Phase space interrogation of the empirical response modes for seismically excited structures

NASA Astrophysics Data System (ADS)

Paul, Bibhas; George, Riya C.; Mishra, Sudib K.

2017-07-01

Conventional Phase Space Interrogation (PSI) for structural damage assessment relies on exciting the structure with low dimensional chaotic waveform, thereby, significantly limiting their applicability to large structures. The PSI technique is presently extended for structure subjected to seismic excitations. The high dimensionality of the phase space for seismic response(s) are overcome by the Empirical Mode Decomposition (EMD), decomposing the responses to a number of intrinsic low dimensional oscillatory modes, referred as Intrinsic Mode Functions (IMFs). Along with their low dimensionality, a few IMFs, retain sufficient information of the system dynamics to reflect the damage induced changes. The mutually conflicting nature of low-dimensionality and the sufficiency of dynamic information are taken care by the optimal choice of the IMF(s), which is shown to be the third/fourth IMFs. The optimal IMF(s) are employed for the reconstruction of the Phase space attractor following Taken's embedding theorem. The widely referred Changes in Phase Space Topology (CPST) feature is then employed on these Phase portrait(s) to derive the damage sensitive feature, referred as the CPST of the IMFs (CPST-IMF). The legitimacy of the CPST-IMF is established as a damage sensitive feature by assessing its variation with a number of damage scenarios benchmarked in the IASC-ASCE building. The damage localization capability, remarkable tolerance to noise contamination and the robustness under different seismic excitations of the feature are demonstrated.

Three-dimensional perspective software for representation of digital imagery data. [Olympic National Park, Washington

NASA Technical Reports Server (NTRS)

Junkin, B. G.

1980-01-01

A generalized three dimensional perspective software capability was developed within the framework of a low cost computer oriented geographically based information system using the Earth Resources Laboratory Applications Software (ELAS) operating subsystem. This perspective software capability, developed primarily to support data display requirements at the NASA/NSTL Earth Resources Laboratory, provides a means of displaying three dimensional feature space object data in two dimensional picture plane coordinates and makes it possible to overlay different types of information on perspective drawings to better understand the relationship of physical features. An example topographic data base is constructed and is used as the basic input to the plotting module. Examples are shown which illustrate oblique viewing angles that convey spatial concepts and relationships represented by the topographic data planes.

Coupled multiview autoencoders with locality sensitivity for three-dimensional human pose estimation

NASA Astrophysics Data System (ADS)

Yu, Jialin; Sun, Jifeng; Luo, Shasha; Duan, Bichao

2017-09-01

Estimating three-dimensional (3D) human poses from a single camera is usually implemented by searching pose candidates with image descriptors. Existing methods usually suppose that the mapping from feature space to pose space is linear, but in fact, their mapping relationship is highly nonlinear, which heavily degrades the performance of 3D pose estimation. We propose a method to recover 3D pose from a silhouette image. It is based on the multiview feature embedding (MFE) and the locality-sensitive autoencoders (LSAEs). On the one hand, we first depict the manifold regularized sparse low-rank approximation for MFE and then the input image is characterized by a fused feature descriptor. On the other hand, both the fused feature and its corresponding 3D pose are separately encoded by LSAEs. A two-layer back-propagation neural network is trained by parameter fine-tuning and then used to map the encoded 2D features to encoded 3D poses. Our LSAE ensures a good preservation of the local topology of data points. Experimental results demonstrate the effectiveness of our proposed method.

Complex Functions with GeoGebra

ERIC Educational Resources Information Center

Breda, Ana Maria D'azevedo; Dos Santos, José Manuel Dos Santos

2016-01-01

Complex functions, generally feature some interesting peculiarities, seen as extensions of real functions. The visualization of complex functions properties usually requires the simultaneous visualization of two-dimensional spaces. The multiple Windows of GeoGebra, combined with its ability of algebraic computation with complex numbers, allow the…

Fukunaga-Koontz transform based dimensionality reduction for hyperspectral imagery

NASA Astrophysics Data System (ADS)

Ochilov, S.; Alam, M. S.; Bal, A.

2006-05-01

Fukunaga-Koontz Transform based technique offers some attractive properties for desired class oriented dimensionality reduction in hyperspectral imagery. In FKT, feature selection is performed by transforming into a new space where feature classes have complimentary eigenvectors. Dimensionality reduction technique based on these complimentary eigenvector analysis can be described under two classes, desired class and background clutter, such that each basis function best represent one class while carrying the least amount of information from the second class. By selecting a few eigenvectors which are most relevant to desired class, one can reduce the dimension of hyperspectral cube. Since the FKT based technique reduces data size, it provides significant advantages for near real time detection applications in hyperspectral imagery. Furthermore, the eigenvector selection approach significantly reduces computation burden via the dimensionality reduction processes. The performance of the proposed dimensionality reduction algorithm has been tested using real-world hyperspectral dataset.

Self-organizing neural networks--an alternative way of cluster analysis in clinical chemistry.

PubMed

Reibnegger, G; Wachter, H

1996-04-15

Supervised learning schemes have been employed by several workers for training neural networks designed to solve clinical problems. We demonstrate that unsupervised techniques can also produce interesting and meaningful results. Using a data set on the chemical composition of milk from 22 different mammals, we demonstrate that self-organizing feature maps (Kohonen networks) as well as a modified version of error backpropagation technique yield results mimicking conventional cluster analysis. Both techniques are able to project a potentially multi-dimensional input vector onto a two-dimensional space whereby neighborhood relationships remain conserved. Thus, these techniques can be used for reducing dimensionality of complicated data sets and for enhancing comprehensibility of features hidden in the data matrix.

Online dimensionality reduction using competitive learning and Radial Basis Function network.

PubMed

Tomenko, Vladimir

2011-06-01

The general purpose dimensionality reduction method should preserve data interrelations at all scales. Additional desired features include online projection of new data, processing nonlinearly embedded manifolds and large amounts of data. The proposed method, called RBF-NDR, combines these features. RBF-NDR is comprised of two modules. The first module learns manifolds by utilizing modified topology representing networks and geodesic distance in data space and approximates sampled or streaming data with a finite set of reference patterns, thus achieving scalability. Using input from the first module, the dimensionality reduction module constructs mappings between observation and target spaces. Introduction of specific loss function and synthesis of the training algorithm for Radial Basis Function network results in global preservation of data structures and online processing of new patterns. The RBF-NDR was applied for feature extraction and visualization and compared with Principal Component Analysis (PCA), neural network for Sammon's projection (SAMANN) and Isomap. With respect to feature extraction, the method outperformed PCA and yielded increased performance of the model describing wastewater treatment process. As for visualization, RBF-NDR produced superior results compared to PCA and SAMANN and matched Isomap. For the Topic Detection and Tracking corpus, the method successfully separated semantically different topics. Copyright © 2011 Elsevier Ltd. All rights reserved.

Growing a hypercubical output space in a self-organizing feature map.

PubMed

Bauer, H U; Villmann, T

1997-01-01

Neural maps project data from an input space onto a neuron position in a (often lower dimensional) output space grid in a neighborhood preserving way, with neighboring neurons in the output space responding to neighboring data points in the input space. A map-learning algorithm can achieve an optimal neighborhood preservation only, if the output space topology roughly matches the effective structure of the data in the input space. We here present a growth algorithm, called the GSOM or growing self-organizing map, which enhances a widespread map self-organization process, Kohonen's self-organizing feature map (SOFM), by an adaptation of the output space grid during learning. The GSOM restricts the output space structure to the shape of a general hypercubical shape, with the overall dimensionality of the grid and its extensions along the different directions being subject of the adaptation. This constraint meets the demands of many larger information processing systems, of which the neural map can be a part. We apply our GSOM-algorithm to three examples, two of which involve real world data. Using recently developed methods for measuring the degree of neighborhood preservation in neural maps, we find the GSOM-algorithm to produce maps which preserve neighborhoods in a nearly optimal fashion.

A real negative selection algorithm with evolutionary preference for anomaly detection

NASA Astrophysics Data System (ADS)

Yang, Tao; Chen, Wen; Li, Tao

2017-04-01

Traditional real negative selection algorithms (RNSAs) adopt the estimated coverage (c0) as the algorithm termination threshold, and generate detectors randomly. With increasing dimensions, the data samples could reside in the low-dimensional subspace, so that the traditional detectors cannot effectively distinguish these samples. Furthermore, in high-dimensional feature space, c0 cannot exactly reflect the detectors set coverage rate for the nonself space, and it could lead the algorithm to be terminated unexpectedly when the number of detectors is insufficient. These shortcomings make the traditional RNSAs to perform poorly in high-dimensional feature space. Based upon "evolutionary preference" theory in immunology, this paper presents a real negative selection algorithm with evolutionary preference (RNSAP). RNSAP utilizes the "unknown nonself space", "low-dimensional target subspace" and "known nonself feature" as the evolutionary preference to guide the generation of detectors, thus ensuring the detectors can cover the nonself space more effectively. Besides, RNSAP uses redundancy to replace c0 as the termination threshold, in this way RNSAP can generate adequate detectors under a proper convergence rate. The theoretical analysis and experimental result demonstrate that, compared to the classical RNSA (V-detector), RNSAP can achieve a higher detection rate, but with less detectors and computing cost.

GPS test range mission planning

NASA Astrophysics Data System (ADS)

Roberts, Iris P.; Hancock, Thomas P.

The principal features of the Test Range User Mission Planner (TRUMP), a PC-resident tool designed to aid in deploying and utilizing GPS-based test range assets, are reviewed. TRUMP features time history plots of time-space-position information (TSPI); performance based on a dynamic GPS/inertial system simulation; time history plots of TSPI data link connectivity; digital terrain elevation data maps with user-defined cultural features; and two-dimensional coverage plots of ground-based test range assets. Some functions to be added during the next development phase are discussed.

Q-space analysis of light scattering by ice crystals

NASA Astrophysics Data System (ADS)

Heinson, Yuli W.; Maughan, Justin B.; Ding, Jiachen; Chakrabarti, Amitabha; Yang, Ping; Sorensen, Christopher M.

2016-12-01

Q-space analysis is applied to extensive simulations of the single-scattering properties of ice crystals with various habits/shapes over a range of sizes. The analysis uncovers features common to all the shapes: a forward scattering regime with intensity quantitatively related to the Rayleigh scattering by the particle and the internal coupling parameter, followed by a Guinier regime dependent upon the particle size, a complex power law regime with incipient two dimensional diffraction effects, and, in some cases, an enhanced backscattering regime. The effects of significant absorption on the scattering profile are also studied. The overall features found for the ice crystals are similar to features in scattering from same sized spheres.

Clustering by reordering of similarity and Laplacian matrices: Application to galaxy clusters

NASA Astrophysics Data System (ADS)

Mahmoud, E.; Shoukry, A.; Takey, A.

2018-04-01

Similarity metrics, kernels and similarity-based algorithms have gained much attention due to their increasing applications in information retrieval, data mining, pattern recognition and machine learning. Similarity Graphs are often adopted as the underlying representation of similarity matrices and are at the origin of known clustering algorithms such as spectral clustering. Similarity matrices offer the advantage of working in object-object (two-dimensional) space where visualization of clusters similarities is available instead of object-features (multi-dimensional) space. In this paper, sparse ɛ-similarity graphs are constructed and decomposed into strong components using appropriate methods such as Dulmage-Mendelsohn permutation (DMperm) and/or Reverse Cuthill-McKee (RCM) algorithms. The obtained strong components correspond to groups (clusters) in the input (feature) space. Parameter ɛi is estimated locally, at each data point i from a corresponding narrow range of the number of nearest neighbors. Although more advanced clustering techniques are available, our method has the advantages of simplicity, better complexity and direct visualization of the clusters similarities in a two-dimensional space. Also, no prior information about the number of clusters is needed. We conducted our experiments on two and three dimensional, low and high-sized synthetic datasets as well as on an astronomical real-dataset. The results are verified graphically and analyzed using gap statistics over a range of neighbors to verify the robustness of the algorithm and the stability of the results. Combining the proposed algorithm with gap statistics provides a promising tool for solving clustering problems. An astronomical application is conducted for confirming the existence of 45 galaxy clusters around the X-ray positions of galaxy clusters in the redshift range [0.1..0.8]. We re-estimate the photometric redshifts of the identified galaxy clusters and obtain acceptable values compared to published spectroscopic redshifts with a 0.029 standard deviation of their differences.

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

NASA Astrophysics Data System (ADS)

Seyyedi, Seyyed Hossein; Minaei-Bidgoli, Behrouz

2017-03-01

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

Inhomogeneous kinetic effects related to intermittent magnetic discontinuities

NASA Astrophysics Data System (ADS)

Greco, A.; Valentini, F.; Servidio, S.; Matthaeus, W. H.

2012-12-01

A connection between kinetic processes and two-dimensional intermittent plasma turbulence is observed using direct numerical simulations of a hybrid Vlasov-Maxwell model, in which the Vlasov equation is solved for protons, while the electrons are described as a massless fluid. During the development of turbulence, the proton distribution functions depart from the typical configuration of local thermodynamic equilibrium, displaying statistically significant non-Maxwellian features. In particular, temperature anisotropy and distortions are concentrated near coherent structures, generated as the result of the turbulent cascade, such as current sheets, which are nonuniformly distributed in space. Here, the partial variance of increments (PVI) method has been employed to identify high magnetic stress regions within a two-dimensional turbulent pattern. A quantitative association between non-Maxwellian features and coherent structures is established.

Baby de Sitter black holes and dS3/CFT2

NASA Astrophysics Data System (ADS)

de Buyl, Sophie; Detournay, Stéphane; Giribet, Gaston; Ng, Gim Seng

2014-02-01

Unlike three-dimensional Einstein gravity, three-dimensional massive gravity admits asymptotically de Sitter space (dS) black hole solutions. These black holes present interesting features and provide us with toy models to study the dS/CFT correspondence. A remarkable property of these black holes is that they are always in thermal equilibrium with the cosmological horizon of the space that hosts them. This invites us to study the thermodynamics of these solutions within the context of dS/CFT. We study the asymptotic symmetry group of the theory and find that it indeed coincides with the local two-dimensional conformal algebra. The charge algebra associated to the asymptotic Killing vectors consists of two copies of the Virasoro algebra with non-vanishing central extension. We compute the mass and angular momentum of the dS black holes and verify that a naive application of Cardy's formula exactly reproduces the entropy of both the black hole and the cosmological horizon. By adapting the holographic renormalization techniques to the case of dS space, we define the boundary stress tensor of the dual Euclidean conformal field theory.

A hybrid fault diagnosis approach based on mixed-domain state features for rotating machinery.

PubMed

Xue, Xiaoming; Zhou, Jianzhong

2017-01-01

To make further improvement in the diagnosis accuracy and efficiency, a mixed-domain state features data based hybrid fault diagnosis approach, which systematically blends both the statistical analysis approach and the artificial intelligence technology, is proposed in this work for rolling element bearings. For simplifying the fault diagnosis problems, the execution of the proposed method is divided into three steps, i.e., fault preliminary detection, fault type recognition and fault degree identification. In the first step, a preliminary judgment about the health status of the equipment can be evaluated by the statistical analysis method based on the permutation entropy theory. If fault exists, the following two processes based on the artificial intelligence approach are performed to further recognize the fault type and then identify the fault degree. For the two subsequent steps, mixed-domain state features containing time-domain, frequency-domain and multi-scale features are extracted to represent the fault peculiarity under different working conditions. As a powerful time-frequency analysis method, the fast EEMD method was employed to obtain multi-scale features. Furthermore, due to the information redundancy and the submergence of original feature space, a novel manifold learning method (modified LGPCA) is introduced to realize the low-dimensional representations for high-dimensional feature space. Finally, two cases with 12 working conditions respectively have been employed to evaluate the performance of the proposed method, where vibration signals were measured from an experimental bench of rolling element bearing. The analysis results showed the effectiveness and the superiority of the proposed method of which the diagnosis thought is more suitable for practical application. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Visual scan-path analysis with feature space transient fixation moments

NASA Astrophysics Data System (ADS)

Dempere-Marco, Laura; Hu, Xiao-Peng; Yang, Guang-Zhong

2003-05-01

The study of eye movements provides useful insight into the cognitive processes underlying visual search tasks. The analysis of the dynamics of eye movements has often been approached from a purely spatial perspective. In many cases, however, it may not be possible to define meaningful or consistent dynamics without considering the features underlying the scan paths. In this paper, the definition of the feature space has been attempted through the concept of visual similarity and non-linear low dimensional embedding, which defines a mapping from the image space into a low dimensional feature manifold that preserves the intrinsic similarity of image patterns. This has enabled the definition of perceptually meaningful features without the use of domain specific knowledge. Based on this, this paper introduces a new concept called Feature Space Transient Fixation Moments (TFM). The approach presented tackles the problem of feature space representation of visual search through the use of TFM. We demonstrate the practical values of this concept for characterizing the dynamics of eye movements in goal directed visual search tasks. We also illustrate how this model can be used to elucidate the fundamental steps involved in skilled search tasks through the evolution of transient fixation moments.

Application of Generative Topographic Mapping to Gear Failures Monitoring

NASA Astrophysics Data System (ADS)

Liao, Guanglan; Li, Weihua; Shi, Tielin; Rao, Raj B. K. N.

2002-07-01

The Generative Topographic Mapping (GTM) model is introduced as a probabilistic re-formation of the self-organizing map and has already been used in a variety of applications. This paper presents a study of the GTM in industrial gear failures monitoring. Vibration signals are analyzed using the GTM model, and the results show that gear feature data sets can be projected into a two-dimensional space and clustered in different areas according to their conditions, which can classify and identify clearly a gear work condition with cracked or broken tooth compared with the normal condition. With the trace of the image points in the two-dimensional space, the variation of gear work conditions can be observed visually, therefore, the occurrence and varying trend of gear failures can be monitored in time.

Target oriented dimensionality reduction of hyperspectral data by Kernel Fukunaga-Koontz Transform

NASA Astrophysics Data System (ADS)

Binol, Hamidullah; Ochilov, Shuhrat; Alam, Mohammad S.; Bal, Abdullah

2017-02-01

Principal component analysis (PCA) is a popular technique in remote sensing for dimensionality reduction. While PCA is suitable for data compression, it is not necessarily an optimal technique for feature extraction, particularly when the features are exploited in supervised learning applications (Cheriyadat and Bruce, 2003) [1]. Preserving features belonging to the target is very crucial to the performance of target detection/recognition techniques. Fukunaga-Koontz Transform (FKT) based supervised band reduction technique can be used to provide this requirement. FKT achieves feature selection by transforming into a new space in where feature classes have complimentary eigenvectors. Analysis of these eigenvectors under two classes, target and background clutter, can be utilized for target oriented band reduction since each basis functions best represent target class while carrying least information of the background class. By selecting few eigenvectors which are the most relevant to the target class, dimension of hyperspectral data can be reduced and thus, it presents significant advantages for near real time target detection applications. The nonlinear properties of the data can be extracted by kernel approach which provides better target features. Thus, we propose constructing kernel FKT (KFKT) to present target oriented band reduction. The performance of the proposed KFKT based target oriented dimensionality reduction algorithm has been tested employing two real-world hyperspectral data and results have been reported consequently.

Multiple-output support vector machine regression with feature selection for arousal/valence space emotion assessment.

PubMed

Torres-Valencia, Cristian A; Álvarez, Mauricio A; Orozco-Gutiérrez, Alvaro A

2014-01-01

Human emotion recognition (HER) allows the assessment of an affective state of a subject. Until recently, such emotional states were described in terms of discrete emotions, like happiness or contempt. In order to cover a high range of emotions, researchers in the field have introduced different dimensional spaces for emotion description that allow the characterization of affective states in terms of several variables or dimensions that measure distinct aspects of the emotion. One of the most common of such dimensional spaces is the bidimensional Arousal/Valence space. To the best of our knowledge, all HER systems so far have modelled independently, the dimensions in these dimensional spaces. In this paper, we study the effect of modelling the output dimensions simultaneously and show experimentally the advantages in modeling them in this way. We consider a multimodal approach by including features from the Electroencephalogram and a few physiological signals. For modelling the multiple outputs, we employ a multiple output regressor based on support vector machines. We also include an stage of feature selection that is developed within an embedded approach known as Recursive Feature Elimination (RFE), proposed initially for SVM. The results show that several features can be eliminated using the multiple output support vector regressor with RFE without affecting the performance of the regressor. From the analysis of the features selected in smaller subsets via RFE, it can be observed that the signals that are more informative into the arousal and valence space discrimination are the EEG, Electrooculogram/Electromiogram (EOG/EMG) and the Galvanic Skin Response (GSR).

Nonlinear three-dimensional verification of the SPECYL and PIXIE3D magnetohydrodynamics codes for fusion plasmas

NASA Astrophysics Data System (ADS)

Bonfiglio, D.; Chacón, L.; Cappello, S.

2010-08-01

With the increasing impact of scientific discovery via advanced computation, there is presently a strong emphasis on ensuring the mathematical correctness of computational simulation tools. Such endeavor, termed verification, is now at the center of most serious code development efforts. In this study, we address a cross-benchmark nonlinear verification study between two three-dimensional magnetohydrodynamics (3D MHD) codes for fluid modeling of fusion plasmas, SPECYL [S. Cappello and D. Biskamp, Nucl. Fusion 36, 571 (1996)] and PIXIE3D [L. Chacón, Phys. Plasmas 15, 056103 (2008)], in their common limit of application: the simple viscoresistive cylindrical approximation. SPECYL is a serial code in cylindrical geometry that features a spectral formulation in space and a semi-implicit temporal advance, and has been used extensively to date for reversed-field pinch studies. PIXIE3D is a massively parallel code in arbitrary curvilinear geometry that features a conservative, solenoidal finite-volume discretization in space, and a fully implicit temporal advance. The present study is, in our view, a first mandatory step in assessing the potential of any numerical 3D MHD code for fluid modeling of fusion plasmas. Excellent agreement is demonstrated over a wide range of parameters for several fusion-relevant cases in both two- and three-dimensional geometries.

Nonlinear three-dimensional verification of the SPECYL and PIXIE3D magnetohydrodynamics codes for fusion plasmas

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

Bonfiglio, Daniele; Chacon, Luis; Cappello, Susanna

2010-01-01

With the increasing impact of scientific discovery via advanced computation, there is presently a strong emphasis on ensuring the mathematical correctness of computational simulation tools. Such endeavor, termed verification, is now at the center of most serious code development efforts. In this study, we address a cross-benchmark nonlinear verification study between two three-dimensional magnetohydrodynamics (3D MHD) codes for fluid modeling of fusion plasmas, SPECYL [S. Cappello and D. Biskamp, Nucl. Fusion 36, 571 (1996)] and PIXIE3D [L. Chacon, Phys. Plasmas 15, 056103 (2008)], in their common limit of application: the simple viscoresistive cylindrical approximation. SPECYL is a serial code inmore » cylindrical geometry that features a spectral formulation in space and a semi-implicit temporal advance, and has been used extensively to date for reversed-field pinch studies. PIXIE3D is a massively parallel code in arbitrary curvilinear geometry that features a conservative, solenoidal finite-volume discretization in space, and a fully implicit temporal advance. The present study is, in our view, a first mandatory step in assessing the potential of any numerical 3D MHD code for fluid modeling of fusion plasmas. Excellent agreement is demonstrated over a wide range of parameters for several fusion-relevant cases in both two- and three-dimensional geometries.« less

Stereo Image Ranging For An Autonomous Robot Vision System

NASA Astrophysics Data System (ADS)

Holten, James R.; Rogers, Steven K.; Kabrisky, Matthew; Cross, Steven

1985-12-01

The principles of stereo vision for three-dimensional data acquisition are well-known and can be applied to the problem of an autonomous robot vehicle. Coincidental points in the two images are located and then the location of that point in a three-dimensional space can be calculated using the offset of the points and knowledge of the camera positions and geometry. This research investigates the application of artificial intelligence knowledge representation techniques as a means to apply heuristics to relieve the computational intensity of the low level image processing tasks. Specifically a new technique for image feature extraction is presented. This technique, the Queen Victoria Algorithm, uses formal language productions to process the image and characterize its features. These characterized features are then used for stereo image feature registration to obtain the required ranging information. The results can be used by an autonomous robot vision system for environmental modeling and path finding.

Reducing the two-loop large-scale structure power spectrum to low-dimensional, radial integrals

DOE PAGES

Schmittfull, Marcel; Vlah, Zvonimir

2016-11-28

Modeling the large-scale structure of the universe on nonlinear scales has the potential to substantially increase the science return of upcoming surveys by increasing the number of modes available for model comparisons. One way to achieve this is to model nonlinear scales perturbatively. Unfortunately, this involves high-dimensional loop integrals that are cumbersome to evaluate. Here, trying to simplify this, we show how two-loop (next-to-next-to-leading order) corrections to the density power spectrum can be reduced to low-dimensional, radial integrals. Many of those can be evaluated with a one-dimensional fast Fourier transform, which is significantly faster than the five-dimensional Monte-Carlo integrals thatmore » are needed otherwise. The general idea of this fast fourier transform perturbation theory method is to switch between Fourier and position space to avoid convolutions and integrate over orientations, leaving only radial integrals. This reformulation is independent of the underlying shape of the initial linear density power spectrum and should easily accommodate features such as those from baryonic acoustic oscillations. We also discuss how to account for halo bias and redshift space distortions.« less

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

NASA Astrophysics Data System (ADS)

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

2015-03-01

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

LDA boost classification: boosting by topics

NASA Astrophysics Data System (ADS)

Lei, La; Qiao, Guo; Qimin, Cao; Qitao, Li

2012-12-01

AdaBoost is an efficacious classification algorithm especially in text categorization (TC) tasks. The methodology of setting up a classifier committee and voting on the documents for classification can achieve high categorization precision. However, traditional Vector Space Model can easily lead to the curse of dimensionality and feature sparsity problems; so it affects classification performance seriously. This article proposed a novel classification algorithm called LDABoost based on boosting ideology which uses Latent Dirichlet Allocation (LDA) to modeling the feature space. Instead of using words or phrase, LDABoost use latent topics as the features. In this way, the feature dimension is significantly reduced. Improved Naïve Bayes (NB) is designed as the weaker classifier which keeps the efficiency advantage of classic NB algorithm and has higher precision. Moreover, a two-stage iterative weighted method called Cute Integration in this article is proposed for improving the accuracy by integrating weak classifiers into strong classifier in a more rational way. Mutual Information is used as metrics of weights allocation. The voting information and the categorization decision made by basis classifiers are fully utilized for generating the strong classifier. Experimental results reveals LDABoost making categorization in a low-dimensional space, it has higher accuracy than traditional AdaBoost algorithms and many other classic classification algorithms. Moreover, its runtime consumption is lower than different versions of AdaBoost, TC algorithms based on support vector machine and Neural Networks.

Palm vein recognition based on directional empirical mode decomposition

NASA Astrophysics Data System (ADS)

Lee, Jen-Chun; Chang, Chien-Ping; Chen, Wei-Kuei

2014-04-01

Directional empirical mode decomposition (DEMD) has recently been proposed to make empirical mode decomposition suitable for the processing of texture analysis. Using DEMD, samples are decomposed into a series of images, referred to as two-dimensional intrinsic mode functions (2-D IMFs), from finer to large scale. A DEMD-based 2 linear discriminant analysis (LDA) for palm vein recognition is proposed. The proposed method progresses through three steps: (i) a set of 2-D IMF features of various scale and orientation are extracted using DEMD, (ii) the 2LDA method is then applied to reduce the dimensionality of the feature space in both the row and column directions, and (iii) the nearest neighbor classifier is used for classification. We also propose two strategies for using the set of 2-D IMF features: ensemble DEMD vein representation (EDVR) and multichannel DEMD vein representation (MDVR). In experiments using palm vein databases, the proposed MDVR-based 2LDA method achieved recognition accuracy of 99.73%, thereby demonstrating its feasibility for palm vein recognition.

Unsupervised spike sorting based on discriminative subspace learning.

PubMed

Keshtkaran, Mohammad Reza; Yang, Zhi

2014-01-01

Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. In this paper, we present two unsupervised spike sorting algorithms based on discriminative subspace learning. The first algorithm simultaneously learns the discriminative feature subspace and performs clustering. It uses histogram of features in the most discriminative projection to detect the number of neurons. The second algorithm performs hierarchical divisive clustering that learns a discriminative 1-dimensional subspace for clustering in each level of the hierarchy until achieving almost unimodal distribution in the subspace. The algorithms are tested on synthetic and in-vivo data, and are compared against two widely used spike sorting methods. The comparative results demonstrate that our spike sorting methods can achieve substantially higher accuracy in lower dimensional feature space, and they are highly robust to noise. Moreover, they provide significantly better cluster separability in the learned subspace than in the subspace obtained by principal component analysis or wavelet transform.

A Generic multi-dimensional feature extraction method using multiobjective genetic programming.

PubMed

Zhang, Yang; Rockett, Peter I

2009-01-01

In this paper, we present a generic feature extraction method for pattern classification using multiobjective genetic programming. This not only evolves the (near-)optimal set of mappings from a pattern space to a multi-dimensional decision space, but also simultaneously optimizes the dimensionality of that decision space. The presented framework evolves vector-to-vector feature extractors that maximize class separability. We demonstrate the efficacy of our approach by making statistically-founded comparisons with a wide variety of established classifier paradigms over a range of datasets and find that for most of the pairwise comparisons, our evolutionary method delivers statistically smaller misclassification errors. At very worst, our method displays no statistical difference in a few pairwise comparisons with established classifier/dataset combinations; crucially, none of the misclassification results produced by our method is worse than any comparator classifier. Although principally focused on feature extraction, feature selection is also performed as an implicit side effect; we show that both feature extraction and selection are important to the success of our technique. The presented method has the practical consequence of obviating the need to exhaustively evaluate a large family of conventional classifiers when faced with a new pattern recognition problem in order to attain a good classification accuracy.

Drug-target interaction prediction using ensemble learning and dimensionality reduction.

PubMed

Ezzat, Ali; Wu, Min; Li, Xiao-Li; Kwoh, Chee-Keong

2017-10-01

Experimental prediction of drug-target interactions is expensive, time-consuming and tedious. Fortunately, computational methods help narrow down the search space for interaction candidates to be further examined via wet-lab techniques. Nowadays, the number of attributes/features for drugs and targets, as well as the amount of their interactions, are increasing, making these computational methods inefficient or occasionally prohibitive. This motivates us to derive a reduced feature set for prediction. In addition, since ensemble learning techniques are widely used to improve the classification performance, it is also worthwhile to design an ensemble learning framework to enhance the performance for drug-target interaction prediction. In this paper, we propose a framework for drug-target interaction prediction leveraging both feature dimensionality reduction and ensemble learning. First, we conducted feature subspacing to inject diversity into the classifier ensemble. Second, we applied three different dimensionality reduction methods to the subspaced features. Third, we trained homogeneous base learners with the reduced features and then aggregated their scores to derive the final predictions. For base learners, we selected two classifiers, namely Decision Tree and Kernel Ridge Regression, resulting in two variants of ensemble models, EnsemDT and EnsemKRR, respectively. In our experiments, we utilized AUC (Area under ROC Curve) as an evaluation metric. We compared our proposed methods with various state-of-the-art methods under 5-fold cross validation. Experimental results showed EnsemKRR achieving the highest AUC (94.3%) for predicting drug-target interactions. In addition, dimensionality reduction helped improve the performance of EnsemDT. In conclusion, our proposed methods produced significant improvements for drug-target interaction prediction. Copyright © 2017 Elsevier Inc. All rights reserved.

Improving Classification of Protein Interaction Articles Using Context Similarity-Based Feature Selection.

PubMed

Chen, Yifei; Sun, Yuxing; Han, Bing-Qing

2015-01-01

Protein interaction article classification is a text classification task in the biological domain to determine which articles describe protein-protein interactions. Since the feature space in text classification is high-dimensional, feature selection is widely used for reducing the dimensionality of features to speed up computation without sacrificing classification performance. Many existing feature selection methods are based on the statistical measure of document frequency and term frequency. One potential drawback of these methods is that they treat features separately. Hence, first we design a similarity measure between the context information to take word cooccurrences and phrase chunks around the features into account. Then we introduce the similarity of context information to the importance measure of the features to substitute the document and term frequency. Hence we propose new context similarity-based feature selection methods. Their performance is evaluated on two protein interaction article collections and compared against the frequency-based methods. The experimental results reveal that the context similarity-based methods perform better in terms of the F1 measure and the dimension reduction rate. Benefiting from the context information surrounding the features, the proposed methods can select distinctive features effectively for protein interaction article classification.

Fermion localization on a split brane

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

Chumbes, A. E. R.; Vasquez, A. E. O.; Hott, M. B.

2011-05-15

In this work we analyze the localization of fermions on a brane embedded in five-dimensional, warped and nonwarped, space-time. In both cases we use the same nonlinear theoretical model with a nonpolynomial potential featuring a self-interacting scalar field whose minimum energy solution is a soliton (a kink) which can be continuously deformed into a two-kink. Thus a single brane splits into two branes. The behavior of spin 1/2 fermions wave functions on the split brane depends on the coupling of fermions to the scalar field and on the geometry of the space-time.

Sample-space-based feature extraction and class preserving projection for gene expression data.

PubMed

Wang, Wenjun

2013-01-01

In order to overcome the problems of high computational complexity and serious matrix singularity for feature extraction using Principal Component Analysis (PCA) and Fisher's Linear Discrinimant Analysis (LDA) in high-dimensional data, sample-space-based feature extraction is presented, which transforms the computation procedure of feature extraction from gene space to sample space by representing the optimal transformation vector with the weighted sum of samples. The technique is used in the implementation of PCA, LDA, Class Preserving Projection (CPP) which is a new method for discriminant feature extraction proposed, and the experimental results on gene expression data demonstrate the effectiveness of the method.

