, rigorous formulations of the strain and kinetic energies for straight and circular beam elements are presented. These formulations include rotating coordinate system effects and geometric nonlinearities to allow for the evaluation of vertical axis wind turbines, the motivating problem for this research. Hierarchic finite element spaces, based on extensions of the polynomial orders used to approximate the displacement variables, are constructed. The developed models are implemented into a general purpose computer program for evaluation. Quality control procedures are examined for a diverse set of sample problems. These procedures include estimating discretization errors in energy norm and natural frequencies, performing static and dynamic equilibrium checks, observing convergence for qualities of interest, and comparison with more exacting theories and experimental data. It is demonstrated that p-extensions produce exponential rates of convergence in the approximation of strain energy and natural frequencies for the class of problems investigated.

A Hierarchical Algorithm for Fast Debye Summation with Applications to Small Angle Scattering

PubMed Central

Gumerov, Nail A.; Berlin, Konstantin; Fushman, David; Duraiswami, Ramani

2012-01-01

Debye summation, which involves the summation of sinc functions of distances between all pair of atoms in three dimensional space, arises in computations performed in crystallography, small/wide angle X-ray scattering (SAXS/WAXS) and small angle neutron scattering (SANS). Direct evaluation of Debye summation has quadratic complexity, which results in computational bottleneck when determining crystal properties, or running structure refinement protocols that involve SAXS or SANS, even for moderately sized molecules. We present a fast approximation algorithm that efficiently computes the summation to any prescribed accuracy ε in linear time. The algorithm is similar to the fast multipole method (FMM), and is based on a hierarchical spatial decomposition of the molecule coupled with local harmonic expansions and translation of these expansions. An even more efficient implementation is possible when the scattering profile is all that is required, as in small angle scattering reconstruction (SAS) of macromolecules. We examine the relationship of the proposed algorithm to existing approximate methods for profile computations, and show that these methods may result in inaccurate profile computations, unless an error bound derived in this paper is used. Our theoretical and computational results show orders of magnitude improvement in computation complexity over existing methods, while maintaining prescribed accuracy. PMID:22707386

Testing higher-order Lagrangian perturbation theory against numerical simulation. 1: Pancake models

NASA Technical Reports Server (NTRS)

Buchert, T.; Melott, A. L.; Weiss, A. G.

1993-01-01

We present results showing an improvement of the accuracy of perturbation theory as applied to cosmological structure formation for a useful range of quasi-linear scales. The Lagrangian theory of gravitational instability of an Einstein-de Sitter dust cosmogony investigated and solved up to the third order is compared with numerical simulations. In this paper we study the dynamics of pancake models as a first step. In previous work the accuracy of several analytical approximations for the modeling of large-scale structure in the mildly non-linear regime was analyzed in the same way, allowing for direct comparison of the accuracy of various approximations. In particular, the Zel'dovich approximation (hereafter ZA) as a subclass of the first-order Lagrangian perturbation solutions was found to provide an excellent approximation to the density field in the mildly non-linear regime (i.e. up to a linear r.m.s. density contrast of sigma is approximately 2). The performance of ZA in hierarchical clustering models can be greatly improved by truncating the initial power spectrum (smoothing the initial data). We here explore whether this approximation can be further improved with higher-order corrections in the displacement mapping from homogeneity. We study a single pancake model (truncated power-spectrum with power-spectrum with power-index n = -1) using cross-correlation statistics employed in previous work. We found that for all statistical methods used the higher-order corrections improve the results obtained for the first-order solution up to the stage when sigma (linear theory) is approximately 1. While this improvement can be seen for all spatial scales, later stages retain this feature only above a certain scale which is increasing with time. However, third-order is not much improvement over second-order at any stage. The total breakdown of the perturbation approach is observed at the stage, where sigma (linear theory) is approximately 2, which corresponds to the onset of hierarchical clustering. This success is found at a considerable higher non-linearity than is usual for perturbation theory. Whether a truncation of the initial power-spectrum in hierarchical models retains this improvement will be analyzed in a forthcoming work.

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

Implementation of Hybrid V-Cycle Multilevel Methods for Mixed Finite Element Systems with Penalty

NASA Technical Reports Server (NTRS)

Lai, Chen-Yao G.

1996-01-01

The goal of this paper is the implementation of hybrid V-cycle hierarchical multilevel methods for the indefinite discrete systems which arise when a mixed finite element approximation is used to solve elliptic boundary value problems. By introducing a penalty parameter, the perturbed indefinite system can be reduced to a symmetric positive definite system containing the small penalty parameter for the velocity unknown alone. We stabilize the hierarchical spatial decomposition approach proposed by Cai, Goldstein, and Pasciak for the reduced system. We demonstrate that the relative condition number of the preconditioner is bounded uniformly with respect to the penalty parameter, the number of levels and possible jumps of the coefficients as long as they occur only across the edges of the coarsest elements.

Hierarchical Metal–Organic Framework Hybrids: Perturbation-Assisted Nanofusion Synthesis

DOE PAGES

Yue, Yanfeng; Fulvio, Pasquale F.; Dai, Sheng

2015-12-04

Metal–organic frameworks (MOFs) represent a new family of microporous materials; however, microporous–mesoporous hierarchical MOF materials have been less investigated because of the lack of simple, reliable methods to introduce mesopores to the crystalline microporous particles. State-of-the-art MOF hierarchical materials have been prepared by ligand extension methods or by using a template, resulting in intrinsic mesopores of longer ligands or replicated pores from template agents, respectively. However, mesoporous MOF materials obtained through ligand extension often collapse in the absence of guest molecules, which dramatically reduces the size of the pore aperture. Although the template-directed strategy allows for the preparation of hierarchicalmore » materials with larger mesopores, the latter requires a template removal step, which may result in the collapse of the implemented mesopores. Recently, a general template-free synthesis of hierarchical microporous crystalline frameworks, such as MOFs and Prussian blue analogues (PBAs), has been reported. Our new method is based on the kinetically controlled precipitation (perturbation), with simultaneous condensation and redissolution of polymorphic nanocrystallites in the mother liquor. This method further eliminates the use of extended organic ligands and the micropores do not collapse upon removal of trapped guest solvent molecules, thus yielding hierarchical MOF materials with intriguing porosity in the gram scale. The hierarchical MOF materials prepared in this way exhibited exceptional properties when tested for the adsorption of large organic dyes over their corresponding microporous frameworks, due to the enhanced pore accessibility and electrolyte diffusion within the mesopores. As for PBAs, the pore size distribution of these materials can be tailored by changing the metals substituting Fe cations in the PB lattice. For these, the textural mesopores increased from approximately 10 nm for Cu analogue (mesoCuHCF), to 16 nm in Co substituted compound (mesoCoHCF), and to as large as 30 nm for the Ni derivative (mesoNiHCF). And while bulk PB and analogues have a higher capacitance than hierarchical analogues for Na-batteries, the increased accessibility to the microporous channels of PBAs allow for faster intercalated ion exchange and diffusion than in bulk PBA crystals. Therefore, hierarchical PBAs are promising candidates for electrodes in future electrochemical energy storage devices with faster charge–discharge rates than batteries, namely pseudocapacitors. Finally, this new synthetic method opens the possibility to prepare hierarchical materials having bimodal distribution of mesopores, and to tailor the structural properties of MOFs for different applications, including contrasting agents for MRI, and drug delivery.« less

Wavelet Algorithms for Illumination Computations

NASA Astrophysics Data System (ADS)

Schroder, Peter

One of the core problems of computer graphics is the computation of the equilibrium distribution of light in a scene. This distribution is given as the solution to a Fredholm integral equation of the second kind involving an integral over all surfaces in the scene. In the general case such solutions can only be numerically approximated, and are generally costly to compute, due to the geometric complexity of typical computer graphics scenes. For this computation both Monte Carlo and finite element techniques (or hybrid approaches) are typically used. A simplified version of the illumination problem is known as radiosity, which assumes that all surfaces are diffuse reflectors. For this case hierarchical techniques, first introduced by Hanrahan et al. (32), have recently gained prominence. The hierarchical approaches lead to an asymptotic improvement when only finite precision is required. The resulting algorithms have cost proportional to O(k^2 + n) versus the usual O(n^2) (k is the number of input surfaces, n the number of finite elements into which the input surfaces are meshed). Similarly a hierarchical technique has been introduced for the more general radiance problem (which allows glossy reflectors) by Aupperle et al. (6). In this dissertation we show the equivalence of these hierarchical techniques to the use of a Haar wavelet basis in a general Galerkin framework. By so doing, we come to a deeper understanding of the properties of the numerical approximations used and are able to extend the hierarchical techniques to higher orders. In particular, we show the correspondence of the geometric arguments underlying hierarchical methods to the theory of Calderon-Zygmund operators and their sparse realization in wavelet bases. The resulting wavelet algorithms for radiosity and radiance are analyzed and numerical results achieved with our implementation are reported. We find that the resulting algorithms achieve smaller and smoother errors at equivalent work.

Compressed-sensing-based content-driven hierarchical reconstruction: Theory and application to C-arm cone-beam tomography

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

Langet, Hélène; Laboratoire des Signaux et Systèmes, CentraleSupélec, Gif-sur-Yvette F-91192; Center for Visual Computing, CentraleSupélec, Châtenay-Malabry F-92295

2015-09-15

Purpose: This paper addresses the reconstruction of x-ray cone-beam computed tomography (CBCT) for interventional C-arm systems. Subsampling of CBCT is a significant issue with C-arms due to their slow rotation and to the low frame rate of their flat panel x-ray detectors. The aim of this work is to propose a novel method able to handle the subsampling artifacts generally observed with analytical reconstruction, through a content-driven hierarchical reconstruction based on compressed sensing. Methods: The central idea is to proceed with a hierarchical method where the most salient features (high intensities or gradients) are reconstructed first to reduce the artifactsmore » these features induce. These artifacts are addressed first because their presence contaminates less salient features. Several hierarchical schemes aiming at streak artifacts reduction are introduced for C-arm CBCT: the empirical orthogonal matching pursuit approach with the ℓ{sub 0} pseudonorm for reconstructing sparse vessels; a convex variant using homotopy with the ℓ{sub 1}-norm constraint of compressed sensing, for reconstructing sparse vessels over a nonsparse background; homotopy with total variation (TV); and a novel empirical extension to nonlinear diffusion (NLD). Such principles are implemented with penalized iterative filtered backprojection algorithms. For soft-tissue imaging, the authors compare the use of TV and NLD filters as sparsity constraints, both optimized with the alternating direction method of multipliers, using a threshold for TV and a nonlinear weighting for NLD. Results: The authors show on simulated data that their approach provides fast convergence to good approximations of the solution of the TV-constrained minimization problem introduced by the compressed sensing theory. Using C-arm CBCT clinical data, the authors show that both TV and NLD can deliver improved image quality by reducing streaks. Conclusions: A flexible compressed-sensing-based algorithmic approach is proposed that is able to accommodate for a wide range of constraints. It is successfully applied to C-arm CBCT images that may not be so well approximated by piecewise constant functions.« less

Hierarchical quantum master equation approach to electronic-vibrational coupling in nonequilibrium transport through nanosystems

NASA Astrophysics Data System (ADS)

Schinabeck, C.; Erpenbeck, A.; Härtle, R.; Thoss, M.

2016-11-01

Within the hierarchical quantum master equation (HQME) framework, an approach is presented, which allows a numerically exact description of nonequilibrium charge transport in nanosystems with strong electronic-vibrational coupling. The method is applied to a generic model of vibrationally coupled transport considering a broad spectrum of parameters ranging from the nonadiabatic to the adiabatic regime and including both resonant and off-resonant transport. We show that nonequilibrium effects are important in all these regimes. In particular, in the off-resonant transport regime, the inelastic cotunneling signal is analyzed for a vibrational mode in full nonequilibrium, revealing a complex interplay of different transport processes and deviations from the commonly used G0/2 rule of thumb. In addition, the HQME approach is used to benchmark approximate master equation and nonequilibrium Green's function methods.

3D hierarchical interface-enriched finite element method: Implementation and applications

NASA Astrophysics Data System (ADS)

Soghrati, Soheil; Ahmadian, Hossein

2015-10-01

A hierarchical interface-enriched finite element method (HIFEM) is proposed for the mesh-independent treatment of 3D problems with intricate morphologies. The HIFEM implements a recursive algorithm for creating enrichment functions that capture gradient discontinuities in nonconforming finite elements cut by arbitrary number and configuration of materials interfaces. The method enables the mesh-independent simulation of multiphase problems with materials interfaces that are in close proximity or contact while providing a straightforward general approach for evaluating the enrichments. In this manuscript, we present a detailed discussion on the implementation issues and required computational geometry considerations associated with the HIFEM approximation of thermal and mechanical responses of 3D problems. A convergence study is provided to investigate the accuracy and convergence rate of the HIFEM and compare them with standard FEM benchmark solutions. We will also demonstrate the application of this mesh-independent method for simulating the thermal and mechanical responses of two composite materials systems with complex microstructures.

Adaptive variable structure hierarchical fuzzy control for a class of high-order nonlinear dynamic systems.

PubMed

Mansouri, Mohammad; Teshnehlab, Mohammad; Aliyari Shoorehdeli, Mahdi

2015-05-01

In this paper, a novel adaptive hierarchical fuzzy control system based on the variable structure control is developed for a class of SISO canonical nonlinear systems in the presence of bounded disturbances. It is assumed that nonlinear functions of the systems be completely unknown. Switching surfaces are incorporated into the hierarchical fuzzy control scheme to ensure the system stability. A fuzzy soft switching system decides the operation area of the hierarchical fuzzy control and variable structure control systems. All the nonlinearly appeared parameters of conclusion parts of fuzzy blocks located in different layers of the hierarchical fuzzy control system are adjusted through adaptation laws deduced from the defined Lyapunov function. The proposed hierarchical fuzzy control system reduces the number of rules and consequently the number of tunable parameters with respect to the ordinary fuzzy control system. Global boundedness of the overall adaptive system and the desired precision are achieved using the proposed adaptive control system. In this study, an adaptive hierarchical fuzzy system is used for two objectives; it can be as a function approximator or a control system based on an intelligent-classic approach. Three theorems are proven to investigate the stability of the nonlinear dynamic systems. The important point about the proposed theorems is that they can be applied not only to hierarchical fuzzy controllers with different structures of hierarchical fuzzy controller, but also to ordinary fuzzy controllers. Therefore, the proposed algorithm is more general. To show the effectiveness of the proposed method four systems (two mechanical, one mathematical and one chaotic) are considered in simulations. Simulation results demonstrate the validity, efficiency and feasibility of the proposed approach to control of nonlinear dynamic systems. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

INVESTIGATING DIFFERENCES IN BRAIN FUNCTIONAL NETWORKS USING HIERARCHICAL COVARIATE-ADJUSTED INDEPENDENT COMPONENT ANALYSIS.

PubMed

Shi, Ran; Guo, Ying

2016-12-01

Human brains perform tasks via complex functional networks consisting of separated brain regions. A popular approach to characterize brain functional networks in fMRI studies is independent component analysis (ICA), which is a powerful method to reconstruct latent source signals from their linear mixtures. In many fMRI studies, an important goal is to investigate how brain functional networks change according to specific clinical and demographic variabilities. Existing ICA methods, however, cannot directly incorporate covariate effects in ICA decomposition. Heuristic post-ICA analysis to address this need can be inaccurate and inefficient. In this paper, we propose a hierarchical covariate-adjusted ICA (hc-ICA) model that provides a formal statistical framework for estimating covariate effects and testing differences between brain functional networks. Our method provides a more reliable and powerful statistical tool for evaluating group differences in brain functional networks while appropriately controlling for potential confounding factors. We present an analytically tractable EM algorithm to obtain maximum likelihood estimates of our model. We also develop a subspace-based approximate EM that runs significantly faster while retaining high accuracy. To test the differences in functional networks, we introduce a voxel-wise approximate inference procedure which eliminates the need of computationally expensive covariance matrix estimation and inversion. We demonstrate the advantages of our methods over the existing method via simulation studies. We apply our method to an fMRI study to investigate differences in brain functional networks associated with post-traumatic stress disorder (PTSD).

An accessible method for implementing hierarchical models with spatio-temporal abundance data

USGS Publications Warehouse

Ross, Beth E.; Hooten, Melvin B.; Koons, David N.

2012-01-01

A common goal in ecology and wildlife management is to determine the causes of variation in population dynamics over long periods of time and across large spatial scales. Many assumptions must nevertheless be overcome to make appropriate inference about spatio-temporal variation in population dynamics, such as autocorrelation among data points, excess zeros, and observation error in count data. To address these issues, many scientists and statisticians have recommended the use of Bayesian hierarchical models. Unfortunately, hierarchical statistical models remain somewhat difficult to use because of the necessary quantitative background needed to implement them, or because of the computational demands of using Markov Chain Monte Carlo algorithms to estimate parameters. Fortunately, new tools have recently been developed that make it more feasible for wildlife biologists to fit sophisticated hierarchical Bayesian models (i.e., Integrated Nested Laplace Approximation, ‘INLA’). We present a case study using two important game species in North America, the lesser and greater scaup, to demonstrate how INLA can be used to estimate the parameters in a hierarchical model that decouples observation error from process variation, and accounts for unknown sources of excess zeros as well as spatial and temporal dependence in the data. Ultimately, our goal was to make unbiased inference about spatial variation in population trends over time.

Efficient electronic structure theory via hierarchical scale-adaptive coupled-cluster formalism: I. Theory and computational complexity analysis

NASA Astrophysics Data System (ADS)

Lyakh, Dmitry I.

2018-03-01

A novel reduced-scaling, general-order coupled-cluster approach is formulated by exploiting hierarchical representations of many-body tensors, combined with the recently suggested formalism of scale-adaptive tensor algebra. Inspired by the hierarchical techniques from the renormalisation group approach, H/H2-matrix algebra and fast multipole method, the computational scaling reduction in our formalism is achieved via coarsening of quantum many-body interactions at larger interaction scales, thus imposing a hierarchical structure on many-body tensors of coupled-cluster theory. In our approach, the interaction scale can be defined on any appropriate Euclidean domain (spatial domain, momentum-space domain, energy domain, etc.). We show that the hierarchically resolved many-body tensors can reduce the storage requirements to O(N), where N is the number of simulated quantum particles. Subsequently, we prove that any connected many-body diagram consisting of a finite number of arbitrary-order tensors, e.g. an arbitrary coupled-cluster diagram, can be evaluated in O(NlogN) floating-point operations. On top of that, we suggest an additional approximation to further reduce the computational complexity of higher order coupled-cluster equations, i.e. equations involving higher than double excitations, which otherwise would introduce a large prefactor into formal O(NlogN) scaling.

Facile synthesis of hierarchical CoMn2O4 microspheres with porous and micro-/nanostructural morphology as anode electrodes for lithium-ion batteries

NASA Astrophysics Data System (ADS)

Li, Yana; Hou, Xianhua; Li, Yajie; Ru, Qiang; Wang, Shaofeng; Hu, Shejun; Lam, Kwok-ho

2017-09-01

Hierarchical CoMn2O4 microspheres assembled by nanoparticles have been successfully synthesized by a facile hydrothermal method and a subsequent annealing treatment. XRD detection indicate the crystal structure. SEM and TEM results reveal the 3-dimensional porous and micro-/nanostructural microsphere assembled by nanoparticles with a size of 20-100 nm. The CoMn2O4 electrode show initial specific discharge capacity of approximately 1546 mAh/g at the current rates 100 mA/g with a coulombic efficiency of 66.7% and remarkable specific capacities (1029-485 mAh/g) at various current rates (100-2800 mA/g). [Figure not available: see fulltext.

Accelerating Electrostatic Surface Potential Calculation with Multiscale Approximation on Graphics Processing Units

PubMed Central

Anandakrishnan, Ramu; Scogland, Tom R. W.; Fenley, Andrew T.; Gordon, John C.; Feng, Wu-chun; Onufriev, Alexey V.

2010-01-01

Tools that compute and visualize biomolecular electrostatic surface potential have been used extensively for studying biomolecular function. However, determining the surface potential for large biomolecules on a typical desktop computer can take days or longer using currently available tools and methods. Two commonly used techniques to speed up these types of electrostatic computations are approximations based on multi-scale coarse-graining and parallelization across multiple processors. This paper demonstrates that for the computation of electrostatic surface potential, these two techniques can be combined to deliver significantly greater speed-up than either one separately, something that is in general not always possible. Specifically, the electrostatic potential computation, using an analytical linearized Poisson Boltzmann (ALPB) method, is approximated using the hierarchical charge partitioning (HCP) multiscale method, and parallelized on an ATI Radeon 4870 graphical processing unit (GPU). The implementation delivers a combined 934-fold speed-up for a 476,040 atom viral capsid, compared to an equivalent non-parallel implementation on an Intel E6550 CPU without the approximation. This speed-up is significantly greater than the 42-fold speed-up for the HCP approximation alone or the 182-fold speed-up for the GPU alone. PMID:20452792

How Hierarchical Topics Evolve in Large Text Corpora.

PubMed

Cui, Weiwei; Liu, Shixia; Wu, Zhuofeng; Wei, Hao

2014-12-01

Using a sequence of topic trees to organize documents is a popular way to represent hierarchical and evolving topics in text corpora. However, following evolving topics in the context of topic trees remains difficult for users. To address this issue, we present an interactive visual text analysis approach to allow users to progressively explore and analyze the complex evolutionary patterns of hierarchical topics. The key idea behind our approach is to exploit a tree cut to approximate each tree and allow users to interactively modify the tree cuts based on their interests. In particular, we propose an incremental evolutionary tree cut algorithm with the goal of balancing 1) the fitness of each tree cut and the smoothness between adjacent tree cuts; 2) the historical and new information related to user interests. A time-based visualization is designed to illustrate the evolving topics over time. To preserve the mental map, we develop a stable layout algorithm. As a result, our approach can quickly guide users to progressively gain profound insights into evolving hierarchical topics. We evaluate the effectiveness of the proposed method on Amazon's Mechanical Turk and real-world news data. The results show that users are able to successfully analyze evolving topics in text data.

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

"Tools For Analysis and Visualization of Large Time- Varying CFD Data Sets"

NASA Technical Reports Server (NTRS)

Wilhelms, Jane; vanGelder, Allen

1999-01-01

During the four years of this grant (including the one year extension), we have explored many aspects of the visualization of large CFD (Computational Fluid Dynamics) datasets. These have included new direct volume rendering approaches, hierarchical methods, volume decimation, error metrics, parallelization, hardware texture mapping, and methods for analyzing and comparing images. First, we implemented an extremely general direct volume rendering approach that can be used to render rectilinear, curvilinear, or tetrahedral grids, including overlapping multiple zone grids, and time-varying grids. Next, we developed techniques for associating the sample data with a k-d tree, a simple hierarchial data model to approximate samples in the regions covered by each node of the tree, and an error metric for the accuracy of the model. We also explored a new method for determining the accuracy of approximate models based on the light field method described at ACM SIGGRAPH (Association for Computing Machinery Special Interest Group on Computer Graphics) '96. In our initial implementation, we automatically image the volume from 32 approximately evenly distributed positions on the surface of an enclosing tessellated sphere. We then calculate differences between these images under different conditions of volume approximation or decimation.

Yet another method for triangulation and contouring for automated cartography

NASA Technical Reports Server (NTRS)

De Floriani, L.; Falcidieno, B.; Nasy, G.; Pienovi, C.

1982-01-01

An algorithm is presented for hierarchical subdivision of a set of three-dimensional surface observations. The data structure used for obtaining the desired triangulation is also singularly appropriate for extracting contours. Some examples are presented, and the results obtained are compared with those given by Delaunay triangulation. The data points selected by the algorithm provide a better approximation to the desired surface than do randomly selected points.

Speed-up of the volumetric method of moments for the approximate RCS of large arbitrary-shaped dielectric targets

NASA Astrophysics Data System (ADS)

Moreno, Javier; Somolinos, Álvaro; Romero, Gustavo; González, Iván; Cátedra, Felipe

2017-08-01

A method for the rigorous computation of the electromagnetic scattering of large dielectric volumes is presented. One goal is to simplify the analysis of large dielectric targets with translational symmetries taken advantage of their Toeplitz symmetry. Then, the matrix-fill stage of the Method of Moments is efficiently obtained because the number of coupling terms to compute is reduced. The Multilevel Fast Multipole Method is applied to solve the problem. Structured meshes are obtained efficiently to approximate the dielectric volumes. The regular mesh grid is achieved by using parallelepipeds whose centres have been identified as internal to the target. The ray casting algorithm is used to classify the parallelepiped centres. It may become a bottleneck when too many points are evaluated in volumes defined by parametric surfaces, so a hierarchical algorithm is proposed to minimize the number of evaluations. Measurements and analytical results are included for validation purposes.

The hierarchical stability of the seven known large size ratio triple asteroids using the empirical stability parameters.

PubMed

Liu, Xiaodong; Baoyin, Hexi; Marchis, Franck

In this study, the hierarchical stability of the seven known large size ratio triple asteroids is investigated. The effect of the solar gravity and primary's J 2 are considered. The force function is expanded in terms of mass ratios based on the Hill's approximation and the large size ratio property. The empirical stability parameters are used to examine the hierarchical stability of the triple asteroids. It is found that the all the known large size ratio triple asteroid systems are hierarchically stable. This study provides useful information for future evolutions of the triple asteroids.

Approximate Bayesian Computation by Subset Simulation using hierarchical state-space models

NASA Astrophysics Data System (ADS)

Vakilzadeh, Majid K.; Huang, Yong; Beck, James L.; Abrahamsson, Thomas

2017-02-01

A new multi-level Markov Chain Monte Carlo algorithm for Approximate Bayesian Computation, ABC-SubSim, has recently appeared that exploits the Subset Simulation method for efficient rare-event simulation. ABC-SubSim adaptively creates a nested decreasing sequence of data-approximating regions in the output space that correspond to increasingly closer approximations of the observed output vector in this output space. At each level, multiple samples of the model parameter vector are generated by a component-wise Metropolis algorithm so that the predicted output corresponding to each parameter value falls in the current data-approximating region. Theoretically, if continued to the limit, the sequence of data-approximating regions would converge on to the observed output vector and the approximate posterior distributions, which are conditional on the data-approximation region, would become exact, but this is not practically feasible. In this paper we study the performance of the ABC-SubSim algorithm for Bayesian updating of the parameters of dynamical systems using a general hierarchical state-space model. We note that the ABC methodology gives an approximate posterior distribution that actually corresponds to an exact posterior where a uniformly distributed combined measurement and modeling error is added. We also note that ABC algorithms have a problem with learning the uncertain error variances in a stochastic state-space model and so we treat them as nuisance parameters and analytically integrate them out of the posterior distribution. In addition, the statistical efficiency of the original ABC-SubSim algorithm is improved by developing a novel strategy to regulate the proposal variance for the component-wise Metropolis algorithm at each level. We demonstrate that Self-regulated ABC-SubSim is well suited for Bayesian system identification by first applying it successfully to model updating of a two degree-of-freedom linear structure for three cases: globally, locally and un-identifiable model classes, and then to model updating of a two degree-of-freedom nonlinear structure with Duffing nonlinearities in its interstory force-deflection relationship.

Efficient Parallel Formulations of Hierarchical Methods and Their Applications

NASA Astrophysics Data System (ADS)

Grama, Ananth Y.

1996-01-01

Hierarchical methods such as the Fast Multipole Method (FMM) and Barnes-Hut (BH) are used for rapid evaluation of potential (gravitational, electrostatic) fields in particle systems. They are also used for solving integral equations using boundary element methods. The linear systems arising from these methods are dense and are solved iteratively. Hierarchical methods reduce the complexity of the core matrix-vector product from O(n^2) to O(n log n) and the memory requirement from O(n^2) to O(n). We have developed highly scalable parallel formulations of a hybrid FMM/BH method that are capable of handling arbitrarily irregular distributions. We apply these formulations to astrophysical simulations of Plummer and Gaussian galaxies. We have used our parallel formulations to solve the integral form of the Laplace equation. We show that our parallel hierarchical mat-vecs yield high efficiency and overall performance even on relatively small problems. A problem containing approximately 200K nodes takes under a second to compute on 256 processors and yet yields over 85% efficiency. The efficiency and raw performance is expected to increase for bigger problems. For the 200K node problem, our code delivers about 5 GFLOPS of performance on a 256 processor T3D. This is impressive considering the fact that the problem has floating point divides and roots, and very little locality resulting in poor cache performance. A dense matrix-vector product of the same dimensions would require about 0.5 TeraBytes of memory and about 770 TeraFLOPS of computing speed. Clearly, if the loss in accuracy resulting from the use of hierarchical methods is acceptable, our code yields significant savings in time and memory. We also study the convergence of a GMRES solver built around this mat-vec. We accelerate the convergence of the solver using three preconditioning techniques: diagonal scaling, block-diagonal preconditioning, and inner-outer preconditioning. We study the performance and parallel efficiency of these preconditioned solvers. Using this solver, we solve dense linear systems with hundreds of thousands of unknowns. Solving a 105K unknown problem takes about 10 minutes on a 64 processor T3D. Until very recently, boundary element problems of this magnitude could not even be generated, let alone solved.

Hierarchical sinuous-antenna phased array for millimeter wavelengths

NASA Astrophysics Data System (ADS)

Cukierman, Ari; Lee, Adrian T.; Raum, Christopher; Suzuki, Aritoki; Westbrook, Benjamin

2018-03-01

We present the design, fabrication, and measured performance of a hierarchical sinuous-antenna phased array coupled to superconducting transition-edge-sensor (TES) bolometers for millimeter wavelengths. The architecture allows for dual-polarization wideband sensitivity with a beam width that is approximately frequency-independent. We report on measurements of a prototype device, which uses three levels of triangular phased arrays to synthesize beams that are approximately constant in width across three frequency bands covering a 3:1 bandwidth. The array element is a lens-coupled sinuous antenna. The device consists of an array of hemispherical lenses coupled to a lithographed wafer, which integrates TESs, planar sinuous antennas, and microwave circuitry including band-defining filters. The approximately frequency-independent beam widths improve coupling to telescope optics and keep the sensitivity of an experiment close to optimal across a broad frequency range. The design can be straightforwardly modified for use with non-TES lithographed cryogenic detectors such as kinetic inductance detectors. Additionally, we report on the design and measurements of a broadband 180° hybrid that can simplify the design of future multichroic focal planes including but not limited to hierarchical phased arrays.

EVOLUTION OF THE VELOCITY-DISPERSION FUNCTION OF LUMINOUS RED GALAXIES: A HIERARCHICAL BAYESIAN MEASUREMENT

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

Shu Yiping; Bolton, Adam S.; Dawson, Kyle S.

2012-04-15

We present a hierarchical Bayesian determination of the velocity-dispersion function of approximately 430,000 massive luminous red galaxies observed at relatively low spectroscopic signal-to-noise ratio (S/N {approx} 3-5 per 69 km s{sup -1}) by the Baryon Oscillation Spectroscopic Survey (BOSS) of the Sloan Digital Sky Survey III. We marginalize over spectroscopic redshift errors, and use the full velocity-dispersion likelihood function for each galaxy to make a self-consistent determination of the velocity-dispersion distribution parameters as a function of absolute magnitude and redshift, correcting as well for the effects of broadband magnitude errors on our binning. Parameterizing the distribution at each point inmore » the luminosity-redshift plane with a log-normal form, we detect significant evolution in the width of the distribution toward higher intrinsic scatter at higher redshifts. Using a subset of deep re-observations of BOSS galaxies, we demonstrate that our distribution-parameter estimates are unbiased regardless of spectroscopic S/N. We also show through simulation that our method introduces no systematic parameter bias with redshift. We highlight the advantage of the hierarchical Bayesian method over frequentist 'stacking' of spectra, and illustrate how our measured distribution parameters can be adopted as informative priors for velocity-dispersion measurements from individual noisy spectra.« less

Accelerating electrostatic surface potential calculation with multi-scale approximation on graphics processing units.

PubMed

Anandakrishnan, Ramu; Scogland, Tom R W; Fenley, Andrew T; Gordon, John C; Feng, Wu-chun; Onufriev, Alexey V

2010-06-01

Tools that compute and visualize biomolecular electrostatic surface potential have been used extensively for studying biomolecular function. However, determining the surface potential for large biomolecules on a typical desktop computer can take days or longer using currently available tools and methods. Two commonly used techniques to speed-up these types of electrostatic computations are approximations based on multi-scale coarse-graining and parallelization across multiple processors. This paper demonstrates that for the computation of electrostatic surface potential, these two techniques can be combined to deliver significantly greater speed-up than either one separately, something that is in general not always possible. Specifically, the electrostatic potential computation, using an analytical linearized Poisson-Boltzmann (ALPB) method, is approximated using the hierarchical charge partitioning (HCP) multi-scale method, and parallelized on an ATI Radeon 4870 graphical processing unit (GPU). The implementation delivers a combined 934-fold speed-up for a 476,040 atom viral capsid, compared to an equivalent non-parallel implementation on an Intel E6550 CPU without the approximation. This speed-up is significantly greater than the 42-fold speed-up for the HCP approximation alone or the 182-fold speed-up for the GPU alone. Copyright (c) 2010 Elsevier Inc. All rights reserved.

Simplified approach to the mixed time-averaging semiclassical initial value representation for the calculation of dense vibrational spectra

NASA Astrophysics Data System (ADS)

Buchholz, Max; Grossmann, Frank; Ceotto, Michele

2018-03-01

We present and test an approximate method for the semiclassical calculation of vibrational spectra. The approach is based on the mixed time-averaging semiclassical initial value representation method, which is simplified to a form that contains a filter to remove contributions from approximately harmonic environmental degrees of freedom. This filter comes at no additional numerical cost, and it has no negative effect on the accuracy of peaks from the anharmonic system of interest. The method is successfully tested for a model Hamiltonian and then applied to the study of the frequency shift of iodine in a krypton matrix. Using a hierarchic model with up to 108 normal modes included in the calculation, we show how the dynamical interaction between iodine and krypton yields results for the lowest excited iodine peaks that reproduce experimental findings to a high degree of accuracy.

Hierarchical matrices implemented into the boundary integral approaches for gravity field modelling

NASA Astrophysics Data System (ADS)

Čunderlík, Róbert; Vipiana, Francesca

2017-04-01

Boundary integral approaches applied for gravity field modelling have been recently developed to solve the geodetic boundary value problems numerically, or to process satellite observations, e.g. from the GOCE satellite mission. In order to obtain numerical solutions of "cm-level" accuracy, such approaches require very refined level of the disretization or resolution. This leads to enormous memory requirements that need to be reduced. An implementation of the Hierarchical Matrices (H-matrices) can significantly reduce a numerical complexity of these approaches. A main idea of the H-matrices is based on an approximation of the entire system matrix that is split into a family of submatrices. Large submatrices are stored in factorized representation, while small submatrices are stored in standard representation. This allows reducing memory requirements significantly while improving the efficiency. The poster presents our preliminary results of implementations of the H-matrices into the existing boundary integral approaches based on the boundary element method or the method of fundamental solution.

Galactic archaeology in action space

NASA Astrophysics Data System (ADS)

Sanderson, Robyn

2009-05-01

Working in action space offers an instructive alternative view of the process of hierarchical assembly in galaxies, but performing the necessary canonical transformation formally requires both complete phase space information of a stellar population and knowledge of the correct galactic potential, neither of which is generally available. I use the approximate-action method pioneered by MacMillan and Binney (2008) to examine the remnant of a late-time merger in M31, which was modeled by Fardal et al. (2007).

Hierarchical fractional-step approximations and parallel kinetic Monte Carlo algorithms

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

Arampatzis, Giorgos, E-mail: garab@math.uoc.gr; Katsoulakis, Markos A., E-mail: markos@math.umass.edu; Plechac, Petr, E-mail: plechac@math.udel.edu

2012-10-01

We present a mathematical framework for constructing and analyzing parallel algorithms for lattice kinetic Monte Carlo (KMC) simulations. The resulting algorithms have the capacity to simulate a wide range of spatio-temporal scales in spatially distributed, non-equilibrium physiochemical processes with complex chemistry and transport micro-mechanisms. Rather than focusing on constructing exactly the stochastic trajectories, our approach relies on approximating the evolution of observables, such as density, coverage, correlations and so on. More specifically, we develop a spatial domain decomposition of the Markov operator (generator) that describes the evolution of all observables according to the kinetic Monte Carlo algorithm. This domain decompositionmore » corresponds to a decomposition of the Markov generator into a hierarchy of operators and can be tailored to specific hierarchical parallel architectures such as multi-core processors or clusters of Graphical Processing Units (GPUs). Based on this operator decomposition, we formulate parallel Fractional step kinetic Monte Carlo algorithms by employing the Trotter Theorem and its randomized variants; these schemes, (a) are partially asynchronous on each fractional step time-window, and (b) are characterized by their communication schedule between processors. The proposed mathematical framework allows us to rigorously justify the numerical and statistical consistency of the proposed algorithms, showing the convergence of our approximating schemes to the original serial KMC. The approach also provides a systematic evaluation of different processor communicating schedules. We carry out a detailed benchmarking of the parallel KMC schemes using available exact solutions, for example, in Ising-type systems and we demonstrate the capabilities of the method to simulate complex spatially distributed reactions at very large scales on GPUs. Finally, we discuss work load balancing between processors and propose a re-balancing scheme based on probabilistic mass transport methods.« less

A topological hierarchy for functions on triangulated surfaces.

PubMed

Bremer, Peer-Timo; Edelsbrunner, Herbert; Hamann, Bernd; Pascucci, Valerio

2004-01-01

We combine topological and geometric methods to construct a multiresolution representation for a function over a two-dimensional domain. In a preprocessing stage, we create the Morse-Smale complex of the function and progressively simplify its topology by cancelling pairs of critical points. Based on a simple notion of dependency among these cancellations, we construct a hierarchical data structure supporting traversal and reconstruction operations similarly to traditional geometry-based representations. We use this data structure to extract topologically valid approximations that satisfy error bounds provided at runtime.

Facile preparation of hierarchically porous diatomite/MFI-type zeolite composites and their performance of benzene adsorption: the effects of NaOH etching pretreatment.

PubMed

Yu, Wenbin; Yuan, Peng; Liu, Dong; Deng, Liangliang; Yuan, Weiwei; Tao, Bo; Cheng, Hefa; Chen, Fanrong

2015-03-21

Hierarchically porous diatomite/MFI-type zeolite (Dt/Z) composites with excellent benzene adsorption performance were prepared. The hierarchical porosity was generated from the microporous zeolite coated at the surface of diatom frustules and from the macroporous diatomite support. A facile NaOH etching method was employed for the first time to treat the frustule support, followed by hydrothermal growth of MFI-type zeolite at the surface of frustules previously seeded with nanocrystalline silicalite-1 (Sil-1). NaOH etching enlarged the pores on diatom frustules and further increased the coated zeolite contents (W(z)). The central macropore size of the diatom frustules increased from approximately 200-500 nm to 400-1000 nm after NaOH etching. The W(z) could reach 61.2%, while the macroporosity of the composites was largely preserved due to more voids for zeolite coating being formed by NaOH etching. The Dt/Z composites exhibited higher benzene adsorption capacity per unit mass of zeolite and less mass transfer resistance than Sil-1, evaluated via a method of breakthrough curves. These results demonstrate that etching of a diatomite support is a facile but crucial process for the preparation of Dt/Z composites, enabling the resulting composites to become promising candidates for uses in volatile organic compounds emission control. Copyright © 2014 Elsevier B.V. All rights reserved.

Sampling-free Bayesian inversion with adaptive hierarchical tensor representations

NASA Astrophysics Data System (ADS)

Eigel, Martin; Marschall, Manuel; Schneider, Reinhold

2018-03-01

A sampling-free approach to Bayesian inversion with an explicit polynomial representation of the parameter densities is developed, based on an affine-parametric representation of a linear forward model. This becomes feasible due to the complete treatment in function spaces, which requires an efficient model reduction technique for numerical computations. The advocated perspective yields the crucial benefit that error bounds can be derived for all occuring approximations, leading to provable convergence subject to the discretization parameters. Moreover, it enables a fully adaptive a posteriori control with automatic problem-dependent adjustments of the employed discretizations. The method is discussed in the context of modern hierarchical tensor representations, which are used for the evaluation of a random PDE (the forward model) and the subsequent high-dimensional quadrature of the log-likelihood, alleviating the ‘curse of dimensionality’. Numerical experiments demonstrate the performance and confirm the theoretical results.

Hierarchical Volume Representation with 3{radical}2 Subdivision and Trivariate B-Spline Wavelets

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

Linsen, L; Gray, JT; Pascucci, V

2002-01-11

Multiresolution methods provide a means for representing data at multiple levels of detail. They are typically based on a hierarchical data organization scheme and update rules needed for data value computation. We use a data organization that is based on what we call n{radical}2 subdivision. The main advantage of subdivision, compared to quadtree (n = 2) or octree (n = 3) organizations, is that the number of vertices is only doubled in each subdivision step instead of multiplied by a factor of four or eight, respectively. To update data values we use n-variate B-spline wavelets, which yields better approximations formore » each level of detail. We develop a lifting scheme for n = 2 and n = 3 based on the n{radical}2-subdivision scheme. We obtain narrow masks that could also provide a basis for view-dependent visualization and adaptive refinement.« less

Approximate message passing with restricted Boltzmann machine priors

NASA Astrophysics Data System (ADS)

Tramel, Eric W.; Drémeau, Angélique; Krzakala, Florent

2016-07-01

Approximate message passing (AMP) has been shown to be an excellent statistical approach to signal inference and compressed sensing problems. The AMP framework provides modularity in the choice of signal prior; here we propose a hierarchical form of the Gauss-Bernoulli prior which utilizes a restricted Boltzmann machine (RBM) trained on the signal support to push reconstruction performance beyond that of simple i.i.d. priors for signals whose support can be well represented by a trained binary RBM. We present and analyze two methods of RBM factorization and demonstrate how these affect signal reconstruction performance within our proposed algorithm. Finally, using the MNIST handwritten digit dataset, we show experimentally that using an RBM allows AMP to approach oracle-support performance.

Bayesian cross-validation for model evaluation and selection, with application to the North American Breeding Bird Survey

USGS Publications Warehouse

Link, William; Sauer, John R.

2016-01-01

The analysis of ecological data has changed in two important ways over the last 15 years. The development and easy availability of Bayesian computational methods has allowed and encouraged the fitting of complex hierarchical models. At the same time, there has been increasing emphasis on acknowledging and accounting for model uncertainty. Unfortunately, the ability to fit complex models has outstripped the development of tools for model selection and model evaluation: familiar model selection tools such as Akaike's information criterion and the deviance information criterion are widely known to be inadequate for hierarchical models. In addition, little attention has been paid to the evaluation of model adequacy in context of hierarchical modeling, i.e., to the evaluation of fit for a single model. In this paper, we describe Bayesian cross-validation, which provides tools for model selection and evaluation. We describe the Bayesian predictive information criterion and a Bayesian approximation to the BPIC known as the Watanabe-Akaike information criterion. We illustrate the use of these tools for model selection, and the use of Bayesian cross-validation as a tool for model evaluation, using three large data sets from the North American Breeding Bird Survey.

Identification of multivariable nonlinear systems in the presence of colored noises using iterative hierarchical least squares algorithm.

PubMed

Jafari, Masoumeh; Salimifard, Maryam; Dehghani, Maryam

2014-07-01

This paper presents an efficient method for identification of nonlinear Multi-Input Multi-Output (MIMO) systems in the presence of colored noises. The method studies the multivariable nonlinear Hammerstein and Wiener models, in which, the nonlinear memory-less block is approximated based on arbitrary vector-based basis functions. The linear time-invariant (LTI) block is modeled by an autoregressive moving average with exogenous (ARMAX) model which can effectively describe the moving average noises as well as the autoregressive and the exogenous dynamics. According to the multivariable nature of the system, a pseudo-linear-in-the-parameter model is obtained which includes two different kinds of unknown parameters, a vector and a matrix. Therefore, the standard least squares algorithm cannot be applied directly. To overcome this problem, a Hierarchical Least Squares Iterative (HLSI) algorithm is used to simultaneously estimate the vector and the matrix of unknown parameters as well as the noises. The efficiency of the proposed identification approaches are investigated through three nonlinear MIMO case studies. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Size-dependent elastic/inelastic behavior of enamel over millimeter and nanometer length scales.

PubMed

Ang, Siang Fung; Bortel, Emely L; Swain, Michael V; Klocke, Arndt; Schneider, Gerold A

2010-03-01

The microstructure of enamel like most biological tissues has a hierarchical structure which determines their mechanical behavior. However, current studies of the mechanical behavior of enamel lack a systematic investigation of these hierarchical length scales. In this study, we performed macroscopic uni-axial compression tests and the spherical indentation with different indenter radii to probe enamel's elastic/inelastic transition over four hierarchical length scales, namely: 'bulk enamel' (mm), 'multiple-rod' (10's microm), 'intra-rod' (100's nm with multiple crystallites) and finally 'single-crystallite' (10's nm with an area of approximately one hydroxyapatite crystallite). The enamel's elastic/inelastic transitions were observed at 0.4-17 GPa depending on the length scale and were compared with the values of synthetic hydroxyapatite crystallites. The elastic limit of a material is important as it provides insights into the deformability of the material before fracture. At the smallest investigated length scale (contact radius approximately 20 nm), elastic limit is followed by plastic deformation. At the largest investigated length scale (contact size approximately 2 mm), only elastic then micro-crack induced response was observed. A map of elastic/inelastic regions of enamel from millimeter to nanometer length scale is presented. Possible underlying mechanisms are also discussed. (c) 2009 Elsevier Ltd. All rights reserved.

Hierarchical mutual information for the comparison of hierarchical community structures in complex networks

NASA Astrophysics Data System (ADS)

Perotti, Juan Ignacio; Tessone, Claudio Juan; Caldarelli, Guido

2015-12-01

The quest for a quantitative characterization of community and modular structure of complex networks produced a variety of methods and algorithms to classify different networks. However, it is not clear if such methods provide consistent, robust, and meaningful results when considering hierarchies as a whole. Part of the problem is the lack of a similarity measure for the comparison of hierarchical community structures. In this work we give a contribution by introducing the hierarchical mutual information, which is a generalization of the traditional mutual information and makes it possible to compare hierarchical partitions and hierarchical community structures. The normalized version of the hierarchical mutual information should behave analogously to the traditional normalized mutual information. Here the correct behavior of the hierarchical mutual information is corroborated on an extensive battery of numerical experiments. The experiments are performed on artificial hierarchies and on the hierarchical community structure of artificial and empirical networks. Furthermore, the experiments illustrate some of the practical applications of the hierarchical mutual information, namely the comparison of different community detection methods and the study of the consistency, robustness, and temporal evolution of the hierarchical modular structure of networks.

Hierarchic models for laminated plates. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Actis, Ricardo Luis

1991-01-01

Structural plates and shells are three-dimensional bodies, one dimension of which happens to be much smaller than the other two. Thus, the quality of a plate or shell model must be judged on the basis of how well its exact solution approximates the corresponding three-dimensional problem. Of course, the exact solution depends not only on the choice of the model but also on the topology, material properties, loading and constraints. The desired degree of approximation depends on the analyst's goals in performing the analysis. For these reasons models have to be chosen adaptively. Hierarchic sequences of models make adaptive selection of the model which is best suited for the purposes of a particular analysis possible. The principles governing the formulation of hierarchic models for laminated plates are presented. The essential features of the hierarchic models described models are: (1) the exact solutions corresponding to the hierarchic sequence of models converge to the exact solution of the corresponding problem of elasticity for a fixed laminate thickness; and (2) the exact solution of each model converges to the same limit as the exact solution of the corresponding problem of elasticity with respect to the laminate thickness approaching zero. The formulation is based on one parameter (beta) which characterizes the hierarchic sequence of models, and a set of constants whose influence was assessed by a numerical sensitivity study. The recommended selection of these constants results in the number of fields increasing by three for each increment in the power of beta. Numerical examples analyzed with the proposed sequence of models are included and good correlation with the reference solutions was found. Results were obtained for laminated strips (plates in cylindrical bending) and for square and rectangular plates with uniform loading and with homogeneous boundary conditions. Cross-ply and angle-ply laminates were evaluated and the results compared with those of MSC/PROBE. Hierarchic models make the computation of any engineering data possible to an arbitrary level of precision within the framework of the theory of elasticity.

POLLUTION PREVENTION IN THE EARLY STAGES OF HIERARCHICAL PROCESS DESIGN

EPA Science Inventory

Hierarchical methods are often used in the conceptual stages of process design to synthesize and evaluate process alternatives. In this work, the methods of hierarchical process design will be focused on environmental aspects. In particular, the design methods will be coupled to ...

An improved computational approach for multilevel optimum design

NASA Technical Reports Server (NTRS)

Haftka, R. T.

1984-01-01

A penalty-function algorithm employing Newton's method with approximate second derivatives (Haftka and Starnes, 1980) is developed for two-level hierarchical design optimization problems. The difficulties posed by discontinuous behavior in typical multilevel problems are explained and illustrated for the case of a three-bar truss; the algorithm is formulated; and its advantages are demonstrated in the problem of a portal framework having three beams (described by six cross-section parameters), subjected to two loading conditions, and to be constructed in six different materials for comparison. The final design parameters are listed in a table.

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

Chen, Chao; Pouransari, Hadi; Rajamanickam, Sivasankaran

We present a parallel hierarchical solver for general sparse linear systems on distributed-memory machines. For large-scale problems, this fully algebraic algorithm is faster and more memory-efficient than sparse direct solvers because it exploits the low-rank structure of fill-in blocks. Depending on the accuracy of low-rank approximations, the hierarchical solver can be used either as a direct solver or as a preconditioner. The parallel algorithm is based on data decomposition and requires only local communication for updating boundary data on every processor. Moreover, the computation-to-communication ratio of the parallel algorithm is approximately the volume-to-surface-area ratio of the subdomain owned by everymore » processor. We also provide various numerical results to demonstrate the versatility and scalability of the parallel algorithm.« less

Detecting concerted demographic response across community assemblages using hierarchical approximate Bayesian computation.

PubMed

Chan, Yvonne L; Schanzenbach, David; Hickerson, Michael J

2014-09-01

Methods that integrate population-level sampling from multiple taxa into a single community-level analysis are an essential addition to the comparative phylogeographic toolkit. Detecting how species within communities have demographically tracked each other in space and time is important for understanding the effects of future climate and landscape changes and the resulting acceleration of extinctions, biological invasions, and potential surges in adaptive evolution. Here, we present a statistical framework for such an analysis based on hierarchical approximate Bayesian computation (hABC) with the goal of detecting concerted demographic histories across an ecological assemblage. Our method combines population genetic data sets from multiple taxa into a single analysis to estimate: 1) the proportion of a community sample that demographically expanded in a temporally clustered pulse and 2) when the pulse occurred. To validate the accuracy and utility of this new approach, we use simulation cross-validation experiments and subsequently analyze an empirical data set of 32 avian populations from Australia that are hypothesized to have expanded from smaller refugia populations in the late Pleistocene. The method can accommodate data set heterogeneity such as variability in effective population size, mutation rates, and sample sizes across species and exploits the statistical strength from the simultaneous analysis of multiple species. This hABC framework used in a multitaxa demographic context can increase our understanding of the impact of historical climate change by determining what proportion of the community responded in concert or independently and can be used with a wide variety of comparative phylogeographic data sets as biota-wide DNA barcoding data sets accumulate. © The Author 2014. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

Facial animation on an anatomy-based hierarchical face model

NASA Astrophysics Data System (ADS)

Zhang, Yu; Prakash, Edmond C.; Sung, Eric

2003-04-01

In this paper we propose a new hierarchical 3D facial model based on anatomical knowledge that provides high fidelity for realistic facial expression animation. Like real human face, the facial model has a hierarchical biomechanical structure, incorporating a physically-based approximation to facial skin tissue, a set of anatomically-motivated facial muscle actuators and underlying skull structure. The deformable skin model has multi-layer structure to approximate different types of soft tissue. It takes into account the nonlinear stress-strain relationship of the skin and the fact that soft tissue is almost incompressible. Different types of muscle models have been developed to simulate distribution of the muscle force on the skin due to muscle contraction. By the presence of the skull model, our facial model takes advantage of both more accurate facial deformation and the consideration of facial anatomy during the interactive definition of facial muscles. Under the muscular force, the deformation of the facial skin is evaluated using numerical integration of the governing dynamic equations. The dynamic facial animation algorithm runs at interactive rate with flexible and realistic facial expressions to be generated.

Hierarchical Ensemble Methods for Protein Function Prediction

PubMed Central

2014-01-01

Protein function prediction is a complex multiclass multilabel classification problem, characterized by multiple issues such as the incompleteness of the available annotations, the integration of multiple sources of high dimensional biomolecular data, the unbalance of several functional classes, and the difficulty of univocally determining negative examples. Moreover, the hierarchical relationships between functional classes that characterize both the Gene Ontology and FunCat taxonomies motivate the development of hierarchy-aware prediction methods that showed significantly better performances than hierarchical-unaware “flat” prediction methods. In this paper, we provide a comprehensive review of hierarchical methods for protein function prediction based on ensembles of learning machines. According to this general approach, a separate learning machine is trained to learn a specific functional term and then the resulting predictions are assembled in a “consensus” ensemble decision, taking into account the hierarchical relationships between classes. The main hierarchical ensemble methods proposed in the literature are discussed in the context of existing computational methods for protein function prediction, highlighting their characteristics, advantages, and limitations. Open problems of this exciting research area of computational biology are finally considered, outlining novel perspectives for future research. PMID:25937954

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

PubMed

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

2015-01-01

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

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

PubMed Central

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

2015-01-01

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

Hierarchic plate and shell models based on p-extension

NASA Technical Reports Server (NTRS)

Szabo, Barna A.; Sahrmann, Glenn J.

1988-01-01

Formulations of finite element models for beams, arches, plates and shells based on the principle of virtual work was studied. The focus is on computer implementation of hierarchic sequences of finite element models suitable for numerical solution of a large variety of practical problems which may concurrently contain thin and thick plates and shells, stiffeners, and regions where three dimensional representation is required. The approximate solutions corresponding to the hierarchic sequence of models converge to the exact solution of the fully three dimensional model. The stopping criterion is based on: (1) estimation of the relative error in energy norm; (2) equilibrium tests, and (3) observation of the convergence of quantities of interest.

Hierarchical Spatial Concept Formation Based on Multimodal Information for Human Support Robots.

PubMed

Hagiwara, Yoshinobu; Inoue, Masakazu; Kobayashi, Hiroyoshi; Taniguchi, Tadahiro

2018-01-01

In this paper, we propose a hierarchical spatial concept formation method based on the Bayesian generative model with multimodal information e.g., vision, position and word information. Since humans have the ability to select an appropriate level of abstraction according to the situation and describe their position linguistically, e.g., "I am in my home" and "I am in front of the table," a hierarchical structure of spatial concepts is necessary in order for human support robots to communicate smoothly with users. The proposed method enables a robot to form hierarchical spatial concepts by categorizing multimodal information using hierarchical multimodal latent Dirichlet allocation (hMLDA). Object recognition results using convolutional neural network (CNN), hierarchical k-means clustering result of self-position estimated by Monte Carlo localization (MCL), and a set of location names are used, respectively, as features in vision, position, and word information. Experiments in forming hierarchical spatial concepts and evaluating how the proposed method can predict unobserved location names and position categories are performed using a robot in the real world. Results verify that, relative to comparable baseline methods, the proposed method enables a robot to predict location names and position categories closer to predictions made by humans. As an application example of the proposed method in a home environment, a demonstration in which a human support robot moves to an instructed place based on human speech instructions is achieved based on the formed hierarchical spatial concept.

Hierarchical Spatial Concept Formation Based on Multimodal Information for Human Support Robots

PubMed Central

Hagiwara, Yoshinobu; Inoue, Masakazu; Kobayashi, Hiroyoshi; Taniguchi, Tadahiro

2018-01-01

In this paper, we propose a hierarchical spatial concept formation method based on the Bayesian generative model with multimodal information e.g., vision, position and word information. Since humans have the ability to select an appropriate level of abstraction according to the situation and describe their position linguistically, e.g., “I am in my home” and “I am in front of the table,” a hierarchical structure of spatial concepts is necessary in order for human support robots to communicate smoothly with users. The proposed method enables a robot to form hierarchical spatial concepts by categorizing multimodal information using hierarchical multimodal latent Dirichlet allocation (hMLDA). Object recognition results using convolutional neural network (CNN), hierarchical k-means clustering result of self-position estimated by Monte Carlo localization (MCL), and a set of location names are used, respectively, as features in vision, position, and word information. Experiments in forming hierarchical spatial concepts and evaluating how the proposed method can predict unobserved location names and position categories are performed using a robot in the real world. Results verify that, relative to comparable baseline methods, the proposed method enables a robot to predict location names and position categories closer to predictions made by humans. As an application example of the proposed method in a home environment, a demonstration in which a human support robot moves to an instructed place based on human speech instructions is achieved based on the formed hierarchical spatial concept. PMID:29593521

Predictors of Drinking Water Boiling and Bottled Water Consumption in Rural China: A Hierarchical Modeling Approach.

PubMed

Cohen, Alasdair; Zhang, Qi; Luo, Qing; Tao, Yong; Colford, John M; Ray, Isha

2017-06-20

Approximately two billion people drink unsafe water. Boiling is the most commonly used household water treatment (HWT) method globally and in China. HWT can make water safer, but sustained adoption is rare and bottled water consumption is growing. To successfully promote HWT, an understanding of associated socioeconomic factors is critical. We collected survey data and water samples from 450 rural households in Guangxi Province, China. Covariates were grouped into blocks to hierarchically construct modified Poisson models and estimate risk ratios (RR) associated with boiling methods, bottled water, and untreated water. Female-headed households were most likely to boil (RR = 1.36, p < 0.01), and among boilers those using electric kettles rather than pots had higher income proxies (e.g., per capita TV ownership RR = 1.42, p < 0.01). Higher-income households with younger, literate, and male heads were more likely to purchase (frequently contaminated) bottled water, or use electric kettles if they boiled. Our findings show that boiling is not an undifferentiated practice, but one with different methods of varying effectiveness, environmental impact, and adoption across socioeconomic strata. Our results can inform programs to promote safer and more efficient boiling using electric kettles, and suggest that if rural China's economy continues to grow then bottled water use will increase.

Hierarchical socioeconomic fractality: The rich, the poor, and the middle-class

NASA Astrophysics Data System (ADS)

Eliazar, Iddo; Cohen, Morrel H.

2014-05-01

Since the seminal work of the Italian economist Vilfredo Pareto, the study of wealth and income has been a topic of active scientific exploration engaging researches ranging from economics and political science to econophysics and complex systems. This paper investigates the intrinsic fractality of wealth and income. To that end we introduce and characterize three forms of socioeconomic scale-invariance-poor fractality, rich fractality, and middle-class fractality-and construct hierarchical fractal approximations of general wealth and income distributions, based on the stitching of these three forms of fractality. Intertwining the theoretical results with real-world empirical data we then establish that the three forms of socioeconomic fractality-amalgamated into a composite hierarchical structure-underlie the distributions of wealth and income in human societies. We further establish that the hierarchical socioeconomic fractality of wealth and income is also displayed by empirical rank distributions observed across the sciences.

Efficient Bayesian hierarchical functional data analysis with basis function approximations using Gaussian-Wishart processes.

PubMed

Yang, Jingjing; Cox, Dennis D; Lee, Jong Soo; Ren, Peng; Choi, Taeryon

2017-12-01

Functional data are defined as realizations of random functions (mostly smooth functions) varying over a continuum, which are usually collected on discretized grids with measurement errors. In order to accurately smooth noisy functional observations and deal with the issue of high-dimensional observation grids, we propose a novel Bayesian method based on the Bayesian hierarchical model with a Gaussian-Wishart process prior and basis function representations. We first derive an induced model for the basis-function coefficients of the functional data, and then use this model to conduct posterior inference through Markov chain Monte Carlo methods. Compared to the standard Bayesian inference that suffers serious computational burden and instability in analyzing high-dimensional functional data, our method greatly improves the computational scalability and stability, while inheriting the advantage of simultaneously smoothing raw observations and estimating the mean-covariance functions in a nonparametric way. In addition, our method can naturally handle functional data observed on random or uncommon grids. Simulation and real studies demonstrate that our method produces similar results to those obtainable by the standard Bayesian inference with low-dimensional common grids, while efficiently smoothing and estimating functional data with random and high-dimensional observation grids when the standard Bayesian inference fails. In conclusion, our method can efficiently smooth and estimate high-dimensional functional data, providing one way to resolve the curse of dimensionality for Bayesian functional data analysis with Gaussian-Wishart processes. © 2017, The International Biometric Society.

A review of surrogate models and their application to groundwater modeling

NASA Astrophysics Data System (ADS)

Asher, M. J.; Croke, B. F. W.; Jakeman, A. J.; Peeters, L. J. M.

2015-08-01

The spatially and temporally variable parameters and inputs to complex groundwater models typically result in long runtimes which hinder comprehensive calibration, sensitivity, and uncertainty analysis. Surrogate modeling aims to provide a simpler, and hence faster, model which emulates the specified output of a more complex model in function of its inputs and parameters. In this review paper, we summarize surrogate modeling techniques in three categories: data-driven, projection, and hierarchical-based approaches. Data-driven surrogates approximate a groundwater model through an empirical model that captures the input-output mapping of the original model. Projection-based models reduce the dimensionality of the parameter space by projecting the governing equations onto a basis of orthonormal vectors. In hierarchical or multifidelity methods the surrogate is created by simplifying the representation of the physical system, such as by ignoring certain processes, or reducing the numerical resolution. In discussing the application to groundwater modeling of these methods, we note several imbalances in the existing literature: a large body of work on data-driven approaches seemingly ignores major drawbacks to the methods; only a fraction of the literature focuses on creating surrogates to reproduce outputs of fully distributed groundwater models, despite these being ubiquitous in practice; and a number of the more advanced surrogate modeling methods are yet to be fully applied in a groundwater modeling context.