Surrogate modelling for the prediction of spatial fields based on simultaneous dimensionality reduction of high-dimensional input/output spaces.

PubMed

Crevillén-García, D

2018-04-01

Time-consuming numerical simulators for solving groundwater flow and dissolution models of physico-chemical processes in deep aquifers normally require some of the model inputs to be defined in high-dimensional spaces in order to return realistic results. Sometimes, the outputs of interest are spatial fields leading to high-dimensional output spaces. Although Gaussian process emulation has been satisfactorily used for computing faithful and inexpensive approximations of complex simulators, these have been mostly applied to problems defined in low-dimensional input spaces. In this paper, we propose a method for simultaneously reducing the dimensionality of very high-dimensional input and output spaces in Gaussian process emulators for stochastic partial differential equation models while retaining the qualitative features of the original models. This allows us to build a surrogate model for the prediction of spatial fields in such time-consuming simulators. We apply the methodology to a model of convection and dissolution processes occurring during carbon capture and storage.

Efficient Stochastic Inversion Using Adjoint Models and Kernel-PCA

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

Thimmisetty, Charanraj A.; Zhao, Wenju; Chen, Xiao

2017-10-18

Performing stochastic inversion on a computationally expensive forward simulation model with a high-dimensional uncertain parameter space (e.g. a spatial random field) is computationally prohibitive even when gradient information can be computed efficiently. Moreover, the ‘nonlinear’ mapping from parameters to observables generally gives rise to non-Gaussian posteriors even with Gaussian priors, thus hampering the use of efficient inversion algorithms designed for models with Gaussian assumptions. In this paper, we propose a novel Bayesian stochastic inversion methodology, which is characterized by a tight coupling between the gradient-based Langevin Markov Chain Monte Carlo (LMCMC) method and a kernel principal component analysis (KPCA). Thismore » approach addresses the ‘curse-of-dimensionality’ via KPCA to identify a low-dimensional feature space within the high-dimensional and nonlinearly correlated parameter space. In addition, non-Gaussian posterior distributions are estimated via an efficient LMCMC method on the projected low-dimensional feature space. We will demonstrate this computational framework by integrating and adapting our recent data-driven statistics-on-manifolds constructions and reduction-through-projection techniques to a linear elasticity model.« less

Implementing quantum Ricci curvature

NASA Astrophysics Data System (ADS)

Klitgaard, N.; Loll, R.

2018-05-01

Quantum Ricci curvature has been introduced recently as a new, geometric observable characterizing the curvature properties of metric spaces, without the need for a smooth structure. Besides coordinate invariance, its key features are scalability, computability, and robustness. We demonstrate that these properties continue to hold in the context of nonperturbative quantum gravity, by evaluating the quantum Ricci curvature numerically in two-dimensional Euclidean quantum gravity, defined in terms of dynamical triangulations. Despite the well-known, highly nonclassical properties of the underlying quantum geometry, its Ricci curvature can be matched well to that of a five-dimensional round sphere.

Single Channel EEG Artifact Identification Using Two-Dimensional Multi-Resolution Analysis.

PubMed

Taherisadr, Mojtaba; Dehzangi, Omid; Parsaei, Hossein

2017-12-13

As a diagnostic monitoring approach, electroencephalogram (EEG) signals can be decoded by signal processing methodologies for various health monitoring purposes. However, EEG recordings are contaminated by other interferences, particularly facial and ocular artifacts generated by the user. This is specifically an issue during continuous EEG recording sessions, and is therefore a key step in using EEG signals for either physiological monitoring and diagnosis or brain-computer interface to identify such artifacts from useful EEG components. In this study, we aim to design a new generic framework in order to process and characterize EEG recording as a multi-component and non-stationary signal with the aim of localizing and identifying its component (e.g., artifact). In the proposed method, we gather three complementary algorithms together to enhance the efficiency of the system. Algorithms include time-frequency (TF) analysis and representation, two-dimensional multi-resolution analysis (2D MRA), and feature extraction and classification. Then, a combination of spectro-temporal and geometric features are extracted by combining key instantaneous TF space descriptors, which enables the system to characterize the non-stationarities in the EEG dynamics. We fit a curvelet transform (as a MRA method) to 2D TF representation of EEG segments to decompose the given space to various levels of resolution. Such a decomposition efficiently improves the analysis of the TF spaces with different characteristics (e.g., resolution). Our experimental results demonstrate that the combination of expansion to TF space, analysis using MRA, and extracting a set of suitable features and applying a proper predictive model is effective in enhancing the EEG artifact identification performance. We also compare the performance of the designed system with another common EEG signal processing technique-namely, 1D wavelet transform. Our experimental results reveal that the proposed method outperforms 1D wavelet.

An Autonomous Star Identification Algorithm Based on One-Dimensional Vector Pattern for Star Sensors

PubMed Central

Luo, Liyan; Xu, Luping; Zhang, Hua

2015-01-01

In order to enhance the robustness and accelerate the recognition speed of star identification, an autonomous star identification algorithm for star sensors is proposed based on the one-dimensional vector pattern (one_DVP). In the proposed algorithm, the space geometry information of the observed stars is used to form the one-dimensional vector pattern of the observed star. The one-dimensional vector pattern of the same observed star remains unchanged when the stellar image rotates, so the problem of star identification is simplified as the comparison of the two feature vectors. The one-dimensional vector pattern is adopted to build the feature vector of the star pattern, which makes it possible to identify the observed stars robustly. The characteristics of the feature vector and the proposed search strategy for the matching pattern make it possible to achieve the recognition result as quickly as possible. The simulation results demonstrate that the proposed algorithm can effectively accelerate the star identification. Moreover, the recognition accuracy and robustness by the proposed algorithm are better than those by the pyramid algorithm, the modified grid algorithm, and the LPT algorithm. The theoretical analysis and experimental results show that the proposed algorithm outperforms the other three star identification algorithms. PMID:26198233

An Autonomous Star Identification Algorithm Based on One-Dimensional Vector Pattern for Star Sensors.

PubMed

Luo, Liyan; Xu, Luping; Zhang, Hua

2015-07-07

In order to enhance the robustness and accelerate the recognition speed of star identification, an autonomous star identification algorithm for star sensors is proposed based on the one-dimensional vector pattern (one_DVP). In the proposed algorithm, the space geometry information of the observed stars is used to form the one-dimensional vector pattern of the observed star. The one-dimensional vector pattern of the same observed star remains unchanged when the stellar image rotates, so the problem of star identification is simplified as the comparison of the two feature vectors. The one-dimensional vector pattern is adopted to build the feature vector of the star pattern, which makes it possible to identify the observed stars robustly. The characteristics of the feature vector and the proposed search strategy for the matching pattern make it possible to achieve the recognition result as quickly as possible. The simulation results demonstrate that the proposed algorithm can effectively accelerate the star identification. Moreover, the recognition accuracy and robustness by the proposed algorithm are better than those by the pyramid algorithm, the modified grid algorithm, and the LPT algorithm. The theoretical analysis and experimental results show that the proposed algorithm outperforms the other three star identification algorithms.

An advanced scanning method for space-borne hyper-spectral imaging system

NASA Astrophysics Data System (ADS)

Wang, Yue-ming; Lang, Jun-Wei; Wang, Jian-Yu; Jiang, Zi-Qing

2011-08-01

Space-borne hyper-spectral imagery is an important means for the studies and applications of earth science. High cost efficiency could be acquired by optimized system design. In this paper, an advanced scanning method is proposed, which contributes to implement both high temporal and spatial resolution imaging system. Revisit frequency and effective working time of space-borne hyper-spectral imagers could be greatly improved by adopting two-axis scanning system if spatial resolution and radiometric accuracy are not harshly demanded. In order to avoid the quality degradation caused by image rotation, an idea of two-axis rotation has been presented based on the analysis and simulation of two-dimensional scanning motion path and features. Further improvement of the imagers' detection ability under the conditions of small solar altitude angle and low surface reflectance can be realized by the Ground Motion Compensation on pitch axis. The structure and control performance are also described. An intelligent integration technology of two-dimensional scanning and image motion compensation is elaborated in this paper. With this technology, sun-synchronous hyper-spectral imagers are able to pay quick visit to hot spots, acquiring both high spatial and temporal resolution hyper-spectral images, which enables rapid response of emergencies. The result has reference value for developing operational space-borne hyper-spectral imagers.

Feature space analysis of MRI

NASA Astrophysics Data System (ADS)

Soltanian-Zadeh, Hamid; Windham, Joe P.; Peck, Donald J.

1997-04-01

This paper presents development and performance evaluation of an MRI feature space method. The method is useful for: identification of tissue types; segmentation of tissues; and quantitative measurements on tissues, to obtain information that can be used in decision making (diagnosis, treatment planning, and evaluation of treatment). The steps of the work accomplished are as follows: (1) Four T2-weighted and two T1-weighted images (before and after injection of Gadolinium) were acquired for ten tumor patients. (2) Images were analyed by two image analysts according to the following algorithm. The intracranial brain tissues were segmented from the scalp and background. The additive noise was suppressed using a multi-dimensional non-linear edge- preserving filter which preserves partial volume information on average. Image nonuniformities were corrected using a modified lowpass filtering approach. The resulting images were used to generate and visualize an optimal feature space. Cluster centers were identified on the feature space. Then images were segmented into normal tissues and different zones of the tumor. (3) Biopsy samples were extracted from each patient and were subsequently analyzed by the pathology laboratory. (4) Image analysis results were compared to each other and to the biopsy results. Pre- and post-surgery feature spaces were also compared. The proposed algorithm made it possible to visualize the MRI feature space and to segment the image. In all cases, the operators were able to find clusters for normal and abnormal tissues. Also, clusters for different zones of the tumor were found. Based on the clusters marked for each zone, the method successfully segmented the image into normal tissues (white matter, gray matter, and CSF) and different zones of the lesion (tumor, cyst, edema, radiation necrosis, necrotic core, and infiltrated tumor). The results agreed with those obtained from the biopsy samples. Comparison of pre- to post-surgery and radiation feature spaces confirmed that the tumor was not present in the second study but radiation necrosis was generated as a result of radiation.

EM in high-dimensional spaces.

PubMed

Draper, Bruce A; Elliott, Daniel L; Hayes, Jeremy; Baek, Kyungim

2005-06-01

This paper considers fitting a mixture of Gaussians model to high-dimensional data in scenarios where there are fewer data samples than feature dimensions. Issues that arise when using principal component analysis (PCA) to represent Gaussian distributions inside Expectation-Maximization (EM) are addressed, and a practical algorithm results. Unlike other algorithms that have been proposed, this algorithm does not try to compress the data to fit low-dimensional models. Instead, it models Gaussian distributions in the (N - 1)-dimensional space spanned by the N data samples. We are able to show that this algorithm converges on data sets where low-dimensional techniques do not.

Detection of epileptiform activity in EEG signals based on time-frequency and non-linear analysis

PubMed Central

Gajic, Dragoljub; Djurovic, Zeljko; Gligorijevic, Jovan; Di Gennaro, Stefano; Savic-Gajic, Ivana

2015-01-01

We present a new technique for detection of epileptiform activity in EEG signals. After preprocessing of EEG signals we extract representative features in time, frequency and time-frequency domain as well as using non-linear analysis. The features are extracted in a few frequency sub-bands of clinical interest since these sub-bands showed much better discriminatory characteristics compared with the whole frequency band. Then we optimally reduce the dimension of feature space to two using scatter matrices. A decision about the presence of epileptiform activity in EEG signals is made by quadratic classifiers designed in the reduced two-dimensional feature space. The accuracy of the technique was tested on three sets of electroencephalographic (EEG) signals recorded at the University Hospital Bonn: surface EEG signals from healthy volunteers, intracranial EEG signals from the epilepsy patients during the seizure free interval from within the seizure focus and intracranial EEG signals of epileptic seizures also from within the seizure focus. An overall detection accuracy of 98.7% was achieved. PMID:25852534

Face recognition by applying wavelet subband representation and kernel associative memory.

PubMed

Zhang, Bai-Ling; Zhang, Haihong; Ge, Shuzhi Sam

2004-01-01

In this paper, we propose an efficient face recognition scheme which has two features: 1) representation of face images by two-dimensional (2-D) wavelet subband coefficients and 2) recognition by a modular, personalised classification method based on kernel associative memory models. Compared to PCA projections and low resolution "thumb-nail" image representations, wavelet subband coefficients can efficiently capture substantial facial features while keeping computational complexity low. As there are usually very limited samples, we constructed an associative memory (AM) model for each person and proposed to improve the performance of AM models by kernel methods. Specifically, we first applied kernel transforms to each possible training pair of faces sample and then mapped the high-dimensional feature space back to input space. Our scheme using modular autoassociative memory for face recognition is inspired by the same motivation as using autoencoders for optical character recognition (OCR), for which the advantages has been proven. By associative memory, all the prototypical faces of one particular person are used to reconstruct themselves and the reconstruction error for a probe face image is used to decide if the probe face is from the corresponding person. We carried out extensive experiments on three standard face recognition datasets, the FERET data, the XM2VTS data, and the ORL data. Detailed comparisons with earlier published results are provided and our proposed scheme offers better recognition accuracy on all of the face datasets.

Synthesis of Joint Volumes, Visualization of Paths, and Revision of Viewing Sequences in a Multi-dimensional Seismic Data Viewer

NASA Astrophysics Data System (ADS)

Chen, D. M.; Clapp, R. G.; Biondi, B.

2006-12-01

Ricksep is a freely-available interactive viewer for multi-dimensional data sets. The viewer is very useful for simultaneous display of multiple data sets from different viewing angles, animation of movement along a path through the data space, and selection of local regions for data processing and information extraction. Several new viewing features are added to enhance the program's functionality in the following three aspects. First, two new data synthesis algorithms are created to adaptively combine information from a data set with mostly high-frequency content, such as seismic data, and another data set with mainly low-frequency content, such as velocity data. Using the algorithms, these two data sets can be synthesized into a single data set which resembles the high-frequency data set on a local scale and at the same time resembles the low- frequency data set on a larger scale. As a result, the originally separated high and low-frequency details can now be more accurately and conveniently studied together. Second, a projection algorithm is developed to display paths through the data space. Paths are geophysically important because they represent wells into the ground. Two difficulties often associated with tracking paths are that they normally cannot be seen clearly inside multi-dimensional spaces and depth information is lost along the direction of projection when ordinary projection techniques are used. The new algorithm projects samples along the path in three orthogonal directions and effectively restores important depth information by using variable projection parameters which are functions of the distance away from the path. Multiple paths in the data space can be generated using different character symbols as positional markers, and users can easily create, modify, and view paths in real time. Third, a viewing history list is implemented which enables Ricksep's users to create, edit and save a recipe for the sequence of viewing states. Then, the recipe can be loaded into an active Ricksep session, after which the user can navigate to any state in the sequence and modify the sequence from that state. Typical uses of this feature are undoing and redoing viewing commands and animating a sequence of viewing states. The theoretical discussion are carried out and several examples using real seismic data are provided to show how these new Ricksep features provide more convenient, accurate ways to manipulate multi-dimensional data sets.

Three-Dimensional Messages for Interstellar Communication

NASA Astrophysics Data System (ADS)

Vakoch, Douglas A.

One of the challenges facing independently evolved civilizations separated by interstellar distances is to communicate information unique to one civilization. One commonly proposed solution is to begin with two-dimensional pictorial representations of mathematical concepts and physical objects, in the hope that this will provide a foundation for overcoming linguistic barriers. However, significant aspects of such representations are highly conventional, and may not be readily intelligible to a civilization with different conventions. The process of teaching conventions of representation may be facilitated by the use of three-dimensional representations redundantly encoded in multiple formats (e.g., as both vectors and as rasters). After having illustrated specific conventions for representing mathematical objects in a three-dimensional space, this method can be used to describe a physical environment shared by transmitter and receiver: a three-dimensional space defined by the transmitter--receiver axis, and containing stars within that space. This method can be extended to show three-dimensional representations varying over time. Having clarified conventions for representing objects potentially familiar to both sender and receiver, novel objects can subsequently be depicted. This is illustrated through sequences showing interactions between human beings, which provide information about human behavior and personality. Extensions of this method may allow the communication of such culture-specific features as aesthetic judgments and religious beliefs. Limitations of this approach will be noted, with specific reference to ETI who are not primarily visual.

Variable importance in nonlinear kernels (VINK): classification of digitized histopathology.

PubMed

Ginsburg, Shoshana; Ali, Sahirzeeshan; Lee, George; Basavanhally, Ajay; Madabhushi, Anant

2013-01-01

Quantitative histomorphometry is the process of modeling appearance of disease morphology on digitized histopathology images via image-based features (e.g., texture, graphs). Due to the curse of dimensionality, building classifiers with large numbers of features requires feature selection (which may require a large training set) or dimensionality reduction (DR). DR methods map the original high-dimensional features in terms of eigenvectors and eigenvalues, which limits the potential for feature transparency or interpretability. Although methods exist for variable selection and ranking on embeddings obtained via linear DR schemes (e.g., principal components analysis (PCA)), similar methods do not yet exist for nonlinear DR (NLDR) methods. In this work we present a simple yet elegant method for approximating the mapping between the data in the original feature space and the transformed data in the kernel PCA (KPCA) embedding space; this mapping provides the basis for quantification of variable importance in nonlinear kernels (VINK). We show how VINK can be implemented in conjunction with the popular Isomap and Laplacian eigenmap algorithms. VINK is evaluated in the contexts of three different problems in digital pathology: (1) predicting five year PSA failure following radical prostatectomy, (2) predicting Oncotype DX recurrence risk scores for ER+ breast cancers, and (3) distinguishing good and poor outcome p16+ oropharyngeal tumors. We demonstrate that subsets of features identified by VINK provide similar or better classification or regression performance compared to the original high dimensional feature sets.

Optimal linear and nonlinear feature extraction based on the minimization of the increased risk of misclassification. [Bayes theorem - statistical analysis/data processing

NASA Technical Reports Server (NTRS)

Defigueiredo, R. J. P.

1974-01-01

General classes of nonlinear and linear transformations were investigated for the reduction of the dimensionality of the classification (feature) space so that, for a prescribed dimension m of this space, the increase of the misclassification risk is minimized.

Trading spaces: building three-dimensional nets from two-dimensional tilings

PubMed Central

Castle, Toen; Evans, Myfanwy E.; Hyde, Stephen T.; Ramsden, Stuart; Robins, Vanessa

2012-01-01

We construct some examples of finite and infinite crystalline three-dimensional nets derived from symmetric reticulations of homogeneous two-dimensional spaces: elliptic (S2), Euclidean (E2) and hyperbolic (H2) space. Those reticulations are edges and vertices of simple spherical, planar and hyperbolic tilings. We show that various projections of the simplest symmetric tilings of those spaces into three-dimensional Euclidean space lead to topologically and geometrically complex patterns, including multiple interwoven nets and tangled nets that are otherwise difficult to generate ab initio in three dimensions. PMID:24098839

Odor Impression Prediction from Mass Spectra.

PubMed

Nozaki, Yuji; Nakamoto, Takamichi

2016-01-01

The sense of smell arises from the perception of odors from chemicals. However, the relationship between the impression of odor and the numerous physicochemical parameters has yet to be understood owing to its complexity. As such, there is no established general method for predicting the impression of odor of a chemical only from its physicochemical properties. In this study, we designed a novel predictive model based on an artificial neural network with a deep structure for predicting odor impression utilizing the mass spectra of chemicals, and we conducted a series of computational analyses to evaluate its performance. Feature vectors extracted from the original high-dimensional space using two autoencoders equipped with both input and output layers in the model are used to build a mapping function from the feature space of mass spectra to the feature space of sensory data. The results of predictions obtained by the proposed new method have notable accuracy (R≅0.76) in comparison with a conventional method (R≅0.61).

Fast multi-dimensional NMR by minimal sampling

NASA Astrophysics Data System (ADS)

Kupče, Ēriks; Freeman, Ray

2008-03-01

A new scheme is proposed for very fast acquisition of three-dimensional NMR spectra based on minimal sampling, instead of the customary step-wise exploration of all of evolution space. The method relies on prior experiments to determine accurate values for the evolving frequencies and intensities from the two-dimensional 'first planes' recorded by setting t1 = 0 or t2 = 0. With this prior knowledge, the entire three-dimensional spectrum can be reconstructed by an additional measurement of the response at a single location (t1∗,t2∗) where t1∗ and t2∗ are fixed values of the evolution times. A key feature is the ability to resolve problems of overlap in the acquisition dimension. Applied to a small protein, agitoxin, the three-dimensional HNCO spectrum is obtained 35 times faster than systematic Cartesian sampling of the evolution domain. The extension to multi-dimensional spectroscopy is outlined.

Deep neural networks for texture classification-A theoretical analysis.

PubMed

Basu, Saikat; Mukhopadhyay, Supratik; Karki, Manohar; DiBiano, Robert; Ganguly, Sangram; Nemani, Ramakrishna; Gayaka, Shreekant

2018-01-01

We investigate the use of Deep Neural Networks for the classification of image datasets where texture features are important for generating class-conditional discriminative representations. To this end, we first derive the size of the feature space for some standard textural features extracted from the input dataset and then use the theory of Vapnik-Chervonenkis dimension to show that hand-crafted feature extraction creates low-dimensional representations which help in reducing the overall excess error rate. As a corollary to this analysis, we derive for the first time upper bounds on the VC dimension of Convolutional Neural Network as well as Dropout and Dropconnect networks and the relation between excess error rate of Dropout and Dropconnect networks. The concept of intrinsic dimension is used to validate the intuition that texture-based datasets are inherently higher dimensional as compared to handwritten digits or other object recognition datasets and hence more difficult to be shattered by neural networks. We then derive the mean distance from the centroid to the nearest and farthest sampling points in an n-dimensional manifold and show that the Relative Contrast of the sample data vanishes as dimensionality of the underlying vector space tends to infinity. Copyright © 2017 Elsevier Ltd. All rights reserved.

Efficient Data Mining for Local Binary Pattern in Texture Image Analysis

PubMed Central

Kwak, Jin Tae; Xu, Sheng; Wood, Bradford J.

2015-01-01

Local binary pattern (LBP) is a simple gray scale descriptor to characterize the local distribution of the grey levels in an image. Multi-resolution LBP and/or combinations of the LBPs have shown to be effective in texture image analysis. However, it is unclear what resolutions or combinations to choose for texture analysis. Examining all the possible cases is impractical and intractable due to the exponential growth in a feature space. This limits the accuracy and time- and space-efficiency of LBP. Here, we propose a data mining approach for LBP, which efficiently explores a high-dimensional feature space and finds a relatively smaller number of discriminative features. The features can be any combinations of LBPs. These may not be achievable with conventional approaches. Hence, our approach not only fully utilizes the capability of LBP but also maintains the low computational complexity. We incorporated three different descriptors (LBP, local contrast measure, and local directional derivative measure) with three spatial resolutions and evaluated our approach using two comprehensive texture databases. The results demonstrated the effectiveness and robustness of our approach to different experimental designs and texture images. PMID:25767332

Sampling and Visualizing Creases with Scale-Space Particles

PubMed Central

Kindlmann, Gordon L.; Estépar, Raúl San José; Smith, Stephen M.; Westin, Carl-Fredrik

2010-01-01

Particle systems have gained importance as a methodology for sampling implicit surfaces and segmented objects to improve mesh generation and shape analysis. We propose that particle systems have a significantly more general role in sampling structure from unsegmented data. We describe a particle system that computes samplings of crease features (i.e. ridges and valleys, as lines or surfaces) that effectively represent many anatomical structures in scanned medical data. Because structure naturally exists at a range of sizes relative to the image resolution, computer vision has developed the theory of scale-space, which considers an n-D image as an (n + 1)-D stack of images at different blurring levels. Our scale-space particles move through continuous four-dimensional scale-space according to spatial constraints imposed by the crease features, a particle-image energy that draws particles towards scales of maximal feature strength, and an inter-particle energy that controls sampling density in space and scale. To make scale-space practical for large three-dimensional data, we present a spline-based interpolation across scale from a small number of pre-computed blurrings at optimally selected scales. The configuration of the particle system is visualized with tensor glyphs that display information about the local Hessian of the image, and the scale of the particle. We use scale-space particles to sample the complex three-dimensional branching structure of airways in lung CT, and the major white matter structures in brain DTI. PMID:19834216

An unsupervised classification approach for analysis of Landsat data to monitor land reclamation in Belmont county, Ohio

NASA Technical Reports Server (NTRS)

Brumfield, J. O.; Bloemer, H. H. L.; Campbell, W. J.

1981-01-01

Two unsupervised classification procedures for analyzing Landsat data used to monitor land reclamation in a surface mining area in east central Ohio are compared for agreement with data collected from the corresponding locations on the ground. One procedure is based on a traditional unsupervised-clustering/maximum-likelihood algorithm sequence that assumes spectral groupings in the Landsat data in n-dimensional space; the other is based on a nontraditional unsupervised-clustering/canonical-transformation/clustering algorithm sequence that not only assumes spectral groupings in n-dimensional space but also includes an additional feature-extraction technique. It is found that the nontraditional procedure provides an appreciable improvement in spectral groupings and apparently increases the level of accuracy in the classification of land cover categories.

Entanglement by Path Identity.

PubMed

Krenn, Mario; Hochrainer, Armin; Lahiri, Mayukh; Zeilinger, Anton

2017-02-24

Quantum entanglement is one of the most prominent features of quantum mechanics and forms the basis of quantum information technologies. Here we present a novel method for the creation of quantum entanglement in multipartite and high-dimensional systems. The two ingredients are (i) superposition of photon pairs with different origins and (ii) aligning photons such that their paths are identical. We explain the experimentally feasible creation of various classes of multiphoton entanglement encoded in polarization as well as in high-dimensional Hilbert spaces-starting only from nonentangled photon pairs. For two photons, arbitrary high-dimensional entanglement can be created. The idea of generating entanglement by path identity could also apply to quantum entities other than photons. We discovered the technique by analyzing the output of a computer algorithm. This shows that computer designed quantum experiments can be inspirations for new techniques.

Transition probabilities for non self-adjoint Hamiltonians in infinite dimensional Hilbert spaces

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

Bagarello, F., E-mail: fabio.bagarello@unipa.it

In a recent paper we have introduced several possible inequivalent descriptions of the dynamics and of the transition probabilities of a quantum system when its Hamiltonian is not self-adjoint. Our analysis was carried out in finite dimensional Hilbert spaces. This is useful, but quite restrictive since many physically relevant quantum systems live in infinite dimensional Hilbert spaces. In this paper we consider this situation, and we discuss some applications to well known models, introduced in the literature in recent years: the extended harmonic oscillator, the Swanson model and a generalized version of the Landau levels Hamiltonian. Not surprisingly we willmore » find new interesting features not previously found in finite dimensional Hilbert spaces, useful for a deeper comprehension of this kind of physical systems.« less

A face and palmprint recognition approach based on discriminant DCT feature extraction.

PubMed

Jing, Xiao-Yuan; Zhang, David

2004-12-01

In the field of image processing and recognition, discrete cosine transform (DCT) and linear discrimination are two widely used techniques. Based on them, we present a new face and palmprint recognition approach in this paper. It first uses a two-dimensional separability judgment to select the DCT frequency bands with favorable linear separability. Then from the selected bands, it extracts the linear discriminative features by an improved Fisherface method and performs the classification by the nearest neighbor classifier. We detailedly analyze theoretical advantages of our approach in feature extraction. The experiments on face databases and palmprint database demonstrate that compared to the state-of-the-art linear discrimination methods, our approach obtains better classification performance. It can significantly improve the recognition rates for face and palmprint data and effectively reduce the dimension of feature space.

A Cross-Lingual Similarity Measure for Detecting Biomedical Term Translations

PubMed Central

Bollegala, Danushka; Kontonatsios, Georgios; Ananiadou, Sophia

2015-01-01

Bilingual dictionaries for technical terms such as biomedical terms are an important resource for machine translation systems as well as for humans who would like to understand a concept described in a foreign language. Often a biomedical term is first proposed in English and later it is manually translated to other languages. Despite the fact that there are large monolingual lexicons of biomedical terms, only a fraction of those term lexicons are translated to other languages. Manually compiling large-scale bilingual dictionaries for technical domains is a challenging task because it is difficult to find a sufficiently large number of bilingual experts. We propose a cross-lingual similarity measure for detecting most similar translation candidates for a biomedical term specified in one language (source) from another language (target). Specifically, a biomedical term in a language is represented using two types of features: (a) intrinsic features that consist of character n-grams extracted from the term under consideration, and (b) extrinsic features that consist of unigrams and bigrams extracted from the contextual windows surrounding the term under consideration. We propose a cross-lingual similarity measure using each of those feature types. First, to reduce the dimensionality of the feature space in each language, we propose prototype vector projection (PVP)—a non-negative lower-dimensional vector projection method. Second, we propose a method to learn a mapping between the feature spaces in the source and target language using partial least squares regression (PLSR). The proposed method requires only a small number of training instances to learn a cross-lingual similarity measure. The proposed PVP method outperforms popular dimensionality reduction methods such as the singular value decomposition (SVD) and non-negative matrix factorization (NMF) in a nearest neighbor prediction task. Moreover, our experimental results covering several language pairs such as English–French, English–Spanish, English–Greek, and English–Japanese show that the proposed method outperforms several other feature projection methods in biomedical term translation prediction tasks. PMID:26030738

GridMass: a fast two-dimensional feature detection method for LC/MS.

PubMed

Treviño, Victor; Yañez-Garza, Irma-Luz; Rodriguez-López, Carlos E; Urrea-López, Rafael; Garza-Rodriguez, Maria-Lourdes; Barrera-Saldaña, Hugo-Alberto; Tamez-Peña, José G; Winkler, Robert; Díaz de-la-Garza, Rocío-Isabel

2015-01-01

One of the initial and critical procedures for the analysis of metabolomics data using liquid chromatography and mass spectrometry is feature detection. Feature detection is the process to detect boundaries of the mass surface from raw data. It consists of detected abundances arranged in a two-dimensional (2D) matrix of mass/charge and elution time. MZmine 2 is one of the leading software environments that provide a full analysis pipeline for these data. However, the feature detection algorithms provided in MZmine 2 are based mainly on the analysis of one-dimension at a time. We propose GridMass, an efficient algorithm for 2D feature detection. The algorithm is based on landing probes across the chromatographic space that are moved to find local maxima providing accurate boundary estimations. We tested GridMass on a controlled marker experiment, on plasma samples, on plant fruits, and in a proteome sample. Compared with other algorithms, GridMass is faster and may achieve comparable or better sensitivity and specificity. As a proof of concept, GridMass has been implemented in Java under the MZmine 2 environment and is available at http://www.bioinformatica.mty.itesm.mx/GridMass and MASSyPup. It has also been submitted to the MZmine 2 developing community. Copyright © 2015 John Wiley & Sons, Ltd.

Computing a Comprehensible Model for Spam Filtering

NASA Astrophysics Data System (ADS)

Ruiz-Sepúlveda, Amparo; Triviño-Rodriguez, José L.; Morales-Bueno, Rafael

In this paper, we describe the application of the Desicion Tree Boosting (DTB) learning model to spam email filtering.This classification task implies the learning in a high dimensional feature space. So, it is an example of how the DTB algorithm performs in such feature space problems. In [1], it has been shown that hypotheses computed by the DTB model are more comprehensible that the ones computed by another ensemble methods. Hence, this paper tries to show that the DTB algorithm maintains the same comprehensibility of hypothesis in high dimensional feature space problems while achieving the performance of other ensemble methods. Four traditional evaluation measures (precision, recall, F1 and accuracy) have been considered for performance comparison between DTB and others models usually applied to spam email filtering. The size of the hypothesis computed by a DTB is smaller and more comprehensible than the hypothesis computed by Adaboost and Naïve Bayes.

Binary classification of items of interest in a repeatable process

DOEpatents

Abell, Jeffrey A.; Spicer, John Patrick; Wincek, Michael Anthony; Wang, Hui; Chakraborty, Debejyo

2014-06-24

A system includes host and learning machines in electrical communication with sensors positioned with respect to an item of interest, e.g., a weld, and memory. The host executes instructions from memory to predict a binary quality status of the item. The learning machine receives signals from the sensor(s), identifies candidate features, and extracts features from the candidates that are more predictive of the binary quality status relative to other candidate features. The learning machine maps the extracted features to a dimensional space that includes most of the items from a passing binary class and excludes all or most of the items from a failing binary class. The host also compares the received signals for a subsequent item of interest to the dimensional space to thereby predict, in real time, the binary quality status of the subsequent item of interest.

Direct k-space imaging of Mahan cones at clean and Bi-covered Cu(111) surfaces

NASA Astrophysics Data System (ADS)

Winkelmann, Aimo; Akin Ünal, A.; Tusche, Christian; Ellguth, Martin; Chiang, Cheng-Tien; Kirschner, Jürgen

2012-08-01

Using a specifically tailored experimental approach, we revisit the exemplary effect of photoemission from quasi-free electronic states in crystals. Applying a momentum microscope, we measure photoelectron momentum patterns emitted into the complete half-space above the sample after excitation from a linearly polarized laser light source. By the application of a fully three-dimensional (3D) geometrical model of direct optical transitions, we explain the characteristic intensity distributions that are formed by the photoelectrons in k-space under the combination of energy conservation and crystal momentum conservation in the 3D bulk as well as at the two-dimensional (2D) surface. For bismuth surface alloys on Cu(111), the energy-resolved photoelectron momentum patterns allow us to identify specific emission processes in which bulk excited electrons are subsequently diffracted by an atomic 2D surface grating. The polarization dependence of the observed intensity features in momentum space is explained based on the different relative orientations of characteristic reciprocal space directions with respect to the electric field vector of the incident light.

Cross-indexing of binary SIFT codes for large-scale image search.

PubMed

Liu, Zhen; Li, Houqiang; Zhang, Liyan; Zhou, Wengang; Tian, Qi

2014-05-01

In recent years, there has been growing interest in mapping visual features into compact binary codes for applications on large-scale image collections. Encoding high-dimensional data as compact binary codes reduces the memory cost for storage. Besides, it benefits the computational efficiency since the computation of similarity can be efficiently measured by Hamming distance. In this paper, we propose a novel flexible scale invariant feature transform (SIFT) binarization (FSB) algorithm for large-scale image search. The FSB algorithm explores the magnitude patterns of SIFT descriptor. It is unsupervised and the generated binary codes are demonstrated to be dispreserving. Besides, we propose a new searching strategy to find target features based on the cross-indexing in the binary SIFT space and original SIFT space. We evaluate our approach on two publicly released data sets. The experiments on large-scale partial duplicate image retrieval system demonstrate the effectiveness and efficiency of the proposed algorithm.

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

Grillot, L.R.; Anderton, P.W.; Haselton, T.M.

The Espoir oil field, located approximately 13 km offshore Ivory Coast, was discovered in 1980 by a joint venture comprised of Phillips Petroleum Company, AGIP, SEDCO Energy, and PETROCI. Following the discovery, a three-dimensional seismic survey was recorded by GSI in 1981-1982 to provide detailed seismic coverage of Espoir field and adjacent features. The seismic program consisted of 7700 line-km of data acquired in a single survey area that is located on the edge of the continental shelf and extends into deep water. In comparison with previous two-dimensional seismic surveys, the three-dimensional data provided several improvements in interpretation and mappingmore » including: (1) sharper definition of structural features, (2) reliable correlations of horizons and fault traces between closely spaced tracks, (3) detailed time contour maps from time-slice sections, and (4) improved velocity model for depth conversion. The improved mapping helped us identify additional well locations; the results of these wells compared favorably with the interpretation made prior to drilling.« less

Unconstrained handwritten numeral recognition based on radial basis competitive and cooperative networks with spatio-temporal feature representation.

PubMed

Lee, S; Pan, J J

1996-01-01

This paper presents a new approach to representation and recognition of handwritten numerals. The approach first transforms a two-dimensional (2-D) spatial representation of a numeral into a three-dimensional (3-D) spatio-temporal representation by identifying the tracing sequence based on a set of heuristic rules acting as transformation operators. A multiresolution critical-point segmentation method is then proposed to extract local feature points, at varying degrees of scale and coarseness. A new neural network architecture, referred to as radial-basis competitive and cooperative network (RCCN), is presented especially for handwritten numeral recognition. RCCN is a globally competitive and locally cooperative network with the capability of self-organizing hidden units to progressively achieve desired network performance, and functions as a universal approximator of arbitrary input-output mappings. Three types of RCCNs are explored: input-space RCCN (IRCCN), output-space RCCN (ORCCN), and bidirectional RCCN (BRCCN). Experiments against handwritten zip code numerals acquired by the U.S. Postal Service indicated that the proposed method is robust in terms of variations, deformations, transformations, and corruption, achieving about 97% recognition rate.

Effect of finite sample size on feature selection and classification: a simulation study.

PubMed

Way, Ted W; Sahiner, Berkman; Hadjiiski, Lubomir M; Chan, Heang-Ping

2010-02-01

The small number of samples available for training and testing is often the limiting factor in finding the most effective features and designing an optimal computer-aided diagnosis (CAD) system. Training on a limited set of samples introduces bias and variance in the performance of a CAD system relative to that trained with an infinite sample size. In this work, the authors conducted a simulation study to evaluate the performances of various combinations of classifiers and feature selection techniques and their dependence on the class distribution, dimensionality, and the training sample size. The understanding of these relationships will facilitate development of effective CAD systems under the constraint of limited available samples. Three feature selection techniques, the stepwise feature selection (SFS), sequential floating forward search (SFFS), and principal component analysis (PCA), and two commonly used classifiers, Fisher's linear discriminant analysis (LDA) and support vector machine (SVM), were investigated. Samples were drawn from multidimensional feature spaces of multivariate Gaussian distributions with equal or unequal covariance matrices and unequal means, and with equal covariance matrices and unequal means estimated from a clinical data set. Classifier performance was quantified by the area under the receiver operating characteristic curve Az. The mean Az values obtained by resubstitution and hold-out methods were evaluated for training sample sizes ranging from 15 to 100 per class. The number of simulated features available for selection was chosen to be 50, 100, and 200. It was found that the relative performance of the different combinations of classifier and feature selection method depends on the feature space distributions, the dimensionality, and the available training sample sizes. The LDA and SVM with radial kernel performed similarly for most of the conditions evaluated in this study, although the SVM classifier showed a slightly higher hold-out performance than LDA for some conditions and vice versa for other conditions. PCA was comparable to or better than SFS and SFFS for LDA at small samples sizes, but inferior for SVM with polynomial kernel. For the class distributions simulated from clinical data, PCA did not show advantages over the other two feature selection methods. Under this condition, the SVM with radial kernel performed better than the LDA when few training samples were available, while LDA performed better when a large number of training samples were available. None of the investigated feature selection-classifier combinations provided consistently superior performance under the studied conditions for different sample sizes and feature space distributions. In general, the SFFS method was comparable to the SFS method while PCA may have an advantage for Gaussian feature spaces with unequal covariance matrices. The performance of the SVM with radial kernel was better than, or comparable to, that of the SVM with polynomial kernel under most conditions studied.

Swarms: Optimum aggregations of spacecraft

NASA Technical Reports Server (NTRS)

Mayer, H. L.

1980-01-01

Swarms are aggregations of spacecraft or elements of a space system which are cooperative in function, but physically isolated or only loosely connected. For some missions the swarm configuration may be optimum compared to a group of completely independent spacecraft or a complex rigidly integrated spacecraft or space platform. General features of swarms are induced by considering an ensemble of 26 swarms, examples ranging from Earth centered swarms for commercial application to swarms for exploring minor planets. A concept for a low altitude swarm as a substitute for a space platform is proposed and a preliminary design studied. The salient design feature is the web of tethers holding the 30 km swarm in a rigid two dimensional array in the orbital plane. A mathematical discussion and tutorial in tether technology and in some aspects of the distribution of services (mass, energy, and information to swarm elements) are included.

High dimensional feature reduction via projection pursuit

NASA Technical Reports Server (NTRS)

Jimenez, Luis; Landgrebe, David

1994-01-01

The recent development of more sophisticated remote sensing systems enables the measurement of radiation in many more spectral intervals than previously possible. An example of that technology is the AVIRIS system, which collects image data in 220 bands. As a result of this, new algorithms must be developed in order to analyze the more complex data effectively. Data in a high dimensional space presents a substantial challenge, since intuitive concepts valid in a 2-3 dimensional space to not necessarily apply in higher dimensional spaces. For example, high dimensional space is mostly empty. This results from the concentration of data in the corners of hypercubes. Other examples may be cited. Such observations suggest the need to project data to a subspace of a much lower dimension on a problem specific basis in such a manner that information is not lost. Projection Pursuit is a technique that will accomplish such a goal. Since it processes data in lower dimensions, it should avoid many of the difficulties of high dimensional spaces. In this paper, we begin the investigation of some of the properties of Projection Pursuit for this purpose.

Mapping the Indonesian territory, based on pollution, social demography and geographical data, using self organizing feature map

NASA Astrophysics Data System (ADS)

Hernawati, Kuswari; Insani, Nur; Bambang S. H., M.; Nur Hadi, W.; Sahid

2017-08-01

This research aims to mapping the 33 (thirty-three) provinces in Indonesia, based on the data on air, water and soil pollution, as well as social demography and geography data, into a clustered model. The method used in this study was unsupervised method that combines the basic concept of Kohonen or Self-Organizing Feature Maps (SOFM). The method is done by providing the design parameters for the model based on data related directly/ indirectly to pollution, which are the demographic and social data, pollution levels of air, water and soil, as well as the geographical situation of each province. The parameters used consists of 19 features/characteristics, including the human development index, the number of vehicles, the availability of the plant's water absorption and flood prevention, as well as geographic and demographic situation. The data used were secondary data from the Central Statistics Agency (BPS), Indonesia. The data are mapped into SOFM from a high-dimensional vector space into two-dimensional vector space according to the closeness of location in term of Euclidean distance. The resulting outputs are represented in clustered grouping. Thirty-three provinces are grouped into five clusters, where each cluster has different features/characteristics and level of pollution. The result can used to help the efforts on prevention and resolution of pollution problems on each cluster in an effective and efficient way.

Predict subcellular locations of singleplex and multiplex proteins by semi-supervised learning and dimension-reducing general mode of Chou's PseAAC.

PubMed

Pacharawongsakda, Eakasit; Theeramunkong, Thanaruk

2013-12-01

Predicting protein subcellular location is one of major challenges in Bioinformatics area since such knowledge helps us understand protein functions and enables us to select the targeted proteins during drug discovery process. While many computational techniques have been proposed to improve predictive performance for protein subcellular location, they have several shortcomings. In this work, we propose a method to solve three main issues in such techniques; i) manipulation of multiplex proteins which may exist or move between multiple cellular compartments, ii) handling of high dimensionality in input and output spaces and iii) requirement of sufficient labeled data for model training. Towards these issues, this work presents a new computational method for predicting proteins which have either single or multiple locations. The proposed technique, namely iFLAST-CORE, incorporates the dimensionality reduction in the feature and label spaces with co-training paradigm for semi-supervised multi-label classification. For this purpose, the Singular Value Decomposition (SVD) is applied to transform the high-dimensional feature space and label space into the lower-dimensional spaces. After that, due to limitation of labeled data, the co-training regression makes use of unlabeled data by predicting the target values in the lower-dimensional spaces of unlabeled data. In the last step, the component of SVD is used to project labels in the lower-dimensional space back to those in the original space and an adaptive threshold is used to map a numeric value to a binary value for label determination. A set of experiments on viral proteins and gram-negative bacterial proteins evidence that our proposed method improve the classification performance in terms of various evaluation metrics such as Aiming (or Precision), Coverage (or Recall) and macro F-measure, compared to the traditional method that uses only labeled data.

Modeling Three-Dimensional Flow in Confined Aquifers by Superposition of Both Two- and Three-Dimensional Analytic Functions

NASA Astrophysics Data System (ADS)

Haitjema, Henk M.

1985-10-01

A technique is presented to incorporate three-dimensional flow in a Dupuit-Forchheimer model. The method is based on superposition of approximate analytic solutions to both two- and three-dimensional flow features in a confined aquifer of infinite extent. Three-dimensional solutions are used in the domain of interest, while farfield conditions are represented by two-dimensional solutions. Approximate three- dimensional solutions have been derived for a partially penetrating well and a shallow creek. Each of these solutions satisfies the condition that no flow occurs across the confining layers of the aquifer. Because of this condition, the flow at some distance of a three-dimensional feature becomes nearly horizontal. Consequently, remotely from a three-dimensional feature, its three-dimensional solution is replaced by a corresponding two-dimensional one. The latter solution is trivial as compared to its three-dimensional counterpart, and its use greatly enhances the computational efficiency of the model. As an example, the flow is modeled between a partially penetrating well and a shallow creek that occur in a regional aquifer system.

Multiparticle dynamics in the E-phi tracking code ESME

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

James A. MacLachlan

2002-06-21

ESME has developed over a twenty year period from its origins as a program for modeling rf gymnastics to a rather general facility for that fraction of beam dynamics of synchrotrons and storage rings which can be properly treated in the two dimensional longitudinal phase space. The features of this program which serve particularly for multiparticle calculations are described, some underling principles are noted, and illustrative results are given.

Multiparticle Dynamics in the E-φ Tracking Code ESME

NASA Astrophysics Data System (ADS)

MacLachlan, James A.

2002-12-01

ESME has developed over a twenty year period from its origins as a program for modeling rf gymnastics to a rather general facility for that fraction of beam dynamics of synchrotrons and storage rings which can be properly treated in the two dimensional longitudinal phase space. The features of this program which serve particularly for multiparticle calculations are described, some uderlying principles are noted, and illustrative results are given.

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

PubMed

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

2007-09-01

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

Wigner's quantum phase-space current in weakly-anharmonic weakly-excited two-state systems

NASA Astrophysics Data System (ADS)

Kakofengitis, Dimitris; Steuernagel, Ole

2017-09-01

There are no phase-space trajectories for anharmonic quantum systems, but Wigner's phase-space representation of quantum mechanics features Wigner current J . This current reveals fine details of quantum dynamics —finer than is ordinarily thought accessible according to quantum folklore invoking Heisenberg's uncertainty principle. Here, we focus on the simplest, most intuitive, and analytically accessible aspects of J. We investigate features of J for bound states of time-reversible, weakly-anharmonic one-dimensional quantum-mechanical systems which are weakly-excited. We establish that weakly-anharmonic potentials can be grouped into three distinct classes: hard, soft, and odd potentials. We stress connections between each other and the harmonic case. We show that their Wigner current fieldline patterns can be characterised by J's discrete stagnation points, how these arise and how a quantum system's dynamics is constrained by the stagnation points' topological charge conservation. We additionally show that quantum dynamics in phase space, in the case of vanishing Planck constant ℏ or vanishing anharmonicity, does not pointwise converge to classical dynamics.

Analytic study of solutions for a (3 + 1) -dimensional generalized KP equation

NASA Astrophysics Data System (ADS)

Gao, Hui; Cheng, Wenguang; Xu, Tianzhou; Wang, Gangwei

2018-03-01

The (3 + 1) -dimensional generalized KP (gKP) equation is an important nonlinear partial differential equation in theoretical and mathematical physics which can be used to describe nonlinear wave motion. Through the Hirota bilinear method, one-solition, two-solition and N-solition solutions are derived via symbolic computation. Two classes of lump solutions, rationally localized in all directions in space, to the dimensionally reduced cases in (2 + 1)-dimensions, are constructed by using a direct method based on the Hirota bilinear form of the equation. It implies that we can derive the lump solutions of the reduced gKP equation from positive quadratic function solutions to the aforementioned bilinear equation. Meanwhile, we get interaction solutions between a lump and a kink of the gKP equation. The lump appears from a kink and is swallowed by it with the change of time. This work offers a possibility which can enrich the variety of the dynamical features of solutions for higher-dimensional nonlinear evolution equations.

Multisensor Analysis of Spectral Dimensionality and Soil Diversity in the Great Central Valley of California.

PubMed

Sousa, Daniel; Small, Christopher

2018-02-14

Planned hyperspectral satellite missions and the decreased revisit time of multispectral imaging offer the potential for data fusion to leverage both the spectral resolution of hyperspectral sensors and the temporal resolution of multispectral constellations. Hyperspectral imagery can also be used to better understand fundamental properties of multispectral data. In this analysis, we use five flight lines from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) archive with coincident Landsat 8 acquisitions over a spectrally diverse region of California to address the following questions: (1) How much of the spectral dimensionality of hyperspectral data is captured in multispectral data?; (2) Is the characteristic pyramidal structure of the multispectral feature space also present in the low order dimensions of the hyperspectral feature space at comparable spatial scales?; (3) How much variability in rock and soil substrate endmembers (EMs) present in hyperspectral data is captured by multispectral sensors? We find nearly identical partitions of variance, low-order feature space topologies, and EM spectra for hyperspectral and multispectral image composites. The resulting feature spaces and EMs are also very similar to those from previous global multispectral analyses, implying that the fundamental structure of the global feature space is present in our relatively small spatial subset of California. Finally, we find that the multispectral dataset well represents the substrate EM variability present in the study area - despite its inability to resolve narrow band absorptions. We observe a tentative but consistent physical relationship between the gradation of substrate reflectance in the feature space and the gradation of sand versus clay content in the soil classification system.

Multisensor Analysis of Spectral Dimensionality and Soil Diversity in the Great Central Valley of California

PubMed Central

Small, Christopher

2018-01-01

Planned hyperspectral satellite missions and the decreased revisit time of multispectral imaging offer the potential for data fusion to leverage both the spectral resolution of hyperspectral sensors and the temporal resolution of multispectral constellations. Hyperspectral imagery can also be used to better understand fundamental properties of multispectral data. In this analysis, we use five flight lines from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) archive with coincident Landsat 8 acquisitions over a spectrally diverse region of California to address the following questions: (1) How much of the spectral dimensionality of hyperspectral data is captured in multispectral data?; (2) Is the characteristic pyramidal structure of the multispectral feature space also present in the low order dimensions of the hyperspectral feature space at comparable spatial scales?; (3) How much variability in rock and soil substrate endmembers (EMs) present in hyperspectral data is captured by multispectral sensors? We find nearly identical partitions of variance, low-order feature space topologies, and EM spectra for hyperspectral and multispectral image composites. The resulting feature spaces and EMs are also very similar to those from previous global multispectral analyses, implying that the fundamental structure of the global feature space is present in our relatively small spatial subset of California. Finally, we find that the multispectral dataset well represents the substrate EM variability present in the study area – despite its inability to resolve narrow band absorptions. We observe a tentative but consistent physical relationship between the gradation of substrate reflectance in the feature space and the gradation of sand versus clay content in the soil classification system. PMID:29443900

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

NASA Astrophysics Data System (ADS)

Liu, Changying; Wu, Xinyuan

2017-07-01

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

Human Activity Recognition in AAL Environments Using Random Projections.

PubMed

Damaševičius, Robertas; Vasiljevas, Mindaugas; Šalkevičius, Justas; Woźniak, Marcin

2016-01-01

Automatic human activity recognition systems aim to capture the state of the user and its environment by exploiting heterogeneous sensors attached to the subject's body and permit continuous monitoring of numerous physiological signals reflecting the state of human actions. Successful identification of human activities can be immensely useful in healthcare applications for Ambient Assisted Living (AAL), for automatic and intelligent activity monitoring systems developed for elderly and disabled people. In this paper, we propose the method for activity recognition and subject identification based on random projections from high-dimensional feature space to low-dimensional projection space, where the classes are separated using the Jaccard distance between probability density functions of projected data. Two HAR domain tasks are considered: activity identification and subject identification. The experimental results using the proposed method with Human Activity Dataset (HAD) data are presented.

Human Activity Recognition in AAL Environments Using Random Projections

PubMed Central

Damaševičius, Robertas; Vasiljevas, Mindaugas; Šalkevičius, Justas; Woźniak, Marcin

2016-01-01

Automatic human activity recognition systems aim to capture the state of the user and its environment by exploiting heterogeneous sensors attached to the subject's body and permit continuous monitoring of numerous physiological signals reflecting the state of human actions. Successful identification of human activities can be immensely useful in healthcare applications for Ambient Assisted Living (AAL), for automatic and intelligent activity monitoring systems developed for elderly and disabled people. In this paper, we propose the method for activity recognition and subject identification based on random projections from high-dimensional feature space to low-dimensional projection space, where the classes are separated using the Jaccard distance between probability density functions of projected data. Two HAR domain tasks are considered: activity identification and subject identification. The experimental results using the proposed method with Human Activity Dataset (HAD) data are presented. PMID:27413392

High-resolution Self-Organizing Maps for advanced visualization and dimension reduction.

PubMed

Saraswati, Ayu; Nguyen, Van Tuc; Hagenbuchner, Markus; Tsoi, Ah Chung

2018-05-04

Kohonen's Self Organizing feature Map (SOM) provides an effective way to project high dimensional input features onto a low dimensional display space while preserving the topological relationships among the input features. Recent advances in algorithms that take advantages of modern computing hardware introduced the concept of high resolution SOMs (HRSOMs). This paper investigates the capabilities and applicability of the HRSOM as a visualization tool for cluster analysis and its suitabilities to serve as a pre-processor in ensemble learning models. The evaluation is conducted on a number of established benchmarks and real-world learning problems, namely, the policeman benchmark, two web spam detection problems, a network intrusion detection problem, and a malware detection problem. It is found that the visualization resulted from an HRSOM provides new insights concerning these learning problems. It is furthermore shown empirically that broad benefits from the use of HRSOMs in both clustering and classification problems can be expected. Copyright © 2018 Elsevier Ltd. All rights reserved.

Topological patterns of mesh textures in serpentinites

NASA Astrophysics Data System (ADS)

Miyazawa, M.; Suzuki, A.; Shimizu, H.; Okamoto, A.; Hiraoka, Y.; Obayashi, I.; Tsuji, T.; Ito, T.

2017-12-01

Serpentinization is a hydration process that forms serpentine minerals and magnetite within the oceanic lithosphere. Microfractures crosscut these minerals during the reactions, and the structures look like mesh textures. It has been known that the patterns of microfractures and the system evolutions are affected by the hydration reaction and fluid transport in fractures and within matrices. This study aims at quantifying the topological patterns of the mesh textures and understanding possible conditions of fluid transport and reaction during serpentinization in the oceanic lithosphere. Two-dimensional simulation by the distinct element method (DEM) generates fracture patterns due to serpentinization. The microfracture patterns are evaluated by persistent homology, which measures features of connected components of a topological space and encodes multi-scale topological features in the persistence diagrams. The persistence diagrams of the different mesh textures are evaluated by principal component analysis to bring out the strong patterns of persistence diagrams. This approach help extract feature values of fracture patterns from high-dimensional and complex datasets.

Feature extraction with deep neural networks by a generalized discriminant analysis.

PubMed

Stuhlsatz, André; Lippel, Jens; Zielke, Thomas

2012-04-01

We present an approach to feature extraction that is a generalization of the classical linear discriminant analysis (LDA) on the basis of deep neural networks (DNNs). As for LDA, discriminative features generated from independent Gaussian class conditionals are assumed. This modeling has the advantages that the intrinsic dimensionality of the feature space is bounded by the number of classes and that the optimal discriminant function is linear. Unfortunately, linear transformations are insufficient to extract optimal discriminative features from arbitrarily distributed raw measurements. The generalized discriminant analysis (GerDA) proposed in this paper uses nonlinear transformations that are learnt by DNNs in a semisupervised fashion. We show that the feature extraction based on our approach displays excellent performance on real-world recognition and detection tasks, such as handwritten digit recognition and face detection. In a series of experiments, we evaluate GerDA features with respect to dimensionality reduction, visualization, classification, and detection. Moreover, we show that GerDA DNNs can preprocess truly high-dimensional input data to low-dimensional representations that facilitate accurate predictions even if simple linear predictors or measures of similarity are used.

Investigation of growth features in several hydraulic fractures

NASA Astrophysics Data System (ADS)

Bykov, Alexander; Galybin, Alexander; Evdokimov, Alexander; Zavialova, Natalia; Zavialov, Ivan; Negodiaev, Sergey; Perepechkin, Ilia

2017-04-01

In this paper we simulate the growth of three or more interacting hydraulic fractures in the horizontal well with a cross flow of fluid between them. Calculation of the dynamics of cracks is performed in three dimensional space. The computation of the movement of fracturing fluid with proppant is performed in the two-dimensional space (the flow was averaged along crack aperture). For determining the hydraulic pipe resistance coefficient we used a generalization of the Reynolds number for fluids with power rheology and a generalization of the von Karman equation made by Dodge and Meiner. The calculations showed that the first crack was developing faster than the rest in homogeneous medium. During the steady loading the outer cracks pinch the inner cracks and it was shown that only the first and last fracture develop in extreme case. It is also possible to simulate the parameters at which the two developing outer cracks pinch the central one in the horizontal direction. In this case, the central crack may grow in the vertical direction.

Resonance-Based Time-Frequency Manifold for Feature Extraction of Ship-Radiated Noise.

PubMed

Yan, Jiaquan; Sun, Haixin; Chen, Hailan; Junejo, Naveed Ur Rehman; Cheng, En

2018-03-22

In this paper, a novel time-frequency signature using resonance-based sparse signal decomposition (RSSD), phase space reconstruction (PSR), time-frequency distribution (TFD) and manifold learning is proposed for feature extraction of ship-radiated noise, which is called resonance-based time-frequency manifold (RTFM). This is suitable for analyzing signals with oscillatory, non-stationary and non-linear characteristics in a situation of serious noise pollution. Unlike the traditional methods which are sensitive to noise and just consider one side of oscillatory, non-stationary and non-linear characteristics, the proposed RTFM can provide the intact feature signature of all these characteristics in the form of a time-frequency signature by the following steps: first, RSSD is employed on the raw signal to extract the high-oscillatory component and abandon the low-oscillatory component. Second, PSR is performed on the high-oscillatory component to map the one-dimensional signal to the high-dimensional phase space. Third, TFD is employed to reveal non-stationary information in the phase space. Finally, manifold learning is applied to the TFDs to fetch the intrinsic non-linear manifold. A proportional addition of the top two RTFMs is adopted to produce the improved RTFM signature. All of the case studies are validated on real audio recordings of ship-radiated noise. Case studies of ship-radiated noise on different datasets and various degrees of noise pollution manifest the effectiveness and robustness of the proposed method.

Resonance-Based Time-Frequency Manifold for Feature Extraction of Ship-Radiated Noise

PubMed Central

Yan, Jiaquan; Sun, Haixin; Chen, Hailan; Junejo, Naveed Ur Rehman; Cheng, En

2018-01-01

In this paper, a novel time-frequency signature using resonance-based sparse signal decomposition (RSSD), phase space reconstruction (PSR), time-frequency distribution (TFD) and manifold learning is proposed for feature extraction of ship-radiated noise, which is called resonance-based time-frequency manifold (RTFM). This is suitable for analyzing signals with oscillatory, non-stationary and non-linear characteristics in a situation of serious noise pollution. Unlike the traditional methods which are sensitive to noise and just consider one side of oscillatory, non-stationary and non-linear characteristics, the proposed RTFM can provide the intact feature signature of all these characteristics in the form of a time-frequency signature by the following steps: first, RSSD is employed on the raw signal to extract the high-oscillatory component and abandon the low-oscillatory component. Second, PSR is performed on the high-oscillatory component to map the one-dimensional signal to the high-dimensional phase space. Third, TFD is employed to reveal non-stationary information in the phase space. Finally, manifold learning is applied to the TFDs to fetch the intrinsic non-linear manifold. A proportional addition of the top two RTFMs is adopted to produce the improved RTFM signature. All of the case studies are validated on real audio recordings of ship-radiated noise. Case studies of ship-radiated noise on different datasets and various degrees of noise pollution manifest the effectiveness and robustness of the proposed method. PMID:29565288

Aesthetics by Numbers: Links between Perceived Texture Qualities and Computed Visual Texture Properties.

PubMed

Jacobs, Richard H A H; Haak, Koen V; Thumfart, Stefan; Renken, Remco; Henson, Brian; Cornelissen, Frans W

2016-01-01

Our world is filled with texture. For the human visual system, this is an important source of information for assessing environmental and material properties. Indeed-and presumably for this reason-the human visual system has regions dedicated to processing textures. Despite their abundance and apparent relevance, only recently the relationships between texture features and high-level judgments have captured the interest of mainstream science, despite long-standing indications for such relationships. In this study, we explore such relationships, as these might be used to predict perceived texture qualities. This is relevant, not only from a psychological/neuroscience perspective, but also for more applied fields such as design, architecture, and the visual arts. In two separate experiments, observers judged various qualities of visual textures such as beauty, roughness, naturalness, elegance, and complexity. Based on factor analysis, we find that in both experiments, ~75% of the variability in the judgments could be explained by a two-dimensional space, with axes that are closely aligned to the beauty and roughness judgments. That a two-dimensional judgment space suffices to capture most of the variability in the perceived texture qualities suggests that observers use a relatively limited set of internal scales on which to base various judgments, including aesthetic ones. Finally, for both of these judgments, we determined the relationship with a large number of texture features computed for each of the texture stimuli. We find that the presence of lower spatial frequencies, oblique orientations, higher intensity variation, higher saturation, and redness correlates with higher beauty ratings. Features that captured image intensity and uniformity correlated with roughness ratings. Therefore, a number of computational texture features are predictive of these judgments. This suggests that perceived texture qualities-including the aesthetic appreciation-are sufficiently universal to be predicted-with reasonable accuracy-based on the computed feature content of the textures.

Aesthetics by Numbers: Links between Perceived Texture Qualities and Computed Visual Texture Properties

PubMed Central

Jacobs, Richard H. A. H.; Haak, Koen V.; Thumfart, Stefan; Renken, Remco; Henson, Brian; Cornelissen, Frans W.

2016-01-01

Our world is filled with texture. For the human visual system, this is an important source of information for assessing environmental and material properties. Indeed—and presumably for this reason—the human visual system has regions dedicated to processing textures. Despite their abundance and apparent relevance, only recently the relationships between texture features and high-level judgments have captured the interest of mainstream science, despite long-standing indications for such relationships. In this study, we explore such relationships, as these might be used to predict perceived texture qualities. This is relevant, not only from a psychological/neuroscience perspective, but also for more applied fields such as design, architecture, and the visual arts. In two separate experiments, observers judged various qualities of visual textures such as beauty, roughness, naturalness, elegance, and complexity. Based on factor analysis, we find that in both experiments, ~75% of the variability in the judgments could be explained by a two-dimensional space, with axes that are closely aligned to the beauty and roughness judgments. That a two-dimensional judgment space suffices to capture most of the variability in the perceived texture qualities suggests that observers use a relatively limited set of internal scales on which to base various judgments, including aesthetic ones. Finally, for both of these judgments, we determined the relationship with a large number of texture features computed for each of the texture stimuli. We find that the presence of lower spatial frequencies, oblique orientations, higher intensity variation, higher saturation, and redness correlates with higher beauty ratings. Features that captured image intensity and uniformity correlated with roughness ratings. Therefore, a number of computational texture features are predictive of these judgments. This suggests that perceived texture qualities—including the aesthetic appreciation—are sufficiently universal to be predicted—with reasonable accuracy—based on the computed feature content of the textures. PMID:27493628

Dimensionality reduction in epidemic spreading models

NASA Astrophysics Data System (ADS)

Frasca, M.; Rizzo, A.; Gallo, L.; Fortuna, L.; Porfiri, M.

2015-09-01

Complex dynamical systems often exhibit collective dynamics that are well described by a reduced set of key variables in a low-dimensional space. Such a low-dimensional description offers a privileged perspective to understand the system behavior across temporal and spatial scales. In this work, we propose a data-driven approach to establish low-dimensional representations of large epidemic datasets by using a dimensionality reduction algorithm based on isometric features mapping (ISOMAP). We demonstrate our approach on synthetic data for epidemic spreading in a population of mobile individuals. We find that ISOMAP is successful in embedding high-dimensional data into a low-dimensional manifold, whose topological features are associated with the epidemic outbreak. Across a range of simulation parameters and model instances, we observe that epidemic outbreaks are embedded into a family of closed curves in a three-dimensional space, in which neighboring points pertain to instants that are close in time. The orientation of each curve is unique to a specific outbreak, and the coordinates correlate with the number of infected individuals. A low-dimensional description of epidemic spreading is expected to improve our understanding of the role of individual response on the outbreak dynamics, inform the selection of meaningful global observables, and, possibly, aid in the design of control and quarantine procedures.

AdS3 to dS3 transition in the near horizon of asymptotically de Sitter solutions

NASA Astrophysics Data System (ADS)

Sadeghian, S.; Vahidinia, M. H.

2017-08-01

We consider two solutions of Einstein-Λ theory which admit the extremal vanishing horizon (EVH) limit, odd-dimensional multispinning Kerr black hole (in the presence of cosmological constant) and cosmological soliton. We show that the near horizon EVH geometry of Kerr has a three-dimensional maximally symmetric subspace whose curvature depends on rotational parameters and the cosmological constant. In the Kerr-dS case, this subspace interpolates between AdS3 , three-dimensional flat and dS3 by varying rotational parameters, while the near horizon of the EVH cosmological soliton always has a dS3 . The feature of the EVH cosmological soliton is that it is regular everywhere on the horizon. In the near EVH case, these three-dimensional parts turn into the corresponding locally maximally symmetric spacetimes with a horizon: Kerr-dS3 , flat space cosmology or BTZ black hole. We show that their thermodynamics match with the thermodynamics of the original near EVH black holes. We also briefly discuss the holographic two-dimensional CFT dual to the near horizon of EVH solutions.

Tonal Interface to MacroMolecules (TIMMol): A Textual and Tonal Tool for Molecular Visualization

ERIC Educational Resources Information Center

Cordes, Timothy J.; Carlson, C. Britt; Forest, Katrina T.

2008-01-01

We developed the three-dimensional visualization software, Tonal Interface to MacroMolecules or TIMMol, for studying atomic coordinates of protein structures. Key features include audio tones indicating x, y, z location, identification of the cursor location in one-dimensional and three-dimensional space, textual output that can be easily linked…

Dynamic of consumer groups and response of commodity markets by principal component analysis

NASA Astrophysics Data System (ADS)

Nobi, Ashadun; Alam, Shafiqul; Lee, Jae Woo

2017-09-01

This study investigates financial states and group dynamics by applying principal component analysis to the cross-correlation coefficients of the daily returns of commodity futures. The eigenvalues of the cross-correlation matrix in the 6-month timeframe displays similar values during 2010-2011, but decline following 2012. A sharp drop in eigenvalue implies the significant change of the market state. Three commodity sectors, energy, metals and agriculture, are projected into two dimensional spaces consisting of two principal components (PC). We observe that they form three distinct clusters in relation to various sectors. However, commodities with distinct features have intermingled with one another and scattered during severe crises, such as the European sovereign debt crises. We observe the notable change of the position of two dimensional spaces of groups during financial crises. By considering the first principal component (PC1) within the 6-month moving timeframe, we observe that commodities of the same group change states in a similar pattern, and the change of states of one group can be used as a warning for other group.