A Hierarchical Multivariate Bayesian Approach to Ensemble Model output Statistics in Atmospheric Prediction

DTIC Science & Technology

2017-09-01

efficacy of statistical post-processing methods downstream of these dynamical model components with a hierarchical multivariate Bayesian approach to...Bayesian hierarchical modeling, Markov chain Monte Carlo methods , Metropolis algorithm, machine learning, atmospheric prediction 15. NUMBER OF PAGES...scale processes. However, this dissertation explores the efficacy of statistical post-processing methods downstream of these dynamical model components

Data graphing methods, articles of manufacture, and computing devices

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

Wong, Pak Chung; Mackey, Patrick S.; Cook, Kristin A.

Data graphing methods, articles of manufacture, and computing devices are described. In one aspect, a method includes accessing a data set, displaying a graphical representation including data of the data set which is arranged according to a first of different hierarchical levels, wherein the first hierarchical level represents the data at a first of a plurality of different resolutions which respectively correspond to respective ones of the hierarchical levels, selecting a portion of the graphical representation wherein the data of the portion is arranged according to the first hierarchical level at the first resolution, modifying the graphical representation by arrangingmore » the data of the portion according to a second of the hierarchal levels at a second of the resolutions, and after the modifying, displaying the graphical representation wherein the data of the portion is arranged according to the second hierarchal level at the second resolution.« less

Advances in Applications of Hierarchical Bayesian Methods with Hydrological Models

NASA Astrophysics Data System (ADS)

Alexander, R. B.; Schwarz, G. E.; Boyer, E. W.

2017-12-01

Mechanistic and empirical watershed models are increasingly used to inform water resource decisions. Growing access to historical stream measurements and data from in-situ sensor technologies has increased the need for improved techniques for coupling models with hydrological measurements. Techniques that account for the intrinsic uncertainties of both models and measurements are especially needed. Hierarchical Bayesian methods provide an efficient modeling tool for quantifying model and prediction uncertainties, including those associated with measurements. Hierarchical methods can also be used to explore spatial and temporal variations in model parameters and uncertainties that are informed by hydrological measurements. We used hierarchical Bayesian methods to develop a hybrid (statistical-mechanistic) SPARROW (SPAtially Referenced Regression On Watershed attributes) model of long-term mean annual streamflow across diverse environmental and climatic drainages in 18 U.S. hydrological regions. Our application illustrates the use of a new generation of Bayesian methods that offer more advanced computational efficiencies than the prior generation. Evaluations of the effects of hierarchical (regional) variations in model coefficients and uncertainties on model accuracy indicates improved prediction accuracies (median of 10-50%) but primarily in humid eastern regions, where model uncertainties are one-third of those in arid western regions. Generally moderate regional variability is observed for most hierarchical coefficients. Accounting for measurement and structural uncertainties, using hierarchical state-space techniques, revealed the effects of spatially-heterogeneous, latent hydrological processes in the "localized" drainages between calibration sites; this improved model precision, with only minor changes in regional coefficients. Our study can inform advances in the use of hierarchical methods with hydrological models to improve their integration with stream measurements.

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

Zhou, Yi, E-mail: zhouyihn@163.com; Huang, Yan; Li, Dang

Graphical abstract: SEM images of the samples synthesized at different hydrothermal temperatures for 8 h: (a) 75; (b) 100; (c) 120; and (d) 140°C, followed by calcination at 450 °C for 2 h. Highlights: ► Effects of calcination temperature on the phase transformation were studied. ► Effects of hydrothermal temperature and time on the morphology growth were studied. ► A two-stage reaction mechanism for the formation was presented. ► The photocatalytic activity was evaluated under sunlight irradiation. ► Effects of calcination temperature on the photocatalytic activity were studied. - Abstract: Novel three-dimensional sea-urchin-like hierarchical TiO{sub 2} superstructures were synthesized onmore » a Ti plate in a mixture of H{sub 2}O{sub 2} and NaOH aqueous solution by a facile one-pot hydrothermal method at a low temperature, followed by protonation and calcination. The results of series of electron microscopy characterizations suggested that the hierarchical TiO{sub 2} superstructures consisted of numerous one-dimensional nanostructures. The microspheres were approximately 2–4 μm in diameter, and the one-dimensional TiO{sub 2} nanostructures were up to 600–700 nm long. A two-stage reaction mechanism, i.e., initial growth and then assembly, was proposed for the formation of these architectures. The three-dimensional sea-urchin-like hierarchical TiO{sub 2} microstructures showed excellent photocatalytic activity for the degradation of Rhodamine B aqueous solution under sunlight irradiation, which was attributed to the special three-dimensional hierarchical superstructure, and increased number of surface active sites. This novel superstructure has promising use in practical aqueous purification.« less

Broadband locally resonant metamaterials with graded hierarchical architecture

NASA Astrophysics Data System (ADS)

Liu, Chenchen; Reina, Celia

2018-03-01

We investigate the effect of hierarchical designs on the bandgap structure of periodic lattice systems with inner resonators. A detailed parameter study reveals various interesting features of structures with two levels of hierarchy as compared with one level systems with identical static mass. In particular: (i) their overall bandwidth is approximately equal, yet bounded above by the bandwidth of the single-resonator system; (ii) the number of bandgaps increases with the level of hierarchy; and (iii) the spectrum of bandgap frequencies is also enlarged. Taking advantage of these features, we propose graded hierarchical structures with ultra-broadband properties. These designs are validated over analogous continuum models via finite element simulations, demonstrating their capability to overcome the bandwidth narrowness that is typical of resonant metamaterials.

Spatio-temporal interpolation of precipitation during monsoon periods in Pakistan

NASA Astrophysics Data System (ADS)

Hussain, Ijaz; Spöck, Gunter; Pilz, Jürgen; Yu, Hwa-Lung

2010-08-01

Spatio-temporal estimation of precipitation over a region is essential to the modeling of hydrologic processes for water resources management. The changes of magnitude and space-time heterogeneity of rainfall observations make space-time estimation of precipitation a challenging task. In this paper we propose a Box-Cox transformed hierarchical Bayesian multivariate spatio-temporal interpolation method for the skewed response variable. The proposed method is applied to estimate space-time monthly precipitation in the monsoon periods during 1974-2000, and 27-year monthly average precipitation data are obtained from 51 stations in Pakistan. The results of transformed hierarchical Bayesian multivariate spatio-temporal interpolation are compared to those of non-transformed hierarchical Bayesian interpolation by using cross-validation. The software developed by [11] is used for Bayesian non-stationary multivariate space-time interpolation. It is observed that the transformed hierarchical Bayesian method provides more accuracy than the non-transformed hierarchical Bayesian method.

Prediction of Human Phenotype Ontology terms by means of hierarchical ensemble methods.

PubMed

Notaro, Marco; Schubach, Max; Robinson, Peter N; Valentini, Giorgio

2017-10-12

The prediction of human gene-abnormal phenotype associations is a fundamental step toward the discovery of novel genes associated with human disorders, especially when no genes are known to be associated with a specific disease. In this context the Human Phenotype Ontology (HPO) provides a standard categorization of the abnormalities associated with human diseases. While the problem of the prediction of gene-disease associations has been widely investigated, the related problem of gene-phenotypic feature (i.e., HPO term) associations has been largely overlooked, even if for most human genes no HPO term associations are known and despite the increasing application of the HPO to relevant medical problems. Moreover most of the methods proposed in literature are not able to capture the hierarchical relationships between HPO terms, thus resulting in inconsistent and relatively inaccurate predictions. We present two hierarchical ensemble methods that we formally prove to provide biologically consistent predictions according to the hierarchical structure of the HPO. The modular structure of the proposed methods, that consists in a "flat" learning first step and a hierarchical combination of the predictions in the second step, allows the predictions of virtually any flat learning method to be enhanced. The experimental results show that hierarchical ensemble methods are able to predict novel associations between genes and abnormal phenotypes with results that are competitive with state-of-the-art algorithms and with a significant reduction of the computational complexity. Hierarchical ensembles are efficient computational methods that guarantee biologically meaningful predictions that obey the true path rule, and can be used as a tool to improve and make consistent the HPO terms predictions starting from virtually any flat learning method. The implementation of the proposed methods is available as an R package from the CRAN repository.

A study of the application of singular perturbation theory. [development of a real time algorithm for optimal three dimensional aircraft maneuvers

NASA Technical Reports Server (NTRS)

Mehra, R. K.; Washburn, R. B.; Sajan, S.; Carroll, J. V.

1979-01-01

A hierarchical real time algorithm for optimal three dimensional control of aircraft is described. Systematic methods are developed for real time computation of nonlinear feedback controls by means of singular perturbation theory. The results are applied to a six state, three control variable, point mass model of an F-4 aircraft. Nonlinear feedback laws are presented for computing the optimal control of throttle, bank angle, and angle of attack. Real Time capability is assessed on a TI 9900 microcomputer. The breakdown of the singular perturbation approximation near the terminal point is examined Continuation methods are examined to obtain exact optimal trajectories starting from the singular perturbation solutions.

Food-web based unified model of macro- and microevolution.

PubMed

Chowdhury, Debashish; Stauffer, Dietrich

2003-10-01

We incorporate the generic hierarchical architecture of foodwebs into a "unified" model that describes both micro- and macroevolutions within a single theoretical framework. This model describes the microevolution in detail by accounting for the birth, ageing, and natural death of individual organisms as well as prey-predator interactions on a hierarchical dynamic food web. It also provides a natural description of random mutations and speciation (origination) of species as well as their extinctions. The distribution of lifetimes of species follows an approximate power law only over a limited regime.

Quark Yukawa pattern from spontaneous breaking of flavour SU(3) 3

NASA Astrophysics Data System (ADS)

Nardi, Enrico

2015-10-01

A SU(3)Q × SU(3)u × SU(3)d invariant scalar potential breaking spontaneously the quark flavour symmetry can explain the Standard Model flavour puzzle. The approximate alignment in flavour space of the vacuum expectation values of the up and down 'Yukawa fields' results as a dynamical effect. The observed quark mixing angles, the weak CP violating phase, and hierarchical quark masses can be all reproduced at the cost of introducing additional (auxiliary) scalar multiplets, but without the need of introducing hierarchical parameters.

Sampling schemes and parameter estimation for nonlinear Bernoulli-Gaussian sparse models

NASA Astrophysics Data System (ADS)

Boudineau, Mégane; Carfantan, Hervé; Bourguignon, Sébastien; Bazot, Michael

2016-06-01

We address the sparse approximation problem in the case where the data are approximated by the linear combination of a small number of elementary signals, each of these signals depending non-linearly on additional parameters. Sparsity is explicitly expressed through a Bernoulli-Gaussian hierarchical model in a Bayesian framework. Posterior mean estimates are computed using Markov Chain Monte-Carlo algorithms. We generalize the partially marginalized Gibbs sampler proposed in the linear case in [1], and build an hybrid Hastings-within-Gibbs algorithm in order to account for the nonlinear parameters. All model parameters are then estimated in an unsupervised procedure. The resulting method is evaluated on a sparse spectral analysis problem. It is shown to converge more efficiently than the classical joint estimation procedure, with only a slight increase of the computational cost per iteration, consequently reducing the global cost of the estimation procedure.

The traveling salesman problem: a hierarchical model.

PubMed

Graham, S M; Joshi, A; Pizlo, Z

2000-10-01

Our review of prior literature on spatial information processing in perception, attention, and memory indicates that these cognitive functions involve similar mechanisms based on a hierarchical architecture. The present study extends the application of hierarchical models to the area of problem solving. First, we report results of an experiment in which human subjects were tested on a Euclidean traveling salesman problem (TSP) with 6 to 30 cities. The subject's solutions were either optimal or near-optimal in length and were produced in a time that was, on average, a linear function of the number of cities. Next, the performance of the subjects is compared with that of five representative artificial intelligence and operations research algorithms, that produce approximate solutions for Euclidean problems. None of these algorithms was found to be an adequate psychological model. Finally, we present a new algorithm for solving the TSP, which is based on a hierarchical pyramid architecture. The performance of this new algorithm is quite similar to the performance of the subjects.

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

Naoz, Smadar; Li, Gongjie; Zanardi, Macarena

The secular approximation of the hierarchical three body systems has been proven to be very useful in addressing many astrophysical systems, from planets to stars to black holes. In such a system, two objects are on a tight orbit and the tertiary is on a much wider orbit. Here, we study the dynamics of a system by taking the tertiary mass to zero and solve the hierarchical three body system up to the octupole level of approximation. We find a rich dynamics that the outer orbit undergoes due to gravitational perturbations from the inner binary. The nominal result of themore » precession of the nodes is mostly limited for the lowest order of approximation; however, when the octupole level of approximation is introduced, the system becomes chaotic, as expected, and the tertiary oscillates below and above 90°, similarly to the non-test particle flip behavior. We provide the Hamiltonian of the system and investigate the dynamics of the system from the quadrupole to the octupole level of approximations. We also analyze the chaotic and quasi-periodic orbital evolution by studying the surfaces of sections. Furthermore, including general relativity, we showcase the long-term evolution of individual debris disk particles under the influence of a far-away interior eccentric planet. We show that this dynamics can naturally result in retrograde objects and a puffy disk after a long timescale evolution (a few Gyr) for initially aligned configuration.« less

The hierarchically organized splitting of chromosome bands into sub-bands analyzed by multicolor banding (MCB).

PubMed

Lehrer, H; Weise, A; Michel, S; Starke, H; Mrasek, K; Heller, A; Kuechler, A; Claussen, U; Liehr, T

2004-01-01

To clarify the nature of chromosome sub-bands in more detail, the multicolor banding (MCB) probe-set for chromosome 5 was hybridized to normal metaphase spreads of GTG band levels at approximately 850, approximately 550, approximately 400 and approximately 300. It could be observed that as the chromosomes became shorter, more of the initial 39 MCB pseudo-colors disappeared, ending with 18 MCB pseudo-colored bands at the approximately 300-band level. The hierarchically organized splitting of bands into sub-bands was analyzed by comparing the disappearance or appearance of pseudo-color bands of the four different band levels. The regions to split first are telomere-near, centromere-near and in 5q23-->q31, followed by 5p15, 5p14, and all GTG dark bands in 5q apart from 5q12 and 5q32 and finalized by sub-band building in 5p15.2, 5q21.2-->q21.3, 5q23.1 and 5q34. The direction of band splitting towards the centromere or the telomere could be assigned to each band separately. Pseudo-colors assigned to GTG-light bands were resistant to band splitting. These observations are in concordance with the recently proposed concept of chromosome region-specific protein swelling. Copyright 2003 S. Karger AG, Basel

A reward optimization method based on action subrewards in hierarchical reinforcement learning.

PubMed

Fu, Yuchen; Liu, Quan; Ling, Xionghong; Cui, Zhiming

2014-01-01

Reinforcement learning (RL) is one kind of interactive learning methods. Its main characteristics are "trial and error" and "related reward." A hierarchical reinforcement learning method based on action subrewards is proposed to solve the problem of "curse of dimensionality," which means that the states space will grow exponentially in the number of features and low convergence speed. The method can reduce state spaces greatly and choose actions with favorable purpose and efficiency so as to optimize reward function and enhance convergence speed. Apply it to the online learning in Tetris game, and the experiment result shows that the convergence speed of this algorithm can be enhanced evidently based on the new method which combines hierarchical reinforcement learning algorithm and action subrewards. The "curse of dimensionality" problem is also solved to a certain extent with hierarchical method. All the performance with different parameters is compared and analyzed as well.

Cognitive mechanisms underlying third graders' arithmetic skills: Expanding the pathways to mathematics model.

PubMed

Träff, Ulf; Olsson, Linda; Skagerlund, Kenny; Östergren, Rickard

2018-03-01

A modified pathways to mathematics model was used to examine the cognitive mechanisms underlying arithmetic skills in third graders. A total of 269 children were assessed on tasks tapping the four pathways and arithmetic skills. A path analysis showed that symbolic number processing was directly supported by the linguistic and approximate quantitative pathways. The direct contribution from the four pathways to arithmetic proficiency varied; the linguistic pathway supported single-digit arithmetic and word problem solving, whereas the approximate quantitative pathway supported only multi-digit calculation. The spatial processing and verbal working memory pathways supported only arithmetic word problem solving. The notion of hierarchical levels of arithmetic was supported by the results, and the different levels were supported by different constellations of pathways. However, the strongest support to the hierarchical levels of arithmetic were provided by the proximal arithmetic skills. Copyright © 2017 Elsevier Inc. All rights reserved.

Sharp peaks in the conductance of a double quantum dot and a quantum-dot spin valve at high temperatures: A hierarchical quantum master equation approach

NASA Astrophysics Data System (ADS)

Wenderoth, S.; Bätge, J.; Härtle, R.

2016-09-01

We study sharp peaks in the conductance-voltage characteristics of a double quantum dot and a quantum dot spin valve that are located around zero bias. The peaks share similarities with a Kondo peak but can be clearly distinguished, in particular as they occur at high temperatures. The underlying physical mechanism is a strong current suppression that is quenched in bias-voltage dependent ways by exchange interactions. Our theoretical results are based on the quantum master equation methodology, including the Born-Markov approximation and a numerically exact, hierarchical scheme, which we extend here to the spin-valve case. The comparison of exact and approximate results allows us to reveal the underlying physical mechanisms, the role of first-, second- and beyond-second-order processes and the robustness of the effect.

Bayesian Computation for Log-Gaussian Cox Processes: A Comparative Analysis of Methods

PubMed Central

Teng, Ming; Nathoo, Farouk S.; Johnson, Timothy D.

2017-01-01

The Log-Gaussian Cox Process is a commonly used model for the analysis of spatial point pattern data. Fitting this model is difficult because of its doubly-stochastic property, i.e., it is an hierarchical combination of a Poisson process at the first level and a Gaussian Process at the second level. Various methods have been proposed to estimate such a process, including traditional likelihood-based approaches as well as Bayesian methods. We focus here on Bayesian methods and several approaches that have been considered for model fitting within this framework, including Hamiltonian Monte Carlo, the Integrated nested Laplace approximation, and Variational Bayes. We consider these approaches and make comparisons with respect to statistical and computational efficiency. These comparisons are made through several simulation studies as well as through two applications, the first examining ecological data and the second involving neuroimaging data. PMID:29200537

Hierarchical pictorial structures for simultaneously localizing multiple organs in volumetric pre-scan CT

NASA Astrophysics Data System (ADS)

Montillo, Albert; Song, Qi; Das, Bipul; Yin, Zhye

2015-03-01

Parsing volumetric computed tomography (CT) into 10 or more salient organs simultaneously is a challenging task with many applications such as personalized scan planning and dose reporting. In the clinic, pre-scan data can come in the form of very low dose volumes acquired just prior to the primary scan or from an existing primary scan. To localize organs in such diverse data, we propose a new learning based framework that we call hierarchical pictorial structures (HPS) which builds multiple levels of models in a tree-like hierarchy that mirrors the natural decomposition of human anatomy from gross structures to finer structures. Each node of our hierarchical model learns (1) the local appearance and shape of structures, and (2) a generative global model that learns probabilistic, structural arrangement. Our main contribution is twofold. First we embed the pictorial structures approach in a hierarchical framework which reduces test time image interpretation and allows for the incorporation of additional geometric constraints that robustly guide model fitting in the presence of noise. Second we guide our HPS framework with the probabilistic cost maps extracted using random decision forests using volumetric 3D HOG features which makes our model fast to train and fast to apply to novel test data and posses a high degree of invariance to shape distortion and imaging artifacts. All steps require approximate 3 mins to compute and all organs are located with suitably high accuracy for our clinical applications such as personalized scan planning for radiation dose reduction. We assess our method using a database of volumetric CT scans from 81 subjects with widely varying age and pathology and with simulated ultra-low dose cadaver pre-scan data.

A Distributed-Memory Package for Dense Hierarchically Semi-Separable Matrix Computations Using Randomization

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

Rouet, François-Henry; Li, Xiaoye S.; Ghysels, Pieter

In this paper, we present a distributed-memory library for computations with dense structured matrices. A matrix is considered structured if its off-diagonal blocks can be approximated by a rank-deficient matrix with low numerical rank. Here, we use Hierarchically Semi-Separable (HSS) representations. Such matrices appear in many applications, for example, finite-element methods, boundary element methods, and so on. Exploiting this structure allows for fast solution of linear systems and/or fast computation of matrix-vector products, which are the two main building blocks of matrix computations. The compression algorithm that we use, that computes the HSS form of an input dense matrix, reliesmore » on randomized sampling with a novel adaptive sampling mechanism. We discuss the parallelization of this algorithm and also present the parallelization of structured matrix-vector product, structured factorization, and solution routines. The efficiency of the approach is demonstrated on large problems from different academic and industrial applications, on up to 8,000 cores. Finally, this work is part of a more global effort, the STRUctured Matrices PACKage (STRUMPACK) software package for computations with sparse and dense structured matrices. Hence, although useful on their own right, the routines also represent a step in the direction of a distributed-memory sparse solver.« less

A Distributed-Memory Package for Dense Hierarchically Semi-Separable Matrix Computations Using Randomization

DOE PAGES

Rouet, François-Henry; Li, Xiaoye S.; Ghysels, Pieter; ...

2016-06-30

In this paper, we present a distributed-memory library for computations with dense structured matrices. A matrix is considered structured if its off-diagonal blocks can be approximated by a rank-deficient matrix with low numerical rank. Here, we use Hierarchically Semi-Separable (HSS) representations. Such matrices appear in many applications, for example, finite-element methods, boundary element methods, and so on. Exploiting this structure allows for fast solution of linear systems and/or fast computation of matrix-vector products, which are the two main building blocks of matrix computations. The compression algorithm that we use, that computes the HSS form of an input dense matrix, reliesmore » on randomized sampling with a novel adaptive sampling mechanism. We discuss the parallelization of this algorithm and also present the parallelization of structured matrix-vector product, structured factorization, and solution routines. The efficiency of the approach is demonstrated on large problems from different academic and industrial applications, on up to 8,000 cores. Finally, this work is part of a more global effort, the STRUctured Matrices PACKage (STRUMPACK) software package for computations with sparse and dense structured matrices. Hence, although useful on their own right, the routines also represent a step in the direction of a distributed-memory sparse solver.« less

Carbon composition with hierarchical porosity, and methods of preparation

DOEpatents

Mayes, Richard T; Dai, Sheng

2014-10-21

A method for fabricating a porous carbon material possessing a hierarchical porosity, the method comprising subjecting a precursor composition to a curing step followed by a carbonization step, the precursor composition comprising: (i) a templating component comprised of a block copolymer, (ii) a phenolic component, (iii) a dione component in which carbonyl groups are adjacent, and (iv) an acidic component, wherein said carbonization step comprises heating the precursor composition at a carbonizing temperature for sufficient time to convert the precursor composition to a carbon material possessing a hierarchical porosity comprised of mesopores and macropores. Also described are the resulting hierarchical porous carbon material, a capacitive deionization device in which the porous carbon material is incorporated, as well as methods for desalinating water by use of said capacitive deionization device.

Wavelets and molecular structure

NASA Astrophysics Data System (ADS)

Carson, Mike

1996-08-01

The wavelet method offers possibilities for display, editing, and topological comparison of proteins at a user-specified level of detail. Wavelets are a mathematical tool that first found application in signal processing. The multiresolution analysis of a signal via wavelets provides a hierarchical series of `best' lower-resolution approximations. B-spline ribbons model the protein fold, with one control point per residue. Wavelet analysis sets limits on the information required to define the winding of the backbone through space, suggesting a recognizable fold is generated from a number of points equal to 1/4 or less the number of residues. Wavelets applied to surfaces and volumes show promise in structure-based drug design.

Prediction of Solvent Physical Properties using the Hierarchical Clustering Method

EPA Science Inventory

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

Three-Dimensional Hierarchical MoS2 Nanosheets/Ultralong N-Doped Carbon Nanotubes as High-Performance Electromagnetic Wave Absorbing Material.

PubMed

Liu, Lianlian; Zhang, Shen; Yan, Feng; Li, Chunyan; Zhu, Chunling; Zhang, Xitian; Chen, Yujin

2018-04-25

Here, we report a simple method to grow thin MoS 2 nanosheets (NSs) on the ultralong nitrogen-doped carbon nanotubes through anion-exchange reaction. The MoS 2 NSs are grown on ultralong nitrogen-doped carbon nanotube surfaces, leading to an interesting three-dimensional hierarchical structure. The fabricated hybrid nanotubes have a length of approximately 100 μm, where the MoS 2 nanosheets have a thickness of less than 7.5 nm. The hybrid nanotubes show excellent electromagnetic wave attenuation performance, with the effective absorption bandwidth of 5.4 GHz at the thicknesses of 2.5 mm, superior to the pure MoS 2 nanosheets and the MoS 2 nanosheets grown on the short N-doped carbon nanotube surfaces. The experimental results indicate that the direct growth of MoS 2 on the ultralong nitrogen-doped carbon nanotube surfaces is a key factor for the enhanced electromagnetic wave attenuation property. The results open the avenue for the development of ultralong transition metal dichalcogenides for electromagnetic wave absorbers.

Interaction of light with hematite hierarchical structures: Experiments and simulations

NASA Astrophysics Data System (ADS)

Distaso, Monica; Zhuromskyy, Oleksander; Seemann, Benjamin; Pflug, Lukas; Mačković, Mirza; Encina, Ezequiel; Taylor, Robin Klupp; Müller, Rolf; Leugering, Günter; Spiecker, Erdmann; Peschel, Ulf; Peukert, Wolfgang

2017-03-01

Mesocrystalline particles have been recognized as a class of multifunctional materials with potential applications in different fields. However, the internal organization of nanocomposite mesocrystals and its influence on the final properties have not yet been investigated. In this paper, a novel strategy based on electrodynamic simulations is developed to shed light on how the internal structure of mesocrystals influences their optical properties. In a first instance, a unified design protocol is reported for the fabrication of hematite/PVP particles with different morphologies such as pseudo-cubes, rods-like and apple-like structures and controlled particle size distributions. The optical properties of hematite/PVP mesocrystals are effectively simulated by taking their aggregate and nanocomposite structure into consideration. The superposition T-Matrix approach accounts for the aggregate nature of mesocrystalline particles and validate the effective medium approximation used in the framework of the Mie theory and electromagnetic simulation such as Finite Element Method. The approach described in our paper provides the framework to understand and predict the optical properties of mesocrystals and more general, of hierarchical nanostructured particles.

CyClus: a fast, comprehensive cylindrical interface approximation clustering/reranking method for rigid-body protein-protein docking decoys.

PubMed

Omori, Satoshi; Kitao, Akio

2013-06-01

We propose a fast clustering and reranking method, CyClus, for protein-protein docking decoys. This method enables comprehensive clustering of whole decoys generated by rigid-body docking using cylindrical approximation of the protein-proteininterface and hierarchical clustering procedures. We demonstrate the clustering and reranking of 54,000 decoy structures generated by ZDOCK for each complex within a few minutes. After parameter tuning for the test set in ZDOCK benchmark 2.0 with the ZDOCK and ZRANK scoring functions, blind tests for the incremental data in ZDOCK benchmark 3.0 and 4.0 were conducted. CyClus successfully generated smaller subsets of decoys containing near-native decoys. For example, the number of decoys required to create subsets containing near-native decoys with 80% probability was reduced from 22% to 50% of the number required in the original ZDOCK. Although specific ZDOCK and ZRANK results were demonstrated, the CyClus algorithm was designed to be more general and can be applied to a wide range of decoys and scoring functions by adjusting just two parameters, p and T. CyClus results were also compared to those from ClusPro. Copyright © 2013 Wiley Periodicals, Inc.

Hierarchically ordered mesoporous carbon/graphene composites as supercapacitor electrode materials.

PubMed

Song, Yanjie; Li, Zhu; Guo, Kunkun; Shao, Ting

2016-08-25

Hierarchically ordered mesoporous carbon/graphene (OMC/G) composites have been fabricated by means of a solvent-evaporation-induced self-assembly (EISA) method. The structures of these composites are characterized by X-ray diffraction, transmission electron microscopy, Raman spectroscopy and nitrogen adsorption-desorption at 77 K. These results indicate that OMC/G composites possess the hierarchically ordered hexagonal p6mm mesostructure with the lattice unit parameter and pore diameter close to 10 nm and 3 nm, respectively. The specific surface area of OMC/G composites after KOH activation is high up to 2109.2 m(2) g(-1), which is significantly greater than OMC after activation (1474.6 m(2) g(-1)). Subsequently, the resulting OMC/G composites as supercapacitor electrode materials exhibit an outstanding capacitance as high as 329.5 F g(-1) in 6 M KOH electrolyte at a current density of 0.5 A g(-1), which is much higher than both OMC (234.2 F g(-1)) and a sample made by mechanical mixing of OMC with graphene (217.7 F g(-1)). In addition, the obtained OMC/G composites display good cyclic stability, and the final capacitance retention is approximately 96% after 5000 cycles. These ordered mesopores in the OMC/G composites are beneficial to the accessibility and rapid diffusion of the electrolyte, while graphene in OMC/G composites can also facilitate the transport of electrons during the processes of charging and discharging owing to its high conductivity, thereby leading to an excellent energy storage performance. The method demonstrated in this work would open up a new route to design and develop graphene-based architectures for supercapacitor applications.

Atomic structure solution of the complex quasicrystal approximant Al77Rh15Ru8 from electron diffraction data.

PubMed

Samuha, Shmuel; Mugnaioli, Enrico; Grushko, Benjamin; Kolb, Ute; Meshi, Louisa

2014-12-01

The crystal structure of the novel Al77Rh15Ru8 phase (which is an approximant of decagonal quasicrystals) was determined using modern direct methods (MDM) applied to automated electron diffraction tomography (ADT) data. The Al77Rh15Ru8 E-phase is orthorhombic [Pbma, a = 23.40 (5), b = 16.20 (4) and c = 20.00 (5) Å] and has one of the most complicated intermetallic structures solved solely by electron diffraction methods. Its structural model consists of 78 unique atomic positions in the unit cell (19 Rh/Ru and 59 Al). Precession electron diffraction (PED) patterns and high-resolution electron microscopy (HRTEM) images were used for the validation of the proposed atomic model. The structure of the E-phase is described using hierarchical packing of polyhedra and a single type of tiling in the form of a parallelogram. Based on this description, the structure of the E-phase is compared with that of the ε6-phase formed in Al-Rh-Ru at close compositions.

Synthesis of hollow silica spheres with hierarchical shell structure by the dual action of liquid indium microbeads in vapor-liquid-solid growth.

PubMed

Wang, Jian-Tao; Wang, Hui; Ou, Xue-Mei; Lee, Chun-Sing; Zhang, Xiao-Hong

2011-07-05

Geometry-based adhesion arising from hierarchical surface structure enables microspheres to adhere to cells strongly, which is essential for inorganic microcapsules that function as drug delivery or diagnostic imaging agents. However, constructing a hierarchical structure on the outer shell of the products via the current microcapsule synthesis method is difficult. This work presents a novel approach to fabricating hollow microspheres with a hierarchical shell structure through the vapor-liquid-solid (VLS) process in which liquid indium droplets act as both templates for the formation of silica capsules and catalysts for the growth of hierarchical shell structure. This hierarchical shell structure offers the hollow microsphere an enhanced geometry-based adhesion. The results provide a facile method for fabricating hollow spheres and enriching their function through tailoring the geometry of their outer shells. © 2011 American Chemical Society

Hierarchical Shared Control of Cane-Type Walking-Aid Robot

PubMed Central

Tao, Chunjing

2017-01-01

A hierarchical shared-control method of the walking-aid robot for both human motion intention recognition and the obstacle emergency-avoidance method based on artificial potential field (APF) is proposed in this paper. The human motion intention is obtained from the interaction force measurements of the sensory system composed of 4 force-sensing registers (FSR) and a torque sensor. Meanwhile, a laser-range finder (LRF) forward is applied to detect the obstacles and try to guide the operator based on the repulsion force calculated by artificial potential field. An obstacle emergency-avoidance method which comprises different control strategies is also assumed according to the different states of obstacles or emergency cases. To ensure the user's safety, the hierarchical shared-control method combines the intention recognition method with the obstacle emergency-avoidance method based on the distance between the walking-aid robot and the obstacles. At last, experiments validate the effectiveness of the proposed hierarchical shared-control method. PMID:29093805

Parameterization of aquatic ecosystem functioning and its natural variation: Hierarchical Bayesian modelling of plankton food web dynamics

NASA Astrophysics Data System (ADS)

Norros, Veera; Laine, Marko; Lignell, Risto; Thingstad, Frede

2017-10-01

Methods for extracting empirically and theoretically sound parameter values are urgently needed in aquatic ecosystem modelling to describe key flows and their variation in the system. Here, we compare three Bayesian formulations for mechanistic model parameterization that differ in their assumptions about the variation in parameter values between various datasets: 1) global analysis - no variation, 2) separate analysis - independent variation and 3) hierarchical analysis - variation arising from a shared distribution defined by hyperparameters. We tested these methods, using computer-generated and empirical data, coupled with simplified and reasonably realistic plankton food web models, respectively. While all methods were adequate, the simulated example demonstrated that a well-designed hierarchical analysis can result in the most accurate and precise parameter estimates and predictions, due to its ability to combine information across datasets. However, our results also highlighted sensitivity to hyperparameter prior distributions as an important caveat of hierarchical analysis. In the more complex empirical example, hierarchical analysis was able to combine precise identification of parameter values with reasonably good predictive performance, although the ranking of the methods was less straightforward. We conclude that hierarchical Bayesian analysis is a promising tool for identifying key ecosystem-functioning parameters and their variation from empirical datasets.

A set of parallel, implicit methods for a reconstructed discontinuous Galerkin method for compressible flows on 3D hybrid grids

DOE PAGES

Xia, Yidong; Luo, Hong; Frisbey, Megan; ...

2014-07-01

A set of implicit methods are proposed for a third-order hierarchical WENO reconstructed discontinuous Galerkin method for compressible flows on 3D hybrid grids. An attractive feature in these methods are the application of the Jacobian matrix based on the P1 element approximation, resulting in a huge reduction of memory requirement compared with DG (P2). Also, three approaches -- analytical derivation, divided differencing, and automatic differentiation (AD) are presented to construct the Jacobian matrix respectively, where the AD approach shows the best robustness. A variety of compressible flow problems are computed to demonstrate the fast convergence property of the implemented flowmore » solver. Furthermore, an SPMD (single program, multiple data) programming paradigm based on MPI is proposed to achieve parallelism. The numerical results on complex geometries indicate that this low-storage implicit method can provide a viable and attractive DG solution for complicated flows of practical importance.« less

DESIGNING EFFICIENT, ECONOMIC AND ENVIRONMENTALLY FRIENDLY CHEMICAL PROCESSES

EPA Science Inventory

A catalytic reforming process has been studied using hierarchical design and simulation calculations. Approximations for the fugitive emissions indicate which streams allow the most value to be lost and which have the highest potential environmental impact. One can use this infor...

Tubular structured hierarchical mesoporous titania material derived from natural cellulosic substances and application as photocatalyst for degradation of methylene blue

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

Huang, Haiqing; Liu, Xiaoyan; Huang, Jianguo, E-mail: jghuang@zju.edu.cn

Graphical abstract: Bio-inspired, tubular structured hierarchical mesoporous titania material with high photocatalytic activity under UV light was fabricated employing natural cellulosic substance (cotton) as hard template and cetyltrimethylammonium bromide (CTAB) surfactant as soft template using a one-pot sol-gel method. Highlights: {yields} Tubular structured mesoporous titania material was fabricated by sol-gel method. {yields} The titania material faithfully recorded the hierarchical structure of the template substrate (cotton). {yields} The titania material exhibited high photocatalytic activity in decomposition of methylene blue. -- Abstract: Bio-inspired, tubular structured hierarchical mesoporous titania material was designed and fabricated employing natural cellulosic substance (cotton) as hard template andmore » cetyltrimethylammonium bromide (CTAB) surfactant as soft template by one-pot sol-gel method. The tubular structured hierarchical mesoporous titania material processes large specific surface area (40.23 m{sup 2}/g) and shows high photocatalytic activity in the photodegradation of methylene blue under UV light irradiation.« less

Band-gap analysis of a novel lattice with a hierarchical periodicity using the spectral element method

NASA Astrophysics Data System (ADS)

Wu, Zhijing; Li, Fengming; Zhang, Chuanzeng

2018-05-01

Inspired by the hierarchical structures of butterfly wing surfaces, a new kind of lattice structures with a two-order hierarchical periodicity is proposed and designed, and the band-gap properties are investigated by the spectral element method (SEM). The equations of motion of the whole structure are established considering the macro and micro periodicities of the system. The efficiency of the SEM is exploited in the modeling process and validated by comparing the results with that of the finite element method (FEM). Based on the highly accurate results in the frequency domain, the dynamic behaviors of the proposed two-order hierarchical structures are analyzed. An original and interesting finding is the existence of the distinct macro and micro stop-bands in the given frequency domain. The mechanisms for these two types of band-gaps are also explored. Finally, the relations between the hierarchical periodicities and the different types of the stop-bands are investigated by analyzing the parametrical influences.

Effects of hierarchical structures and insulating liquid media on adhesion

NASA Astrophysics Data System (ADS)

Yang, Weixu; Wang, Xiaoli; Li, Hanqing; Song, Xintao

2017-11-01

Effects of hierarchical structures and insulating liquid media on adhesion are investigated through a numerical adhesive contact model established in this paper, in which hierarchical structures are considered by introducing the height distribution into the surface gap equation, and media are taken into account through the Hamaker constant in Lifshitz-Hamaker approach. Computational methods such as inexact Newton method, bi-conjugate stabilized (Bi-CGSTAB) method and fast Fourier transform (FFT) technique are employed to obtain the adhesive force. It is shown that hierarchical structured surface exhibits excellent anti-adhesive properties compared with flat, micro or nano structured surfaces. Adhesion force is more dependent on the sizes of nanostructures than those of microstructures, and the optimal ranges of nanostructure pitch and maximum height for small adhesion force are presented. Insulating liquid media effectively decrease the adhesive interaction and 1-bromonaphthalene exhibits the smallest adhesion force among the five selected media. In addition, effects of hierarchical structures with optimal sizes on reducing adhesion are more obvious than those of the selected insulating liquid media.

A reliable method of manufacturing metallic hierarchical superhydrophobic surfaces

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

Pogreb, Roman; Whyman, Gene; Barayev, Reuven

2009-06-01

A method of manufacturing hierarchical metallic surfaces demonstrating superhydrophobic properties is presented. The surfaces showed apparent contact angles as high as 153 deg. and sliding angles of 10 deg. for 50-100 {mu}l droplets. The Cassie-like model [A. B. D. Cassie and S. Baxter, Trans. Faraday Soc. 40, 546 (1944)], considering the hierarchical topography of the relief, predicts apparent contact angles in a satisfactory agreement with the measured values.

Assessing dose–response effects of national essential medicine policy in China: comparison of two methods for handling data with a stepped wedge-like design and hierarchical structure

PubMed Central

Ren, Yan; Yang, Min; Li, Qian; Pan, Jay; Chen, Fei; Li, Xiaosong; Meng, Qun

2017-01-01

Objectives To introduce multilevel repeated measures (RM) models and compare them with multilevel difference-in-differences (DID) models in assessing the linear relationship between the length of the policy intervention period and healthcare outcomes (dose–response effect) for data from a stepped-wedge design with a hierarchical structure. Design The implementation of national essential medicine policy (NEMP) in China was a stepped-wedge-like design of five time points with a hierarchical structure. Using one key healthcare outcome from the national NEMP surveillance data as an example, we illustrate how a series of multilevel DID models and one multilevel RM model can be fitted to answer some research questions on policy effects. Setting Routinely and annually collected national data on China from 2008 to 2012. Participants 34 506 primary healthcare facilities in 2675 counties of 31 provinces. Outcome measures Agreement and differences in estimates of dose–response effect and variation in such effect between the two methods on the logarithm-transformed total number of outpatient visits per facility per year (LG-OPV). Results The estimated dose–response effect was approximately 0.015 according to four multilevel DID models and precisely 0.012 from one multilevel RM model. Both types of model estimated an increase in LG-OPV by 2.55 times from 2009 to 2012, but 2–4.3 times larger SEs of those estimates were found by the multilevel DID models. Similar estimates of mean effects of covariates and random effects of the average LG-OPV among all levels in the example dataset were obtained by both types of model. Significant variances in the dose–response among provinces, counties and facilities were estimated, and the ‘lowest’ or ‘highest’ units by their dose–response effects were pinpointed only by the multilevel RM model. Conclusions For examining dose–response effect based on data from multiple time points with hierarchical structure and the stepped wedge-like designs, multilevel RM models are more efficient, convenient and informative than the multilevel DID models. PMID:28399510

Gating Mechanisms of Mechanosensitive Channels of Large Conductance, I: A Continuum Mechanics-Based Hierarchical Framework

PubMed Central

Chen, Xi; Cui, Qiang; Tang, Yuye; Yoo, Jejoong; Yethiraj, Arun

2008-01-01

A hierarchical simulation framework that integrates information from molecular dynamics (MD) simulations into a continuum model is established to study the mechanical response of mechanosensitive channel of large-conductance (MscL) using the finite element method (FEM). The proposed MD-decorated FEM (MDeFEM) approach is used to explore the detailed gating mechanisms of the MscL in Escherichia coli embedded in a palmitoyloleoylphosphatidylethanolamine lipid bilayer. In Part I of this study, the framework of MDeFEM is established. The transmembrane and cytoplasmic helices are taken to be elastic rods, the loops are modeled as springs, and the lipid bilayer is approximated by a three-layer sheet. The mechanical properties of the continuum components, as well as their interactions, are derived from molecular simulations based on atomic force fields. In addition, analytical closed-form continuum model and elastic network model are established to complement the MDeFEM approach and to capture the most essential features of gating. In Part II of this study, the detailed gating mechanisms of E. coli-MscL under various types of loading are presented and compared with experiments, structural model, and all-atom simulations, as well as the analytical models established in Part I. It is envisioned that such a hierarchical multiscale framework will find great value in the study of a variety of biological processes involving complex mechanical deformations such as muscle contraction and mechanotransduction. PMID:18390626

Ray tracing a three-dimensional scene using a hierarchical data structure

DOEpatents

Wald, Ingo; Boulos, Solomon; Shirley, Peter

2012-09-04

Ray tracing a three-dimensional scene made up of geometric primitives that are spatially partitioned into a hierarchical data structure. One example embodiment is a method for ray tracing a three-dimensional scene made up of geometric primitives that are spatially partitioned into a hierarchical data structure. In this example embodiment, the hierarchical data structure includes at least a parent node and a corresponding plurality of child nodes. The method includes a first act of determining that a first active ray in the packet hits the parent node and a second act of descending to each of the plurality of child nodes.

Hierarchically nanostructured hydroxyapatite: hydrothermal synthesis, morphology control, growth mechanism, and biological activity

PubMed Central

Ma, Ming-Guo

2012-01-01

Hierarchically nanosized hydroxyapatite (HA) with flower-like structure assembled from nanosheets consisting of nanorod building blocks was successfully synthesized by using CaCl2, NaH2PO4, and potassium sodium tartrate via a hydrothermal method at 200°C for 24 hours. The effects of heating time and heating temperature on the products were investigated. As a chelating ligand and template molecule, the potassium sodium tartrate plays a key role in the formation of hierarchically nanostructured HA. On the basis of experimental results, a possible mechanism based on soft-template and self-assembly was proposed for the formation and growth of the hierarchically nanostructured HA. Cytotoxicity experiments indicated that the hierarchically nanostructured HA had good biocompatibility. It was shown by in-vitro experiments that mesenchymal stem cells could attach to the hierarchically nanostructured HA after being cultured for 48 hours. Objective The purpose of this study was to develop facile and effective methods for the synthesis of novel hydroxyapatite (HA) with hierarchical nanostructures assembled from independent and discrete nanobuilding blocks. Methods A simple hydrothermal approach was applied to synthesize HA by using CaCl2, NaH2PO4, and potassium sodium tartrate at 200°C for 24 hours. The cell cytotoxicity of the hierarchically nanostructured HA was tested by MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay. Results HA displayed the flower-like structure assembled from nanosheets consisting of nanorod building blocks. The potassium sodium tartrate was used as a chelating ligand, inducing the formation and self-assembly of HA nanorods. The heating time and heating temperature influenced the aggregation and morphology of HA. The cell viability did not decrease with the increasing concentration of hierarchically nanostructured HA added. Conclusion A novel, simple and reliable hydrothermal route had been developed for the synthesis of hierarchically nanosized HA with flower-like structure assembled from nanosheets consisting of nanorod building blocks. The HA with the hierarchical nanostructure was formed via a soft-template assisted self-assembly mechanism. The hierarchically nanostructured HA has a good biocompatibility and essentially no in-vitro cytotoxicity. PMID:22619527

Hierarchical cluster-tendency analysis of the group structure in the foreign exchange market

NASA Astrophysics Data System (ADS)

Wu, Xin-Ye; Zheng, Zhi-Gang

2013-08-01

A hierarchical cluster-tendency (HCT) method in analyzing the group structure of networks of the global foreign exchange (FX) market is proposed by combining the advantages of both the minimal spanning tree (MST) and the hierarchical tree (HT). Fifty currencies of the top 50 World GDP in 2010 according to World Bank's database are chosen as the underlying system. By using the HCT method, all nodes in the FX market network can be "colored" and distinguished. We reveal that the FX networks can be divided into two groups, i.e., the Asia-Pacific group and the Pan-European group. The results given by the hierarchical cluster-tendency method agree well with the formerly observed geographical aggregation behavior in the FX market. Moreover, an oil-resource aggregation phenomenon is discovered by using our method. We find that gold could be a better numeraire for the weekly-frequency FX data.

The method of parallel-hierarchical transformation for rapid recognition of dynamic images using GPGPU technology

NASA Astrophysics Data System (ADS)

Timchenko, Leonid; Yarovyi, Andrii; Kokriatskaya, Nataliya; Nakonechna, Svitlana; Abramenko, Ludmila; Ławicki, Tomasz; Popiel, Piotr; Yesmakhanova, Laura

2016-09-01

The paper presents a method of parallel-hierarchical transformations for rapid recognition of dynamic images using GPU technology. Direct parallel-hierarchical transformations based on cluster CPU-and GPU-oriented hardware platform. Mathematic models of training of the parallel hierarchical (PH) network for the transformation are developed, as well as a training method of the PH network for recognition of dynamic images. This research is most topical for problems on organizing high-performance computations of super large arrays of information designed to implement multi-stage sensing and processing as well as compaction and recognition of data in the informational structures and computer devices. This method has such advantages as high performance through the use of recent advances in parallelization, possibility to work with images of ultra dimension, ease of scaling in case of changing the number of nodes in the cluster, auto scan of local network to detect compute nodes.

A hierarchical approach to reliability modeling of fault-tolerant systems. M.S. Thesis

NASA Technical Reports Server (NTRS)

Gossman, W. E.

1986-01-01

A methodology for performing fault tolerant system reliability analysis is presented. The method decomposes a system into its subsystems, evaluates vent rates derived from the subsystem's conditional state probability vector and incorporates those results into a hierarchical Markov model of the system. This is done in a manner that addresses failure sequence dependence associated with the system's redundancy management strategy. The method is derived for application to a specific system definition. Results are presented that compare the hierarchical model's unreliability prediction to that of a more complicated tandard Markov model of the system. The results for the example given indicate that the hierarchical method predicts system unreliability to a desirable level of accuracy while achieving significant computational savings relative to component level Markov model of the system.

The MIL-88A-Derived Fe3O4-Carbon Hierarchical Nanocomposites for Electrochemical Sensing

PubMed Central

Wang, Li; Zhang, Yayun; Li, Xia; Xie, Yingzhen; He, Juan; Yu, Jie; Song, Yonghai

2015-01-01

Metal or metal oxides/carbon nanocomposites with hierarchical superstructures have become one of the most promising functional materials in sensor, catalysis, energy conversion, etc. In this work, novel hierarchical Fe3O4/carbon superstructures have been fabricated based on metal-organic frameworks (MOFs)-derived method. Three kinds of Fe-MOFs (MIL-88A) with different morphologies were prepared beforehand as templates, and then pyrolyzed to fabricate the corresponding novel hierarchical Fe3O4/carbon superstructures. The systematic studies on the thermal decomposition process of the three kinds of MIL-88A and the effect of template morphology on the products were carried out in detail. Scanning electron microscopy, transmission electron microscopy, X-ray powder diffraction, X-ray photoelectron spectroscopy and thermal analysis were employed to investigate the hierarchical Fe3O4/carbon superstructures. Based on these resulted hierarchical Fe3O4/carbon superstructures, a novel and sensitive nonenzymatic N-acetyl cysteine sensor was developed. The porous and hierarchical superstructures and large surface area of the as-formed Fe3O4/carbon superstructures eventually contributed to the good electrocatalytic activity of the prepared sensor towards the oxidation of N-acetyl cysteine. The proposed preparation method of the hierarchical Fe3O4/carbon superstructures is simple, efficient, cheap and easy to mass production. It might open up a new way for hierarchical superstructures preparation. PMID:26387535

On the importance of avoiding shortcuts in applying cognitive models to hierarchical data.

PubMed

Boehm, Udo; Marsman, Maarten; Matzke, Dora; Wagenmakers, Eric-Jan

2018-06-12

Psychological experiments often yield data that are hierarchically structured. A number of popular shortcut strategies in cognitive modeling do not properly accommodate this structure and can result in biased conclusions. To gauge the severity of these biases, we conducted a simulation study for a two-group experiment. We first considered a modeling strategy that ignores the hierarchical data structure. In line with theoretical results, our simulations showed that Bayesian and frequentist methods that rely on this strategy are biased towards the null hypothesis. Secondly, we considered a modeling strategy that takes a two-step approach by first obtaining participant-level estimates from a hierarchical cognitive model and subsequently using these estimates in a follow-up statistical test. Methods that rely on this strategy are biased towards the alternative hypothesis. Only hierarchical models of the multilevel data lead to correct conclusions. Our results are particularly relevant for the use of hierarchical Bayesian parameter estimates in cognitive modeling.

CuO-Decorated ZnO Hierarchical Nanostructures as Efficient and Established Sensing Materials for H2S Gas Sensors

PubMed Central

Vuong, Nguyen Minh; Chinh, Nguyen Duc; Huy, Bui The; Lee, Yong-Ill

2016-01-01

Highly sensitive hydrogen sulfide (H2S) gas sensors were developed from CuO-decorated ZnO semiconducting hierarchical nanostructures. The ZnO hierarchical nanostructure was fabricated by an electrospinning method following hydrothermal and heat treatment. CuO decoration of ZnO hierarchical structures was carried out by a wet method. The H2S gas-sensing properties were examined at different working temperatures using various quantities of CuO as the variable. CuO decoration of the ZnO hierarchical structure was observed to promote sensitivity for H2S gas higher than 30 times at low working temperature (200 °C) compared with that in the nondecorated hierarchical structure. The sensing mechanism of the hybrid sensor structure is also discussed. The morphology and characteristics of the samples were examined by scanning electron microscopy (SEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), UV-vis absorption, photoluminescence (PL), and electrical measurements. PMID:27231026

Delineating the joint hierarchical structure of clinical and personality disorders in an outpatient psychiatric sample.

PubMed

Forbes, Miriam K; Kotov, Roman; Ruggero, Camilo J; Watson, David; Zimmerman, Mark; Krueger, Robert F

2017-11-01

A large body of research has focused on identifying the optimal number of dimensions - or spectra - to model individual differences in psychopathology. Recently, it has become increasingly clear that ostensibly competing models with varying numbers of spectra can be synthesized in empirically derived hierarchical structures. We examined the convergence between top-down (bass-ackwards or sequential principal components analysis) and bottom-up (hierarchical agglomerative cluster analysis) statistical methods for elucidating hierarchies to explicate the joint hierarchical structure of clinical and personality disorders. Analyses examined 24 clinical and personality disorders based on semi-structured clinical interviews in an outpatient psychiatric sample (n=2900). The two methods of hierarchical analysis converged on a three-tier joint hierarchy of psychopathology. At the lowest tier, there were seven spectra - disinhibition, antagonism, core thought disorder, detachment, core internalizing, somatoform, and compulsivity - that emerged in both methods. These spectra were nested under the same three higher-order superspectra in both methods: externalizing, broad thought dysfunction, and broad internalizing. In turn, these three superspectra were nested under a single general psychopathology spectrum, which represented the top tier of the hierarchical structure. The hierarchical structure mirrors and extends upon past research, with the inclusion of a novel compulsivity spectrum, and the finding that psychopathology is organized in three superordinate domains. This hierarchy can thus be used as a flexible and integrative framework to facilitate psychopathology research with varying levels of specificity (i.e., focusing on the optimal level of detailed information, rather than the optimal number of factors). Copyright © 2017 Elsevier Inc. All rights reserved.

Controllable synthesis of layered Co-Ni hydroxide hierarchical structures for high-performance hybrid supercapacitors

NASA Astrophysics Data System (ADS)

Yuan, Peng; Zhang, Ning; Zhang, Dan; Liu, Tao; Chen, Limiao; Ma, Renzhi; Qiu, Guanzhou; Liu, Xiaohe

2016-01-01

A facile solvothermal method is developed for synthesizing layered Co-Ni hydroxide hierarchical structures by using hexamethylenetetramine (HMT) as alkaline reagent. The electrochemical measurements reveal that the specific capacitances of layered bimetallic (Co-Ni) hydroxides are generally superior to those of layered monometallic (Co, Ni) hydroxides. The as-prepared Co0.5Ni0.5 hydroxide hierarchical structures possesses the highest specific capacitance of 1767 F g-1 at a galvanic current density of 1 A g-1 and an outstanding specific capacitance retention of 87% after 1000 cycles. In comparison with the dispersed nanosheets of Co-Ni hydroxide, layered hydroxide hierarchical structures show much superior electrochemical performance. This study provides a promising method to construct hierarchical structures with controllable transition-metal compositions for enhancing the electrochemical performance in hybrid supercapacitors.

Rapid fabrication of hierarchically structured supramolecular nanocomposite thin films in one minute

DOEpatents

Xu, Ting; Kao, Joseph

2016-11-08

Functional nanocomposites containing nanoparticles of different chemical compositions may exhibit new properties to meet demands for advanced technology. It is imperative to simultaneously achieve hierarchical structural control and to develop rapid, scalable fabrication to minimize degradation of nanoparticle properties and for compatibility with nanomanufacturing. The assembly kinetics of supramolecular nanocomposite in thin films is governed by the energetic cost arising from defects, the chain mobility, and the activation energy for inter-domain diffusion. By optimizing only one parameter, the solvent fraction in the film, the assembly kinetics can be precisely tailored to produce hierarchically structured thin films of supramolecular nanocomposites in approximately one minute. Moreover, the strong wavelength dependent optical anisotropy in the nanocomposite highlights their potential applications for light manipulation and information transmission. The present invention opens a new avenue in designing manufacture-friendly continuous processing for the fabrication of functional nanocomposite thin films.

Hierarchical Diagnosis of Vocal Fold Disorders

NASA Astrophysics Data System (ADS)

Nikkhah-Bahrami, Mansour; Ahmadi-Noubari, Hossein; Seyed Aghazadeh, Babak; Khadivi Heris, Hossein

This paper explores the use of hierarchical structure for diagnosis of vocal fold disorders. The hierarchical structure is initially used to train different second-level classifiers. At the first level normal and pathological signals have been distinguished. Next, pathological signals have been classified into neurogenic and organic vocal fold disorders. At the final level, vocal fold nodules have been distinguished from polyps in organic disorders category. For feature selection at each level of hierarchy, the reconstructed signal at each wavelet packet decomposition sub-band in 5 levels of decomposition with mother wavelet of (db10) is used to extract the nonlinear features of self-similarity and approximate entropy. Also, wavelet packet coefficients are used to measure energy and Shannon entropy features at different spectral sub-bands. Davies-Bouldin criterion has been employed to find the most discriminant features. Finally, support vector machines have been adopted as classifiers at each level of hierarchy resulting in the diagnosis accuracy of 92%.

Approximation abilities of neuro-fuzzy networks

NASA Astrophysics Data System (ADS)

Mrówczyńska, Maria

2010-01-01

The paper presents the operation of two neuro-fuzzy systems of an adaptive type, intended for solving problems of the approximation of multi-variable functions in the domain of real numbers. Neuro-fuzzy systems being a combination of the methodology of artificial neural networks and fuzzy sets operate on the basis of a set of fuzzy rules "if-then", generated by means of the self-organization of data grouping and the estimation of relations between fuzzy experiment results. The article includes a description of neuro-fuzzy systems by Takaga-Sugeno-Kang (TSK) and Wang-Mendel (WM), and in order to complement the problem in question, a hierarchical structural self-organizing method of teaching a fuzzy network. A multi-layer structure of the systems is a structure analogous to the structure of "classic" neural networks. In its final part the article presents selected areas of application of neuro-fuzzy systems in the field of geodesy and surveying engineering. Numerical examples showing how the systems work concerned: the approximation of functions of several variables to be used as algorithms in the Geographic Information Systems (the approximation of a terrain model), the transformation of coordinates, and the prediction of a time series. The accuracy characteristics of the results obtained have been taken into consideration.

Control of discrete event systems modeled as hierarchical state machines

NASA Technical Reports Server (NTRS)

Brave, Y.; Heymann, M.

1991-01-01

The authors examine a class of discrete event systems (DESs) modeled as asynchronous hierarchical state machines (AHSMs). For this class of DESs, they provide an efficient method for testing reachability, which is an essential step in many control synthesis procedures. This method utilizes the asynchronous nature and hierarchical structure of AHSMs, thereby illustrating the advantage of the AHSM representation as compared with its equivalent (flat) state machine representation. An application of the method is presented where an online minimally restrictive solution is proposed for the problem of maintaining a controlled AHSM within prescribed legal bounds.

Universal Method for Creating Hierarchical Wrinkles on Thin-Film Surfaces.

PubMed

Jung, Woo-Bin; Cho, Kyeong Min; Lee, Won-Kyu; Odom, Teri W; Jung, Hee-Tae

2018-01-10

One of the most interesting topics in physical science and materials science is the creation of complex wrinkled structures on thin-film surfaces because of their several advantages of high surface area, localized strain, and stress tolerance. In this study, a significant step was taken toward solving limitations imposed by the fabrication of previous artificial wrinkles. A universal method for preparing hierarchical three-dimensional wrinkle structures of thin films on a multiple scale (e.g., nanometers to micrometers) by sequential wrinkling with different skin layers was developed. Notably, this method was not limited to specific materials, and it was applicable to fabricating hierarchical wrinkles on all of the thin-film surfaces tested thus far, including those of metals, two-dimensional and one-dimensional materials, and polymers. The hierarchical wrinkles with multiscale structures were prepared by sequential wrinkling, in which a sacrificial layer was used as the additional skin layer between sequences. For example, a hierarchical MoS 2 wrinkle exhibited highly enhanced catalytic behavior because of the superaerophobicity and effective surface area, which are related to topological effects. As the developed method can be adopted to a majority of thin films, it is thought to be a universal method for enhancing the physical properties of various materials.

Optimal Power Flow Pursuit

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

Dall'Anese, Emiliano; Simonetto, Andrea

This paper considers distribution networks featuring inverter-interfaced distributed energy resources, and develops distributed feedback controllers that continuously drive the inverter output powers to solutions of AC optimal power flow (OPF) problems. Particularly, the controllers update the power setpoints based on voltage measurements as well as given (time-varying) OPF targets, and entail elementary operations implementable onto low-cost microcontrollers that accompany power-electronics interfaces of gateways and inverters. The design of the control framework is based on suitable linear approximations of the AC power-flow equations as well as Lagrangian regularization methods. Convergence and OPF-target tracking capabilities of the controllers are analytically established. Overall,more » the proposed method allows to bypass traditional hierarchical setups where feedback control and optimization operate at distinct time scales, and to enable real-time optimization of distribution systems.« less

Ranging through Gabor logons-a consistent, hierarchical approach.

PubMed

Chang, C; Chatterjee, S

1993-01-01

In this work, the correspondence problem in stereo vision is handled by matching two sets of dense feature vectors. Inspired by biological evidence, these feature vectors are generated by a correlation between a bank of Gabor sensors and the intensity image. The sensors consist of two-dimensional Gabor filters at various scales (spatial frequencies) and orientations, which bear close resemblance to the receptive field profiles of simple V1 cells in visual cortex. A hierarchical, stochastic relaxation method is then used to obtain the dense stereo disparities. Unlike traditional hierarchical methods for stereo, feature based hierarchical processing yields consistent disparities. To avoid false matchings due to static occlusion, a dual matching, based on the imaging geometry, is used.

Hierarchical semi-numeric method for pairwise fuzzy group decision making.

PubMed

Marimin, M; Umano, M; Hatono, I; Tamura, H

2002-01-01

Gradual improvements to a single-level semi-numeric method, i.e., linguistic labels preference representation by fuzzy sets computation for pairwise fuzzy group decision making are summarized. The method is extended to solve multiple criteria hierarchical structure pairwise fuzzy group decision-making problems. The problems are hierarchically structured into focus, criteria, and alternatives. Decision makers express their evaluations of criteria and alternatives based on each criterion by using linguistic labels. The labels are converted into and processed in triangular fuzzy numbers (TFNs). Evaluations of criteria yield relative criteria weights. Evaluations of the alternatives, based on each criterion, yield a degree of preference for each alternative or a degree of satisfaction for each preference value. By using a neat ordered weighted average (OWA) or a fuzzy weighted average operator, solutions obtained based on each criterion are aggregated into final solutions. The hierarchical semi-numeric method is suitable for solving a larger and more complex pairwise fuzzy group decision-making problem. The proposed method has been verified and applied to solve some real cases and is compared to Saaty's (1996) analytic hierarchy process (AHP) method.