Numerical generation of two-dimensional grids by the use of Poisson equations with grid control at boundaries

NASA Technical Reports Server (NTRS)

Sorenson, R. L.; Steger, J. L.

1980-01-01

A method for generating boundary-fitted, curvilinear, two dimensional grids by the use of the Poisson equations is presented. Grids of C-type and O-type were made about airfoils and other shapes, with circular, rectangular, cascade-type, and other outer boundary shapes. Both viscous and inviscid spacings were used. In all cases, two important types of grid control can be exercised at both inner and outer boundaries. First is arbitrary control of the distances between the boundaries and the adjacent lines of the same coordinate family, i.e., stand-off distances. Second is arbitrary control of the angles with which lines of the opposite coordinate family intersect the boundaries. Thus, both grid cell size (or aspect ratio) and grid cell skewness are controlled at boundaries. Reasonable cell size and shape are ensured even in cases wherein extreme boundary shapes would tend to cause skewness or poorly controlled grid spacing. An inherent feature of the Poisson equations is that lines in the interior of the grid smoothly connect the boundary points (the grid mapping functions are second order differentiable).

Collaborated measurement of three-dimensional position and orientation errors of assembled miniature devices with two vision systems

NASA Astrophysics Data System (ADS)

Wang, Xiaodong; Zhang, Wei; Luo, Yi; Yang, Weimin; Chen, Liang

2013-01-01

In assembly of miniature devices, the position and orientation of the parts to be assembled should be guaranteed during or after assembly. In some cases, the relative position or orientation errors among the parts can not be measured from only one direction using visual method, because of visual occlusion or for the features of parts located in a three-dimensional way. An automatic assembly system for precise miniature devices is introduced. In the modular assembly system, two machine vision systems were employed for measurement of the three-dimensionally distributed assembly errors. High resolution CCD cameras and high position repeatability precision stages were integrated to realize high precision measurement in large work space. The two cameras worked in collaboration in measurement procedure to eliminate the influence of movement errors of the rotational or translational stages. A set of templates were designed for calibration of the vision systems and evaluation of the system's measurement accuracy.

Effective degrees of freedom of a random walk on a fractal

NASA Astrophysics Data System (ADS)

Balankin, Alexander S.

2015-12-01

We argue that a non-Markovian random walk on a fractal can be treated as a Markovian process in a fractional dimensional space with a suitable metric. This allows us to define the fractional dimensional space allied to the fractal as the ν -dimensional space Fν equipped with the metric induced by the fractal topology. The relation between the number of effective spatial degrees of freedom of walkers on the fractal (ν ) and fractal dimensionalities is deduced. The intrinsic time of random walk in Fν is inferred. The Laplacian operator in Fν is constructed. This allows us to map physical problems on fractals into the corresponding problems in Fν. In this way, essential features of physics on fractals are revealed. Particularly, subdiffusion on path-connected fractals is elucidated. The Coulomb potential of a point charge on a fractal embedded in the Euclidean space is derived. Intriguing attributes of some types of fractals are highlighted.

Margin-maximizing feature elimination methods for linear and nonlinear kernel-based discriminant functions.

PubMed

Aksu, Yaman; Miller, David J; Kesidis, George; Yang, Qing X

2010-05-01

Feature selection for classification in high-dimensional spaces can improve generalization, reduce classifier complexity, and identify important, discriminating feature "markers." For support vector machine (SVM) classification, a widely used technique is recursive feature elimination (RFE). We demonstrate that RFE is not consistent with margin maximization, central to the SVM learning approach. We thus propose explicit margin-based feature elimination (MFE) for SVMs and demonstrate both improved margin and improved generalization, compared with RFE. Moreover, for the case of a nonlinear kernel, we show that RFE assumes that the squared weight vector 2-norm is strictly decreasing as features are eliminated. We demonstrate this is not true for the Gaussian kernel and, consequently, RFE may give poor results in this case. MFE for nonlinear kernels gives better margin and generalization. We also present an extension which achieves further margin gains, by optimizing only two degrees of freedom--the hyperplane's intercept and its squared 2-norm--with the weight vector orientation fixed. We finally introduce an extension that allows margin slackness. We compare against several alternatives, including RFE and a linear programming method that embeds feature selection within the classifier design. On high-dimensional gene microarray data sets, University of California at Irvine (UCI) repository data sets, and Alzheimer's disease brain image data, MFE methods give promising results.

Knowledge Driven Image Mining with Mixture Density Mercer Kernels

NASA Technical Reports Server (NTRS)

Srivastava, Ashok N.; Oza, Nikunj

2004-01-01

This paper presents a new methodology for automatic knowledge driven image mining based on the theory of Mercer Kernels; which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly infinite dimensional feature space. In that high dimensional feature space, linear clustering, prediction, and classification algorithms can be applied and the results can be mapped back down to the original image space. Thus, highly nonlinear structure in the image can be recovered through the use of well-known linear mathematics in the feature space. This process has a number of advantages over traditional methods in that it allows for nonlinear interactions to be modelled with only a marginal increase in computational costs. In this paper, we present the theory of Mercer Kernels, describe its use in image mining, discuss a new method to generate Mercer Kernels directly from data, and compare the results with existing algorithms on data from the MODIS (Moderate Resolution Spectral Radiometer) instrument taken over the Arctic region. We also discuss the potential application of these methods on the Intelligent Archive, a NASA initiative for developing a tagged image data warehouse for the Earth Sciences.

Knowledge Driven Image Mining with Mixture Density Mercer Kernals

NASA Technical Reports Server (NTRS)

Srivastava, Ashok N.; Oza, Nikunj

2004-01-01

This paper presents a new methodology for automatic knowledge driven image mining based on the theory of Mercer Kernels, which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly infinite dimensional feature space. In that high dimensional feature space, linear clustering, prediction, and classification algorithms can be applied and the results can be mapped back down to the original image space. Thus, highly nonlinear structure in the image can be recovered through the use of well-known linear mathematics in the feature space. This process has a number of advantages over traditional methods in that it allows for nonlinear interactions to be modelled with only a marginal increase in computational costs. In this paper we present the theory of Mercer Kernels; describe its use in image mining, discuss a new method to generate Mercer Kernels directly from data, and compare the results with existing algorithms on data from the MODIS (Moderate Resolution Spectral Radiometer) instrument taken over the Arctic region. We also discuss the potential application of these methods on the Intelligent Archive, a NASA initiative for developing a tagged image data warehouse for the Earth Sciences.

Elucidating high-dimensional cancer hallmark annotation via enriched ontology.

PubMed

Yan, Shankai; Wong, Ka-Chun

2017-09-01

Cancer hallmark annotation is a promising technique that could discover novel knowledge about cancer from the biomedical literature. The automated annotation of cancer hallmarks could reveal relevant cancer transformation processes in the literature or extract the articles that correspond to the cancer hallmark of interest. It acts as a complementary approach that can retrieve knowledge from massive text information, advancing numerous focused studies in cancer research. Nonetheless, the high-dimensional nature of cancer hallmark annotation imposes a unique challenge. To address the curse of dimensionality, we compared multiple cancer hallmark annotation methods on 1580 PubMed abstracts. Based on the insights, a novel approach, UDT-RF, which makes use of ontological features is proposed. It expands the feature space via the Medical Subject Headings (MeSH) ontology graph and utilizes novel feature selections for elucidating the high-dimensional cancer hallmark annotation space. To demonstrate its effectiveness, state-of-the-art methods are compared and evaluated by a multitude of performance metrics, revealing the full performance spectrum on the full set of cancer hallmarks. Several case studies are conducted, demonstrating how the proposed approach could reveal novel insights into cancers. https://github.com/cskyan/chmannot. Copyright © 2017 Elsevier Inc. All rights reserved.

Two-dimensional PCA-based human gait identification

NASA Astrophysics Data System (ADS)

Chen, Jinyan; Wu, Rongteng

2012-11-01

It is very necessary to recognize person through visual surveillance automatically for public security reason. Human gait based identification focus on recognizing human by his walking video automatically using computer vision and image processing approaches. As a potential biometric measure, human gait identification has attracted more and more researchers. Current human gait identification methods can be divided into two categories: model-based methods and motion-based methods. In this paper a two-Dimensional Principal Component Analysis and temporal-space analysis based human gait identification method is proposed. Using background estimation and image subtraction we can get a binary images sequence from the surveillance video. By comparing the difference of two adjacent images in the gait images sequence, we can get a difference binary images sequence. Every binary difference image indicates the body moving mode during a person walking. We use the following steps to extract the temporal-space features from the difference binary images sequence: Projecting one difference image to Y axis or X axis we can get two vectors. Project every difference image in the difference binary images sequence to Y axis or X axis difference binary images sequence we can get two matrixes. These two matrixes indicate the styles of one walking. Then Two-Dimensional Principal Component Analysis(2DPCA) is used to transform these two matrixes to two vectors while at the same time keep the maximum separability. Finally the similarity of two human gait images is calculated by the Euclidean distance of the two vectors. The performance of our methods is illustrated using the CASIA Gait Database.

Tensor networks from kinematic space

DOE PAGES

Czech, Bartlomiej; Lamprou, Lampros; McCandlish, Samuel; ...

2016-07-20

We point out that the MERA network for the ground state of a 1+1-dimensional conformal field theory has the same structural features as kinematic space — the geometry of CFT intervals. In holographic theories kinematic space becomes identified with the space of bulk geodesics studied in integral geometry. We argue that in these settings MERA is best viewed as a discretization of the space of bulk geodesics rather than of the bulk geometry itself. As a test of this kinematic proposal, we compare the MERA representation of the thermofield-double state with the space of geodesics in the two-sided BTZ geometry,more » obtaining a detailed agreement which includes the entwinement sector. In conclusion, we discuss how the kinematic proposal can be extended to excited states by generalizing MERA to a broader class of compression networks.« less

A Novel Method Using Abstract Convex Underestimation in Ab-Initio Protein Structure Prediction for Guiding Search in Conformational Feature Space.

PubMed

Hao, Xiao-Hu; Zhang, Gui-Jun; Zhou, Xiao-Gen; Yu, Xu-Feng

2016-01-01

To address the searching problem of protein conformational space in ab-initio protein structure prediction, a novel method using abstract convex underestimation (ACUE) based on the framework of evolutionary algorithm was proposed. Computing such conformations, essential to associate structural and functional information with gene sequences, is challenging due to the high-dimensionality and rugged energy surface of the protein conformational space. As a consequence, the dimension of protein conformational space should be reduced to a proper level. In this paper, the high-dimensionality original conformational space was converted into feature space whose dimension is considerably reduced by feature extraction technique. And, the underestimate space could be constructed according to abstract convex theory. Thus, the entropy effect caused by searching in the high-dimensionality conformational space could be avoided through such conversion. The tight lower bound estimate information was obtained to guide the searching direction, and the invalid searching area in which the global optimal solution is not located could be eliminated in advance. Moreover, instead of expensively calculating the energy of conformations in the original conformational space, the estimate value is employed to judge if the conformation is worth exploring to reduce the evaluation time, thereby making computational cost lower and the searching process more efficient. Additionally, fragment assembly and the Monte Carlo method are combined to generate a series of metastable conformations by sampling in the conformational space. The proposed method provides a novel technique to solve the searching problem of protein conformational space. Twenty small-to-medium structurally diverse proteins were tested, and the proposed ACUE method was compared with It Fix, HEA, Rosetta and the developed method LEDE without underestimate information. Test results show that the ACUE method can more rapidly and more efficiently obtain the near-native protein structure.

Creating Body Shapes From Verbal Descriptions by Linking Similarity Spaces.

PubMed

Hill, Matthew Q; Streuber, Stephan; Hahn, Carina A; Black, Michael J; O'Toole, Alice J

2016-11-01

Brief verbal descriptions of people's bodies (e.g., "curvy," "long-legged") can elicit vivid mental images. The ease with which these mental images are created belies the complexity of three-dimensional body shapes. We explored the relationship between body shapes and body descriptions and showed that a small number of words can be used to generate categorically accurate representations of three-dimensional bodies. The dimensions of body-shape variation that emerged in a language-based similarity space were related to major dimensions of variation computed directly from three-dimensional laser scans of 2,094 bodies. This relationship allowed us to generate three-dimensional models of people in the shape space using only their coordinates on analogous dimensions in the language-based description space. Human descriptions of photographed bodies and their corresponding models matched closely. The natural mapping between the spaces illustrates the role of language as a concise code for body shape that captures perceptually salient global and local body features. © The Author(s) 2016.

A Selective Overview of Variable Selection in High Dimensional Feature Space

PubMed Central

Fan, Jianqing

2010-01-01

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

Caustic Skeleton & Cosmic Web

NASA Astrophysics Data System (ADS)

Feldbrugge, Job; van de Weygaert, Rien; Hidding, Johan; Feldbrugge, Joost

2018-05-01

We present a general formalism for identifying the caustic structure of a dynamically evolving mass distribution, in an arbitrary dimensional space. The identification of caustics in fluids with Hamiltonian dynamics, viewed in Lagrangian space, corresponds to the classification of singularities in Lagrangian catastrophe theory. On the basis of this formalism we develop a theoretical framework for the dynamics of the formation of the cosmic web, and specifically those aspects that characterize its unique nature: its complex topological connectivity and multiscale spinal structure of sheetlike membranes, elongated filaments and compact cluster nodes. Given the collisionless nature of the gravitationally dominant dark matter component in the universe, the presented formalism entails an accurate description of the spatial organization of matter resulting from the gravitationally driven formation of cosmic structure. The present work represents a significant extension of the work by Arnol'd et al. [1], who classified the caustics that develop in one- and two-dimensional systems that evolve according to the Zel'dovich approximation. His seminal work established the defining role of emerging singularities in the formation of nonlinear structures in the universe. At the transition from the linear to nonlinear structure evolution, the first complex features emerge at locations where different fluid elements cross to establish multistream regions. Involving a complex folding of the 6-D sheetlike phase-space distribution, it manifests itself in the appearance of infinite density caustic features. The classification and characterization of these mass element foldings can be encapsulated in caustic conditions on the eigenvalue and eigenvector fields of the deformation tensor field. In this study we introduce an alternative and transparent proof for Lagrangian catastrophe theory. This facilitates the derivation of the caustic conditions for general Lagrangian fluids, with arbitrary dynamics. Most important in the present context is that it allows us to follow and describe the full three-dimensional geometric and topological complexity of the purely gravitationally evolving nonlinear cosmic matter field. While generic and statistical results can be based on the eigenvalue characteristics, one of our key findings is that of the significance of the eigenvector field of the deformation field for outlining the entire spatial structure of the caustic skeleton emerging from a primordial density field. In this paper we explicitly consider the caustic conditions for the three-dimensional Zel'dovich approximation, extending earlier work on those for one- and two-dimensional fluids towards the full spatial richness of the cosmic web. In an accompanying publication, we apply this towards a full three-dimensional study of caustics in the formation of the cosmic web and evaluate in how far it manages to outline and identify the intricate skeletal features in the corresponding N-body simulations.

Visual classification of medical data using MLP mapping.

PubMed

Cağatay Güler, E; Sankur, B; Kahya, Y P; Raudys, S

1998-05-01

In this work we discuss the design of a novel non-linear mapping method for visual classification based on multilayer perceptrons (MLP) and assigned class target values. In training the perceptron, one or more target output values for each class in a 2-dimensional space are used. In other words, class membership information is interpreted visually as closeness to target values in a 2D feature space. This mapping is obtained by training the multilayer perceptron (MLP) using class membership information, input data and judiciously chosen target values. Weights are estimated in such a way that each training feature of the corresponding class is forced to be mapped onto the corresponding 2-dimensional target value.

A Dimensionally Aligned Signal Projection for Classification of Unintended Radiated Emissions

DOE PAGES

Vann, Jason Michael; Karnowski, Thomas P.; Kerekes, Ryan; ...

2017-04-24

Characterization of unintended radiated emissions (URE) from electronic devices plays an important role in many research areas from electromagnetic interference to nonintrusive load monitoring to information system security. URE can provide insights for applications ranging from load disaggregation and energy efficiency to condition-based maintenance of equipment-based upon detected fault conditions. URE characterization often requires subject matter expertise to tailor transforms and feature extractors for the specific electrical devices of interest. We present a novel approach, named dimensionally aligned signal projection (DASP), for projecting aligned signal characteristics that are inherent to the physical implementation of many commercial electronic devices. These projectionsmore » minimize the need for an intimate understanding of the underlying physical circuitry and significantly reduce the number of features required for signal classification. We present three possible DASP algorithms that leverage frequency harmonics, modulation alignments, and frequency peak spacings, along with a two-dimensional image manipulation method for statistical feature extraction. To demonstrate the ability of DASP to generate relevant features from URE, we measured the conducted URE from 14 residential electronic devices using a 2 MS/s collection system. Furthermore, a linear discriminant analysis classifier was trained using DASP generated features and was blind tested resulting in a greater than 90% classification accuracy for each of the DASP algorithms and an accuracy of 99.1% when DASP features are used in combination. Furthermore, we show that a rank reduced feature set of the combined DASP algorithms provides a 98.9% classification accuracy with only three features and outperforms a set of spectral features in terms of general classification as well as applicability across a broad number of devices.« less

A Dimensionally Aligned Signal Projection for Classification of Unintended Radiated Emissions

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

Vann, Jason Michael; Karnowski, Thomas P.; Kerekes, Ryan

Characterization of unintended radiated emissions (URE) from electronic devices plays an important role in many research areas from electromagnetic interference to nonintrusive load monitoring to information system security. URE can provide insights for applications ranging from load disaggregation and energy efficiency to condition-based maintenance of equipment-based upon detected fault conditions. URE characterization often requires subject matter expertise to tailor transforms and feature extractors for the specific electrical devices of interest. We present a novel approach, named dimensionally aligned signal projection (DASP), for projecting aligned signal characteristics that are inherent to the physical implementation of many commercial electronic devices. These projectionsmore » minimize the need for an intimate understanding of the underlying physical circuitry and significantly reduce the number of features required for signal classification. We present three possible DASP algorithms that leverage frequency harmonics, modulation alignments, and frequency peak spacings, along with a two-dimensional image manipulation method for statistical feature extraction. To demonstrate the ability of DASP to generate relevant features from URE, we measured the conducted URE from 14 residential electronic devices using a 2 MS/s collection system. Furthermore, a linear discriminant analysis classifier was trained using DASP generated features and was blind tested resulting in a greater than 90% classification accuracy for each of the DASP algorithms and an accuracy of 99.1% when DASP features are used in combination. Furthermore, we show that a rank reduced feature set of the combined DASP algorithms provides a 98.9% classification accuracy with only three features and outperforms a set of spectral features in terms of general classification as well as applicability across a broad number of devices.« less

Marginal Space Deep Learning: Efficient Architecture for Volumetric Image Parsing.

PubMed

Ghesu, Florin C; Krubasik, Edward; Georgescu, Bogdan; Singh, Vivek; Yefeng Zheng; Hornegger, Joachim; Comaniciu, Dorin

2016-05-01

Robust and fast solutions for anatomical object detection and segmentation support the entire clinical workflow from diagnosis, patient stratification, therapy planning, intervention and follow-up. Current state-of-the-art techniques for parsing volumetric medical image data are typically based on machine learning methods that exploit large annotated image databases. Two main challenges need to be addressed, these are the efficiency in scanning high-dimensional parametric spaces and the need for representative image features which require significant efforts of manual engineering. We propose a pipeline for object detection and segmentation in the context of volumetric image parsing, solving a two-step learning problem: anatomical pose estimation and boundary delineation. For this task we introduce Marginal Space Deep Learning (MSDL), a novel framework exploiting both the strengths of efficient object parametrization in hierarchical marginal spaces and the automated feature design of Deep Learning (DL) network architectures. In the 3D context, the application of deep learning systems is limited by the very high complexity of the parametrization. More specifically 9 parameters are necessary to describe a restricted affine transformation in 3D, resulting in a prohibitive amount of billions of scanning hypotheses. The mechanism of marginal space learning provides excellent run-time performance by learning classifiers in clustered, high-probability regions in spaces of gradually increasing dimensionality. To further increase computational efficiency and robustness, in our system we learn sparse adaptive data sampling patterns that automatically capture the structure of the input. Given the object localization, we propose a DL-based active shape model to estimate the non-rigid object boundary. Experimental results are presented on the aortic valve in ultrasound using an extensive dataset of 2891 volumes from 869 patients, showing significant improvements of up to 45.2% over the state-of-the-art. To our knowledge, this is the first successful demonstration of the DL potential to detection and segmentation in full 3D data with parametrized representations.

Quantum Transport Properties in Two-Dimensional and Low Dimensional Systems

NASA Astrophysics Data System (ADS)

Fang, Hao

1991-02-01

The quantum transport properties in quasi two -dimensional and zero-dimensional systems have been studied at magnetic field of 0 - 8T and low temperatures down to 1.3K. In the (100) Si inversion layer, we investigated the effect of valley splitting on the value of the enhanced effective g factor by the tilted magnetic field measurement. The valley splitting is determined from the beat effect on samples with measurable valley splitting behavior due to misorientation effects. Experimental results illustrate that the effective g factor is enhanced by many body interactions and that the valley splitting has no obvious effect on the g-value. A simulation calculation with a Gaussian distribution of density of states has been carried out and the simulated results are in an excellent agreement with the experimental data. A new and very simple technique has been developed for fabricating two-dimensional periodic submicron structures with feature sizes down to about 300 A. The etching mask is made by coating the material surface with a monolayer of close-packed uniform latex particles. We have demonstrated the formation of a quasi zero-dimensional quantum dot array and performed capacitance measurements on GaAs/AlGaAs heterostructure samples with periodicities ranging from 3000 to 4000 A. A series of nearly equally spaced peaks in a curve of the derivative of capacitance with respect to gate voltage, which corresponds to the energy levels formed by the lateral electric confining potential, is observed. The energy spacings and effective dot widths estimated from a simple parabolic potential model are consistent with the experimental data. Novel magnetoresistance oscillations in a two -dimensional electron gas modulated by a two-dimensional triangular superlattice potential are observed in GaAs/AlGaAs heterostructures. The new oscillations appear at very low magnetic fields and the peak positions are directly determined by the magnetic field and the periodicity of the modulation structure. New oscillation results from the modulation-broadened Landau bandwidth and the induced density of states variation with magnetic field. Physical explanations and theoretical approaches for the commensurability problem in a two-dimensional triangular superlattice potential are presented. The differences in oscillation frequencies and phase factors for two kinds of samples correlate with structures differing in degree of depletion and the resulting geometry.

Penalized Ordinal Regression Methods for Predicting Stage of Cancer in High-Dimensional Covariate Spaces.

PubMed

Gentry, Amanda Elswick; Jackson-Cook, Colleen K; Lyon, Debra E; Archer, Kellie J

2015-01-01

The pathological description of the stage of a tumor is an important clinical designation and is considered, like many other forms of biomedical data, an ordinal outcome. Currently, statistical methods for predicting an ordinal outcome using clinical, demographic, and high-dimensional correlated features are lacking. In this paper, we propose a method that fits an ordinal response model to predict an ordinal outcome for high-dimensional covariate spaces. Our method penalizes some covariates (high-throughput genomic features) without penalizing others (such as demographic and/or clinical covariates). We demonstrate the application of our method to predict the stage of breast cancer. In our model, breast cancer subtype is a nonpenalized predictor, and CpG site methylation values from the Illumina Human Methylation 450K assay are penalized predictors. The method has been made available in the ordinalgmifs package in the R programming environment.

Three-dimensional marginal separation

NASA Technical Reports Server (NTRS)

Duck, Peter W.

1988-01-01

The three dimensional marginal separation of a boundary layer along a line of symmetry is considered. The key equation governing the displacement function is derived, and found to be a nonlinear integral equation in two space variables. This is solved iteratively using a pseudo-spectral approach, based partly in double Fourier space, and partly in physical space. Qualitatively, the results are similar to previously reported two dimensional results (which are also computed to test the accuracy of the numerical scheme); however quantitatively the three dimensional results are much different.

Spectral-Spatial Shared Linear Regression for Hyperspectral Image Classification.

PubMed

Haoliang Yuan; Yuan Yan Tang

2017-04-01

Classification of the pixels in hyperspectral image (HSI) is an important task and has been popularly applied in many practical applications. Its major challenge is the high-dimensional small-sized problem. To deal with this problem, lots of subspace learning (SL) methods are developed to reduce the dimension of the pixels while preserving the important discriminant information. Motivated by ridge linear regression (RLR) framework for SL, we propose a spectral-spatial shared linear regression method (SSSLR) for extracting the feature representation. Comparing with RLR, our proposed SSSLR has the following two advantages. First, we utilize a convex set to explore the spatial structure for computing the linear projection matrix. Second, we utilize a shared structure learning model, which is formed by original data space and a hidden feature space, to learn a more discriminant linear projection matrix for classification. To optimize our proposed method, an efficient iterative algorithm is proposed. Experimental results on two popular HSI data sets, i.e., Indian Pines and Salinas demonstrate that our proposed methods outperform many SL methods.

Application of Hyperspectral Techniques to Monitoring and Management of Invasive Plant Species Infestation

DTIC Science & Technology

2008-01-01

the sensor is a data cloud in multi- dimensional space with each band generating an axis of dimension. When the data cloud is viewed in two or three...endmember of interest is not a true endmember in the data space . A ) B) Figure 8: Linear mixture models. A ) two- dimensional ...multi- dimensional space . A classifier is a computer algorithm that takes

Dynamic adaptive learning for decision-making supporting systems

NASA Astrophysics Data System (ADS)

He, Haibo; Cao, Yuan; Chen, Sheng; Desai, Sachi; Hohil, Myron E.

2008-03-01

This paper proposes a novel adaptive learning method for data mining in support of decision-making systems. Due to the inherent characteristics of information ambiguity/uncertainty, high dimensionality and noisy in many homeland security and defense applications, such as surveillances, monitoring, net-centric battlefield, and others, it is critical to develop autonomous learning methods to efficiently learn useful information from raw data to help the decision making process. The proposed method is based on a dynamic learning principle in the feature spaces. Generally speaking, conventional approaches of learning from high dimensional data sets include various feature extraction (principal component analysis, wavelet transform, and others) and feature selection (embedded approach, wrapper approach, filter approach, and others) methods. However, very limited understandings of adaptive learning from different feature spaces have been achieved. We propose an integrative approach that takes advantages of feature selection and hypothesis ensemble techniques to achieve our goal. Based on the training data distributions, a feature score function is used to provide a measurement of the importance of different features for learning purpose. Then multiple hypotheses are iteratively developed in different feature spaces according to their learning capabilities. Unlike the pre-set iteration steps in many of the existing ensemble learning approaches, such as adaptive boosting (AdaBoost) method, the iterative learning process will automatically stop when the intelligent system can not provide a better understanding than a random guess in that particular subset of feature spaces. Finally, a voting algorithm is used to combine all the decisions from different hypotheses to provide the final prediction results. Simulation analyses of the proposed method on classification of different US military aircraft databases show the effectiveness of this method.

Intelligent feature selection techniques for pattern classification of Lamb wave signals

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

Hinders, Mark K.; Miller, Corey A.

2014-02-18

Lamb wave interaction with flaws is a complex, three-dimensional phenomenon, which often frustrates signal interpretation schemes based on mode arrival time shifts predicted by dispersion curves. As the flaw severity increases, scattering and mode conversion effects will often dominate the time-domain signals, obscuring available information about flaws because multiple modes may arrive on top of each other. Even for idealized flaw geometries the scattering and mode conversion behavior of Lamb waves is very complex. Here, multi-mode Lamb waves in a metal plate are propagated across a rectangular flat-bottom hole in a sequence of pitch-catch measurements corresponding to the double crossholemore » tomography geometry. The flaw is sequentially deepened, with the Lamb wave measurements repeated at each flaw depth. Lamb wave tomography reconstructions are used to identify which waveforms have interacted with the flaw and thereby carry information about its depth. Multiple features are extracted from each of the Lamb wave signals using wavelets, which are then fed to statistical pattern classification algorithms that identify flaw severity. In order to achieve the highest classification accuracy, an optimal feature space is required but it’s never known a priori which features are going to be best. For structural health monitoring we make use of the fact that physical flaws, such as corrosion, will only increase over time. This allows us to identify feature vectors which are topologically well-behaved by requiring that sequential classes “line up” in feature vector space. An intelligent feature selection routine is illustrated that identifies favorable class distributions in multi-dimensional feature spaces using computational homology theory. Betti numbers and formal classification accuracies are calculated for each feature space subset to establish a correlation between the topology of the class distribution and the corresponding classification accuracy.« less

An enhanced data visualization method for diesel engine malfunction classification using multi-sensor signals.

PubMed

Li, Yiqing; Wang, Yu; Zi, Yanyang; Zhang, Mingquan

2015-10-21

The various multi-sensor signal features from a diesel engine constitute a complex high-dimensional dataset. The non-linear dimensionality reduction method, t-distributed stochastic neighbor embedding (t-SNE), provides an effective way to implement data visualization for complex high-dimensional data. However, irrelevant features can deteriorate the performance of data visualization, and thus, should be eliminated a priori. This paper proposes a feature subset score based t-SNE (FSS-t-SNE) data visualization method to deal with the high-dimensional data that are collected from multi-sensor signals. In this method, the optimal feature subset is constructed by a feature subset score criterion. Then the high-dimensional data are visualized in 2-dimension space. According to the UCI dataset test, FSS-t-SNE can effectively improve the classification accuracy. An experiment was performed with a large power marine diesel engine to validate the proposed method for diesel engine malfunction classification. Multi-sensor signals were collected by a cylinder vibration sensor and a cylinder pressure sensor. Compared with other conventional data visualization methods, the proposed method shows good visualization performance and high classification accuracy in multi-malfunction classification of a diesel engine.

An Enhanced Data Visualization Method for Diesel Engine Malfunction Classification Using Multi-Sensor Signals

PubMed Central

Li, Yiqing; Wang, Yu; Zi, Yanyang; Zhang, Mingquan

2015-01-01

The various multi-sensor signal features from a diesel engine constitute a complex high-dimensional dataset. The non-linear dimensionality reduction method, t-distributed stochastic neighbor embedding (t-SNE), provides an effective way to implement data visualization for complex high-dimensional data. However, irrelevant features can deteriorate the performance of data visualization, and thus, should be eliminated a priori. This paper proposes a feature subset score based t-SNE (FSS-t-SNE) data visualization method to deal with the high-dimensional data that are collected from multi-sensor signals. In this method, the optimal feature subset is constructed by a feature subset score criterion. Then the high-dimensional data are visualized in 2-dimension space. According to the UCI dataset test, FSS-t-SNE can effectively improve the classification accuracy. An experiment was performed with a large power marine diesel engine to validate the proposed method for diesel engine malfunction classification. Multi-sensor signals were collected by a cylinder vibration sensor and a cylinder pressure sensor. Compared with other conventional data visualization methods, the proposed method shows good visualization performance and high classification accuracy in multi-malfunction classification of a diesel engine. PMID:26506347

Three-dimensional spectral analysis of compositional heterogeneity at Arruntia crater on (4) Vesta using Dawn FC

NASA Astrophysics Data System (ADS)

Thangjam, Guneshwar; Nathues, Andreas; Mengel, Kurt; Schäfer, Michael; Hoffmann, Martin; Cloutis, Edward A.; Mann, Paul; Müller, Christian; Platz, Thomas; Schäfer, Tanja

2016-03-01

We introduce an innovative three-dimensional spectral approach (three band parameter space with polyhedrons) that can be used for both qualitative and quantitative analyzes improving the characterization of surface compositional heterogeneity of (4) Vesta. It is an advanced and more robust methodology compared to the standard two-dimensional spectral approach (two band parameter space). The Dawn Framing Camera (FC) color data obtained during High Altitude Mapping Orbit (resolution ∼ 60 m/pixel) is used. The main focus is on the howardite-eucrite-diogenite (HED) lithologies containing carbonaceous chondritic material, olivine, and impact-melt. The archived spectra of HEDs and their mixtures, from RELAB, HOSERLab and USGS databases as well as our laboratory-measured spectra are used for this study. Three-dimensional convex polyhedrons are defined using computed band parameter values of laboratory spectra. Polyhedrons based on the parameters of Band Tilt (R0.92μm/R0.96μm), Mid Ratio ((R0.75μm/R0.83μm)/(R0.83μm/R0.92μm)) and reflectance at 0.55 μm (R0.55μm) are chosen for the present analysis. An algorithm in IDL programming language is employed to assign FC data points to the respective polyhedrons. The Arruntia region in the northern hemisphere of Vesta is selected for a case study because of its geological and mineralogical importance. We observe that this region is eucrite-dominated howarditic in composition. The extent of olivine-rich exposures within an area of 2.5 crater radii is ∼12% larger than the previous finding (Thangjam, G. et al. [2014]. Meteorit. Planet. Sci. 49, 1831-1850). Lithologies of nearly pure CM2-chondrite, olivine, glass, and diogenite are not found in this region. Although there are no unambiguous spectral features of impact melt, the investigation of morphological features using FC clear filter data from Low Altitude Mapping Orbit (resolution ∼ 18 m/pixel) suggests potential impact-melt features inside and outside of the crater. Our spectral approach can be extended to the entire Vestan surface to study the heterogeneous surface composition and its geology.

Genome U-Plot: a whole genome visualization.

PubMed

Gaitatzes, Athanasios; Johnson, Sarah H; Smadbeck, James B; Vasmatzis, George

2018-05-15

The ability to produce and analyze whole genome sequencing (WGS) data from samples with structural variations (SV) generated the need to visualize such abnormalities in simplified plots. Conventional two-dimensional representations of WGS data frequently use either circular or linear layouts. There are several diverse advantages regarding both these representations, but their major disadvantage is that they do not use the two-dimensional space very efficiently. We propose a layout, termed the Genome U-Plot, which spreads the chromosomes on a two-dimensional surface and essentially quadruples the spatial resolution. We present the Genome U-Plot for producing clear and intuitive graphs that allows researchers to generate novel insights and hypotheses by visualizing SVs such as deletions, amplifications, and chromoanagenesis events. The main features of the Genome U-Plot are its layered layout, its high spatial resolution and its improved aesthetic qualities. We compare conventional visualization schemas with the Genome U-Plot using visualization metrics such as number of line crossings and crossing angle resolution measures. Based on our metrics, we improve the readability of the resulting graph by at least 2-fold, making apparent important features and making it easy to identify important genomic changes. A whole genome visualization tool with high spatial resolution and improved aesthetic qualities. An implementation and documentation of the Genome U-Plot is publicly available at https://github.com/gaitat/GenomeUPlot. vasmatzis.george@mayo.edu. Supplementary data are available at Bioinformatics online.

Unbiased feature selection in learning random forests for high-dimensional data.

PubMed

Nguyen, Thanh-Tung; Huang, Joshua Zhexue; Nguyen, Thuy Thi

2015-01-01

Random forests (RFs) have been widely used as a powerful classification method. However, with the randomization in both bagging samples and feature selection, the trees in the forest tend to select uninformative features for node splitting. This makes RFs have poor accuracy when working with high-dimensional data. Besides that, RFs have bias in the feature selection process where multivalued features are favored. Aiming at debiasing feature selection in RFs, we propose a new RF algorithm, called xRF, to select good features in learning RFs for high-dimensional data. We first remove the uninformative features using p-value assessment, and the subset of unbiased features is then selected based on some statistical measures. This feature subset is then partitioned into two subsets. A feature weighting sampling technique is used to sample features from these two subsets for building trees. This approach enables one to generate more accurate trees, while allowing one to reduce dimensionality and the amount of data needed for learning RFs. An extensive set of experiments has been conducted on 47 high-dimensional real-world datasets including image datasets. The experimental results have shown that RFs with the proposed approach outperformed the existing random forests in increasing the accuracy and the AUC measures.

Median filtering detection using variation of neighboring line pairs for image forensics

NASA Astrophysics Data System (ADS)

Rhee, Kang Hyeon

2016-09-01

Attention to tampering by median filtering (MF) has recently increased in digital image forensics. For the MF detection (MFD), this paper presents a feature vector that is extracted from two kinds of variations between the neighboring line pairs: the row and column directions. Of these variations in the proposed method, one is defined by a gradient difference of the intensity values between the neighboring line pairs, and the other is defined by a coefficient difference of the Fourier transform (FT) between the neighboring line pairs. Subsequently, the constructed 19-dimensional feature vector is composed of these two parts. One is the extracted 9-dimensional from the space domain of an image and the other is the 10-dimensional from the frequency domain of an image. The feature vector is trained in a support vector machine classifier for MFD in the altered images. As a result, in the measured performances of the experimental items, the area under the receiver operating characteristic curve (AUC, ROC) by the sensitivity (PTP: the true positive rate) and 1-specificity (PFP: the false-positive rate) are above 0.985 and the classification ratios are also above 0.979. Pe (a minimal average decision error) ranges from 0 to 0.024, and PTP at PFP=0.01 ranges from 0.965 to 0.996. It is confirmed that the grade evaluation of the proposed variation-based MF detection method is rated as "Excellent (A)" by AUC is above 0.9.

Multiscale statistics of trajectories with applications to fluid particles in turbulence and football players

NASA Astrophysics Data System (ADS)

Schneider, Kai; Kadoch, Benjamin; Bos, Wouter

2017-11-01

The angle between two subsequent particle displacement increments is evaluated as a function of the time lag. The directional change of particles can thus be quantified at different scales and multiscale statistics can be performed. Flow dependent and geometry dependent features can be distinguished. The mean angle satisfies scaling behaviors for short time lags based on the smoothness of the trajectories. For intermediate time lags a power law behavior can be observed for some turbulent flows, which can be related to Kolmogorov scaling. The long time behavior depends on the confinement geometry of the flow. We show that the shape of the probability distribution function of the directional change can be well described by a Fischer distribution. Results for two-dimensional (direct and inverse cascade) and three-dimensional turbulence with and without confinement, illustrate the properties of the proposed multiscale statistics. The presented Monte-Carlo simulations allow disentangling geometry dependent and flow independent features. Finally, we also analyze trajectories of football players, which are, in general, not randomly spaced on a field.

Spectral properties near the Mott transition in the two-dimensional t-J model with next-nearest-neighbor hopping

NASA Astrophysics Data System (ADS)

Kohno, Masanori

2018-05-01

The single-particle spectral properties of the two-dimensional t-J model with next-nearest-neighbor hopping are investigated near the Mott transition by using cluster perturbation theory. The spectral features are interpreted by considering the effects of the next-nearest-neighbor hopping on the shift of the spectral-weight distribution of the two-dimensional t-J model. Various anomalous features observed in hole-doped and electron-doped high-temperature cuprate superconductors are collectively explained in the two-dimensional t-J model with next-nearest-neighbor hopping near the Mott transition.

Visualizing and enhancing a deep learning framework using patients age and gender for chest x-ray image retrieval

NASA Astrophysics Data System (ADS)

Anavi, Yaron; Kogan, Ilya; Gelbart, Elad; Geva, Ofer; Greenspan, Hayit

2016-03-01

We explore the combination of text metadata, such as patients' age and gender, with image-based features, for X-ray chest pathology image retrieval. We focus on a feature set extracted from a pre-trained deep convolutional network shown in earlier work to achieve state-of-the-art results. Two distance measures are explored: a descriptor-based measure, which computes the distance between image descriptors, and a classification-based measure, which performed by a comparison of the corresponding SVM classification probabilities. We show that retrieval results increase once the age and gender information combined with the features extracted from the last layers of the network, with best results using the classification-based scheme. Visualization of the X-ray data is presented by embedding the high dimensional deep learning features in a 2-D dimensional space while preserving the pairwise distances using the t-SNE algorithm. The 2-D visualization gives the unique ability to find groups of X-ray images that are similar to the query image and among themselves, which is a characteristic we do not see in a 1-D traditional ranking.

Effective degrees of freedom of a random walk on a fractal.

PubMed

Balankin, Alexander S

2015-12-01

We argue that a non-Markovian random walk on a fractal can be treated as a Markovian process in a fractional dimensional space with a suitable metric. This allows us to define the fractional dimensional space allied to the fractal as the ν-dimensional space F(ν) equipped with the metric induced by the fractal topology. The relation between the number of effective spatial degrees of freedom of walkers on the fractal (ν) and fractal dimensionalities is deduced. The intrinsic time of random walk in F(ν) is inferred. The Laplacian operator in F(ν) is constructed. This allows us to map physical problems on fractals into the corresponding problems in F(ν). In this way, essential features of physics on fractals are revealed. Particularly, subdiffusion on path-connected fractals is elucidated. The Coulomb potential of a point charge on a fractal embedded in the Euclidean space is derived. Intriguing attributes of some types of fractals are highlighted.

Hofstadter butterfly evolution in the space of two-dimensional Bravais lattices

NASA Astrophysics Data System (ADS)

Yılmaz, F.; Oktel, M. Ö.

2017-06-01

The self-similar energy spectrum of a particle in a periodic potential under a magnetic field, known as the Hofstadter butterfly, is determined by the lattice geometry as well as the external field. Recent realizations of artificial gauge fields and adjustable optical lattices in cold-atom experiments necessitate the consideration of these self-similar spectra for the most general two-dimensional lattice. In a previous work [F. Yılmaz et al., Phys. Rev. A 91, 063628 (2015), 10.1103/PhysRevA.91.063628], we investigated the evolution of the spectrum for an experimentally realized lattice which was tuned by changing the unit-cell structure but keeping the square Bravais lattice fixed. We now consider all possible Bravais lattices in two dimensions and investigate the structure of the Hofstadter butterfly as the lattice is deformed between lattices with different point-symmetry groups. We model the optical lattice with a sinusoidal real-space potential and obtain the tight-binding model for any lattice geometry by calculating the Wannier functions. We introduce the magnetic field via Peierls substitution and numerically calculate the energy spectrum. The transition between the two most symmetric lattices, i.e., the triangular and the square lattices, displays the importance of bipartite symmetry featuring deformation as well as closing of some of the major energy gaps. The transitions from the square to rectangular lattice and from the triangular to centered rectangular lattices are analyzed in terms of coupling of one-dimensional chains. We calculate the Chern numbers of the major gaps and Chern number transfer between bands during the transitions. We use gap Chern numbers to identify distinct topological regions in the space of Bravais lattices.

Efficient and robust computation of PDF features from diffusion MR signal.

PubMed

Assemlal, Haz-Edine; Tschumperlé, David; Brun, Luc

2009-10-01

We present a method for the estimation of various features of the tissue micro-architecture using the diffusion magnetic resonance imaging. The considered features are designed from the displacement probability density function (PDF). The estimation is based on two steps: first the approximation of the signal by a series expansion made of Gaussian-Laguerre and Spherical Harmonics functions; followed by a projection on a finite dimensional space. Besides, we propose to tackle the problem of the robustness to Rician noise corrupting in-vivo acquisitions. Our feature estimation is expressed as a variational minimization process leading to a variational framework which is robust to noise. This approach is very flexible regarding the number of samples and enables the computation of a large set of various features of the local tissues structure. We demonstrate the effectiveness of the method with results on both synthetic phantom and real MR datasets acquired in a clinical time-frame.

Sparse representation of multi parametric DCE-MRI features using K-SVD for classifying gene expression based breast cancer recurrence risk

NASA Astrophysics Data System (ADS)

Mahrooghy, Majid; Ashraf, Ahmed B.; Daye, Dania; Mies, Carolyn; Rosen, Mark; Feldman, Michael; Kontos, Despina

2014-03-01

We evaluate the prognostic value of sparse representation-based features by applying the K-SVD algorithm on multiparametric kinetic, textural, and morphologic features in breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). K-SVD is an iterative dimensionality reduction method that optimally reduces the initial feature space by updating the dictionary columns jointly with the sparse representation coefficients. Therefore, by using K-SVD, we not only provide sparse representation of the features and condense the information in a few coefficients but also we reduce the dimensionality. The extracted K-SVD features are evaluated by a machine learning algorithm including a logistic regression classifier for the task of classifying high versus low breast cancer recurrence risk as determined by a validated gene expression assay. The features are evaluated using ROC curve analysis and leave one-out cross validation for different sparse representation and dimensionality reduction numbers. Optimal sparse representation is obtained when the number of dictionary elements is 4 (K=4) and maximum non-zero coefficients is 2 (L=2). We compare K-SVD with ANOVA based feature selection for the same prognostic features. The ROC results show that the AUC of the K-SVD based (K=4, L=2), the ANOVA based, and the original features (i.e., no dimensionality reduction) are 0.78, 0.71. and 0.68, respectively. From the results, it can be inferred that by using sparse representation of the originally extracted multi-parametric, high-dimensional data, we can condense the information on a few coefficients with the highest predictive value. In addition, the dimensionality reduction introduced by K-SVD can prevent models from over-fitting.

Gene masking - a technique to improve accuracy for cancer classification with high dimensionality in microarray data.

PubMed

Saini, Harsh; Lal, Sunil Pranit; Naidu, Vimal Vikash; Pickering, Vincel Wince; Singh, Gurmeet; Tsunoda, Tatsuhiko; Sharma, Alok

2016-12-05

High dimensional feature space generally degrades classification in several applications. In this paper, we propose a strategy called gene masking, in which non-contributing dimensions are heuristically removed from the data to improve classification accuracy. Gene masking is implemented via a binary encoded genetic algorithm that can be integrated seamlessly with classifiers during the training phase of classification to perform feature selection. It can also be used to discriminate between features that contribute most to the classification, thereby, allowing researchers to isolate features that may have special significance. This technique was applied on publicly available datasets whereby it substantially reduced the number of features used for classification while maintaining high accuracies. The proposed technique can be extremely useful in feature selection as it heuristically removes non-contributing features to improve the performance of classifiers.

The Three-Dimensional Morphology of VY Canis Majoris. II. Polarimetry and the Line-of-Sight Distribution of the Ejecta

NASA Astrophysics Data System (ADS)

Jones, Terry Jay; Humphreys, Roberta M.; Helton, L. Andrew; Gui, Changfeng; Huang, Xiang

2007-06-01

We use imaging polarimetry taken with the HST Advanced Camera for Surveys High Resolution Camera to explore the three-dimensional structure of the circumstellar dust distribution around the red supergiant VY Canis Majoris. The polarization vectors of the nebulosity surrounding VY CMa show a strong centrosymmetric pattern in all directions except directly east and range from 10% to 80% in fractional polarization. In regions that are optically thin, and therefore likely to have only single scattering, we use the fractional polarization and photometric color to locate the physical position of the dust along the line of sight. Most of the individual arclike features and clumps seen in the intensity image are also features in the fractional polarization map. These features must be distinct geometric objects. If they were just local density enhancements, the fractional polarization would not change so abruptly at the edge of the feature. The location of these features in the ejecta of VY CMa using polarimetry provides a determination of their three-dimensional geometry independent of, but in close agreement with, the results from our study of their kinematics (Paper I). Based on observations with the NASA/ESA Hubble Space Telescope, obtained at the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-26555.

Quasiclassical analysis of Bloch oscillations in non-Hermitian tight-binding lattices

NASA Astrophysics Data System (ADS)

Graefe, E. M.; Korsch, H. J.; Rush, A.

2016-07-01

Many features of Bloch oscillations in one-dimensional quantum lattices with a static force can be described by quasiclassical considerations for example by means of the acceleration theorem, at least for Hermitian systems. Here the quasiclassical approach is extended to non-Hermitian lattices, which are of increasing interest. The analysis is based on a generalised non-Hermitian phase space dynamics developed recently. Applications to a single-band tight-binding system demonstrate that many features of the quantum dynamics can be understood from this classical description qualitatively and even quantitatively. Two non-Hermitian and PT-symmetric examples are studied, a Hatano-Nelson lattice with real coupling constants and a system with purely imaginary couplings, both for initially localised states in space or in momentum. It is shown that the time-evolution of the norm of the wave packet and the expectation values of position and momentum can be described in a classical picture.

Experimental two-dimensional quantum walk on a photonic chip

PubMed Central

Lin, Xiao-Feng; Feng, Zhen; Chen, Jing-Yuan; Gao, Jun; Sun, Ke; Wang, Chao-Yue; Lai, Peng-Cheng; Xu, Xiao-Yun; Wang, Yao; Qiao, Lu-Feng; Yang, Ai-Lin

2018-01-01

Quantum walks, in virtue of the coherent superposition and quantum interference, have exponential superiority over their classical counterpart in applications of quantum searching and quantum simulation. The quantum-enhanced power is highly related to the state space of quantum walks, which can be expanded by enlarging the photon number and/or the dimensions of the evolution network, but the former is considerably challenging due to probabilistic generation of single photons and multiplicative loss. We demonstrate a two-dimensional continuous-time quantum walk by using the external geometry of photonic waveguide arrays, rather than the inner degree of freedoms of photons. Using femtosecond laser direct writing, we construct a large-scale three-dimensional structure that forms a two-dimensional lattice with up to 49 × 49 nodes on a photonic chip. We demonstrate spatial two-dimensional quantum walks using heralded single photons and single photon–level imaging. We analyze the quantum transport properties via observing the ballistic evolution pattern and the variance profile, which agree well with simulation results. We further reveal the transient nature that is the unique feature for quantum walks of beyond one dimension. An architecture that allows a quantum walk to freely evolve in all directions and at a large scale, combining with defect and disorder control, may bring up powerful and versatile quantum walk machines for classically intractable problems. PMID:29756040

Experimental two-dimensional quantum walk on a photonic chip.

PubMed

Tang, Hao; Lin, Xiao-Feng; Feng, Zhen; Chen, Jing-Yuan; Gao, Jun; Sun, Ke; Wang, Chao-Yue; Lai, Peng-Cheng; Xu, Xiao-Yun; Wang, Yao; Qiao, Lu-Feng; Yang, Ai-Lin; Jin, Xian-Min

2018-05-01

Quantum walks, in virtue of the coherent superposition and quantum interference, have exponential superiority over their classical counterpart in applications of quantum searching and quantum simulation. The quantum-enhanced power is highly related to the state space of quantum walks, which can be expanded by enlarging the photon number and/or the dimensions of the evolution network, but the former is considerably challenging due to probabilistic generation of single photons and multiplicative loss. We demonstrate a two-dimensional continuous-time quantum walk by using the external geometry of photonic waveguide arrays, rather than the inner degree of freedoms of photons. Using femtosecond laser direct writing, we construct a large-scale three-dimensional structure that forms a two-dimensional lattice with up to 49 × 49 nodes on a photonic chip. We demonstrate spatial two-dimensional quantum walks using heralded single photons and single photon-level imaging. We analyze the quantum transport properties via observing the ballistic evolution pattern and the variance profile, which agree well with simulation results. We further reveal the transient nature that is the unique feature for quantum walks of beyond one dimension. An architecture that allows a quantum walk to freely evolve in all directions and at a large scale, combining with defect and disorder control, may bring up powerful and versatile quantum walk machines for classically intractable problems.

The use of virtual reality to reimagine two-dimensional representations of three-dimensional spaces

NASA Astrophysics Data System (ADS)

Fath, Elaine

2015-03-01

A familiar realm in the world of two-dimensional art is the craft of taking a flat canvas and creating, through color, size, and perspective, the illusion of a three-dimensional space. Using well-explored tricks of logic and sight, impossible landscapes such as those by surrealists de Chirico or Salvador Dalí seem to be windows into new and incredible spaces which appear to be simultaneously feasible and utterly nonsensical. As real-time 3D imaging becomes increasingly prevalent as an artistic medium, this process takes on an additional layer of depth: no longer is two-dimensional space restricted to strategies of light, color, line and geometry to create the impression of a three-dimensional space. A digital interactive environment is a space laid out in three dimensions, allowing the user to explore impossible environments in a way that feels very real. In this project, surrealist two-dimensional art was researched and reimagined: what would stepping into a de Chirico or a Magritte look and feel like, if the depth and distance created by light and geometry were not simply single-perspective illusions, but fully formed and explorable spaces? 3D environment-building software is allowing us to step into these impossible spaces in ways that 2D representations leave us yearning for. This art project explores what we gain--and what gets left behind--when these impossible spaces become doors, rather than windows. Using sketching, Maya 3D rendering software, and the Unity Engine, surrealist art was reimagined as a fully navigable real-time digital environment. The surrealist movement and its key artists were researched for their use of color, geometry, texture, and space and how these elements contributed to their work as a whole, which often conveys feelings of unexpectedness or uneasiness. The end goal was to preserve these feelings while allowing the viewer to actively engage with the space.

Comparative assessment of techniques for initial pose estimation using monocular vision

NASA Astrophysics Data System (ADS)

Sharma, Sumant; D`Amico, Simone

2016-06-01

This work addresses the comparative assessment of initial pose estimation techniques for monocular navigation to enable formation-flying and on-orbit servicing missions. Monocular navigation relies on finding an initial pose, i.e., a coarse estimate of the attitude and position of the space resident object with respect to the camera, based on a minimum number of features from a three dimensional computer model and a single two dimensional image. The initial pose is estimated without the use of fiducial markers, without any range measurements or any apriori relative motion information. Prior work has been done to compare different pose estimators for terrestrial applications, but there is a lack of functional and performance characterization of such algorithms in the context of missions involving rendezvous operations in the space environment. Use of state-of-the-art pose estimation algorithms designed for terrestrial applications is challenging in space due to factors such as limited on-board processing power, low carrier to noise ratio, and high image contrasts. This paper focuses on performance characterization of three initial pose estimation algorithms in the context of such missions and suggests improvements.

Multi-perspective analysis and spatiotemporal mapping of air pollution monitoring data.

PubMed

Kolovos, Alexander; Skupin, André; Jerrett, Michael; Christakos, George

2010-09-01

Space-time data analysis and assimilation techniques in atmospheric sciences typically consider input from monitoring measurements. The input is often processed in a manner that acknowledges characteristics of the measurements (e.g., underlying patterns, fluctuation features) under conditions of uncertainty; it also leads to the derivation of secondary information that serves study-oriented goals, and provides input to space-time prediction techniques. We present a novel approach that blends a rigorous space-time prediction model (Bayesian maximum entropy, BME) with a cognitively informed visualization of high-dimensional data (spatialization). The combined BME and spatialization approach (BME-S) is used to study monthly averaged NO2 and mean annual SO4 measurements in California over the 15-year period 1988-2002. Using the original scattered measurements of these two pollutants BME generates spatiotemporal predictions on a regular grid across the state. Subsequently, the prediction network undergoes the spatialization transformation into a lower-dimensional geometric representation, aimed at revealing patterns and relationships that exist within the input data. The proposed BME-S provides a powerful spatiotemporal framework to study a variety of air pollution data sources.

Compactification on phase space

NASA Astrophysics Data System (ADS)

Lovelady, Benjamin; Wheeler, James

2016-03-01

A major challenge for string theory is to understand the dimensional reduction required for comparison with the standard model. We propose reducing the dimension of the compactification by interpreting some of the extra dimensions as the energy-momentum portion of a phase-space. Such models naturally arise as generalized quotients of the conformal group called biconformal spaces. By combining the standard Kaluza-Klein approach with such a conformal gauge theory, we may start from the conformal group of an n-dimensional Euclidean space to form a 2n-dimensional quotient manifold with symplectic structure. A pair of involutions leads naturally to two n-dimensional Lorentzian manifolds. For n = 5, this leaves only two extra dimensions, with a countable family of possible compactifications and an SO(5) Yang-Mills field on the fibers. Starting with n=6 leads to 4-dimensional compactification of the phase space. In the latter case, if the two dimensions each from spacetime and momentum space are compactified onto spheres, then there is an SU(2)xSU(2) (left-right symmetric electroweak) field between phase and configuration space and an SO(6) field on the fibers. Such a theory, with minor additional symmetry breaking, could contain all parts of the standard model.

Using 2D correlation analysis to enhance spectral information available from highly spatially resolved AFM-IR spectra

NASA Astrophysics Data System (ADS)

Marcott, Curtis; Lo, Michael; Hu, Qichi; Kjoller, Kevin; Boskey, Adele; Noda, Isao

2014-07-01

The recent combination of atomic force microscopy and infrared spectroscopy (AFM-IR) has led to the ability to obtain IR spectra with nanoscale spatial resolution, nearly two orders-of-magnitude better than conventional Fourier transform infrared (FT-IR) microspectroscopy. This advanced methodology can lead to significantly sharper spectral features than are typically seen in conventional IR spectra of inhomogeneous materials, where a wider range of molecular environments are coaveraged by the larger sample cross section being probed. In this work, two-dimensional (2D) correlation analysis is used to examine position sensitive spectral variations in datasets of closely spaced AFM-IR spectra. This analysis can reveal new key insights, providing a better understanding of the new spectral information that was previously hidden under broader overlapped spectral features. Two examples of the utility of this new approach are presented. Two-dimensional correlation analysis of a set of AFM-IR spectra were collected at 200-nm increments along a line through a nucleation site generated by remelting a small spot on a thin film of poly(3-hydroxybutyrate-co-3-hydroxyhexanoate). There are two different crystalline carbonyl band components near 1720 cm-1 that sequentially disappear before a band at 1740 cm-1 due to more disordered material appears. In the second example, 2D correlation analysis of a series of AFM-IR spectra spaced every 1 μm of a thin cross section of a bone sample measured outward from an osteon center of bone growth. There are many changes in the amide I and phosphate band contours, suggesting changes in the bone structure are occurring as the bone matures.

Hörmander multipliers on two-dimensional dyadic Hardy spaces

NASA Astrophysics Data System (ADS)

Daly, J.; Fridli, S.

2008-12-01

In this paper we are interested in conditions on the coefficients of a two-dimensional Walsh multiplier operator that imply the operator is bounded on certain of the Hardy type spaces Hp, 0

Space plasma contractor research, 1988

NASA Technical Reports Server (NTRS)

Williams, John D.; Wilbur, Paul J.

1989-01-01

Results of experiments conducted on hollow cathode-based plasma contractors are reported. Specific tests in which attempts were made to vary plasma conditions in the simulated ionospheric plasma are described. Experimental results showing the effects of contractor flowrate and ion collecting surface size on contactor performance and contactor plasma plume geometry are presented. In addition to this work, one-dimensional solutions to spherical and cylindircal space-charge limited double-sheath problems are developed. A technique is proposed that can be used to apply these solutions to the problem of current flow through elongated double-sheaths that separate two cold plasmas. Two conference papers which describe the essential features of the plasma contacting process and present data that should facilitate calibration of comprehensive numerical models of the plasma contacting process are also included.

Distinction of Green Sweet Peppers by Using Various Color Space Models and Computation of 3 Dimensional Location Coordinates of Recognized Green Sweet Peppers Based on Parallel Stereovision System

NASA Astrophysics Data System (ADS)

Bachche, Shivaji; Oka, Koichi

2013-06-01

This paper presents the comparative study of various color space models to determine the suitable color space model for detection of green sweet peppers. The images were captured by using CCD cameras and infrared cameras and processed by using Halcon image processing software. The LED ring around the camera neck was used as an artificial lighting to enhance the feature parameters. For color images, CieLab, YIQ, YUV, HSI and HSV whereas for infrared images, grayscale color space models were selected for image processing. In case of color images, HSV color space model was found more significant with high percentage of green sweet pepper detection followed by HSI color space model as both provides information in terms of hue/lightness/chroma or hue/lightness/saturation which are often more relevant to discriminate the fruit from image at specific threshold value. The overlapped fruits or fruits covered by leaves can be detected in better way by using HSV color space model as the reflection feature from fruits had higher histogram than reflection feature from leaves. The IR 80 optical filter failed to distinguish fruits from images as filter blocks useful information on features. Computation of 3D coordinates of recognized green sweet peppers was also conducted in which Halcon image processing software provides location and orientation of the fruits accurately. The depth accuracy of Z axis was examined in which 500 to 600 mm distance between cameras and fruits was found significant to compute the depth distance precisely when distance between two cameras maintained to 100 mm.

Similarity solutions of some two-space-dimensional nonlinear wave evolution equations

NASA Technical Reports Server (NTRS)

Redekopp, L. G.

1980-01-01

Similarity reductions of the two-space-dimensional versions of the Korteweg-de Vries, modified Korteweg-de Vries, Benjamin-Davis-Ono, and nonlinear Schroedinger equations are presented, and some solutions of the reduced equations are discussed. Exact dispersive solutions of the two-dimensional Korteweg-de Vries equation are obtained, and the similarity solution of this equation is shown to be reducible to the second Painleve transcendent.

Lagrangian statistics in weakly forced two-dimensional turbulence.

PubMed

Rivera, Michael K; Ecke, Robert E

2016-01-01

Measurements of Lagrangian single-point and multiple-point statistics in a quasi-two-dimensional stratified layer system are reported. The system consists of a layer of salt water over an immiscible layer of Fluorinert and is forced electromagnetically so that mean-squared vorticity is injected at a well-defined spatial scale ri. Simultaneous cascades develop in which enstrophy flows predominately to small scales whereas energy cascades, on average, to larger scales. Lagrangian correlations and one- and two-point displacements are measured for random initial conditions and for initial positions within topological centers and saddles. Some of the behavior of these quantities can be understood in terms of the trapping characteristics of long-lived centers, the slow motion near strong saddles, and the rapid fluctuations outside of either centers or saddles. We also present statistics of Lagrangian velocity fluctuations using energy spectra in frequency space and structure functions in real space. We compare with complementary Eulerian velocity statistics. We find that simultaneous inverse energy and enstrophy ranges present in spectra are not directly echoed in real-space moments of velocity difference. Nevertheless, the spectral ranges line up well with features of moment ratios, indicating that although the moments are not exhibiting unambiguous scaling, the behavior of the probability distribution functions is changing over short ranges of length scales. Implications for understanding weakly forced 2D turbulence with simultaneous inverse and direct cascades are discussed.

Global Interior Robot Localisation by a Colour Content Image Retrieval System

NASA Astrophysics Data System (ADS)

Chaari, A.; Lelandais, S.; Montagne, C.; Ahmed, M. Ben

2007-12-01

We propose a new global localisation approach to determine a coarse position of a mobile robot in structured indoor space using colour-based image retrieval techniques. We use an original method of colour quantisation based on the baker's transformation to extract a two-dimensional colour pallet combining as well space and vicinity-related information as colourimetric aspect of the original image. We conceive several retrieving approaches bringing to a specific similarity measure [InlineEquation not available: see fulltext.] integrating the space organisation of colours in the pallet. The baker's transformation provides a quantisation of the image into a space where colours that are nearby in the original space are also nearby in the output space, thereby providing dimensionality reduction and invariance to minor changes in the image. Whereas the distance [InlineEquation not available: see fulltext.] provides for partial invariance to translation, sight point small changes, and scale factor. In addition to this study, we developed a hierarchical search module based on the logic classification of images following rooms. This hierarchical module reduces the searching indoor space and ensures an improvement of our system performances. Results are then compared with those brought by colour histograms provided with several similarity measures. In this paper, we focus on colour-based features to describe indoor images. A finalised system must obviously integrate other type of signature like shape and texture.

Evolutionary Algorithm Based Feature Optimization for Multi-Channel EEG Classification.

PubMed

Wang, Yubo; Veluvolu, Kalyana C

2017-01-01

The most BCI systems that rely on EEG signals employ Fourier based methods for time-frequency decomposition for feature extraction. The band-limited multiple Fourier linear combiner is well-suited for such band-limited signals due to its real-time applicability. Despite the improved performance of these techniques in two channel settings, its application in multiple-channel EEG is not straightforward and challenging. As more channels are available, a spatial filter will be required to eliminate the noise and preserve the required useful information. Moreover, multiple-channel EEG also adds the high dimensionality to the frequency feature space. Feature selection will be required to stabilize the performance of the classifier. In this paper, we develop a new method based on Evolutionary Algorithm (EA) to solve these two problems simultaneously. The real-valued EA encodes both the spatial filter estimates and the feature selection into its solution and optimizes it with respect to the classification error. Three Fourier based designs are tested in this paper. Our results show that the combination of Fourier based method with covariance matrix adaptation evolution strategy (CMA-ES) has the best overall performance.

Effective traffic features selection algorithm for cyber-attacks samples

NASA Astrophysics Data System (ADS)

Li, Yihong; Liu, Fangzheng; Du, Zhenyu

2018-05-01

By studying the defense scheme of Network attacks, this paper propose an effective traffic features selection algorithm based on k-means++ clustering to deal with the problem of high dimensionality of traffic features which extracted from cyber-attacks samples. Firstly, this algorithm divide the original feature set into attack traffic feature set and background traffic feature set by the clustering. Then, we calculates the variation of clustering performance after removing a certain feature. Finally, evaluating the degree of distinctiveness of the feature vector according to the result. Among them, the effective feature vector is whose degree of distinctiveness exceeds the set threshold. The purpose of this paper is to select out the effective features from the extracted original feature set. In this way, it can reduce the dimensionality of the features so as to reduce the space-time overhead of subsequent detection. The experimental results show that the proposed algorithm is feasible and it has some advantages over other selection algorithms.

A holographic model of the Kondo effect

NASA Astrophysics Data System (ADS)

Erdmenger, Johanna; Hoyos, Carlos; O'Bannon, Andy; Wu, Jackson

2013-12-01

We propose a model of the Kondo effect based on the Anti-de Sitter/Conformal Field Theory (AdS/CFT) correspondence, also known as holography. The Kondo effect is the screening of a magnetic impurity coupled anti-ferromagnetically to a bath of conduction electrons at low temperatures. In a (1+1)-dimensional CFT description, the Kondo effect is a renormalization group flow triggered by a marginally relevant (0+1)-dimensional operator between two fixed points with the same Kac-Moody current algebra. In the large- N limit, with spin SU( N) and charge U(1) symmetries, the Kondo effect appears as a (0+1)-dimensional second-order mean-field transition in which the U(1) charge symmetry is spontaneously broken. Our holographic model, which combines the CFT and large- N descriptions, is a Chern-Simons gauge field in (2+1)-dimensional AdS space, AdS 3, dual to the Kac-Moody current, coupled to a holographic superconductor along an AdS 2 sub-space. Our model exhibits several characteristic features of the Kondo effect, including a dynamically generated scale, a resistivity with power-law behavior in temperature at low temperatures, and a spectral flow producing a phase shift. Our holographic Kondo model may be useful for studying many open problems involving impurities, including for example the Kondo lattice problem.

A series of three-dimensional lanthanide coordination polymers with rutile and unprecedented rutile-related topologies.

PubMed

Qin, Chao; Wang, Xin-Long; Wang, En-Bo; Su, Zhong-Min

2005-10-03

The complexes of formulas Ln(pydc)(Hpydc) (Ln = Sm (1), Eu (2), Gd (3); H2pydc = pyridine-2,5-dicarboxylic acid) and Ln(pydc)(bc)(H2O) (Ln = Sm (4), Gd (5); Hbc = benzenecarboxylic acid) have been synthesized under hydrothermal conditions and characterized by elemental analysis, IR, TG analysis, and single-crystal X-ray diffraction. Compounds 1-3 are isomorphous and crystallize in the orthorhombic system, space group Pbcn. Their final three-dimensional racemic frameworks can be considered as being constructed by helix-linked scalelike sheets. Compounds 4 and 5 are isostructural and crystallize in the monoclinic system, space group P2(1)/c. pydc ligands bridge dinuclear lanthanide centers to form the three-dimensional frameworks featuring hexagonal channels along the a-axis that are occupied by one-end-coordinated bc ligands. From the topological point of view, the five three-dimensional nets are binodal with six- and three-connected nodes, the former of which exhibit a rutile-related (4.6(2))(2)(4(2).6(9).8(4)) topology that is unprecedented within coordination frames, and the latter two species display a distorted rutile (4.6(2))(2)(4(2).6(10).8(3)) topology. Furthermore, the luminescent properties of 2 were studied.