Robust feature estimation by non-rigid hierarchical image registration and its application in disparity measurement

NASA Astrophysics Data System (ADS)

Badshah, Amir; Choudhry, Aadil Jaleel; Ullah, Shan

2017-03-01

Industries are moving towards automation in order to increase productivity and ensure quality. Variety of electronic and electromagnetic systems are being employed to assist human operator in fast and accurate quality inspection of products. Majority of these systems are equipped with cameras and rely on diverse image processing algorithms. Information is lost in 2D image, therefore acquiring accurate 3D data from 2D images is an open issue. FAST, SURF and SIFT are well-known spatial domain techniques for features extraction and henceforth image registration to find correspondence between images. The efficiency of these methods is measured in terms of the number of perfect matches found. A novel fast and robust technique for stereo-image processing is proposed. It is based on non-rigid registration using modified normalized phase correlation. The proposed method registers two images in hierarchical fashion using quad-tree structure. The registration process works through global to local level resulting in robust matches even in presence of blur and noise. The computed matches can further be utilized to determine disparity and depth for industrial product inspection. The same can be used in driver assistance systems. The preliminary tests on Middlebury dataset produced satisfactory results. The execution time for a 413 x 370 stereo-pair is 500ms approximately on a low cost DSP.

Scale of association: hierarchical linear models and the measurement of ecological systems

Treesearch

Sean M. McMahon; Jeffrey M. Diez

2007-01-01

A fundamental challenge to understanding patterns in ecological systems lies in employing methods that can analyse, test and draw inference from measured associations between variables across scales. Hierarchical linear models (HLM) use advanced estimation algorithms to measure regression relationships and variance-covariance parameters in hierarchically structured...

Graphical Evaluation of Hierarchical Clustering Schemes. Technical Report No. 1.

ERIC Educational Resources Information Center

Halff, Henry M.

Graphical methods for evaluating the fit of Johnson's hierarchical clustering schemes are presented together with an example. These evaluation methods examine the extent to which the clustering algorithm can minimize the overlap of the distributions of intracluster and intercluster distances. (Author)

Learning topic models by belief propagation.

PubMed

Zeng, Jia; Cheung, William K; Liu, Jiming

2013-05-01

Latent Dirichlet allocation (LDA) is an important hierarchical Bayesian model for probabilistic topic modeling, which attracts worldwide interest and touches on many important applications in text mining, computer vision and computational biology. This paper represents the collapsed LDA as a factor graph, which enables the classic loopy belief propagation (BP) algorithm for approximate inference and parameter estimation. Although two commonly used approximate inference methods, such as variational Bayes (VB) and collapsed Gibbs sampling (GS), have gained great success in learning LDA, the proposed BP is competitive in both speed and accuracy, as validated by encouraging experimental results on four large-scale document datasets. Furthermore, the BP algorithm has the potential to become a generic scheme for learning variants of LDA-based topic models in the collapsed space. To this end, we show how to learn two typical variants of LDA-based topic models, such as author-topic models (ATM) and relational topic models (RTM), using BP based on the factor graph representations.

Combining qualitative and quantitative spatial and temporal information in a hierarchical structure: Approximate reasoning for plan execution monitoring

NASA Technical Reports Server (NTRS)

Hoebel, Louis J.

1993-01-01

The problem of plan generation (PG) and the problem of plan execution monitoring (PEM), including updating, queries, and resource-bounded replanning, have different reasoning and representation requirements. PEM requires the integration of qualitative and quantitative information. PEM is the receiving of data about the world in which a plan or agent is executing. The problem is to quickly determine the relevance of the data, the consistency of the data with respect to the expected effects, and if execution should continue. Only spatial and temporal aspects of the plan are addressed for relevance in this work. Current temporal reasoning systems are deficient in computational aspects or expressiveness. This work presents a hybrid qualitative and quantitative system that is fully expressive in its assertion language while offering certain computational efficiencies. In order to proceed, methods incorporating approximate reasoning using hierarchies, notions of locality, constraint expansion, and absolute parameters need be used and are shown to be useful for the anytime nature of PEM.

A Graph-Embedding Approach to Hierarchical Visual Word Mergence.

PubMed

Wang, Lei; Liu, Lingqiao; Zhou, Luping

2017-02-01

Appropriately merging visual words are an effective dimension reduction method for the bag-of-visual-words model in image classification. The approach of hierarchically merging visual words has been extensively employed, because it gives a fully determined merging hierarchy. Existing supervised hierarchical merging methods take different approaches and realize the merging process with various formulations. In this paper, we propose a unified hierarchical merging approach built upon the graph-embedding framework. Our approach is able to merge visual words for any scenario, where a preferred structure and an undesired structure are defined, and, therefore, can effectively attend to all kinds of requirements for the word-merging process. In terms of computational efficiency, we show that our algorithm can seamlessly integrate a fast search strategy developed in our previous work and, thus, well maintain the state-of-the-art merging speed. To the best of our survey, the proposed approach is the first one that addresses the hierarchical visual word mergence in such a flexible and unified manner. As demonstrated, it can maintain excellent image classification performance even after a significant dimension reduction, and outperform all the existing comparable visual word-merging methods. In a broad sense, our work provides an open platform for applying, evaluating, and developing new criteria for hierarchical word-merging tasks.

Enhancing adaptive sparse grid approximations and improving refinement strategies using adjoint-based a posteriori error estimates

DOE PAGES

Jakeman, J. D.; Wildey, T.

2015-01-01

In this paper we present an algorithm for adaptive sparse grid approximations of quantities of interest computed from discretized partial differential equations. We use adjoint-based a posteriori error estimates of the interpolation error in the sparse grid to enhance the sparse grid approximation and to drive adaptivity. We show that utilizing these error estimates provides significantly more accurate functional values for random samples of the sparse grid approximation. We also demonstrate that alternative refinement strategies based upon a posteriori error estimates can lead to further increases in accuracy in the approximation over traditional hierarchical surplus based strategies. Throughout this papermore » we also provide and test a framework for balancing the physical discretization error with the stochastic interpolation error of the enhanced sparse grid approximation.« less

Method and system for rendering and interacting with an adaptable computing environment

DOEpatents

Osbourn, Gordon Cecil [Albuquerque, NM; Bouchard, Ann Marie [Albuquerque, NM

2012-06-12

An adaptable computing environment is implemented with software entities termed "s-machines", which self-assemble into hierarchical data structures capable of rendering and interacting with the computing environment. A hierarchical data structure includes a first hierarchical s-machine bound to a second hierarchical s-machine. The first hierarchical s-machine is associated with a first layer of a rendering region on a display screen and the second hierarchical s-machine is associated with a second layer of the rendering region overlaying at least a portion of the first layer. A screen element s-machine is linked to the first hierarchical s-machine. The screen element s-machine manages data associated with a screen element rendered to the display screen within the rendering region at the first layer.

Hierarchical Recurrent Neural Hashing for Image Retrieval With Hierarchical Convolutional Features.

PubMed

Lu, Xiaoqiang; Chen, Yaxiong; Li, Xuelong

Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep learning architectures can learn more effective image representation features. However, these methods only use semantic features to generate hash codes by shallow projection but ignore texture details. In this paper, we proposed a novel hashing method, namely hierarchical recurrent neural hashing (HRNH), to exploit hierarchical recurrent neural network to generate effective hash codes. There are three contributions of this paper. First, a deep hashing method is proposed to extensively exploit both spatial details and semantic information, in which, we leverage hierarchical convolutional features to construct image pyramid representation. Second, our proposed deep network can exploit directly convolutional feature maps as input to preserve the spatial structure of convolutional feature maps. Finally, we propose a new loss function that considers the quantization error of binarizing the continuous embeddings into the discrete binary codes, and simultaneously maintains the semantic similarity and balanceable property of hash codes. Experimental results on four widely used data sets demonstrate that the proposed HRNH can achieve superior performance over other state-of-the-art hashing methods.Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep learning architectures can learn more effective image representation features. However, these methods only use semantic features to generate hash codes by shallow projection but ignore texture details. In this paper, we proposed a novel hashing method, namely hierarchical recurrent neural hashing (HRNH), to exploit hierarchical recurrent neural network to generate effective hash codes. There are three contributions of this paper. First, a deep hashing method is proposed to extensively exploit both spatial details and semantic information, in which, we leverage hierarchical convolutional features to construct image pyramid representation. Second, our proposed deep network can exploit directly convolutional feature maps as input to preserve the spatial structure of convolutional feature maps. Finally, we propose a new loss function that considers the quantization error of binarizing the continuous embeddings into the discrete binary codes, and simultaneously maintains the semantic similarity and balanceable property of hash codes. Experimental results on four widely used data sets demonstrate that the proposed HRNH can achieve superior performance over other state-of-the-art hashing methods.

Hierarchical modeling and inference in ecology: The analysis of data from populations, metapopulations and communities

USGS Publications Warehouse

Royle, J. Andrew; Dorazio, Robert M.

2008-01-01

A guide to data collection, modeling and inference strategies for biological survey data using Bayesian and classical statistical methods. This book describes a general and flexible framework for modeling and inference in ecological systems based on hierarchical models, with a strict focus on the use of probability models and parametric inference. Hierarchical models represent a paradigm shift in the application of statistics to ecological inference problems because they combine explicit models of ecological system structure or dynamics with models of how ecological systems are observed. The principles of hierarchical modeling are developed and applied to problems in population, metapopulation, community, and metacommunity systems. The book provides the first synthetic treatment of many recent methodological advances in ecological modeling and unifies disparate methods and procedures. The authors apply principles of hierarchical modeling to ecological problems, including * occurrence or occupancy models for estimating species distribution * abundance models based on many sampling protocols, including distance sampling * capture-recapture models with individual effects * spatial capture-recapture models based on camera trapping and related methods * population and metapopulation dynamic models * models of biodiversity, community structure and dynamics.

Fabrication and condensation characteristics of metallic superhydrophobic surface with hierarchical micro-nano structures

NASA Astrophysics Data System (ADS)

Chu, Fuqiang; Wu, Xiaomin

2016-05-01

Metallic superhydrophobic surfaces have various applications in aerospace, refrigeration and other engineering fields due to their excellent water repellent characteristics. This study considers a simple but widely applicable fabrication method using a two simultaneous chemical reactions method to prepare the acid-salt mixed solutions to process the metal surfaces with surface deposition and surface etching to construct hierarchical micro-nano structures on the surface and then modify the surface with low surface-energy materials. Al-based and Cu-based superhydrophobic surfaces were fabricated using this method. The Al-based superhydrophobic surface had a water contact angle of 164° with hierarchical micro-nano structures similar to the lotus leaves. The Cu-based surface had a water contact angle of 157° with moss-like hierarchical micro-nano structures. Droplet condensation experiments were also performed on these two superhydrophobic surfaces to investigate their condensation characteristics. The results show that the Al-based superhydrophobic surface has lower droplet density, higher droplet jumping probability, slower droplet growth rate and lower surface coverage due to the more structured hierarchical structures.

Refinements in the hierarchical structure of externalizing psychiatric disorders: Patterns of lifetime liability from mid-adolescence through early adulthood.

PubMed

Farmer, Richard F; Seeley, John R; Kosty, Derek B; Lewinsohn, Peter M

2009-11-01

Research on hierarchical modeling of psychopathology has frequently identified 2 higher order latent factors, internalizing and externalizing. When based on the comorbidity of psychiatric diagnoses, the externalizing domain has usually been modeled as a single latent factor. Multivariate studies of externalizing symptom features, however, suggest multidimensionality. To address this apparent contradiction, confirmatory factor analytic methods and information-theoretic criteria were used to evaluate 4 theoretically plausible measurement models based on lifetime comorbidity patterns of 7 putative externalizing disorders. Diagnostic information was collected at 4 assessment waves from an age-based cohort of 816 persons between the ages of 14 and 33. A 2-factor model that distinguished oppositional behavior disorders (attention-deficit/hyperactivity disorder, oppositional defiant disorder) from social norm violation disorders (conduct disorder, adult antisocial behavior, alcohol use disorder, cannabis use disorder, hard drug use disorder) demonstrated consistently good fit and superior approximating abilities. Analyses of psychosocial outcomes measured at the last assessment wave supported the validity of this 2-factor model. Implications of this research for the theoretical understanding of domain-related disorders and the organization of classification systems are discussed. PsycINFO Database Record 2009 APA, all rights reserved.

TOPICAL REVIEW: Nonlinear aspects of the renormalization group flows of Dyson's hierarchical model

NASA Astrophysics Data System (ADS)

Meurice, Y.

2007-06-01

We review recent results concerning the renormalization group (RG) transformation of Dyson's hierarchical model (HM). This model can be seen as an approximation of a scalar field theory on a lattice. We introduce the HM and show that its large group of symmetry simplifies drastically the blockspinning procedure. Several equivalent forms of the recursion formula are presented with unified notations. Rigourous and numerical results concerning the recursion formula are summarized. It is pointed out that the recursion formula of the HM is inequivalent to both Wilson's approximate recursion formula and Polchinski's equation in the local potential approximation (despite the very small difference with the exponents of the latter). We draw a comparison between the RG of the HM and functional RG equations in the local potential approximation. The construction of the linear and nonlinear scaling variables is discussed in an operational way. We describe the calculation of non-universal critical amplitudes in terms of the scaling variables of two fixed points. This question appears as a problem of interpolation between these fixed points. Universal amplitude ratios are calculated. We discuss the large-N limit and the complex singularities of the critical potential calculable in this limit. The interpolation between the HM and more conventional lattice models is presented as a symmetry breaking problem. We briefly introduce models with an approximate supersymmetry. One important goal of this review is to present a configuration space counterpart, suitable for lattice formulations, of functional RG equations formulated in momentum space (often called exact RG equations and abbreviated ERGE).

Hierarchical Coarse-Graining Via a Generalized Yvon-Born Green Framework: Many-Body Correlations, Mappings, and Structural Accuracy

NASA Astrophysics Data System (ADS)

Rudzinski, Joseph F.

Atomically-detailed molecular dynamics simulations have emerged as one of the most powerful theoretic tools for studying complex, condensed-phase systems. Despite their ability to provide incredible molecular insight, these simulations are insufficient for investigating complex biological processes, e.g., protein folding or molecular aggregation, on relevant length and time scales. The increasing scope and sophistication of atomically-detailed models has motivated the development of "hierarchical" approaches, which parameterize a low resolution, coarse-grained (CG) model based on simulations of an atomically-detailed model. The utility of hierarchical CG models depends on their ability to accurately incorporate the correct physics of the underlying model. One approach for ensuring this "consistency" between the models is to parameterize the CG model to reproduce the structural ensemble generated by the high resolution model. The many-body potential of mean force is the proper CG energy function for reproducing all structural distributions of the atomically-detailed model, at the CG level of resolution. However, this CG potential is a configuration-dependent free energy function that is generally too complicated to represent or simulate. The multiscale coarse-graining (MS-CG) method employs a generalized Yvon-Born-Green (g-YBG) relation to directly determine a variationally optimal approximation to the many-body potential of mean force. The MS-CG/g-YBG method provides a convenient and transparent framework for investigating the equilibrium structure of the system, at the CG level of resolution. In this work, we investigate the fundamental limitations and approximations of the MS-CG/g-YBG method. Throughout the work, we propose several theoretic constructs to directly relate the MS-CG/g-YBG method to other popular structure-based CG approaches. We investigate the physical interpretation of the MS-CG/g-YBG correlation matrix, the quantity responsible for disentangling the various contributions to the average force on a CG site. We then employ an iterative extension of the MS-CG/g-YBG method that improves the accuracy of a particular set of low order correlation functions relative to the original MS-CG/g-YBG model. We demonstrate that this method provides a powerful framework for identifying the precise source of error in an MS-CG/g-YBG model. We then propose a method for identifying an optimal CG representation, prior to the development of the CG model. We employ these techniques together to demonstrate that in the cases where the MS-CG/g-YBG method fails to determine an accurate model, a fundamental problem likely exists with the chosen CG representation or interaction set. Additionally, we explicitly demonstrate that while the iterative model successfully improves the accuracy of the low order structure, it does so by distorting the higher order structural correlations relative to the underlying model. Finally, we apply these methods to investigate the utility of the MS-CG/g- YBG method for developing models for systems with complex intramolecular structure. Overall, our results demonstrate the power of the g-YBG framework for developing accurate CG models and for investigating the driving forces of equilibrium structures for complex condensed-phase systems. This work also explicitly motivates future development of bottom-up CG methods and highlights some outstanding problems in the field. iii.

A Hierarchical Modeling for Reactive Power Optimization With Joint Transmission and Distribution Networks by Curve Fitting

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

Ding, Tao; Li, Cheng; Huang, Can

Here, in order to solve the reactive power optimization with joint transmission and distribution networks, a hierarchical modeling method is proposed in this paper. It allows the reactive power optimization of transmission and distribution networks to be performed separately, leading to a master–slave structure and improves traditional centralized modeling methods by alleviating the big data problem in a control center. Specifically, the transmission-distribution-network coordination issue of the hierarchical modeling method is investigated. First, a curve-fitting approach is developed to provide a cost function of the slave model for the master model, which reflects the impacts of each slave model. Second,more » the transmission and distribution networks are decoupled at feeder buses, and all the distribution networks are coordinated by the master reactive power optimization model to achieve the global optimality. Finally, numerical results on two test systems verify the effectiveness of the proposed hierarchical modeling and curve-fitting methods.« less

A Hierarchical Modeling for Reactive Power Optimization With Joint Transmission and Distribution Networks by Curve Fitting

DOE PAGES

Ding, Tao; Li, Cheng; Huang, Can; ...

2017-01-09

Here, in order to solve the reactive power optimization with joint transmission and distribution networks, a hierarchical modeling method is proposed in this paper. It allows the reactive power optimization of transmission and distribution networks to be performed separately, leading to a master–slave structure and improves traditional centralized modeling methods by alleviating the big data problem in a control center. Specifically, the transmission-distribution-network coordination issue of the hierarchical modeling method is investigated. First, a curve-fitting approach is developed to provide a cost function of the slave model for the master model, which reflects the impacts of each slave model. Second,more » the transmission and distribution networks are decoupled at feeder buses, and all the distribution networks are coordinated by the master reactive power optimization model to achieve the global optimality. Finally, numerical results on two test systems verify the effectiveness of the proposed hierarchical modeling and curve-fitting methods.« less

A new hierarchical method to find community structure in networks

NASA Astrophysics Data System (ADS)

Saoud, Bilal; Moussaoui, Abdelouahab

2018-04-01

Community structure is very important to understand a network which represents a context. Many community detection methods have been proposed like hierarchical methods. In our study, we propose a new hierarchical method for community detection in networks based on genetic algorithm. In this method we use genetic algorithm to split a network into two networks which maximize the modularity. Each new network represents a cluster (community). Then we repeat the splitting process until we get one node at each cluster. We use the modularity function to measure the strength of the community structure found by our method, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our method are highly effective at discovering community structure in both computer-generated and real-world network data.

Variational Bayesian identification and prediction of stochastic nonlinear dynamic causal models.

PubMed

Daunizeau, J; Friston, K J; Kiebel, S J

2009-11-01

In this paper, we describe a general variational Bayesian approach for approximate inference on nonlinear stochastic dynamic models. This scheme extends established approximate inference on hidden-states to cover: (i) nonlinear evolution and observation functions, (ii) unknown parameters and (precision) hyperparameters and (iii) model comparison and prediction under uncertainty. Model identification or inversion entails the estimation of the marginal likelihood or evidence of a model. This difficult integration problem can be finessed by optimising a free-energy bound on the evidence using results from variational calculus. This yields a deterministic update scheme that optimises an approximation to the posterior density on the unknown model variables. We derive such a variational Bayesian scheme in the context of nonlinear stochastic dynamic hierarchical models, for both model identification and time-series prediction. The computational complexity of the scheme is comparable to that of an extended Kalman filter, which is critical when inverting high dimensional models or long time-series. Using Monte-Carlo simulations, we assess the estimation efficiency of this variational Bayesian approach using three stochastic variants of chaotic dynamic systems. We also demonstrate the model comparison capabilities of the method, its self-consistency and its predictive power.

Approximation methods for stochastic petri nets

NASA Technical Reports Server (NTRS)

Jungnitz, Hauke Joerg

1992-01-01

Stochastic Marked Graphs are a concurrent decision free formalism provided with a powerful synchronization mechanism generalizing conventional Fork Join Queueing Networks. In some particular cases the analysis of the throughput can be done analytically. Otherwise the analysis suffers from the classical state explosion problem. Embedded in the divide and conquer paradigm, approximation techniques are introduced for the analysis of stochastic marked graphs and Macroplace/Macrotransition-nets (MPMT-nets), a new subclass introduced herein. MPMT-nets are a subclass of Petri nets that allow limited choice, concurrency and sharing of resources. The modeling power of MPMT is much larger than that of marked graphs, e.g., MPMT-nets can model manufacturing flow lines with unreliable machines and dataflow graphs where choice and synchronization occur. The basic idea leads to the notion of a cut to split the original net system into two subnets. The cuts lead to two aggregated net systems where one of the subnets is reduced to a single transition. A further reduction leads to a basic skeleton. The generalization of the idea leads to multiple cuts, where single cuts can be applied recursively leading to a hierarchical decomposition. Based on the decomposition, a response time approximation technique for the performance analysis is introduced. Also, delay equivalence, which has previously been introduced in the context of marked graphs by Woodside et al., Marie's method and flow equivalent aggregation are applied to the aggregated net systems. The experimental results show that response time approximation converges quickly and shows reasonable accuracy in most cases. The convergence of Marie's method and flow equivalent aggregation are applied to the aggregated net systems. The experimental results show that response time approximation converges quickly and shows reasonable accuracy in most cases. The convergence of Marie's is slower, but the accuracy is generally better. Delay equivalence often fails to converge, while flow equivalent aggregation can lead to potentially bad results if a strong dependence of the mean completion time on the interarrival process exists.

Graphical Methods for Reducing, Visualizing and Analyzing Large Data Sets Using Hierarchical Terminologies

PubMed Central

Jing, Xia; Cimino, James J.

2011-01-01

Objective: To explore new graphical methods for reducing and analyzing large data sets in which the data are coded with a hierarchical terminology. Methods: We use a hierarchical terminology to organize a data set and display it in a graph. We reduce the size and complexity of the data set by considering the terminological structure and the data set itself (using a variety of thresholds) as well as contributions of child level nodes to parent level nodes. Results: We found that our methods can reduce large data sets to manageable size and highlight the differences among graphs. The thresholds used as filters to reduce the data set can be used alone or in combination. We applied our methods to two data sets containing information about how nurses and physicians query online knowledge resources. The reduced graphs make the differences between the two groups readily apparent. Conclusions: This is a new approach to reduce size and complexity of large data sets and to simplify visualization. This approach can be applied to any data sets that are coded with hierarchical terminologies. PMID:22195119

Managing Clustered Data Using Hierarchical Linear Modeling

ERIC Educational Resources Information Center

Warne, Russell T.; Li, Yan; McKyer, E. Lisako J.; Condie, Rachel; Diep, Cassandra S.; Murano, Peter S.

2012-01-01

Researchers in nutrition research often use cluster or multistage sampling to gather participants for their studies. These sampling methods often produce violations of the assumption of data independence that most traditional statistics share. Hierarchical linear modeling is a statistical method that can overcome violations of the independence…

[Study of the accidental thymus involution during the formation of hierarchic communities by a novel physical method for recording the social stress].

PubMed

Kulikov, A V; Arkhipova, L V; Kulikov, D A; Smirnova, G N; Kulikova, P A

2013-01-01

A novel method for recording the aggressive behavior in newly formed hierarchic communities has been developed. A temporal and age-related dynamics of the accidental thymus involution in mammals has been studied.

Correlates and Risk Markers for Sleep Disturbance in Participants of the Autism Treatment Network

ERIC Educational Resources Information Center

Hollway, Jill A.; Aman, Michael G.; Butter, Eric

2013-01-01

We explored possible cognitive, behavioral, emotional, and physiological risk markers for sleep disturbance in children with autism spectrum disorders. Data from 1,583 children in the Autism Treatment Network were analyzed. Approximately 45 potential predictors were analyzed using hierarchical regression modeling. As medication could confound…

Identifying Hierarchical and Overlapping Protein Complexes Based on Essential Protein-Protein Interactions and “Seed-Expanding” Method

PubMed Central

Ren, Jun; Zhou, Wei; Wang, Jianxin

2014-01-01

Many evidences have demonstrated that protein complexes are overlapping and hierarchically organized in PPI networks. Meanwhile, the large size of PPI network wants complex detection methods have low time complexity. Up to now, few methods can identify overlapping and hierarchical protein complexes in a PPI network quickly. In this paper, a novel method, called MCSE, is proposed based on λ-module and “seed-expanding.” First, it chooses seeds as essential PPIs or edges with high edge clustering values. Then, it identifies protein complexes by expanding each seed to a λ-module. MCSE is suitable for large PPI networks because of its low time complexity. MCSE can identify overlapping protein complexes naturally because a protein can be visited by different seeds. MCSE uses the parameter λ_th to control the range of seed expanding and can detect a hierarchical organization of protein complexes by tuning the value of λ_th. Experimental results of S. cerevisiae show that this hierarchical organization is similar to that of known complexes in MIPS database. The experimental results also show that MCSE outperforms other previous competing algorithms, such as CPM, CMC, Core-Attachment, Dpclus, HC-PIN, MCL, and NFC, in terms of the functional enrichment and matching with known protein complexes. PMID:25143945

CLUSFAVOR 5.0: hierarchical cluster and principal-component analysis of microarray-based transcriptional profiles

PubMed Central

Peterson, Leif E

2002-01-01

CLUSFAVOR (CLUSter and Factor Analysis with Varimax Orthogonal Rotation) 5.0 is a Windows-based computer program for hierarchical cluster and principal-component analysis of microarray-based transcriptional profiles. CLUSFAVOR 5.0 standardizes input data; sorts data according to gene-specific coefficient of variation, standard deviation, average and total expression, and Shannon entropy; performs hierarchical cluster analysis using nearest-neighbor, unweighted pair-group method using arithmetic averages (UPGMA), or furthest-neighbor joining methods, and Euclidean, correlation, or jack-knife distances; and performs principal-component analysis. PMID:12184816

Microparticles with hierarchical porosity

DOEpatents

Petsev, Dimiter N; Atanassov, Plamen; Pylypenko, Svitlana; Carroll, Nick; Olson, Tim

2012-12-18

The present disclosure provides oxide microparticles with engineered hierarchical porosity and methods of manufacturing the same. Also described are structures that are formed by templating, impregnating, and/or precipitating the oxide microparticles and method for forming the same. Suitable applications include catalysts, electrocatalysts, electrocatalysts support materials, capacitors, drug delivery systems, sensors and chromatography.

Complex Applications of HLM in Studies of Science and Mathematics Achievement: Cross-Classified Random Effects Models

ERIC Educational Resources Information Center

Moreno, Mario; Harwell, Michael; Guzey, S. Selcen; Phillips, Alison; Moore, Tamara J.

2016-01-01

Hierarchical linear models have become a familiar method for accounting for a hierarchical data structure in studies of science and mathematics achievement. This paper illustrates the use of cross-classified random effects models (CCREMs), which are likely less familiar. The defining characteristic of CCREMs is a hierarchical data structure…

Recognition and characterization of hierarchical interstellar structure. II - Structure tree statistics

NASA Technical Reports Server (NTRS)

Houlahan, Padraig; Scalo, John

1992-01-01

A new method of image analysis is described, in which images partitioned into 'clouds' are represented by simplified skeleton images, called structure trees, that preserve the spatial relations of the component clouds while disregarding information concerning their sizes and shapes. The method can be used to discriminate between images of projected hierarchical (multiply nested) and random three-dimensional simulated collections of clouds constructed on the basis of observed interstellar properties, and even intermediate systems formed by combining random and hierarchical simulations. For a given structure type, the method can distinguish between different subclasses of models with different parameters and reliably estimate their hierarchical parameters: average number of children per parent, scale reduction factor per level of hierarchy, density contrast, and number of resolved levels. An application to a column density image of the Taurus complex constructed from IRAS data is given. Moderately strong evidence for a hierarchical structural component is found, and parameters of the hierarchy, as well as the average volume filling factor and mass efficiency of fragmentation per level of hierarchy, are estimated. The existence of nested structure contradicts models in which large molecular clouds are supposed to fragment, in a single stage, into roughly stellar-mass cores.

Receiver function deconvolution using transdimensional hierarchical Bayesian inference

NASA Astrophysics Data System (ADS)

Kolb, J. M.; Lekić, V.

2014-06-01

Teleseismic waves can convert from shear to compressional (Sp) or compressional to shear (Ps) across impedance contrasts in the subsurface. Deconvolving the parent waveforms (P for Ps or S for Sp) from the daughter waveforms (S for Ps or P for Sp) generates receiver functions which can be used to analyse velocity structure beneath the receiver. Though a variety of deconvolution techniques have been developed, they are all adversely affected by background and signal-generated noise. In order to take into account the unknown noise characteristics, we propose a method based on transdimensional hierarchical Bayesian inference in which both the noise magnitude and noise spectral character are parameters in calculating the likelihood probability distribution. We use a reversible-jump implementation of a Markov chain Monte Carlo algorithm to find an ensemble of receiver functions whose relative fits to the data have been calculated while simultaneously inferring the values of the noise parameters. Our noise parametrization is determined from pre-event noise so that it approximates observed noise characteristics. We test the algorithm on synthetic waveforms contaminated with noise generated from a covariance matrix obtained from observed noise. We show that the method retrieves easily interpretable receiver functions even in the presence of high noise levels. We also show that we can obtain useful estimates of noise amplitude and frequency content. Analysis of the ensemble solutions produced by our method can be used to quantify the uncertainties associated with individual receiver functions as well as with individual features within them, providing an objective way for deciding which features warrant geological interpretation. This method should make possible more robust inferences on subsurface structure using receiver function analysis, especially in areas of poor data coverage or under noisy station conditions.

Multitrace/singletrace formulations and Domain Decomposition Methods for the solution of Helmholtz transmission problems for bounded composite scatterers

NASA Astrophysics Data System (ADS)

Jerez-Hanckes, Carlos; Pérez-Arancibia, Carlos; Turc, Catalin

2017-12-01

We present Nyström discretizations of multitrace/singletrace formulations and non-overlapping Domain Decomposition Methods (DDM) for the solution of Helmholtz transmission problems for bounded composite scatterers with piecewise constant material properties. We investigate the performance of DDM with both classical Robin and optimized transmission boundary conditions. The optimized transmission boundary conditions incorporate square root Fourier multiplier approximations of Dirichlet to Neumann operators. While the multitrace/singletrace formulations as well as the DDM that use classical Robin transmission conditions are not particularly well suited for Krylov subspace iterative solutions of high-contrast high-frequency Helmholtz transmission problems, we provide ample numerical evidence that DDM with optimized transmission conditions constitute efficient computational alternatives for these type of applications. In the case of large numbers of subdomains with different material properties, we show that the associated DDM linear system can be efficiently solved via hierarchical Schur complements elimination.

Airfoil Design and Optimization by the One-Shot Method

NASA Technical Reports Server (NTRS)

Kuruvila, G.; Taasan, Shlomo; Salas, M. D.

1995-01-01

An efficient numerical approach for the design of optimal aerodynamic shapes is presented in this paper. The objective of any optimization problem is to find the optimum of a cost function subject to a certain state equation (governing equation of the flow field) and certain side constraints. As in classical optimal control methods, the present approach introduces a costate variable (Lagrange multiplier) to evaluate the gradient of the cost function. High efficiency in reaching the optimum solution is achieved by using a multigrid technique and updating the shape in a hierarchical manner such that smooth (low-frequency) changes are done separately from high-frequency changes. Thus, the design variables are changed on a grid where their changes produce nonsmooth (high-frequency) perturbations that can be damped efficiently by the multigrid. The cost of solving the optimization problem is approximately two to three times the cost of the equivalent analysis problem.

Stochastic Dynamics through Hierarchically Embedded Markov Chains

NASA Astrophysics Data System (ADS)

Vasconcelos, Vítor V.; Santos, Fernando P.; Santos, Francisco C.; Pacheco, Jorge M.

2017-02-01

Studying dynamical phenomena in finite populations often involves Markov processes of significant mathematical and/or computational complexity, which rapidly becomes prohibitive with increasing population size or an increasing number of individual configuration states. Here, we develop a framework that allows us to define a hierarchy of approximations to the stationary distribution of general systems that can be described as discrete Markov processes with time invariant transition probabilities and (possibly) a large number of states. This results in an efficient method for studying social and biological communities in the presence of stochastic effects—such as mutations in evolutionary dynamics and a random exploration of choices in social systems—including situations where the dynamics encompasses the existence of stable polymorphic configurations, thus overcoming the limitations of existing methods. The present formalism is shown to be general in scope, widely applicable, and of relevance to a variety of interdisciplinary problems.

Stochastic Dynamics through Hierarchically Embedded Markov Chains.

PubMed

Vasconcelos, Vítor V; Santos, Fernando P; Santos, Francisco C; Pacheco, Jorge M

2017-02-03

Studying dynamical phenomena in finite populations often involves Markov processes of significant mathematical and/or computational complexity, which rapidly becomes prohibitive with increasing population size or an increasing number of individual configuration states. Here, we develop a framework that allows us to define a hierarchy of approximations to the stationary distribution of general systems that can be described as discrete Markov processes with time invariant transition probabilities and (possibly) a large number of states. This results in an efficient method for studying social and biological communities in the presence of stochastic effects-such as mutations in evolutionary dynamics and a random exploration of choices in social systems-including situations where the dynamics encompasses the existence of stable polymorphic configurations, thus overcoming the limitations of existing methods. The present formalism is shown to be general in scope, widely applicable, and of relevance to a variety of interdisciplinary problems.

Airfoil optimization by the one-shot method

NASA Technical Reports Server (NTRS)

Kuruvila, G.; Taasan, Shlomo; Salas, M. D.

1994-01-01

An efficient numerical approach for the design of optimal aerodynamic shapes is presented in this paper. The objective of any optimization problem is to find the optimum of a cost function subject to a certain state equation (Governing equation of the flow field) and certain side constraints. As in classical optimal control methods, the present approach introduces a costate variable (Language multiplier) to evaluate the gradient of the cost function. High efficiency in reaching the optimum solution is achieved by using a multigrid technique and updating the shape in a hierarchical manner such that smooth (low-frequency) changes are done separately from high-frequency changes. Thus, the design variables are changed on a grid where their changes produce nonsmooth (high-frequency) perturbations that can be damped efficiently by the multigrid. The cost of solving the optimization problem is approximately two to three times the cost of the equivalent analysis problem.

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

Wang, Yong; State Key Laboratory of Multiphase Complex System, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100080; Zhu, Qingshan, E-mail: qszhu@home.ipe.ac.cn

{beta}-Ni(OH){sub 2} hierarchical micro-flowers, hierarchical hollow microspheres and nanosheets were synthesized via a facile, single-step and selected-control hydrothermal method. Both hierarchical micro-flowers and hierarchical hollow microspheres were built from two-dimensional nanosheets with thickness of 50-100 nm. The as-obtained products were characterized by Brunauer-Emmett-Teller (BET) surface area analysis, X-ray powder diffraction (XRD) and field emission scanning electron microscopy (FESEM). It was observed that marked morphological changes in {beta}-Ni(OH){sub 2} depended on the initial concentrations of Ni{sup 2+} ions and glycine. A possible growth mechanism was proposed based on experimental results. In addition, the effect of morphology on the electrochemical properties wasmore » also investigated. Both hierarchical micro-flowers and hierarchical hollow microspheres exhibited enhanced specific capacity and high-rate discharge ability as compared with pure Ni(OH){sub 2} nanosheets. Investigations confirmed that hierarchical structures had a pronounced influence upon the electrochemical performance of nickel hydroxide.« less

Ultra-facile fabrication of phosphorus doped egg-like hierarchic porous carbon with superior supercapacitance performance by microwave irradiation combining with self-activation strategy

NASA Astrophysics Data System (ADS)

Zhang, Deyi; Han, Mei; Li, Yubing; He, Jingjing; Wang, Bing; Wang, Kunjie; Feng, Huixia

2017-12-01

Herein, we report an ultra-facile fabrication method for a phosphorus doped egg-like hierarchic porous carbon by microwave irradiation combining with self-activation strategy under air atmosphere. Comparing with the traditional pyrolytic carbonization method, the reported method exhibits incomparable merits, such as high energy efficiency, ultra-fast and inert atmosphere protection absent fabrication process. Similar morphology and graphitization degree with the sample fabricated by the traditional pyrolytic carbonization method under inert atmosphere protection for 2 h can be easily achieved by the reported microwave irradiation method just for 3 min under ambient atmosphere. The samples fabricated by the reported method display a unique phosphorus doped egg-like hierarchic porous structure, high specific surface area (1642 m2 g-1) and large pore volume (2.04 cm3 g-1). Specific capacitance of the samples fabricated by the reported method reaches up to 209 F g-1, and over 96.2% of initial capacitance remains as current density increasing from 0.5 to 20 A g-1, indicating the superior capacitance performance of the fabricated samples. The hierarchic porous structure, opened microporosity, additional pseudocapacitance, high electrolyte-accessible surface area and good conductivity make essential contribution to its superior capacitance performance.

Fractal multi-level organisation of human groups in a virtual world.

PubMed

Fuchs, Benedikt; Sornette, Didier; Thurner, Stefan

2014-10-06

Humans are fundamentally social. They form societies which consist of hierarchically layered nested groups of various quality, size, and structure. The anthropologic literature has classified these groups as support cliques, sympathy groups, bands, cognitive groups, tribes, linguistic groups, and so on. Anthropologic data show that, on average, each group consists of approximately three subgroups. However, a general understanding of the structural dependence of groups at different layers is largely missing. We extend these early findings to a very large high-precision large-scale internet-based social network data. We analyse the organisational structure of a complete, multi-relational, large social multiplex network of a human society consisting of about 400,000 odd players of an open-ended massive multiplayer online game for which we know all about their various group memberships at different layers. Remarkably, the online players' society exhibits the same type of structured hierarchical layers as found in hunter-gatherer societies. Our findings suggest that the hierarchical organisation of human society is deeply nested in human psychology.

Fractal multi-level organisation of human groups in a virtual world

PubMed Central

Fuchs, Benedikt; Sornette, Didier; Thurner, Stefan

2014-01-01

Humans are fundamentally social. They form societies which consist of hierarchically layered nested groups of various quality, size, and structure. The anthropologic literature has classified these groups as support cliques, sympathy groups, bands, cognitive groups, tribes, linguistic groups, and so on. Anthropologic data show that, on average, each group consists of approximately three subgroups. However, a general understanding of the structural dependence of groups at different layers is largely missing. We extend these early findings to a very large high-precision large-scale internet-based social network data. We analyse the organisational structure of a complete, multi-relational, large social multiplex network of a human society consisting of about 400,000 odd players of an open-ended massive multiplayer online game for which we know all about their various group memberships at different layers. Remarkably, the online players' society exhibits the same type of structured hierarchical layers as found in hunter-gatherer societies. Our findings suggest that the hierarchical organisation of human society is deeply nested in human psychology. PMID:25283998

Fractal multi-level organisation of human groups in a virtual world

NASA Astrophysics Data System (ADS)

Fuchs, Benedikt; Sornette, Didier; Thurner, Stefan

2014-10-01

Humans are fundamentally social. They form societies which consist of hierarchically layered nested groups of various quality, size, and structure. The anthropologic literature has classified these groups as support cliques, sympathy groups, bands, cognitive groups, tribes, linguistic groups, and so on. Anthropologic data show that, on average, each group consists of approximately three subgroups. However, a general understanding of the structural dependence of groups at different layers is largely missing. We extend these early findings to a very large high-precision large-scale internet-based social network data. We analyse the organisational structure of a complete, multi-relational, large social multiplex network of a human society consisting of about 400,000 odd players of an open-ended massive multiplayer online game for which we know all about their various group memberships at different layers. Remarkably, the online players' society exhibits the same type of structured hierarchical layers as found in hunter-gatherer societies. Our findings suggest that the hierarchical organisation of human society is deeply nested in human psychology.

A Hierarchical Modeling Approach to Data Analysis and Study Design in a Multi-Site Experimental fMRI Study

ERIC Educational Resources Information Center

Zhou, Bo; Konstorum, Anna; Duong, Thao; Tieu, Kinh H.; Wells, William M.; Brown, Gregory G.; Stern, Hal S.; Shahbaba, Babak

2013-01-01

We propose a hierarchical Bayesian model for analyzing multi-site experimental fMRI studies. Our method takes the hierarchical structure of the data (subjects are nested within sites, and there are multiple observations per subject) into account and allows for modeling between-site variation. Using posterior predictive model checking and model…

Automatic thoracic anatomy segmentation on CT images using hierarchical fuzzy models and registration

NASA Astrophysics Data System (ADS)

Sun, Kaioqiong; Udupa, Jayaram K.; Odhner, Dewey; Tong, Yubing; Torigian, Drew A.

2014-03-01

This paper proposes a thoracic anatomy segmentation method based on hierarchical recognition and delineation guided by a built fuzzy model. Labeled binary samples for each organ are registered and aligned into a 3D fuzzy set representing the fuzzy shape model for the organ. The gray intensity distributions of the corresponding regions of the organ in the original image are recorded in the model. The hierarchical relation and mean location relation between different organs are also captured in the model. Following the hierarchical structure and location relation, the fuzzy shape model of different organs is registered to the given target image to achieve object recognition. A fuzzy connected delineation method is then used to obtain the final segmentation result of organs with seed points provided by recognition. The hierarchical structure and location relation integrated in the model provide the initial parameters for registration and make the recognition efficient and robust. The 3D fuzzy model combined with hierarchical affine registration ensures that accurate recognition can be obtained for both non-sparse and sparse organs. The results on real images are presented and shown to be better than a recently reported fuzzy model-based anatomy recognition strategy.

Testing comparative phylogeographic models of marine vicariance and dispersal using a hierarchical Bayesian approach

PubMed Central

2008-01-01

Background Marine allopatric speciation is an enigma because pelagic larval dispersal can potentially connect disjunct populations thereby preventing reproductive and morphological divergence. Here we present a new hierarchical approximate Bayesian computation model (HABC) that tests two hypotheses of marine allopatric speciation: 1.) "soft vicariance", where a speciation involves fragmentation of a large widespread ancestral species range that was previously connected by long distance gene flow; and 2.) peripatric colonization, where speciations in peripheral archipelagos emerge from sweepstakes colonizations from central source regions. The HABC approach analyzes all the phylogeographic datasets at once in order to make across taxon-pair inferences about biogeographic processes while explicitly allowing for uncertainty in the demographic differences within each taxon-pair. Our method uses comparative phylogeographic data that consists of single locus mtDNA sequences from multiple co-distributed taxa containing pairs of central and peripheral populations. We use the method on two comparative phylogeographic data sets consisting of cowrie gastropod endemics co-distributed in the Hawaiian (11 taxon-pairs) and Marquesan archipelagos (7 taxon-pairs). Results Given the Marquesan data, we find strong evidence of simultaneous colonization across all seven cowrie gastropod endemics co-distributed in the Marquesas. In contrast, the lower sample sizes in the Hawaiian data lead to greater uncertainty associated with the Hawaiian estimates. Although, the hyper-parameter estimates point to soft vicariance in a subset of the 11 Hawaiian taxon-pairs, the hyper-prior and hyper-posterior are too similar to make a definitive conclusion. Both results are not inconsistent with what is known about the geologic history of the archipelagos. Simulations verify that our method can successfully distinguish these two histories across a wide range of conditions given sufficient sampling. Conclusion Although soft vicariance and colonization are likely to produce similar genetic patterns when a single taxon-pair is used, our hierarchical Bayesian model can potentially detect if either history is a dominant process across co-distributed taxon-pairs. As comparative phylogeographic datasets grow to include > 100 co-distributed taxon-pairs, the HABC approach will be well suited to dissect temporal patterns in community assembly and evolution, thereby providing a bridge linking comparative phylogeography with community ecology. PMID:19038027

High-Quality Hollow Closed-Pore Silica Antireflection Coatings Based on Styrene-Acrylate Emulsion @ Organic-Inorganic Silica Precursor.

PubMed

Guo, Zhaolong; Zhao, Haixin; Zhao, Wei; Wang, Tao; Kong, Depeng; Chen, Taojing; Zhang, Xiaoyan

2016-05-11

Making use of a facile and low-cost way for the preparation of a hierarchically organized novel hollow closed-pore silica antireflective coating (CHAR) with tailored optical properties and a mechanical reliability is of great interest in the field of solar photovoltaic technology. The process mainly contains two aspects: (1) a styrene-acrylate emulsion @ organic-inorganic silica precursor (SA@OISP) core/shell hierarchical nanostructure, consisting of a sacrificial styrene-acrylate (SA) primary template, was fabricated using a sol-gel method; (2) the self-assembly of the nanostructures leads to SA@OISP nanospheres forming the high-quality hollow closed-pore silica antireflection coating (CHAR) by a dip-coating process and a subsequent calcination treatment. The resulting SA@OISP nanospheres have a mean diameter of 65.2 nm and contained a SA soft core with a mean diameter of approximately 54.8 nm and an organic-inorganic silica precursor (OISP) shell with a thickness of approximately 6-10 nm. Furthermore, the prepared CHAR film exhibited a high transmittance and good ruggedness. An average transmittance (TAV) of 97.64% was obtained, and the value is close to the ideal single-layered antireflection coating (98.09%) over a broad range of wavelengths (from 380 to 1100 nm). The CHAR film showed a stable TAV, with attenuation values of less than 0.8% and 0.43% after the abrasion test and the damp heat test, respectively. The conversion efficiency of the CHAR coating cover solar modules tends to be increased by 3.75%. The promising results obtained in this study suggest that the CHAR film was considered as an essential component of the solar module and were expected to provide additional solar energy harvest under extreme outdoor climates.

Segmentation of knee cartilage by using a hierarchical active shape model based on multi-resolution transforms in magnetic resonance images

NASA Astrophysics Data System (ADS)

León, Madeleine; Escalante-Ramirez, Boris

2013-11-01

Knee osteoarthritis (OA) is characterized by the morphological degeneration of cartilage. Efficient segmentation of cartilage is important for cartilage damage diagnosis and to support therapeutic responses. We present a method for knee cartilage segmentation in magnetic resonance images (MRI). Our method incorporates the Hermite Transform to obtain a hierarchical decomposition of contours which describe knee cartilage shapes. Then, we compute a statistical model of the contour of interest from a set of training images. Thereby, our Hierarchical Active Shape Model (HASM) captures a large range of shape variability even from a small group of training samples, improving segmentation accuracy. The method was trained with a training set of 16- MRI of knee and tested with leave-one-out method.

Dissipative time-dependent quantum transport theory.

PubMed

Zhang, Yu; Yam, Chi Yung; Chen, GuanHua

2013-04-28

A dissipative time-dependent quantum transport theory is developed to treat the transient current through molecular or nanoscopic devices in presence of electron-phonon interaction. The dissipation via phonon is taken into account by introducing a self-energy for the electron-phonon coupling in addition to the self-energy caused by the electrodes. Based on this, a numerical method is proposed. For practical implementation, the lowest order expansion is employed for the weak electron-phonon coupling case and the wide-band limit approximation is adopted for device and electrodes coupling. The corresponding hierarchical equation of motion is derived, which leads to an efficient and accurate time-dependent treatment of inelastic effect on transport for the weak electron-phonon interaction. The resulting method is applied to a one-level model system and a gold wire described by tight-binding model to demonstrate its validity and the importance of electron-phonon interaction for the quantum transport. As it is based on the effective single-electron model, the method can be readily extended to time-dependent density functional theory.

The Differential Effects of Preschool: Evidence from Virginia

ERIC Educational Resources Information Center

Huang, Francis L.; Invernizzi, Marcia A.; Drake, E. Allison

2012-01-01

This study investigated the differential and persistent effects of a state-funded pre-K program, the Virginia Preschool Initiative (VPI). We analyzed data from a cohort of over 60,000 students nested in approximately 1000 schools from the beginning of kindergarten to the end of first grade using two-level hierarchical logistic regression models.…

Bridging Literacy Acquisition and Self-Regulated Learning: Using a SRL Framework to Support Struggling Readers

ERIC Educational Resources Information Center

Holtzheuser, Sierra; McNamara, John

2014-01-01

Reading is conceptualized as a hierarchy of component skills where lower order emergent literacy skills set the foundation for higher order reading skills such as fluency and comprehension. Approximately 20% of readers struggle within this hierarchical process (Fielding, Kerr, & Rosier, 2007). Struggling readers are susceptible to the Matthew…

Influence of Valley Floor Landforms on Stream Ecosystems

Treesearch

Stanley V. Gregory; Gary A. Lamberti; Kelly M. S. Moore

1989-01-01

A hierarchical perspective of relationships between valley floor landforms, riparian plant communities, and aquatic ecosystems has been developed, based on studies of two fifth-order basins in the Cascade Mountains of Oregon. Retention of dissolved nitrogen and leaves were approximately 2-3 times greater in unconstrained reaches than in constrained reaches. Both valley...

Microstructure Hierarchical Model of Competitive e+-Ps Trapping in Nanostructurized Substances: from Nanoparticle-Uniform to Nanoparticle-Biased Systems.

PubMed

Shpotyuk, Oleh; Ingram, Adam; Bujňáková, Zdenka; Baláž, Peter

2017-12-01

Microstructure hierarchical model considering the free-volume elements at the level of interacting crystallites (non-spherical approximation) and the agglomerates of these crystallites (spherical approximation) was developed to describe free-volume evolution in mechanochemically milled As 4 S 4 /ZnS composites employing positron annihilation spectroscopy in a lifetime measuring mode. Positron lifetime spectra were reconstructed from unconstrained three-term decomposition procedure and further subjected to parameterization using x3-x2-coupling decomposition algorithm. Intrinsic inhomogeneities due to coarse-grained As 4 S 4 and fine-grained ZnS nanoparticles were adequately described in terms of substitution trapping in positron and positronium (Ps) (bound positron-electron) states due to interfacial triple junctions between contacting particles and own free-volume defects in boundary compounds. Compositionally dependent nanostructurization in As 4 S 4 /ZnS nanocomposite system was imagined as conversion from o-Ps trapping sites to positron traps. The calculated trapping parameters that were shown could be useful to characterize adequately the nanospace filling in As 4 S 4 /ZnS composites.

A facile approach toward transition metal oxide hierarchical structures and their lithium storage properties.

PubMed

Zhang, Cuimiao; Chen, Jing; Zeng, Yi; Rui, Xianhong; Zhu, Jixin; Zhang, Wenyu; Xu, Chen; Lim, Tuti Mariana; Hng, Huey Hoon; Yan, Qingyu

2012-06-21

A simple, non-template, non-surfactant and environmentally friendly hydrothermal method is presented based on the controlled release of the reactants into the reaction solvents to induce slow nucleation and growth of three-dimensional hierarchical nanostructures of transition metal oxides. This method is a general approach, which can be used to prepare Co(3)O(4), CuO, and Ni(OH)(2)/NiO. These metal oxides with hierarchical nanostructures can be used as anode materials for lithium-ion batteries with good Li storage performance, e.g. high specific capacities and stable cyclability.

On the design of a hierarchical SS7 network: A graph theoretical approach

NASA Astrophysics Data System (ADS)

Krauss, Lutz; Rufa, Gerhard

1994-04-01

This contribution is concerned with the design of Signaling System No. 7 networks based on graph theoretical methods. A hierarchical network topology is derived by combining the advantage of the hierarchical network structure with the realization of node disjoint routes between nodes of the network. By using specific features of this topology, we develop an algorithm to construct circle-free routing data and to assure bidirectionality also in case of failure situations. The methods described are based on the requirements that the network topology, as well as the routing data, may be easily changed.

Patterned Diblock Co-Polymer Thin Films as Templates for Advanced Anisotropic Metal Nanostructures.

PubMed

Roth, Stephan V; Santoro, Gonzalo; Risch, Johannes F H; Yu, Shun; Schwartzkopf, Matthias; Boese, Torsten; Döhrmann, Ralph; Zhang, Peng; Besner, Bastian; Bremer, Philipp; Rukser, Dieter; Rübhausen, Michael A; Terrill, Nick J; Staniec, Paul A; Yao, Yuan; Metwalli, Ezzeldin; Müller-Buschbaum, Peter

2015-06-17

We demonstrate glancing-angle deposition of gold on a nanostructured diblock copolymer, namely polystyrene-block-poly(methyl methacrylate) thin film. Exploiting the selective wetting of gold on the polystyrene block, we are able to fabricate directional hierarchical structures. We prove the asymmetric growth of the gold nanoparticles and are able to extract the different growth laws by in situ scattering methods. The optical anisotropy of these hierarchical hybrid materials is further probed by angular resolved spectroscopic methods. This approach enables us to tailor functional hierarchical layers in nanodevices, such as nanoantennae arrays, organic photovoltaics, and sensor electronics.

Assessing dose-response effects of national essential medicine policy in China: comparison of two methods for handling data with a stepped wedge-like design and hierarchical structure.

PubMed

Ren, Yan; Yang, Min; Li, Qian; Pan, Jay; Chen, Fei; Li, Xiaosong; Meng, Qun

2017-02-22

To introduce multilevel repeated measures (RM) models and compare them with multilevel difference-in-differences (DID) models in assessing the linear relationship between the length of the policy intervention period and healthcare outcomes (dose-response effect) for data from a stepped-wedge design with a hierarchical structure. The implementation of national essential medicine policy (NEMP) in China was a stepped-wedge-like design of five time points with a hierarchical structure. Using one key healthcare outcome from the national NEMP surveillance data as an example, we illustrate how a series of multilevel DID models and one multilevel RM model can be fitted to answer some research questions on policy effects. Routinely and annually collected national data on China from 2008 to 2012. 34 506 primary healthcare facilities in 2675 counties of 31 provinces. Agreement and differences in estimates of dose-response effect and variation in such effect between the two methods on the logarithm-transformed total number of outpatient visits per facility per year (LG-OPV). The estimated dose-response effect was approximately 0.015 according to four multilevel DID models and precisely 0.012 from one multilevel RM model. Both types of model estimated an increase in LG-OPV by 2.55 times from 2009 to 2012, but 2-4.3 times larger SEs of those estimates were found by the multilevel DID models. Similar estimates of mean effects of covariates and random effects of the average LG-OPV among all levels in the example dataset were obtained by both types of model. Significant variances in the dose-response among provinces, counties and facilities were estimated, and the 'lowest' or 'highest' units by their dose-response effects were pinpointed only by the multilevel RM model. For examining dose-response effect based on data from multiple time points with hierarchical structure and the stepped wedge-like designs, multilevel RM models are more efficient, convenient and informative than the multilevel DID models. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

A New Runge-Kutta Discontinuous Galerkin Method with Conservation Constraint to Improve CFL Condition for Solving Conservation Laws

PubMed Central

Xu, Zhiliang; Chen, Xu-Yan; Liu, Yingjie

2014-01-01

We present a new formulation of the Runge-Kutta discontinuous Galerkin (RKDG) method [9, 8, 7, 6] for solving conservation Laws with increased CFL numbers. The new formulation requires the computed RKDG solution in a cell to satisfy additional conservation constraint in adjacent cells and does not increase the complexity or change the compactness of the RKDG method. Numerical computations for solving one-dimensional and two-dimensional scalar and systems of nonlinear hyperbolic conservation laws are performed with approximate solutions represented by piecewise quadratic and cubic polynomials, respectively. The hierarchical reconstruction [17, 33] is applied as a limiter to eliminate spurious oscillations in discontinuous solutions. From both numerical experiments and the analytic estimate of the CFL number of the newly formulated method, we find that: 1) this new formulation improves the CFL number over the original RKDG formulation by at least three times or more and thus reduces the overall computational cost; and 2) the new formulation essentially does not compromise the resolution of the numerical solutions of shock wave problems compared with ones computed by the RKDG method. PMID:25414520

Flexible fabrication of biomimetic compound eye array via two-step thermal reflow of simply pre-modeled hierarchic microstructures

NASA Astrophysics Data System (ADS)

Huang, Shengzhou; Li, Mujun; Shen, Lianguan; Qiu, Jinfeng; Zhou, Youquan

2017-06-01

A flexible fabrication method for the biomimetic compound eye (BCE) array is proposed. In this method, a triple-layer sandwich-like coating configuration was introduced, and the required hierarchic microstructures are formed with a simple single-scan exposure in maskless digital lithography. Taking advantage of the difference of glass transition point (Tg) between photoresists of each layer, the pre-formed hierarchic microstructures are in turn reflowed to the curved substrate and the BCE ommatidia in a two-step thermal reflow process. To avoid affecting the spherical substrate formed in the first thermal reflow, a non-contact strategy was proposed in the second reflow process. The measurement results were in good agreement with the designed BCE profiles. Results also showed that the fabricated BCE had good performances in optical test. The presented method is flexible, convenient, low-cost and can easily adapt to the fabrications of other optical elements with hierarchic microstructures.

Hierarchical screening for multiple mental disorders.

PubMed

Batterham, Philip J; Calear, Alison L; Sunderland, Matthew; Carragher, Natacha; Christensen, Helen; Mackinnon, Andrew J

2013-10-01

There is a need for brief, accurate screening when assessing multiple mental disorders. Two-stage hierarchical screening, consisting of brief pre-screening followed by a battery of disorder-specific scales for those who meet diagnostic criteria, may increase the efficiency of screening without sacrificing precision. This study tested whether more efficient screening could be gained using two-stage hierarchical screening than by administering multiple separate tests. Two Australian adult samples (N=1990) with high rates of psychopathology were recruited using Facebook advertising to examine four methods of hierarchical screening for four mental disorders: major depressive disorder, generalised anxiety disorder, panic disorder and social phobia. Using K6 scores to determine whether full screening was required did not increase screening efficiency. However, pre-screening based on two decision tree approaches or item gating led to considerable reductions in the mean number of items presented per disorder screened, with estimated item reductions of up to 54%. The sensitivity of these hierarchical methods approached 100% relative to the full screening battery. Further testing of the hierarchical screening approach based on clinical criteria and in other samples is warranted. The results demonstrate that a two-phase hierarchical approach to screening multiple mental disorders leads to considerable increases efficiency gains without reducing accuracy. Screening programs should take advantage of prescreeners based on gating items or decision trees to reduce the burden on respondents. © 2013 Elsevier B.V. All rights reserved.

Hydrothermal Fabrication of WO3 Hierarchical Architectures: Structure, Growth and Response

PubMed Central

Wu, Chuan-Sheng

2015-01-01

Recently hierarchical architectures, consisting of two-dimensional (2D) nanostructures, are of great interest for potential applications in energy and environmental. Here, novel rose-like WO3 hierarchical architectures were successfully synthesized via a facile hydrothermal method. The as-prepared WO3 hierarchical architectures were in fact assembled by numerous nanosheets with an average thickness of ~30 nm. We found that the oxalic acid played a significant role in governing morphologies of WO3 during hydrothermal process. Based on comparative studies, a possible formation mechanism was also proposed in detail. Furthermore, gas-sensing measurement showed that the well-defined 3D WO3 hierarchical architectures exhibited the excellent gas sensing properties towards CO. PMID:28347062

Fabrication of micro/nano hierarchical structures with analysis on the surface mechanics

NASA Astrophysics Data System (ADS)

Jheng, Yu-Sheng; Lee, Yeeu-Chang

2016-10-01

Biomimicry refers to the imitation of mechanisms and features found in living creatures using artificial methods. This study used optical lithography, colloidal lithography, and dry etching to mimic the micro/nano hierarchical structures covering the soles of gecko feet. We measured the static contact angle and contact angle hysteresis to reveal the behavior of liquid drops on the hierarchical structures. Pulling tests were also performed to measure the resistance of movement between the hierarchical structures and a testing plate. Our results reveal that hierarchical structures at the micro-/nano-scale are considerably hydrophobic, they provide good flow characteristics, and they generate more contact force than do surfaces with micro-scale cylindrical structures.

Progressive Precision Surface Design

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

Duchaineau, M; Joy, KJ

2002-01-11

We introduce a novel wavelet decomposition algorithm that makes a number of powerful new surface design operations practical. Wavelets, and hierarchical representations generally, have held promise to facilitate a variety of design tasks in a unified way by approximating results very precisely, thus avoiding a proliferation of undergirding mathematical representations. However, traditional wavelet decomposition is defined from fine to coarse resolution, thus limiting its efficiency for highly precise surface manipulation when attempting to create new non-local editing methods. Our key contribution is the progressive wavelet decomposition algorithm, a general-purpose coarse-to-fine method for hierarchical fitting, based in this paper on anmore » underlying multiresolution representation called dyadic splines. The algorithm requests input via a generic interval query mechanism, allowing a wide variety of non-local operations to be quickly implemented. The algorithm performs work proportionate to the tiny compressed output size, rather than to some arbitrarily high resolution that would otherwise be required, thus increasing performance by several orders of magnitude. We describe several design operations that are made tractable because of the progressive decomposition. Free-form pasting is a generalization of the traditional control-mesh edit, but for which the shape of the change is completely general and where the shape can be placed using a free-form deformation within the surface domain. Smoothing and roughening operations are enhanced so that an arbitrary loop in the domain specifies the area of effect. Finally, the sculpting effect of moving a tool shape along a path is simulated.« less

Rigorous Free-Fermion Entanglement Renormalization from Wavelet Theory

NASA Astrophysics Data System (ADS)

Haegeman, Jutho; Swingle, Brian; Walter, Michael; Cotler, Jordan; Evenbly, Glen; Scholz, Volkher B.

2018-01-01

We construct entanglement renormalization schemes that provably approximate the ground states of noninteracting-fermion nearest-neighbor hopping Hamiltonians on the one-dimensional discrete line and the two-dimensional square lattice. These schemes give hierarchical quantum circuits that build up the states from unentangled degrees of freedom. The circuits are based on pairs of discrete wavelet transforms, which are approximately related by a "half-shift": translation by half a unit cell. The presence of the Fermi surface in the two-dimensional model requires a special kind of circuit architecture to properly capture the entanglement in the ground state. We show how the error in the approximation can be controlled without ever performing a variational optimization.