Mean apparent propagator (MAP) MRI: a novel diffusion imaging method for mapping tissue microstructure.

PubMed

Özarslan, Evren; Koay, Cheng Guan; Shepherd, Timothy M; Komlosh, Michal E; İrfanoğlu, M Okan; Pierpaoli, Carlo; Basser, Peter J

2013-09-01

Diffusion-weighted magnetic resonance (MR) signals reflect information about underlying tissue microstructure and cytoarchitecture. We propose a quantitative, efficient, and robust mathematical and physical framework for representing diffusion-weighted MR imaging (MRI) data obtained in "q-space," and the corresponding "mean apparent propagator (MAP)" describing molecular displacements in "r-space." We also define and map novel quantitative descriptors of diffusion that can be computed robustly using this MAP-MRI framework. We describe efficient analytical representation of the three-dimensional q-space MR signal in a series expansion of basis functions that accurately describes diffusion in many complex geometries. The lowest order term in this expansion contains a diffusion tensor that characterizes the Gaussian displacement distribution, equivalent to diffusion tensor MRI (DTI). Inclusion of higher order terms enables the reconstruction of the true average propagator whose projection onto the unit "displacement" sphere provides an orientational distribution function (ODF) that contains only the orientational dependence of the diffusion process. The representation characterizes novel features of diffusion anisotropy and the non-Gaussian character of the three-dimensional diffusion process. Other important measures this representation provides include the return-to-the-origin probability (RTOP), and its variants for diffusion in one- and two-dimensions-the return-to-the-plane probability (RTPP), and the return-to-the-axis probability (RTAP), respectively. These zero net displacement probabilities measure the mean compartment (pore) volume and cross-sectional area in distributions of isolated pores irrespective of the pore shape. MAP-MRI represents a new comprehensive framework to model the three-dimensional q-space signal and transform it into diffusion propagators. Experiments on an excised marmoset brain specimen demonstrate that MAP-MRI provides several novel, quantifiable parameters that capture previously obscured intrinsic features of nervous tissue microstructure. This should prove helpful for investigating the functional organization of normal and pathologic nervous tissue. Copyright © 2013 Elsevier Inc. All rights reserved.

3D chromosome rendering from Hi-C data using virtual reality

NASA Astrophysics Data System (ADS)

Zhu, Yixin; Selvaraj, Siddarth; Weber, Philip; Fang, Jennifer; Schulze, Jürgen P.; Ren, Bing

2015-01-01

Most genome browsers display DNA linearly, using single-dimensional depictions that are useful to examine certain epigenetic mechanisms such as DNA methylation. However, these representations are insufficient to visualize intrachromosomal interactions and relationships between distal genome features. Relationships between DNA regions may be difficult to decipher or missed entirely if those regions are distant in one dimension but could be spatially proximal when mapped to three-dimensional space. For example, the visualization of enhancers folding over genes is only fully expressed in three-dimensional space. Thus, to accurately understand DNA behavior during gene expression, a means to model chromosomes is essential. Using coordinates generated from Hi-C interaction frequency data, we have created interactive 3D models of whole chromosome structures and its respective domains. We have also rendered information on genomic features such as genes, CTCF binding sites, and enhancers. The goal of this article is to present the procedure, findings, and conclusions of our models and renderings.

Structural optimization via a design space hierarchy

NASA Technical Reports Server (NTRS)

Vanderplaats, G. N.

1976-01-01

Mathematical programming techniques provide a general approach to automated structural design. An iterative method is proposed in which design is treated as a hierarchy of subproblems, one being locally constrained and the other being locally unconstrained. It is assumed that the design space is locally convex in the case of good initial designs and that the objective and constraint functions are continuous, with continuous first derivatives. A general design algorithm is outlined for finding a move direction which will decrease the value of the objective function while maintaining a feasible design. The case of one-dimensional search in a two-variable design space is discussed. Possible applications are discussed. A major feature of the proposed algorithm is its application to problems which are inherently ill-conditioned, such as design of structures for optimum geometry.

Geometry of the generalized Bloch sphere for qutrits

NASA Astrophysics Data System (ADS)

Goyal, Sandeep K.; Neethi Simon, B.; Singh, Rajeev; Simon, Sudhavathani

2016-04-01

The geometry of the generalized Bloch sphere Ω3, the state space of a qutrit, is studied. Closed form expressions for Ω3, its boundary ∂Ω3, and the set of extremals {{{Ω }}}3{{ext}} are obtained by use of an elementary observation. These expressions and analytic methods are used to classify the 28 two-sections and the 56 three-sections of Ω3 into unitary equivalence classes, completing the works of earlier authors. It is shown, in particular, that there are families of two-sections and of three-sections which are equivalent geometrically but not unitarily, a feature that does not appear to have been appreciated earlier. A family of three-sections of obese-tetrahedral shape whose symmetry corresponds to the 24-element tetrahedral point group T d is examined in detail. This symmetry is traced to the natural reduction of the adjoint representation of SU(3), the symmetry underlying Ω3, into direct sum of the two-dimensional and the two (inequivalent) three-dimensional irreducible representations of T d .

Data driven analysis of rain events: feature extraction, clustering, microphysical /macro physical relationship

NASA Astrophysics Data System (ADS)

Djallel Dilmi, Mohamed; Mallet, Cécile; Barthes, Laurent; Chazottes, Aymeric

2017-04-01

The study of rain time series records is mainly carried out using rainfall rate or rain accumulation parameters estimated on a fixed integration time (typically 1 min, 1 hour or 1 day). In this study we used the concept of rain event. In fact, the discrete and intermittent natures of rain processes make the definition of some features inadequate when defined on a fixed duration. Long integration times (hour, day) lead to mix rainy and clear air periods in the same sample. Small integration time (seconds, minutes) will lead to noisy data with a great sensibility to detector characteristics. The analysis on the whole rain event instead of individual short duration samples of a fixed duration allows to clarify relationships between features, in particular between macro physical and microphysical ones. This approach allows suppressing the intra-event variability partly due to measurement uncertainties and allows focusing on physical processes. An algorithm based on Genetic Algorithm (GA) and Self Organising Maps (SOM) is developed to obtain a parsimonious characterisation of rain events using a minimal set of variables. The use of self-organizing map (SOM) is justified by the fact that it allows to map a high dimensional data space in a two-dimensional space while preserving as much as possible the initial space topology in an unsupervised way. The obtained SOM allows providing the dependencies between variables and consequently removing redundant variables leading to a minimal subset of only five features (the event duration, the rain rate peak, the rain event depth, the event rain rate standard deviation and the absolute rain rate variation of order 0.5). To confirm relevance of the five selected features the corresponding SOM is analyzed. This analysis shows clearly the existence of relationships between features. It also shows the independence of the inter-event time (IETp) feature or the weak dependence of the Dry percentage in event (Dd%e) feature. This confirms that a rain time series can be considered by an alternation of independent rain event and no rain period. The five selected feature are used to perform a hierarchical clustering of the events. The well-known division between stratiform and convective events appears clearly. This classification into two classes is then refined in 5 fairly homogeneous subclasses. The data driven analysis performed on whole rain events instead of fixed length samples allows identifying strong relationships between macrophysics (based on rain rate) and microphysics (based on raindrops) features. We show that among the 5 identified subclasses some of them have specific microphysics characteristics. Obtaining information on microphysical characteristics of rainfall events from rain gauges measurement suggests many implications in development of the quantitative precipitation estimation (QPE), for the improvement of rain rate retrieval algorithm in remote sensing context.

Stargate GTM: Bridging Descriptor and Activity Spaces.

PubMed

Gaspar, Héléna A; Baskin, Igor I; Marcou, Gilles; Horvath, Dragos; Varnek, Alexandre

2015-11-23

Predicting the activity profile of a molecule or discovering structures possessing a specific activity profile are two important goals in chemoinformatics, which could be achieved by bridging activity and molecular descriptor spaces. In this paper, we introduce the "Stargate" version of the Generative Topographic Mapping approach (S-GTM) in which two different multidimensional spaces (e.g., structural descriptor space and activity space) are linked through a common 2D latent space. In the S-GTM algorithm, the manifolds are trained simultaneously in two initial spaces using the probabilities in the 2D latent space calculated as a weighted geometric mean of probability distributions in both spaces. S-GTM has the following interesting features: (1) activities are involved during the training procedure; therefore, the method is supervised, unlike conventional GTM; (2) using molecular descriptors of a given compound as input, the model predicts a whole activity profile, and (3) using an activity profile as input, areas populated by relevant chemical structures can be detected. To assess the performance of S-GTM prediction models, a descriptor space (ISIDA descriptors) of a set of 1325 GPCR ligands was related to a B-dimensional (B = 1 or 8) activity space corresponding to pKi values for eight different targets. S-GTM outperforms conventional GTM for individual activities and performs similarly to the Lasso multitask learning algorithm, although it is still slightly less accurate than the Random Forest method.

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

PubMed Central

Tian, Xin

2014-01-01

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

Complex Environmental Data Modelling Using Adaptive General Regression Neural Networks

NASA Astrophysics Data System (ADS)

Kanevski, Mikhail

2015-04-01

The research deals with an adaptation and application of Adaptive General Regression Neural Networks (GRNN) to high dimensional environmental data. GRNN [1,2,3] are efficient modelling tools both for spatial and temporal data and are based on nonparametric kernel methods closely related to classical Nadaraya-Watson estimator. Adaptive GRNN, using anisotropic kernels, can be also applied for features selection tasks when working with high dimensional data [1,3]. In the present research Adaptive GRNN are used to study geospatial data predictability and relevant feature selection using both simulated and real data case studies. The original raw data were either three dimensional monthly precipitation data or monthly wind speeds embedded into 13 dimensional space constructed by geographical coordinates and geo-features calculated from digital elevation model. GRNN were applied in two different ways: 1) adaptive GRNN with the resulting list of features ordered according to their relevancy; and 2) adaptive GRNN applied to evaluate all possible models N [in case of wind fields N=(2^13 -1)=8191] and rank them according to the cross-validation error. In both cases training were carried out applying leave-one-out procedure. An important result of the study is that the set of the most relevant features depends on the month (strong seasonal effect) and year. The predictabilities of precipitation and wind field patterns, estimated using the cross-validation and testing errors of raw and shuffled data, were studied in detail. The results of both approaches were qualitatively and quantitatively compared. In conclusion, Adaptive GRNN with their ability to select features and efficient modelling of complex high dimensional data can be widely used in automatic/on-line mapping and as an integrated part of environmental decision support systems. 1. Kanevski M., Pozdnoukhov A., Timonin V. Machine Learning for Spatial Environmental Data. Theory, applications and software. EPFL Press. With a CD: data, software, guides. (2009). 2. Kanevski M. Spatial Predictions of Soil Contamination Using General Regression Neural Networks. Systems Research and Information Systems, Volume 8, number 4, 1999. 3. Robert S., Foresti L., Kanevski M. Spatial prediction of monthly wind speeds in complex terrain with adaptive general regression neural networks. International Journal of Climatology, 33 pp. 1793-1804, 2013.

Thermal Non-Equilibrium Flows in Three Space Dimensions

NASA Astrophysics Data System (ADS)

Zeng, Yanni

2016-01-01

We study the equations describing the motion of a thermal non-equilibrium gas in three space dimensions. It is a hyperbolic system of six equations with a relaxation term. The dissipation mechanism induced by the relaxation is weak in the sense that the Shizuta-Kawashima criterion is violated. This implies that a perturbation of a constant equilibrium state consists of two parts: one decays in time while the other stays. In fact, the entropy wave grows weakly along the particle path as the process is irreversible. We study thermal properties related to the well-posedness of the nonlinear system. We also obtain a detailed pointwise estimate on the Green's function for the Cauchy problem when the system is linearized around an equilibrium constant state. The Green's function provides a complete picture of the wave pattern, with an exact and explicit leading term. Comparing with existing results for one dimensional flows, our results reveal a new feature of three dimensional flows: not only does the entropy wave not decay, but the velocity also contains a non-decaying part, strongly coupled with its decaying one. The new feature is supported by the second order approximation via the Chapman-Enskog expansions, which are the Navier-Stokes equations with vanished shear viscosity and heat conductivity.

Generalized Weierstrass-Mandelbrot Function Model for Actual Stocks Markets Indexes with Nonlinear Characteristics

NASA Astrophysics Data System (ADS)

Zhang, L.; Yu, C.; Sun, J. Q.

2015-03-01

It is difficult to simulate the dynamical behavior of actual financial markets indexes effectively, especially when they have nonlinear characteristics. So it is significant to propose a mathematical model with these characteristics. In this paper, we investigate a generalized Weierstrass-Mandelbrot function (WMF) model with two nonlinear characteristics: fractal dimension D where 2 > D > 1.5 and Hurst exponent (H) where 1 > H > 0.5 firstly. And then we study the dynamical behavior of H for WMF as D and the spectrum of the time series γ change in three-dimensional space, respectively. Because WMF and the actual stock market indexes have two common features: fractal behavior using fractal dimension and long memory effect by Hurst exponent, we study the relationship between WMF and the actual stock market indexes. We choose a random value of γ and fixed value of D for WMF to simulate the S&P 500 indexes at different time ranges. As shown in the simulation results of three-dimensional space, we find that γ is important in WMF model and different γ may have the same effect for the nonlinearity of WMF. Then we calculate the skewness and kurtosis of actual Daily S&P 500 index in different time ranges which can be used to choose the value of γ. Based on these results, we choose appropriate γ, D and initial value into WMF to simulate Daily S&P 500 indexes. Using the fit line method in two-dimensional space for the simulated values, we find that the generalized WMF model is effective for simulating different actual stock market indexes in different time ranges. It may be useful for understanding the dynamical behavior of many different financial markets.

A Hybrid Semi-Supervised Anomaly Detection Model for High-Dimensional Data.

PubMed

Song, Hongchao; Jiang, Zhuqing; Men, Aidong; Yang, Bo

2017-01-01

Anomaly detection, which aims to identify observations that deviate from a nominal sample, is a challenging task for high-dimensional data. Traditional distance-based anomaly detection methods compute the neighborhood distance between each observation and suffer from the curse of dimensionality in high-dimensional space; for example, the distances between any pair of samples are similar and each sample may perform like an outlier. In this paper, we propose a hybrid semi-supervised anomaly detection model for high-dimensional data that consists of two parts: a deep autoencoder (DAE) and an ensemble k -nearest neighbor graphs- ( K -NNG-) based anomaly detector. Benefiting from the ability of nonlinear mapping, the DAE is first trained to learn the intrinsic features of a high-dimensional dataset to represent the high-dimensional data in a more compact subspace. Several nonparametric KNN-based anomaly detectors are then built from different subsets that are randomly sampled from the whole dataset. The final prediction is made by all the anomaly detectors. The performance of the proposed method is evaluated on several real-life datasets, and the results confirm that the proposed hybrid model improves the detection accuracy and reduces the computational complexity.

A Hybrid Semi-Supervised Anomaly Detection Model for High-Dimensional Data

PubMed Central

Jiang, Zhuqing; Men, Aidong; Yang, Bo

2017-01-01

Anomaly detection, which aims to identify observations that deviate from a nominal sample, is a challenging task for high-dimensional data. Traditional distance-based anomaly detection methods compute the neighborhood distance between each observation and suffer from the curse of dimensionality in high-dimensional space; for example, the distances between any pair of samples are similar and each sample may perform like an outlier. In this paper, we propose a hybrid semi-supervised anomaly detection model for high-dimensional data that consists of two parts: a deep autoencoder (DAE) and an ensemble k-nearest neighbor graphs- (K-NNG-) based anomaly detector. Benefiting from the ability of nonlinear mapping, the DAE is first trained to learn the intrinsic features of a high-dimensional dataset to represent the high-dimensional data in a more compact subspace. Several nonparametric KNN-based anomaly detectors are then built from different subsets that are randomly sampled from the whole dataset. The final prediction is made by all the anomaly detectors. The performance of the proposed method is evaluated on several real-life datasets, and the results confirm that the proposed hybrid model improves the detection accuracy and reduces the computational complexity. PMID:29270197

Formation of Spiral-Arm Spurs and Bound Clouds in Vertically Stratified Galactic Gas Disks

NASA Astrophysics Data System (ADS)

Kim, Woong-Tae; Ostriker, Eve C.

2006-07-01

We investigate the growth of spiral-arm substructure in vertically stratified, self-gravitating, galactic gas disks, using local numerical MHD simulations. Our new models extend our previous two-dimensional studies, which showed that a magnetized spiral shock in a thin disk can undergo magneto-Jeans instability (MJI), resulting in regularly spaced interarm spur structures and massive gravitationally bound fragments. Similar spur (or ``feather'') features have recently been seen in high-resolution observations of several galaxies. Here we consider two sets of numerical models: two-dimensional simulations that use a ``thick-disk'' gravitational kernel, and three-dimensional simulations with explicit vertical stratification. Both models adopt an isothermal equation of state with cs=7 km s-1. When disks are sufficiently magnetized and self-gravitating, the result in both sorts of models is the growth of spiral-arm substructure similar to that in our previous razor-thin models. Reduced self-gravity due to nonzero disk thickness increases the spur spacing to ~10 times the Jeans length at the arm peak. Bound clouds that form from spur fragmentation have masses ~(1-3)×107 Msolar each, similar to the largest observed GMCs. The mass-to-flux ratios and specific angular momenta of the bound condensations are lower than large-scale galactic values, as is true for observed GMCs. We find that unmagnetized or weakly magnetized two-dimensional models are unstable to the ``wiggle instability'' previously identified by Wada & Koda. However, our fully three-dimensional models do not show this effect. Nonsteady motions and strong vertical shear prevent coherent vortical structures from forming, evidently suppressing the wiggle instability. We also find no clear traces of Parker instability in the nonlinear spiral arm substructures that emerge, although conceivably Parker modes may help seed the MJI at early stages since azimuthal wavelengths are similar.

Blended particle filters for large-dimensional chaotic dynamical systems

PubMed Central

Majda, Andrew J.; Qi, Di; Sapsis, Themistoklis P.

2014-01-01

A major challenge in contemporary data science is the development of statistically accurate particle filters to capture non-Gaussian features in large-dimensional chaotic dynamical systems. Blended particle filters that capture non-Gaussian features in an adaptively evolving low-dimensional subspace through particles interacting with evolving Gaussian statistics on the remaining portion of phase space are introduced here. These blended particle filters are constructed in this paper through a mathematical formalism involving conditional Gaussian mixtures combined with statistically nonlinear forecast models compatible with this structure developed recently with high skill for uncertainty quantification. Stringent test cases for filtering involving the 40-dimensional Lorenz 96 model with a 5-dimensional adaptive subspace for nonlinear blended filtering in various turbulent regimes with at least nine positive Lyapunov exponents are used here. These cases demonstrate the high skill of the blended particle filter algorithms in capturing both highly non-Gaussian dynamical features as well as crucial nonlinear statistics for accurate filtering in extreme filtering regimes with sparse infrequent high-quality observations. The formalism developed here is also useful for multiscale filtering of turbulent systems and a simple application is sketched below. PMID:24825886

Dimensionality reduction of collective motion by principal manifolds

NASA Astrophysics Data System (ADS)

Gajamannage, Kelum; Butail, Sachit; Porfiri, Maurizio; Bollt, Erik M.

2015-01-01

While the existence of low-dimensional embedding manifolds has been shown in patterns of collective motion, the current battery of nonlinear dimensionality reduction methods is not amenable to the analysis of such manifolds. This is mainly due to the necessary spectral decomposition step, which limits control over the mapping from the original high-dimensional space to the embedding space. Here, we propose an alternative approach that demands a two-dimensional embedding which topologically summarizes the high-dimensional data. In this sense, our approach is closely related to the construction of one-dimensional principal curves that minimize orthogonal error to data points subject to smoothness constraints. Specifically, we construct a two-dimensional principal manifold directly in the high-dimensional space using cubic smoothing splines, and define the embedding coordinates in terms of geodesic distances. Thus, the mapping from the high-dimensional data to the manifold is defined in terms of local coordinates. Through representative examples, we show that compared to existing nonlinear dimensionality reduction methods, the principal manifold retains the original structure even in noisy and sparse datasets. The principal manifold finding algorithm is applied to configurations obtained from a dynamical system of multiple agents simulating a complex maneuver called predator mobbing, and the resulting two-dimensional embedding is compared with that of a well-established nonlinear dimensionality reduction method.

Flow Interactions of Two- and Three-Dimensional Networked Bio-Inspired Control Elements in an In-Line Arrangement.

PubMed

Kurt, Melike; Moored, Keith

2018-04-19

We present experiments that examine the modes of interaction, the collective performance and the role of three-dimensionality in two pitching propulsors in an in-line arrangement. Both two-dimensional foils and three-dimensional rectangular wings of $AR = 2$ are examined. \\kwm{In contrast to previous work, two interaction modes distinguished as the coherent and branched wake modes are not observed to be directly linked to the propulsive efficiency, although they are linked to peak thrust performance and minimum power consumption as previously described \\cite[]{boschitsch2014propulsive}.} \\kwm{In fact, in closely-spaced propulsors peak propulsive efficiency of the follower occurs near its minimum power and this condition \\kwm{ reveals a} branched wake mode. Alternatively, for propulsors spaced far apart peak propulsive efficiency of the follower occurs near its peak thrust and this condition \\kwm{reveals a} coherent wake mode.} By examining the collective performance, it is discovered that there is an optimal spacing between the propulsors to maximize the collective efficiency. For two-dimensional foils the optimal spacing of $X^* = 0.75$ and the synchrony of $\\phi = 2\\pi /3$ leads to a collective efficiency and thrust enhancement of 50\\% and 32\\%, respectively, as compared to two isolated foils. In comparison, for $AR = 2$ wings the optimal spacing of $X^* = 0.25$ and the synchrony of $\\phi = 7\\pi /6$ leads to a collective efficiency and thrust enhancement of 30\\% and 22\\%, respectively. In addition, at the optimal conditions the collective lateral force coefficients in both the two- and three-dimensional cases are negligible, while operating off these conditions can lead to non-negligible lateral forces. Finally, the peak efficiency of the collective and the follower are shown to have opposite trends with increasing spacing in two- and three-dimensional flows. This is correlated to the breakdown of the impinging vortex on the follower wing in three-dimensions. These results can aid in the design of networked bio-inspired control elements that through integrated sensing can synchronize to three-dimensional flow interactions. © 2018 IOP Publishing Ltd.

Long-range two-dimensional superstructure in the superconducting electron-doped cuprate Pr 0.88 LaCe 0.12 CuO 4

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

Campbell, B. J.; Rosenkranz, S.; Kang, H. J.

2015-07-01

Utilizing single-crystal synchrotron x-ray scattering, we observe distorted CuO 2 planes in the electron- doped superconductor Pr 1-xLaCe xCuO 4+δ , x =0.12. Resolution-limited rods of scattering are indicative of a long-range two-dimensional 2√2 × 2√2 superstructure in the a-b plane, adhering to planar space-group symmetry p4gm, which is subject to stacking disorder perpendicular to the planes. This superstructure is present only in annealed, superconducting samples, but not in the as-grown, nonsuperconducting samples. These long-range distortions of the CuO 2 planes, which are generally considered to be detrimental to superconductivity, have avoided detection to date due to the challenges ofmore » observing and interpreting subtle diffuse-scattering features.« less

Balancing Newtonian gravity and spin to create localized structures

NASA Astrophysics Data System (ADS)

Bush, Michael; Lindner, John

2015-03-01

Using geometry and Newtonian physics, we design localized structures that do not require electromagnetic or other forces to resist implosion or explosion. In two-dimensional Euclidean space, we find an equilibrium configuration of a rotating ring of massive dust whose inward gravity is the centripetal force that spins it. We find similar solutions in three-dimensional Euclidean and hyperbolic spaces, but only in the limit of vanishing mass. Finally, in three-dimensional Euclidean space, we generalize the two-dimensional result by finding an equilibrium configuration of a spherical shell of massive dust that supports itself against gravitational collapse by spinning isoclinically in four dimensions so its three-dimensional acceleration is everywhere inward. These Newtonian ``atoms'' illuminate classical physics and geometry.

Fractal geometry in an expanding, one-dimensional, Newtonian universe.

PubMed

Miller, Bruce N; Rouet, Jean-Louis; Le Guirriec, Emmanuel

2007-09-01

Observations of galaxies over large distances reveal the possibility of a fractal distribution of their positions. The source of fractal behavior is the lack of a length scale in the two body gravitational interaction. However, even with new, larger, sample sizes from recent surveys, it is difficult to extract information concerning fractal properties with confidence. Similarly, three-dimensional N-body simulations with a billion particles only provide a thousand particles per dimension, far too small for accurate conclusions. With one-dimensional models these limitations can be overcome by carrying out simulations with on the order of a quarter of a million particles without compromising the computation of the gravitational force. Here the multifractal properties of two of these models that incorporate different features of the dynamical equations governing the evolution of a matter dominated universe are compared. For each model at least two scaling regions are identified. By employing criteria from dynamical systems theory it is shown that only one of them can be geometrically significant. The results share important similarities with galaxy observations, such as hierarchical clustering and apparent bifractal geometry. They also provide insights concerning possible constraints on length and time scales for fractal structure. They clearly demonstrate that fractal geometry evolves in the mu (position, velocity) space. The observed patterns are simply a shadow (projection) of higher-dimensional structure.

Multiview alignment hashing for efficient image search.

PubMed

Liu, Li; Yu, Mengyang; Shao, Ling

2015-03-01

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

Locally Linear Embedding of Local Orthogonal Least Squares Images for Face Recognition

NASA Astrophysics Data System (ADS)

Hafizhelmi Kamaru Zaman, Fadhlan

2018-03-01

Dimensionality reduction is very important in face recognition since it ensures that high-dimensionality data can be mapped to lower dimensional space without losing salient and integral facial information. Locally Linear Embedding (LLE) has been previously used to serve this purpose, however, the process of acquiring LLE features requires high computation and resources. To overcome this limitation, we propose a locally-applied Local Orthogonal Least Squares (LOLS) model can be used as initial feature extraction before the application of LLE. By construction of least squares regression under orthogonal constraints we can preserve more discriminant information in the local subspace of facial features while reducing the overall features into a more compact form that we called LOLS images. LLE can then be applied on the LOLS images to maps its representation into a global coordinate system of much lower dimensionality. Several experiments carried out using publicly available face datasets such as AR, ORL, YaleB, and FERET under Single Sample Per Person (SSPP) constraint demonstrates that our proposed method can reduce the time required to compute LLE features while delivering better accuracy when compared to when either LLE or OLS alone is used. Comparison against several other feature extraction methods and more recent feature-learning method such as state-of-the-art Convolutional Neural Networks (CNN) also reveal the superiority of the proposed method under SSPP constraint.

Feature extraction and classification algorithms for high dimensional data

NASA Technical Reports Server (NTRS)

Lee, Chulhee; Landgrebe, David

1993-01-01

Feature extraction and classification algorithms for high dimensional data are investigated. Developments with regard to sensors for Earth observation are moving in the direction of providing much higher dimensional multispectral imagery than is now possible. In analyzing such high dimensional data, processing time becomes an important factor. With large increases in dimensionality and the number of classes, processing time will increase significantly. To address this problem, a multistage classification scheme is proposed which reduces the processing time substantially by eliminating unlikely classes from further consideration at each stage. Several truncation criteria are developed and the relationship between thresholds and the error caused by the truncation is investigated. Next an approach to feature extraction for classification is proposed based directly on the decision boundaries. It is shown that all the features needed for classification can be extracted from decision boundaries. A characteristic of the proposed method arises by noting that only a portion of the decision boundary is effective in discriminating between classes, and the concept of the effective decision boundary is introduced. The proposed feature extraction algorithm has several desirable properties: it predicts the minimum number of features necessary to achieve the same classification accuracy as in the original space for a given pattern recognition problem; and it finds the necessary feature vectors. The proposed algorithm does not deteriorate under the circumstances of equal means or equal covariances as some previous algorithms do. In addition, the decision boundary feature extraction algorithm can be used both for parametric and non-parametric classifiers. Finally, some problems encountered in analyzing high dimensional data are studied and possible solutions are proposed. First, the increased importance of the second order statistics in analyzing high dimensional data is recognized. By investigating the characteristics of high dimensional data, the reason why the second order statistics must be taken into account in high dimensional data is suggested. Recognizing the importance of the second order statistics, there is a need to represent the second order statistics. A method to visualize statistics using a color code is proposed. By representing statistics using color coding, one can easily extract and compare the first and the second statistics.

On low-energy effective action in three-dimensional = 2 and = 4 supersymmetric electrodynamics

NASA Astrophysics Data System (ADS)

Buchbinder, I. L.; Merzlikin, B. S.; Samsonov, I. B.

2013-11-01

We discuss general structure of low-energy effective actions in = 2 and = 4 three-dimensional supersymmetric electrodynamics (SQED) in gauge superfield sector. There are specific terms in the effective action having no four-dimensional analogs. Some of these terms are responsible for the moduli space metric in the Coulomb branch of the theory. We find two-loop quantum corrections to the moduli space metric in the = 2 SQED and show that in the = 4 SQED the moduli space does not receive two-loop quantum corrections.

High-Order Central WENO Schemes for Multi-Dimensional Hamilton-Jacobi Equations

NASA Technical Reports Server (NTRS)

Bryson, Steve; Levy, Doron; Biegel, Bryan (Technical Monitor)

2002-01-01

We present new third- and fifth-order Godunov-type central schemes for approximating solutions of the Hamilton-Jacobi (HJ) equation in an arbitrary number of space dimensions. These are the first central schemes for approximating solutions of the HJ equations with an order of accuracy that is greater than two. In two space dimensions we present two versions for the third-order scheme: one scheme that is based on a genuinely two-dimensional Central WENO reconstruction, and another scheme that is based on a simpler dimension-by-dimension reconstruction. The simpler dimension-by-dimension variant is then extended to a multi-dimensional fifth-order scheme. Our numerical examples in one, two and three space dimensions verify the expected order of accuracy of the schemes.

The formation method of the feature space for the identification of fatigued bills

NASA Astrophysics Data System (ADS)

Kang, Dongshik; Oshiro, Ayumu; Ozawa, Kenji; Mitsui, Ikugo

2014-10-01

Fatigued bills make a trouble such as the paper jam in a bill handling machine. In the discrimination of fatigued bills using an acoustic signal, the variation of an observed bill sound is considered to be one of causes in misclassification. Therefore a technique has demanded in order to make the classification of fatigued bills more efficient. In this paper, we proposed the algorithm that extracted feature quantity of bill sound from acoustic signal using the frequency difference, and carried out discrimination experiment of fatigued bill money by Support Vector Machine(SVM). The feature quantity of frequency difference can represent the frequency components of an acoustic signal is varied by the fatigued degree of bill money. The generalization performance of SVM does not depend on the size of dimensions of the feature space, even in a high dimensional feature space such as bill-acoustic signals. Furthermore, SVM can induce an optimal classifier which considers the combination of features by the virtue of polynomial kernel functions.

Experimental identification of a comb-shaped chaotic region in multiple parameter spaces simulated by the Hindmarsh—Rose neuron model

NASA Astrophysics Data System (ADS)

Jia, Bing

2014-03-01

A comb-shaped chaotic region has been simulated in multiple two-dimensional parameter spaces using the Hindmarsh—Rose (HR) neuron model in many recent studies, which can interpret almost all of the previously simulated bifurcation processes with chaos in neural firing patterns. In the present paper, a comb-shaped chaotic region in a two-dimensional parameter space was reproduced, which presented different processes of period-adding bifurcations with chaos with changing one parameter and fixed the other parameter at different levels. In the biological experiments, different period-adding bifurcation scenarios with chaos by decreasing the extra-cellular calcium concentration were observed from some neural pacemakers at different levels of extra-cellular 4-aminopyridine concentration and from other pacemakers at different levels of extra-cellular caesium concentration. By using the nonlinear time series analysis method, the deterministic dynamics of the experimental chaotic firings were investigated. The period-adding bifurcations with chaos observed in the experiments resembled those simulated in the comb-shaped chaotic region using the HR model. The experimental results show that period-adding bifurcations with chaos are preserved in different two-dimensional parameter spaces, which provides evidence of the existence of the comb-shaped chaotic region and a demonstration of the simulation results in different two-dimensional parameter spaces in the HR neuron model. The results also present relationships between different firing patterns in two-dimensional parameter spaces.

Online Condition Monitoring of Bearings to Support Total Productive Maintenance in the Packaging Materials Industry.

PubMed

Gligorijevic, Jovan; Gajic, Dragoljub; Brkovic, Aleksandar; Savic-Gajic, Ivana; Georgieva, Olga; Di Gennaro, Stefano

2016-03-01

The packaging materials industry has already recognized the importance of Total Productive Maintenance as a system of proactive techniques for improving equipment reliability. Bearing faults, which often occur gradually, represent one of the foremost causes of failures in the industry. Therefore, detection of their faults in an early stage is quite important to assure reliable and efficient operation. We present a new automated technique for early fault detection and diagnosis in rolling-element bearings based on vibration signal analysis. Following the wavelet decomposition of vibration signals into a few sub-bands of interest, the standard deviation of obtained wavelet coefficients is extracted as a representative feature. Then, the feature space dimension is optimally reduced to two using scatter matrices. In the reduced two-dimensional feature space the fault detection and diagnosis is carried out by quadratic classifiers. Accuracy of the technique has been tested on four classes of the recorded vibrations signals, i.e., normal, with the fault of inner race, outer race, and ball operation. The overall accuracy of 98.9% has been achieved. The new technique can be used to support maintenance decision-making processes and, thus, to increase reliability and efficiency in the industry by preventing unexpected faulty operation of bearings.

Online Condition Monitoring of Bearings to Support Total Productive Maintenance in the Packaging Materials Industry

PubMed Central

Gligorijevic, Jovan; Gajic, Dragoljub; Brkovic, Aleksandar; Savic-Gajic, Ivana; Georgieva, Olga; Di Gennaro, Stefano

2016-01-01

The packaging materials industry has already recognized the importance of Total Productive Maintenance as a system of proactive techniques for improving equipment reliability. Bearing faults, which often occur gradually, represent one of the foremost causes of failures in the industry. Therefore, detection of their faults in an early stage is quite important to assure reliable and efficient operation. We present a new automated technique for early fault detection and diagnosis in rolling-element bearings based on vibration signal analysis. Following the wavelet decomposition of vibration signals into a few sub-bands of interest, the standard deviation of obtained wavelet coefficients is extracted as a representative feature. Then, the feature space dimension is optimally reduced to two using scatter matrices. In the reduced two-dimensional feature space the fault detection and diagnosis is carried out by quadratic classifiers. Accuracy of the technique has been tested on four classes of the recorded vibrations signals, i.e., normal, with the fault of inner race, outer race, and ball operation. The overall accuracy of 98.9% has been achieved. The new technique can be used to support maintenance decision-making processes and, thus, to increase reliability and efficiency in the industry by preventing unexpected faulty operation of bearings. PMID:26938541

Online 3D Ear Recognition by Combining Global and Local Features.

PubMed

Liu, Yahui; Zhang, Bob; Lu, Guangming; Zhang, David

2016-01-01

The three-dimensional shape of the ear has been proven to be a stable candidate for biometric authentication because of its desirable properties such as universality, uniqueness, and permanence. In this paper, a special laser scanner designed for online three-dimensional ear acquisition was described. Based on the dataset collected by our scanner, two novel feature classes were defined from a three-dimensional ear image: the global feature class (empty centers and angles) and local feature class (points, lines, and areas). These features are extracted and combined in an optimal way for three-dimensional ear recognition. Using a large dataset consisting of 2,000 samples, the experimental results illustrate the effectiveness of fusing global and local features, obtaining an equal error rate of 2.2%.

Online 3D Ear Recognition by Combining Global and Local Features

PubMed Central

Liu, Yahui; Zhang, Bob; Lu, Guangming; Zhang, David

2016-01-01

The three-dimensional shape of the ear has been proven to be a stable candidate for biometric authentication because of its desirable properties such as universality, uniqueness, and permanence. In this paper, a special laser scanner designed for online three-dimensional ear acquisition was described. Based on the dataset collected by our scanner, two novel feature classes were defined from a three-dimensional ear image: the global feature class (empty centers and angles) and local feature class (points, lines, and areas). These features are extracted and combined in an optimal way for three-dimensional ear recognition. Using a large dataset consisting of 2,000 samples, the experimental results illustrate the effectiveness of fusing global and local features, obtaining an equal error rate of 2.2%. PMID:27935955

Wigner functions from the two-dimensional wavelet group.

PubMed

Ali, S T; Krasowska, A E; Murenzi, R

2000-12-01

Following a general procedure developed previously [Ann. Henri Poincaré 1, 685 (2000)], here we construct Wigner functions on a phase space related to the similitude group in two dimensions. Since the group space in this case is topologically homeomorphic to the phase space in question, the Wigner functions so constructed may also be considered as being functions on the group space itself. Previously the similitude group was used to construct wavelets for two-dimensional image analysis; we discuss here the connection between the wavelet transform and the Wigner function.

A new method for recognizing quadric surfaces from range data and its application to telerobotics and automation

NASA Technical Reports Server (NTRS)

Alvertos, Nicolas; Dcunha, Ivan

1992-01-01

A feature set of two dimensional curves is obtained after intersecting symmetric objects like spheres, cones, cylinders, ellipsoids, paraboloids, and parallelepipeds with two planes. After determining the location and orientation of the objects in space, these objects are aligned so as to lie on a plane parallel to a suitable coordinate system. These objects are then intersected with a horizontal and a vertical plane. Experiments were carried out with range images of sphere and cylinder. The 3-D discriminant approach was used to recognize quadric surfaces made up of simulated data. Its application to real data was also studied.

a Probabilistic Embedding Clustering Method for Urban Structure Detection

NASA Astrophysics Data System (ADS)

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

2017-09-01

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

Simplifying the representation of complex free-energy landscapes using sketch-map

PubMed Central

Ceriotti, Michele; Tribello, Gareth A.; Parrinello, Michele

2011-01-01

A new scheme, sketch-map, for obtaining a low-dimensional representation of the region of phase space explored during an enhanced dynamics simulation is proposed. We show evidence, from an examination of the distribution of pairwise distances between frames, that some features of the free-energy surface are inherently high-dimensional. This makes dimensionality reduction problematic because the data does not satisfy the assumptions made in conventional manifold learning algorithms We therefore propose that when dimensionality reduction is performed on trajectory data one should think of the resultant embedding as a quickly sketched set of directions rather than a road map. In other words, the embedding tells one about the connectivity between states but does not provide the vectors that correspond to the slow degrees of freedom. This realization informs the development of sketch-map, which endeavors to reproduce the proximity information from the high-dimensionality description in a space of lower dimensionality even when a faithful embedding is not possible. PMID:21730167

Study of reflection and transport in the microwave photo-excited GaAs/AlGaAs two dimensional electron system

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

Ye, Tianyu; Mani, Ramesh G.; Wegscheider, Werner

2013-12-04

We present the results of a concurrent experimental study of microwave reflection and transport in the GaAs/AlGaAs two dimensional electron gas system and correlate observed features in the reflection with the observed transport features. The experimental results are compared with expectations based on theory.

Crossing the dividing surface of transition state theory. IV. Dynamical regularity and dimensionality reduction as key features of reactive trajectories

NASA Astrophysics Data System (ADS)

Lorquet, J. C.

2017-04-01

The atom-diatom interaction is studied by classical mechanics using Jacobi coordinates (R, r, θ). Reactivity criteria that go beyond the simple requirement of transition state theory (i.e., PR* > 0) are derived in terms of specific initial conditions. Trajectories that exactly fulfill these conditions cross the conventional dividing surface used in transition state theory (i.e., the plane in configuration space passing through a saddle point of the potential energy surface and perpendicular to the reaction coordinate) only once. Furthermore, they are observed to be strikingly similar and to form a tightly packed bundle of perfectly collimated trajectories in the two-dimensional (R, r) configuration space, although their angular motion is highly specific for each one. Particular attention is paid to symmetrical transition states (i.e., either collinear or T-shaped with C2v symmetry) for which decoupling between angular and radial coordinates is observed, as a result of selection rules that reduce to zero Coriolis couplings between modes that belong to different irreducible representations. Liapunov exponents are equal to zero and Hamilton's characteristic function is planar in that part of configuration space that is visited by reactive trajectories. Detailed consideration is given to the concept of average reactive trajectory, which starts right from the saddle point and which is shown to be free of curvature-induced Coriolis coupling. The reaction path Hamiltonian model, together with a symmetry-based separation of the angular degree of freedom, provides an appropriate framework that leads to the formulation of an effective two-dimensional Hamiltonian. The success of the adiabatic approximation in this model is due to the symmetry of the transition state, not to a separation of time scales. Adjacent trajectories, i.e., those that do not exactly fulfill the reactivity conditions have similar characteristics, but the quality of the approximation is lower. At higher energies, these characteristics persist, but to a lesser degree. Recrossings of the dividing surface then become much more frequent and the phase space volumes of initial conditions that generate recrossing-free trajectories decrease. Altogether, one ends up with an additional illustration of the concept of reactive cylinder (or conduit) in phase space that reactive trajectories must follow. Reactivity is associated with dynamical regularity and dimensionality reduction, whatever the shape of the potential energy surface, no matter how strong its anharmonicity, and whatever the curvature of its reaction path. Both simplifying features persist during the entire reactive process, up to complete separation of fragments. The ergodicity assumption commonly assumed in statistical theories is inappropriate for reactive trajectories.

Rarefied gas flow through two-dimensional nozzles

NASA Technical Reports Server (NTRS)

De Witt, Kenneth J.; Jeng, Duen-Ren; Keith, Theo G., Jr.; Chung, Chan-Hong

1989-01-01

A kinetic theory analysis is made of the flow of a rarefied gas from one reservoir to another through two-dimensional nozzles with arbitrary curvature. The Boltzmann equation simplified by a model collision integral is solved by means of finite-difference approximations with the discrete ordinate method. The physical space is transformed by a general grid generation technique and the velocity space is transformed to a polar coordinate system. A numerical code is developed which can be applied to any two-dimensional passage of complicated geometry for the flow regimes from free-molecular to slip. Numerical values of flow quantities can be calculated for the entire physical space including both inside the nozzle and in the outside plume. Predictions are made for the case of parallel slots and compared with existing literature data. Also, results for the cases of convergent or divergent slots and two-dimensional nozzles with arbitrary curvature at arbitrary knudsen number are presented.

Vibration and stress analysis of soft-bonded shuttle insulation tiles. Modal analysis with compact widely space stringers

NASA Technical Reports Server (NTRS)

Ojalvo, I. U.; Austin, F.; Levy, A.

1974-01-01

An efficient iterative procedure is described for the vibration and modal stress analysis of reusable surface insulation (RSI) of multi-tiled space shuttle panels. The method, which is quite general, is rapidly convergent and highly useful for this application. A user-oriented computer program based upon this procedure and titled RESIST (REusable Surface Insulation Stresses) has been prepared for the analysis of compact, widely spaced, stringer-stiffened panels. RESIST, which uses finite element methods, obtains three dimensional tile stresses in the isolator, arrestor (if any) and RSI materials. Two dimensional stresses are obtained in the tile coating and the stringer-stiffened primary structure plate. A special feature of the program is that all the usual detailed finite element grid data is generated internally from a minimum of input data. The program can accommodate tile idealizations with up to 850 nodes (2550 degrees-of-freedom) and primary structure idealizations with a maximum of 10,000 degrees-of-freedom. The primary structure vibration capability is achieved through the development of a new rapid eigenvalue program named ALARM (Automatic LArge Reduction of Matrices to tridiagonal form).

Lagrangian statistics in weakly forced two-dimensional turbulence

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

Rivera, Michael K.; Ecke, Robert E.

Measurements of Lagrangian single-point and multiple-point statistics in a quasi-two-dimensional stratified layer system are reported. The system consists of a layer of salt water over an immiscible layer of Fluorinert and is forced electromagnetically so that mean-squared vorticity is injected at a well-defined spatial scale r i. Simultaneous cascades develop in which enstrophy flows predominately to small scales whereas energy cascades, on average, to larger scales. Lagrangian correlations and one- and two-point displacements are measured for random initial conditions and for initial positions within topological centers and saddles. Some of the behavior of these quantities can be understood in termsmore » of the trapping characteristics of long-lived centers, the slow motion near strong saddles, and the rapid fluctuations outside of either centers or saddles. We also present statistics of Lagrangian velocity fluctuations using energy spectra in frequency space and structure functions in real space. We compare with complementary Eulerian velocity statistics. We find that simultaneous inverse energy and enstrophy ranges present in spectra are not directly echoed in real-space moments of velocity difference. Nevertheless, the spectral ranges line up well with features of moment ratios, indicating that although the moments are not exhibiting unambiguous scaling, the behavior of the probability distribution functions is changing over short ranges of length scales. Furthermore, implications for understanding weakly forced 2D turbulence with simultaneous inverse and direct cascades are discussed.« less

Lagrangian statistics in weakly forced two-dimensional turbulence

DOE PAGES

Rivera, Michael K.; Ecke, Robert E.

2016-01-14

Measurements of Lagrangian single-point and multiple-point statistics in a quasi-two-dimensional stratified layer system are reported. The system consists of a layer of salt water over an immiscible layer of Fluorinert and is forced electromagnetically so that mean-squared vorticity is injected at a well-defined spatial scale r i. Simultaneous cascades develop in which enstrophy flows predominately to small scales whereas energy cascades, on average, to larger scales. Lagrangian correlations and one- and two-point displacements are measured for random initial conditions and for initial positions within topological centers and saddles. Some of the behavior of these quantities can be understood in termsmore » of the trapping characteristics of long-lived centers, the slow motion near strong saddles, and the rapid fluctuations outside of either centers or saddles. We also present statistics of Lagrangian velocity fluctuations using energy spectra in frequency space and structure functions in real space. We compare with complementary Eulerian velocity statistics. We find that simultaneous inverse energy and enstrophy ranges present in spectra are not directly echoed in real-space moments of velocity difference. Nevertheless, the spectral ranges line up well with features of moment ratios, indicating that although the moments are not exhibiting unambiguous scaling, the behavior of the probability distribution functions is changing over short ranges of length scales. Furthermore, implications for understanding weakly forced 2D turbulence with simultaneous inverse and direct cascades are discussed.« less

A novel weld seam detection method for space weld seam of narrow butt joint in laser welding

NASA Astrophysics Data System (ADS)

Shao, Wen Jun; Huang, Yu; Zhang, Yong

2018-02-01

Structured light measurement is widely used for weld seam detection owing to its high measurement precision and robust. However, there is nearly no geometrical deformation of the stripe projected onto weld face, whose seam width is less than 0.1 mm and without misalignment. So, it's very difficult to ensure an exact retrieval of the seam feature. This issue is raised as laser welding for butt joint of thin metal plate is widely applied. Moreover, measurement for the seam width, seam center and the normal vector of the weld face at the same time during welding process is of great importance to the welding quality but rarely reported. Consequently, a seam measurement method based on vision sensor for space weld seam of narrow butt joint is proposed in this article. Three laser stripes with different wave length are project on the weldment, in which two red laser stripes are designed and used to measure the three dimensional profile of the weld face by the principle of optical triangulation, and the third green laser stripe is used as light source to measure the edge and the centerline of the seam by the principle of passive vision sensor. The corresponding image process algorithm is proposed to extract the centerline of the red laser stripes as well as the seam feature. All these three laser stripes are captured and processed in a single image so that the three dimensional position of the space weld seam can be obtained simultaneously. Finally, the result of experiment reveals that the proposed method can meet the precision demand of space narrow butt joint.

An efficient sampling algorithm for uncertain abnormal data detection in biomedical image processing and disease prediction.

PubMed

Liu, Fei; Zhang, Xi; Jia, Yan

2015-01-01

In this paper, we propose a computer information processing algorithm that can be used for biomedical image processing and disease prediction. A biomedical image is considered a data object in a multi-dimensional space. Each dimension is a feature that can be used for disease diagnosis. We introduce a new concept of the top (k1,k2) outlier. It can be used to detect abnormal data objects in the multi-dimensional space. This technique focuses on uncertain space, where each data object has several possible instances with distinct probabilities. We design an efficient sampling algorithm for the top (k1,k2) outlier in uncertain space. Some improvement techniques are used for acceleration. Experiments show our methods' high accuracy and high efficiency.

Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images.

PubMed

Zhang, Lefei; Zhang, Qian; Du, Bo; Huang, Xin; Tang, Yuan Yan; Tao, Dacheng

2018-01-01

In hyperspectral remote sensing data mining, it is important to take into account of both spectral and spatial information, such as the spectral signature, texture feature, and morphological property, to improve the performances, e.g., the image classification accuracy. In a feature representation point of view, a nature approach to handle this situation is to concatenate the spectral and spatial features into a single but high dimensional vector and then apply a certain dimension reduction technique directly on that concatenated vector before feed it into the subsequent classifier. However, multiple features from various domains definitely have different physical meanings and statistical properties, and thus such concatenation has not efficiently explore the complementary properties among different features, which should benefit for boost the feature discriminability. Furthermore, it is also difficult to interpret the transformed results of the concatenated vector. Consequently, finding a physically meaningful consensus low dimensional feature representation of original multiple features is still a challenging task. In order to address these issues, we propose a novel feature learning framework, i.e., the simultaneous spectral-spatial feature selection and extraction algorithm, for hyperspectral images spectral-spatial feature representation and classification. Specifically, the proposed method learns a latent low dimensional subspace by projecting the spectral-spatial feature into a common feature space, where the complementary information has been effectively exploited, and simultaneously, only the most significant original features have been transformed. Encouraging experimental results on three public available hyperspectral remote sensing datasets confirm that our proposed method is effective and efficient.

Coherent Structures and Spectral Energy Transfer in Turbulent Plasma: A Space-Filter Approach.

PubMed

Camporeale, E; Sorriso-Valvo, L; Califano, F; Retinò, A

2018-03-23

Plasma turbulence at scales of the order of the ion inertial length is mediated by several mechanisms, including linear wave damping, magnetic reconnection, the formation and dissipation of thin current sheets, and stochastic heating. It is now understood that the presence of localized coherent structures enhances the dissipation channels and the kinetic features of the plasma. However, no formal way of quantifying the relationship between scale-to-scale energy transfer and the presence of spatial structures has been presented so far. In the Letter we quantify such a relationship analyzing the results of a two-dimensional high-resolution Hall magnetohydrodynamic simulation. In particular, we employ the technique of space filtering to derive a spectral energy flux term which defines, in any point of the computational domain, the signed flux of spectral energy across a given wave number. The characterization of coherent structures is performed by means of a traditional two-dimensional wavelet transformation. By studying the correlation between the spectral energy flux and the wavelet amplitude, we demonstrate the strong relationship between scale-to-scale transfer and coherent structures. Furthermore, by conditioning one quantity with respect to the other, we are able for the first time to quantify the inhomogeneity of the turbulence cascade induced by topological structures in the magnetic field. Taking into account the low space-filling factor of coherent structures (i.e., they cover a small portion of space), it emerges that 80% of the spectral energy transfer (both in the direct and inverse cascade directions) is localized in about 50% of space, and 50% of the energy transfer is localized in only 25% of space.

Coherent Structures and Spectral Energy Transfer in Turbulent Plasma: A Space-Filter Approach

NASA Astrophysics Data System (ADS)

Camporeale, E.; Sorriso-Valvo, L.; Califano, F.; Retinò, A.

2018-03-01

Plasma turbulence at scales of the order of the ion inertial length is mediated by several mechanisms, including linear wave damping, magnetic reconnection, the formation and dissipation of thin current sheets, and stochastic heating. It is now understood that the presence of localized coherent structures enhances the dissipation channels and the kinetic features of the plasma. However, no formal way of quantifying the relationship between scale-to-scale energy transfer and the presence of spatial structures has been presented so far. In the Letter we quantify such a relationship analyzing the results of a two-dimensional high-resolution Hall magnetohydrodynamic simulation. In particular, we employ the technique of space filtering to derive a spectral energy flux term which defines, in any point of the computational domain, the signed flux of spectral energy across a given wave number. The characterization of coherent structures is performed by means of a traditional two-dimensional wavelet transformation. By studying the correlation between the spectral energy flux and the wavelet amplitude, we demonstrate the strong relationship between scale-to-scale transfer and coherent structures. Furthermore, by conditioning one quantity with respect to the other, we are able for the first time to quantify the inhomogeneity of the turbulence cascade induced by topological structures in the magnetic field. Taking into account the low space-filling factor of coherent structures (i.e., they cover a small portion of space), it emerges that 80% of the spectral energy transfer (both in the direct and inverse cascade directions) is localized in about 50% of space, and 50% of the energy transfer is localized in only 25% of space.

Echocardiography Comparison Between Two and Three Dimensional Echocardiograms

NASA Technical Reports Server (NTRS)

2003-01-01

Echocardiography uses sound waves to image the heart and other organs. Developing a compact version of the latest technology improved the ease of monitoring crew member health, a critical task during long space flights. NASA researchers plan to adapt the three-dimensional (3-D) echocardiogram for space flight. The two-dimensional (2-D) echocardiogram utilized in orbit on the International Space Station (ISS) was effective, but difficult to use with precision. A heart image from a 2-D echocardiogram (left) is of a better quality than that from a 3-D device (right), but the 3-D imaging procedure is more user-friendly.

Stochastic solution to quantum dynamics

NASA Technical Reports Server (NTRS)

John, Sarah; Wilson, John W.

1994-01-01

The quantum Liouville equation in the Wigner representation is solved numerically by using Monte Carlo methods. For incremental time steps, the propagation is implemented as a classical evolution in phase space modified by a quantum correction. The correction, which is a momentum jump function, is simulated in the quasi-classical approximation via a stochastic process. The technique, which is developed and validated in two- and three- dimensional momentum space, extends an earlier one-dimensional work. Also, by developing a new algorithm, the application to bound state motion in an anharmonic quartic potential shows better agreement with exact solutions in two-dimensional phase space.

Wigner surmises and the two-dimensional homogeneous Poisson point process.

PubMed

Sakhr, Jamal; Nieminen, John M

2006-04-01

We derive a set of identities that relate the higher-order interpoint spacing statistics of the two-dimensional homogeneous Poisson point process to the Wigner surmises for the higher-order spacing distributions of eigenvalues from the three classical random matrix ensembles. We also report a remarkable identity that equates the second-nearest-neighbor spacing statistics of the points of the Poisson process and the nearest-neighbor spacing statistics of complex eigenvalues from Ginibre's ensemble of 2 x 2 complex non-Hermitian random matrices.

Dynamics of a neuron model in different two-dimensional parameter-spaces

NASA Astrophysics Data System (ADS)

Rech, Paulo C.

2011-03-01

We report some two-dimensional parameter-space diagrams numerically obtained for the multi-parameter Hindmarsh-Rose neuron model. Several different parameter planes are considered, and we show that regardless of the combination of parameters, a typical scenario is preserved: for all choice of two parameters, the parameter-space presents a comb-shaped chaotic region immersed in a large periodic region. We also show that exist regions close these chaotic region, separated by the comb teeth, organized themselves in period-adding bifurcation cascades.

Why is the World four-dimensional? Hermann Weyl’s 1955 argument and the topology of causation

NASA Astrophysics Data System (ADS)

De Bianchi, Silvia

2017-08-01

This paper approaches the question of space dimensionality by discussing a neglected argument proposed by Hermann Weyl in 1955. In Why is the World Four-Dimensional? (1955), Weyl offered a different argument from the one generally attributed to him and presented in Raum-Zeit-Materie. In the first sections of the paper, this new argument and its features are spelled-out, and in the last section, I shall develop some useful remarks on the concept of topology of causation that can still inform our reflection on the dimensionality of the world.

Static and dynamic friction in sliding colloidal monolayers

PubMed Central

Vanossi, Andrea; Manini, Nicola; Tosatti, Erio

2012-01-01

In a pioneer experiment, Bohlein et al. realized the controlled sliding of two-dimensional colloidal crystals over laser-generated periodic or quasi-periodic potentials. Here we present realistic simulations and arguments that besides reproducing the main experimentally observed features give a first theoretical demonstration of the potential impact of colloid sliding in nanotribology. The free motion of solitons and antisolitons in the sliding of hard incommensurate crystals is contrasted with the soliton–antisoliton pair nucleation at the large static friction threshold Fs when the two lattices are commensurate and pinned. The frictional work directly extracted from particles’ velocities can be analyzed as a function of classic tribological parameters, including speed, spacing, and amplitude of the periodic potential (representing, respectively, the mismatch of the sliding interface and the corrugation, or “load”). These and other features suggestive of further experiments and insights promote colloid sliding to a unique friction study instrument. PMID:23019582

The oligonucleotide frequency derived error gradient and its application to the binning of metagenome fragments

PubMed Central

2009-01-01

Background The characterisation, or binning, of metagenome fragments is an important first step to further downstream analysis of microbial consortia. Here, we propose a one-dimensional signature, OFDEG, derived from the oligonucleotide frequency profile of a DNA sequence, and show that it is possible to obtain a meaningful phylogenetic signal for relatively short DNA sequences. The one-dimensional signal is essentially a compact representation of higher dimensional feature spaces of greater complexity and is intended to improve on the tetranucleotide frequency feature space preferred by current compositional binning methods. Results We compare the fidelity of OFDEG against tetranucleotide frequency in both an unsupervised and semi-supervised setting on simulated metagenome benchmark data. Four tests were conducted using assembler output of Arachne and phrap, and for each, performance was evaluated on contigs which are greater than or equal to 8 kbp in length and contigs which are composed of at least 10 reads. Using both G-C content in conjunction with OFDEG gave an average accuracy of 96.75% (semi-supervised) and 95.19% (unsupervised), versus 94.25% (semi-supervised) and 82.35% (unsupervised) for tetranucleotide frequency. Conclusion We have presented an observation of an alternative characteristic of DNA sequences. The proposed feature representation has proven to be more beneficial than the existing tetranucleotide frequency space to the metagenome binning problem. We do note, however, that our observation of OFDEG deserves further anlaysis and investigation. Unsupervised clustering revealed OFDEG related features performed better than standard tetranucleotide frequency in representing a relevant organism specific signal. Further improvement in binning accuracy is given by semi-supervised classification using OFDEG. The emphasis on a feature-driven, bottom-up approach to the problem of binning reveals promising avenues for future development of techniques to characterise short environmental sequences without bias toward cultivable organisms. PMID:19958473

A consensus embedding approach for segmentation of high resolution in vivo prostate magnetic resonance imagery

NASA Astrophysics Data System (ADS)

Viswanath, Satish; Rosen, Mark; Madabhushi, Anant

2008-03-01

Current techniques for localization of prostatic adenocarcinoma (CaP) via blinded trans-rectal ultrasound biopsy are associated with a high false negative detection rate. While high resolution endorectal in vivo Magnetic Resonance (MR) prostate imaging has been shown to have improved contrast and resolution for CaP detection over ultrasound, similarity in intensity characteristics between benign and cancerous regions on MR images contribute to a high false positive detection rate. In this paper, we present a novel unsupervised segmentation method that employs manifold learning via consensus schemes for detection of cancerous regions from high resolution 1.5 Tesla (T) endorectal in vivo prostate MRI. A significant contribution of this paper is a method to combine multiple weak, lower-dimensional representations of high dimensional feature data in a way analogous to classifier ensemble schemes, and hence create a stable and accurate reduced dimensional representation. After correcting for MR image intensity artifacts, such as bias field inhomogeneity and intensity non-standardness, our algorithm extracts over 350 3D texture features at every spatial location in the MR scene at multiple scales and orientations. Non-linear dimensionality reduction schemes such as Locally Linear Embedding (LLE) and Graph Embedding (GE) are employed to create multiple low dimensional data representations of this high dimensional texture feature space. Our novel consensus embedding method is used to average object adjacencies from within the multiple low dimensional projections so that class relationships are preserved. Unsupervised consensus clustering is then used to partition the objects in this consensus embedding space into distinct classes. Quantitative evaluation on 18 1.5 T prostate MR data against corresponding histology obtained from the multi-site ACRIN trials show a sensitivity of 92.65% and a specificity of 82.06%, which suggests that our method is successfully able to detect suspicious regions in the prostate.

Detection of relationships among multi-modal brain imaging meta-features via information flow.

PubMed

Miller, Robyn L; Vergara, Victor M; Calhoun, Vince D

2018-01-15

Neuroscientists and clinical researchers are awash in data from an ever-growing number of imaging and other bio-behavioral modalities. This flow of brain imaging data, taken under resting and various task conditions, combines with available cognitive measures, behavioral information, genetic data plus other potentially salient biomedical and environmental information to create a rich but diffuse data landscape. The conditions being studied with brain imaging data are often extremely complex and it is common for researchers to employ more than one imaging, behavioral or biological data modality (e.g., genetics) in their investigations. While the field has advanced significantly in its approach to multimodal data, the vast majority of studies still ignore joint information among two or more features or modalities. We propose an intuitive framework based on conditional probabilities for understanding information exchange between features in what we are calling a feature meta-space; that is, a space consisting of many individual featurae spaces. Features can have any dimension and can be drawn from any data source or modality. No a priori assumptions are made about the functional form (e.g., linear, polynomial, exponential) of captured inter-feature relationships. We demonstrate the framework's ability to identify relationships between disparate features of varying dimensionality by applying it to a large multi-site, multi-modal clinical dataset, balance between schizophrenia patients and controls. In our application it exposes both expected (previously observed) relationships, and novel relationships rarely considered investigated by clinical researchers. To the best of our knowledge there is not presently a comparably efficient way to capture relationships of indeterminate functional form between features of arbitrary dimension and type. We are introducing this method as an initial foray into a space that remains relatively underpopulated. The framework we propose is powerful, intuitive and very efficiently provides a high-level overview of a massive data space. In our application it exposes both expected relationships and relationships very rarely considered worth investigating by clinical researchers. Copyright © 2017 Elsevier B.V. All rights reserved.

Vacuum solutions of five dimensional Einstein equations generated by inverse scattering method. II. Production of the black ring solution

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

Tomizawa, Shinya; Nozawa, Masato

2006-06-15

We study vacuum solutions of five-dimensional Einstein equations generated by the inverse scattering method. We reproduce the black ring solution which was found by Emparan and Reall by taking the Euclidean Levi-Civita metric plus one-dimensional flat space as a seed. This transformation consists of two successive processes; the first step is to perform the three-solitonic transformation of the Euclidean Levi-Civita metric with one-dimensional flat space as a seed. The resulting metric is the Euclidean C-metric with extra one-dimensional flat space. The second is to perform the two-solitonic transformation by taking it as a new seed. Our result may serve asmore » a stepping stone to find new exact solutions in higher dimensions.« less

SUTRA (Saturated-Unsaturated Transport). A Finite-Element Simulation Model for Saturated-Unsaturated, Fluid-Density-Dependent Ground-Water Flow with Energy Transport or Chemically-Reactive Single-Species Solute Transport.

DTIC Science & Technology

1984-12-30

as three dimensional, when the assumption is made that all SUTRA parameters and coefficients have a constant value in the third space direction. A...finite element. The type of element employed by SUTRA for two-dimensional simulation is a quadrilateral which has a finite thickness in the third ... space dimension. This type of a quad- rilateral element and a typical two-dimensional mesh is shown in Figure 3.1. - All twelve edges of the two

Intelligent Control of a Sensor-Actuator System via Kernelized Least-Squares Policy Iteration

PubMed Central

Liu, Bo; Chen, Sanfeng; Li, Shuai; Liang, Yongsheng

2012-01-01

In this paper a new framework, called Compressive Kernelized Reinforcement Learning (CKRL), for computing near-optimal policies in sequential decision making with uncertainty is proposed via incorporating the non-adaptive data-independent Random Projections and nonparametric Kernelized Least-squares Policy Iteration (KLSPI). Random Projections are a fast, non-adaptive dimensionality reduction framework in which high-dimensionality data is projected onto a random lower-dimension subspace via spherically random rotation and coordination sampling. KLSPI introduce kernel trick into the LSPI framework for Reinforcement Learning, often achieving faster convergence and providing automatic feature selection via various kernel sparsification approaches. In this approach, policies are computed in a low-dimensional subspace generated by projecting the high-dimensional features onto a set of random basis. We first show how Random Projections constitute an efficient sparsification technique and how our method often converges faster than regular LSPI, while at lower computational costs. Theoretical foundation underlying this approach is a fast approximation of Singular Value Decomposition (SVD). Finally, simulation results are exhibited on benchmark MDP domains, which confirm gains both in computation time and in performance in large feature spaces. PMID:22736969

Groupwise registration of cardiac perfusion MRI sequences using normalized mutual information in high dimension

NASA Astrophysics Data System (ADS)

Hamrouni, Sameh; Rougon, Nicolas; Pr"teux, Françoise

2011-03-01

In perfusion MRI (p-MRI) exams, short-axis (SA) image sequences are captured at multiple slice levels along the long-axis of the heart during the transit of a vascular contrast agent (Gd-DTPA) through the cardiac chambers and muscle. Compensating cardio-thoracic motions is a requirement for enabling computer-aided quantitative assessment of myocardial ischaemia from contrast-enhanced p-MRI sequences. The classical paradigm consists of registering each sequence frame on a reference image using some intensity-based matching criterion. In this paper, we introduce a novel unsupervised method for the spatio-temporal groupwise registration of cardiac p-MRI exams based on normalized mutual information (NMI) between high-dimensional feature distributions. Here, local contrast enhancement curves are used as a dense set of spatio-temporal features, and statistically matched through variational optimization to a target feature distribution derived from a registered reference template. The hard issue of probability density estimation in high-dimensional state spaces is bypassed by using consistent geometric entropy estimators, allowing NMI to be computed directly from feature samples. Specifically, a computationally efficient kth-nearest neighbor (kNN) estimation framework is retained, leading to closed-form expressions for the gradient flow of NMI over finite- and infinite-dimensional motion spaces. This approach is applied to the groupwise alignment of cardiac p-MRI exams using a free-form Deformation (FFD) model for cardio-thoracic motions. Experiments on simulated and natural datasets suggest its accuracy and robustness for registering p-MRI exams comprising more than 30 frames.

Online feature selection with streaming features.

PubMed

Wu, Xindong; Yu, Kui; Ding, Wei; Wang, Hao; Zhu, Xingquan

2013-05-01

We propose a new online feature selection framework for applications with streaming features where the knowledge of the full feature space is unknown in advance. We define streaming features as features that flow in one by one over time whereas the number of training examples remains fixed. This is in contrast with traditional online learning methods that only deal with sequentially added observations, with little attention being paid to streaming features. The critical challenges for Online Streaming Feature Selection (OSFS) include 1) the continuous growth of feature volumes over time, 2) a large feature space, possibly of unknown or infinite size, and 3) the unavailability of the entire feature set before learning starts. In the paper, we present a novel Online Streaming Feature Selection method to select strongly relevant and nonredundant features on the fly. An efficient Fast-OSFS algorithm is proposed to improve feature selection performance. The proposed algorithms are evaluated extensively on high-dimensional datasets and also with a real-world case study on impact crater detection. Experimental results demonstrate that the algorithms achieve better compactness and higher prediction accuracy than existing streaming feature selection algorithms.