Hierarchical structures of amorphous solids characterized by persistent homology

PubMed Central

Hiraoka, Yasuaki; Nakamura, Takenobu; Hirata, Akihiko; Escolar, Emerson G.; Matsue, Kaname; Nishiura, Yasumasa

2016-01-01

This article proposes a topological method that extracts hierarchical structures of various amorphous solids. The method is based on the persistence diagram (PD), a mathematical tool for capturing shapes of multiscale data. The input to the PDs is given by an atomic configuration and the output is expressed as 2D histograms. Then, specific distributions such as curves and islands in the PDs identify meaningful shape characteristics of the atomic configuration. Although the method can be applied to a wide variety of disordered systems, it is applied here to silica glass, the Lennard-Jones system, and Cu-Zr metallic glass as standard examples of continuous random network and random packing structures. In silica glass, the method classified the atomic rings as short-range and medium-range orders and unveiled hierarchical ring structures among them. These detailed geometric characterizations clarified a real space origin of the first sharp diffraction peak and also indicated that PDs contain information on elastic response. Even in the Lennard-Jones system and Cu-Zr metallic glass, the hierarchical structures in the atomic configurations were derived in a similar way using PDs, although the glass structures and properties substantially differ from silica glass. These results suggest that the PDs provide a unified method that extracts greater depth of geometric information in amorphous solids than conventional methods. PMID:27298351

A new artefacts resistant method for automatic lineament extraction using Multi-Hillshade Hierarchic Clustering (MHHC)

NASA Astrophysics Data System (ADS)

Šilhavý, Jakub; Minár, Jozef; Mentlík, Pavel; Sládek, Ján

2016-07-01

This paper presents a new method of automatic lineament extraction which includes the removal of the 'artefacts effect' which is associated with the process of raster based analysis. The core of the proposed Multi-Hillshade Hierarchic Clustering (MHHC) method incorporates a set of variously illuminated and rotated hillshades in combination with hierarchic clustering of derived 'protolineaments'. The algorithm also includes classification into positive and negative lineaments. MHHC was tested in two different territories in Bohemian Forest and Central Western Carpathians. The original vector-based algorithm was developed for comparison of the individual lineaments proximity. Its use confirms the compatibility of manual and automatic extraction and their similar relationships to structural data in the study areas.

Monitoring Farmland Loss Caused by Urbanization in Beijing from Modis Time Series Using Hierarchical Hidden Markov Model

NASA Astrophysics Data System (ADS)

Yuan, Y.; Meng, Y.; Chen, Y. X.; Jiang, C.; Yue, A. Z.

2018-04-01

In this study, we proposed a method to map urban encroachment onto farmland using satellite image time series (SITS) based on the hierarchical hidden Markov model (HHMM). In this method, the farmland change process is decomposed into three hierarchical levels, i.e., the land cover level, the vegetation phenology level, and the SITS level. Then a three-level HHMM is constructed to model the multi-level semantic structure of farmland change process. Once the HHMM is established, a change from farmland to built-up could be detected by inferring the underlying state sequence that is most likely to generate the input time series. The performance of the method is evaluated on MODIS time series in Beijing. Results on both simulated and real datasets demonstrate that our method improves the change detection accuracy compared with the HMM-based method.

Hierarchical Co(OH)2 nanostructures/glassy carbon electrode derived from Co(BTC) metal-organic frameworks for glucose sensing

NASA Astrophysics Data System (ADS)

He, Juan; Lu, Xingping; Yu, Jie; Wang, Li; Song, Yonghai

2016-07-01

A novel Co(OH)2/glassy carbon electrode (GCE) has been fabricated via metal-organic framework (MOF)-directed method. In the strategy, the Co(BTC, 1,3,5-benzentricarboxylic acid) MOFs/GCE was firstly prepared by alternately immersing GCE in Co2+ and BTC solution based on a layer-by-layer method. And then, the Co(OH)2 with hierarchical flake nanostructure/GCE was constructed by immersing Co(BTC) MOFs/GCE into 0.1 M NaOH solution at room temperature. Such strategy improves the distribution of hierarchical Co(OH)2 nanostructures on electrode surface greatly, enhances the stability of nanomaterials on the electrode surface, and increases the use efficiency of the Co(OH)2 nanostructures. Scanning electron microscopy, energy dispersive X-ray spectroscopy, X-ray powder diffraction, energy dispersive spectroscopy, Fourier transform infrared spectroscopy, and Raman spectra were used to characterize the Co(BTC) MOFs/GCE and Co(OH)2/GCE. Based on the hierarchical Co(OH)2 nanostructures/GCE, a novel and sensitive nonenzymatic glucose sensor was developed. The good performance of the resulted sensor toward the detection of glucose was ascribed to hierarchical flake nanostructures, good mechanical stability, excellent distribution, and large specific surface area of Co(OH)2 nanostructures. The proposed preparation method is simple, efficient, and cheap .

Hierarchical Heteroclinics in Dynamical Model of Cognitive Processes: Chunking

NASA Astrophysics Data System (ADS)

Afraimovich, Valentin S.; Young, Todd R.; Rabinovich, Mikhail I.

Combining the results of brain imaging and nonlinear dynamics provides a new hierarchical vision of brain network functionality that is helpful in understanding the relationship of the network to different mental tasks. Using these ideas it is possible to build adequate models for the description and prediction of different cognitive activities in which the number of variables is usually small enough for analysis. The dynamical images of different mental processes depend on their temporal organization and, as a rule, cannot be just simple attractors since cognition is characterized by transient dynamics. The mathematical image for a robust transient is a stable heteroclinic channel consisting of a chain of saddles connected by unstable separatrices. We focus here on hierarchical chunking dynamics that can represent several cognitive activities. Chunking is the dynamical phenomenon that means dividing a long information chain into shorter items. Chunking is known to be important in many processes of perception, learning, memory and cognition. We prove that in the phase space of the model that describes chunking there exists a new mathematical object — heteroclinic sequence of heteroclinic cycles — using the technique of slow-fast approximations. This new object serves as a skeleton of motions reflecting sequential features of hierarchical chunking dynamics and is an adequate image of the chunking processing.

Colorimetric sensing of anions in water using ratiometric indicator-displacement assay.

PubMed

Feng, Liang; Li, Hui; Li, Xiao; Chen, Liang; Shen, Zheng; Guan, Yafeng

2012-09-19

The analysis of anions in water presents a difficult challenge due to their low charge-to-radius ratio, and the ability to discriminate among similar anions often remains problematic. The use of a 3×6 ratiometric indicator-displacement assay (RIDA) array for the colorimetric detection and identification of ten anions in water is reported. The sensor array consists of different combinations of colorimetric indicators and metal cations. The colorimetric indicators chelate with metal cations, forming the color changes. Upon the addition of anions, anions compete with the indicator ligands according to solubility product constants (K(sp)). The indicator-metal chelate compound changes color back dramatically when the competition of anions wins. The color changes of the RIDA array were used as a digital representation of the array response and analyzed with standard statistical methods, including principal component analysis and hierarchical clustering analysis. No confusion or errors in classification by hierarchical clustering analysis were observed in 44 trials. The limit of detection was calculated approximately, and most limits of detections of anions are well below μM level using our RIDA array. The pH effect, temperature influence, interfering anions were also investigated, and the RIDA array shows the feasibility of real sample testing. Copyright © 2012 Elsevier B.V. All rights reserved.

Hydrothermal preparation of hierarchical ZIF-L nanostructures for enhanced CO2 capture.

PubMed

Ding, Bing; Wang, Xianbiao; Xu, Yongfei; Feng, Shaojie; Ding, Yi; Pan, Yang; Xu, Weifan; Wang, Huanting

2018-06-01

A zeolitic imidazolate framework (ZIF-L) with hierarchical morphology was synthesized through hydrothermal method. The hierarchical product consists of ZIF-L leaves with length of several micrometers, width of 1 ∼ 2 μm and thickness of ∼300 nm cross connected symmetrically. It was found that the hydrothermal temperature is crucial for the formation of such hierarchical nanostructure. The formation mechanism was investigated to be a secondary crystal growth process. The hierarchical ZIF-L has larger surface area compared with the two-dimensional (2D) ZIF-L leaves. Subsequently, the hierarchical ZIF-L exhibited enhanced CO 2 adsorption capacity (1.56 mmol·g -1 ) as compared with that of the reported two-dimensional ZIF-L leaves (0.94 mmol·g -1 ). This work not only reveals a new strategy for the formation of hierarchical ZIF-L nanostructures, but also supplies a potential material for CO 2 capture. Copyright © 2018 Elsevier Inc. All rights reserved.

Towards a unification of the hierarchical reference theory and the self-consistent Ornstein-Zernike approximation.

PubMed

Reiner, A; Høye, J S

2005-12-01

The hierarchical reference theory and the self-consistent Ornstein-Zernike approximation are two liquid state theories that both furnish a largely satisfactory description of the critical region as well as phase coexistence and the equation of state in general. Furthermore, there are a number of similarities that suggest the possibility of a unification of both theories. As a first step towards this goal, we consider the problem of combining the lowest order gamma expansion result for the incorporation of a Fourier component of the interaction with the requirement of consistency between internal and free energies, leaving aside the compressibility relation. For simplicity, we restrict ourselves to a simplified lattice gas that is expected to display the same qualitative behavior as more elaborate models. It turns out that the analytically tractable mean spherical approximation is a solution to this problem, as are several of its generalizations. Analysis of the characteristic equations shows the potential for a practical scheme and yields necessary conditions that any closure to the Ornstein-Zernike relation must fulfill for the consistency problem to be well posed and to have a unique differentiable solution. These criteria are expected to remain valid for more general discrete and continuous systems, even if consistency with the compressibility route is also enforced where possible explicit solutions will require numerical evaluations.

Validity of the t-plot method to assess microporosity in hierarchical micro/mesoporous materials.

PubMed

Galarneau, Anne; Villemot, François; Rodriguez, Jeremy; Fajula, François; Coasne, Benoit

2014-11-11

The t-plot method is a well-known technique which allows determining the micro- and/or mesoporous volumes and the specific surface area of a sample by comparison with a reference adsorption isotherm of a nonporous material having the same surface chemistry. In this paper, the validity of the t-plot method is discussed in the case of hierarchical porous materials exhibiting both micro- and mesoporosities. Different hierarchical zeolites with MCM-41 type ordered mesoporosity are prepared using pseudomorphic transformation. For comparison, we also consider simple mechanical mixtures of microporous and mesoporous materials. We first show an intrinsic failure of the t-plot method; this method does not describe the fact that, for a given surface chemistry and pressure, the thickness of the film adsorbed in micropores or small mesopores (< 10σ, σ being the diameter of the adsorbate) increases with decreasing the pore size (curvature effect). We further show that such an effect, which arises from the fact that the surface area and, hence, the free energy of the curved gas/liquid interface decreases with increasing the film thickness, is captured using the simple thermodynamical model by Derjaguin. The effect of such a drawback on the ability of the t-plot method to estimate the micro- and mesoporous volumes of hierarchical samples is then discussed, and an abacus is given to correct the underestimated microporous volume by the t-plot method.

Sparse Representation with Spatio-Temporal Online Dictionary Learning for Efficient Video Coding.

PubMed

Dai, Wenrui; Shen, Yangmei; Tang, Xin; Zou, Junni; Xiong, Hongkai; Chen, Chang Wen

2016-07-27

Classical dictionary learning methods for video coding suer from high computational complexity and interfered coding eciency by disregarding its underlying distribution. This paper proposes a spatio-temporal online dictionary learning (STOL) algorithm to speed up the convergence rate of dictionary learning with a guarantee of approximation error. The proposed algorithm incorporates stochastic gradient descents to form a dictionary of pairs of 3-D low-frequency and highfrequency spatio-temporal volumes. In each iteration of the learning process, it randomly selects one sample volume and updates the atoms of dictionary by minimizing the expected cost, rather than optimizes empirical cost over the complete training data like batch learning methods, e.g. K-SVD. Since the selected volumes are supposed to be i.i.d. samples from the underlying distribution, decomposition coecients attained from the trained dictionary are desirable for sparse representation. Theoretically, it is proved that the proposed STOL could achieve better approximation for sparse representation than K-SVD and maintain both structured sparsity and hierarchical sparsity. It is shown to outperform batch gradient descent methods (K-SVD) in the sense of convergence speed and computational complexity, and its upper bound for prediction error is asymptotically equal to the training error. With lower computational complexity, extensive experiments validate that the STOL based coding scheme achieves performance improvements than H.264/AVC or HEVC as well as existing super-resolution based methods in ratedistortion performance and visual quality.

Selective Template Wetting Routes to Hierarchical Polymer Films: Polymer Nanotubes from Phase-Separated Films via Solvent Annealing.

PubMed

Ko, Hao-Wen; Cheng, Ming-Hsiang; Chi, Mu-Huan; Chang, Chun-Wei; Chen, Jiun-Tai

2016-03-01

We demonstrate a novel wetting method to prepare hierarchical polymer films with polymer nanotubes on selective regions. This strategy is based on the selective wetting abilities of polymer chains, annealed in different solvent vapors, into the nanopores of porous templates. Phase-separated films of polystyrene (PS) and poly(methyl methacrylate) (PMMA), two commonly used polymers, are prepared as a model system. After anodic aluminum oxide (AAO) templates are placed on the films, the samples are annealed in vapors of acetic acid, in which the PMMA chains are swollen and wet the nanopores of the AAO templates selectively. As a result, hierarchical polymer films containing PMMA nanotubes can be obtained after the AAO templates are removed. The distribution of the PMMA nanotubes of the hierarchical polymer films can also be controlled by changing the compositions of the polymer blends. This work not only presents a novel method to fabricate hierarchical polymer films with polymer nanotubes on selective regions, but also gives a deeper understanding in the selective wetting ability of polymer chains in solvent vapors.

Hierarchical Bayesian sparse image reconstruction with application to MRFM.

PubMed

Dobigeon, Nicolas; Hero, Alfred O; Tourneret, Jean-Yves

2009-09-01

This paper presents a hierarchical Bayesian model to reconstruct sparse images when the observations are obtained from linear transformations and corrupted by an additive white Gaussian noise. Our hierarchical Bayes model is well suited to such naturally sparse image applications as it seamlessly accounts for properties such as sparsity and positivity of the image via appropriate Bayes priors. We propose a prior that is based on a weighted mixture of a positive exponential distribution and a mass at zero. The prior has hyperparameters that are tuned automatically by marginalization over the hierarchical Bayesian model. To overcome the complexity of the posterior distribution, a Gibbs sampling strategy is proposed. The Gibbs samples can be used to estimate the image to be recovered, e.g., by maximizing the estimated posterior distribution. In our fully Bayesian approach, the posteriors of all the parameters are available. Thus, our algorithm provides more information than other previously proposed sparse reconstruction methods that only give a point estimate. The performance of the proposed hierarchical Bayesian sparse reconstruction method is illustrated on synthetic data and real data collected from a tobacco virus sample using a prototype MRFM instrument.

What are hierarchical models and how do we analyze them?

USGS Publications Warehouse

Royle, Andy

2016-01-01

In this chapter we provide a basic definition of hierarchical models and introduce the two canonical hierarchical models in this book: site occupancy and N-mixture models. The former is a hierarchical extension of logistic regression and the latter is a hierarchical extension of Poisson regression. We introduce basic concepts of probability modeling and statistical inference including likelihood and Bayesian perspectives. We go through the mechanics of maximizing the likelihood and characterizing the posterior distribution by Markov chain Monte Carlo (MCMC) methods. We give a general perspective on topics such as model selection and assessment of model fit, although we demonstrate these topics in practice in later chapters (especially Chapters 5, 6, 7, and 10 Chapter 5 Chapter 6 Chapter 7 Chapter 10)

Hierarchical porous ZnO microflowers with ultra-high ethanol gas-sensing at low concentration

NASA Astrophysics Data System (ADS)

Song, Liming; Yue, He; Li, Haiying; Liu, Li; Li, Yu; Du, Liting; Duan, Haojie; Klyui, N. I.

2018-05-01

Hierarchical porous and non-porous ZnO microflowers have been successfully fabricated by hydrothermal method. Their crystal structure, morphology and gas-sensing properties were studied by X-ray diffraction (XRD), scanning electron microscopy (SEM), and chemical gas sensing intelligent analysis system (CGS). Compared with hierarchical non-porous ZnO microflowers, hierarchical porous ZnO microflowers exhibited ultra-high sensitivity with 50 ppm ethanol at 260 °C and the response is 110, which is 1.8 times higher than that of non-porous ZnO microflowers. Moreover, the lowest concentration limit of hierarchical porous ZnO microflowers (non-porous ZnO microflowers) to ethanol is 0.1 (1) ppm, the response value is 1.6 (1).

Hierarchical optimization for neutron scattering problems

DOE PAGES

Bao, Feng; Archibald, Rick; Bansal, Dipanshu; ...

2016-03-14

In this study, we present a scalable optimization method for neutron scattering problems that determines confidence regions of simulation parameters in lattice dynamics models used to fit neutron scattering data for crystalline solids. The method uses physics-based hierarchical dimension reduction in both the computational simulation domain and the parameter space. We demonstrate for silicon that after a few iterations the method converges to parameters values (interatomic force-constants) computed with density functional theory simulations.

Hierarchical optimization for neutron scattering problems

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

Bao, Feng; Archibald, Rick; Bansal, Dipanshu

In this study, we present a scalable optimization method for neutron scattering problems that determines confidence regions of simulation parameters in lattice dynamics models used to fit neutron scattering data for crystalline solids. The method uses physics-based hierarchical dimension reduction in both the computational simulation domain and the parameter space. We demonstrate for silicon that after a few iterations the method converges to parameters values (interatomic force-constants) computed with density functional theory simulations.

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

Ghysels, Pieter; Li, Xiaoye S.; Rouet, Francois -Henry

Here, we present a sparse linear system solver that is based on a multifrontal variant of Gaussian elimination and exploits low-rank approximation of the resulting dense frontal matrices. We use hierarchically semiseparable (HSS) matrices, which have low-rank off-diagonal blocks, to approximate the frontal matrices. For HSS matrix construction, a randomized sampling algorithm is used together with interpolative decompositions. The combination of the randomized compression with a fast ULV HSS factoriz ation leads to a solver with lower computational complexity than the standard multifrontal method for many applications, resulting in speedups up to 7 fold for problems in our test suite.more » The implementation targets many-core systems by using task parallelism with dynamic runtime scheduling. Numerical experiments show performance improvements over state-of-the-art sparse direct solvers. The implementation achieves high performance and good scalability on a range of modern shared memory parallel systems, including the Intel Xeon Phi (MIC). The code is part of a software package called STRUMPACK - STRUctured Matrices PACKage, which also has a distributed memory component for dense rank-structured matrices.« less

An Efficient Multicore Implementation of a Novel HSS-Structured Multifrontal Solver Using Randomized Sampling

DOE PAGES

Ghysels, Pieter; Li, Xiaoye S.; Rouet, Francois -Henry; ...

2016-10-27

Here, we present a sparse linear system solver that is based on a multifrontal variant of Gaussian elimination and exploits low-rank approximation of the resulting dense frontal matrices. We use hierarchically semiseparable (HSS) matrices, which have low-rank off-diagonal blocks, to approximate the frontal matrices. For HSS matrix construction, a randomized sampling algorithm is used together with interpolative decompositions. The combination of the randomized compression with a fast ULV HSS factoriz ation leads to a solver with lower computational complexity than the standard multifrontal method for many applications, resulting in speedups up to 7 fold for problems in our test suite.more » The implementation targets many-core systems by using task parallelism with dynamic runtime scheduling. Numerical experiments show performance improvements over state-of-the-art sparse direct solvers. The implementation achieves high performance and good scalability on a range of modern shared memory parallel systems, including the Intel Xeon Phi (MIC). The code is part of a software package called STRUMPACK - STRUctured Matrices PACKage, which also has a distributed memory component for dense rank-structured matrices.« less

Topology of foreign exchange markets using hierarchical structure methods

NASA Astrophysics Data System (ADS)

Naylor, Michael J.; Rose, Lawrence C.; Moyle, Brendan J.

2007-08-01

This paper uses two physics derived hierarchical techniques, a minimal spanning tree and an ultrametric hierarchical tree, to extract a topological influence map for major currencies from the ultrametric distance matrix for 1995-2001. We find that these two techniques generate a defined and robust scale free network with meaningful taxonomy. The topology is shown to be robust with respect to method, to time horizon and is stable during market crises. This topology, appropriately used, gives a useful guide to determining the underlying economic or regional causal relationships for individual currencies and to understanding the dynamics of exchange rate price determination as part of a complex network.

Forested plant associations of the Colville National Forest.

Treesearch

Clinton K. Williams; Brian F. Kelley; Bradley G. Smith; Terry R. Lillybridge

1995-01-01

A classification of forest vegetation is presented for the Colville National Forest in northeastern Washington State. It is based on potential vegetation with the plant association as the basic unit. The classification is based on a sample of approximately 229 intensive plots and 282 reconnaissance plots distributed across the forest from 1980 to 1983. The hierarchical...

Fine scale population genetic structure and within tree distribution of mating types of Venturia effusa, cause of pecan scab in the USA

USDA-ARS?s Scientific Manuscript database

Scab (caused by Venturia effusa) is the major disease of pecan in the southeastern USA. There is no information available on the fine scale population genetic diversity. Four cv. Wichita trees (populations) were sampled hierarchically. Within each tree canopy, 4 approximately evenly spaced terminals...

The Role of Grief, Anxiety, and Depressive Symptoms in the Use of Bereavement Services

ERIC Educational Resources Information Center

Bergman, Elizabeth J.; Haley, William E.; Small, Brent J.

2010-01-01

This study examined the role of psychological distress in the use of bereavement services at six months post-loss by 250 bereaved spouses in the Changing Lives of Older Couples study. Approximately 52% (129) used services, commonly provided by physicians and clergy. Hierarchical logistic regression analyses indicated that Black race, higher…

Hierarchical LiFePO4 with a controllable growth of the (010) facet for lithium-ion batteries.

PubMed

Guo, Binbin; Ruan, Hongcheng; Zheng, Cheng; Fei, Hailong; Wei, Mingdeng

2013-09-27

Hierarchically structured LiFePO4 was successfully synthesized by ionic liquid solvothermal method. These hierarchically structured LiFePO4 samples were constructed from nanostructured platelets with their (010) facets mainly exposed. To the best of our knowledge, facet control of a hierarchical LiFePO4 crystal has not been reported yet. Based on a series of experimental results, a tentative mechanism for the formation of these hierarchical structures was proposed. After these hierarchically structured LiFePO4 samples were coated with a thin carbon layer and used as cathode materials for lithium-ion batteries, they exhibited excellent high-rate discharge capability and cycling stability. For instance, a capacity of 95% can be maintained for the LiFePO4 sample at a rate as high as 20 C, even after 1000 cycles.

Hierarchical porous poly (ether sulfone) membranes with excellent capacity retention for vanadium flow battery application

NASA Astrophysics Data System (ADS)

Chen, Dongju; Li, Dandan; Li, Xianfeng

2017-06-01

A hierarchical poly (ether sulfone) (PES) porous membrane is facilely fabricated via a hard template method for vanadium flow battery (VFB) application. The construction of this hierarchical porous membrane is prepared via removing templates (phenolphthalein). The pore size can be well controlled by optimizing the template content in the cast solution, ensuring the membrane conductivity and selectively. The prepared hierarchical porous membrane can combine high ion selectivity with high proton conductivity, which renders a good electrochemical performance in a VFB. The optimized hierarchical porous membrane shows a columbic efficiency of 94.52% and energy efficiency of 81.66% along with a superior ability to maintain stable capacity over extended cycling at a current density of 80 mA cm-2. The characteristics of low cost, proven chemical stability and high electrochemical performance afford the hierarchical PES porous membrane great prospect in VFB application.

Image Search Reranking With Hierarchical Topic Awareness.

PubMed

Tian, Xinmei; Yang, Linjun; Lu, Yijuan; Tian, Qi; Tao, Dacheng

2015-10-01

With much attention from both academia and industrial communities, visual search reranking has recently been proposed to refine image search results obtained from text-based image search engines. Most of the traditional reranking methods cannot capture both relevance and diversity of the search results at the same time. Or they ignore the hierarchical topic structure of search result. Each topic is treated equally and independently. However, in real applications, images returned for certain queries are naturally in hierarchical organization, rather than simple parallel relation. In this paper, a new reranking method "topic-aware reranking (TARerank)" is proposed. TARerank describes the hierarchical topic structure of search results in one model, and seamlessly captures both relevance and diversity of the image search results simultaneously. Through a structured learning framework, relevance and diversity are modeled in TARerank by a set of carefully designed features, and then the model is learned from human-labeled training samples. The learned model is expected to predict reranking results with high relevance and diversity for testing queries. To verify the effectiveness of the proposed method, we collect an image search dataset and conduct comparison experiments on it. The experimental results demonstrate that the proposed TARerank outperforms the existing relevance-based and diversified reranking methods.

Merging K-means with hierarchical clustering for identifying general-shaped groups.

PubMed

Peterson, Anna D; Ghosh, Arka P; Maitra, Ranjan

2018-01-01

Clustering partitions a dataset such that observations placed together in a group are similar but different from those in other groups. Hierarchical and K -means clustering are two approaches but have different strengths and weaknesses. For instance, hierarchical clustering identifies groups in a tree-like structure but suffers from computational complexity in large datasets while K -means clustering is efficient but designed to identify homogeneous spherically-shaped clusters. We present a hybrid non-parametric clustering approach that amalgamates the two methods to identify general-shaped clusters and that can be applied to larger datasets. Specifically, we first partition the dataset into spherical groups using K -means. We next merge these groups using hierarchical methods with a data-driven distance measure as a stopping criterion. Our proposal has the potential to reveal groups with general shapes and structure in a dataset. We demonstrate good performance on several simulated and real datasets.

Highly Transparent Water-Repelling Surfaces based on Biomimetic Hierarchical Structure

NASA Astrophysics Data System (ADS)

Wooh, Sanghyuk; Koh, Jai; Yoon, Hyunsik; Char, Kookheon

2013-03-01

Nature is a great source of inspiration for creating unique structures with special functions. The representative examples of water-repelling surfaces in nature, such as lotus leaves, rose petals, and insect wings, consist of an array of bumps (or long hairs) and nanoscale surface features with different dimension scales. Herein, we introduced a method of realizing multi-dimensional hierarchical structures and water-repellancy of the surfaces with different drop impact scenarios. The multi-dimensional hierarchical structures were fabricated by soft imprinting method with TiO2 nanoparticle pastes. In order to achieve the enhanced hydrophobicity, fluorinated moieties were attached to the etched surfaces to lower the surface energy. As a result, super-hydrophobic surfaces with high transparency were realized (over 176° water contact angle), and for further investigation, these hierarchical surfaces with different drop impact scenarios were characterized by varying the impact speed, drop size, and the geometry of the surfaces.

When mechanism matters: Bayesian forecasting using models of ecological diffusion

USGS Publications Warehouse

Hefley, Trevor J.; Hooten, Mevin B.; Russell, Robin E.; Walsh, Daniel P.; Powell, James A.

2017-01-01

Ecological diffusion is a theory that can be used to understand and forecast spatio-temporal processes such as dispersal, invasion, and the spread of disease. Hierarchical Bayesian modelling provides a framework to make statistical inference and probabilistic forecasts, using mechanistic ecological models. To illustrate, we show how hierarchical Bayesian models of ecological diffusion can be implemented for large data sets that are distributed densely across space and time. The hierarchical Bayesian approach is used to understand and forecast the growth and geographic spread in the prevalence of chronic wasting disease in white-tailed deer (Odocoileus virginianus). We compare statistical inference and forecasts from our hierarchical Bayesian model to phenomenological regression-based methods that are commonly used to analyse spatial occurrence data. The mechanistic statistical model based on ecological diffusion led to important ecological insights, obviated a commonly ignored type of collinearity, and was the most accurate method for forecasting.

Report on the sixth blind test of organic crystal structure prediction methods

PubMed Central

Reilly, Anthony M.; Cooper, Richard I.; Adjiman, Claire S.; Bhattacharya, Saswata; Boese, A. Daniel; Brandenburg, Jan Gerit; Bygrave, Peter J.; Bylsma, Rita; Campbell, Josh E.; Car, Roberto; Case, David H.; Chadha, Renu; Cole, Jason C.; Cosburn, Katherine; Cuppen, Herma M.; Curtis, Farren; Day, Graeme M.; DiStasio Jr, Robert A.; Dzyabchenko, Alexander; van Eijck, Bouke P.; Elking, Dennis M.; van den Ende, Joost A.; Facelli, Julio C.; Ferraro, Marta B.; Fusti-Molnar, Laszlo; Gatsiou, Christina-Anna; Gee, Thomas S.; de Gelder, René; Ghiringhelli, Luca M.; Goto, Hitoshi; Grimme, Stefan; Guo, Rui; Hofmann, Detlef W. M.; Hoja, Johannes; Hylton, Rebecca K.; Iuzzolino, Luca; Jankiewicz, Wojciech; de Jong, Daniël T.; Kendrick, John; de Klerk, Niek J. J.; Ko, Hsin-Yu; Kuleshova, Liudmila N.; Li, Xiayue; Lohani, Sanjaya; Leusen, Frank J. J.; Lund, Albert M.; Lv, Jian; Ma, Yanming; Marom, Noa; Masunov, Artëm E.; McCabe, Patrick; McMahon, David P.; Meekes, Hugo; Metz, Michael P.; Misquitta, Alston J.; Mohamed, Sharmarke; Monserrat, Bartomeu; Needs, Richard J.; Neumann, Marcus A.; Nyman, Jonas; Obata, Shigeaki; Oberhofer, Harald; Oganov, Artem R.; Orendt, Anita M.; Pagola, Gabriel I.; Pantelides, Constantinos C.; Pickard, Chris J.; Podeszwa, Rafal; Price, Louise S.; Price, Sarah L.; Pulido, Angeles; Read, Murray G.; Reuter, Karsten; Schneider, Elia; Schober, Christoph; Shields, Gregory P.; Singh, Pawanpreet; Sugden, Isaac J.; Szalewicz, Krzysztof; Taylor, Christopher R.; Tkatchenko, Alexandre; Tuckerman, Mark E.; Vacarro, Francesca; Vasileiadis, Manolis; Vazquez-Mayagoitia, Alvaro; Vogt, Leslie; Wang, Yanchao; Watson, Rona E.; de Wijs, Gilles A.; Yang, Jack; Zhu, Qiang; Groom, Colin R.

2016-01-01

The sixth blind test of organic crystal structure prediction (CSP) methods has been held, with five target systems: a small nearly rigid molecule, a polymorphic former drug candidate, a chloride salt hydrate, a co-crystal and a bulky flexible molecule. This blind test has seen substantial growth in the number of participants, with the broad range of prediction methods giving a unique insight into the state of the art in the field. Significant progress has been seen in treating flexible molecules, usage of hierarchical approaches to ranking structures, the application of density-functional approximations, and the establishment of new workflows and ‘best practices’ for performing CSP calculations. All of the targets, apart from a single potentially disordered Z′ = 2 polymorph of the drug candidate, were predicted by at least one submission. Despite many remaining challenges, it is clear that CSP methods are becoming more applicable to a wider range of real systems, including salts, hydrates and larger flexible molecules. The results also highlight the potential for CSP calculations to complement and augment experimental studies of organic solid forms. PMID:27484368

Construction of hierarchical porous NiCo{sub 2}O{sub 4} films composed of nanowalls as cathode materials for high-performance supercapacitor

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

Zheng, Qingyun, E-mail: hizhengqingyun@126.com; Zhang, Xiangyang; Shen, Youming

Graphical abstract: Hydrothermal-synthesized NiCo{sub 2}O{sub 4} mesowall films exhibit porous structure and high capacity as well as good cycling life for supercapacitor application. - Highlights: • Hierarchical porous NiCo{sub 2}O{sub 4} nanowall films are prepared by a hydrothermal method. • NiCo{sub 2}O{sub 4} nanowall films show excellent electrochemical performance. • Hierarchical porous film structure is favorable for fast ion/electron transfer. - Abstract: Hierarchical porous NiCo{sub 2}O{sub 4} films composed of nanowalls on nickel foam are synthesized via a facile hydrothermal method. Besides the mesoporous walls, the NiCo{sub 2}O{sub 4} nanowalls are interconnected with each other to form hierarchical porous structure.more » These unique porous structured films possess a high specific surface area. The supercapacitor performance of the hierarchical porous NiCo{sub 2}O{sub 4} film is fully characterized. A high capacity of 130 mA h g{sup −1} is achieved at 2 A g{sup −1} with 97% capacity maintained after 2,000 cycles. Importantly, 75.6% of capacity is retained when the current density changes from 3 A g{sup −1} to 36 A g{sup −1}. The superior electrochemical performance is mainly due to the unique hierarchical porous structure with large surface area as well as shorter diffusion length for ion and charge transport.« less

Convex Clustering: An Attractive Alternative to Hierarchical Clustering

PubMed Central

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

2015-01-01

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

Convex clustering: an attractive alternative to hierarchical clustering.

PubMed

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

2015-05-01

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

New heterogeneous test statistics for the unbalanced fixed-effect nested design.

PubMed

Guo, Jiin-Huarng; Billard, L; Luh, Wei-Ming

2011-05-01

When the underlying variances are unknown or/and unequal, using the conventional F test is problematic in the two-factor hierarchical data structure. Prompted by the approximate test statistics (Welch and Alexander-Govern methods), the authors develop four new heterogeneous test statistics to test factor A and factor B nested within A for the unbalanced fixed-effect two-stage nested design under variance heterogeneity. The actual significance levels and statistical power of the test statistics were compared in a simulation study. The results show that the proposed procedures maintain better Type I error rate control and have greater statistical power than those obtained by the conventional F test in various conditions. Therefore, the proposed test statistics are recommended in terms of robustness and easy implementation. ©2010 The British Psychological Society.

Parameterizations for ensemble Kalman inversion

NASA Astrophysics Data System (ADS)

Chada, Neil K.; Iglesias, Marco A.; Roininen, Lassi; Stuart, Andrew M.

2018-05-01

The use of ensemble methods to solve inverse problems is attractive because it is a derivative-free methodology which is also well-adapted to parallelization. In its basic iterative form the method produces an ensemble of solutions which lie in the linear span of the initial ensemble. Choice of the parameterization of the unknown field is thus a key component of the success of the method. We demonstrate how both geometric ideas and hierarchical ideas can be used to design effective parameterizations for a number of applied inverse problems arising in electrical impedance tomography, groundwater flow and source inversion. In particular we show how geometric ideas, including the level set method, can be used to reconstruct piecewise continuous fields, and we show how hierarchical methods can be used to learn key parameters in continuous fields, such as length-scales, resulting in improved reconstructions. Geometric and hierarchical ideas are combined in the level set method to find piecewise constant reconstructions with interfaces of unknown topology.

Dealing with missing standard deviation and mean values in meta-analysis of continuous outcomes: a systematic review.

PubMed

Weir, Christopher J; Butcher, Isabella; Assi, Valentina; Lewis, Stephanie C; Murray, Gordon D; Langhorne, Peter; Brady, Marian C

2018-03-07

Rigorous, informative meta-analyses rely on availability of appropriate summary statistics or individual participant data. For continuous outcomes, especially those with naturally skewed distributions, summary information on the mean or variability often goes unreported. While full reporting of original trial data is the ideal, we sought to identify methods for handling unreported mean or variability summary statistics in meta-analysis. We undertook two systematic literature reviews to identify methodological approaches used to deal with missing mean or variability summary statistics. Five electronic databases were searched, in addition to the Cochrane Colloquium abstract books and the Cochrane Statistics Methods Group mailing list archive. We also conducted cited reference searching and emailed topic experts to identify recent methodological developments. Details recorded included the description of the method, the information required to implement the method, any underlying assumptions and whether the method could be readily applied in standard statistical software. We provided a summary description of the methods identified, illustrating selected methods in example meta-analysis scenarios. For missing standard deviations (SDs), following screening of 503 articles, fifteen methods were identified in addition to those reported in a previous review. These included Bayesian hierarchical modelling at the meta-analysis level; summary statistic level imputation based on observed SD values from other trials in the meta-analysis; a practical approximation based on the range; and algebraic estimation of the SD based on other summary statistics. Following screening of 1124 articles for methods estimating the mean, one approximate Bayesian computation approach and three papers based on alternative summary statistics were identified. Illustrative meta-analyses showed that when replacing a missing SD the approximation using the range minimised loss of precision and generally performed better than omitting trials. When estimating missing means, a formula using the median, lower quartile and upper quartile performed best in preserving the precision of the meta-analysis findings, although in some scenarios, omitting trials gave superior results. Methods based on summary statistics (minimum, maximum, lower quartile, upper quartile, median) reported in the literature facilitate more comprehensive inclusion of randomised controlled trials with missing mean or variability summary statistics within meta-analyses.

A hierarchical transition state search algorithm

NASA Astrophysics Data System (ADS)

del Campo, Jorge M.; Köster, Andreas M.

2008-07-01

A hierarchical transition state search algorithm is developed and its implementation in the density functional theory program deMon2k is described. This search algorithm combines the double ended saddle interpolation method with local uphill trust region optimization. A new formalism for the incorporation of the distance constrain in the saddle interpolation method is derived. The similarities between the constrained optimizations in the local trust region method and the saddle interpolation are highlighted. The saddle interpolation and local uphill trust region optimizations are validated on a test set of 28 representative reactions. The hierarchical transition state search algorithm is applied to an intramolecular Diels-Alder reaction with several internal rotors, which makes automatic transition state search rather challenging. The obtained reaction mechanism is discussed in the context of the experimentally observed product distribution.

Hierarchical and coupling model of factors influencing vessel traffic flow.

PubMed

Liu, Zhao; Liu, Jingxian; Li, Huanhuan; Li, Zongzhi; Tan, Zhirong; Liu, Ryan Wen; Liu, Yi

2017-01-01

Understanding the characteristics of vessel traffic flow is crucial in maintaining navigation safety, efficiency, and overall waterway transportation management. Factors influencing vessel traffic flow possess diverse features such as hierarchy, uncertainty, nonlinearity, complexity, and interdependency. To reveal the impact mechanism of the factors influencing vessel traffic flow, a hierarchical model and a coupling model are proposed in this study based on the interpretative structural modeling method. The hierarchical model explains the hierarchies and relationships of the factors using a graph. The coupling model provides a quantitative method that explores interaction effects of factors using a coupling coefficient. The coupling coefficient is obtained by determining the quantitative indicators of the factors and their weights. Thereafter, the data obtained from Port of Tianjin is used to verify the proposed coupling model. The results show that the hierarchical model of the factors influencing vessel traffic flow can explain the level, structure, and interaction effect of the factors; the coupling model is efficient in analyzing factors influencing traffic volumes. The proposed method can be used for analyzing increases in vessel traffic flow in waterway transportation system.

Emulsion-Assisted Polymerization-Induced Hierarchical Self-Assembly of Giant Sea Urchin-like Aggregates in a Large Scale.

PubMed

Xu, Qingsong; Huang, Tong; Li, Shanlong; Li, Ke; Li, Chuanlong; Liu, Yannan; Wang, Yuling; Yu, Chunyang; Zhou, Yongfeng

2018-05-09

Hierarchical solution self-assembly has nowadays become an important biomimetic method to prepare highly complex and multifunctional supramolecular structures. However, despites the great progress, it is still highly challenging to prepare hierarchical self-assemblies in a large scale since the self-assembly processes are generally performed at high dilution. Herein, we report an emulsion-assisted polymerization-induced self-assembly (EAPISA) method with the advantages of in-situ self-assembly process, scalable preparation and facile functionalization to prepare hierarchical multiscale sea urchin-like aggregates (SUAs). It also extends horizons of PISA in monomers and in polymerization method. The obtained SUAs from amphiphilic alternating copolymers represent a novel self-assembled structure with micron-sized rattan ball-like capsule (RBC) acting as the hollow core body and radiating nanotubes tens of micrometers in length as the hollow spines. They can effectively capture model proteins at an ultra-low concentration (≈10 nM) after functionalized with amino groups through click copolymerization. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Hierarchical and coupling model of factors influencing vessel traffic flow

PubMed Central

Liu, Jingxian; Li, Huanhuan; Li, Zongzhi; Tan, Zhirong; Liu, Ryan Wen; Liu, Yi

2017-01-01

Understanding the characteristics of vessel traffic flow is crucial in maintaining navigation safety, efficiency, and overall waterway transportation management. Factors influencing vessel traffic flow possess diverse features such as hierarchy, uncertainty, nonlinearity, complexity, and interdependency. To reveal the impact mechanism of the factors influencing vessel traffic flow, a hierarchical model and a coupling model are proposed in this study based on the interpretative structural modeling method. The hierarchical model explains the hierarchies and relationships of the factors using a graph. The coupling model provides a quantitative method that explores interaction effects of factors using a coupling coefficient. The coupling coefficient is obtained by determining the quantitative indicators of the factors and their weights. Thereafter, the data obtained from Port of Tianjin is used to verify the proposed coupling model. The results show that the hierarchical model of the factors influencing vessel traffic flow can explain the level, structure, and interaction effect of the factors; the coupling model is efficient in analyzing factors influencing traffic volumes. The proposed method can be used for analyzing increases in vessel traffic flow in waterway transportation system. PMID:28414747

Global/local processing of hierarchical visual stimuli in a conflict-choice task by capuchin monkeys (Sapajus spp.).

PubMed

Truppa, Valentina; Carducci, Paola; De Simone, Diego Antonio; Bisazza, Angelo; De Lillo, Carlo

2017-03-01

In the last two decades, comparative research has addressed the issue of how the global and local levels of structure of visual stimuli are processed by different species, using Navon-type hierarchical figures, i.e. smaller local elements that form larger global configurations. Determining whether or not the variety of procedures adopted to test different species with hierarchical figures are equivalent is of crucial importance to ensure comparability of results. Among non-human species, global/local processing has been extensively studied in tufted capuchin monkeys using matching-to-sample tasks with hierarchical patterns. Local dominance has emerged consistently in these New World primates. In the present study, we assessed capuchins' processing of hierarchical stimuli with a method frequently adopted in studies of global/local processing in non-primate species: the conflict-choice task. Different from the matching-to-sample procedure, this task involved processing local and global information retained in long-term memory. Capuchins were trained to discriminate between consistent hierarchical stimuli (similar global and local shape) and then tested with inconsistent hierarchical stimuli (different global and local shapes). We found that capuchins preferred the hierarchical stimuli featuring the correct local elements rather than those with the correct global configuration. This finding confirms that capuchins' local dominance, typically observed using matching-to-sample procedures, is also expressed as a local preference in the conflict-choice task. Our study adds to the growing body of comparative studies on visual grouping functions by demonstrating that the methods most frequently used in the literature on global/local processing produce analogous results irrespective of extent of the involvement of memory processes.

A Hierarchical Clustering Methodology for the Estimation of Toxicity

EPA Science Inventory

A Quantitative Structure Activity Relationship (QSAR) methodology based on hierarchical clustering was developed to predict toxicological endpoints. This methodology utilizes Ward's method to divide a training set into a series of structurally similar clusters. The structural sim...

Hierarchical hybrid control of manipulators: Artificial intelligence in large scale integrated circuits

NASA Technical Reports Server (NTRS)

Greene, P. H.

1972-01-01

Both in practical engineering and in control of muscular systems, low level subsystems automatically provide crude approximations to the proper response. Through low level tuning of these approximations, the proper response variant can emerge from standardized high level commands. Such systems are expressly suited to emerging large scale integrated circuit technology. A computer, using symbolic descriptions of subsystem responses, can select and shape responses of low level digital or analog microcircuits. A mathematical theory that reveals significant informational units in this style of control and software for realizing such information structures are formulated.

Bayesian Variable Selection for Hierarchical Gene-Environment and Gene-Gene Interactions

PubMed Central

Liu, Changlu; Ma, Jianzhong; Amos, Christopher I.

2014-01-01

We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions and gene by environment interactions in the same model. Our approach incorporates the natural hierarchical structure between the main effects and interaction effects into a mixture model, such that our methods tend to remove the irrelevant interaction effects more effectively, resulting in more robust and parsimonious models. We consider both strong and weak hierarchical models. For a strong hierarchical model, both of the main effects between interacting factors must be present for the interactions to be considered in the model development, while for a weak hierarchical model, only one of the two main effects is required to be present for the interaction to be evaluated. Our simulation results show that the proposed strong and weak hierarchical mixture models work well in controlling false positive rates and provide a powerful approach for identifying the predisposing effects and interactions in gene-environment interaction studies, in comparison with the naive model that does not impose this hierarchical constraint in most of the scenarios simulated. We illustrated our approach using data for lung cancer and cutaneous melanoma. PMID:25154630

An Improved Hierarchical Genetic Algorithm for Sheet Cutting Scheduling with Process Constraints

PubMed Central

Rao, Yunqing; Qi, Dezhong; Li, Jinling

2013-01-01

For the first time, an improved hierarchical genetic algorithm for sheet cutting problem which involves n cutting patterns for m non-identical parallel machines with process constraints has been proposed in the integrated cutting stock model. The objective of the cutting scheduling problem is minimizing the weighted completed time. A mathematical model for this problem is presented, an improved hierarchical genetic algorithm (ant colony—hierarchical genetic algorithm) is developed for better solution, and a hierarchical coding method is used based on the characteristics of the problem. Furthermore, to speed up convergence rates and resolve local convergence issues, a kind of adaptive crossover probability and mutation probability is used in this algorithm. The computational result and comparison prove that the presented approach is quite effective for the considered problem. PMID:24489491

An improved hierarchical genetic algorithm for sheet cutting scheduling with process constraints.

PubMed

Rao, Yunqing; Qi, Dezhong; Li, Jinling

2013-01-01

For the first time, an improved hierarchical genetic algorithm for sheet cutting problem which involves n cutting patterns for m non-identical parallel machines with process constraints has been proposed in the integrated cutting stock model. The objective of the cutting scheduling problem is minimizing the weighted completed time. A mathematical model for this problem is presented, an improved hierarchical genetic algorithm (ant colony--hierarchical genetic algorithm) is developed for better solution, and a hierarchical coding method is used based on the characteristics of the problem. Furthermore, to speed up convergence rates and resolve local convergence issues, a kind of adaptive crossover probability and mutation probability is used in this algorithm. The computational result and comparison prove that the presented approach is quite effective for the considered problem.

Multimodal Hierarchical Dirichlet Process-Based Active Perception by a Robot

PubMed Central

Taniguchi, Tadahiro; Yoshino, Ryo; Takano, Toshiaki

2018-01-01

In this paper, we propose an active perception method for recognizing object categories based on the multimodal hierarchical Dirichlet process (MHDP). The MHDP enables a robot to form object categories using multimodal information, e.g., visual, auditory, and haptic information, which can be observed by performing actions on an object. However, performing many actions on a target object requires a long time. In a real-time scenario, i.e., when the time is limited, the robot has to determine the set of actions that is most effective for recognizing a target object. We propose an active perception for MHDP method that uses the information gain (IG) maximization criterion and lazy greedy algorithm. We show that the IG maximization criterion is optimal in the sense that the criterion is equivalent to a minimization of the expected Kullback–Leibler divergence between a final recognition state and the recognition state after the next set of actions. However, a straightforward calculation of IG is practically impossible. Therefore, we derive a Monte Carlo approximation method for IG by making use of a property of the MHDP. We also show that the IG has submodular and non-decreasing properties as a set function because of the structure of the graphical model of the MHDP. Therefore, the IG maximization problem is reduced to a submodular maximization problem. This means that greedy and lazy greedy algorithms are effective and have a theoretical justification for their performance. We conducted an experiment using an upper-torso humanoid robot and a second one using synthetic data. The experimental results show that the method enables the robot to select a set of actions that allow it to recognize target objects quickly and accurately. The numerical experiment using the synthetic data shows that the proposed method can work appropriately even when the number of actions is large and a set of target objects involves objects categorized into multiple classes. The results support our theoretical outcomes. PMID:29872389

Multimodal Hierarchical Dirichlet Process-Based Active Perception by a Robot.

PubMed

Taniguchi, Tadahiro; Yoshino, Ryo; Takano, Toshiaki

2018-01-01

In this paper, we propose an active perception method for recognizing object categories based on the multimodal hierarchical Dirichlet process (MHDP). The MHDP enables a robot to form object categories using multimodal information, e.g., visual, auditory, and haptic information, which can be observed by performing actions on an object. However, performing many actions on a target object requires a long time. In a real-time scenario, i.e., when the time is limited, the robot has to determine the set of actions that is most effective for recognizing a target object. We propose an active perception for MHDP method that uses the information gain (IG) maximization criterion and lazy greedy algorithm. We show that the IG maximization criterion is optimal in the sense that the criterion is equivalent to a minimization of the expected Kullback-Leibler divergence between a final recognition state and the recognition state after the next set of actions. However, a straightforward calculation of IG is practically impossible. Therefore, we derive a Monte Carlo approximation method for IG by making use of a property of the MHDP. We also show that the IG has submodular and non-decreasing properties as a set function because of the structure of the graphical model of the MHDP. Therefore, the IG maximization problem is reduced to a submodular maximization problem. This means that greedy and lazy greedy algorithms are effective and have a theoretical justification for their performance. We conducted an experiment using an upper-torso humanoid robot and a second one using synthetic data. The experimental results show that the method enables the robot to select a set of actions that allow it to recognize target objects quickly and accurately. The numerical experiment using the synthetic data shows that the proposed method can work appropriately even when the number of actions is large and a set of target objects involves objects categorized into multiple classes. The results support our theoretical outcomes.

Hierarchical mean-field approach to the J1-J2 Heisenberg model on a square lattice

NASA Astrophysics Data System (ADS)

Isaev, L.; Ortiz, G.; Dukelsky, J.

2009-01-01

We study the quantum phase diagram and excitation spectrum of the frustrated J1-J2 spin-1/2 Heisenberg Hamiltonian. A hierarchical mean-field approach, at the heart of which lies the idea of identifying relevant degrees of freedom, is developed. Thus, by performing educated, manifestly symmetry-preserving mean-field approximations, we unveil fundamental properties of the system. We then compare various coverings of the square lattice with plaquettes, dimers, and other degrees of freedom, and show that only the symmetric plaquette covering, which reproduces the original Bravais lattice, leads to the known phase diagram. The intermediate quantum paramagnetic phase is shown to be a (singlet) plaquette crystal, connected with the neighboring Néel phase by a continuous phase transition. We also introduce fluctuations around the hierarchical mean-field solutions, and demonstrate that in the paramagnetic phase the ground and first excited states are separated by a finite gap, which closes in the Néel and columnar phases. Our results suggest that the quantum phase transition between Néel and paramagnetic phases can be properly described within the Ginzburg-Landau-Wilson paradigm.

Hierarchical mean-field approach to the J1-J2 Heisenberg model on a square lattice

NASA Astrophysics Data System (ADS)

Isaev, Leonid; Ortiz, Gerardo; Dukelsky, Jorge

2009-03-01

We study the quantum phase diagram and excitation spectrum of the frustrated J1-J2 spin-1/2 Heisenberg Hamiltonian. A hierarchical mean-field approach, at the heart of which lies the idea of identifying relevant degrees of freedom, is developed. Thus, by performing educated, manifestly symmetry preserving mean-field approximations, we unveil fundamental properties of the system. We then compare various coverings of the square lattice with plaquettes, dimers and other degrees of freedom, and show that only the symmetric plaquette covering, which reproduces the original Bravais lattice, leads to the known phase diagram. The intermediate quantum paramagnetic phase is shown to be a (singlet) plaquette crystal, connected with the neighbouring N'eel phase by a continuous phase transition. We also introduce fluctuations around the hierarchical mean-field solutions, and demonstrate that in the paramagnetic phase the ground and first excited states are separated by a finite gap, which closes in the N'eel and columnar phases. Our results suggest that the quantum phase transition between N'eel and paramagnetic phases can be properly described within the Ginzburg-Landau-Wilson paradigm.

Reuse, Recycle, Reweigh: Combating Influenza through Efficient Sequential Bayesian Computation for Massive Data.

PubMed

Tom, Jennifer A; Sinsheimer, Janet S; Suchard, Marc A

Massive datasets in the gigabyte and terabyte range combined with the availability of increasingly sophisticated statistical tools yield analyses at the boundary of what is computationally feasible. Compromising in the face of this computational burden by partitioning the dataset into more tractable sizes results in stratified analyses, removed from the context that justified the initial data collection. In a Bayesian framework, these stratified analyses generate intermediate realizations, often compared using point estimates that fail to account for the variability within and correlation between the distributions these realizations approximate. However, although the initial concession to stratify generally precludes the more sensible analysis using a single joint hierarchical model, we can circumvent this outcome and capitalize on the intermediate realizations by extending the dynamic iterative reweighting MCMC algorithm. In doing so, we reuse the available realizations by reweighting them with importance weights, recycling them into a now tractable joint hierarchical model. We apply this technique to intermediate realizations generated from stratified analyses of 687 influenza A genomes spanning 13 years allowing us to revisit hypotheses regarding the evolutionary history of influenza within a hierarchical statistical framework.

Reuse, Recycle, Reweigh: Combating Influenza through Efficient Sequential Bayesian Computation for Massive Data

PubMed Central

Tom, Jennifer A.; Sinsheimer, Janet S.; Suchard, Marc A.

2015-01-01

Massive datasets in the gigabyte and terabyte range combined with the availability of increasingly sophisticated statistical tools yield analyses at the boundary of what is computationally feasible. Compromising in the face of this computational burden by partitioning the dataset into more tractable sizes results in stratified analyses, removed from the context that justified the initial data collection. In a Bayesian framework, these stratified analyses generate intermediate realizations, often compared using point estimates that fail to account for the variability within and correlation between the distributions these realizations approximate. However, although the initial concession to stratify generally precludes the more sensible analysis using a single joint hierarchical model, we can circumvent this outcome and capitalize on the intermediate realizations by extending the dynamic iterative reweighting MCMC algorithm. In doing so, we reuse the available realizations by reweighting them with importance weights, recycling them into a now tractable joint hierarchical model. We apply this technique to intermediate realizations generated from stratified analyses of 687 influenza A genomes spanning 13 years allowing us to revisit hypotheses regarding the evolutionary history of influenza within a hierarchical statistical framework. PMID:26681992

Approximating gecko setae via direct laser lithography

NASA Astrophysics Data System (ADS)

Tricinci, Omar; Eason, Eric V.; Filippeschi, Carlo; Mondini, Alessio; Mazzolai, Barbara; Pugno, Nicola M.; Cutkosky, Mark R.; Greco, Francesco; Mattoli, Virgilio

2018-07-01

The biomimetic replication of dry adhesion present in the gecko’s foot has attracted great interest in recent years. All the microfabrication techniques used so far were not able to faithfully reproduce the hierarchical and complex three-dimensional geometry of the gecko’s setae, with features at the micro- and nano-scale, thus reducing the effectiveness that such conformal morphology could provide. By means of direct laser lithography we fabricated artificial hairs that faithfully reproduce the natural model. This technique allows the fabrication of three-dimensional microstructures with outstanding results in terms of reproducibility and resolution at the micro- and nano-scale. It was possible to get very close to the morphology of the natural gecko setae, especially concerning the hierarchical shape. We designed several morphologies for the setae and studied the effects in terms of adhesion and friction performances compared to the natural counterpart, showing the interplay between morphology, dimensional scaling and materials. Direct laser lithography promises great applications in the biomimetics field, paving the way to the implementation of the concept of hierarchical bioinspired dry adhesives.

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

NASA Astrophysics Data System (ADS)

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

2017-07-01

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

Enhanced lithium storage performance of hierarchical CuO nanomaterials with surface fractal characteristics

NASA Astrophysics Data System (ADS)

Li, Ang; He, Renyue; Bian, Zhuo; Song, Huaihe; Chen, Xiaohong; Zhou, Jisheng

2018-06-01

Self-assembled hierarchical CuO nanostructures with fractal structures were prepared by a mild method and exhibited excellent lithium storage properties, certain of which even demonstrated a high reversible capacity of 827 mAh g-1 at a rate of 0.1 C. An interesting phenomenon was observed that the electrochemical performance varies along with the structure complexity, and the products with higher surface factal dimensions exhibited larger capability and better cyclability. Structural and electrochemical analysis methods were used to explore the lithiation kinetics of the samples and the reasons for the outstanding electrochemical performances related to the complexities of hierarchical nanostructures and the irregularities of surface and mass distribution.

Utilizing Hierarchical Clustering to improve Efficiency of Self-Organizing Feature Map to Identify Hydrological Homogeneous Regions

NASA Astrophysics Data System (ADS)

Farsadnia, Farhad; Ghahreman, Bijan

2016-04-01

Hydrologic homogeneous group identification is considered both fundamental and applied research in hydrology. Clustering methods are among conventional methods to assess the hydrological homogeneous regions. Recently, Self-Organizing feature Map (SOM) method has been applied in some studies. However, the main problem of this method is the interpretation on the output map of this approach. Therefore, SOM is used as input to other clustering algorithms. The aim of this study is to apply a two-level Self-Organizing feature map and Ward hierarchical clustering method to determine the hydrologic homogenous regions in North and Razavi Khorasan provinces. At first by principal component analysis, we reduced SOM input matrix dimension, then the SOM was used to form a two-dimensional features map. To determine homogeneous regions for flood frequency analysis, SOM output nodes were used as input into the Ward method. Generally, the regions identified by the clustering algorithms are not statistically homogeneous. Consequently, they have to be adjusted to improve their homogeneity. After adjustment of the homogeneity regions by L-moment tests, five hydrologic homogeneous regions were identified. Finally, adjusted regions were created by a two-level SOM and then the best regional distribution function and associated parameters were selected by the L-moment approach. The results showed that the combination of self-organizing maps and Ward hierarchical clustering by principal components as input is more effective than the hierarchical method, by principal components or standardized inputs to achieve hydrologic homogeneous regions.

Hierarchical multistage MCMC follow-up of continuous gravitational wave candidates

NASA Astrophysics Data System (ADS)

Ashton, G.; Prix, R.

2018-05-01

Leveraging Markov chain Monte Carlo optimization of the F statistic, we introduce a method for the hierarchical follow-up of continuous gravitational wave candidates identified by wide-parameter space semicoherent searches. We demonstrate parameter estimation for continuous wave sources and develop a framework and tools to understand and control the effective size of the parameter space, critical to the success of the method. Monte Carlo tests of simulated signals in noise demonstrate that this method is close to the theoretical optimal performance.

Multidisciplinary optimization for engineering systems - Achievements and potential

NASA Technical Reports Server (NTRS)

Sobieszczanski-Sobieski, Jaroslaw

1989-01-01

The currently common sequential design process for engineering systems is likely to lead to suboptimal designs. Recently developed decomposition methods offer an alternative for coming closer to optimum by breaking the large task of system optimization into smaller, concurrently executed and, yet, coupled tasks, identified with engineering disciplines or subsystems. The hierarchic and non-hierarchic decompositions are discussed and illustrated by examples. An organization of a design process centered on the non-hierarchic decomposition is proposed.

Multidisciplinary optimization for engineering systems: Achievements and potential

NASA Technical Reports Server (NTRS)

Sobieszczanski-Sobieski, Jaroslaw

1989-01-01

The currently common sequential design process for engineering systems is likely to lead to suboptimal designs. Recently developed decomposition methods offer an alternative for coming closer to optimum by breaking the large task of system optimization into smaller, concurrently executed and, yet, coupled tasks, identified with engineering disciplines or subsystems. The hierarchic and non-hierarchic decompositions are discussed and illustrated by examples. An organization of a design process centered on the non-hierarchic decomposition is proposed.

Modelling habitat associations with fingernail clam (Family: Sphaeriidae) counts at multiple spatial scales using hierarchical count models

USGS Publications Warehouse

Gray, B.R.; Haro, R.J.; Rogala, J.T.; Sauer, J.S.

2005-01-01

1. Macroinvertebrate count data often exhibit nested or hierarchical structure. Examples include multiple measurements along each of a set of streams, and multiple synoptic measurements from each of a set of ponds. With data exhibiting hierarchical structure, outcomes at both sampling (e.g. Within stream) and aggregated (e.g. Stream) scales are often of interest. Unfortunately, methods for modelling hierarchical count data have received little attention in the ecological literature. 2. We demonstrate the use of hierarchical count models using fingernail clam (Family: Sphaeriidae) count data and habitat predictors derived from sampling and aggregated spatial scales. The sampling scale corresponded to that of a standard Ponar grab (0.052 m(2)) and the aggregated scale to impounded and backwater regions within 38-197 km reaches of the Upper Mississippi River. Impounded and backwater regions were resampled annually for 10 years. Consequently, measurements on clams were nested within years. Counts were treated as negative binomial random variates, and means from each resampling event as random departures from the impounded and backwater region grand means. 3. Clam models were improved by the addition of covariates that varied at both the sampling and regional scales. Substrate composition varied at the sampling scale and was associated with model improvements, and reductions (for a given mean) in variance at the sampling scale. Inorganic suspended solids (ISS) levels, measured in the summer preceding sampling, also yielded model improvements and were associated with reductions in variances at the regional rather than sampling scales. ISS levels were negatively associated with mean clam counts. 4. Hierarchical models allow hierarchically structured data to be modelled without ignoring information specific to levels of the hierarchy. In addition, information at each hierarchical level may be modelled as functions of covariates that themselves vary by and within levels. As a result, hierarchical models provide researchers and resource managers with a method for modelling hierarchical data that explicitly recognises both the sampling design and the information contained in the corresponding data.

Latino Adolescents' Civic Development in the United States: Research Results from the IEA Civic Education Study

ERIC Educational Resources Information Center

Torney-Purta, Judith; Barber, Carolyn H.; Wilkenfeld, Britt

2007-01-01

Many studies have reported gaps between Latino and non-Latino adolescents in academic and political outcomes. The current study presents possible explanations for such gaps, both at the individual and school level. Hierarchical linear modeling is employed to examine data from 2,811 American ninth graders (approximately 14 years of age) who had…

The Influences of Inductive Instruction and Resources on Students' Attitudes toward Reading: Evidence from PISA 2009

ERIC Educational Resources Information Center

Jhang, Fang-Hua

2014-01-01

The declining trend in the positive reading attitude of students' has concerned scholars. This paper aims to apply a 3-level hierarchical linear model to analyse how inductive instruction and resources influence both students' positive and negative attitudes towards reading. Approximately 470,000 15-year-old students, and their school principals,…

Can an unbroken flavour symmetry provide an approximate description of lepton masses and mixing?

NASA Astrophysics Data System (ADS)

Reyimuaji, Y.; Romanino, A.

2018-03-01

We provide a complete answer to the following question: what are the flavour groups and representations providing, in the symmetric limit, an approximate description of lepton masses and mixings? We assume that neutrino masses are described by the Weinberg operator. We show that the pattern of lepton masses and mixings only depends on the dimension, type (real, pseudoreal, complex), and equivalence of the irreducible components of the flavour representation, and we find only six viable cases. In all cases the neutrinos are either anarchical or have an inverted hierarchical spectrum. In the context of SU(5) unification, only the anarchical option is allowed. Therefore, if the hint of a normal hierarchical spectrum were confirmed, we would conclude (under the above assumption) that symmetry breaking effects must play a leading order role in the understanding of neutrino flavour observables. In order to obtain the above results, we develop a simple algorithm to determine the form of the lepton masses and mixings directly from the structure of the decomposition of the flavour representation in irreducible components, without the need to specify the form of the lepton mass matrices.

Synchronization of Hierarchical Time-Varying Neural Networks Based on Asynchronous and Intermittent Sampled-Data Control.

PubMed

Xiong, Wenjun; Patel, Ragini; Cao, Jinde; Zheng, Wei Xing

In this brief, our purpose is to apply asynchronous and intermittent sampled-data control methods to achieve the synchronization of hierarchical time-varying neural networks. The asynchronous and intermittent sampled-data controllers are proposed for two reasons: 1) the controllers may not transmit the control information simultaneously and 2) the controllers cannot always exist at any time . The synchronization is then discussed for a kind of hierarchical time-varying neural networks based on the asynchronous and intermittent sampled-data controllers. Finally, the simulation results are given to illustrate the usefulness of the developed criteria.In this brief, our purpose is to apply asynchronous and intermittent sampled-data control methods to achieve the synchronization of hierarchical time-varying neural networks. The asynchronous and intermittent sampled-data controllers are proposed for two reasons: 1) the controllers may not transmit the control information simultaneously and 2) the controllers cannot always exist at any time . The synchronization is then discussed for a kind of hierarchical time-varying neural networks based on the asynchronous and intermittent sampled-data controllers. Finally, the simulation results are given to illustrate the usefulness of the developed criteria.

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

Giunta, G.; Belouettar, S.

In this paper, the static response of three-dimensional beams made of functionally graded materials is investigated through a family of hierarchical one-dimensional finite elements. A wide variety of elements is proposed differing by the kinematic formulation and the number of nodes per elements along the beam axis. Elements’ stiffness matrix and load vector are derived in a unified nuclear form that does not depend upon the a priori expansion order over the cross-section nor the finite element approximation along the beam axis. Results are validated towards three-dimensional finite element models as well as equivalent Navier-type analytical solutions. The numerical investigationsmore » show that accurate and efficient solutions (when compared with full three-dimensional FEM solutions) can be obtained by the proposed family of hierarchical one-dimensional elements’ family.« less

An Investigation of Possible Hierarchical Dependency of Four Piaget-Type Tasks under Two Methods of Presentation to Third-, Fifth-, and Seventh-Grade Children.

ERIC Educational Resources Information Center

Phillips, Darrell Gordon

The purpose of this study was to investigate a proposed model for the acquisition of the concept of displacement volume and to compare two methods of conservation task presentation. A 12-stage hierarchical model for the acquisition of the concept was proposed, based on four primary assumptions: (1) concept attainment can be measured by…

Personalized Medicine in Veterans with Traumatic Brain Injuries

DTIC Science & Technology

2014-07-01

9 control cases are subjected to unsupervised hierarchical clustering analysis using the UPGMA algorithm with cosine correlation as the similarity...in unsu- pervised hierarchical clustering by the Un- weighted Pair-Group Method using Arithmetic averages ( UPGMA ) based on cosine correlation of row...of log2 trans- formed MAS5.0 signal values; probe set cluster- ing was performed by the UPGMA method using Cosine correlation as the similarity

A Facile Method to Fabricate Anisotropic Hydrogels with Perfectly Aligned Hierarchical Fibrous Structures.

PubMed

Mredha, Md Tariful Islam; Guo, Yun Zhou; Nonoyama, Takayuki; Nakajima, Tasuku; Kurokawa, Takayuki; Gong, Jian Ping

2018-03-01

Natural structural materials (such as tendons and ligaments) are comprised of multiscale hierarchical architectures, with dimensions ranging from nano- to macroscale, which are difficult to mimic synthetically. Here a bioinspired, facile method to fabricate anisotropic hydrogels with perfectly aligned multiscale hierarchical fibrous structures similar to those of tendons and ligaments is reported. The method includes drying a diluted physical hydrogel in air by confining its length direction. During this process, sufficiently high tensile stress is built along the length direction to align the polymer chains and multiscale fibrous structures (from nano- to submicro- to microscale) are spontaneously formed in the bulk material, which are well-retained in the reswollen gel. The method is useful for relatively rigid polymers (such as alginate and cellulose), which are susceptible to mechanical signal. By controlling the drying with or without prestretching, the degree of alignment, size of superstructures, and the strength of supramolecular interactions can be tuned, which sensitively influence the strength and toughness of the hydrogels. The mechanical properties are comparable with those of natural ligaments. This study provides a general strategy for designing hydrogels with highly ordered hierarchical structures, which opens routes for the development of many functional biomimetic materials for biomedical applications. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Modelling the mechanics of partially mineralized collagen fibrils, fibres and tissue

PubMed Central

Liu, Yanxin; Thomopoulos, Stavros; Chen, Changqing; Birman, Victor; Buehler, Markus J.; Genin, Guy M.

2014-01-01

Progressive stiffening of collagen tissue by bioapatite mineral is important physiologically, but the details of this stiffening are uncertain. Unresolved questions about the details of the accommodation of bioapatite within and upon collagen's hierarchical structure have posed a central hurdle, but recent microscopy data resolve several major questions. These data suggest how collagen accommodates bioapatite at the lowest relevant hierarchical level (collagen fibrils), and suggest several possibilities for the progressive accommodation of bioapatite at higher hierarchical length scales (fibres and tissue). We developed approximations for the stiffening of collagen across spatial hierarchies based upon these data, and connected models across hierarchies levels to estimate mineralization-dependent tissue-level mechanics. In the five possible sequences of mineralization studied, percolation of the bioapatite phase proved to be an important determinant of the degree of stiffening by bioapatite. The models were applied to study one important instance of partially mineralized tissue, which occurs at the attachment of tendon to bone. All sequences of mineralization considered reproduced experimental observations of a region of tissue between tendon and bone that is more compliant than either tendon or bone, but the size and nature of this region depended strongly upon the sequence of mineralization. These models and observations have implications for engineered tissue scaffolds at the attachment of tendon to bone, bone development and graded biomimetic attachment of dissimilar hierarchical materials in general. PMID:24352669

Fabrication of hierarchical porous ZnO-Al2O3 microspheres with enhanced adsorption performance

NASA Astrophysics Data System (ADS)

Lei, Chunsheng; Pi, Meng; Xu, Difa; Jiang, Chuanjia; Cheng, Bei

2017-12-01

Hierarchical porous ZnO-Al2O3 microspheres were fabricated through a simple hydrothermal route. The as-prepared hierarchical porous ZnO-Al2O3 composites were utilized as adsorbents to remove organic dye Congo red (CR) from water. The ZnO-Al2O3 composites had morphology of microspheres with diameters in the range of 12-16 μm, which were assembled by nanosheets with thicknesses of approximately 60 nm. The adsorption kinetics of CR onto the ZnO-Al2O3 composites was properly fitted by the pseudo-second-order kinetic model. The equilibrium adsorption data were perfectly described by the Langmuir isotherm and had a maximum adsorption capacity that reached 397 mg/g, which was significantly higher than the value of the pure alumina (Al2O3) and zinc oxide (ZnO) samples. The superior CR removal efficiency of the ZnO-Al2O3 composites was attributed to its well-developed hierarchical porous structures and larger specific surface area (201 m2/g), which were conducive to the diffusion and adsorption of CR molecules. Moreover, the regeneration study reveals that the ZnO-Al2O3 composites have suitable stability and reusability. The results also indicate that the as-prepared sample can act as a highly effective adsorbent in anionic dye removal from wastewater.

A novel method for a multi-level hierarchical composite with brick-and-mortar structure

PubMed Central

Brandt, Kristina; Wolff, Michael F. H.; Salikov, Vitalij; Heinrich, Stefan; Schneider, Gerold A.

2013-01-01

The fascination for hierarchically structured hard tissues such as enamel or nacre arises from their unique structure-properties-relationship. During the last decades this numerously motivated the synthesis of composites, mimicking the brick-and-mortar structure of nacre. However, there is still a lack in synthetic engineering materials displaying a true hierarchical structure. Here, we present a novel multi-step processing route for anisotropic 2-level hierarchical composites by combining different coating techniques on different length scales. It comprises polymer-encapsulated ceramic particles as building blocks for the first level, followed by spouted bed spray granulation for a second level, and finally directional hot pressing to anisotropically consolidate the composite. The microstructure achieved reveals a brick-and-mortar hierarchical structure with distinct, however not yet optimized mechanical properties on each level. It opens up a completely new processing route for the synthesis of multi-level hierarchically structured composites, giving prospects to multi-functional structure-properties relationships. PMID:23900554

A novel method for a multi-level hierarchical composite with brick-and-mortar structure.

PubMed

Brandt, Kristina; Wolff, Michael F H; Salikov, Vitalij; Heinrich, Stefan; Schneider, Gerold A

2013-01-01

The fascination for hierarchically structured hard tissues such as enamel or nacre arises from their unique structure-properties-relationship. During the last decades this numerously motivated the synthesis of composites, mimicking the brick-and-mortar structure of nacre. However, there is still a lack in synthetic engineering materials displaying a true hierarchical structure. Here, we present a novel multi-step processing route for anisotropic 2-level hierarchical composites by combining different coating techniques on different length scales. It comprises polymer-encapsulated ceramic particles as building blocks for the first level, followed by spouted bed spray granulation for a second level, and finally directional hot pressing to anisotropically consolidate the composite. The microstructure achieved reveals a brick-and-mortar hierarchical structure with distinct, however not yet optimized mechanical properties on each level. It opens up a completely new processing route for the synthesis of multi-level hierarchically structured composites, giving prospects to multi-functional structure-properties relationships.

A novel method for a multi-level hierarchical composite with brick-and-mortar structure

NASA Astrophysics Data System (ADS)

Brandt, Kristina; Wolff, Michael F. H.; Salikov, Vitalij; Heinrich, Stefan; Schneider, Gerold A.

2013-07-01

The fascination for hierarchically structured hard tissues such as enamel or nacre arises from their unique structure-properties-relationship. During the last decades this numerously motivated the synthesis of composites, mimicking the brick-and-mortar structure of nacre. However, there is still a lack in synthetic engineering materials displaying a true hierarchical structure. Here, we present a novel multi-step processing route for anisotropic 2-level hierarchical composites by combining different coating techniques on different length scales. It comprises polymer-encapsulated ceramic particles as building blocks for the first level, followed by spouted bed spray granulation for a second level, and finally directional hot pressing to anisotropically consolidate the composite. The microstructure achieved reveals a brick-and-mortar hierarchical structure with distinct, however not yet optimized mechanical properties on each level. It opens up a completely new processing route for the synthesis of multi-level hierarchically structured composites, giving prospects to multi-functional structure-properties relationships.

Hierarchical virtual screening approaches in small molecule drug discovery.

PubMed

Kumar, Ashutosh; Zhang, Kam Y J

2015-01-01

Virtual screening has played a significant role in the discovery of small molecule inhibitors of therapeutic targets in last two decades. Various ligand and structure-based virtual screening approaches are employed to identify small molecule ligands for proteins of interest. These approaches are often combined in either hierarchical or parallel manner to take advantage of the strength and avoid the limitations associated with individual methods. Hierarchical combination of ligand and structure-based virtual screening approaches has received noteworthy success in numerous drug discovery campaigns. In hierarchical virtual screening, several filters using ligand and structure-based approaches are sequentially applied to reduce a large screening library to a number small enough for experimental testing. In this review, we focus on different hierarchical virtual screening strategies and their application in the discovery of small molecule modulators of important drug targets. Several virtual screening studies are discussed to demonstrate the successful application of hierarchical virtual screening in small molecule drug discovery. Copyright © 2014 Elsevier Inc. All rights reserved.

Highly efficient decomposition of organic dye by aqueous-solid phase transfer and in situ photocatalysis using hierarchical copper phthalocyanine hollow spheres.

PubMed

Zhang, Mingyi; Shao, Changlu; Guo, Zengcai; Zhang, Zhenyi; Mu, Jingbo; Zhang, Peng; Cao, Tieping; Liu, Yichun

2011-07-01

The hierarchical tetranitro copper phthalocyanine (TNCuPc) hollow spheres were fabricated by a simple solvothermal method. The formation mechanism was proposed based on the evolution of morphology as a function of solvothermal time, which involved the initial formation of nanoparticles followed by their self-aggregation to microspheres and transformation into hierarchical hollow spheres by Ostwald ripening. Furthermore, the hierarchical TNCuPc hollow spheres exhibited high adsorption capacity and excellent simultaneously visible-light-driven photocatalytic performance for Rhodamine B (RB) under visible light. A possible mechanism for the "aqueous-solid phase transfer and in situ photocatalysis" was suggested. Repetitive tests showed that the hierarchical TNCuPc hollow spheres maintained high catalytic activity over several cycles, and it had a better regeneration capability under mild conditions.

Multiscale stochastic simulations for tensile testing of nanotube-based macroscopic cables.

PubMed

Pugno, Nicola M; Bosia, Federico; Carpinteri, Alberto

2008-08-01

Thousands of multiscale stochastic simulations are carried out in order to perform the first in-silico tensile tests of carbon nanotube (CNT)-based macroscopic cables with varying length. The longest treated cable is the space-elevator megacable but more realistic shorter cables are also considered in this bottom-up investigation. Different sizes, shapes, and concentrations of defects are simulated, resulting in cable macrostrengths not larger than approximately 10 GPa, which is much smaller than the theoretical nanotube strength (approximately 100 GPa). No best-fit parameters are present in the multiscale simulations: the input at level 1 is directly estimated from nanotensile tests of CNTs, whereas its output is considered as the input for the level 2, and so on up to level 5, corresponding to the megacable. Thus, five hierarchical levels are used to span lengths from that of a single nanotube (approximately 100 nm) to that of the space-elevator megacable (approximately 100 Mm).

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

Franović, Igor, E-mail: franovic@ipb.ac.rs; Todorović, Kristina; Burić, Nikola

We use the mean-field approach to analyze the collective dynamics in macroscopic networks of stochastic Fitzhugh-Nagumo units with delayed couplings. The conditions for validity of the two main approximations behind the model, called the Gaussian approximation and the Quasi-independence approximation, are examined. It is shown that the dynamics of the mean-field model may indicate in a self-consistent fashion the parameter domains where the Quasi-independence approximation fails. Apart from a network of globally coupled units, we also consider the paradigmatic setup of two interacting assemblies to demonstrate how our framework may be extended to hierarchical and modular networks. In both cases,more » the mean-field model can be used to qualitatively analyze the stability of the system, as well as the scenarios for the onset and the suppression of the collective mode. In quantitative terms, the mean-field model is capable of predicting the average oscillation frequency corresponding to the global variables of the exact system.« less

Density functional theory for molecular and periodic systems using density fitting and continuous fast multipole method: Analytical gradients.

PubMed

Łazarski, Roman; Burow, Asbjörn Manfred; Grajciar, Lukáš; Sierka, Marek

2016-10-30

A full implementation of analytical energy gradients for molecular and periodic systems is reported in the TURBOMOLE program package within the framework of Kohn-Sham density functional theory using Gaussian-type orbitals as basis functions. Its key component is a combination of density fitting (DF) approximation and continuous fast multipole method (CFMM) that allows for an efficient calculation of the Coulomb energy gradient. For exchange-correlation part the hierarchical numerical integration scheme (Burow and Sierka, Journal of Chemical Theory and Computation 2011, 7, 3097) is extended to energy gradients. Computational efficiency and asymptotic O(N) scaling behavior of the implementation is demonstrated for various molecular and periodic model systems, with the largest unit cell of hematite containing 640 atoms and 19,072 basis functions. The overall computational effort of energy gradient is comparable to that of the Kohn-Sham matrix formation. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

The Hierarchical Structure of Formal Operational Tasks.

ERIC Educational Resources Information Center

Bart, William M.; Mertens, Donna M.

1979-01-01

The hierarchical structure of the formal operational period of Piaget's theory of cognitive development was explored through the application of ordering theoretical methods to a set of data that systematically utilized the various formal operational schemes. Results suggested a common structure underlying task performance. (Author/BH)

Efficiently computing and deriving topological relation matrices between complex regions with broad boundaries

NASA Astrophysics Data System (ADS)

Du, Shihong; Guo, Luo; Wang, Qiao; Qin, Qimin

The extended 9-intersection matrix is used to formalize topological relations between uncertain regions while it is designed to satisfy the requirements at a concept level, and to deal with the complex regions with broad boundaries (CBBRs) as a whole without considering their hierarchical structures. In contrast to simple regions with broad boundaries, CBBRs have complex hierarchical structures. Therefore, it is necessary to take into account the complex hierarchical structure and to represent the topological relations between all regions in CBBRs as a relation matrix, rather than using the extended 9-intersection matrix to determine topological relations. In this study, a tree model is first used to represent the intrinsic configuration of CBBRs hierarchically. Then, the reasoning tables are presented for deriving topological relations between child, parent and sibling regions from the relations between two given regions in CBBRs. Finally, based on the reasoning, efficient methods are proposed to compute and derive the topological relation matrix. The proposed methods can be incorporated into spatial databases to facilitate geometric-oriented applications.

Superhydrophobic Surface Based on a Coral-Like Hierarchical Structure of ZnO

PubMed Central

Wu, Jun; Xia, Jun; Lei, Wei; Wang, Baoping

2010-01-01

Background Fabrication of superhydrophobic surfaces has attracted much interest in the past decade. The fabrication methods that have been studied are chemical vapour deposition, the sol-gel method, etching technique, electrochemical deposition, the layer-by-layer deposition, and so on. Simple and inexpensive methods for manufacturing environmentally stable superhydrophobic surfaces have also been proposed lately. However, work referring to the influence of special structures on the wettability, such as hierarchical ZnO nanostructures, is rare. Methodology This study presents a simple and reproducible method to fabricate a superhydrophobic surface with micro-scale roughness based on zinc oxide (ZnO) hierarchical structure, which is grown by the hydrothermal method with an alkaline aqueous solution. Coral-like structures of ZnO were fabricated on a glass substrate with a micro-scale roughness, while the antennas of the coral formed the nano-scale roughness. The fresh ZnO films exhibited excellent superhydrophilicity (the apparent contact angle for water droplet was about 0°), while the ability to be wet could be changed to superhydrophobicity after spin-coating Teflon (the apparent contact angle greater than 168°). The procedure reported here can be applied to substrates consisting of other materials and having various shapes. Results The new process is convenient and environmentally friendly compared to conventional methods. Furthermore, the hierarchical structure generates the extraordinary solid/gas/liquid three-phase contact interface, which is the essential characteristic for a superhydrophobic surface. PMID:21209931

Classification of high resolution remote sensing image based on geo-ontology and conditional random fields

NASA Astrophysics Data System (ADS)

Hong, Liang

2013-10-01

The availability of high spatial resolution remote sensing data provides new opportunities for urban land-cover classification. More geometric details can be observed in the high resolution remote sensing image, Also Ground objects in the high resolution remote sensing image have displayed rich texture, structure, shape and hierarchical semantic characters. More landscape elements are represented by a small group of pixels. Recently years, the an object-based remote sensing analysis methodology is widely accepted and applied in high resolution remote sensing image processing. The classification method based on Geo-ontology and conditional random fields is presented in this paper. The proposed method is made up of four blocks: (1) the hierarchical ground objects semantic framework is constructed based on geoontology; (2) segmentation by mean-shift algorithm, which image objects are generated. And the mean-shift method is to get boundary preserved and spectrally homogeneous over-segmentation regions ;(3) the relations between the hierarchical ground objects semantic and over-segmentation regions are defined based on conditional random fields framework ;(4) the hierarchical classification results are obtained based on geo-ontology and conditional random fields. Finally, high-resolution remote sensed image data -GeoEye, is used to testify the performance of the presented method. And the experimental results have shown the superiority of this method to the eCognition method both on the effectively and accuracy, which implies it is suitable for the classification of high resolution remote sensing image.

Morphology evolution of hierarchical ZnO nanostructures modulated by supersaturation and growth temperature

NASA Astrophysics Data System (ADS)

Yan, Youguo; Zhou, Lixia; Yu, Lianqing; Zhang, Ye

2008-07-01

Three kinds of ZnO hierarchical structures, nanocombs with tube- and needle-shaped teeth and hierarchical nanorod arrays, were successfully synthesized through the chemical vapor deposition method. Combining the experimental parameters, the microcosmic growing conditions (growth temperature and supersaturation) along the flux was discussed at length, and, based on the conclusions, three reasonable growth processes were proposed. The results and discussions were beneficial to further realize the relation between the growing behavior of the nanomaterial and microcosmic conditions, and the hierarchical nanostructures obtained were also expected to have potential applications as functional blocks in future nanodevices. Furthermore, the study of photoluminescence further indicated that the physical properties were strongly dependent on the crystal structure.

A novel snowflake-like SnO2 hierarchical architecture with superior gas sensing properties

NASA Astrophysics Data System (ADS)

Li, Yanqiong

2018-02-01

Snowflake-like SnO2 hierarchical architecture has been synthesized via a facile hydrothermal method and followed by calcination. The SnO2 hierarchical structures are assembled with thin nanoflakes blocks, which look like snowflake shape. A possible mechanism for the formation of the SnO2 hierarchical structures is speculated. Moreover, gas sensing tests show that the sensor based on snowflake-like SnO2 architectures exhibited excellent gas sensing properties. The enhancement may be attributed to its unique structures, in which the porous feature on the snowflake surface could further increase the active surface area of the materials and provide facile pathways for the target gas.

Dominance, Information, and Hierarchical Scaling of Variance Space.

ERIC Educational Resources Information Center

Ceurvorst, Robert W.; Krus, David J.

1979-01-01

A method for computation of dominance relations and for construction of their corresponding hierarchical structures is presented. The link between dominance and variance allows integration of the mathematical theory of information with least squares statistical procedures without recourse to logarithmic transformations of the data. (Author/CTM)

Zinc oxide hierarchical nanostructures for photocatalysis

NASA Astrophysics Data System (ADS)

Yukhnovets, O.; Semenova, A. A.; Levkevich, E. A.; Maximov, A. I.; Moshnikov, V. A.

2018-03-01

In this work, we perform the study of zinc oxide hierarchical structures synthesized by the low-temperature hydrothermal method. The paper considers morphological properties of obtained structures. Photocatalytic activity of samples was analysed by methyl orange degradation under UV irradiation. The sufficient decrease in methyl orange has been demonstrated.

Hierarchical fameworks for distributional and life history data: Implementation of a new ecoinformatics tool

EPA Science Inventory

Background/Questions/Methods Threats to marine and estuarine species operate over many spatial scales, from nutrient enrichment at the watershed/estuary scale to climate change at a global scale. To address this range of environmental issues, we developed a hierarchical framewor...

Method for implementation of recursive hierarchical segmentation on parallel computers

NASA Technical Reports Server (NTRS)

Tilton, James C. (Inventor)

2005-01-01

A method, computer readable storage, and apparatus for implementing a recursive hierarchical segmentation algorithm on a parallel computing platform. The method includes setting a bottom level of recursion that defines where a recursive division of an image into sections stops dividing, and setting an intermediate level of recursion where the recursive division changes from a parallel implementation into a serial implementation. The segmentation algorithm is implemented according to the set levels. The method can also include setting a convergence check level of recursion with which the first level of recursion communicates with when performing a convergence check.

Down-Regulation of Olfactory Receptors in Response to Traumatic Brain Injury Promotes Risk for Alzheimers Disease

DTIC Science & Technology

2015-12-01

group assignment of samples in unsupervised hierarchical clustering by the Unweighted Pair-Group Method using Arithmetic averages ( UPGMA ) based on...log2 transformed MAS5.0 signal values; probe set clustering was performed by the UPGMA method using Cosine correlation as the similarity met- ric. For...differentially-regulated genes identified were subjected to unsupervised hierarchical clustering analysis using the UPGMA algorithm with cosine correlation as

Indirect estimates of natal dispersal distance from genetic data in a stream-dwelling fish (Mogurnda adspersa).

PubMed

Shipham, Ashlee; Schmidt, Daniel J; Hughes, Jane M

2013-01-01

Recent work has highlighted the need to account for hierarchical patterns of genetic structure when estimating evolutionary and ecological parameters of interest. This caution is particularly relevant to studies of riverine organisms, where hierarchical structure appears to be commonplace. Here, we indirectly estimate dispersal distance in a hierarchically structured freshwater fish, Mogurnda adspersa. Microsatellite and mitochondrial DNA (mtDNA) data were obtained for 443 individuals across 27 sites separated by an average of 1.3 km within creeks of southeastern Queensland, Australia. Significant genetic structure was found among sites (mtDNA Φ(ST) = 0.508; microsatellite F(ST) = 0.225, F'(ST) = 0.340). Various clustering methods produced congruent patterns of hierarchical structure reflecting stream architecture. Partial mantel tests identified contiguous sets of sample sites where isolation by distance (IBD) explained F(ST) variation without significant contribution of hierarchical structure. Analysis of mean natal dispersal distance (σ) within sets of IBD-linked sample sites suggested most dispersal occurs over less than 1 km, and the average effective density (D(e)) was estimated at 11.5 individuals km(-1); indicating sedentary behavior and small effective population size are responsible for the remarkable patterns of genetic structure observed. Our results demonstrate that Rousset's regression-based method is applicable to estimating the scale of dispersal in riverine organisms and that identifying contiguous populations that satisfy the assumptions of this model is achievable with genetic clustering methods and partial correlations.

Hierarchical Boltzmann simulations and model error estimation

NASA Astrophysics Data System (ADS)

Torrilhon, Manuel; Sarna, Neeraj

2017-08-01

A hierarchical simulation approach for Boltzmann's equation should provide a single numerical framework in which a coarse representation can be used to compute gas flows as accurately and efficiently as in computational fluid dynamics, but a subsequent refinement allows to successively improve the result to the complete Boltzmann result. We use Hermite discretization, or moment equations, for the steady linearized Boltzmann equation for a proof-of-concept of such a framework. All representations of the hierarchy are rotationally invariant and the numerical method is formulated on fully unstructured triangular and quadrilateral meshes using a implicit discontinuous Galerkin formulation. We demonstrate the performance of the numerical method on model problems which in particular highlights the relevance of stability of boundary conditions on curved domains. The hierarchical nature of the method allows also to provide model error estimates by comparing subsequent representations. We present various model errors for a flow through a curved channel with obstacles.

Wavelet compression of multichannel ECG data by enhanced set partitioning in hierarchical trees algorithm.

PubMed

Sharifahmadian, Ershad

2006-01-01

The set partitioning in hierarchical trees (SPIHT) algorithm is very effective and computationally simple technique for image and signal compression. Here the author modified the algorithm which provides even better performance than the SPIHT algorithm. The enhanced set partitioning in hierarchical trees (ESPIHT) algorithm has performance faster than the SPIHT algorithm. In addition, the proposed algorithm reduces the number of bits in a bit stream which is stored or transmitted. I applied it to compression of multichannel ECG data. Also, I presented a specific procedure based on the modified algorithm for more efficient compression of multichannel ECG data. This method employed on selected records from the MIT-BIH arrhythmia database. According to experiments, the proposed method attained the significant results regarding compression of multichannel ECG data. Furthermore, in order to compress one signal which is stored for a long time, the proposed multichannel compression method can be utilized efficiently.

Clustering PPI data by combining FA and SHC method.

PubMed

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

2015-01-01

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

Clustering PPI data by combining FA and SHC method

PubMed Central

2015-01-01

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

Hierarchical Adaptive Means (HAM) clustering for hardware-efficient, unsupervised and real-time spike sorting.

PubMed

Paraskevopoulou, Sivylla E; Wu, Di; Eftekhar, Amir; Constandinou, Timothy G

2014-09-30

This work presents a novel unsupervised algorithm for real-time adaptive clustering of neural spike data (spike sorting). The proposed Hierarchical Adaptive Means (HAM) clustering method combines centroid-based clustering with hierarchical cluster connectivity to classify incoming spikes using groups of clusters. It is described how the proposed method can adaptively track the incoming spike data without requiring any past history, iteration or training and autonomously determines the number of spike classes. Its performance (classification accuracy) has been tested using multiple datasets (both simulated and recorded) achieving a near-identical accuracy compared to k-means (using 10-iterations and provided with the number of spike classes). Also, its robustness in applying to different feature extraction methods has been demonstrated by achieving classification accuracies above 80% across multiple datasets. Last but crucially, its low complexity, that has been quantified through both memory and computation requirements makes this method hugely attractive for future hardware implementation. Copyright © 2014 Elsevier B.V. All rights reserved.

Hierarchical Si/ZnO trunk-branch nanostructure for photocurrent enhancement

PubMed Central

2014-01-01

Hierarchical Si/ZnO trunk-branch nanostructures (NSs) have been synthesized by hot wire assisted chemical vapor deposition method for trunk Si nanowires (NWs) on indium tin oxide (ITO) substrate and followed by the vapor transport condensation (VTC) method for zinc oxide (ZnO) nanorods (NRs) which was laterally grown from each Si nanowires (NWs). A spin coating method has been used for zinc oxide (ZnO) seeding. This method is better compared with other group where they used sputtering method for the same process. The sputtering method only results in the growth of ZnO NRs on top of the Si trunk. Our method shows improvement by having the growth evenly distributed on the lateral sides and caps of the Si trunks, resulting in pine-leave-like NSs. Field emission scanning electron microscope image shows the hierarchical nanostructures resembling the shape of the leaves of pine trees. Single crystalline structure for the ZnO branch grown laterally from the crystalline Si trunk has been identified by using a lattice-resolved transmission electron microscope. A preliminary photoelectrochemical (PEC) cell testing has been setup to characterize the photocurrent of sole array of ZnO NR growth by both hydrothermal-grown (HTG) method and VTC method on ITO substrates. VTC-grown ZnO NRs showed greater photocurrent effect due to its better structural properties. The measured photocurrent was also compared with the array of hierarchical Si/ZnO trunk-branch NSs. The cell with the array of Si/ZnO trunk-branch NSs revealed four-fold magnitude enhancement in photocurrent density compared with the sole array of ZnO NRs obtain from VTC processes. PMID:25246872

Space-Time Smoothing of Complex Survey Data: Small Area Estimation for Child Mortality.

PubMed

Mercer, Laina D; Wakefield, Jon; Pantazis, Athena; Lutambi, Angelina M; Masanja, Honorati; Clark, Samuel

2015-12-01

Many people living in low and middle-income countries are not covered by civil registration and vital statistics systems. Consequently, a wide variety of other types of data including many household sample surveys are used to estimate health and population indicators. In this paper we combine data from sample surveys and demographic surveillance systems to produce small area estimates of child mortality through time. Small area estimates are necessary to understand geographical heterogeneity in health indicators when full-coverage vital statistics are not available. For this endeavor spatio-temporal smoothing is beneficial to alleviate problems of data sparsity. The use of conventional hierarchical models requires careful thought since the survey weights may need to be considered to alleviate bias due to non-random sampling and non-response. The application that motivated this work is estimation of child mortality rates in five-year time intervals in regions of Tanzania. Data come from Demographic and Health Surveys conducted over the period 1991-2010 and two demographic surveillance system sites. We derive a variance estimator of under five years child mortality that accounts for the complex survey weighting. For our application, the hierarchical models we consider include random effects for area, time and survey and we compare models using a variety of measures including the conditional predictive ordinate (CPO). The method we propose is implemented via the fast and accurate integrated nested Laplace approximation (INLA).

Array-based, parallel hierarchical mesh refinement algorithms for unstructured meshes

DOE PAGES

Ray, Navamita; Grindeanu, Iulian; Zhao, Xinglin; ...

2016-08-18

In this paper, we describe an array-based hierarchical mesh refinement capability through uniform refinement of unstructured meshes for efficient solution of PDE's using finite element methods and multigrid solvers. A multi-degree, multi-dimensional and multi-level framework is designed to generate the nested hierarchies from an initial coarse mesh that can be used for a variety of purposes such as in multigrid solvers/preconditioners, to do solution convergence and verification studies and to improve overall parallel efficiency by decreasing I/O bandwidth requirements (by loading smaller meshes and in memory refinement). We also describe a high-order boundary reconstruction capability that can be used tomore » project the new points after refinement using high-order approximations instead of linear projection in order to minimize and provide more control on geometrical errors introduced by curved boundaries.The capability is developed under the parallel unstructured mesh framework "Mesh Oriented dAtaBase" (MOAB Tautges et al. (2004)). We describe the underlying data structures and algorithms to generate such hierarchies in parallel and present numerical results for computational efficiency and effect on mesh quality. Furthermore, we also present results to demonstrate the applicability of the developed capability to study convergence properties of different point projection schemes for various mesh hierarchies and to a multigrid finite-element solver for elliptic problems.« less

Facile synthesis of mesoporous silica sublayer with hierarchical pore structure on ceramic membrane using anionic polyelectrolyte.

PubMed

Kang, Taewook; Oh, Seogil; Kim, Honggon; Yi, Jongheop

2005-06-21

A facile method for introducing mesoporous silica sublayer onto the surface of a ceramic membrane for use in liquid-phase separation is described. To reduce the electrostatic repulsion between the mesoporous silica sol and the ceramic membrane in highly acidic conditions (pH < 2), thus facilitating the approach of hydrolyzed silica sol to the surface of the membrane, poly(sodium 4-styrenesulfonate) (Na+PSS-, denoted as PSS-) was used as an ionic linker. The use of PSS- led to a significant reduction in positive charge on the ceramic membrane, as confirmed by experimental titration data. Consistent with the titration results, the amount of mesoporous silica particles on the surface of the ceramic membrane was low, in the absence of PSS- treatment, whereas mesoporous silica sublayer with hierarchical pore structure was produced, when 1 wt % PSS- was used. The results show that mesoporous silica grows in the confined surface, eventually forming a multistacked surface architecture. The mesoporous silica sublayer contained uniform, ordered (P6 mm) mesopores of ca. 7.5 nm from mesoporous silica as well as macropores ( approximately mum) from interparticle voids, as evidenced by transmission electron microscopy and scanning electron microscopy analyses. The morphologies of the supported mesoporous silica could be manipulated, thus permitting the generation of uniform needlelike forms or uniform spheroid particles by varying the concentration of PSS-.

Parallel implementation of approximate atomistic models of the AMOEBA polarizable model

NASA Astrophysics Data System (ADS)

Demerdash, Omar; Head-Gordon, Teresa

2016-11-01

In this work we present a replicated data hybrid OpenMP/MPI implementation of a hierarchical progression of approximate classical polarizable models that yields speedups of up to ∼10 compared to the standard OpenMP implementation of the exact parent AMOEBA polarizable model. In addition, our parallel implementation exhibits reasonable weak and strong scaling. The resulting parallel software will prove useful for those who are interested in how molecular properties converge in the condensed phase with respect to the MBE, it provides a fruitful test bed for exploring different electrostatic embedding schemes, and offers an interesting possibility for future exascale computing paradigms.

Remarks on Hierarchic Control for a Linearized Micropolar Fluids System in Moving Domains

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

Jesus, Isaías Pereira de, E-mail: isaias@ufpi.edu.br

We study a Stackelberg strategy subject to the evolutionary linearized micropolar fluids equations in domains with moving boundaries, considering a Nash multi-objective equilibrium (non necessarily cooperative) for the “follower players” (as is called in the economy field) and an optimal problem for the leader player with approximate controllability objective. We will obtain the following main results: the existence and uniqueness of Nash equilibrium and its characterization, the approximate controllability of the linearized micropolar system with respect to the leader control and the existence and uniqueness of the Stackelberg–Nash problem, where the optimality system for the leader is given.

A hierarchical classification method for finger knuckle print recognition

NASA Astrophysics Data System (ADS)

Kong, Tao; Yang, Gongping; Yang, Lu

2014-12-01

Finger knuckle print has recently been seen as an effective biometric technique. In this paper, we propose a hierarchical classification method for finger knuckle print recognition, which is rooted in traditional score-level fusion methods. In the proposed method, we firstly take Gabor feature as the basic feature for finger knuckle print recognition and then a new decision rule is defined based on the predefined threshold. Finally, the minor feature speeded-up robust feature is conducted for these users, who cannot be recognized by the basic feature. Extensive experiments are performed to evaluate the proposed method, and experimental results show that it can achieve a promising performance.

Hierarchical Bayesian modelling of mobility metrics for hazard model input calibration

NASA Astrophysics Data System (ADS)

Calder, Eliza; Ogburn, Sarah; Spiller, Elaine; Rutarindwa, Regis; Berger, Jim

2015-04-01

In this work we present a method to constrain flow mobility input parameters for pyroclastic flow models using hierarchical Bayes modeling of standard mobility metrics such as H/L and flow volume etc. The advantage of hierarchical modeling is that it can leverage the information in global dataset for a particular mobility metric in order to reduce the uncertainty in modeling of an individual volcano, especially important where individual volcanoes have only sparse datasets. We use compiled pyroclastic flow runout data from Colima, Merapi, Soufriere Hills, Unzen and Semeru volcanoes, presented in an open-source database FlowDat (https://vhub.org/groups/massflowdatabase). While the exact relationship between flow volume and friction varies somewhat between volcanoes, dome collapse flows originating from the same volcano exhibit similar mobility relationships. Instead of fitting separate regression models for each volcano dataset, we use a variation of the hierarchical linear model (Kass and Steffey, 1989). The model presents a hierarchical structure with two levels; all dome collapse flows and dome collapse flows at specific volcanoes. The hierarchical model allows us to assume that the flows at specific volcanoes share a common distribution of regression slopes, then solves for that distribution. We present comparisons of the 95% confidence intervals on the individual regression lines for the data set from each volcano as well as those obtained from the hierarchical model. The results clearly demonstrate the advantage of considering global datasets using this technique. The technique developed is demonstrated here for mobility metrics, but can be applied to many other global datasets of volcanic parameters. In particular, such methods can provide a means to better contain parameters for volcanoes for which we only have sparse data, a ubiquitous problem in volcanology.

Carbon fiber reinforced hierarchical orthogrid stiffened cylinder: Fabrication and testing

NASA Astrophysics Data System (ADS)

Wu, Hao; Lai, Changlian; Sun, Fangfang; Li, Ming; Ji, Bin; Wei, Weiyi; Liu, Debo; Zhang, Xi; Fan, Hualin

2018-04-01

To get strong, stiff and light cylindrical shell, carbon fiber reinforced hierarchical orthogrid stiffened cylinders are designed and fabricated. The cylinder is stiffened by two-scale orthogrid. The primary orthogrid has thick and high ribs and contains several sub-orthogrid cells whose rib is much thinner and lower. The primary orthogrid stiffens the bending rigidity of the cylinder to resist the global instability while the sub-orthogrid stiffens the bending rigidity of the skin enclosed by the primary orthogrid to resist local buckling. The cylinder is fabricated by filament winding method based on a silicone rubber mandrel with hierarchical grooves. Axial compression tests are performed to reveal the failure modes. With hierarchical stiffeners, the cylinder fails at skin fracture and has high specific strength. The cylinder will fail at end crushing if the end of the cylinder is not thickened. Global instability and local buckling are well restricted by the hierarchical stiffeners.

Distinct Photovoltaic Performance of Hierarchical Nanostructures Self-Assembled from Multiblock Copolymers.

PubMed

Xu, Zhanwen; Lin, Jiaping; Zhang, Liangshun; Wang, Liquan; Wang, Gengchao; Tian, Xiaohui; Jiang, Tao

2018-06-14

We applied a multi-scale approach coupling dissipative particle dynamics method with a drift-diffusion model to elucidate the photovoltaic properties of multiblock copolymers consisting of alternating electron donor and acceptor blocks. A series of hierarchical lamellae-in-lamellar structures were obtained from the self-assembly of the multiblock copolymers. A distinct improvement in photovoltaic performance upon the morphology transformation from lamella to lamellae-in-lamella was observed. The hierarchical lamellae-in-lamellar structures significantly enhanced exciton dissociation and charge carrier transport, which consequently contributed to the improved photovoltaic performance. Based on our theoretical calculations, the hierarchical nanostructures can achieve a much enhanced energy conversion efficiency, improved by around 25% compared with that of general ones, through structure modulation on number and size of the small-length-scale domains. Our findings are supported by recent experimental evidence and yield guidelines for designing hierarchical materials with improved photovoltaic properties.

Metal oxide nanostructures with hierarchical morphology

DOEpatents

Ren, Zhifeng; Lao, Jing Yu; Banerjee, Debasish

2007-11-13

The present invention relates generally to metal oxide materials with varied symmetrical nanostructure morphologies. In particular, the present invention provides metal oxide materials comprising one or more metallic oxides with three-dimensionally ordered nanostructural morphologies, including hierarchical morphologies. The present invention also provides methods for producing such metal oxide materials.

Hierarchical Bayesian Models of Subtask Learning

ERIC Educational Resources Information Center

Anglim, Jeromy; Wynton, Sarah K. A.

2015-01-01

The current study used Bayesian hierarchical methods to challenge and extend previous work on subtask learning consistency. A general model of individual-level subtask learning was proposed focusing on power and exponential functions with constraints to test for inconsistency. To study subtask learning, we developed a novel computer-based booking…

Hierarchical Network Models for Education Research: Hierarchical Latent Space Models

ERIC Educational Resources Information Center

Sweet, Tracy M.; Thomas, Andrew C.; Junker, Brian W.

2013-01-01

Intervention studies in school systems are sometimes aimed not at changing curriculum or classroom technique, but rather at changing the way that teachers, teaching coaches, and administrators in schools work with one another--in short, changing the professional social networks of educators. Current methods of social network analysis are…

Types of Online Hierarchical Repository Structures

ERIC Educational Resources Information Center

Hershkovitz, Arnon; Azran, Ronit; Hardof-Jaffe, Sharon; Nachmias, Rafi

2011-01-01

This study presents an empirical investigation of online hierarchical repositories of items presented to university students in Web-supported course websites, using Web mining methods. To this end, data from 1747 courses were collected, and the use of online repositories of content items in these courses was examined. At a later stage, courses…

Hierarchical analysis of forest bird species-environment relationships in the Oregon Coast Range

Treesearch

Samuel A. Cushman; Kevin McGarigal

2004-01-01

Species in biological communities respond to environmental variation simultaneously across a range of organizational levels. Accordingly, it is important to quantify the effects of environmental factors at multiple levels on species distribution and abundance. Hierarchical methods that explicitly separate the independent and confounded influences of environmental...

Evaluation of Validity and Reliability for Hierarchical Scales Using Latent Variable Modeling

ERIC Educational Resources Information Center

Raykov, Tenko; Marcoulides, George A.

2012-01-01

A latent variable modeling method is outlined, which accomplishes estimation of criterion validity and reliability for a multicomponent measuring instrument with hierarchical structure. The approach provides point and interval estimates for the scale criterion validity and reliability coefficients, and can also be used for testing composite or…

A novel alignment-free method for detection of lateral genetic transfer based on TF-IDF.

PubMed

Cong, Yingnan; Chan, Yao-Ban; Ragan, Mark A

2016-07-25

Lateral genetic transfer (LGT) plays an important role in the evolution of microbes. Existing computational methods for detecting genomic regions of putative lateral origin scale poorly to large data. Here, we propose a novel method based on TF-IDF (Term Frequency-Inverse Document Frequency) statistics to detect not only regions of lateral origin, but also their origin and direction of transfer, in sets of hierarchically structured nucleotide or protein sequences. This approach is based on the frequency distributions of k-mers in the sequences. If a set of contiguous k-mers appears sufficiently more frequently in another phyletic group than in its own, we infer that they have been transferred from the first group to the second. We performed rigorous tests of TF-IDF using simulated and empirical datasets. With the simulated data, we tested our method under different parameter settings for sequence length, substitution rate between and within groups and post-LGT, deletion rate, length of transferred region and k size, and found that we can detect LGT events with high precision and recall. Our method performs better than an established method, ALFY, which has high recall but low precision. Our method is efficient, with runtime increasing approximately linearly with sequence length.

A Hierarchical Model Predictive Tracking Control for Independent Four-Wheel Driving/Steering Vehicles with Coaxial Steering Mechanism

NASA Astrophysics Data System (ADS)

Itoh, Masato; Hagimori, Yuki; Nonaka, Kenichiro; Sekiguchi, Kazuma

2016-09-01

In this study, we apply a hierarchical model predictive control to omni-directional mobile vehicle, and improve the tracking performance. We deal with an independent four-wheel driving/steering vehicle (IFWDS) equipped with four coaxial steering mechanisms (CSM). The coaxial steering mechanism is a special one composed of two steering joints on the same axis. In our previous study with respect to IFWDS with ideal steering, we proposed a model predictive tracking control. However, this method did not consider constraints of the coaxial steering mechanism which causes delay of steering. We also proposed a model predictive steering control considering constraints of this mechanism. In this study, we propose a hierarchical system combining above two control methods for IFWDS. An upper controller, which deals with vehicle kinematics, runs a model predictive tracking control, and a lower controller, which considers constraints of coaxial steering mechanism, runs a model predictive steering control which tracks the predicted steering angle optimized an upper controller. We verify the superiority of this method by comparing this method with the previous method.

Biomimetic synthesis of hierarchical crystalline hydroxyapatite fibers in large-scale

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

Xing, Chaogang; Ge, Suxiang; Huang, Baojun

Highlights: ► Crystalline hierarchical hydroxyapatite (HAp) fibers are synthesized. ► We employ a biomimetic route by using cotton cloth as a natural bio-template. ► We study the effects of pH, ultrasonic cleaning time, and calcination temperature. ► We obtain an optimized reaction condition. ► This is a low cost method for production of hierarchical HAp fibers. -- Abstract: Crystalline hierarchical hydroxyapatite [Ca{sub 10}(PO{sub 4}){sub 6}(OH){sub 2}, HAp)] fibers were successfully synthesized via a biomimetic route by using cotton cloth as a natural bio-template. The effects of pH value, aging time, ultrasonic cleaning time, and calcination temperature on the purity andmore » morphology of the resulting hydroxyapatite (HAp) were monitored by scanning election microscope (SEM), X-ray diffraction (XRD), and infrared spectrophotometer (IR) to obtain an optimized reaction condition, namely, pH 9, ultrasonic cleaning for 1 min, aging for 24 h, and calcination at 600 °C for 4 h. We found that the natural cellulose could not only control the morphology of HAp but also lower its phase transformation temperature. The impact of this method lies in its low cost and successful production of large-scale patterning of three-dimensional hierarchical HAp fibers.« less

GOTHIC: Gravitational oct-tree code accelerated by hierarchical time step controlling

NASA Astrophysics Data System (ADS)

Miki, Yohei; Umemura, Masayuki

2017-04-01

The tree method is a widely implemented algorithm for collisionless N-body simulations in astrophysics well suited for GPU(s). Adopting hierarchical time stepping can accelerate N-body simulations; however, it is infrequently implemented and its potential remains untested in GPU implementations. We have developed a Gravitational Oct-Tree code accelerated by HIerarchical time step Controlling named GOTHIC, which adopts both the tree method and the hierarchical time step. The code adopts some adaptive optimizations by monitoring the execution time of each function on-the-fly and minimizes the time-to-solution by balancing the measured time of multiple functions. Results of performance measurements with realistic particle distribution performed on NVIDIA Tesla M2090, K20X, and GeForce GTX TITAN X, which are representative GPUs of the Fermi, Kepler, and Maxwell generation of GPUs, show that the hierarchical time step achieves a speedup by a factor of around 3-5 times compared to the shared time step. The measured elapsed time per step of GOTHIC is 0.30 s or 0.44 s on GTX TITAN X when the particle distribution represents the Andromeda galaxy or the NFW sphere, respectively, with 224 = 16,777,216 particles. The averaged performance of the code corresponds to 10-30% of the theoretical single precision peak performance of the GPU.

Aircraft optimization by a system approach: Achievements and trends

NASA Technical Reports Server (NTRS)

Sobieszczanski-Sobieski, Jaroslaw

1992-01-01

Recently emerging methodology for optimal design of aircraft treated as a system of interacting physical phenomena and parts is examined. The methodology is found to coalesce into methods for hierarchic, non-hierarchic, and hybrid systems all dependent on sensitivity analysis. A separate category of methods has also evolved independent of sensitivity analysis, hence suitable for discrete problems. References and numerical applications are cited. Massively parallel computer processing is seen as enabling technology for practical implementation of the methodology.

Down-Regulation of Olfactory Receptors in Response to Traumatic Brain Injury Promotes Risk for Alzheimer’s Disease

DTIC Science & Technology

2013-10-01

correct group assignment of samples in unsupervised hierarchical clustering by the Unweighted Pair-Group Method using Arithmetic averages ( UPGMA ) based on...centering of log2 transformed MAS5.0 signal values; probe set clustering was performed by the UPGMA method using Cosine correlation as the similarity met...A) The 108 differentially-regulated genes identified were subjected to unsupervised hierarchical clustering analysis using the UPGMA algorithm with

Rare-event statistics and modular invariance

NASA Astrophysics Data System (ADS)

Nechaev, S. K.; Polovnikov, K.

2018-01-01

Simple geometric arguments based on constructing the Euclid orchard are presented, which explain the equivalence of various types of distributions that result from rare-event statistics. In particular, the spectral density of the exponentially weighted ensemble of linear polymer chains is examined for its number-theoretic properties. It can be shown that the eigenvalue statistics of the corresponding adjacency matrices in the sparse regime show a peculiar hierarchical structure and are described by the popcorn (Thomae) function discontinuous in the dense set of rational numbers. Moreover, the spectral edge density distribution exhibits Lifshitz tails, reminiscent of 1D Anderson localization. Finally, a continuous approximation for the popcorn function is suggested based on the Dedekind η-function, and the hierarchical ultrametric structure of the popcorn-like distributions is demonstrated to be related to hidden SL(2,Z) modular symmetry.

How Markovian is exciton dynamics in purple bacteria?

NASA Astrophysics Data System (ADS)

Vaughan, Felix; Linden, Noah; Manby, Frederick R.

2017-03-01

We investigate the extent to which the dynamics of excitons in the light-harvesting complex LH2 of purple bacteria can be described using a Markovian approximation. To analyse the degree of non-Markovianity in these systems, we introduce a measure based on fitting Lindblad dynamics, as well as employing a recently introduced trace-distance measure. We apply these measures to a chromophore-dimer model of exciton dynamics and use the hierarchical equation-of-motion method to take into account the broad, low-frequency phonon bath. With a smooth phonon bath, small amounts of non-Markovianity are present according to the trace-distance measure, but the dynamics is poorly described by a Lindblad master equation unless the excitonic dimer coupling strength is modified. Inclusion of underdamped, high-frequency modes leads to significant deviations from Markovian evolution in both measures. In particular, we find that modes that are nearly resonant with gaps in the excitonic spectrum produce dynamics that deviate most strongly from the Lindblad approximation, despite the trace distance measuring larger amounts of non-Markovianity for higher frequency modes. Overall we find that the detailed structure in the high-frequency region of the spectral density has a significant impact on the nature of the dynamics of excitons.

Application of quasi-steady state methods to molecular motor transport on microtubules in fungal hyphae.

PubMed

Dauvergne, Duncan; Edelstein-Keshet, Leah

2015-08-21

We consider bidirectional transport of cargo by molecular motors dynein and kinesin that walk along microtubules, and/or diffuse in the cell. The motors compete to transport cargo in opposite directions with respect to microtubule polarity (towards the plus or minus end of the microtubule). In recent work, Gou et al. (2014) used a hierarchical set of models, each consisting of continuum transport equations to track the evolution of motors and their cargo (early endosomes) in the specific case of the fungus Ustilago maydis. We complement their work using a framework of quasi-steady state analysis developed by Newby and Bressloff (2010) and Bressloff and Newby (2013) to reduce the models to an approximating steady state Fokker-Plank equation. This analysis allows us to find analytic approximations to the steady state solutions in many cases where the full models are not easily solved. Consequently, we can make predictions about parameter dependence of the resulting spatial distributions. We also characterize the overall rates of bulk transport and diffusion, and how these are related to state transition parameters, motor speeds, microtubule polarity distribution, and specific assumptions made. Copyright © 2015 Elsevier Ltd. All rights reserved.

Au functionalized ZnO rose-like hierarchical structures and their enhanced NO2 sensing performance

NASA Astrophysics Data System (ADS)

Shingange, K.; Swart, H. C.; Mhlongo, G. H.

2018-04-01

Herein, we present ZnO rose-like hierarchical nanostructures employed as support to Au nanoparticles to produce Au functionalized three dimensional (3D) ZnO hierarchical nanostructures (Au/ZnO) for NO2 detection using a microwave-assisted method. Comparative analysis of NO2 sensing performance between the pristine ZnO and Au/ZnO rose-like structures at 300 °C revealed improved NO2 response and rapid response-recovery times with Au incorporation owing to a combination of high surface accessibility induced by hierarchical nanostructure design and catalytic activity of the small Au nanoparticles. Structural and optical analyses acquired from X-ray diffraction, scanning electron microscopy, transmission electron microscope and photoluminescence spectroscopy were also performed.

Eddington's demon: inferring galaxy mass functions and other distributions from uncertain data

NASA Astrophysics Data System (ADS)

Obreschkow, D.; Murray, S. G.; Robotham, A. S. G.; Westmeier, T.

2018-03-01

We present a general modified maximum likelihood (MML) method for inferring generative distribution functions from uncertain and biased data. The MML estimator is identical to, but easier and many orders of magnitude faster to compute than the solution of the exact Bayesian hierarchical modelling of all measurement errors. As a key application, this method can accurately recover the mass function (MF) of galaxies, while simultaneously dealing with observational uncertainties (Eddington bias), complex selection functions and unknown cosmic large-scale structure. The MML method is free of binning and natively accounts for small number statistics and non-detections. Its fast implementation in the R-package dftools is equally applicable to other objects, such as haloes, groups, and clusters, as well as observables other than mass. The formalism readily extends to multidimensional distribution functions, e.g. a Choloniewski function for the galaxy mass-angular momentum distribution, also handled by dftools. The code provides uncertainties and covariances for the fitted model parameters and approximate Bayesian evidences. We use numerous mock surveys to illustrate and test the MML method, as well as to emphasize the necessity of accounting for observational uncertainties in MFs of modern galaxy surveys.

A Hierarchical Learning Control Framework for an Aerial Manipulation System

NASA Astrophysics Data System (ADS)

Ma, Le; Chi, yanxun; Li, Jiapeng; Li, Zhongsheng; Ding, Yalei; Liu, Lixing

2017-07-01

A hierarchical learning control framework for an aerial manipulation system is proposed. Firstly, the mechanical design of aerial manipulation system is introduced and analyzed, and the kinematics and the dynamics based on Newton-Euler equation are modeled. Secondly, the framework of hierarchical learning for this system is presented, in which flight platform and manipulator are controlled by different controller respectively. The RBF (Radial Basis Function) neural networks are employed to estimate parameters and control. The Simulation and experiment demonstrate that the methods proposed effective and advanced.

Hierarchical porous graphene/polyaniline composite film with superior rate performance for flexible supercapacitors.

PubMed

Meng, Yuena; Wang, Kai; Zhang, Yajie; Wei, Zhixiang

2013-12-23

A highly flexible graphene free-standing film with hierarchical structure is prepared by a facile template method. With a porous structure, the film can be easily bent and cut, and forms a composite with another material as a scaffold. The 3D graphene film exhibits excellent rate capability and its capacitance is further improved by forming a composite with polyaniline nanowire arrays. The flexible hierarchical composite proves to be an excellent electrode material for flexible supercapacitors. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Fabrication of Advanced Thermoelectric Materials by Hierarchical Nanovoid Generation

NASA Technical Reports Server (NTRS)

Park, Yeonjoon (Inventor); Elliott, James R. (Inventor); Stoakley, Diane M. (Inventor); Chu, Sang-Hyon (Inventor); King, Glen C. (Inventor); Kim, Jae-Woo (Inventor); Choi, Sang Hyouk (Inventor); Lillehei, Peter T. (Inventor)

2011-01-01

A novel method to prepare an advanced thermoelectric material has hierarchical structures embedded with nanometer-sized voids which are key to enhancement of the thermoelectric performance. Solution-based thin film deposition technique enables preparation of stable film of thermoelectric material and void generator (voigen). A subsequent thermal process creates hierarchical nanovoid structure inside the thermoelectric material. Potential application areas of this advanced thermoelectric material with nanovoid structure are commercial applications (electronics cooling), medical and scientific applications (biological analysis device, medical imaging systems), telecommunications, and defense and military applications (night vision equipments).

Hierarchical Parallelism in Finite Difference Analysis of Heat Conduction

NASA Technical Reports Server (NTRS)

Padovan, Joseph; Krishna, Lala; Gute, Douglas

1997-01-01

Based on the concept of hierarchical parallelism, this research effort resulted in highly efficient parallel solution strategies for very large scale heat conduction problems. Overall, the method of hierarchical parallelism involves the partitioning of thermal models into several substructured levels wherein an optimal balance into various associated bandwidths is achieved. The details are described in this report. Overall, the report is organized into two parts. Part 1 describes the parallel modelling methodology and associated multilevel direct, iterative and mixed solution schemes. Part 2 establishes both the formal and computational properties of the scheme.

Three-dimensional information hierarchical encryption based on computer-generated holograms

NASA Astrophysics Data System (ADS)

Kong, Dezhao; Shen, Xueju; Cao, Liangcai; Zhang, Hao; Zong, Song; Jin, Guofan

2016-12-01

A novel approach for encrypting three-dimensional (3-D) scene information hierarchically based on computer-generated holograms (CGHs) is proposed. The CGHs of the layer-oriented 3-D scene information are produced by angular-spectrum propagation algorithm at different depths. All the CGHs are then modulated by different chaotic random phase masks generated by the logistic map. Hierarchical encryption encoding is applied when all the CGHs are accumulated one by one, and the reconstructed volume of the 3-D scene information depends on permissions of different users. The chaotic random phase masks could be encoded into several parameters of the chaotic sequences to simplify the transmission and preservation of the keys. Optical experiments verify the proposed method and numerical simulations show the high key sensitivity, high security, and application flexibility of the method.

Assessment of Gait Characteristics in Total Knee Arthroplasty Patients Using a Hierarchical Partial Least Squares Method.

PubMed

Wang, Wei; Ackland, David C; McClelland, Jodie A; Webster, Kate E; Halgamuge, Saman

2018-01-01

Quantitative gait analysis is an important tool in objective assessment and management of total knee arthroplasty (TKA) patients. Studies evaluating gait patterns in TKA patients have tended to focus on discrete data such as spatiotemporal information, joint range of motion and peak values of kinematics and kinetics, or consider selected principal components of gait waveforms for analysis. These strategies may not have the capacity to capture small variations in gait patterns associated with each joint across an entire gait cycle, and may ultimately limit the accuracy of gait classification. The aim of this study was to develop an automatic feature extraction method to analyse patterns from high-dimensional autocorrelated gait waveforms. A general linear feature extraction framework was proposed and a hierarchical partial least squares method derived for discriminant analysis of multiple gait waveforms. The effectiveness of this strategy was verified using a dataset of joint angle and ground reaction force waveforms from 43 patients after TKA surgery and 31 healthy control subjects. Compared with principal component analysis and partial least squares methods, the hierarchical partial least squares method achieved generally better classification performance on all possible combinations of waveforms, with the highest classification accuracy . The novel hierarchical partial least squares method proposed is capable of capturing virtually all significant differences between TKA patients and the controls, and provides new insights into data visualization. The proposed framework presents a foundation for more rigorous classification of gait, and may ultimately be used to evaluate the effects of interventions such as surgery and rehabilitation.

The Stability of Tidal Equilibrium for Hierarchical Star-Planet-Moon Systems

NASA Astrophysics Data System (ADS)

Adams, Fred C.

2018-04-01

Motivated by the current search for exomoons, this talk considers the stability of tidal equilibrium for hierarchical three-body systems containing a star, a planet, and a moon. In this treatment, the energy and angular momentum budgets include contributions from the planetary orbit, lunar orbit, stellar spin, planetary spin, and lunar spin. The goal is to determine the optimized energy state of the system subject to the constraint of constant angular momentum. Due to the lack of a closed form solution for the full three-body problem, however, we must use use an approximate description of the orbits. We first consider the Keplerian limit and find that the critical energy states are saddle points, rather than minima, so that these hierarchical systems have no stable tidal equilibrium states. We then generalize the calculation so that the lunar orbit is described by a time-averaged version of the circular restricted three-body problem. In this latter case, the critical energy state is a shallow minimum, so that a tidal equilibrium state exists. In both cases, however, the lunar orbit for the critical point lies outside the boundary (roughly half the Hill radius) where (previous) numerical simulations indicate dynamical instability.

Create and Publish a Hierarchical Progressive Survey (HiPS)

NASA Astrophysics Data System (ADS)

Fernique, P.; Boch, T.; Pineau, F.; Oberto, A.

2014-05-01

Since 2009, the CDS promotes a method for visualizing based on the HEALPix sky tessellation. This method, called “Hierarchical Progressive Survey" or HiPS, allows one to display a survey progressively. It is particularly suited for all-sky surveys or deep fields. This visualization method is now integrated in several applications, notably Aladin, the SiTools/MIZAR CNES framework, and the recent HTML5 “Aladin Lite". Also, more than one hundred surveys are already available in this view mode. In this article, we will present the progress concerning this method and its recent adaptation to the astronomical catalogs such as the GAIA simulation.

Assessing Student Achievement in Large-Scale Educational Programs Using Hierarchical Propensity Scores

ERIC Educational Resources Information Center

Vaughan, Angela L.; Lalonde, Trent L.; Jenkins-Guarnieri, Michael A.

2014-01-01

Many researchers assessing the efficacy of educational programs face challenges due to issues with non-randomization and the likelihood of dependence between nested subjects. The purpose of the study was to demonstrate a rigorous research methodology using a hierarchical propensity score matching method that can be utilized in contexts where…

Social Influence on Information Technology Adoption and Sustained Use in Healthcare: A Hierarchical Bayesian Learning Method Analysis

ERIC Educational Resources Information Center

Hao, Haijing

2013-01-01

Information technology adoption and diffusion is currently a significant challenge in the healthcare delivery setting. This thesis includes three papers that explore social influence on information technology adoption and sustained use in the healthcare delivery environment using conventional regression models and novel hierarchical Bayesian…

Measuring Service Quality in Higher Education: Development of a Hierarchical Model (HESQUAL)

ERIC Educational Resources Information Center

Teeroovengadum, Viraiyan; Kamalanabhan, T. J.; Seebaluck, Ashley Keshwar

2016-01-01

Purpose: This paper aims to develop and empirically test a hierarchical model for measuring service quality in higher education. Design/methodology/approach: The first phase of the study consisted of qualitative research methods and a comprehensive literature review, which allowed the development of a conceptual model comprising 53 service quality…

Intraclass Correlation Coefficients in Hierarchical Designs: Evaluation Using Latent Variable Modeling

ERIC Educational Resources Information Center

Raykov, Tenko

2011-01-01

Interval estimation of intraclass correlation coefficients in hierarchical designs is discussed within a latent variable modeling framework. A method accomplishing this aim is outlined, which is applicable in two-level studies where participants (or generally lower-order units) are clustered within higher-order units. The procedure can also be…

Generating Hierarchical Document Indices from Common Denominators in Large Document Collections.

ERIC Educational Resources Information Center

O'Kane, Kevin C.

1996-01-01

Describes an algorithm for computer generation of hierarchical indexes for document collections. The resulting index, when presented with a graphical interface, provides users with a view of a document collection that permits general browsing and informal search activities via an access method that requires no keyboard entry or prior knowledge of…

Testing Theories of Linguistic Constituency with Configural Learning: The Case of the English Syllable

ERIC Educational Resources Information Center

Kapatsinski, Vsevolod

2009-01-01

This article proposes and tests an experimental method to assess the psychological reality of hierarchical theories of constituent structure in particular domains. I show that a hierarchical theory of constituent structure necessarily makes the prediction that an association between constituents should be easier to learn than an association…

Determining Predictor Importance in Hierarchical Linear Models Using Dominance Analysis

ERIC Educational Resources Information Center

Luo, Wen; Azen, Razia

2013-01-01

Dominance analysis (DA) is a method used to evaluate the relative importance of predictors that was originally proposed for linear regression models. This article proposes an extension of DA that allows researchers to determine the relative importance of predictors in hierarchical linear models (HLM). Commonly used measures of model adequacy in…

Bio-inspired Fabrication of Complex Hierarchical Structure in Silicon.

PubMed

Gao, Yang; Peng, Zhengchun; Shi, Tielin; Tan, Xianhua; Zhang, Deqin; Huang, Qiang; Zou, Chuanping; Liao, Guanglan

2015-08-01

In this paper, we developed a top-down method to fabricate complex three dimensional silicon structure, which was inspired by the hierarchical micro/nanostructure of the Morpho butterfly scales. The fabrication procedure includes photolithography, metal masking, and both dry and wet etching techniques. First, microscale photoresist grating pattern was formed on the silicon (111) wafer. Trenches with controllable rippled structures on the sidewalls were etched by inductively coupled plasma reactive ion etching Bosch process. Then, Cr film was angled deposited on the bottom of the ripples by electron beam evaporation, followed by anisotropic wet etching of the silicon. The simple fabrication method results in large scale hierarchical structure on a silicon wafer. The fabricated Si structure has multiple layers with uniform thickness of hundreds nanometers. We conducted both light reflection and heat transfer experiments on this structure. They exhibited excellent antireflection performance for polarized ultraviolet, visible and near infrared wavelengths. And the heat flux of the structure was significantly enhanced. As such, we believe that these bio-inspired hierarchical silicon structure will have promising applications in photovoltaics, sensor technology and photonic crystal devices.

Robust Pedestrian Classification Based on Hierarchical Kernel Sparse Representation.

PubMed

Sun, Rui; Zhang, Guanghai; Yan, Xiaoxing; Gao, Jun

2016-08-16

Vision-based pedestrian detection has become an active topic in computer vision and autonomous vehicles. It aims at detecting pedestrians appearing ahead of the vehicle using a camera so that autonomous vehicles can assess the danger and take action. Due to varied illumination and appearance, complex background and occlusion pedestrian detection in outdoor environments is a difficult problem. In this paper, we propose a novel hierarchical feature extraction and weighted kernel sparse representation model for pedestrian classification. Initially, hierarchical feature extraction based on a CENTRIST descriptor is used to capture discriminative structures. A max pooling operation is used to enhance the invariance of varying appearance. Then, a kernel sparse representation model is proposed to fully exploit the discrimination information embedded in the hierarchical local features, and a Gaussian weight function as the measure to effectively handle the occlusion in pedestrian images. Extensive experiments are conducted on benchmark databases, including INRIA, Daimler, an artificially generated dataset and a real occluded dataset, demonstrating the more robust performance of the proposed method compared to state-of-the-art pedestrian classification methods.

Robust Pedestrian Classification Based on Hierarchical Kernel Sparse Representation

PubMed Central

Sun, Rui; Zhang, Guanghai; Yan, Xiaoxing; Gao, Jun

2016-01-01

Vision-based pedestrian detection has become an active topic in computer vision and autonomous vehicles. It aims at detecting pedestrians appearing ahead of the vehicle using a camera so that autonomous vehicles can assess the danger and take action. Due to varied illumination and appearance, complex background and occlusion pedestrian detection in outdoor environments is a difficult problem. In this paper, we propose a novel hierarchical feature extraction and weighted kernel sparse representation model for pedestrian classification. Initially, hierarchical feature extraction based on a CENTRIST descriptor is used to capture discriminative structures. A max pooling operation is used to enhance the invariance of varying appearance. Then, a kernel sparse representation model is proposed to fully exploit the discrimination information embedded in the hierarchical local features, and a Gaussian weight function as the measure to effectively handle the occlusion in pedestrian images. Extensive experiments are conducted on benchmark databases, including INRIA, Daimler, an artificially generated dataset and a real occluded dataset, demonstrating the more robust performance of the proposed method compared to state-of-the-art pedestrian classification methods. PMID:27537888

Design and Development of Novel Hierarchically Ordered Block Copolymer-Magnetoelectric Particle Nanocomposites

DTIC Science & Technology

2012-06-08

microscopy (JEOL 1200EX STEM). The volume fraction of BaTiO3 nanoparticles in the PS-BTO nanocomposites was determined by thermogravimetric analysis ...encapsulated in the 9.8±0.4-nm NP was approximately only 13.8% based on the thermogravimetric analysis (TGA) measurement (Supporting Material Section...the properties of the constituents and their spatial arrangement. Nanocomposites of polymer/ nanoparticle , formed by incorporating nanoparticles into

Analysis of Student and School Level Variables Related to Mathematics Self-Efficacy Level Based on PISA 2012 Results for China-Shanghai, Turkey, and Greece

ERIC Educational Resources Information Center

Usta, H. Gonca

2016-01-01

This study aims to analyze the student and school level variables that affect students' self-efficacy levels in mathematics in China-Shanghai, Turkey, and Greece based on PISA 2012 results. In line with this purpose, the hierarchical linear regression model (HLM) was employed. The interschool variability is estimated at approximately 17% in…

Multilevel Optimization Framework for Hierarchical Stiffened Shells Accelerated by Adaptive Equivalent Strategy

NASA Astrophysics Data System (ADS)

Wang, Bo; Tian, Kuo; Zhao, Haixin; Hao, Peng; Zhu, Tianyu; Zhang, Ke; Ma, Yunlong

2017-06-01

In order to improve the post-buckling optimization efficiency of hierarchical stiffened shells, a multilevel optimization framework accelerated by adaptive equivalent strategy is presented in this paper. Firstly, the Numerical-based Smeared Stiffener Method (NSSM) for hierarchical stiffened shells is derived by means of the numerical implementation of asymptotic homogenization (NIAH) method. Based on the NSSM, a reasonable adaptive equivalent strategy for hierarchical stiffened shells is developed from the concept of hierarchy reduction. Its core idea is to self-adaptively decide which hierarchy of the structure should be equivalent according to the critical buckling mode rapidly predicted by NSSM. Compared with the detailed model, the high prediction accuracy and efficiency of the proposed model is highlighted. On the basis of this adaptive equivalent model, a multilevel optimization framework is then established by decomposing the complex entire optimization process into major-stiffener-level and minor-stiffener-level sub-optimizations, during which Fixed Point Iteration (FPI) is employed to accelerate convergence. Finally, the illustrative examples of the multilevel framework is carried out to demonstrate its efficiency and effectiveness to search for the global optimum result by contrast with the single-level optimization method. Remarkably, the high efficiency and flexibility of the adaptive equivalent strategy is indicated by compared with the single equivalent strategy.

Assessment of Differential Item Functioning in Health-Related Outcomes: A Simulation and Empirical Analysis with Hierarchical Polytomous Data

PubMed Central

Sharafi, Zahra

2017-01-01

Background The purpose of this study was to evaluate the effectiveness of two methods of detecting differential item functioning (DIF) in the presence of multilevel data and polytomously scored items. The assessment of DIF with multilevel data (e.g., patients nested within hospitals, hospitals nested within districts) from large-scale assessment programs has received considerable attention but very few studies evaluated the effect of hierarchical structure of data on DIF detection for polytomously scored items. Methods The ordinal logistic regression (OLR) and hierarchical ordinal logistic regression (HOLR) were utilized to assess DIF in simulated and real multilevel polytomous data. Six factors (DIF magnitude, grouping variable, intraclass correlation coefficient, number of clusters, number of participants per cluster, and item discrimination parameter) with a fully crossed design were considered in the simulation study. Furthermore, data of Pediatric Quality of Life Inventory™ (PedsQL™) 4.0 collected from 576 healthy school children were analyzed. Results Overall, results indicate that both methods performed equivalently in terms of controlling Type I error and detection power rates. Conclusions The current study showed negligible difference between OLR and HOLR in detecting DIF with polytomously scored items in a hierarchical structure. Implications and considerations while analyzing real data were also discussed. PMID:29312463

Hierarchical Protein Free Energy Landscapes from Variationally Enhanced Sampling.

PubMed

Shaffer, Patrick; Valsson, Omar; Parrinello, Michele

2016-12-13

In recent work, we demonstrated that it is possible to obtain approximate representations of high-dimensional free energy surfaces with variationally enhanced sampling ( Shaffer, P.; Valsson, O.; Parrinello, M. Proc. Natl. Acad. Sci. , 2016 , 113 , 17 ). The high-dimensional spaces considered in that work were the set of backbone dihedral angles of a small peptide, Chignolin, and the high-dimensional free energy surface was approximated as the sum of many two-dimensional terms plus an additional term which represents an initial estimate. In this paper, we build on that work and demonstrate that we can calculate high-dimensional free energy surfaces of very high accuracy by incorporating additional terms. The additional terms apply to a set of collective variables which are more coarse than the base set of collective variables. In this way, it is possible to build hierarchical free energy surfaces, which are composed of terms that act on different length scales. We test the accuracy of these free energy landscapes for the proteins Chignolin and Trp-cage by constructing simple coarse-grained models and comparing results from the coarse-grained model to results from atomistic simulations. The approach described in this paper is ideally suited for problems in which the free energy surface has important features on different length scales or in which there is some natural hierarchy.

Texturing of continuous LOD meshes with the hierarchical texture atlas

NASA Astrophysics Data System (ADS)

Birkholz, Hermann

2006-02-01

For the rendering of detailed virtual environments, trade-offs have to be made between image quality and rendering time. An immersive experience of virtual reality always demands high frame-rates with the best reachable image qual-ity. Continuous Level of Detail (cLoD) triangle-meshes provide an continuous spectrum of detail for a triangle mesh that can be used to create view-dependent approximations of the environment in real-time. This enables the rendering with a constant number of triangles and thus with constant frame-rates. Normally the construction of such cLoD mesh representations leads to the loss of all texture information of the original mesh. To overcome this problem, a parameter domain can be created, in order to map the surface properties (colour, texture, normal) to it. This parameter domain can be used to map the surface properties back to arbitrary approximations of the original mesh. The parameter domain is often a simplified version of the mesh to be parameterised. This limits the reachable simplification to the domain mesh which has to map the surface of the original mesh with the least possible stretch. In this paper, a hierarchical domain mesh is presented, that scales between very coarse domain meshes and good property-mapping.

On the Calculation of Uncertainty Statistics with Error Bounds for CFD Calculations Containing Random Parameters and Fields

NASA Technical Reports Server (NTRS)

Barth, Timothy J.

2016-01-01

This chapter discusses the ongoing development of combined uncertainty and error bound estimates for computational fluid dynamics (CFD) calculations subject to imposed random parameters and random fields. An objective of this work is the construction of computable error bound formulas for output uncertainty statistics that guide CFD practitioners in systematically determining how accurately CFD realizations should be approximated and how accurately uncertainty statistics should be approximated for output quantities of interest. Formal error bounds formulas for moment statistics that properly account for the presence of numerical errors in CFD calculations and numerical quadrature errors in the calculation of moment statistics have been previously presented in [8]. In this past work, hierarchical node-nested dense and sparse tensor product quadratures are used to calculate moment statistics integrals. In the present work, a framework has been developed that exploits the hierarchical structure of these quadratures in order to simplify the calculation of an estimate of the quadrature error needed in error bound formulas. When signed estimates of realization error are available, this signed error may also be used to estimate output quantity of interest probability densities as a means to assess the impact of realization error on these density estimates. Numerical results are presented for CFD problems with uncertainty to demonstrate the capabilities of this framework.

Resolution of Singularities Introduced by Hierarchical Structure in Deep Neural Networks.

PubMed

Nitta, Tohru

2017-10-01

We present a theoretical analysis of singular points of artificial deep neural networks, resulting in providing deep neural network models having no critical points introduced by a hierarchical structure. It is considered that such deep neural network models have good nature for gradient-based optimization. First, we show that there exist a large number of critical points introduced by a hierarchical structure in deep neural networks as straight lines, depending on the number of hidden layers and the number of hidden neurons. Second, we derive a sufficient condition for deep neural networks having no critical points introduced by a hierarchical structure, which can be applied to general deep neural networks. It is also shown that the existence of critical points introduced by a hierarchical structure is determined by the rank and the regularity of weight matrices for a specific class of deep neural networks. Finally, two kinds of implementation methods of the sufficient conditions to have no critical points are provided. One is a learning algorithm that can avoid critical points introduced by the hierarchical structure during learning (called avoidant learning algorithm). The other is a neural network that does not have some critical points introduced by the hierarchical structure as an inherent property (called avoidant neural network).

Hierarchical Kohonenen net for anomaly detection in network security.

PubMed

Sarasamma, Suseela T; Zhu, Qiuming A; Huff, Julie

2005-04-01

A novel multilevel hierarchical Kohonen Net (K-Map) for an intrusion detection system is presented. Each level of the hierarchical map is modeled as a simple winner-take-all K-Map. One significant advantage of this multilevel hierarchical K-Map is its computational efficiency. Unlike other statistical anomaly detection methods such as nearest neighbor approach, K-means clustering or probabilistic analysis that employ distance computation in the feature space to identify the outliers, our approach does not involve costly point-to-point computation in organizing the data into clusters. Another advantage is the reduced network size. We use the classification capability of the K-Map on selected dimensions of data set in detecting anomalies. Randomly selected subsets that contain both attacks and normal records from the KDD Cup 1999 benchmark data are used to train the hierarchical net. We use a confidence measure to label the clusters. Then we use the test set from the same KDD Cup 1999 benchmark to test the hierarchical net. We show that a hierarchical K-Map in which each layer operates on a small subset of the feature space is superior to a single-layer K-Map operating on the whole feature space in detecting a variety of attacks in terms of detection rate as well as false positive rate.

Sugar apple-shaped TiO2 hierarchical spheres for highly efficient dye-sensitized solar cells

NASA Astrophysics Data System (ADS)

Lei, Bing-Xin; Zeng, Li-Li; Zhang, Ping; Qiao, He-Kang; Sun, Zhen-Fan

2014-05-01

The sugar apple-shaped TiO2 hierarchical spheres are prepared by a facile hydrothermal method using polyethylene glycol 600 as stabilized reagent, (NH4)2TiF6 and urea as starting materials at 180 °C. The characterizations show that the TiO2 hierarchical sphere has well-defined pyramid-shaped crystal facets. The as-prepared TiO2 hierarchical spheres are crystalline of the anatase phase, with a diameter of about 2-4 μm and a surface area of 36.846 m2 g-1. The optical investigation evidences that the sugar apple-shaped TiO2 hierarchical sphere film exhibits a prominent light scattering effect at a wavelength range of 600-800 nm due to the unique hierarchical morphology. Furthermore, the sugar apple-shaped TiO2 hierarchical spheres are deposited as the scattering layer to balance the dye adsorption and light scattering effect in DSSCs and a 7.20% solar energy conversion efficiency is demonstrated, indicating an improvement compared with the P25 cell (6.68%). Based on the optical and electrochemical investigations, the high conversion efficiency is mainly due to the effective suppression of the back reaction of the injected electron with the I3- in the electrolyte and excellent light scattering ability.

Mesoporous titanium dioxide (TiO2) with hierarchically 3D dendrimeric architectures: formation mechanism and highly enhanced photocatalytic activity.

PubMed

Li, Xiao-Yun; Chen, Li-Hua; Rooke, Joanna Claire; Deng, Zhao; Hu, Zhi-Yi; Wang, Shao-Zhuan; Wang, Li; Li, Yu; Krief, Alain; Su, Bao-Lian

2013-03-15

Mesoporous TiO(2) with a hierarchically 3D dendrimeric nanostructure comprised of nanoribbon building units has been synthesized via a spontaneous self-formation process from various titanium alkoxides. These hierarchically 3D dendrimeric architectures can be obtained by a very facile, template-free method, by simply dropping a titanium butoxide precursor into methanol solution. The novel configuration of the mesoporous TiO(2) nanostructure in nanoribbon building units yields a high surface area. The calcined samples show significantly enhanced photocatalytic activity and degradation rates owing to the mesoporosity and their improved crystallinity after calcination. Furthermore, the 3D dendrimeric architectures can be preserved after phase transformation from amorphous TiO(2) to anatase or rutile, which occurs during calcination. In addition, the spontaneous self-formation process of mesoporous TiO(2) with hierarchically 3D dendrimeric architectures from the hydrolysis and condensation reaction of titanium butoxide in methanol has been followed by in situ optical microscopy (OM), revealing the secret on the formation of hierarchically 3D dendrimeric nanostructures. Moreover, mesoporous TiO(2) nanostructures with similar hierarchically 3D dendrimeric architectures can also be obtained using other titanium alkoxides. The porosities and nanostructures of the resultant products were characterized by SEM, TEM, XRD, and N(2) adsorption-desorption measurements. The present work provides a facile and reproducible method for the synthesis of novel mesoporous TiO(2) nanoarchitectures, which in turn could herald the fabrication of more efficient photocatalysts. Copyright © 2012 Elsevier Inc. All rights reserved.

Functional architecture of Escherichia coli: new insights provided by a natural decomposition approach.

PubMed

Freyre-González, Julio A; Alonso-Pavón, José A; Treviño-Quintanilla, Luis G; Collado-Vides, Julio

2008-10-27

Previous studies have used different methods in an effort to extract the modular organization of transcriptional regulatory networks. However, these approaches are not natural, as they try to cluster strongly connected genes into a module or locate known pleiotropic transcription factors in lower hierarchical layers. Here, we unravel the transcriptional regulatory network of Escherichia coli by separating it into its key elements, thus revealing its natural organization. We also present a mathematical criterion, based on the topological features of the transcriptional regulatory network, to classify the network elements into one of two possible classes: hierarchical or modular genes. We found that modular genes are clustered into physiologically correlated groups validated by a statistical analysis of the enrichment of the functional classes. Hierarchical genes encode transcription factors responsible for coordinating module responses based on general interest signals. Hierarchical elements correlate highly with the previously studied global regulators, suggesting that this could be the first mathematical method to identify global regulators. We identified a new element in transcriptional regulatory networks never described before: intermodular genes. These are structural genes that integrate, at the promoter level, signals coming from different modules, and therefore from different physiological responses. Using the concept of pleiotropy, we have reconstructed the hierarchy of the network and discuss the role of feedforward motifs in shaping the hierarchical backbone of the transcriptional regulatory network. This study sheds new light on the design principles underpinning the organization of transcriptional regulatory networks, showing a novel nonpyramidal architecture composed of independent modules globally governed by hierarchical transcription factors, whose responses are integrated by intermodular genes.

Models and Methods of Aggregating Linguistic Information in Multi-criteria Hierarchical Quality Assessment Systems

NASA Astrophysics Data System (ADS)

Azarnova, T. V.; Titova, I. A.; Barkalov, S. A.

2018-03-01

The article presents an algorithm for obtaining an integral assessment of the quality of an organization from the perspective of customers, based on the method of aggregating linguistic information on a multilevel hierarchical system of quality assessment. The algorithm is of a constructive nature, it provides not only the possibility of obtaining an integral evaluation, but also the development of a quality improvement strategy based on the method of linguistic decomposition, which forms the minimum set of areas of work with clients whose quality change will allow obtaining the required level of integrated quality assessment.

A Feasible One-Step Synthesis of Hierarchical Zeolite Beta with Uniform Nanocrystals via CTAB

PubMed Central

Zhang, Weimin; Hu, Sufang; Qin, Bo; Li, Ruifeng

2018-01-01

A hierarchical zeolite Beta has been prepared by a feasible one-pot and one-step method, which is suitable for application in industrial production. The synthesis is a simple hydrothermal process with low-cost raw materials, without adding alcohol or adding seeds, and without aging, recrystallization, and other complex steps. The hierarchical zeolite Beta is a uniform nanocrystal (20–50 nm) aggregation with high external surface area (300 m2/g) and mesoporous volume (0.50 cm3/g), with the mesoporous structure composed of intercrystal and intracrystal pores. As an acid catalyst in benzylation of naphthalene with benzyl chloride, the hierarchical zeolite Beta has shown high activity in the bulky molecule reaction due to its introduction of mesostructure. PMID:29695044

A generalized linear factor model approach to the hierarchical framework for responses and response times.

PubMed

Molenaar, Dylan; Tuerlinckx, Francis; van der Maas, Han L J

2015-05-01

We show how the hierarchical model for responses and response times as developed by van der Linden (2007), Fox, Klein Entink, and van der Linden (2007), Klein Entink, Fox, and van der Linden (2009), and Glas and van der Linden (2010) can be simplified to a generalized linear factor model with only the mild restriction that there is no hierarchical model at the item side. This result is valuable as it enables all well-developed modelling tools and extensions that come with these methods. We show that the restriction we impose on the hierarchical model does not influence parameter recovery under realistic circumstances. In addition, we present two illustrative real data analyses to demonstrate the practical benefits of our approach. © 2014 The British Psychological Society.

Nanoscale Analysis of a Hierarchical Hybrid Solar Cell in 3D.

PubMed

Divitini, Giorgio; Stenzel, Ole; Ghadirzadeh, Ali; Guarnera, Simone; Russo, Valeria; Casari, Carlo S; Bassi, Andrea Li; Petrozza, Annamaria; Di Fonzo, Fabio; Schmidt, Volker; Ducati, Caterina

2014-05-01

A quantitative method for the characterization of nanoscale 3D morphology is applied to the investigation of a hybrid solar cell based on a novel hierarchical nanostructured photoanode. A cross section of the solar cell device is prepared by focused ion beam milling in a micropillar geometry, which allows a detailed 3D reconstruction of the titania photoanode by electron tomography. It is found that the hierarchical titania nanostructure facilitates polymer infiltration, thus favoring intermixing of the two semiconducting phases, essential for charge separation. The 3D nanoparticle network is analyzed with tools from stochastic geometry to extract information related to the charge transport in the hierarchical solar cell. In particular, the experimental dataset allows direct visualization of the percolation pathways that contribute to the photocurrent.

Nanoscale Analysis of a Hierarchical Hybrid Solar Cell in 3D

PubMed Central

Divitini, Giorgio; Stenzel, Ole; Ghadirzadeh, Ali; Guarnera, Simone; Russo, Valeria; Casari, Carlo S; Bassi, Andrea Li; Petrozza, Annamaria; Di Fonzo, Fabio; Schmidt, Volker; Ducati, Caterina

2014-01-01

A quantitative method for the characterization of nanoscale 3D morphology is applied to the investigation of a hybrid solar cell based on a novel hierarchical nanostructured photoanode. A cross section of the solar cell device is prepared by focused ion beam milling in a micropillar geometry, which allows a detailed 3D reconstruction of the titania photoanode by electron tomography. It is found that the hierarchical titania nanostructure facilitates polymer infiltration, thus favoring intermixing of the two semiconducting phases, essential for charge separation. The 3D nanoparticle network is analyzed with tools from stochastic geometry to extract information related to the charge transport in the hierarchical solar cell. In particular, the experimental dataset allows direct visualization of the percolation pathways that contribute to the photocurrent. PMID:25834481

Open Systems with Error Bounds: Spin-Boson Model with Spectral Density Variations.

PubMed

Mascherpa, F; Smirne, A; Huelga, S F; Plenio, M B

2017-03-10

In the study of open quantum systems, one of the most common ways to describe environmental effects on the reduced dynamics is through the spectral density. However, in many models this object cannot be computed from first principles and needs to be inferred on phenomenological grounds or fitted to experimental data. Consequently, some uncertainty regarding its form and parameters is unavoidable; this in turn calls into question the accuracy of any theoretical predictions based on a given spectral density. Here, we focus on the spin-boson model as a prototypical open quantum system, find two error bounds on predicted expectation values in terms of the spectral density variation considered, and state a sufficient condition for the strongest one to apply. We further demonstrate an application of our result, by bounding the error brought about by the approximations involved in the hierarchical equations of motion resolution method for spin-boson dynamics.

Mismatch Negativity with Visual-only and Audiovisual Speech

PubMed Central

Ponton, Curtis W.; Bernstein, Lynne E.; Auer, Edward T.

2009-01-01

The functional organization of cortical speech processing is thought to be hierarchical, increasing in complexity and proceeding from primary sensory areas centrifugally. The current study used the mismatch negativity (MMN) obtained with electrophysiology (EEG) to investigate the early latency period of visual speech processing under both visual-only (VO) and audiovisual (AV) conditions. Current density reconstruction (CDR) methods were used to model the cortical MMN generator locations. MMNs were obtained with VO and AV speech stimuli at early latencies (approximately 82-87 ms peak in time waveforms relative to the acoustic onset) and in regions of the right lateral temporal and parietal cortices. Latencies were consistent with bottom-up processing of the visible stimuli. We suggest that a visual pathway extracts phonetic cues from visible speech, and that previously reported effects of AV speech in classical early auditory areas, given later reported latencies, could be attributable to modulatory feedback from visual phonetic processing. PMID:19404730

Variational learning and bits-back coding: an information-theoretic view to Bayesian learning.

PubMed

Honkela, Antti; Valpola, Harri

2004-07-01

The bits-back coding first introduced by Wallace in 1990 and later by Hinton and van Camp in 1993 provides an interesting link between Bayesian learning and information-theoretic minimum-description-length (MDL) learning approaches. The bits-back coding allows interpreting the cost function used in the variational Bayesian method called ensemble learning as a code length in addition to the Bayesian view of misfit of the posterior approximation and a lower bound of model evidence. Combining these two viewpoints provides interesting insights to the learning process and the functions of different parts of the model. In this paper, the problem of variational Bayesian learning of hierarchical latent variable models is used to demonstrate the benefits of the two views. The code-length interpretation provides new views to many parts of the problem such as model comparison and pruning and helps explain many phenomena occurring in learning.

Degree-Pruning Dynamic Programming Approaches to Central Time Series Minimizing Dynamic Time Warping Distance.

PubMed

Sun, Tao; Liu, Hongbo; Yu, Hong; Chen, C L Philip

2016-06-28

The central time series crystallizes the common patterns of the set it represents. In this paper, we propose a global constrained degree-pruning dynamic programming (g(dp)²) approach to obtain the central time series through minimizing dynamic time warping (DTW) distance between two time series. The DTW matching path theory with global constraints is proved theoretically for our degree-pruning strategy, which is helpful to reduce the time complexity and computational cost. Our approach can achieve the optimal solution between two time series. An approximate method to the central time series of multiple time series [called as m_g(dp)²] is presented based on DTW barycenter averaging and our g(dp)² approach by considering hierarchically merging strategy. As illustrated by the experimental results, our approaches provide better within-group sum of squares and robustness than other relevant algorithms.

A Simulation Study of Methods for Selecting Subgroup-Specific Doses in Phase I Trials

PubMed Central

Morita, Satoshi; Thall, Peter F.; Takeda, Kentaro

2016-01-01

Summary Patient heterogeneity may complicate dose-finding in phase I clinical trials if the dose-toxicity curves differ between subgroups. Conducting separate trials within subgroups may lead to infeasibly small sample sizes in subgroups having low prevalence. Alternatively, it is not obvious how to conduct a single trial while accounting for heterogeneity. To address this problem, we consider a generalization of the continual reassessment method (O’Quigley, et al., 1990) based on a hierarchical Bayesian dose-toxicity model that borrows strength between subgroups under the assumption that the subgroups are exchangeable. We evaluate a design using this model that includes subgroup-specific dose selection and safety rules. A simulation study is presented that includes comparison of this method to three alternative approaches, based on non-hierarchical models, that make different types of assumptions about within-subgroup dose-toxicity curves. The simulations show that the hierarchical model-based method is recommended in settings where the dose-toxicity curves are exchangeable between subgroups. We present practical guidelines for application, and provide computer programs for trial simulation and conduct. PMID:28111916

Implementation of hierarchical clustering using k-mer sparse matrix to analyze MERS-CoV genetic relationship

NASA Astrophysics Data System (ADS)

Bustamam, A.; Ulul, E. D.; Hura, H. F. A.; Siswantining, T.

2017-07-01

Hierarchical clustering is one of effective methods in creating a phylogenetic tree based on the distance matrix between DNA (deoxyribonucleic acid) sequences. One of the well-known methods to calculate the distance matrix is k-mer method. Generally, k-mer is more efficient than some distance matrix calculation techniques. The steps of k-mer method are started from creating k-mer sparse matrix, and followed by creating k-mer singular value vectors. The last step is computing the distance amongst vectors. In this paper, we analyze the sequences of MERS-CoV (Middle East Respiratory Syndrome - Coronavirus) DNA by implementing hierarchical clustering using k-mer sparse matrix in order to perform the phylogenetic analysis. Our results show that the ancestor of our MERS-CoV is coming from Egypt. Moreover, we found that the MERS-CoV infection that occurs in one country may not necessarily come from the same country of origin. This suggests that the process of MERS-CoV mutation might not only be influenced by geographical factor.

Improved Model Fitting for the Empirical Green's Function Approach Using Hierarchical Models

NASA Astrophysics Data System (ADS)

Van Houtte, Chris; Denolle, Marine

2018-04-01

Stress drops calculated from source spectral studies currently show larger variability than what is implied by empirical ground motion models. One of the potential origins of the inflated variability is the simplified model-fitting techniques used in most source spectral studies. This study examines a variety of model-fitting methods and shows that the choice of method can explain some of the discrepancy. The preferred method is Bayesian hierarchical modeling, which can reduce bias, better quantify uncertainties, and allow additional effects to be resolved. Two case study earthquakes are examined, the 2016 MW7.1 Kumamoto, Japan earthquake and a MW5.3 aftershock of the 2016 MW7.8 Kaikōura earthquake. By using hierarchical models, the variation of the corner frequency, fc, and the falloff rate, n, across the focal sphere can be retrieved without overfitting the data. Other methods commonly used to calculate corner frequencies may give substantial biases. In particular, if fc was calculated for the Kumamoto earthquake using an ω-square model, the obtained fc could be twice as large as a realistic value.

The analysis of thin walled composite laminated helicopter rotor with hierarchical warping functions and finite element method

NASA Astrophysics Data System (ADS)

Zhu, Dechao; Deng, Zhongmin; Wang, Xingwei

2001-08-01

In the present paper, a series of hierarchical warping functions is developed to analyze the static and dynamic problems of thin walled composite laminated helicopter rotors composed of several layers with single closed cell. This method is the development and extension of the traditional constrained warping theory of thin walled metallic beams, which had been proved very successful since 1940s. The warping distribution along the perimeter of each layer is expanded into a series of successively corrective warping functions with the traditional warping function caused by free torsion or free bending as the first term, and is assumed to be piecewise linear along the thickness direction of layers. The governing equations are derived based upon the variational principle of minimum potential energy for static analysis and Rayleigh Quotient for free vibration analysis. Then the hierarchical finite element method is introduced to form a numerical algorithm. Both static and natural vibration problems of sample box beams are analyzed with the present method to show the main mechanical behavior of the thin walled composite laminated helicopter rotor.

Hierarchical 3-dimensional nickel-iron nanosheet arrays on carbon fiber paper as a novel electrode for non-enzymatic glucose sensing.

PubMed

Kannan, Palanisamy; Maiyalagan, Thandavarayan; Marsili, Enrico; Ghosh, Srabanti; Niedziolka-Jönsson, Joanna; Jönsson-Niedziolka, Martin

2016-01-14

Three-dimensional nickel-iron (3-D/Ni-Fe) nanostructures are exciting candidates for various applications because they produce more reaction-active sites than 1-D and 2-D nanostructured materials and exhibit attractive optical, electrical and catalytic properties. In this work, freestanding 3-D/Ni-Fe interconnected hierarchical nanosheets, hierarchical nanospheres, and porous nanospheres are directly grown on a flexible carbon fiber paper (CFP) substrate by a single-step hydrothermal process. Among the nanostructures, 3-D/Ni-Fe interconnected hierarchical nanosheets show excellent electrochemical properties because of its high conductivity, large specific active surface area, and mesopores on its walls (vide infra). The 3-D/Ni-Fe hierarchical nanosheet array modified CFP substrate is further explored as a novel electrode for electrochemical non-enzymatic glucose sensor application. The 3-D/Ni-Fe hierarchical nanosheet arrays exhibit significant catalytic activity towards the electrochemical oxidation of glucose, as compared to the 3-D/Ni-Fe hierarchical nanospheres, and porous nanospheres. The 3-D/Ni-Fe hierarchical nanosheet arrays can access a large amount of glucose molecules on their surface (mesopore walls) for an efficient electrocatalytic oxidation process. Moreover, 3-D/Ni-Fe hierarchical nanosheet arrays showed higher sensitivity (7.90 μA μM(-1) cm(-2)) with wide linear glucose concentration ranging from 0.05 μM to 0.2 mM, and the low detection limit (LOD) of 0.031 μM (S/N = 3) is achieved by the amperometry method. Further, the 3-D/Ni-Fe hierarchical nanosheet array modified CFP electrode can be demonstrated to have excellent selectivity towards the detection of glucose in the presence of 500-fold excess of major important interferents. All these results indicate that 3-D/Ni-Fe hierarchical nanosheet arrays are promising candidates for non-enzymatic glucose sensing.

Visual question answering using hierarchical dynamic memory networks

NASA Astrophysics Data System (ADS)

Shang, Jiayu; Li, Shiren; Duan, Zhikui; Huang, Junwei

2018-04-01

Visual Question Answering (VQA) is one of the most popular research fields in machine learning which aims to let the computer learn to answer natural language questions with images. In this paper, we propose a new method called hierarchical dynamic memory networks (HDMN), which takes both question attention and visual attention into consideration impressed by Co-Attention method, which is the best (or among the best) algorithm for now. Additionally, we use bi-directional LSTMs, which have a better capability to remain more information from the question and image, to replace the old unit so that we can capture information from both past and future sentences to be used. Then we rebuild the hierarchical architecture for not only question attention but also visual attention. What's more, we accelerate the algorithm via a new technic called Batch Normalization which helps the network converge more quickly than other algorithms. The experimental result shows that our model improves the state of the art on the large COCO-QA dataset, compared with other methods.

Simultaneous formation of multiscale hierarchical surface morphologies through sequential wrinkling and folding

NASA Astrophysics Data System (ADS)

Wang, Yu; Sun, Qingyang; Xiao, Jianliang

2018-02-01

Highly organized hierarchical surface morphologies possess various intriguing properties that could find important potential applications. In this paper, we demonstrate a facile approach to simultaneously form multiscale hierarchical surface morphologies through sequential wrinkling. This method combines surface wrinkling induced by thermal expansion and mechanical strain on a three-layer structure composed of an aluminum film, a hard Polydimethylsiloxane (PDMS) film, and a soft PDMS substrate. Deposition of the aluminum film on hard PDMS induces biaxial wrinkling due to thermal expansion mismatch, and recovering the prestrain in the soft PDMS substrate leads to wrinkling of the hard PDMS film. In total, three orders of wrinkling patterns form in this process, with wavelength and amplitude spanning 3 orders of magnitude in length scale. By increasing the prestrain in the soft PDMS substrate, a hierarchical wrinkling-folding structure was also obtained. This approach can be easily extended to other thin films for fabrication of multiscale hierarchical surface morphologies with potential applications in different areas.

Recognizing Chinese characters in digital ink from non-native language writers using hierarchical models

NASA Astrophysics Data System (ADS)

Bai, Hao; Zhang, Xi-wen

2017-06-01

While Chinese is learned as a second language, its characters are taught step by step from their strokes to components, radicals to components, and their complex relations. Chinese Characters in digital ink from non-native language writers are deformed seriously, thus the global recognition approaches are poorer. So a progressive approach from bottom to top is presented based on hierarchical models. Hierarchical information includes strokes and hierarchical components. Each Chinese character is modeled as a hierarchical tree. Strokes in one Chinese characters in digital ink are classified with Hidden Markov Models and concatenated to the stroke symbol sequence. And then the structure of components in one ink character is extracted. According to the extraction result and the stroke symbol sequence, candidate characters are traversed and scored. Finally, the recognition candidate results are listed by descending. The method of this paper is validated by testing 19815 copies of the handwriting Chinese characters written by foreign students.

A hierarchical clustering methodology for the estimation of toxicity.

PubMed

Martin, Todd M; Harten, Paul; Venkatapathy, Raghuraman; Das, Shashikala; Young, Douglas M

2008-01-01

ABSTRACT A quantitative structure-activity relationship (QSAR) methodology based on hierarchical clustering was developed to predict toxicological endpoints. This methodology utilizes Ward's method to divide a training set into a series of structurally similar clusters. The structural similarity is defined in terms of 2-D physicochemical descriptors (such as connectivity and E-state indices). A genetic algorithm-based technique is used to generate statistically valid QSAR models for each cluster (using the pool of descriptors described above). The toxicity for a given query compound is estimated using the weighted average of the predictions from the closest cluster from each step in the hierarchical clustering assuming that the compound is within the domain of applicability of the cluster. The hierarchical clustering methodology was tested using a Tetrahymena pyriformis acute toxicity data set containing 644 chemicals in the training set and with two prediction sets containing 339 and 110 chemicals. The results from the hierarchical clustering methodology were compared to the results from several different QSAR methodologies.

Bayesian hierarchical functional data analysis via contaminated informative priors.

PubMed

Scarpa, Bruno; Dunson, David B

2009-09-01

A variety of flexible approaches have been proposed for functional data analysis, allowing both the mean curve and the distribution about the mean to be unknown. Such methods are most useful when there is limited prior information. Motivated by applications to modeling of temperature curves in the menstrual cycle, this article proposes a flexible approach for incorporating prior information in semiparametric Bayesian analyses of hierarchical functional data. The proposed approach is based on specifying the distribution of functions as a mixture of a parametric hierarchical model and a nonparametric contamination. The parametric component is chosen based on prior knowledge, while the contamination is characterized as a functional Dirichlet process. In the motivating application, the contamination component allows unanticipated curve shapes in unhealthy menstrual cycles. Methods are developed for posterior computation, and the approach is applied to data from a European fecundability study.

Complexity of major UK companies between 2006 and 2010: Hierarchical structure method approach

NASA Astrophysics Data System (ADS)

Ulusoy, Tolga; Keskin, Mustafa; Shirvani, Ayoub; Deviren, Bayram; Kantar, Ersin; Çaǧrı Dönmez, Cem

2012-11-01

This study reports on topology of the top 40 UK companies that have been analysed for predictive verification of markets for the period 2006-2010, applying the concept of minimal spanning tree and hierarchical tree (HT) analysis. Construction of the minimal spanning tree (MST) and the hierarchical tree (HT) is confined to a brief description of the methodology and a definition of the correlation function between a pair of companies based on the London Stock Exchange (LSE) index in order to quantify synchronization between the companies. A derivation of hierarchical organization and the construction of minimal-spanning and hierarchical trees for the 2006-2008 and 2008-2010 periods have been used and the results validate the predictive verification of applied semantics. The trees are known as useful tools to perceive and detect the global structure, taxonomy and hierarchy in financial data. From these trees, two different clusters of companies in 2006 were detected. They also show three clusters in 2008 and two between 2008 and 2010, according to their proximity. The clusters match each other as regards their common production activities or their strong interrelationship. The key companies are generally given by major economic activities as expected. This work gives a comparative approach between MST and HT methods from statistical physics and information theory with analysis of financial markets that may give new valuable and useful information of the financial market dynamics.

Argument structure hierarchy system and method for facilitating analysis and decision-making processes

DOEpatents

Janssen, Terry

2000-01-01

A system and method for facilitating decision-making comprising a computer program causing linkage of data representing a plurality of argument structure units into a hierarchical argument structure. Each argument structure unit comprises data corresponding to a hypothesis and its corresponding counter-hypothesis, data corresponding to grounds that provide a basis for inference of the hypothesis or its corresponding counter-hypothesis, data corresponding to a warrant linking the grounds to the hypothesis or its corresponding counter-hypothesis, and data corresponding to backing that certifies the warrant. The hierarchical argument structure comprises a top level argument structure unit and a plurality of subordinate level argument structure units. Each of the plurality of subordinate argument structure units comprises at least a portion of the grounds of the argument structure unit to which it is subordinate. Program code located on each of a plurality of remote computers accepts input from one of a plurality of contributors. Each input comprises data corresponding to an argument structure unit in the hierarchical argument structure and supports the hypothesis or its corresponding counter-hypothesis. A second programming code is adapted to combine the inputs into a single hierarchical argument structure. A third computer program code is responsive to the second computer program code and is adapted to represent a degree of support for the hypothesis and its corresponding counter-hypothesis in the single hierarchical argument structure.

HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python.

PubMed

Wiecki, Thomas V; Sofer, Imri; Frank, Michael J

2013-01-01

The diffusion model is a commonly used tool to infer latent psychological processes underlying decision-making, and to link them to neural mechanisms based on response times. Although efficient open source software has been made available to quantitatively fit the model to data, current estimation methods require an abundance of response time measurements to recover meaningful parameters, and only provide point estimates of each parameter. In contrast, hierarchical Bayesian parameter estimation methods are useful for enhancing statistical power, allowing for simultaneous estimation of individual subject parameters and the group distribution that they are drawn from, while also providing measures of uncertainty in these parameters in the posterior distribution. Here, we present a novel Python-based toolbox called HDDM (hierarchical drift diffusion model), which allows fast and flexible estimation of the the drift-diffusion model and the related linear ballistic accumulator model. HDDM requires fewer data per subject/condition than non-hierarchical methods, allows for full Bayesian data analysis, and can handle outliers in the data. Finally, HDDM supports the estimation of how trial-by-trial measurements (e.g., fMRI) influence decision-making parameters. This paper will first describe the theoretical background of the drift diffusion model and Bayesian inference. We then illustrate usage of the toolbox on a real-world data set from our lab. Finally, parameter recovery studies show that HDDM beats alternative fitting methods like the χ(2)-quantile method as well as maximum likelihood estimation. The software and documentation can be downloaded at: http://ski.clps.brown.edu/hddm_docs/

A green surfactant-assisted synthesis of hierarchical TS-1 zeolites with excellent catalytic properties for oxidative desulfurization.

PubMed

Du, Shuting; Li, Fen; Sun, Qiming; Wang, Ning; Jia, Mingjun; Yu, Jihong

2016-02-25

Hierarchical TS-1 zeolites with uniform intracrystalline mesopores have been successfully synthesized through the hydrothermal method by using the green and cheap surfactant Triton X-100 as the mesoporous template. The resultant materials exhibit remarkably enhanced catalytic activity in oxidative desulfurization reactions compared to the conventional TS-1 zeolite.

A hierarchical model for probabilistic independent component analysis of multi-subject fMRI studies

PubMed Central

Tang, Li

2014-01-01

Summary An important goal in fMRI studies is to decompose the observed series of brain images to identify and characterize underlying brain functional networks. Independent component analysis (ICA) has been shown to be a powerful computational tool for this purpose. Classic ICA has been successfully applied to single-subject fMRI data. The extension of ICA to group inferences in neuroimaging studies, however, is challenging due to the unavailability of a pre-specified group design matrix. Existing group ICA methods generally concatenate observed fMRI data across subjects on the temporal domain and then decompose multi-subject data in a similar manner to single-subject ICA. The major limitation of existing methods is that they ignore between-subject variability in spatial distributions of brain functional networks in group ICA. In this paper, we propose a new hierarchical probabilistic group ICA method to formally model subject-specific effects in both temporal and spatial domains when decomposing multi-subject fMRI data. The proposed method provides model-based estimation of brain functional networks at both the population and subject level. An important advantage of the hierarchical model is that it provides a formal statistical framework to investigate similarities and differences in brain functional networks across subjects, e.g., subjects with mental disorders or neurodegenerative diseases such as Parkinson’s as compared to normal subjects. We develop an EM algorithm for model estimation where both the E-step and M-step have explicit forms. We compare the performance of the proposed hierarchical model with that of two popular group ICA methods via simulation studies. We illustrate our method with application to an fMRI study of Zen meditation. PMID:24033125

Hierarchical Address Event Routing for Reconfigurable Large-Scale Neuromorphic Systems.

PubMed

Park, Jongkil; Yu, Theodore; Joshi, Siddharth; Maier, Christoph; Cauwenberghs, Gert

2017-10-01

We present a hierarchical address-event routing (HiAER) architecture for scalable communication of neural and synaptic spike events between neuromorphic processors, implemented with five Xilinx Spartan-6 field-programmable gate arrays and four custom analog neuromophic integrated circuits serving 262k neurons and 262M synapses. The architecture extends the single-bus address-event representation protocol to a hierarchy of multiple nested buses, routing events across increasing scales of spatial distance. The HiAER protocol provides individually programmable axonal delay in addition to strength for each synapse, lending itself toward biologically plausible neural network architectures, and scales across a range of hierarchies suitable for multichip and multiboard systems in reconfigurable large-scale neuromorphic systems. We show approximately linear scaling of net global synaptic event throughput with number of routing nodes in the network, at 3.6×10 7 synaptic events per second per 16k-neuron node in the hierarchy.

A hierarchical storage management (HSM) scheme for cost-effective on-line archival using lossy compression.

PubMed

Avrin, D E; Andriole, K P; Yin, L; Gould, R G; Arenson, R L

2001-03-01

A hierarchical storage management (HSM) scheme for cost-effective on-line archival of image data using lossy compression is described. This HSM scheme also provides an off-site tape backup mechanism and disaster recovery. The full-resolution image data are viewed originally for primary diagnosis, then losslessly compressed and sent off site to a tape backup archive. In addition, the original data are wavelet lossy compressed (at approximately 25:1 for computed radiography, 10:1 for computed tomography, and 5:1 for magnetic resonance) and stored on a large RAID device for maximum cost-effective, on-line storage and immediate retrieval of images for review and comparison. This HSM scheme provides a solution to 4 problems in image archiving, namely cost-effective on-line storage, disaster recovery of data, off-site tape backup for the legal record, and maximum intermediate storage and retrieval through the use of on-site lossy compression.

Molecular-like hierarchical self-assembly of monolayers of mixtures of particles

PubMed Central

Singh, P.; Hossain, M.; Gurupatham, S. K.; Shah, K.; Amah, E.; Ju, D.; Janjua, M.; Nudurupati, S.; Fischer, I.

2014-01-01

We present a technique that uses an externally applied electric field to self-assemble monolayers of mixtures of particles into molecular-like hierarchical arrangements on fluid-liquid interfaces. The arrangements consist of composite particles (analogous to molecules) which are arranged in a pattern. The structure of a composite particle depends on factors such as the relative sizes of the particles and their polarizabilities, and the electric field intensity. If the particles sizes differ by a factor of two or more, the composite particle has a larger particle at its core and several smaller particles form a ring around it. The number of particles in the ring and the spacing between the composite particles depend on their polarizabilities and the electric field intensity. Approximately same sized particles form chains (analogous to polymeric molecules) in which positively and negatively polarized particles alternate. PMID:25510331

A flavor symmetry model for bilarge leptonic mixing and the lepton masses

NASA Astrophysics Data System (ADS)

Ohlsson, Tommy; Seidl, Gerhart

2002-11-01

We present a model for leptonic mixing and the lepton masses based on flavor symmetries and higher-dimensional mass operators. The model predicts bilarge leptonic mixing (i.e., the mixing angles θ12 and θ23 are large and the mixing angle θ13 is small) and an inverted hierarchical neutrino mass spectrum. Furthermore, it approximately yields the experimental hierarchical mass spectrum of the charged leptons. The obtained values for the leptonic mixing parameters and the neutrino mass squared differences are all in agreement with atmospheric neutrino data, the Mikheyev-Smirnov-Wolfenstein large mixing angle solution of the solar neutrino problem, and consistent with the upper bound on the reactor mixing angle. Thus, we have a large, but not close to maximal, solar mixing angle θ12, a nearly maximal atmospheric mixing angle θ23, and a small reactor mixing angle θ13. In addition, the model predicts θ 12≃ {π}/{4}-θ 13.

Construction of anatase/rutile TiO2 hollow boxes for highly efficient photocatalytic performance

NASA Astrophysics Data System (ADS)

Jia, Changchao; Zhang, Xiao; Yang, Ping

2018-02-01

Hollow TiO2 hierarchical boxes with suitable anatase and rutile ratios were designed for photocatalysis. The unique hierarchical structure was fabricated via a Topotactic synthetic method. CaTiO3 cubes were acted as the sacrificial templates to create TiO2 hollow hierarchical boxes with well-defined phase distribution. The phase composition of the hollow TiO2 hierarchical boxes is similar to that of TiO2 P25 nanoparticles (∼80% anatase, and 20% rutile). Compared with nanaoparticles, TiO2 hollow boxes with hierarchical structures exhibited an excellent performance in the photocatalytic degradation of methylene blue organic pollutant. Quantificationally, the degradation rate of the hollow boxes is higher than that of TiO2 P25 nanoparticles by a factor of 2.7. This is ascribed that hollow structure provide an opportunity for using incident light more efficiently. The surface hierarchical and well-organized porous structures are beneficial to supply more active sites and enough transport channels for reactant molecules. The boxes consist of single crystal anatase and rutile combined well with each other, which gives photon-generated carriers transfer efficiently.

Hierarchical Cu4V2.15O9.38 micro-/nanostructures: a lithium intercalating electrode material

NASA Astrophysics Data System (ADS)

Zhou, Liang; Cui, Wangjun; Wu, Jiamin; Zhao, Qingfei; Li, Hexing; Xia, Yongyao; Wang, Yunhua; Yu, Chengzhong

2011-03-01

Hierarchical Cu4V2.15O9.38 micro-/nanostructures have been prepared by a facile ``forced hydrolysis'' method, from an aqueous peroxovanadate and cupric nitrate solution in the presence of urea. The hierarchical architectures with diameters of 10-20 µm are assembled from flexible nanosheets and rigid nanoplates with widths of 2-4 µm and lengths of 5-10 µm in a radiative way. The preliminary electrochemical properties of Cu4V2.15O9.38 have been investigated for the first time and correlated with its structure. This material delivers a large discharge capacity of 471 mA h g-1 above 1.5 V, thus making it an interesting electrode material for primary lithium ion batteries used in implantable cardioverter defibrillators.Hierarchical Cu4V2.15O9.38 micro-/nanostructures have been prepared by a facile ``forced hydrolysis'' method, from an aqueous peroxovanadate and cupric nitrate solution in the presence of urea. The hierarchical architectures with diameters of 10-20 µm are assembled from flexible nanosheets and rigid nanoplates with widths of 2-4 µm and lengths of 5-10 µm in a radiative way. The preliminary electrochemical properties of Cu4V2.15O9.38 have been investigated for the first time and correlated with its structure. This material delivers a large discharge capacity of 471 mA h g-1 above 1.5 V, thus making it an interesting electrode material for primary lithium ion batteries used in implantable cardioverter defibrillators. Electronic supplementary information (ESI) available: SEM images of hierarchical Cu4V2.15O9.38, CV curves of the electrode and discharge profiles of the cell made from Cu4V2.15O9.38 hierarchical structures, XRD pattern and SEM images of layered vanadium oxide hydrate, structure model of Cu4V2.15O9.38. See DOI: 10.1039/c0nr00657b

Basics of Bayesian methods.

PubMed

Ghosh, Sujit K

2010-01-01

Bayesian methods are rapidly becoming popular tools for making statistical inference in various fields of science including biology, engineering, finance, and genetics. One of the key aspects of Bayesian inferential method is its logical foundation that provides a coherent framework to utilize not only empirical but also scientific information available to a researcher. Prior knowledge arising from scientific background, expert judgment, or previously collected data is used to build a prior distribution which is then combined with current data via the likelihood function to characterize the current state of knowledge using the so-called posterior distribution. Bayesian methods allow the use of models of complex physical phenomena that were previously too difficult to estimate (e.g., using asymptotic approximations). Bayesian methods offer a means of more fully understanding issues that are central to many practical problems by allowing researchers to build integrated models based on hierarchical conditional distributions that can be estimated even with limited amounts of data. Furthermore, advances in numerical integration methods, particularly those based on Monte Carlo methods, have made it possible to compute the optimal Bayes estimators. However, there is a reasonably wide gap between the background of the empirically trained scientists and the full weight of Bayesian statistical inference. Hence, one of the goals of this chapter is to bridge the gap by offering elementary to advanced concepts that emphasize linkages between standard approaches and full probability modeling via Bayesian methods.

Detecting Corresponding Vertex Pairs between Planar Tessellation Datasets with Agglomerative Hierarchical Cell-Set Matching.

PubMed

Huh, Yong; Yu, Kiyun; Park, Woojin

2016-01-01

This paper proposes a method to detect corresponding vertex pairs between planar tessellation datasets. Applying an agglomerative hierarchical co-clustering, the method finds geometrically corresponding cell-set pairs from which corresponding vertex pairs are detected. Then, the map transformation is performed with the vertex pairs. Since these pairs are independently detected for each corresponding cell-set pairs, the method presents improved matching performance regardless of locally uneven positional discrepancies between dataset. The proposed method was applied to complicated synthetic cell datasets assumed as a cadastral map and a topographical map, and showed an improved result with the F-measures of 0.84 comparing to a previous matching method with the F-measure of 0.48.

Natural Higgs-Flavor-Democracy Solution of the μ Problem of Supersymmetry and the QCD Axion

NASA Astrophysics Data System (ADS)

Kim, Jihn E.

2013-07-01

We show that the hierarchically small μ term in supersymmetric theories is a consequence of two identical pairs of Higgs doublets taking a democratic form for their mass matrix. We briefly discuss the discrete symmetry S2×S2 toward the democratic mass matrix. Then, we show that there results an approximate Peccei-Quinn symmetry and hence the value μ is related to the axion decay constant.

γ-Fe2O3 and Fe3O4 magnetic hierarchically nanostructured hollow microspheres: preparation, formation mechanism, magnetic property, and application in water treatment.

PubMed

Xu, Jing-San; Zhu, Ying-Jie

2012-11-01

In this paper, we report the preparation of γ-Fe(2)O(3) and Fe(3)O(4) magnetic hierarchically nanostructured hollow microspheres by a solvothermal combined with precursor thermal conversion method. These γ-Fe(2)O(3) and Fe(3)O(4) magnetic hierarchically nanostructured hollow microspheres were constructed by three-dimensional self-assembly of nanosheets, forming porous nanostructures. The effects of experimental parameters including molar ratio of reactants and reaction temperature on the precursors were studied. The time-dependent experiments indicated that the Ostwald ripening was responsible for the formation of the hierarchically nanostructured hollow microspheres of the precursors. γ-Fe(2)O(3) and Fe(3)O(4) magnetic hierarchically nanostructured hollow microspheres were obtained by the thermal transformation of the precursor hollow microspheres. Both γ-Fe(2)O(3) and Fe(3)O(4) hierarchically nanostructured hollow microspheres exhibited a superparamagnetic property at room temperature and had the saturation magnetization of 44.2 and 55.4 emu/g, respectively, in the applied magnetic field of 20 KOe. Several kinds of organic pollutants including salicylic acid (SA), methylene blue (MB), and basic fuchsin (BF) were chosen as the model water pollutants to evaluate the removal abilities of γ-Fe(2)O(3) and Fe(3)O(4) magnetic hierarchically nanostructured hollow microspheres. It was found that γ-Fe(2)O(3) hierarchically nanostructured hollow microspheres showed a better adsorption ability over SA than MB and BF. However, Fe(3)O(4) hierarchically nanostructured hollow microspheres had the best performance for adsorbing MB. Copyright © 2012 Elsevier Inc. All rights reserved.

Risk adjustment in the American College of Surgeons National Surgical Quality Improvement Program: a comparison of logistic versus hierarchical modeling.

PubMed

Cohen, Mark E; Dimick, Justin B; Bilimoria, Karl Y; Ko, Clifford Y; Richards, Karen; Hall, Bruce Lee

2009-12-01

Although logistic regression has commonly been used to adjust for risk differences in patient and case mix to permit quality comparisons across hospitals, hierarchical modeling has been advocated as the preferred methodology, because it accounts for clustering of patients within hospitals. It is unclear whether hierarchical models would yield important differences in quality assessments compared with logistic models when applied to American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) data. Our objective was to evaluate differences in logistic versus hierarchical modeling for identifying hospitals with outlying outcomes in the ACS-NSQIP. Data from ACS-NSQIP patients who underwent colorectal operations in 2008 at hospitals that reported at least 100 operations were used to generate logistic and hierarchical prediction models for 30-day morbidity and mortality. Differences in risk-adjusted performance (ratio of observed-to-expected events) and outlier detections from the two models were compared. Logistic and hierarchical models identified the same 25 hospitals as morbidity outliers (14 low and 11 high outliers), but the hierarchical model identified 2 additional high outliers. Both models identified the same eight hospitals as mortality outliers (five low and three high outliers). The values of observed-to-expected events ratios and p values from the two models were highly correlated. Results were similar when data were permitted from hospitals providing < 100 patients. When applied to ACS-NSQIP data, logistic and hierarchical models provided nearly identical results with respect to identification of hospitals' observed-to-expected events ratio outliers. As hierarchical models are prone to implementation problems, logistic regression will remain an accurate and efficient method for performing risk adjustment of hospital quality comparisons.

Evaluating Hierarchical Structure in Music Annotations

PubMed Central

McFee, Brian; Nieto, Oriol; Farbood, Morwaread M.; Bello, Juan Pablo

2017-01-01

Music exhibits structure at multiple scales, ranging from motifs to large-scale functional components. When inferring the structure of a piece, different listeners may attend to different temporal scales, which can result in disagreements when they describe the same piece. In the field of music informatics research (MIR), it is common to use corpora annotated with structural boundaries at different levels. By quantifying disagreements between multiple annotators, previous research has yielded several insights relevant to the study of music cognition. First, annotators tend to agree when structural boundaries are ambiguous. Second, this ambiguity seems to depend on musical features, time scale, and genre. Furthermore, it is possible to tune current annotation evaluation metrics to better align with these perceptual differences. However, previous work has not directly analyzed the effects of hierarchical structure because the existing methods for comparing structural annotations are designed for “flat” descriptions, and do not readily generalize to hierarchical annotations. In this paper, we extend and generalize previous work on the evaluation of hierarchical descriptions of musical structure. We derive an evaluation metric which can compare hierarchical annotations holistically across multiple levels. sing this metric, we investigate inter-annotator agreement on the multilevel annotations of two different music corpora, investigate the influence of acoustic properties on hierarchical annotations, and evaluate existing hierarchical segmentation algorithms against the distribution of inter-annotator agreement. PMID:28824514

Modular and hierarchical structure of social contact networks

NASA Astrophysics Data System (ADS)

Ge, Yuanzheng; Song, Zhichao; Qiu, Xiaogang; Song, Hongbin; Wang, Yong

2013-10-01

Social contact networks exhibit overlapping qualities of communities, hierarchical structure and spatial-correlated nature. We propose a mixing pattern of modular and growing hierarchical structures to reconstruct social contact networks by using an individual’s geospatial distribution information in the real world. The hierarchical structure of social contact networks is defined based on the spatial distance between individuals, and edges among individuals are added in turn from the modular layer to the highest layer. It is a gradual process to construct the hierarchical structure: from the basic modular model up to the global network. The proposed model not only shows hierarchically increasing degree distribution and large clustering coefficients in communities, but also exhibits spatial clustering features of individual distributions. As an evaluation of the method, we reconstruct a hierarchical contact network based on the investigation data of a university. Transmission experiments of influenza H1N1 are carried out on the generated social contact networks, and results show that the constructed network is efficient to reproduce the dynamic process of an outbreak and evaluate interventions. The reproduced spread process exhibits that the spatial clustering of infection is accordant with the clustering of network topology. Moreover, the effect of individual topological character on the spread of influenza is analyzed, and the experiment results indicate that the spread is limited by individual daily contact patterns and local clustering topology rather than individual degree.

Space-Time Smoothing of Complex Survey Data: Small Area Estimation for Child Mortality

PubMed Central

Mercer, Laina D; Wakefield, Jon; Pantazis, Athena; Lutambi, Angelina M; Masanja, Honorati; Clark, Samuel

2016-01-01

Many people living in low and middle-income countries are not covered by civil registration and vital statistics systems. Consequently, a wide variety of other types of data including many household sample surveys are used to estimate health and population indicators. In this paper we combine data from sample surveys and demographic surveillance systems to produce small area estimates of child mortality through time. Small area estimates are necessary to understand geographical heterogeneity in health indicators when full-coverage vital statistics are not available. For this endeavor spatio-temporal smoothing is beneficial to alleviate problems of data sparsity. The use of conventional hierarchical models requires careful thought since the survey weights may need to be considered to alleviate bias due to non-random sampling and non-response. The application that motivated this work is estimation of child mortality rates in five-year time intervals in regions of Tanzania. Data come from Demographic and Health Surveys conducted over the period 1991–2010 and two demographic surveillance system sites. We derive a variance estimator of under five years child mortality that accounts for the complex survey weighting. For our application, the hierarchical models we consider include random effects for area, time and survey and we compare models using a variety of measures including the conditional predictive ordinate (CPO). The method we propose is implemented via the fast and accurate integrated nested Laplace approximation (INLA). PMID:27468328

Marker-Based Hierarchical Segmentation and Classification Approach for Hyperspectral Imagery

NASA Technical Reports Server (NTRS)

Tarabalka, Yuliya; Tilton, James C.; Benediktsson, Jon Atli; Chanussot, Jocelyn

2011-01-01

The Hierarchical SEGmentation (HSEG) algorithm, which is a combination of hierarchical step-wise optimization and spectral clustering, has given good performances for hyperspectral image analysis. This technique produces at its output a hierarchical set of image segmentations. The automated selection of a single segmentation level is often necessary. We propose and investigate the use of automatically selected markers for this purpose. In this paper, a novel Marker-based HSEG (M-HSEG) method for spectral-spatial classification of hyperspectral images is proposed. First, pixelwise classification is performed and the most reliably classified pixels are selected as markers, with the corresponding class labels. Then, a novel constrained marker-based HSEG algorithm is applied, resulting in a spectral-spatial classification map. The experimental results show that the proposed approach yields accurate segmentation and classification maps, and thus is attractive for hyperspectral image analysis.

Biomimetic cellular metals-using hierarchical structuring for energy absorption.

PubMed

Bührig-Polaczek, A; Fleck, C; Speck, T; Schüler, P; Fischer, S F; Caliaro, M; Thielen, M

2016-07-19

Fruit walls as well as nut and seed shells typically perform a multitude of functions. One of the biologically most important functions consists in the direct or indirect protection of the seeds from mechanical damage or other negative environmental influences. This qualifies such biological structures as role models for the development of new materials and components that protect commodities and/or persons from damage caused for example by impacts due to rough handling or crashes. We were able to show how the mechanical properties of metal foam based components can be improved by altering their structure on various hierarchical levels inspired by features and principles important for the impact and/or puncture resistance of the biological role models, rather than by tuning the properties of the bulk material. For this various investigation methods have been established which combine mechanical testing with different imaging methods, as well as with in situ and ex situ mechanical testing methods. Different structural hierarchies especially important for the mechanical deformation and failure behaviour of the biological role models, pomelo fruit (Citrus maxima) and Macadamia integrifolia, were identified. They were abstracted and transferred into corresponding structural principles and thus hierarchically structured bio-inspired metal foams have been designed. A production route for metal based bio-inspired structures by investment casting was successfully established. This allows the production of complex and reliable structures, by implementing and combining different hierarchical structural elements found in the biological concept generators, such as strut design and integration of fibres, as well as by minimising casting defects. To evaluate the structural effects, similar investigation methods and mechanical tests were applied to both the biological role models and the metallic foams. As a result an even deeper quantitative understanding of the form-structure-function relationship of the biological concept generators as well as the bio-inspired metal foams was achieved, on deeper hierarchical levels and overarching different levels.

Hierarchical spatial models of abundance and occurrence from imperfect survey data

USGS Publications Warehouse

Royle, J. Andrew; Kery, M.; Gautier, R.; Schmid, Hans

2007-01-01

Many estimation and inference problems arising from large-scale animal surveys are focused on developing an understanding of patterns in abundance or occurrence of a species based on spatially referenced count data. One fundamental challenge, then, is that it is generally not feasible to completely enumerate ('census') all individuals present in each sample unit. This observation bias may consist of several components, including spatial coverage bias (not all individuals in the Population are exposed to sampling) and detection bias (exposed individuals may go undetected). Thus, observations are biased for the state variable (abundance, occupancy) that is the object of inference. Moreover, data are often sparse for most observation locations, requiring consideration of methods for spatially aggregating or otherwise combining sparse data among sample units. The development of methods that unify spatial statistical models with models accommodating non-detection is necessary to resolve important spatial inference problems based on animal survey data. In this paper, we develop a novel hierarchical spatial model for estimation of abundance and occurrence from survey data wherein detection is imperfect. Our application is focused on spatial inference problems in the Swiss Survey of Common Breeding Birds. The observation model for the survey data is specified conditional on the unknown quadrat population size, N(s). We augment the observation model with a spatial process model for N(s), describing the spatial variation in abundance of the species. The model includes explicit sources of variation in habitat structure (forest, elevation) and latent variation in the form of a correlated spatial process. This provides a model-based framework for combining the spatially referenced samples while at the same time yielding a unified treatment of estimation problems involving both abundance and occurrence. We provide a Bayesian framework for analysis and prediction based on the integrated likelihood, and we use the model to obtain estimates of abundance and occurrence maps for the European Jay (Garrulus glandarius), a widespread, elusive, forest bird. The naive national abundance estimate ignoring imperfect detection and incomplete quadrat coverage was 77 766 territories. Accounting for imperfect detection added approximately 18 000 territories, and adjusting for coverage bias added another 131 000 territories to yield a fully corrected estimate of the national total of about 227 000 territories. This is approximately three times as high as previous estimates that assume every territory is detected in each quadrat.

Hierarchical clustering method for improved prostate cancer imaging in diffuse optical tomography

NASA Astrophysics Data System (ADS)

Kavuri, Venkaiah C.; Liu, Hanli

2013-03-01

We investigate the feasibility of trans-rectal near infrared (NIR) based diffuse optical tomography (DOT) for early detection of prostate cancer using a transrectal ultrasound (TRUS) compatible imaging probe. For this purpose, we designed a TRUS-compatible, NIR-based image system (780nm), in which the photo diodes were placed on the trans-rectal probe. DC signals were recorded and used for estimating the absorption coefficient. We validated the system using laboratory phantoms. For further improvement, we also developed a hierarchical clustering method (HCM) to improve the accuracy of image reconstruction with limited prior information. We demonstrated the method using computer simulations laboratory phantom experiments.

Application of six sigma and AHP in analysis of variable lead time calibration process instrumentation

NASA Astrophysics Data System (ADS)

Rimantho, Dino; Rahman, Tomy Abdul; Cahyadi, Bambang; Tina Hernawati, S.

2017-02-01

Calibration of instrumentation equipment in the pharmaceutical industry is an important activity to determine the true value of a measurement. Preliminary studies indicated that occur lead-time calibration resulted in disruption of production and laboratory activities. This study aimed to analyze the causes of lead-time calibration. Several methods used in this study such as, Six Sigma in order to determine the capability process of the calibration instrumentation of equipment. Furthermore, the method of brainstorming, Pareto diagrams, and Fishbone diagrams were used to identify and analyze the problems. Then, the method of Hierarchy Analytical Process (AHP) was used to create a hierarchical structure and prioritize problems. The results showed that the value of DPMO around 40769.23 which was equivalent to the level of sigma in calibration equipment approximately 3,24σ. This indicated the need for improvements in the calibration process. Furthermore, the determination of problem-solving strategies Lead Time Calibration such as, shortens the schedule preventive maintenance, increase the number of instrument Calibrators, and train personnel. Test results on the consistency of the whole matrix of pairwise comparisons and consistency test showed the value of hierarchy the CR below 0.1.

Direct computation of stochastic flow in reservoirs with uncertain parameters

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

Dainton, M.P.; Nichols, N.K.; Goldwater, M.H.

1997-01-15

A direct method is presented for determining the uncertainty in reservoir pressure, flow, and net present value (NPV) using the time-dependent, one phase, two- or three-dimensional equations of flow through a porous medium. The uncertainty in the solution is modelled as a probability distribution function and is computed from given statistical data for input parameters such as permeability. The method generates an expansion for the mean of the pressure about a deterministic solution to the system equations using a perturbation to the mean of the input parameters. Hierarchical equations that define approximations to the mean solution at each point andmore » to the field convariance of the pressure are developed and solved numerically. The procedure is then used to find the statistics of the flow and the risked value of the field, defined by the NPV, for a given development scenario. This method involves only one (albeit complicated) solution of the equations and contrasts with the more usual Monte-Carlo approach where many such solutions are required. The procedure is applied easily to other physical systems modelled by linear or nonlinear partial differential equations with uncertain data. 14 refs., 14 figs., 3 tabs.« less

An Empirical Analysis of the Impact of Recruitment Patterns on RDS Estimates among a Socially Ordered Population of Female Sex Workers in China

PubMed Central

Yamanis, Thespina J.; Merli, M. Giovanna; Neely, William Whipple; Tian, Felicia Feng; Moody, James; Tu, Xiaowen; Gao, Ersheng

2013-01-01

Respondent-driven sampling (RDS) is a method for recruiting “hidden” populations through a network-based, chain and peer referral process. RDS recruits hidden populations more effectively than other sampling methods and promises to generate unbiased estimates of their characteristics. RDS’s faithful representation of hidden populations relies on the validity of core assumptions regarding the unobserved referral process. With empirical recruitment data from an RDS study of female sex workers (FSWs) in Shanghai, we assess the RDS assumption that participants recruit nonpreferentially from among their network alters. We also present a bootstrap method for constructing the confidence intervals around RDS estimates. This approach uniquely incorporates real-world features of the population under study (e.g., the sample’s observed branching structure). We then extend this approach to approximate the distribution of RDS estimates under various peer recruitment scenarios consistent with the data as a means to quantify the impact of recruitment bias and of rejection bias on the RDS estimates. We find that the hierarchical social organization of FSWs leads to recruitment biases by constraining RDS recruitment across social classes and introducing bias in the RDS estimates. PMID:24288418

Computer systems and methods for the query and visualization of multidimensional databases

DOEpatents

Stolte, Chris; Tang, Diane L.; Hanrahan, Patrick

2006-08-08

A method and system for producing graphics. A hierarchical structure of a database is determined. A visual table, comprising a plurality of panes, is constructed by providing a specification that is in a language based on the hierarchical structure of the database. In some cases, this language can include fields that are in the database schema. The database is queried to retrieve a set of tuples in accordance with the specification. A subset of the set of tuples is associated with a pane in the plurality of panes.

Computer systems and methods for the query and visualization of multidimensional database

DOEpatents

Stolte, Chris; Tang, Diane L.; Hanrahan, Patrick

2010-05-11

A method and system for producing graphics. A hierarchical structure of a database is determined. A visual table, comprising a plurality of panes, is constructed by providing a specification that is in a language based on the hierarchical structure of the database. In some cases, this language can include fields that are in the database schema. The database is queried to retrieve a set of tuples in accordance with the specification. A subset of the set of tuples is associated with a pane in the plurality of panes.

Recent activities within the Aeroservoelasticity Branch at the NASA Langley Research Center

NASA Technical Reports Server (NTRS)

Noll, Thomas E.; Perry, Boyd, III; Gilbert, Michael G.

1989-01-01

The objective of research in aeroservoelasticity at the NASA Langley Research Center is to enhance the modeling, analysis, and multidisciplinary design methodologies for obtaining multifunction digital control systems for application to flexible flight vehicles. Recent accomplishments are discussed, and a status report on current activities within the Aeroservoelasticity Branch is presented. In the area of modeling, improvements to the Minimum-State Method of approximating unsteady aerodynamics are shown to provide precise, low-order aeroservoelastic models for design and simulation activities. Analytical methods based on Matched Filter Theory and Random Process Theory to provide efficient and direct predictions of the critical gust profile and the time-correlated gust loads for linear structural design considerations are also discussed. Two research projects leading towards improved design methodology are summarized. The first program is developing an integrated structure/control design capability based on hierarchical problem decomposition, multilevel optimization and analytical sensitivities. The second program provides procedures for obtaining low-order, robust digital control laws for aeroelastic applications. In terms of methodology validation and application the current activities associated with the Active Flexible Wing project are reviewed.

Assessing the relationship between family mealtime communication and adolescent emotional well-being using the experience sampling method.

PubMed

Offer, Shira

2013-06-01

While most prior research has focused on the frequency of family meals the issue of which elements of family mealtime are most salient for adolescents' well-being has remained overlooked. The current study used the experience sampling method, a unique form of time diary, and survey data drawn from the 500 Family Study (N = 237 adolescents with 8122 observations) to examine the association between family mealtime communication and teens' emotional well-being. Results showed that in approximately half of the time spent on family meals (3 h per week on average) adolescents reported talking to their parents. Hierarchical linear model analyses revealed that controlling for the quality of family relationships family mealtime communication was significantly associated with higher positive affect and engagement and with lower negative affect and stress. Findings suggest that family meals constitute an important site for communication between teens and parents that is beneficial to adolescents' emotional well-being. Copyright © 2013 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

Classifying dysmorphic syndromes by using artificial neural network based hierarchical decision tree.

PubMed

Özdemir, Merve Erkınay; Telatar, Ziya; Eroğul, Osman; Tunca, Yusuf

2018-05-01

Dysmorphic syndromes have different facial malformations. These malformations are significant to an early diagnosis of dysmorphic syndromes and contain distinctive information for face recognition. In this study we define the certain features of each syndrome by considering facial malformations and classify Fragile X, Hurler, Prader Willi, Down, Wolf Hirschhorn syndromes and healthy groups automatically. The reference points are marked on the face images and ratios between the points' distances are taken into consideration as features. We suggest a neural network based hierarchical decision tree structure in order to classify the syndrome types. We also implement k-nearest neighbor (k-NN) and artificial neural network (ANN) classifiers to compare classification accuracy with our hierarchical decision tree. The classification accuracy is 50, 73 and 86.7% with k-NN, ANN and hierarchical decision tree methods, respectively. Then, the same images are shown to a clinical expert who achieve a recognition rate of 46.7%. We develop an efficient system to recognize different syndrome types automatically in a simple, non-invasive imaging data, which is independent from the patient's age, sex and race at high accuracy. The promising results indicate that our method can be used for pre-diagnosis of the dysmorphic syndromes by clinical experts.

Using GOMS models and hypertext to create representations of medical procedures for online display

NASA Technical Reports Server (NTRS)

Gugerty, Leo; Halgren, Shannon; Gosbee, John; Rudisill, Marianne

1991-01-01

This study investigated two methods to improve organization and presentation of computer-based medical procedures. A literature review suggested that the GOMS (goals, operators, methods, and selecton rules) model can assist in rigorous task analysis, which can then help generate initial design ideas for the human-computer interface. GOMS model are hierarchical in nature, so this study also investigated the effect of hierarchical, hypertext interfaces. We used a 2 x 2 between subjects design, including the following independent variables: procedure organization - GOMS model based vs. medical-textbook based; navigation type - hierarchical vs. linear (booklike). After naive subjects studies the online procedures, measures were taken of their memory for the content and the organization of the procedures. This design was repeated for two medical procedures. For one procedure, subjects who studied GOMS-based and hierarchical procedures remembered more about the procedures than other subjects. The results for the other procedure were less clear. However, data for both procedures showed a 'GOMSification effect'. That is, when asked to do a free recall of a procedure, subjects who had studies a textbook procedure often recalled key information in a location inconsistent with the procedure they actually studied, but consistent with the GOMS-based procedure.

Hierarchical Mesoporous Lithium-Rich Li[Li0.2Ni0.2Mn0.6]O2 Cathode Material Synthesized via Ice Templating for Lithium-Ion Battery.

PubMed

Li, Yu; Wu, Chuan; Bai, Ying; Liu, Lu; Wang, Hui; Wu, Feng; Zhang, Na; Zou, Yufeng

2016-07-27

Tuning hierarchical micro/nanostructure of electrode materials is a sought-after means to reinforce their electrochemical performance in the energy storage field. Herein, we introduce a type of hierarchical mesoporous Li[Li0.2Ni0.2Mn0.6]O2 microsphere composed of nanoparticles synthesized via an ice templating combined coprecipitation strategy. It is a low-cost, eco-friendly, and easily operated method using ice as a template to control material with homogeneous morphology and rich porous channels. The as-prepared material exhibits remarkably enhanced electrochemical performances with higher capacity, more excellent cycling stability and more superior rate property, compared with the sample prepared by conventional coprecipitation method. It has satisfactory initial discharge capacities of 280.1 mAh g(-1) at 0.1 C, 207.1 mAh g(-1) at 2 C, and 152.4 mAh g(-1) at 5 C, as well as good cycle performance. The enhanced electrochemical performance can be ascribed to the stable hierarchical microsized structure and the improved lithium-ion diffusion kinetics from the highly porous structure.

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

Liu Shengwei; Yu Jiaguo

Bi{sub 2}WO{sub 6} hierarchical multilayered flower-like assemblies are fabricated on a large scale by a simple hydrothermal method in the presence of polymeric poly(sodium 4-styrenesulfonate). Such 3D Bi{sub 2}WO{sub 6} assemblies are constructed from orderly arranged 2D layers, which are further composed of a large number of interconnected nanoplates with a mean side length of ca. 50 nm. The bimodal mesopores associated with such hierarchical assembly exhibit peak mesopore size of ca. 4 nm for the voids within a layer, and peak mesopore size of ca. 40 nm corresponding to the interspaces between stacked layers, respectively. The formation process ismore » discussed on the basis of the results of time-dependent experiments, which support a novel 'coupled cooperative assembly and localized ripening' formation mechanism. More interestingly, we have noticed that the collective effect related to such hierarchical assembly induces a significantly enhanced optical absorbance in the UV-visible region. This work may shed some light on the design of complex architectures and exploitation of their potential applications. - Graphical abstract: Bi{sub 2}WO{sub 6} hierarchical multilayered flower-like assemblies are fabricated on a large scale by a simple hydrothermal method in the presence of polymeric poly(sodium 4-styrenesulfonate)« less

Decomposition and extraction: a new framework for visual classification.

PubMed

Fang, Yuqiang; Chen, Qiang; Sun, Lin; Dai, Bin; Yan, Shuicheng

2014-08-01

In this paper, we present a novel framework for visual classification based on hierarchical image decomposition and hybrid midlevel feature extraction. Unlike most midlevel feature learning methods, which focus on the process of coding or pooling, we emphasize that the mechanism of image composition also strongly influences the feature extraction. To effectively explore the image content for the feature extraction, we model a multiplicity feature representation mechanism through meaningful hierarchical image decomposition followed by a fusion step. In particularly, we first propose a new hierarchical image decomposition approach in which each image is decomposed into a series of hierarchical semantical components, i.e, the structure and texture images. Then, different feature extraction schemes can be adopted to match the decomposed structure and texture processes in a dissociative manner. Here, two schemes are explored to produce property related feature representations. One is based on a single-stage network over hand-crafted features and the other is based on a multistage network, which can learn features from raw pixels automatically. Finally, those multiple midlevel features are incorporated by solving a multiple kernel learning task. Extensive experiments are conducted on several challenging data sets for visual classification, and experimental results demonstrate the effectiveness of the proposed method.

One-step synthesis of highly efficient nanocatalysts on the supports with hierarchical pores using porous ionic liquid-water gel.

PubMed

Kang, Xinchen; Zhang, Jianling; Shang, Wenting; Wu, Tianbin; Zhang, Peng; Han, Buxing; Wu, Zhonghua; Mo, Guang; Xing, Xueqing

2014-03-12

Stable porous ionic liquid-water gel induced by inorganic salts was created for the first time. The porous gel was used to develop a one-step method to synthesize supported metal nanocatalysts. Au/SiO2, Ru/SiO2, Pd/Cu(2-pymo)2 metal-organic framework (Cu-MOF), and Au/polyacrylamide (PAM) were synthesized, in which the supports had hierarchical meso- and macropores, the size of the metal nanocatalysts could be very small (<1 nm), and the size distribution was very narrow even when the metal loading amount was as high as 8 wt %. The catalysts were extremely active, selective, and stable for oxidative esterification of benzyl alcohol to methyl benzoate, benzene hydrogenation to cyclohexane, and oxidation of benzyl alcohol to benzaldehyde because they combined the advantages of the nanocatalysts of small size and hierarchical porosity of the supports. In addition, this method is very simple.

Construction of hierarchically porous metal–organic frameworks through linker labilization

DOE PAGES

Yuan, Shuai; Zou, Lanfang; Qin, Jun-Sheng; ...

2017-05-25

One major goal of metal–organic framework (MOF) research is the expansion of pore size and volume. Although many approaches have been attempted to increase the pore size of MOF materials, it is still a challenge to construct MOFs with precisely customized pore apertures for specific applications. W present a new method, namely linker labilization, to increase the MOF porosity and pore size, giving rise to hierarchical-pore architectures. Microporous MOFs with robust metal nodes and pro-labile linkers were initially synthesized. The mesopores were subsequently created as crystal defects through the splitting of a pro-labile-linker and the removal of the linker fragmentsmore » by acid treatment. We also demonstrate that linker labilization method can create controllable hierarchical porous structures in stable MOFs, which facilitates the diffusion and adsorption process of guest molecules to improve the performances of MOFs in adsorption and catalysis.« less

Biomimetic wall-shaped hierarchical microstructure for gecko-like attachment.

PubMed

Kasem, Haytam; Tsipenyuk, Alexey; Varenberg, Michael

2015-04-21

Most biological hairy adhesive systems involved in locomotion rely on spatula-shaped terminal elements, whose operation has been actively studied during the last decade. However, though functional principles underlying their amazing performance are now well understood, due to technical difficulties in manufacturing the complex structure of hierarchical spatulate systems, a biomimetic surface structure featuring true shear-induced dynamic attachment still remains elusive. To try bridging this gap, a novel method of manufacturing gecko-like attachment surfaces is devised based on a laser-micromachining technology. This method overcomes the inherent disadvantages of photolithography techniques and opens wide perspectives for future production of gecko-like attachment systems. Advanced smart-performance surfaces featuring thin-film-based hierarchical shear-activated elements are fabricated and found capable of generating friction force of several tens of times the contact load, which makes a significant step forward towards a true gecko-like adhesive.

Construction of hierarchically porous metal-organic frameworks through linker labilization

NASA Astrophysics Data System (ADS)

Yuan, Shuai; Zou, Lanfang; Qin, Jun-Sheng; Li, Jialuo; Huang, Lan; Feng, Liang; Wang, Xuan; Bosch, Mathieu; Alsalme, Ali; Cagin, Tahir; Zhou, Hong-Cai

2017-05-01

A major goal of metal-organic framework (MOF) research is the expansion of pore size and volume. Although many approaches have been attempted to increase the pore size of MOF materials, it is still a challenge to construct MOFs with precisely customized pore apertures for specific applications. Herein, we present a new method, namely linker labilization, to increase the MOF porosity and pore size, giving rise to hierarchical-pore architectures. Microporous MOFs with robust metal nodes and pro-labile linkers were initially synthesized. The mesopores were subsequently created as crystal defects through the splitting of a pro-labile-linker and the removal of the linker fragments by acid treatment. We demonstrate that linker labilization method can create controllable hierarchical porous structures in stable MOFs, which facilitates the diffusion and adsorption process of guest molecules to improve the performances of MOFs in adsorption and catalysis.

Computer-assisted quantification of the skull deformity for craniosynostosis from 3D head CT images using morphological descriptor and hierarchical classification

NASA Astrophysics Data System (ADS)

Lee, Min Jin; Hong, Helen; Shim, Kyu Won; Kim, Yong Oock

2017-03-01

This paper proposes morphological descriptors representing the degree of skull deformity for craniosynostosis in head CT images and a hierarchical classifier model distinguishing among normal and different types of craniosynostosis. First, to compare deformity surface model with mean normal surface model, mean normal surface models are generated for each age range and the mean normal surface model is deformed to the deformity surface model via multi-level threestage registration. Second, four shape features including local distance and area ratio indices are extracted in each five cranial bone. Finally, hierarchical SVM classifier is proposed to distinguish between the normal and deformity. As a result, the proposed method showed improved classification results compared to traditional cranial index. Our method can be used for the early diagnosis, surgical planning and postsurgical assessment of craniosynostosis as well as quantitative analysis of skull deformity.

Construction of hierarchically porous metal–organic frameworks through linker labilization

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

Yuan, Shuai; Zou, Lanfang; Qin, Jun-Sheng

One major goal of metal–organic framework (MOF) research is the expansion of pore size and volume. Although many approaches have been attempted to increase the pore size of MOF materials, it is still a challenge to construct MOFs with precisely customized pore apertures for specific applications. W present a new method, namely linker labilization, to increase the MOF porosity and pore size, giving rise to hierarchical-pore architectures. Microporous MOFs with robust metal nodes and pro-labile linkers were initially synthesized. The mesopores were subsequently created as crystal defects through the splitting of a pro-labile-linker and the removal of the linker fragmentsmore » by acid treatment. We also demonstrate that linker labilization method can create controllable hierarchical porous structures in stable MOFs, which facilitates the diffusion and adsorption process of guest molecules to improve the performances of MOFs in adsorption and catalysis.« less

Scale free effects in world currency exchange network

NASA Astrophysics Data System (ADS)

Górski, A. Z.; Drożdż, S.; Kwapień, J.

2008-11-01

A large collection of daily time series for 60 world currencies' exchange rates is considered. The correlation matrices are calculated and the corresponding Minimal Spanning Tree (MST) graphs are constructed for each of those currencies used as reference for the remaining ones. It is shown that multiplicity of the MST graphs' nodes to a good approximation develops a power like, scale free distribution with the scaling exponent similar as for several other complex systems studied so far. Furthermore, quantitative arguments in favor of the hierarchical organization of the world currency exchange network are provided by relating the structure of the above MST graphs and their scaling exponents to those that are derived from an exactly solvable hierarchical network model. A special status of the USD during the period considered can be attributed to some departures of the MST features, when this currency (or some other tied to it) is used as reference, from characteristics typical to such a hierarchical clustering of nodes towards those that correspond to the random graphs. Even though in general the basic structure of the MST is robust with respect to changing the reference currency some trace of a systematic transition from somewhat dispersed - like the USD case - towards more compact MST topology can be observed when correlations increase.

Hierarchical imputation of systematically and sporadically missing data: An approximate Bayesian approach using chained equations.

PubMed

Jolani, Shahab

2018-03-01

In health and medical sciences, multiple imputation (MI) is now becoming popular to obtain valid inferences in the presence of missing data. However, MI of clustered data such as multicenter studies and individual participant data meta-analysis requires advanced imputation routines that preserve the hierarchical structure of data. In clustered data, a specific challenge is the presence of systematically missing data, when a variable is completely missing in some clusters, and sporadically missing data, when it is partly missing in some clusters. Unfortunately, little is known about how to perform MI when both types of missing data occur simultaneously. We develop a new class of hierarchical imputation approach based on chained equations methodology that simultaneously imputes systematically and sporadically missing data while allowing for arbitrary patterns of missingness among them. Here, we use a random effect imputation model and adopt a simplification over fully Bayesian techniques such as Gibbs sampler to directly obtain draws of parameters within each step of the chained equations. We justify through theoretical arguments and extensive simulation studies that the proposed imputation methodology has good statistical properties in terms of bias and coverage rates of parameter estimates. An illustration is given in a case study with eight individual participant datasets. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Peripheral infrastructure vectors and an extended set of plant parts for the Modular Cloning system

PubMed Central

Kretschmer, Carola; Gruetzner, Ramona; Löfke, Christian; Dagdas, Yasin; Bürstenbinder, Katharina; Marillonnet, Sylvestre

2018-01-01

Standardized DNA assembly strategies facilitate the generation of multigene constructs from collections of building blocks in plant synthetic biology. A common syntax for hierarchical DNA assembly following the Golden Gate principle employing Type IIs restriction endonucleases was recently developed, and underlies the Modular Cloning and GoldenBraid systems. In these systems, transcriptional units and/or multigene constructs are assembled from libraries of standardized building blocks, also referred to as phytobricks, in several hierarchical levels and by iterative Golden Gate reactions. Here, a toolkit containing further modules for the novel DNA assembly standards was developed. Intended for use with Modular Cloning, most modules are also compatible with GoldenBraid. Firstly, a collection of approximately 80 additional phytobricks is provided, comprising e.g. modules for inducible expression systems, promoters or epitope tags. Furthermore, DNA modules were developed for connecting Modular Cloning and Gateway cloning, either for toggling between systems or for standardized Gateway destination vector assembly. Finally, first instances of a “peripheral infrastructure” around Modular Cloning are presented: While available toolkits are designed for the assembly of plant transformation constructs, vectors were created to also use coding sequence-containing phytobricks directly in yeast two hybrid interaction or bacterial infection assays. The presented material will further enhance versatility of hierarchical DNA assembly strategies. PMID:29847550

Hierarchical Approach to 'Atomistic' 3-D MOSFET Simulation

NASA Technical Reports Server (NTRS)

Asenov, Asen; Brown, Andrew R.; Davies, John H.; Saini, Subhash

1999-01-01

We present a hierarchical approach to the 'atomistic' simulation of aggressively scaled sub-0.1 micron MOSFET's. These devices are so small that their characteristics depend on the precise location of dopant atoms within them, not just on their average density. A full-scale three-dimensional drift-diffusion atomistic simulation approach is first described and used to verify more economical, but restricted, options. To reduce processor time and memory requirements at high drain voltage, we have developed a self-consistent option based on a solution of the current continuity equation restricted to a thin slab of the channel. This is coupled to the solution of the Poisson equation in the whole simulation domain in the Gummel iteration cycles. The accuracy of this approach is investigated in comparison to the full self-consistent solution. At low drain voltage, a single solution of the nonlinear Poisson equation is sufficient to extract the current with satisfactory accuracy. In this case, the current is calculated by solving the current continuity equation in a drift approximation only, also in a thin slab containing the MOSFET channel. The regions of applicability for the different components of this hierarchical approach are illustrated in example simulations covering the random dopant-induced threshold voltage fluctuations, threshold voltage lowering, threshold voltage asymmetry, and drain current fluctuations.

Hierarchical classification method and its application in shape representation

NASA Astrophysics Data System (ADS)

Ireton, M. A.; Oakley, John P.; Xydeas, Costas S.

1992-04-01

In this paper we describe a technique for performing shaped-based content retrieval of images from a large database. In order to be able to formulate such user-generated queries about visual objects, we have developed an hierarchical classification technique. This hierarchical classification technique enables similarity matching between objects, with the position in the hierarchy signifying the level of generality to be used in the query. The classification technique is unsupervised, robust, and general; it can be applied to any suitable parameter set. To establish the potential of this classifier for aiding visual querying, we have applied it to the classification of the 2-D outlines of leaves.

Fabrication of hierarchical feather-mimetic polymer nanofibres

NASA Astrophysics Data System (ADS)

Ouyang, Shenshen; Wang, Tao; Zhong, Longgang; Peng, Meiling; Yao, Juming; Wang, Sheng

2018-01-01

In this study, hierarchically feather-mimetic structures formed of poly(m-phenylene isophthalamide) (PMIA) nanofibres were prepared by electrospinning and subsequent crystallisation for superwettability applications. X-ray diffraction measurementsand scanning electron microscopy show that a feather-mimetic structure of crystallised nanoflakes was formed following a hydrothermal treatment process. The nanoflakes formed a nanosized fine texture on top of a coarser-textured membrane, which greatly improved the membrane roughness and yielded a hierarchical topography. After fluorination, the membrane exhibited superamphiphobicity, with surface contact angles of 151° and 136° for water and hexadecane, respectively. The method provides new insight for the design and development of functional bionic membranes based on PMIA.

Optimized detection of shear peaks in weak lensing maps

NASA Astrophysics Data System (ADS)

Marian, Laura; Smith, Robert E.; Hilbert, Stefan; Schneider, Peter

2012-06-01

We present a new method to extract cosmological constraints from weak lensing (WL) peak counts, which we denote as ‘the hierarchical algorithm’. The idea of this method is to combine information from WL maps sequentially smoothed with a series of filters of different size, from the largest down to the smallest, thus increasing the cosmological sensitivity of the resulting peak function. We compare the cosmological constraints resulting from the peak abundance measured in this way and the abundance obtained by using a filter of fixed size, which is the standard practice in WL peak studies. For this purpose, we employ a large set of WL maps generated by ray tracing through N-body simulations, and the Fisher matrix formalism. We find that if low signal-to-noise ratio (?) peaks are included in the analysis (?), the hierarchical method yields constraints significantly better than the single-sized filtering. For a large future survey such as Euclid or Large Synoptic Survey Telescope, combined with information from a cosmic microwave background experiment like Planck, the results for the hierarchical (single-sized) method are Δns= 0.0039 (0.004), ΔΩm= 0.002 (0.0045), Δσ8= 0.003 (0.006) and Δw= 0.019 (0.0525). This forecast is conservative, as we assume no knowledge of the redshifts of the lenses, and consider a single broad bin for the redshifts of the sources. If only peaks with ? are considered, then there is little difference between the results of the two methods. We also examine the statistical properties of the hierarchical peak function: Its covariance matrix has off-diagonal terms for bins with ? and aperture mass of M < 3 × 1014 h-1 M⊙, the higher bins being largely uncorrelated and therefore well described by a Poisson distribution.

Rigorous Approach in Investigation of Seismic Structure and Source Characteristicsin Northeast Asia: Hierarchical and Trans-dimensional Bayesian Inversion

NASA Astrophysics Data System (ADS)

Mustac, M.; Kim, S.; Tkalcic, H.; Rhie, J.; Chen, Y.; Ford, S. R.; Sebastian, N.

2015-12-01

Conventional approaches to inverse problems suffer from non-linearity and non-uniqueness in estimations of seismic structures and source properties. Estimated results and associated uncertainties are often biased by applied regularizations and additional constraints, which are commonly introduced to solve such problems. Bayesian methods, however, provide statistically meaningful estimations of models and their uncertainties constrained by data information. In addition, hierarchical and trans-dimensional (trans-D) techniques are inherently implemented in the Bayesian framework to account for involved error statistics and model parameterizations, and, in turn, allow more rigorous estimations of the same. Here, we apply Bayesian methods throughout the entire inference process to estimate seismic structures and source properties in Northeast Asia including east China, the Korean peninsula, and the Japanese islands. Ambient noise analysis is first performed to obtain a base three-dimensional (3-D) heterogeneity model using continuous broadband waveforms from more than 300 stations. As for the tomography of surface wave group and phase velocities in the 5-70 s band, we adopt a hierarchical and trans-D Bayesian inversion method using Voronoi partition. The 3-D heterogeneity model is further improved by joint inversions of teleseismic receiver functions and dispersion data using a newly developed high-efficiency Bayesian technique. The obtained model is subsequently used to prepare 3-D structural Green's functions for the source characterization. A hierarchical Bayesian method for point source inversion using regional complete waveform data is applied to selected events from the region. The seismic structure and source characteristics with rigorously estimated uncertainties from the novel Bayesian methods provide enhanced monitoring and discrimination of seismic events in northeast Asia.

Room temperature synthesis and highly enhanced visible light photocatalytic activity of porous BiOI/BiOCl composites nanoplates microflowers.

PubMed

Dong, Fan; Sun, Yanjuan; Fu, Min; Wu, Zhongbiao; Lee, S C

2012-06-15

This research represents a highly enhanced visible light photocatalytic removal of 450 ppb level of nitric oxide (NO) in air by utilizing flower-like hierarchical porous BiOI/BiOCl composites synthesized by a room temperature template free method for the first time. The facile synthesis method avoids high temperature treatment, use of organic precursors and production of undesirable organic byproducts during synthesis process. The result indicated that the as-prepared BiOI/BiOCl composites samples were solid solution and were self-assembled hierarchically with single-crystal nanoplates. The aggregation of the self-assembled nanoplates resulted in the formation of 3D hierarchical porous architecture containing tri-model mesopores. The coupling to BiOI with BiOCl led to down-lowered valence band (VB) and up-lifted conduction band (CB) in contrast to BiOI, making the composites suitable for visible light excitation. The BiOI/BiOCl composites samples exhibited highly enhanced visible light photocatalytic activity for removal of NO in air due to the large surface areas and pore volume, hierarchical structure and modified band structure, exceeding that of P25, BiOI, C-doped TiO(2) and Bi(2)WO(6). This research results could provide a cost-effective approach for the synthesis of porous hierarchical materials and enhancement of photocatalyst performance for environmental and energetic applications owing to its low cost and easy scaling up. Copyright © 2012 Elsevier B.V. All rights reserved.

Using Nested Contractions and a Hierarchical Tensor Format To Compute Vibrational Spectra of Molecules with Seven Atoms.

PubMed

Thomas, Phillip S; Carrington, Tucker

2015-12-31

We propose a method for solving the vibrational Schrödinger equation with which one can compute hundreds of energy levels of seven-atom molecules using at most a few gigabytes of memory. It uses nested contractions in conjunction with the reduced-rank block power method (RRBPM) described in J. Chem. Phys. 2014, 140, 174111. Successive basis contractions are organized into a tree, the nodes of which are associated with eigenfunctions of reduced-dimension Hamiltonians. The RRBPM is used recursively to compute eigenfunctions of nodes in bases of products of reduced-dimension eigenfunctions of nodes with fewer coordinates. The corresponding vectors are tensors in what is called CP-format. The final wave functions are therefore represented in a hierarchical CP-format. Computational efficiency and accuracy are significantly improved by representing the Hamiltonian in the same hierarchical format as the wave function. We demonstrate that with this hierarchical RRBPM it is possible to compute energy levels of a 64-D coupled-oscillator model Hamiltonian and also of acetonitrile (CH3CN) and ethylene oxide (C2H4O), for which we use quartic potentials. The most accurate acetonitrile calculation uses 139 MB of memory and takes 3.2 h on a single processor. The most accurate ethylene oxide calculation uses 6.1 GB of memory and takes 14 d on 63 processors. The hierarchical RRBPM shatters the memory barrier that impedes the calculation of vibrational spectra.

Hierarchical graph-based segmentation for extracting road networks from high-resolution satellite images

NASA Astrophysics Data System (ADS)

Alshehhi, Rasha; Marpu, Prashanth Reddy

2017-04-01

Extraction of road networks in urban areas from remotely sensed imagery plays an important role in many urban applications (e.g. road navigation, geometric correction of urban remote sensing images, updating geographic information systems, etc.). It is normally difficult to accurately differentiate road from its background due to the complex geometry of the buildings and the acquisition geometry of the sensor. In this paper, we present a new method for extracting roads from high-resolution imagery based on hierarchical graph-based image segmentation. The proposed method consists of: 1. Extracting features (e.g., using Gabor and morphological filtering) to enhance the contrast between road and non-road pixels, 2. Graph-based segmentation consisting of (i) Constructing a graph representation of the image based on initial segmentation and (ii) Hierarchical merging and splitting of image segments based on color and shape features, and 3. Post-processing to remove irregularities in the extracted road segments. Experiments are conducted on three challenging datasets of high-resolution images to demonstrate the proposed method and compare with other similar approaches. The results demonstrate the validity and superior performance of the proposed method for road extraction in urban areas.

2D automatic body-fitted structured mesh generation using advancing extraction method

NASA Astrophysics Data System (ADS)

Zhang, Yaoxin; Jia, Yafei

2018-01-01

This paper presents an automatic mesh generation algorithm for body-fitted structured meshes in Computational Fluids Dynamics (CFD) analysis using the Advancing Extraction Method (AEM). The method is applicable to two-dimensional domains with complex geometries, which have the hierarchical tree-like topography with extrusion-like structures (i.e., branches or tributaries) and intrusion-like structures (i.e., peninsula or dikes). With the AEM, the hierarchical levels of sub-domains can be identified, and the block boundary of each sub-domain in convex polygon shape in each level can be extracted in an advancing scheme. In this paper, several examples were used to illustrate the effectiveness and applicability of the proposed algorithm for automatic structured mesh generation, and the implementation of the method.

Fabrication of hierarchical CoP nanosheet@microwire arrays via space-confined phosphidation toward high-efficiency water oxidation electrocatalysis under alkaline conditions.

PubMed

Ji, Xuqiang; Zhang, Rong; Shi, Xifeng; Asiri, Abdullah M; Zheng, Baozhan; Sun, Xuping

2018-05-03

In spite of recent advances in the synthesis of transition metal phosphide nanostructures, the simple fabrication of hierarchical arrays with more accessible active sites still remains a great challenge. In this Communication, we report a space-confined phosphidation strategy toward developing hierarchical CoP nanosheet@microwire arrays on nickel foam (CoP NS@MW/NF) using a Co(H2PO4)2·2H3PO4 microwire array as the precursor. The thermally stable nature of the anion in the precursor is key to hierarchical nanostructure formation. When used as a 3D electrode for water oxidation electrocatalysis, such CoP NS@MW/NF needs an overpotential as low as 296 mV to drive a geometrical catalytic current density of 100 mA cm-2 in 1.0 M KOH, outperforming all reported Co phosphide catalysts in alkaline media. This catalyst also shows superior long-term electrochemical durability, maintaining its activity for at least 65 h. This study offers us a general method for facile preparation of hierarchical arrays for applications.

Three-Dimensional ZnO Hierarchical Nanostructures: Solution Phase Synthesis and Applications

PubMed Central

Wang, Xiaoliang; Ahmad, Mashkoor

2017-01-01

Zinc oxide (ZnO) nanostructures have been studied extensively in the past 20 years due to their novel electronic, photonic, mechanical and electrochemical properties. Recently, more attention has been paid to assemble nanoscale building blocks into three-dimensional (3D) complex hierarchical structures, which not only inherit the excellent properties of the single building blocks but also provide potential applications in the bottom-up fabrication of functional devices. This review article focuses on 3D ZnO hierarchical nanostructures, and summarizes major advances in the solution phase synthesis, applications in environment, and electrical/electrochemical devices. We present the principles and growth mechanisms of ZnO nanostructures via different solution methods, with an emphasis on rational control of the morphology and assembly. We then discuss the applications of 3D ZnO hierarchical nanostructures in photocatalysis, field emission, electrochemical sensor, and lithium ion batteries. Throughout the discussion, the relationship between the device performance and the microstructures of 3D ZnO hierarchical nanostructures will be highlighted. This review concludes with a personal perspective on the current challenges and future research. PMID:29137195

Facile synthesis of three dimensional hierarchical Co-Al layered double hydroxides on graphene as high-performance materials for supercapacitor electrode.

PubMed

Hao, Jinhui; Yang, Wenshu; Zhang, Zhe; Lu, Baoping; Ke, Xi; Zhang, Bailin; Tang, Jilin

2014-07-15

A facile simple hydrothermal method combined with a post-solution reaction is developed to grow interconnected three dimensional (3D) hierarchical Co-Al layered double hydroxides (LDHs) on reduced graphene oxide (rGO). The obtained 3D hierarchical rGO-LDHs are characterized by field emission scanning electron microscopy, X-ray diffraction, and X-ray photo-electron spectroscopy. As LDHs nanosheets directly grow on the surface of rGO via chemical covalent bonding, the rGO could provide facile electron transport paths in the electrode for the fast Faradaic reaction. Moreover, benefiting from the rational 3D hierarchical structural, the rGO-LDHs demonstrate excellent electrochemical properties with a combination of high charge storage capacitance, fast rate capability and stable cycling performance. Remarkably, the 3D hierarchical rGO-LDHs exhibit specific capacitance values of 599 F g(-1) at a constant current density of 4 A g(-1). The rGO-LDHs also show high charge-discharge reversibility with an efficiency of 92.4% after 5000 cycles. Copyright © 2014 Elsevier Inc. All rights reserved.

Energy-efficient hierarchical processing in the network of wireless intelligent sensors (WISE)

NASA Astrophysics Data System (ADS)

Raskovic, Dejan

Sensor network nodes have benefited from technological advances in the field of wireless communication, processing, and power sources. However, the processing power of microcontrollers is often not sufficient to perform sophisticated processing, while the power requirements of digital signal processing boards or handheld computers are usually too demanding for prolonged system use. We are matching the intrinsic hierarchical nature of many digital signal-processing applications with the natural hierarchy in distributed wireless networks, and building the hierarchical system of wireless intelligent sensors. Our goal is to build a system that will exploit the hierarchical organization to optimize the power consumption and extend battery life for the given time and memory constraints, while providing real-time processing of sensor signals. In addition, we are designing our system to be able to adapt to the current state of the environment, by dynamically changing the algorithm through procedure replacement. This dissertation presents the analysis of hierarchical environment and methods for energy profiling used to evaluate different system design strategies, and to optimize time-effective and energy-efficient processing.

Application of Bayesian inference to the study of hierarchical organization in self-organized complex adaptive systems

NASA Astrophysics Data System (ADS)

Knuth, K. H.

2001-05-01

We consider the application of Bayesian inference to the study of self-organized structures in complex adaptive systems. In particular, we examine the distribution of elements, agents, or processes in systems dominated by hierarchical structure. We demonstrate that results obtained by Caianiello [1] on Hierarchical Modular Systems (HMS) can be found by applying Jaynes' Principle of Group Invariance [2] to a few key assumptions about our knowledge of hierarchical organization. Subsequent application of the Principle of Maximum Entropy allows inferences to be made about specific systems. The utility of the Bayesian method is considered by examining both successes and failures of the hierarchical model. We discuss how Caianiello's original statements suffer from the Mind Projection Fallacy [3] and we restate his assumptions thus widening the applicability of the HMS model. The relationship between inference and statistical physics, described by Jaynes [4], is reiterated with the expectation that this realization will aid the field of complex systems research by moving away from often inappropriate direct application of statistical mechanics to a more encompassing inferential methodology.

Hierarchical Merging and Large-Scale Structure Within the Horologium-Reticulum Supercluster

NASA Astrophysics Data System (ADS)

Fleenor, M. C.; Rose, J. A.

2003-12-01

The Horologium-Reticulum Supercluster (HRS) covers an area of more than 12 x 12 degrees on the sky centered at approximately α = 3h18m43s, δ = -50°01\\arcmin40\\arcsec. It is second only to the Shapley supercluster in terms of mass concentration in the local 300 Mpc. We have now obtained ˜1450 unpublished redshifts via multi-fiber spectroscopy in this area covering both global and localized regions. On a global scale, approximately 550 spectra of galaxies have been obtained using the six-degree field (6dF) instrument on the UK Schmidt Telescope at the Anglo Australian Observatory (25% coverage down to 17.5 BJ). Spectroscopic studies in the localized regions of the HRS were completed with the fibre optic coupled aperture plate system (FOCAP with 40\\arcmin FOV) on the Anglo-Australian Telescope (90% coverage down to 19.0 BJ). This increase of information doubles the amount of coverage compared to previous redshift data and provides a complementary picture of the area. With ˜3000 redshifts in this region, we are understanding the role of the supercluster environment in structure formation and evolution. Specifically, we are probing the dynamical and morphological characteristics of the HRS complex, comparing these with other known supercluster data for similarities, as well as evaluating the hierarchical merging scenario of structure formation as found in CDM N-body simulations.

N-body dark matter haloes with simple hierarchical histories

NASA Astrophysics Data System (ADS)

Jiang, Lilian; Helly, John C.; Cole, Shaun; Frenk, Carlos S.

2014-05-01

We present a new algorithm which groups the subhaloes found in cosmological N-body simulations by structure finders such as SUBFIND into dark matter haloes whose formation histories are strictly hierarchical. One advantage of these `Dhaloes' over the commonly used friends-of-friends (FoF) haloes is that they retain their individual identity in the cases when FoF haloes are artificially merged by tenuous bridges of particles or by an overlap of their outer diffuse haloes. Dhaloes are thus well suited for modelling galaxy formation and their merger trees form the basis of the Durham semi-analytic galaxy formation model, GALFORM. Applying the Dhalo construction to the Λ cold dark matter Millennium II Simulation, we find that approximately 90 per cent of Dhaloes have a one-to-one, bijective match with a corresponding FoF halo. The remaining 10 per cent are typically secondary components of large FoF haloes. Although the mass functions of both types of haloes are similar, the mass of Dhaloes correlates much more tightly with the virial mass, M200, than FoF haloes. Approximately 80 per cent of FoF and bijective and non-bijective Dhaloes are relaxed according to standard criteria. For these relaxed haloes, all three types have similar concentration-M200 relations and, at fixed mass, the concentration distributions are described accurately by log-normal distributions.

A novel biomimetic approach to the design of high-performance ceramic–metal composites

PubMed Central

Launey, Maximilien E.; Munch, Etienne; Alsem, Daan Hein; Saiz, Eduardo; Tomsia, Antoni P.; Ritchie, Robert O.

2010-01-01

The prospect of extending natural biological design to develop new synthetic ceramic–metal composite materials is examined. Using ice-templating of ceramic suspensions and subsequent metal infiltration, we demonstrate that the concept of ordered hierarchical design can be applied to create fine-scale laminated ceramic–metal (bulk) composites that are inexpensive, lightweight and display exceptional damage-tolerance properties. Specifically, Al2O3/Al–Si laminates with ceramic contents up to approximately 40 vol% and with lamellae thicknesses down to 10 µm were processed and characterized. These structures achieve an excellent fracture toughness of 40 MPa√m at a tensile strength of approximately 300 MPa. Salient toughening mechanisms are described together with further toughening strategies. PMID:19828498

A fast combination method in DSmT and its application to recommender system

PubMed Central

Liu, Yihai

2018-01-01

In many applications involving epistemic uncertainties usually modeled by belief functions, it is often necessary to approximate general (non-Bayesian) basic belief assignments (BBAs) to subjective probabilities (called Bayesian BBAs). This necessity occurs if one needs to embed the fusion result in a system based on the probabilistic framework and Bayesian inference (e.g. tracking systems), or if one needs to make a decision in the decision making problems. In this paper, we present a new fast combination method, called modified rigid coarsening (MRC), to obtain the final Bayesian BBAs based on hierarchical decomposition (coarsening) of the frame of discernment. Regarding this method, focal elements with probabilities are coarsened efficiently to reduce computational complexity in the process of combination by using disagreement vector and a simple dichotomous approach. In order to prove the practicality of our approach, this new approach is applied to combine users’ soft preferences in recommender systems (RSs). Additionally, in order to make a comprehensive performance comparison, the proportional conflict redistribution rule #6 (PCR6) is regarded as a baseline in a range of experiments. According to the results of experiments, MRC is more effective in accuracy of recommendations compared to original Rigid Coarsening (RC) method and comparable in computational time. PMID:29351297

Electromagnetic Extended Finite Elements for High-Fidelity Multimaterial Problems LDRD Final Report

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

Siefert, Christopher; Bochev, Pavel Blagoveston; Kramer, Richard Michael Jack

Surface effects are critical to the accurate simulation of electromagnetics (EM) as current tends to concentrate near material surfaces. Sandia EM applications, which include exploding bridge wires for detonator design, electromagnetic launch of flyer plates for material testing and gun design, lightning blast-through for weapon safety, electromagnetic armor, and magnetic flux compression generators, all require accurate resolution of surface effects. These applications operate in a large deformation regime, where body-fitted meshes are impractical and multimaterial elements are the only feasible option. State-of-the-art methods use various mixture models to approximate the multi-physics of these elements. The empirical nature of these modelsmore » can significantly compromise the accuracy of the simulation in this very important surface region. We propose to substantially improve the predictive capability of electromagnetic simulations by removing the need for empirical mixture models at material surfaces. We do this by developing an eXtended Finite Element Method (XFEM) and an associated Conformal Decomposition Finite Element Method (CDFEM) which satisfy the physically required compatibility conditions at material interfaces. We demonstrate the effectiveness of these methods for diffusion and diffusion-like problems on node, edge and face elements in 2D and 3D. We also present preliminary work on h -hierarchical elements and remap algorithms.« less

Hierarchical and Well-Ordered Porous Copper for Liquid Transport Properties Control.

PubMed

Pham, Quang N; Shao, Bowen; Kim, Yongsung; Won, Yoonjin

2018-05-09

Liquid delivery through interconnected pore network is essential for various interfacial transport applications ranging from energy storage to evaporative cooling. The liquid transport performance in porous media can be significantly improved through the use of hierarchical morphology that leverages transport phenomena at different length scales. Traditional surface engineering techniques using chemical or thermal reactions often show nonuniform surface nanostructuring within three-dimensional pore network due to uncontrollable diffusion and reactivity in geometrically complex porous structures. Here, we demonstrate hierarchical architectures on the basis of crystalline copper inverse opals using an electrochemistry approach, which offers volumetric controllability of structural and surface properties within the complex porous metal. The electrochemical process sequentially combines subtractive and additive steps-electrochemical polishing and electrochemical oxidation-to improve surface wetting properties without sacrificing structural permeability. We report the transport performance of the hierarchical inverse opals by measuring the capillary-driven liquid rise. The capillary performance parameter of hierarchically engineered inverse opal ( K/ R eff = ∼5 × 10 -3 μm) is shown to be higher than that of a typical crystalline inverse opal ( K/ R eff = ∼1 × 10 -3 μm) owing to the enhancement in fluid permeable and hydrophilic pathways. The new surface engineering method presented in this work provides a rational approach in designing hierarchical porous copper for transport performance enhancements.

An approach to separating the levels of hierarchical structure building in language and mathematics.

PubMed

Makuuchi, Michiru; Bahlmann, Jörg; Friederici, Angela D

2012-07-19

We aimed to dissociate two levels of hierarchical structure building in language and mathematics, namely 'first-level' (the build-up of hierarchical structure with externally given elements) and 'second-level' (the build-up of hierarchical structure with internally represented elements produced by first-level processes). Using functional magnetic resonance imaging, we investigated these processes in three domains: sentence comprehension, arithmetic calculation (using Reverse Polish notation, which gives two operands followed by an operator) and a working memory control task. All tasks required the build-up of hierarchical structures at the first- and second-level, resulting in a similar computational hierarchy across language and mathematics, as well as in a working memory control task. Using a novel method that estimates the difference in the integration cost for conditions of different trial durations, we found an anterior-to-posterior functional organization in the prefrontal cortex, according to the level of hierarchy. Common to all domains, the ventral premotor cortex (PMv) supports first-level hierarchy building, while the dorsal pars opercularis (POd) subserves second-level hierarchy building, with lower activation for language compared with the other two tasks. These results suggest that the POd and the PMv support domain-general mechanisms for hierarchical structure building, with the POd being uniquely efficient for language.

Hierarchical trie packet classification algorithm based on expectation-maximization clustering.

PubMed

Bi, Xia-An; Zhao, Junxia

2017-01-01

With the development of computer network bandwidth, packet classification algorithms which are able to deal with large-scale rule sets are in urgent need. Among the existing algorithms, researches on packet classification algorithms based on hierarchical trie have become an important packet classification research branch because of their widely practical use. Although hierarchical trie is beneficial to save large storage space, it has several shortcomings such as the existence of backtracking and empty nodes. This paper proposes a new packet classification algorithm, Hierarchical Trie Algorithm Based on Expectation-Maximization Clustering (HTEMC). Firstly, this paper uses the formalization method to deal with the packet classification problem by means of mapping the rules and data packets into a two-dimensional space. Secondly, this paper uses expectation-maximization algorithm to cluster the rules based on their aggregate characteristics, and thereby diversified clusters are formed. Thirdly, this paper proposes a hierarchical trie based on the results of expectation-maximization clustering. Finally, this paper respectively conducts simulation experiments and real-environment experiments to compare the performances of our algorithm with other typical algorithms, and analyzes the results of the experiments. The hierarchical trie structure in our algorithm not only adopts trie path compression to eliminate backtracking, but also solves the problem of low efficiency of trie updates, which greatly improves the performance of the algorithm.

Spine-like Nanostructured Carbon Interconnected by Graphene for High-performance Supercapacitors

NASA Astrophysics Data System (ADS)

Park, Sang-Hoon; Yoon, Seung-Beom; Kim, Hyun-Kyung; Han, Joong Tark; Park, Hae-Woong; Han, Joah; Yun, Seok-Min; Jeong, Han Gi; Roh, Kwang Chul; Kim, Kwang-Bum

2014-08-01

Recent studies on supercapacitors have focused on the development of hierarchical nanostructured carbons by combining two-dimensional graphene and other conductive sp2 carbons, which differ in dimensionality, to improve their electrochemical performance. Herein, we report a strategy for synthesizing a hierarchical graphene-based carbon material, which we shall refer to as spine-like nanostructured carbon, from a one-dimensional graphitic carbon nanofiber by controlling the local graphene/graphitic structure via an expanding process and a co-solvent exfoliation method. Spine-like nanostructured carbon has a unique hierarchical structure of partially exfoliated graphitic blocks interconnected by thin graphene sheets in the same manner as in the case of ligaments. Owing to the exposed graphene layers and interconnected sp2 carbon structure, this hierarchical nanostructured carbon possesses a large, electrochemically accessible surface area with high electrical conductivity and exhibits high electrochemical performance.

Spine-like nanostructured carbon interconnected by graphene for high-performance supercapacitors.

PubMed

Park, Sang-Hoon; Yoon, Seung-Beom; Kim, Hyun-Kyung; Han, Joong Tark; Park, Hae-Woong; Han, Joah; Yun, Seok-Min; Jeong, Han Gi; Roh, Kwang Chul; Kim, Kwang-Bum

2014-08-19

Recent studies on supercapacitors have focused on the development of hierarchical nanostructured carbons by combining two-dimensional graphene and other conductive sp(2) carbons, which differ in dimensionality, to improve their electrochemical performance. Herein, we report a strategy for synthesizing a hierarchical graphene-based carbon material, which we shall refer to as spine-like nanostructured carbon, from a one-dimensional graphitic carbon nanofiber by controlling the local graphene/graphitic structure via an expanding process and a co-solvent exfoliation method. Spine-like nanostructured carbon has a unique hierarchical structure of partially exfoliated graphitic blocks interconnected by thin graphene sheets in the same manner as in the case of ligaments. Owing to the exposed graphene layers and interconnected sp(2) carbon structure, this hierarchical nanostructured carbon possesses a large, electrochemically accessible surface area with high electrical conductivity and exhibits high electrochemical performance.

Spine-like Nanostructured Carbon Interconnected by Graphene for High-performance Supercapacitors

PubMed Central

Park, Sang-Hoon; Yoon, Seung-Beom; Kim, Hyun-Kyung; Han, Joong Tark; Park, Hae-Woong; Han, Joah; Yun, Seok-Min; Jeong, Han Gi; Roh, Kwang Chul; Kim, Kwang-Bum

2014-01-01

Recent studies on supercapacitors have focused on the development of hierarchical nanostructured carbons by combining two-dimensional graphene and other conductive sp2 carbons, which differ in dimensionality, to improve their electrochemical performance. Herein, we report a strategy for synthesizing a hierarchical graphene-based carbon material, which we shall refer to as spine-like nanostructured carbon, from a one-dimensional graphitic carbon nanofiber by controlling the local graphene/graphitic structure via an expanding process and a co-solvent exfoliation method. Spine-like nanostructured carbon has a unique hierarchical structure of partially exfoliated graphitic blocks interconnected by thin graphene sheets in the same manner as in the case of ligaments. Owing to the exposed graphene layers and interconnected sp2 carbon structure, this hierarchical nanostructured carbon possesses a large, electrochemically accessible surface area with high electrical conductivity and exhibits high electrochemical performance. PMID:25134517

Hierarchical Mesoporous Zinc-Nickel-Cobalt Ternary Oxide Nanowire Arrays on Nickel Foam as High-Performance Electrodes for Supercapacitors.

PubMed

Wu, Chun; Cai, Junjie; Zhang, Qiaobao; Zhou, Xiang; Zhu, Ying; Shen, Pei Kang; Zhang, Kaili

2015-12-09

Nickel foam supported hierarchical mesoporous Zn-Ni-Co ternary oxide (ZNCO) nanowire arrays are synthesized by a simple two-step approach including a hydrothermal method and subsequent calcination process and directly utilized for supercapacitive investigation for the first time. The nickel foam supported hierarchical mesoporous ZNCO nanowire arrays possess an ultrahigh specific capacitance value of 2481.8 F g(-1) at 1 A g(-1) and excellent rate capability of about 91.9% capacitance retention at 5 A g(-1). More importantly, an asymmetric supercapacitor with a high energy density (35.6 Wh kg(-1)) and remarkable cycle stability performance (94% capacitance retention over 3000 cycles) is assembled successfully by employing the ZNCO electrode as positive electrode and activated carbon as negative electrode. The remarkable electrochemical behaviors demonstrate that the nickel foam supported hierarchical mesoporous ZNCO nanowire array electrodes are highly desirable for application as advanced supercapacitor electrodes.

Hierarchical structure of stock price fluctuations in financial markets

NASA Astrophysics Data System (ADS)

Gao, Ya-Chun; Cai, Shi-Min; Wang, Bing-Hong

2012-12-01

The financial market and turbulence have been broadly compared on account of the same quantitative methods and several common stylized facts they share. In this paper, the She-Leveque (SL) hierarchy, proposed to explain the anomalous scaling exponents deviating from Kolmogorov monofractal scaling of the velocity fluctuation in fluid turbulence, is applied to study and quantify the hierarchical structure of stock price fluctuations in financial markets. We therefore observed certain interesting results: (i) the hierarchical structure related to multifractal scaling generally presents in all the stock price fluctuations we investigated. (ii) The quantitatively statistical parameters that describe SL hierarchy are different between developed financial markets and emerging ones, distinctively. (iii) For the high-frequency stock price fluctuation, the hierarchical structure varies with different time periods. All these results provide a novel analogy in turbulence and financial market dynamics and an insight to deeply understand multifractality in financial markets.

A Bayesian hierarchical diffusion model decomposition of performance in Approach–Avoidance Tasks

PubMed Central

Krypotos, Angelos-Miltiadis; Beckers, Tom; Kindt, Merel; Wagenmakers, Eric-Jan

2015-01-01

Common methods for analysing response time (RT) tasks, frequently used across different disciplines of psychology, suffer from a number of limitations such as the failure to directly measure the underlying latent processes of interest and the inability to take into account the uncertainty associated with each individual's point estimate of performance. Here, we discuss a Bayesian hierarchical diffusion model and apply it to RT data. This model allows researchers to decompose performance into meaningful psychological processes and to account optimally for individual differences and commonalities, even with relatively sparse data. We highlight the advantages of the Bayesian hierarchical diffusion model decomposition by applying it to performance on Approach–Avoidance Tasks, widely used in the emotion and psychopathology literature. Model fits for two experimental data-sets demonstrate that the model performs well. The Bayesian hierarchical diffusion model overcomes important limitations of current analysis procedures and provides deeper insight in latent psychological processes of interest. PMID:25491372

Hierarchical Macro-meso-microporous ZSM-5 Zeolite Hollow Fibers With Highly Efficient Catalytic Cracking Capability

PubMed Central

Liu, Jia; Jiang, Guiyuan; Liu, Ying; Di, Jiancheng; Wang, Yajun; Zhao, Zhen; Sun, Qianyao; Xu, Chunming; Gao, Jinsen; Duan, Aijun; Liu, Jian; Wei, Yuechang; Zhao, Yong; Jiang, Lei

2014-01-01

Zeolite fibers have attracted growing interest for a range of new applications because of their structural particularity while maintaining the intrinsic performances of the building blocks of zeolites. The fabrication of uniform zeolite fibers with tunable hierarchical porosity and further exploration of their catalytic potential are of great importance. Here, we present a versatile and facile method for the fabrication of hierarchical ZSM-5 zeolite fibers with macro-meso-microporosity by coaxial electrospinning. Due to the synergistic integration of the suitable acidity and the hierarchical porosity, high yield of propylene and excellent anti-coking stability were demonstrated on the as-prepared ZSM-5 hollow fibers in the catalytic cracking reaction of iso-butane. This work may also provide good model catalysts with uniform wall thickness and tunable porosity for studying a series of important catalytic reactions. PMID:25450726

Deep hierarchical attention network for video description

NASA Astrophysics Data System (ADS)

Li, Shuohao; Tang, Min; Zhang, Jun

2018-03-01

Pairing video to natural language description remains a challenge in computer vision and machine translation. Inspired by image description, which uses an encoder-decoder model for reducing visual scene into a single sentence, we propose a deep hierarchical attention network for video description. The proposed model uses convolutional neural network (CNN) and bidirectional LSTM network as encoders while a hierarchical attention network is used as the decoder. Compared to encoder-decoder models used in video description, the bidirectional LSTM network can capture the temporal structure among video frames. Moreover, the hierarchical attention network has an advantage over single-layer attention network on global context modeling. To make a fair comparison with other methods, we evaluate the proposed architecture with different types of CNN structures and decoders. Experimental results on the standard datasets show that our model has a more superior performance than the state-of-the-art techniques.

Metal hierarchical patterning by direct nanoimprint lithography

PubMed Central

Radha, Boya; Lim, Su Hui; Saifullah, Mohammad S. M.; Kulkarni, Giridhar U.

2013-01-01

Three-dimensional hierarchical patterning of metals is of paramount importance in diverse fields involving photonics, controlling surface wettability and wearable electronics. Conventionally, this type of structuring is tedious and usually involves layer-by-layer lithographic patterning. Here, we describe a simple process of direct nanoimprint lithography using palladium benzylthiolate, a versatile metal-organic ink, which not only leads to the formation of hierarchical patterns but also is amenable to layer-by-layer stacking of the metal over large areas. The key to achieving such multi-faceted patterning is hysteretic melting of ink, enabling its shaping. It undergoes transformation to metallic palladium under gentle thermal conditions without affecting the integrity of the hierarchical patterns on micro- as well as nanoscale. A metallic rice leaf structure showing anisotropic wetting behavior and woodpile-like structures were thus fabricated. Furthermore, this method is extendable for transferring imprinted structures to a flexible substrate to make them robust enough to sustain numerous bending cycles. PMID:23446801

Intensity-based hierarchical clustering in CT-scans: application to interactive segmentation in cardiology

NASA Astrophysics Data System (ADS)

Hadida, Jonathan; Desrosiers, Christian; Duong, Luc

2011-03-01

The segmentation of anatomical structures in Computed Tomography Angiography (CTA) is a pre-operative task useful in image guided surgery. Even though very robust and precise methods have been developed to help achieving a reliable segmentation (level sets, active contours, etc), it remains very time consuming both in terms of manual interactions and in terms of computation time. The goal of this study is to present a fast method to find coarse anatomical structures in CTA with few parameters, based on hierarchical clustering. The algorithm is organized as follows: first, a fast non-parametric histogram clustering method is proposed to compute a piecewise constant mask. A second step then indexes all the space-connected regions in the piecewise constant mask. Finally, a hierarchical clustering is achieved to build a graph representing the connections between the various regions in the piecewise constant mask. This step builds up a structural knowledge about the image. Several interactive features for segmentation are presented, for instance association or disassociation of anatomical structures. A comparison with the Mean-Shift algorithm is presented.

Trans-dimensional and hierarchical Bayesian approaches toward rigorous estimation of seismic sources and structures in the Northeast Asia

NASA Astrophysics Data System (ADS)

Kim, Seongryong; Tkalčić, Hrvoje; Mustać, Marija; Rhie, Junkee; Ford, Sean

2016-04-01

A framework is presented within which we provide rigorous estimations for seismic sources and structures in the Northeast Asia. We use Bayesian inversion methods, which enable statistical estimations of models and their uncertainties based on data information. Ambiguities in error statistics and model parameterizations are addressed by hierarchical and trans-dimensional (trans-D) techniques, which can be inherently implemented in the Bayesian inversions. Hence reliable estimation of model parameters and their uncertainties is possible, thus avoiding arbitrary regularizations and parameterizations. Hierarchical and trans-D inversions are performed to develop a three-dimensional velocity model using ambient noise data. To further improve the model, we perform joint inversions with receiver function data using a newly developed Bayesian method. For the source estimation, a novel moment tensor inversion method is presented and applied to regional waveform data of the North Korean nuclear explosion tests. By the combination of new Bayesian techniques and the structural model, coupled with meaningful uncertainties related to each of the processes, more quantitative monitoring and discrimination of seismic events is possible.

Hierarchical Leak Detection and Localization Method in Natural Gas Pipeline Monitoring Sensor Networks

PubMed Central

Wan, Jiangwen; Yu, Yang; Wu, Yinfeng; Feng, Renjian; Yu, Ning

2012-01-01

In light of the problems of low recognition efficiency, high false rates and poor localization accuracy in traditional pipeline security detection technology, this paper proposes a type of hierarchical leak detection and localization method for use in natural gas pipeline monitoring sensor networks. In the signal preprocessing phase, original monitoring signals are dealt with by wavelet transform technology to extract the single mode signals as well as characteristic parameters. In the initial recognition phase, a multi-classifier model based on SVM is constructed and characteristic parameters are sent as input vectors to the multi-classifier for initial recognition. In the final decision phase, an improved evidence combination rule is designed to integrate initial recognition results for final decisions. Furthermore, a weighted average localization algorithm based on time difference of arrival is introduced for determining the leak point’s position. Experimental results illustrate that this hierarchical pipeline leak detection and localization method could effectively improve the accuracy of the leak point localization and reduce the undetected rate as well as false alarm rate. PMID:22368464

Hierarchical leak detection and localization method in natural gas pipeline monitoring sensor networks.

PubMed

Wan, Jiangwen; Yu, Yang; Wu, Yinfeng; Feng, Renjian; Yu, Ning

2012-01-01

In light of the problems of low recognition efficiency, high false rates and poor localization accuracy in traditional pipeline security detection technology, this paper proposes a type of hierarchical leak detection and localization method for use in natural gas pipeline monitoring sensor networks. In the signal preprocessing phase, original monitoring signals are dealt with by wavelet transform technology to extract the single mode signals as well as characteristic parameters. In the initial recognition phase, a multi-classifier model based on SVM is constructed and characteristic parameters are sent as input vectors to the multi-classifier for initial recognition. In the final decision phase, an improved evidence combination rule is designed to integrate initial recognition results for final decisions. Furthermore, a weighted average localization algorithm based on time difference of arrival is introduced for determining the leak point's position. Experimental results illustrate that this hierarchical pipeline leak detection and localization method could effectively improve the accuracy of the leak point localization and reduce the undetected rate as well as false alarm rate.

Acoustic⁻Seismic Mixed Feature Extraction Based on Wavelet Transform for Vehicle Classification in Wireless Sensor Networks.

PubMed

Zhang, Heng; Pan, Zhongming; Zhang, Wenna

2018-06-07

An acoustic⁻seismic mixed feature extraction method based on the wavelet coefficient energy ratio (WCER) of the target signal is proposed in this study for classifying vehicle targets in wireless sensor networks. The signal was decomposed into a set of wavelet coefficients using the à trous algorithm, which is a concise method used to implement the wavelet transform of a discrete signal sequence. After the wavelet coefficients of the target acoustic and seismic signals were obtained, the energy ratio of each layer coefficient was calculated as the feature vector of the target signals. Subsequently, the acoustic and seismic features were merged into an acoustic⁻seismic mixed feature to improve the target classification accuracy after the acoustic and seismic WCER features of the target signal were simplified using the hierarchical clustering method. We selected the support vector machine method for classification and utilized the data acquired from a real-world experiment to validate the proposed method. The calculated results show that the WCER feature extraction method can effectively extract the target features from target signals. Feature simplification can reduce the time consumption of feature extraction and classification, with no effect on the target classification accuracy. The use of acoustic⁻seismic mixed features effectively improved target classification accuracy by approximately 12% compared with either acoustic signal or seismic signal alone.

Estimating abundance of an open population with an N-mixture model using auxiliary data on animal movements.

PubMed

Ketz, Alison C; Johnson, Therese L; Monello, Ryan J; Mack, John A; George, Janet L; Kraft, Benjamin R; Wild, Margaret A; Hooten, Mevin B; Hobbs, N Thompson

2018-04-01

Accurate assessment of abundance forms a central challenge in population ecology and wildlife management. Many statistical techniques have been developed to estimate population sizes because populations change over time and space and to correct for the bias resulting from animals that are present in a study area but not observed. The mobility of individuals makes it difficult to design sampling procedures that account for movement into and out of areas with fixed jurisdictional boundaries. Aerial surveys are the gold standard used to obtain data of large mobile species in geographic regions with harsh terrain, but these surveys can be prohibitively expensive and dangerous. Estimating abundance with ground-based census methods have practical advantages, but it can be difficult to simultaneously account for temporary emigration and observer error to avoid biased results. Contemporary research in population ecology increasingly relies on telemetry observations of the states and locations of individuals to gain insight on vital rates, animal movements, and population abundance. Analytical models that use observations of movements to improve estimates of abundance have not been developed. Here we build upon existing multi-state mark-recapture methods using a hierarchical N-mixture model with multiple sources of data, including telemetry data on locations of individuals, to improve estimates of population sizes. We used a state-space approach to model animal movements to approximate the number of marked animals present within the study area at any observation period, thereby accounting for a frequently changing number of marked individuals. We illustrate the approach using data on a population of elk (Cervus elaphus nelsoni) in Northern Colorado, USA. We demonstrate substantial improvement compared to existing abundance estimation methods and corroborate our results from the ground based surveys with estimates from aerial surveys during the same seasons. We develop a hierarchical Bayesian N-mixture model using multiple sources of data on abundance, movement and survival to estimate the population size of a mobile species that uses remote conservation areas. The model improves accuracy of inference relative to previous methods for estimating abundance of open populations. © 2018 by the Ecological Society of America.

Photocatalytic properties of hierarchical ZnO flowers synthesized by a sucrose-assisted hydrothermal method

NASA Astrophysics Data System (ADS)

Lv, Wei; Wei, Bo; Xu, Lingling; Zhao, Yan; Gao, Hong; Liu, Jia

2012-10-01

In this work, hierarchical ZnO flowers were synthesized via a sucrose-assisted urea hydrothermal method. The thermogravimetric analysis/differential thermal analysis (TGA-DTA) and Fourier transform infrared spectra (FTIR) showed that sucrose acted as a complexing agent in the synthesis process and assisted combustion during annealing. Photocatalytic activity was evaluated using the degradation of organic dye methyl orange. The sucrose added ZnO flowers showed improved activity, which was mainly attributed to the better crystallinity as confirmed by X-ray photoelectron spectroscopy (XPS) analysis. The effect of sucrose amount on photocatalytic activity was also studied.

Variational Gaussian approximation for Poisson data

NASA Astrophysics Data System (ADS)

Arridge, Simon R.; Ito, Kazufumi; Jin, Bangti; Zhang, Chen

2018-02-01

The Poisson model is frequently employed to describe count data, but in a Bayesian context it leads to an analytically intractable posterior probability distribution. In this work, we analyze a variational Gaussian approximation to the posterior distribution arising from the Poisson model with a Gaussian prior. This is achieved by seeking an optimal Gaussian distribution minimizing the Kullback-Leibler divergence from the posterior distribution to the approximation, or equivalently maximizing the lower bound for the model evidence. We derive an explicit expression for the lower bound, and show the existence and uniqueness of the optimal Gaussian approximation. The lower bound functional can be viewed as a variant of classical Tikhonov regularization that penalizes also the covariance. Then we develop an efficient alternating direction maximization algorithm for solving the optimization problem, and analyze its convergence. We discuss strategies for reducing the computational complexity via low rank structure of the forward operator and the sparsity of the covariance. Further, as an application of the lower bound, we discuss hierarchical Bayesian modeling for selecting the hyperparameter in the prior distribution, and propose a monotonically convergent algorithm for determining the hyperparameter. We present extensive numerical experiments to illustrate the Gaussian approximation and the algorithms.

A STUDY OF HIERARCHICAL ORGANIZATION LOCATION MODEL FOR SERVICE INDUSTRY ENTERPRISE

NASA Astrophysics Data System (ADS)

Okumura, Makoto; Takada, Naoki; Okubo, Kazuaki

The service industry has come to participate in many economic activities, but there are few studies, which analyze on the hierarchical organization location of a service industry enterprise that treats information. We propose a hierarchical organized location model, which endogenously determines the number of hierarchies. Furtheremore, We propose a MCMC based statistical method to obtain parameter distributions corresponding to the observed macro emplowment distribution as well as the saralies paid. By using our model, we analyzed the regional disparities about the number of employees and the wage. We found that the change of the regional disparities is caused by internal factors such as the industrial structural change, rather than external factors such as traffic condition changes.

Bayesian X-ray computed tomography using a three-level hierarchical prior model

NASA Astrophysics Data System (ADS)

Wang, Li; Mohammad-Djafari, Ali; Gac, Nicolas

2017-06-01

In recent decades X-ray Computed Tomography (CT) image reconstruction has been largely developed in both medical and industrial domain. In this paper, we propose using the Bayesian inference approach with a new hierarchical prior model. In the proposed model, a generalised Student-t distribution is used to enforce the Haar transformation of images to be sparse. Comparisons with some state of the art methods are presented. It is shown that by using the proposed model, the sparsity of sparse representation of images is enforced, so that edges of images are preserved. Simulation results are also provided to demonstrate the effectiveness of the new hierarchical model for reconstruction with fewer projections.

New Materials and Methods for Hierarchically Structured Tissue Scaffolds

DTIC Science & Technology

2005-01-01

to the fabrication of hierarchically structured scaffolds. In order to achieve this goal, photopolymerizable materials must be developed that are... photopolymerizable materials that can also be selectively chemically modified during the SL part building process. This paper provides an update on our work...which uses a laser to "write" patterns into a vat containing a photopolymerizable resin. The first step in performing SL is generating a computer

Controlled Fabrication of Silk Protein Sericin Mediated Hierarchical Hybrid Flowers and Their Excellent Adsorption Capability of Heavy Metal Ions of Pb(II), Cd(II) and Hg(II).

PubMed

Koley, Pradyot; Sakurai, Makoto; Aono, Masakazu

2016-01-27

Fabrication of protein-inorganic hybrid materials of innumerable hierarchical patterns plays a major role in the development of multifunctional advanced materials with their improved features in synergistic way. However, effective fabrication and applications of the hybrid structures is limited due to the difficulty in control and production cost. Here, we report the controlled fabrication of complex hybrid flowers with hierarchical porosity through a green and facile coprecipitation method by using industrial waste natural silk protein sericin. The large surface areas and porosity of the microsize hybrid flowers enable water purification through adsorption of different heavy metal ions. The high adsorption capacity depends on their morphology, which is changed largely by sericin concentration in their fabrication. Superior adsorption and greater selectivity of the Pb(II) ions have been confirmed by the characteristic growth of needle-shaped nanowires on the hierarchical surface of the hybrid flowers. These hybrid flowers show excellent thermal stability even after complete evaporation of the protein molecules, significantly increasing the porosity of the flower petals. A simple, cost-effective and environmental friendly fabrication method of the porous flowers will lead to a new solution to water pollution required in the modern industrial society.

Comparison of four methods for deriving hospital standardised mortality ratios from a single hierarchical logistic regression model.

PubMed

Mohammed, Mohammed A; Manktelow, Bradley N; Hofer, Timothy P

2016-04-01

There is interest in deriving case-mix adjusted standardised mortality ratios so that comparisons between healthcare providers, such as hospitals, can be undertaken in the controversial belief that variability in standardised mortality ratios reflects quality of care. Typically standardised mortality ratios are derived using a fixed effects logistic regression model, without a hospital term in the model. This fails to account for the hierarchical structure of the data - patients nested within hospitals - and so a hierarchical logistic regression model is more appropriate. However, four methods have been advocated for deriving standardised mortality ratios from a hierarchical logistic regression model, but their agreement is not known and neither do we know which is to be preferred. We found significant differences between the four types of standardised mortality ratios because they reflect a range of underlying conceptual issues. The most subtle issue is the distinction between asking how an average patient fares in different hospitals versus how patients at a given hospital fare at an average hospital. Since the answers to these questions are not the same and since the choice between these two approaches is not obvious, the extent to which profiling hospitals on mortality can be undertaken safely and reliably, without resolving these methodological issues, remains questionable. © The Author(s) 2012.

A hierarchical model for spatial capture-recapture data

USGS Publications Warehouse

Royle, J. Andrew; Young, K.V.

2008-01-01

Estimating density is a fundamental objective of many animal population studies. Application of methods for estimating population size from ostensibly closed populations is widespread, but ineffective for estimating absolute density because most populations are subject to short-term movements or so-called temporary emigration. This phenomenon invalidates the resulting estimates because the effective sample area is unknown. A number of methods involving the adjustment of estimates based on heuristic considerations are in widespread use. In this paper, a hierarchical model of spatially indexed capture recapture data is proposed for sampling based on area searches of spatial sample units subject to uniform sampling intensity. The hierarchical model contains explicit models for the distribution of individuals and their movements, in addition to an observation model that is conditional on the location of individuals during sampling. Bayesian analysis of the hierarchical model is achieved by the use of data augmentation, which allows for a straightforward implementation in the freely available software WinBUGS. We present results of a simulation study that was carried out to evaluate the operating characteristics of the Bayesian estimator under variable densities and movement patterns of individuals. An application of the model is presented for survey data on the flat-tailed horned lizard (Phrynosoma mcallii) in Arizona, USA.

Assessment of Differential Item Functioning in Health-Related Outcomes: A Simulation and Empirical Analysis with Hierarchical Polytomous Data.

PubMed

Sharafi, Zahra; Mousavi, Amin; Ayatollahi, Seyyed Mohammad Taghi; Jafari, Peyman

2017-01-01

The purpose of this study was to evaluate the effectiveness of two methods of detecting differential item functioning (DIF) in the presence of multilevel data and polytomously scored items. The assessment of DIF with multilevel data (e.g., patients nested within hospitals, hospitals nested within districts) from large-scale assessment programs has received considerable attention but very few studies evaluated the effect of hierarchical structure of data on DIF detection for polytomously scored items. The ordinal logistic regression (OLR) and hierarchical ordinal logistic regression (HOLR) were utilized to assess DIF in simulated and real multilevel polytomous data. Six factors (DIF magnitude, grouping variable, intraclass correlation coefficient, number of clusters, number of participants per cluster, and item discrimination parameter) with a fully crossed design were considered in the simulation study. Furthermore, data of Pediatric Quality of Life Inventory™ (PedsQL™) 4.0 collected from 576 healthy school children were analyzed. Overall, results indicate that both methods performed equivalently in terms of controlling Type I error and detection power rates. The current study showed negligible difference between OLR and HOLR in detecting DIF with polytomously scored items in a hierarchical structure. Implications and considerations while analyzing real data were also discussed.

A hierarchical Bayesian method for vibration-based time domain force reconstruction problems

NASA Astrophysics Data System (ADS)

Li, Qiaofeng; Lu, Qiuhai

2018-05-01

Traditional force reconstruction techniques require prior knowledge on the force nature to determine the regularization term. When such information is unavailable, the inappropriate term is easily chosen and the reconstruction result becomes unsatisfactory. In this paper, we propose a novel method to automatically determine the appropriate q as in ℓq regularization and reconstruct the force history. The method incorporates all to-be-determined variables such as the force history, precision parameters and q into a hierarchical Bayesian formulation. The posterior distributions of variables are evaluated by a Metropolis-within-Gibbs sampler. The point estimates of variables and their uncertainties are given. Simulations of a cantilever beam and a space truss under various loading conditions validate the proposed method in providing adaptive determination of q and better reconstruction performance than existing Bayesian methods.

Controller partitioning for integrated flight/propulsion control implementation

NASA Technical Reports Server (NTRS)

Garg, Sanjay

1993-01-01

The notion of partitioning a centralized controller into a decentralized, hierarchical structure suitable for integrated flight/propulsion control (IFPC) implementation is discussed. A systematic procedure is developed for determining partitioned airframe and engine subsystem controllers (subcontrollers), with the desired interconnection structure, that approximate the closed-loop performance and robustness characteristics of a given centralized controller. The procedure is demonstrated by application to IFPC design for a Short Take-Off and Vertical Landing (STOVL) aircraft in the landing approach to hover transition flight phase.

Partitioning of centralized integrated flight/propulsion control design for decentralized implementation

NASA Technical Reports Server (NTRS)

Garg, Sanjay

1993-01-01

The notion of partitioning a centralized controller into a decentralized, hierarchical structure suitable for integrated flight/propulsion control (IFPC) implementation is discussed. A systematic procedure is developed for determining partitioned airframe and engine subsystem controllers (subcontrollers), with the desired interconnection structure, that approximate the closed-loop performance and robustness characteristics of a given centralized controller. The procedure is demonstrated by application to IFPC design for a short take-off and vertical landing (STOVL) aircraft in the landing-approach-to-hover-transition flight phase.

Hierarchical equations of motion method applied to nonequilibrium heat transport in model molecular junctions: Transient heat current and high-order moments of the current operator

NASA Astrophysics Data System (ADS)

Song, Linze; Shi, Qiang

2017-02-01

We present a theoretical approach to study nonequilibrium quantum heat transport in molecular junctions described by a spin-boson type model. Based on the Feynman-Vernon path integral influence functional formalism, expressions for the average value and high-order moments of the heat current operators are derived, which are further obtained directly from the auxiliary density operators (ADOs) in the hierarchical equations of motion (HEOM) method. Distribution of the heat current is then derived from the high-order moments. As the HEOM method is nonperturbative and capable of treating non-Markovian system-environment interactions, the method can be applied to various problems of nonequilibrium quantum heat transport beyond the weak coupling regime.

Best Merge Region Growing with Integrated Probabilistic Classification for Hyperspectral Imagery

NASA Technical Reports Server (NTRS)

Tarabalka, Yuliya; Tilton, James C.

2011-01-01

A new method for spectral-spatial classification of hyperspectral images is proposed. The method is based on the integration of probabilistic classification within the hierarchical best merge region growing algorithm. For this purpose, preliminary probabilistic support vector machines classification is performed. Then, hierarchical step-wise optimization algorithm is applied, by iteratively merging regions with the smallest Dissimilarity Criterion (DC). The main novelty of this method consists in defining a DC between regions as a function of region statistical and geometrical features along with classification probabilities. Experimental results are presented on a 200-band AVIRIS image of the Northwestern Indiana s vegetation area and compared with those obtained by recently proposed spectral-spatial classification techniques. The proposed method improves classification accuracies when compared to other classification approaches.

A nontransferring dry adhesive with hierarchical polymer nanohairs

PubMed Central

Jeong, Hoon Eui; Lee, Jin-Kwan; Kim, Hong Nam; Moon, Sang Heup; Suh, Kahp Y.

2009-01-01

We present a simple yet robust method for fabricating angled, hierarchically patterned high-aspect-ratio polymer nanohairs to generate directionally sensitive dry adhesives. The slanted polymeric nanostructures were molded from an etched polySi substrate containing slanted nanoholes. An angled etching technique was developed to fabricate slanted nanoholes with flat tips by inserting an etch-stop layer of silicon dioxide. This unique etching method was equipped with a Faraday cage system to control the ion-incident angles in the conventional plasma etching system. The polymeric nanohairs were fabricated with tailored leaning angles, sizes, tip shapes, and hierarchical structures. As a result of controlled leaning angle and bulged flat top of the nanohairs, the replicated, slanted nanohairs showed excellent directional adhesion, exhibiting strong shear attachment (≈26 N/cm2 in maximum) in the angled direction and easy detachment (≈2.2 N/cm2) in the opposite direction, with a hysteresis value of ≈10. In addition to single scale nanohairs, monolithic, micro-nanoscale combined hierarchical hairs were also fabricated by using a 2-step UV-assisted molding technique. These hierarchical nanoscale patterns maintained their adhesive force even on a rough surface (roughness <20 μm) because of an increase in the contact area by the enhanced height of hierarchy, whereas simple nanohairs lost their adhesion strength. To demonstrate the potential applications of the adhesive patch, the dry adhesive was used to transport a large-area glass (47.5 × 37.5 cm2, second-generation TFT-LCD glass), which could replace the current electrostatic transport/holding system with further optimization. PMID:19304801

Hierarchical mesosilicalite nanoformulation integrated with cisplatin exhibits target-specific efficient anticancer activity

NASA Astrophysics Data System (ADS)

Jermy, B. Rabindran; Acharya, Sadananda; Ravinayagam, Vijaya; Alghamdi, Hajer Saleh; Akhtar, Sultan; Basuwaidan, Rehab S.

2018-04-01

Hierarchically structured zeolitic ZSM-5 and meso MCM-41 interlinked domain had an impeccable use as catalysis in many applications. The aim of the study was to develop a new drug delivery nanoformulation, specifically, cisplatin/mesosilicalite using top-down approach for cancer therapy. Hierarchical mesosilicalite with variable porosity was synthesized using alkaline molar solution (0.2 and 0.7 M NaOH) and was loaded with cisplatin through equilibrium adsorption technique. Physico-chemical properties of the nanoformulation (IAUM-56—Imam Abdulrahman Bin Faisal University Mesosilicalite-56) were characterized using X-ray diffraction, surface area analysis (BET), Fourier transformed infrared spectroscopy (FT-IR), diffuse reflectance UV-Vis spectroscopy, and transmission electron microscopy. Drug release study and anticancer activity were assayed on HeLa and MCF7 cancer cells using MTT assay. X-ray diffraction pattern showed interrelated meso- and microphases, while BET analysis revealed considerable mesoporosity formation with a remodulation of isotherm hysteresis indicating the presence of hierarchical pores. FT-IR showed the presence of nanozeolitic subunits into mesostructure with a band at about 550 cm-1. IAUM-56 demonstrated high cytotoxic activity against HeLa cancer cells with an LC50 of 0.02 mg/ml, MCF7 cancer cells with an LC50 of 0.05 mg/ml, and less toxic to normal fibroblast cells with an LC50 of approximately ten times higher at 0.5 mg/ml. Overall, IAUM-56 showed a high rate of sustained release of cisplatin imparting target specific cytotoxic effect against tumor cells with at least tenfold lower toxicity on normal fibroblast cells. Our nanoformulation has the potential use in cancer therapy as a targeted drug delivery system.

Multi person detection and tracking based on hierarchical level-set method

NASA Astrophysics Data System (ADS)

Khraief, Chadia; Benzarti, Faouzi; Amiri, Hamid

2018-04-01

In this paper, we propose an efficient unsupervised method for mutli-person tracking based on hierarchical level-set approach. The proposed method uses both edge and region information in order to effectively detect objects. The persons are tracked on each frame of the sequence by minimizing an energy functional that combines color, texture and shape information. These features are enrolled in covariance matrix as region descriptor. The present method is fully automated without the need to manually specify the initial contour of Level-set. It is based on combined person detection and background subtraction methods. The edge-based is employed to maintain a stable evolution, guide the segmentation towards apparent boundaries and inhibit regions fusion. The computational cost of level-set is reduced by using narrow band technique. Many experimental results are performed on challenging video sequences and show the effectiveness of the proposed method.

Chaotic Signal Denoising Based on Hierarchical Threshold Synchrosqueezed Wavelet Transform

NASA Astrophysics Data System (ADS)

Wang, Wen-Bo; Jing, Yun-yu; Zhao, Yan-chao; Zhang, Lian-Hua; Wang, Xiang-Li

2017-12-01

In order to overcoming the shortcoming of single threshold synchrosqueezed wavelet transform(SWT) denoising method, an adaptive hierarchical threshold SWT chaotic signal denoising method is proposed. Firstly, a new SWT threshold function is constructed based on Stein unbiased risk estimation, which is two order continuous derivable. Then, by using of the new threshold function, a threshold process based on the minimum mean square error was implemented, and the optimal estimation value of each layer threshold in SWT chaotic denoising is obtained. The experimental results of the simulating chaotic signal and measured sunspot signals show that, the proposed method can filter the noise of chaotic signal well, and the intrinsic chaotic characteristic of the original signal can be recovered very well. Compared with the EEMD denoising method and the single threshold SWT denoising method, the proposed method can obtain better denoising result for the chaotic signal.

Methods of Feminist Family Therapy Supervision.

ERIC Educational Resources Information Center

Prouty, Anne M.; Thomas, Volker; Johnson, Scott; Long, Janie K.

2001-01-01

Presents three supervision methods which emerged from a qualitative study of the experiences of feminist family therapy supervisors and the therapists they supervised: the supervision contract, collaborative methods, and hierarchical methods. Provides a description of the participants' experiences of these methods and discusses their fit with…

Validating hierarchical verbal autopsy expert algorithms in a large data set with known causes of death.

PubMed

Kalter, Henry D; Perin, Jamie; Black, Robert E

2016-06-01

Physician assessment historically has been the most common method of analyzing verbal autopsy (VA) data. Recently, the World Health Organization endorsed two automated methods, Tariff 2.0 and InterVA-4, which promise greater objectivity and lower cost. A disadvantage of the Tariff method is that it requires a training data set from a prior validation study, while InterVA relies on clinically specified conditional probabilities. We undertook to validate the hierarchical expert algorithm analysis of VA data, an automated, intuitive, deterministic method that does not require a training data set. Using Population Health Metrics Research Consortium study hospital source data, we compared the primary causes of 1629 neonatal and 1456 1-59 month-old child deaths from VA expert algorithms arranged in a hierarchy to their reference standard causes. The expert algorithms were held constant, while five prior and one new "compromise" neonatal hierarchy, and three former child hierarchies were tested. For each comparison, the reference standard data were resampled 1000 times within the range of cause-specific mortality fractions (CSMF) for one of three approximated community scenarios in the 2013 WHO global causes of death, plus one random mortality cause proportions scenario. We utilized CSMF accuracy to assess overall population-level validity, and the absolute difference between VA and reference standard CSMFs to examine particular causes. Chance-corrected concordance (CCC) and Cohen's kappa were used to evaluate individual-level cause assignment. Overall CSMF accuracy for the best-performing expert algorithm hierarchy was 0.80 (range 0.57-0.96) for neonatal deaths and 0.76 (0.50-0.97) for child deaths. Performance for particular causes of death varied, with fairly flat estimated CSMF over a range of reference values for several causes. Performance at the individual diagnosis level was also less favorable than that for overall CSMF (neonatal: best CCC = 0.23, range 0.16-0.33; best kappa = 0.29, 0.23-0.35; child: best CCC = 0.40, 0.19-0.45; best kappa = 0.29, 0.07-0.35). Expert algorithms in a hierarchy offer an accessible, automated method for assigning VA causes of death. Overall population-level accuracy is similar to that of more complex machine learning methods, but without need for a training data set from a prior validation study.

E2 and SN2 Reactions of X(-) + CH3CH2X (X = F, Cl); an ab Initio and DFT Benchmark Study.

PubMed

Bento, A Patrícia; Solà, Miquel; Bickelhaupt, F Matthias

2008-06-01

We have computed consistent benchmark potential energy surfaces (PESs) for the anti-E2, syn-E2, and SN2 pathways of X(-) + CH3CH2X with X = F and Cl. This benchmark has been used to evaluate the performance of 31 popular density functionals, covering local-density approximation, generalized gradient approximation (GGA), meta-GGA, and hybrid density-functional theory (DFT). The ab initio benchmark has been obtained by exploring the PESs using a hierarchical series of ab initio methods [up to CCSD(T)] in combination with a hierarchical series of Gaussian-type basis sets (up to aug-cc-pVQZ). Our best CCSD(T) estimates show that the overall barriers for the various pathways increase in the order anti-E2 (X = F) < SN2 (X = F) < SN2 (X = Cl) ∼ syn-E2 (X = F) < anti-E2 (X = Cl) < syn-E2 (X = Cl). Thus, anti-E2 dominates for F(-) + CH3CH2F, and SN2 dominates for Cl(-) + CH3CH2Cl, while syn-E2 is in all cases the least favorable pathway. Best overall agreement with our ab initio benchmark is obtained by representatives from each of the three categories of functionals, GGA, meta-GGA, and hybrid DFT, with mean absolute errors in, for example, central barriers of 4.3 (OPBE), 2.2 (M06-L), and 2.0 kcal/mol (M06), respectively. Importantly, the hybrid functional BHandH and the meta-GGA M06-L yield incorrect trends and qualitative features of the PESs (in particular, an erroneous preference for SN2 over the anti-E2 in the case of F(-) + CH3CH2F) even though they are among the best functionals as measured by their small mean absolute errors of 3.3 and 2.2 kcal/mol in reaction barriers. OLYP and B3LYP have somewhat higher mean absolute errors in central barriers (5.6 and 4.8 kcal/mol, respectively), but the error distribution is somewhat more uniform, and as a consequence, the correct trends are reproduced.

Accurate detection of hierarchical communities in complex networks based on nonlinear dynamical evolution

NASA Astrophysics Data System (ADS)

Zhuo, Zhao; Cai, Shi-Min; Tang, Ming; Lai, Ying-Cheng

2018-04-01

One of the most challenging problems in network science is to accurately detect communities at distinct hierarchical scales. Most existing methods are based on structural analysis and manipulation, which are NP-hard. We articulate an alternative, dynamical evolution-based approach to the problem. The basic principle is to computationally implement a nonlinear dynamical process on all nodes in the network with a general coupling scheme, creating a networked dynamical system. Under a proper system setting and with an adjustable control parameter, the community structure of the network would "come out" or emerge naturally from the dynamical evolution of the system. As the control parameter is systematically varied, the community hierarchies at different scales can be revealed. As a concrete example of this general principle, we exploit clustered synchronization as a dynamical mechanism through which the hierarchical community structure can be uncovered. In particular, for quite arbitrary choices of the nonlinear nodal dynamics and coupling scheme, decreasing the coupling parameter from the global synchronization regime, in which the dynamical states of all nodes are perfectly synchronized, can lead to a weaker type of synchronization organized as clusters. We demonstrate the existence of optimal choices of the coupling parameter for which the synchronization clusters encode accurate information about the hierarchical community structure of the network. We test and validate our method using a standard class of benchmark modular networks with two distinct hierarchies of communities and a number of empirical networks arising from the real world. Our method is computationally extremely efficient, eliminating completely the NP-hard difficulty associated with previous methods. The basic principle of exploiting dynamical evolution to uncover hidden community organizations at different scales represents a "game-change" type of approach to addressing the problem of community detection in complex networks.

A Marker-Based Approach for the Automated Selection of a Single Segmentation from a Hierarchical Set of Image Segmentations

NASA Technical Reports Server (NTRS)

Tarabalka, Y.; Tilton, J. C.; Benediktsson, J. A.; Chanussot, J.

2012-01-01

The Hierarchical SEGmentation (HSEG) algorithm, which combines region object finding with region object clustering, has given good performances for multi- and hyperspectral image analysis. This technique produces at its output a hierarchical set of image segmentations. The automated selection of a single segmentation level is often necessary. We propose and investigate the use of automatically selected markers for this purpose. In this paper, a novel Marker-based HSEG (M-HSEG) method for spectral-spatial classification of hyperspectral images is proposed. Two classification-based approaches for automatic marker selection are adapted and compared for this purpose. Then, a novel constrained marker-based HSEG algorithm is applied, resulting in a spectral-spatial classification map. Three different implementations of the M-HSEG method are proposed and their performances in terms of classification accuracies are compared. The experimental results, presented for three hyperspectral airborne images, demonstrate that the proposed approach yields accurate segmentation and classification maps, and thus is attractive for remote sensing image analysis.

Facile method for preparing superoleophobic surfaces with hierarchical microcubic/nanowire structures

NASA Astrophysics Data System (ADS)

Kwak, Wonshik; Hwang, Woonbong

2016-02-01

To facilitate the fabrication of superoleophobic surfaces having hierarchical microcubic/nanowire structures (HMNS), even for low surface tension liquids including octane (surface tension = 21.1 mN m-1), and to understand the influences of surface structures on the oleophobicity, we developed a convenient method to achieve superoleophobic surfaces on aluminum substrates using chemical acid etching, anodization and fluorination treatment. The liquid repellency of the structured surface was validated through observable experimental results the contact and sliding angle measurements. The etching condition required to ensure high surface roughness was established, and an optimal anodizing condition was determined, as a critical parameter in building the superoleophobicity. The microcubic structures formed by acid etching are essential for achieving the formation of the hierarchical structure, and therefore, the nanowire structures formed by anodization lead to an enhancement of the superoleophobicity for low surface tension liquids. Under optimized morphology by microcubic/nanowire structures with fluorination treatment, the contact angle over 150° and the sliding angle less than 10° are achieved even for octane.

Chelating agent-free, vapor-assisted crystallization method to synthesize hierarchical microporous/mesoporous MIL-125 (Ti).

PubMed

McNamara, Nicholas D; Hicks, Jason C

2015-03-11

Titanium-based microporous heterogeneous catalysts are widely studied but are often limited by the accessibility of reactants to active sites. Metal-organic frameworks (MOFs), such as MIL-125 (Ti), exhibit enhanced surface areas due to their high intrinsic microporosity, but the pore diameters of most microporous MOFs are often too small to allow for the diffusion of larger reactants (>7 Å) relevant to petroleum and biomass upgrading. In this work, hierarchical microporous MIL-125 exhibiting significantly enhanced interparticle mesoporosity was synthesized using a chelating-free, vapor-assisted crystallization method. The resulting hierarchical MOF was examined as an active catalyst for the oxidation of dibenzothiophene (DBT) with tert-butyl hydroperoxide and outperformed the solely microporous analogue. This was attributed to greater access of the substrate to surface active sites, as the pores in the microporous analogues were of inadequate size to accommodate DBT. Moreover, thiophene adsorption studies suggested the mesoporous MOF contained larger amounts of unsaturated metal sites that could enhance the observed catalytic activity.

A simple template method for hierarchical dendrites of silver nanorods and their applications in catalysis

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

Gao Peng; Zhang Milin; Hou Hongwei

2008-03-04

A novel strategy has been put forward to prepare hierarchical dendrites of silver nanorods via a simple integration method using 'Devarda's template' as a reducing agent and architecture template with the assistance of ultrasonic waves, in which the template was firstly fabricated and employed. The individual silver dendrite is composed of a long central trunk with secondary branches, which preferentially grew in a parallel direction with a definite angle to the trunk. The results reveal that the dendrites are single crystalline in nature and interestingly prove that the silver single crystal has the preferential orientation in <1 1 1> directionmore » in normal conditions. The contrast experiments demonstrated that both 'Devarda's template' and the ultrasonic irradiation are necessary for building hierarchically silver dendrites in a water system. Moreover, the experimental results show that the dendrites of silver nanorods are the superior electrode materials for the electrochemical sensors to detect directly NO{sub 2}{sup -} in aqueous solution.« less

Extinction of cue-evoked drug-seeking relies on degrading hierarchical instrumental expectancies

PubMed Central

Hogarth, Lee; Retzler, Chris; Munafò, Marcus R.; Tran, Dominic M.D.; Troisi, Joseph R.; Rose, Abigail K.; Jones, Andrew; Field, Matt

2014-01-01

There has long been need for a behavioural intervention that attenuates cue-evoked drug-seeking, but the optimal method remains obscure. To address this, we report three approaches to extinguish cue-evoked drug-seeking measured in a Pavlovian to instrumental transfer design, in non-treatment seeking adult smokers and alcohol drinkers. The results showed that the ability of a drug stimulus to transfer control over a separately trained drug-seeking response was not affected by the stimulus undergoing Pavlovian extinction training in experiment 1, but was abolished by the stimulus undergoing discriminative extinction training in experiment 2, and was abolished by explicit verbal instructions stating that the stimulus did not signal a more effective response-drug contingency in experiment 3. These data suggest that cue-evoked drug-seeking is mediated by a propositional hierarchical instrumental expectancy that the drug-seeking response is more likely to be rewarded in that stimulus. Methods which degraded this hierarchical expectancy were effective in the laboratory, and so may have therapeutic potential. PMID:25011113

Hierarchical nanostructures of copper(II) phthalocyanine on electrospun TiO(2) nanofibers: controllable solvothermal-fabrication and enhanced visible photocatalytic properties.

PubMed

Zhang, Mingyi; Shao, Changlu; Guo, Zengcai; Zhang, Zhenyi; Mu, Jingbo; Cao, Tieping; Liu, Yichun

2011-02-01

In the present work, 2,9,16,23-tetranitrophthalocyanine copper(II) (TNCuPc)/TiO(2) hierarchical nanostructures were successfully fabricated by a simple combination method of electrospinning technique and solvothermal processing. Scanning electron microscopy (SEM), energy-dispersive X-ray (EDX) spectroscopy, X-ray diffraction (XRD), UV-vis diffuse reflectance (DR), Fourier transform infrared spectrum (FT-IR), X-ray photoelectron spectroscopy (XPS), and thermal gravimetric and differential thermal analysis (TG-DTA) were used to characterize the as-synthesized TNCuPc/TiO(2) hierarchical nanostructures. The results showed that the secondary TNCuPc nanostructures were not only successfully grown on the primary TiO(2) nanofibers substrates but also uniformly distributed without aggregation. By adjusting the solvothermal fabrication parameters, the TNCuPc nanowires or nanoflowers were facilely fabricated, and also the loading amounts of TNCuPc could be controlled on the TNCuPc/TiO(2) hierarchical nanostructural nanofibers. And, there might exist the interaction between TNCuPc and TiO(2). A possible mechanism for the formation of TNCuPc/TiO(2) hierarchical nanostructures was suggested. The photocatalytic studies revealed that the TNCuPc/TiO(2) hierarchical nanostructures exhibited enhanced photocatalytic efficiency of photodegradation of Rhodamine B (RB) compared with the pure TNCuPc or TiO(2) nanofibers under visible-light irradiation.

Hierarchical trie packet classification algorithm based on expectation-maximization clustering

PubMed Central

Bi, Xia-an; Zhao, Junxia

2017-01-01

With the development of computer network bandwidth, packet classification algorithms which are able to deal with large-scale rule sets are in urgent need. Among the existing algorithms, researches on packet classification algorithms based on hierarchical trie have become an important packet classification research branch because of their widely practical use. Although hierarchical trie is beneficial to save large storage space, it has several shortcomings such as the existence of backtracking and empty nodes. This paper proposes a new packet classification algorithm, Hierarchical Trie Algorithm Based on Expectation-Maximization Clustering (HTEMC). Firstly, this paper uses the formalization method to deal with the packet classification problem by means of mapping the rules and data packets into a two-dimensional space. Secondly, this paper uses expectation-maximization algorithm to cluster the rules based on their aggregate characteristics, and thereby diversified clusters are formed. Thirdly, this paper proposes a hierarchical trie based on the results of expectation-maximization clustering. Finally, this paper respectively conducts simulation experiments and real-environment experiments to compare the performances of our algorithm with other typical algorithms, and analyzes the results of the experiments. The hierarchical trie structure in our algorithm not only adopts trie path compression to eliminate backtracking, but also solves the problem of low efficiency of trie updates, which greatly improves the performance of the algorithm. PMID:28704476

Approximation, abstraction and decomposition in search and optimization

NASA Technical Reports Server (NTRS)

Ellman, Thomas

1992-01-01

In this paper, I discuss four different areas of my research. One portion of my research has focused on automatic synthesis of search control heuristics for constraint satisfaction problems (CSPs). I have developed techniques for automatically synthesizing two types of heuristics for CSPs: Filtering functions are used to remove portions of a search space from consideration. Another portion of my research is focused on automatic synthesis of hierarchic algorithms for solving constraint satisfaction problems (CSPs). I have developed a technique for constructing hierarchic problem solvers based on numeric interval algebra. Another portion of my research is focused on automatic decomposition of design optimization problems. We are using the design of racing yacht hulls as a testbed domain for this research. Decomposition is especially important in the design of complex physical shapes such as yacht hulls. Another portion of my research is focused on intelligent model selection in design optimization. The model selection problem results from the difficulty of using exact models to analyze the performance of candidate designs.

Adaptive Skin Meshes Coarsening for Biomolecular Simulation

PubMed Central

Shi, Xinwei; Koehl, Patrice

2011-01-01

In this paper, we present efficient algorithms for generating hierarchical molecular skin meshes with decreasing size and guaranteed quality. Our algorithms generate a sequence of coarse meshes for both the surfaces and the bounded volumes. Each coarser surface mesh is adaptive to the surface curvature and maintains the topology of the skin surface with guaranteed mesh quality. The corresponding tetrahedral mesh is conforming to the interface surface mesh and contains high quality tetrahedral that decompose both the interior of the molecule and the surrounding region (enclosed in a sphere). Our hierarchical tetrahedral meshes have a number of advantages that will facilitate fast and accurate multigrid PDE solvers. Firstly, the quality of both the surface triangulations and tetrahedral meshes is guaranteed. Secondly, the interface in the tetrahedral mesh is an accurate approximation of the molecular boundary. In particular, all the boundary points lie on the skin surface. Thirdly, our meshes are Delaunay meshes. Finally, the meshes are adaptive to the geometry. PMID:21779137

Template-free synthesis of nitrogen-doped hierarchical porous carbons for CO2 adsorption and supercapacitor electrodes.

PubMed

Bing, Xuefeng; Wei, Yanju; Wang, Mei; Xu, Sheng; Long, Donghui; Wang, Jitong; Qiao, Wenming; Ling, Licheng

2017-02-15

Nitrogen-doped hierarchical porous carbons (NHPCs) with controllable nitrogen content were prepared via a template-free method by direct carbonization of melamine-resorcinol-terephthaldehyde networks. The synthetic approach is facile and gentle, resulting in a hierarchical pore structure with modest micropores and well-developed meso-/macropores, and allowing the easy adjusting of the nitrogen content in the carbon framework. The micropore structure was generated within the highly cross-linked networks of polymer chains, while the mesopore and macropore structure were formed from the interconnected 3D gel network. The as-prepared NHPC has a large specific surface area of 1150m 2 ·g -1 , and a high nitrogen content of 14.5wt.%. CO 2 adsorption performances were measured between 0°C and 75°C, and a high adsorption capacity of 3.96mmol·g -1 was achieved at 1bar and 0°C. Moreover, these nitrogen-doped hierarchical porous carbons exhibit a great potential to act as electrode materials for supercapacitors, which could deliver high specific capacitance of 214.0F·g -1 with an excellent rate capability of 74.7% from 0.1 to 10 A·g -1 . The appropriate nitrogen doping and well-developed hierarchical porosity could accelerate the ion diffusion and the frequency response for excellent capacitive performance. This kind of new nitrogen-doped hierarchical porous carbons with controllable hierarchical porosity and chemical composition may have a good potential in the future applications. Copyright © 2016 Elsevier Inc. All rights reserved.

Control, responses and modularity of cellular regulatory networks: a control analysis perspective.

PubMed

Bruggeman, F J; Snoep, J L; Westerhoff, H V

2008-11-01

Cells adapt to changes in environmental conditions through the concerted action of signalling, gene expression and metabolic subsystems. The authors will discuss a theoretical framework addressing such integrated systems. This 'hierarchical analysis' was first developed as an extension to a metabolic control analysis. It builds on the phenomenon that often the communication between signalling, gene expression and metabolic subsystems is almost exclusively via regulatory interactions and not via mass flow interactions. This allows for the treatment of the said subsystems as 'levels' in a hierarchical view of the organisation of the molecular reaction network of cells. Such a hierarchical approach has as a major advantage that levels can be analysed conceptually in isolation of each other (from a local intra-level perspective) and at a later stage integrated via their interactions (from a global inter-level perspective). Hereby, it allows for a modular approach with variable scope. A number of different approaches have been developed for the analysis of hierarchical systems, for example hierarchical control analysis and modular response analysis. The authors, here, review these methods and illustrate the strength of these types of analyses using a core model of a system with gene expression, metabolic and signal transduction levels.

Hierarchical core-shell structure of ZnO nanorod@NiO/MoO₂ composite nanosheet arrays for high-performance supercapacitors.

PubMed

Hou, Sucheng; Zhang, Guanhua; Zeng, Wei; Zhu, Jian; Gong, Feilong; Li, Feng; Duan, Huigao

2014-08-27

A hierarchical core-shell structure of ZnO nanorod@NiO/MoO2 composite nanosheet arrays on nickel foam substrate for high-performance supercapacitors was constructed by a two-step solution-based method involving two hydrothermal processes followed by a calcination treatment. Compared to one composed of pure NiO/MoO2 composite nanosheets, the hierarchical core-shell structure electrode displays better pseudocapacitive behaviors in 2 M KOH, including high areal specific capacitance values of 1.18 F cm(-2) at 5 mA cm(-2) and 0.6 F cm(-2) at 30 mA cm(-2) as well as relatively good rate capability at high current densities. Furthermore, it also shows remarkable cycle stability, remaining at 91.7% of the initial value even after 4000 cycles at a current density of 10 mA cm(-2). The enhanced pseudocapacitive behaviors are mainly due to the unique hierarchical core-shell structure and the synergistic effect of combining ZnO nanorod arrays and NiO/MoO2 composite nanosheets. This novel hierarchical core-shell structure shows promise for use in next-generation supercapacitors.

Pore-scale study of effects of macroscopic pores and their distributions on reactive transport in hierarchical porous media

DOE PAGES

Chen, Li; Zhang, Ruiyuan; Min, Ting; ...

2018-05-19

For applications of reactive transport in porous media, optimal porous structures should possess both high surface area for reactive sites loading and low mass transport resistance. Hierarchical porous media with a combination of pores at different scales are designed for this purpose. In this paper, using the lattice Boltzmann method, pore-scale numerical studies are conducted to investigate diffusion-reaction processes in 2D hierarchical porous media generated by self-developed reconstruction scheme. Complex interactions between porous structures and reactive transport are revealed under different conditions. Simulation results show that adding macropores can greatly enhance the mass transport, but at the same time reducemore » the reactive surface, leading to complex change trend of the total reaction rate. Effects of gradient distribution of macropores within the porous medium are also investigated. It is found that a front-loose, back-tight (FLBT) hierarchical structure is desirable for enhancing mass transport, increasing total reaction rate, and improving catalyst utilization. Finally, on the whole, from the viewpoint of reducing cost and improving material performance, hierarchical porous structures, especially gradient structures with the size of macropores gradually decreasing along the transport direction, are desirable for catalyst application.« less

Fabrication of hierarchical porous N-doping carbon membrane by using ;confined nanospace deposition; method for supercapacitor

NASA Astrophysics Data System (ADS)

Wang, Guoxu; Liu, Meng; Du, Juan; Liu, Lei; Yu, Yifeng; Sha, Jitong; Chen, Aibing

2018-03-01

The membrane carbon materials with hierarchical porous architecture are attractive because they can provide more channels for ion transport and shorten the ions transport path. Herein, we develop a facile way based on "confined nanospace deposition" to fabricate N-dopi-ng three dimensional hierarchical porous membrane carbon material (N-THPMC) via coating the nickel nitrate, silicate oligomers and triblock copolymer P123 on the branches of commercial polyamide membrane (PAM). During high temperature treatment, the mesoporous silica layer and Ni species serve as a "confined nanospace" and catalyst respectively, which are indispensable elements for formation of carbon framework, and the gas-phase carbon precursors which derive from the decomposition of PAM are deposited into the "confined nanospace" forming carbon framework. The N-THPMC with hierarchical macro/meso/microporous structure, N-doping (2.9%) and large specific surface area (994m2 g-1) well inherits the membrane morphology and hierarchical porous structure of PAM. The N-THPMC as electrode without binder exhibits a specific capacitance of 252 F g-1 at the current density of 1 A g-1 in 6 M KOH electrolyte and excellent cycling stability of 92.7% even after 5000 cycles.

Pore-scale study of effects of macroscopic pores and their distributions on reactive transport in hierarchical porous media

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

Chen, Li; Zhang, Ruiyuan; Min, Ting

For applications of reactive transport in porous media, optimal porous structures should possess both high surface area for reactive sites loading and low mass transport resistance. Hierarchical porous media with a combination of pores at different scales are designed for this purpose. In this paper, using the lattice Boltzmann method, pore-scale numerical studies are conducted to investigate diffusion-reaction processes in 2D hierarchical porous media generated by self-developed reconstruction scheme. Complex interactions between porous structures and reactive transport are revealed under different conditions. Simulation results show that adding macropores can greatly enhance the mass transport, but at the same time reducemore » the reactive surface, leading to complex change trend of the total reaction rate. Effects of gradient distribution of macropores within the porous medium are also investigated. It is found that a front-loose, back-tight (FLBT) hierarchical structure is desirable for enhancing mass transport, increasing total reaction rate, and improving catalyst utilization. Finally, on the whole, from the viewpoint of reducing cost and improving material performance, hierarchical porous structures, especially gradient structures with the size of macropores gradually decreasing along the transport direction, are desirable for catalyst application.« less

Co3O4 based non-enzymatic glucose sensor with high sensitivity and reliable stability derived from hollow hierarchical architecture.

PubMed

Tian, Liangliang; He, Gege; Cai, Yanhua; Wu, Shenping; Su, Yongyao; Yan, Hengqing; Yang, Cong; Chen, Yanling; Li, Lu

2018-02-16

Inspired by kinetics, the design of hollow hierarchical electrocatalysts through large-scale integration of building blocks is recognized as an effective approach to the achievement of superior electrocatalytic performance. In this work, a hollow, hierarchical Co 3 O 4 architecture (Co 3 O 4 HHA) was constructed using a coordinated etching and precipitation (CEP) method followed by calcination. The resulting Co 3 O 4 HHA electrode exhibited excellent electrocatalytic activity in terms of high sensitivity (839.3 μA mM -1 cm -2 ) and reliable stability in glucose detection. The high sensitivity could be attributed to the large specific surface area (SSA), ample unimpeded penetration diffusion paths and high electron transfer rate originating from the unique two-dimensional (2D) sheet-like character and hollow porous architecture. The hollow hierarchical structure also affords sufficient interspace for accommodation of volume change and structural strain, resulting in enhanced stability. The results indicate that Co 3 O 4 HHA could have potential for application in the design of non-enzymatic glucose sensors, and that the construction of hollow hierarchical architecture provides an efficient way to design highly active, stable electrocatalysts.

Co3O4 based non-enzymatic glucose sensor with high sensitivity and reliable stability derived from hollow hierarchical architecture

NASA Astrophysics Data System (ADS)

Tian, Liangliang; He, Gege; Cai, Yanhua; Wu, Shenping; Su, Yongyao; Yan, Hengqing; Yang, Cong; Chen, Yanling; Li, Lu

2018-02-01

Inspired by kinetics, the design of hollow hierarchical electrocatalysts through large-scale integration of building blocks is recognized as an effective approach to the achievement of superior electrocatalytic performance. In this work, a hollow, hierarchical Co3O4 architecture (Co3O4 HHA) was constructed using a coordinated etching and precipitation (CEP) method followed by calcination. The resulting Co3O4 HHA electrode exhibited excellent electrocatalytic activity in terms of high sensitivity (839.3 μA mM-1 cm-2) and reliable stability in glucose detection. The high sensitivity could be attributed to the large specific surface area (SSA), ample unimpeded penetration diffusion paths and high electron transfer rate originating from the unique two-dimensional (2D) sheet-like character and hollow porous architecture. The hollow hierarchical structure also affords sufficient interspace for accommodation of volume change and structural strain, resulting in enhanced stability. The results indicate that Co3O4 HHA could have potential for application in the design of non-enzymatic glucose sensors, and that the construction of hollow hierarchical architecture provides an efficient way to design highly active, stable electrocatalysts.

Bayesian data analysis in population ecology: motivations, methods, and benefits

USGS Publications Warehouse

Dorazio, Robert

2016-01-01

During the 20th century ecologists largely relied on the frequentist system of inference for the analysis of their data. However, in the past few decades ecologists have become increasingly interested in the use of Bayesian methods of data analysis. In this article I provide guidance to ecologists who would like to decide whether Bayesian methods can be used to improve their conclusions and predictions. I begin by providing a concise summary of Bayesian methods of analysis, including a comparison of differences between Bayesian and frequentist approaches to inference when using hierarchical models. Next I provide a list of problems where Bayesian methods of analysis may arguably be preferred over frequentist methods. These problems are usually encountered in analyses based on hierarchical models of data. I describe the essentials required for applying modern methods of Bayesian computation, and I use real-world examples to illustrate these methods. I conclude by summarizing what I perceive to be the main strengths and weaknesses of using Bayesian methods to solve ecological inference problems.

Bias correction in the hierarchical likelihood approach to the analysis of multivariate survival data.

PubMed

Jeon, Jihyoun; Hsu, Li; Gorfine, Malka

2012-07-01

Frailty models are useful for measuring unobserved heterogeneity in risk of failures across clusters, providing cluster-specific risk prediction. In a frailty model, the latent frailties shared by members within a cluster are assumed to act multiplicatively on the hazard function. In order to obtain parameter and frailty variate estimates, we consider the hierarchical likelihood (H-likelihood) approach (Ha, Lee and Song, 2001. Hierarchical-likelihood approach for frailty models. Biometrika 88, 233-243) in which the latent frailties are treated as "parameters" and estimated jointly with other parameters of interest. We find that the H-likelihood estimators perform well when the censoring rate is low, however, they are substantially biased when the censoring rate is moderate to high. In this paper, we propose a simple and easy-to-implement bias correction method for the H-likelihood estimators under a shared frailty model. We also extend the method to a multivariate frailty model, which incorporates complex dependence structure within clusters. We conduct an extensive simulation study and show that the proposed approach performs very well for censoring rates as high as 80%. We also illustrate the method with a breast cancer data set. Since the H-likelihood is the same as the penalized likelihood function, the proposed bias correction method is also applicable to the penalized likelihood estimators.

Improved Hierarchical Optimization-Based Classification of Hyperspectral Images Using Shape Analysis

NASA Technical Reports Server (NTRS)

Tarabalka, Yuliya; Tilton, James C.

2012-01-01

A new spectral-spatial method for classification of hyperspectral images is proposed. The HSegClas method is based on the integration of probabilistic classification and shape analysis within the hierarchical step-wise optimization algorithm. First, probabilistic support vector machines classification is applied. Then, at each iteration two neighboring regions with the smallest Dissimilarity Criterion (DC) are merged, and classification probabilities are recomputed. The important contribution of this work consists in estimating a DC between regions as a function of statistical, classification and geometrical (area and rectangularity) features. Experimental results are presented on a 102-band ROSIS image of the Center of Pavia, Italy. The developed approach yields more accurate classification results when compared to previously proposed methods.

RHSEG and Subdue: Background and Preliminary Approach for Combining these Technologies for Enhanced Image Data Analysis, Mining and Knowledge Discovery

NASA Technical Reports Server (NTRS)

Tilton, James C.; Cook, Diane J.

2008-01-01

Under a project recently selected for funding by NASA's Science Mission Directorate under the Applied Information Systems Research (AISR) program, Tilton and Cook will design and implement the integration of the Subdue graph based knowledge discovery system, developed at the University of Texas Arlington and Washington State University, with image segmentation hierarchies produced by the RHSEG software, developed at NASA GSFC, and perform pilot demonstration studies of data analysis, mining and knowledge discovery on NASA data. Subdue represents a method for discovering substructures in structural databases. Subdue is devised for general-purpose automated discovery, concept learning, and hierarchical clustering, with or without domain knowledge. Subdue was developed by Cook and her colleague, Lawrence B. Holder. For Subdue to be effective in finding patterns in imagery data, the data must be abstracted up from the pixel domain. An appropriate abstraction of imagery data is a segmentation hierarchy: a set of several segmentations of the same image at different levels of detail in which the segmentations at coarser levels of detail can be produced from simple merges of regions at finer levels of detail. The RHSEG program, a recursive approximation to a Hierarchical Segmentation approach (HSEG), can produce segmentation hierarchies quickly and effectively for a wide variety of images. RHSEG and HSEG were developed at NASA GSFC by Tilton. In this presentation we provide background on the RHSEG and Subdue technologies and present a preliminary analysis on how RHSEG and Subdue may be combined to enhance image data analysis, mining and knowledge discovery.