Universal dynamical properties preclude standard clustering in a large class of biochemical data.

PubMed

Gomez, Florian; Stoop, Ralph L; Stoop, Ruedi

2014-09-01

Clustering of chemical and biochemical data based on observed features is a central cognitive step in the analysis of chemical substances, in particular in combinatorial chemistry, or of complex biochemical reaction networks. Often, for reasons unknown to the researcher, this step produces disappointing results. Once the sources of the problem are known, improved clustering methods might revitalize the statistical approach of compound and reaction search and analysis. Here, we present a generic mechanism that may be at the origin of many clustering difficulties. The variety of dynamical behaviors that can be exhibited by complex biochemical reactions on variation of the system parameters are fundamental system fingerprints. In parameter space, shrimp-like or swallow-tail structures separate parameter sets that lead to stable periodic dynamical behavior from those leading to irregular behavior. We work out the genericity of this phenomenon and demonstrate novel examples for their occurrence in realistic models of biophysics. Although we elucidate the phenomenon by considering the emergence of periodicity in dependence on system parameters in a low-dimensional parameter space, the conclusions from our simple setting are shown to continue to be valid for features in a higher-dimensional feature space, as long as the feature-generating mechanism is not too extreme and the dimension of this space is not too high compared with the amount of available data. For online versions of super-paramagnetic clustering see http://stoop.ini.uzh.ch/research/clustering. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

ProteinShader: illustrative rendering of macromolecules

PubMed Central

Weber, Joseph R

2009-01-01

Background Cartoon-style illustrative renderings of proteins can help clarify structural features that are obscured by space filling or balls and sticks style models, and recent advances in programmable graphics cards offer many new opportunities for improving illustrative renderings. Results The ProteinShader program, a new tool for macromolecular visualization, uses information from Protein Data Bank files to produce illustrative renderings of proteins that approximate what an artist might create by hand using pen and ink. A combination of Hermite and spherical linear interpolation is used to draw smooth, gradually rotating three-dimensional tubes and ribbons with a repeating pattern of texture coordinates, which allows the application of texture mapping, real-time halftoning, and smooth edge lines. This free platform-independent open-source program is written primarily in Java, but also makes extensive use of the OpenGL Shading Language to modify the graphics pipeline. Conclusion By programming to the graphics processor unit, ProteinShader is able to produce high quality images and illustrative rendering effects in real-time. The main feature that distinguishes ProteinShader from other free molecular visualization tools is its use of texture mapping techniques that allow two-dimensional images to be mapped onto the curved three-dimensional surfaces of ribbons and tubes with minimum distortion of the images. PMID:19331660

Visualization of 3-D tensor fields

NASA Technical Reports Server (NTRS)

Hesselink, L.

1996-01-01

Second-order tensor fields have applications in many different areas of physics, such as general relativity and fluid mechanics. The wealth of multivariate information in tensor fields makes them more complex and abstract than scalar and vector fields. Visualization is a good technique for scientists to gain new insights from them. Visualizing a 3-D continuous tensor field is equivalent to simultaneously visualizing its three eigenvector fields. In the past, research has been conducted in the area of two-dimensional tensor fields. It was shown that degenerate points, defined as points where eigenvalues are equal to each other, are the basic singularities underlying the topology of tensor fields. Moreover, it was shown that eigenvectors never cross each other except at degenerate points. Since we live in a three-dimensional world, it is important for us to understand the underlying physics of this world. In this report, we describe a new method for locating degenerate points along with the conditions for classifying them in three-dimensional space. Finally, we discuss some topological features of three-dimensional tensor fields, and interpret topological patterns in terms of physical properties.

Three-dimensional face model reproduction method using multiview images

NASA Astrophysics Data System (ADS)

Nagashima, Yoshio; Agawa, Hiroshi; Kishino, Fumio

1991-11-01

This paper describes a method of reproducing three-dimensional face models using multi-view images for a virtual space teleconferencing system that achieves a realistic visual presence for teleconferencing. The goal of this research, as an integral component of a virtual space teleconferencing system, is to generate a three-dimensional face model from facial images, synthesize images of the model virtually viewed from different angles, and with natural shadow to suit the lighting conditions of the virtual space. The proposed method is as follows: first, front and side view images of the human face are taken by TV cameras. The 3D data of facial feature points are obtained from front- and side-views by an image processing technique based on the color, shape, and correlation of face components. Using these 3D data, the prepared base face models, representing typical Japanese male and female faces, are modified to approximate the input facial image. The personal face model, representing the individual character, is then reproduced. Next, an oblique view image is taken by TV camera. The feature points of the oblique view image are extracted using the same image processing technique. A more precise personal model is reproduced by fitting the boundary of the personal face model to the boundary of the oblique view image. The modified boundary of the personal face model is determined by using face direction, namely rotation angle, which is detected based on the extracted feature points. After the 3D model is established, the new images are synthesized by mapping facial texture onto the model.

A hardware-algorithm co-design approach to optimize seizure detection algorithms for implantable applications.

PubMed

Raghunathan, Shriram; Gupta, Sumeet K; Markandeya, Himanshu S; Roy, Kaushik; Irazoqui, Pedro P

2010-10-30

Implantable neural prostheses that deliver focal electrical stimulation upon demand are rapidly emerging as an alternate therapy for roughly a third of the epileptic patient population that is medically refractory. Seizure detection algorithms enable feedback mechanisms to provide focally and temporally specific intervention. Real-time feasibility and computational complexity often limit most reported detection algorithms to implementations using computers for bedside monitoring or external devices communicating with the implanted electrodes. A comparison of algorithms based on detection efficacy does not present a complete picture of the feasibility of the algorithm with limited computational power, as is the case with most battery-powered applications. We present a two-dimensional design optimization approach that takes into account both detection efficacy and hardware cost in evaluating algorithms for their feasibility in an implantable application. Detection features are first compared for their ability to detect electrographic seizures from micro-electrode data recorded from kainate-treated rats. Circuit models are then used to estimate the dynamic and leakage power consumption of the compared features. A score is assigned based on detection efficacy and the hardware cost for each of the features, then plotted on a two-dimensional design space. An optimal combination of compared features is used to construct an algorithm that provides maximal detection efficacy per unit hardware cost. The methods presented in this paper would facilitate the development of a common platform to benchmark seizure detection algorithms for comparison and feasibility analysis in the next generation of implantable neuroprosthetic devices to treat epilepsy. Copyright © 2010 Elsevier B.V. All rights reserved.

Efficient analysis of three dimensional EUV mask induced imaging artifacts using the waveguide decomposition method

NASA Astrophysics Data System (ADS)

Shao, Feng; Evanschitzky, Peter; Fühner, Tim; Erdmann, Andreas

2009-10-01

This paper employs the Waveguide decomposition method as an efficient rigorous electromagnetic field (EMF) solver to investigate three dimensional mask-induced imaging artifacts in EUV lithography. The major mask diffraction induced imaging artifacts are first identified by applying the Zernike analysis of the mask nearfield spectrum of 2D lines/spaces. Three dimensional mask features like 22nm semidense/dense contacts/posts, isolated elbows and line-ends are then investigated in terms of lithographic results. After that, the 3D mask-induced imaging artifacts such as feature orientation dependent best focus shift, process window asymmetries, and other aberration-like phenomena are explored for the studied mask features. The simulation results can help lithographers to understand the reasons of EUV-specific imaging artifacts and to devise illumination and feature dependent strategies for their compensation in the optical proximity correction (OPC) for EUV masks. At last, an efficient approach using the Zernike analysis together with the Waveguide decomposition technique is proposed to characterize the impact of mask properties for the future OPC process.

Graph theory approach to the eigenvalue problem of large space structures

NASA Technical Reports Server (NTRS)

Reddy, A. S. S. R.; Bainum, P. M.

1981-01-01

Graph theory is used to obtain numerical solutions to eigenvalue problems of large space structures (LSS) characterized by a state vector of large dimensions. The LSS are considered as large, flexible systems requiring both orientation and surface shape control. Graphic interpretation of the determinant of a matrix is employed to reduce a higher dimensional matrix into combinations of smaller dimensional sub-matrices. The reduction is implemented by means of a Boolean equivalent of the original matrices formulated to obtain smaller dimensional equivalents of the original numerical matrix. Computation time becomes less and more accurate solutions are possible. An example is provided in the form of a free-free square plate. Linearized system equations and numerical values of a stiffness matrix are presented, featuring a state vector with 16 components.

Structural and magnetic characterization of the one-dimensional S = 5/2 antiferromagnetic chain system SrMn(VO 4)(OH)

DOE PAGES

Sanjeewa, Liurukara D.; Garlea, Vasile O.; McGuire, Michael A.; ...

2016-06-06

The descloizite-type compound, SrMn(VO 4)(OH), was synthesized as large single crystals (1-2mm) using a high-temperature high-pressure hydrothermal technique. X-ray single crystal structure analysis reveals that the material crystallizes in the acentric orthorhombic space group of P2 12 12 1 (no. 19), Z = 4. The structure exhibits a one-dimensional feature, with [MnO 4] chains propagating along the a-axis which are interconnected by VO 4 tetrahedra. Raman and infrared spectra were obtained to identify the fundamental vanadate and hydroxide vibrational modes. Magnetization data reveal a broad maximum at approximately 80 K, arising from one-dimensional magnetic correlations with intrachain exchange constant ofmore » J/k B = 9.97(3) K between nearest Mn neighbors and a canted antiferromagnetic behavior below T N = 30 K. Single crystal neutron diffraction at 4 K yielded a magnetic structure solution in the lower symmetry of the magnetic space group P2 1 with two unique chains displaying antiferromagnetically ordered Mn moments oriented nearly perpendicular to the chain axis. Lastly, the presence of the Dzyaloshinskii Moriya antisymmetric exchange interaction leads to a slight canting of the spins and gives rise to a weak ferromagnetic component along the chain direction.« less

Methods of editing cloud and atmospheric layer affected pixels from satellite data

NASA Technical Reports Server (NTRS)

Nixon, P. R. (Principal Investigator); Wiegand, C. L.; Richardson, A. J.; Johnson, M. P.

1981-01-01

Plotted transects made from south Texas daytime HCMM data show the effect of subvisible cirrus (SCI) clouds in the emissive (IR) band but the effect is unnoticable in the reflective (VIS) band. The depression of satellite indicated temperatures ws greatest in the center of SCi streamers and tapered off at the edges. Pixels of uncontaminated land and water features in the HCMM test area shared identical VIS and IR digital count combinations with other pixels representing similar features. A minimum of 0.015 percent repeats of identical VIS-IR combinations are characteristic of land and water features in a scene of 30 percent cloud cover. This increases to 0.021 percent of more when the scene is clear. Pixels having shared VIS-IR combinations less than these amounts are considered to be cloud contaminated in the cluster screening method. About twenty percent of SCi was machine indistinguishable from land features in two dimensional spectral space (VIS vs IR).

Velocity Field of the McMurdo Shear Zone from Annual Three-Dimensional Ground Penetrating Radar Imaging and Crevasse Matching

NASA Astrophysics Data System (ADS)

Ray, L.; Jordan, M.; Arcone, S. A.; Kaluzienski, L. M.; Koons, P. O.; Lever, J.; Walker, B.; Hamilton, G. S.

2017-12-01

The McMurdo Shear Zone (MSZ) is a narrow, intensely crevassed strip tens of km long separating the Ross and McMurdo ice shelves (RIS and MIS) and an important pinning feature for the RIS. We derive local velocity fields within the MSZ from two consecutive annual ground penetrating radar (GPR) datasets that reveal complex firn and marine ice crevassing; no englacial features are evident. The datasets were acquired in 2014 and 2015 using robot-towed 400 MHz and 200 MHz GPR over a 5 km x 5.7 km grid. 100 west-to-east transects at 50 m spacing provide three-dimensional maps that reveal the length of many firn crevasses, and their year-to-year structural evolution. Hand labeling of crevasse cross sections near the MSZ western and eastern boundaries reveal matching firn and marine ice crevasses, and more complex and chaotic features between these boundaries. By matching crevasse features from year to year both on the eastern and western boundaries and within the chaotic region, marine ice crevasses along the western and eastern boundaries are shown to align directly with firn crevasses, and the local velocity field is estimated and compared with data from strain rate surveys and remote sensing. While remote sensing provides global velocity fields, crevasse matching indicates greater local complexity attributed to faulting, folding, and rotation.

Local Gram-Schmidt and covariant Lyapunov vectors and exponents for three harmonic oscillator problems

NASA Astrophysics Data System (ADS)

Hoover, Wm. G.; Hoover, Carol G.

2012-02-01

We compare the Gram-Schmidt and covariant phase-space-basis-vector descriptions for three time-reversible harmonic oscillator problems, in two, three, and four phase-space dimensions respectively. The two-dimensional problem can be solved analytically. The three-dimensional and four-dimensional problems studied here are simultaneously chaotic, time-reversible, and dissipative. Our treatment is intended to be pedagogical, for use in an updated version of our book on Time Reversibility, Computer Simulation, and Chaos. Comments are very welcome.

Multimaterial magnetically assisted 3D printing of composite materials.

PubMed

Kokkinis, Dimitri; Schaffner, Manuel; Studart, André R

2015-10-23

3D printing has become commonplace for the manufacturing of objects with unusual geometries. Recent developments that enabled printing of multiple materials indicate that the technology can potentially offer a much wider design space beyond unusual shaping. Here we show that a new dimension in this design space can be exploited through the control of the orientation of anisotropic particles used as building blocks during a direct ink-writing process. Particle orientation control is demonstrated by applying low magnetic fields on deposited inks pre-loaded with magnetized stiff platelets. Multimaterial dispensers and a two-component mixing unit provide additional control over the local composition of the printed material. The five-dimensional design space covered by the proposed multimaterial magnetically assisted 3D printing platform (MM-3D printing) opens the way towards the manufacturing of functional heterogeneous materials with exquisite microstructural features thus far only accessible by biological materials grown in nature.

Direct imaging of atomic-scale ripples in few-layer graphene.

PubMed

Wang, Wei L; Bhandari, Sagar; Yi, Wei; Bell, David C; Westervelt, Robert; Kaxiras, Efthimios

2012-05-09

Graphene has been touted as the prototypical two-dimensional solid of extraordinary stability and strength. However, its very existence relies on out-of-plane ripples as predicted by theory and confirmed by experiments. Evidence of the intrinsic ripples has been reported in the form of broadened diffraction spots in reciprocal space, in which all spatial information is lost. Here we show direct real-space images of the ripples in a few-layer graphene (FLG) membrane resolved at the atomic scale using monochromated aberration-corrected transmission electron microscopy (TEM). The thickness of FLG amplifies the weak local effects of the ripples, resulting in spatially varying TEM contrast that is unique up to inversion symmetry. We compare the characteristic TEM contrast with simulated images based on accurate first-principles calculations of the scattering potential. Our results characterize the ripples in real space and suggest that such features are likely common in ultrathin materials, even in the nanometer-thickness range.

Resolving runaway electron distributions in space, time, and energy

NASA Astrophysics Data System (ADS)

Paz-Soldan, C.; Cooper, C. M.; Aleynikov, P.; Eidietis, N. W.; Lvovskiy, A.; Pace, D. C.; Brennan, D. P.; Hollmann, E. M.; Liu, C.; Moyer, R. A.; Shiraki, D.

2018-05-01

Areas of agreement and disagreement with present-day models of runaway electron (RE) evolution are revealed by measuring MeV-level bremsstrahlung radiation from runaway electrons (REs) with a pinhole camera. Spatially resolved measurements localize the RE beam, reveal energy-dependent RE transport, and can be used to perform full two-dimensional (energy and pitch-angle) inversions of the RE phase-space distribution. Energy-resolved measurements find qualitative agreement with modeling on the role of collisional and synchrotron damping in modifying the RE distribution shape. Measurements are consistent with predictions of phase-space attractors that accumulate REs, with non-monotonic features observed in the distribution. Temporally resolved measurements find qualitative agreement with modeling on the impact of collisional and synchrotron damping in varying the RE growth and decay rate. Anomalous RE loss is observed and found to be largest at low energy. Possible roles for kinetic instability or spatial transport to resolve these anomalies are discussed.

Multimaterial magnetically assisted 3D printing of composite materials

NASA Astrophysics Data System (ADS)

Kokkinis, Dimitri; Schaffner, Manuel; Studart, André R.

2015-10-01

3D printing has become commonplace for the manufacturing of objects with unusual geometries. Recent developments that enabled printing of multiple materials indicate that the technology can potentially offer a much wider design space beyond unusual shaping. Here we show that a new dimension in this design space can be exploited through the control of the orientation of anisotropic particles used as building blocks during a direct ink-writing process. Particle orientation control is demonstrated by applying low magnetic fields on deposited inks pre-loaded with magnetized stiff platelets. Multimaterial dispensers and a two-component mixing unit provide additional control over the local composition of the printed material. The five-dimensional design space covered by the proposed multimaterial magnetically assisted 3D printing platform (MM-3D printing) opens the way towards the manufacturing of functional heterogeneous materials with exquisite microstructural features thus far only accessible by biological materials grown in nature.

Non-reciprocal elastic wave propagation in 2D phononic membranes with spatiotemporally varying material properties

NASA Astrophysics Data System (ADS)

Attarzadeh, M. A.; Nouh, M.

2018-05-01

One-dimensional phononic materials with material fields traveling simultaneously in space and time have been shown to break elastodynamic reciprocity resulting in unique wave propagation features. In the present work, a comprehensive mathematical analysis is presented to characterize and fully predict the non-reciprocal wave dispersion in two-dimensional space. The analytical dispersion relations, in the presence of the spatiotemporal material variations, are validated numerically using finite 2D membranes with a prescribed number of cells. Using omnidirectional excitations at the membrane's center, wave propagations are shown to exhibit directional asymmetry that increases drastically in the direction of the material travel and vanishes in the direction perpendicular to it. The topological nature of the predicted dispersion in different propagation directions are evaluated using the computed Chern numbers. Finally, the degree of the 2D non-reciprocity is quantified using a non-reciprocity index (NRI) which confirms the theoretical dispersion predictions as well as the finite simulations. The presented framework can be extended to plate-type structures as well as 3D spatiotemporally modulated phononic crystals.

SLLE for predicting membrane protein types.

PubMed

Wang, Meng; Yang, Jie; Xu, Zhi-Jie; Chou, Kuo-Chen

2005-01-07

Introduction of the concept of pseudo amino acid composition (PROTEINS: Structure, Function, and Genetics 43 (2001) 246; Erratum: ibid. 44 (2001) 60) has made it possible to incorporate a considerable amount of sequence-order effects by representing a protein sample in terms of a set of discrete numbers, and hence can significantly enhance the prediction quality of membrane protein type. As a continuous effort along such a line, the Supervised Locally Linear Embedding (SLLE) technique for nonlinear dimensionality reduction is introduced (Science 22 (2000) 2323). The advantage of using SLLE is that it can reduce the operational space by extracting the essential features from the high-dimensional pseudo amino acid composition space, and that the cluster-tolerant capacity can be increased accordingly. As a consequence by combining these two approaches, high success rates have been observed during the tests of self-consistency, jackknife and independent data set, respectively, by using the simplest nearest neighbour classifier. The current approach represents a new strategy to deal with the problems of protein attribute prediction, and hence may become a useful vehicle in the area of bioinformatics and proteomics.

Turtle 24.0 diffusion depletion code

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

Altomare, S.; Barry, R.F.

1971-09-01

TURTLE is a two-group, two-dimensional (x-y, x-z, r-z) neutron diffusion code featuring a direct treatment of the nonlinear effects of xenon, enthalpy, and Doppler. Fuel depletion is allowed. TURTLE was written for the study of azimuthal xenon oscillations, but the code is useful for general analysis. The input is simple, fuel management is handled directly, and a boron criticality search is allowed. Ten thousand space points are allowed (over 20,000 with diagonal symmetry). TURTLE is written in FORTRAN IV and is tailored for the present CDC-6600. The program is corecontained. Provision is made to save data on tape for futuremore » reference. ( auth)« less

The effects of mental representation on performance in a navigation task

NASA Technical Reports Server (NTRS)

Barshi, Immanuel; Healy, Alice F.

2002-01-01

In three experiments, we investigated the mental representations employed when instructions were followed that involved navigation in a space displayed as a grid on a computer screen. Performance was affected much more by the number of instructional units than by the number of words per unit. Performance in a three-dimensional space was independent of the number of dimensions along which participants navigated. However, memory for and accuracy in following the instructions were reduced when the task required mentally representing a three-dimensional space, as compared with representing a two-dimensional space, although the words used in the instructions were identical in the two cases. These results demonstrate the interdependence of verbal and spatial memory representations, because individuals' immediate memory for verbal navigation instructions is affected by their mental representation of the space referred to by the instructions.

Unique Zigzag-Shaped Buckling Zn2C Monolayer with Strain-Tunable Band Gap and Negative Poisson Ratio.

PubMed

Meng, Lingbiao; Zhang, Yingjuan; Zhou, Minjie; Zhang, Jicheng; Zhou, Xiuwen; Ni, Shuang; Wu, Weidong

2018-02-19

Designing new materials with reduced dimensionality and distinguished properties has continuously attracted intense interest for materials innovation. Here we report a novel two-dimensional (2D) Zn 2 C monolayer nanomaterial with exceptional structure and properties by means of first-principles calculations. This new Zn 2 C monolayer is composed of quasi-tetrahedral tetracoordinate carbon and quasi-linear bicoordinate zinc, featuring a peculiar zigzag-shaped buckling configuration. The unique coordinate topology endows this natural 2D semiconducting monolayer with strongly strain tunable band gap and unusual negative Poisson ratios. The monolayer has good dynamic and thermal stabilities and is also the lowest-energy structure of 2D space indicated by the particle-swarm optimization (PSO) method, implying its synthetic feasibility. With these intriguing properties the material may find applications in nanoelectronics and micromechanics.

Stern-Gerlach dynamics with quantum propagators

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

Hsu, Bailey C.; Berrondo, Manuel; Van Huele, Jean-Francois S.

2011-01-15

We study the quantum dynamics of a nonrelativistic neutral particle with spin in inhomogeneous external magnetic fields. We first consider fields with one-dimensional inhomogeneities, both unphysical and physical, and construct the corresponding analytic propagators. We then consider fields with two-dimensional inhomogeneities and develop an appropriate numerical propagation method. We propagate initial states exhibiting different degrees of space localization and various initial spin configurations, including both pure and mixed spin states. We study the evolution of their spin densities and identify characteristic features of spin density dynamics, such as the spatial separation of spin components, and spin localization or accumulation. Wemore » compare our approach and our results with the coverage of the Stern-Gerlach effect in the literature, and we focus on nonstandard Stern-Gerlach outcomes, such as radial separation, spin focusing, spin oscillation, and spin flipping.« less

Online signature recognition using principal component analysis and artificial neural network

NASA Astrophysics Data System (ADS)

Hwang, Seung-Jun; Park, Seung-Je; Baek, Joong-Hwan

2016-12-01

In this paper, we propose an algorithm for on-line signature recognition using fingertip point in the air from the depth image acquired by Kinect. We extract 10 statistical features from X, Y, Z axis, which are invariant to changes in shifting and scaling of the signature trajectories in three-dimensional space. Artificial neural network is adopted to solve the complex signature classification problem. 30 dimensional features are converted into 10 principal components using principal component analysis, which is 99.02% of total variances. We implement the proposed algorithm and test to actual on-line signatures. In experiment, we verify the proposed method is successful to classify 15 different on-line signatures. Experimental result shows 98.47% of recognition rate when using only 10 feature vectors.

Cosmic strings: Gravitation without local curvature

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

Helliwell, T.M.; Konkowski, D.A.

1987-05-01

Cosmic strings are very long, thin structures which might stretch over vast reaches of the universe. If they exist, they would have been formed during phase transitions in the very early universe. The space-time surrounding a straight cosmic string is flat but nontrivial: A two-dimensional spatial section is a cone rather than a plane. This feature leads to unique gravitational effects. The flatness of the cone means that many of the gravitational effects can be understood with no mathematics beyond trigonometry. This includes the observational predictions of the double imaging of quasars and the truncation of the images of galaxies.

Dimensional reduction for a SIR type model

NASA Astrophysics Data System (ADS)

Cahyono, Edi; Soeharyadi, Yudi; Mukhsar

2018-03-01

Epidemic phenomena are often modeled in the form of dynamical systems. Such model has also been used to model spread of rumor, spread of extreme ideology, and dissemination of knowledge. Among the simplest is SIR (susceptible, infected and recovered) model, a model that consists of three compartments, and hence three variables. The variables are functions of time which represent the number of subpopulations, namely suspect, infected and recovery. The sum of the three is assumed to be constant. Hence, the model is actually two dimensional which sits in three-dimensional ambient space. This paper deals with the reduction of a SIR type model into two variables in two-dimensional ambient space to understand the geometry and dynamics better. The dynamics is studied, and the phase portrait is presented. The two dimensional model preserves the equilibrium and the stability. The model has been applied for knowledge dissemination, which has been the interest of knowledge management.

Complexity of free energy landscapes of peptides revealed by nonlinear principal component analysis.

PubMed

Nguyen, Phuong H

2006-12-01

Employing the recently developed hierarchical nonlinear principal component analysis (NLPCA) method of Saegusa et al. (Neurocomputing 2004;61:57-70 and IEICE Trans Inf Syst 2005;E88-D:2242-2248), the complexities of the free energy landscapes of several peptides, including triglycine, hexaalanine, and the C-terminal beta-hairpin of protein G, were studied. First, the performance of this NLPCA method was compared with the standard linear principal component analysis (PCA). In particular, we compared two methods according to (1) the ability of the dimensionality reduction and (2) the efficient representation of peptide conformations in low-dimensional spaces spanned by the first few principal components. The study revealed that NLPCA reduces the dimensionality of the considered systems much better, than did PCA. For example, in order to get the similar error, which is due to representation of the original data of beta-hairpin in low dimensional space, one needs 4 and 21 principal components of NLPCA and PCA, respectively. Second, by representing the free energy landscapes of the considered systems as a function of the first two principal components obtained from PCA, we obtained the relatively well-structured free energy landscapes. In contrast, the free energy landscapes of NLPCA are much more complicated, exhibiting many states which are hidden in the PCA maps, especially in the unfolded regions. Furthermore, the study also showed that many states in the PCA maps are mixed up by several peptide conformations, while those of the NLPCA maps are more pure. This finding suggests that the NLPCA should be used to capture the essential features of the systems. (c) 2006 Wiley-Liss, Inc.

A teleoperation training simulator with visual and kinesthetic force virtual reality

NASA Technical Reports Server (NTRS)

Kim, Won S.; Schenker, Paul

1992-01-01

A force-reflecting teleoperation training simulator with a high-fidelity real-time graphics display has been developed for operator training. A novel feature of this simulator is that it enables the operator to feel contact forces and torques through a force-reflecting controller during the execution of the simulated peg-in-hole task, providing the operator with the feel of visual and kinesthetic force virtual reality. A peg-in-hole task is used in our simulated teleoperation trainer as a generic teleoperation task. A quasi-static analysis of a two-dimensional peg-in-hole task model has been extended to a three-dimensional model analysis to compute contact forces and torques for a virtual realization of kinesthetic force feedback. The simulator allows the user to specify force reflection gains and stiffness (compliance) values of the manipulator hand for both the three translational and the three rotational axes in Cartesian space. Three viewing modes are provided for graphics display: single view, two split views, and stereoscopic view.

Space Radar Image of Long Valley, California in 3-D

NASA Image and Video Library

1999-05-01

This three-dimensional perspective view of Long Valley, California was created from data taken by the Spaceborne Imaging Radar-C/X-band Synthetic Aperture Radar on board the space shuttle Endeavour. This image was constructed by overlaying a color composite SIR-C radar image on a digital elevation map. The digital elevation map was produced using radar interferometry, a process by which radar data are acquired on different passes of the space shuttle. The two data passes are compared to obtain elevation information. The interferometry data were acquired on April 13,1994 and on October 3, 1994, during the first and second flights of the SIR-C/X-SAR instrument. The color composite radar image was taken in October and was produced by assigning red to the C-band (horizontally transmitted and vertically received) polarization; green to the C-band (vertically transmitted and received) polarization; and blue to the ratio of the two data sets. Blue areas in the image are smooth and yellow areas are rock outcrops with varying amounts of snow and vegetation. The view is looking north along the northeastern edge of the Long Valley caldera, a volcanic collapse feature created 750,000 years ago and the site of continued subsurface activity. Crowley Lake is the large dark feature in the foreground. http://photojournal.jpl.nasa.gov/catalog/PIA01769

Two-dimensional electromagnetic Child-Langmuir law of a short-pulse electron flow

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

Chen, S. H.; Tai, L. C.; Liu, Y. L.

Two-dimensional electromagnetic particle-in-cell simulations were performed to study the effect of the displacement current and the self-magnetic field on the space charge limited current density or the Child-Langmuir law of a short-pulse electron flow with a propagation distance of {zeta} and an emitting width of W from the classical regime to the relativistic regime. Numerical scaling of the two-dimensional electromagnetic Child-Langmuir law was constructed and it scales with ({zeta}/W) and ({zeta}/W){sup 2} at the classical and relativistic regimes, respectively. Our findings reveal that the displacement current can considerably enhance the space charge limited current density as compared to the well-knownmore » two-dimensional electrostatic Child-Langmuir law even at the classical regime.« less

Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics

NASA Astrophysics Data System (ADS)

Wehmeyer, Christoph; Noé, Frank

2018-06-01

Inspired by the success of deep learning techniques in the physical and chemical sciences, we apply a modification of an autoencoder type deep neural network to the task of dimension reduction of molecular dynamics data. We can show that our time-lagged autoencoder reliably finds low-dimensional embeddings for high-dimensional feature spaces which capture the slow dynamics of the underlying stochastic processes—beyond the capabilities of linear dimension reduction techniques.

Two-dimensional Manifold with Point-like Defects

NASA Astrophysics Data System (ADS)

Gani, V. A.; Dmitriev, A. E.; Rubin, S. G.

We study a class of two-dimensional compact extra spaces isomorphic to the sphere S 2 in the framework of multidimensional gravitation. We show that there exists a family of stationary metrics that depend on the initial (boundary) conditions. All these geometries have a singular point. We also discuss the possibility for these deformed extra spaces to be considered as dark matter candidates.

Mapping the fundamental niches of two freshwater microalgae, Chlorella vulgaris (Trebouxiophyceae) and Peridinium cinctum (Dinophyceae), in 5-dimensional ion space

USDA-ARS?s Scientific Manuscript database

A five dimensional experimental design, i.e. a five component ion mixture design for nitrate, phosphate, potassium, sodium and chloride projected across a total ion concentration gradient of 1-30 mM was utilized to map the ion-based, scenopoetic, or ‘Grinnellian’, niche space for two freshwater alga...

Two-dimensional wavelet transform feature extraction for porous silicon chemical sensors.

PubMed

Murguía, José S; Vergara, Alexander; Vargas-Olmos, Cecilia; Wong, Travis J; Fonollosa, Jordi; Huerta, Ramón

2013-06-27

Designing reliable, fast responding, highly sensitive, and low-power consuming chemo-sensory systems has long been a major goal in chemo-sensing. This goal, however, presents a difficult challenge because having a set of chemo-sensory detectors exhibiting all these aforementioned ideal conditions are still largely un-realizable to-date. This paper presents a unique perspective on capturing more in-depth insights into the physicochemical interactions of two distinct, selectively chemically modified porous silicon (pSi) film-based optical gas sensors by implementing an innovative, based on signal processing methodology, namely the two-dimensional discrete wavelet transform. Specifically, the method consists of using the two-dimensional discrete wavelet transform as a feature extraction method to capture the non-stationary behavior from the bi-dimensional pSi rugate sensor response. Utilizing a comprehensive set of measurements collected from each of the aforementioned optically based chemical sensors, we evaluate the significance of our approach on a complex, six-dimensional chemical analyte discrimination/quantification task problem. Due to the bi-dimensional aspects naturally governing the optical sensor response to chemical analytes, our findings provide evidence that the proposed feature extractor strategy may be a valuable tool to deepen our understanding of the performance of optically based chemical sensors as well as an important step toward attaining their implementation in more realistic chemo-sensing applications. Copyright © 2013 Elsevier B.V. All rights reserved.

Lip-reading aids word recognition most in moderate noise: a Bayesian explanation using high-dimensional feature space.

PubMed

Ma, Wei Ji; Zhou, Xiang; Ross, Lars A; Foxe, John J; Parra, Lucas C

2009-01-01

Watching a speaker's facial movements can dramatically enhance our ability to comprehend words, especially in noisy environments. From a general doctrine of combining information from different sensory modalities (the principle of inverse effectiveness), one would expect that the visual signals would be most effective at the highest levels of auditory noise. In contrast, we find, in accord with a recent paper, that visual information improves performance more at intermediate levels of auditory noise than at the highest levels, and we show that a novel visual stimulus containing only temporal information does the same. We present a Bayesian model of optimal cue integration that can explain these conflicts. In this model, words are regarded as points in a multidimensional space and word recognition is a probabilistic inference process. When the dimensionality of the feature space is low, the Bayesian model predicts inverse effectiveness; when the dimensionality is high, the enhancement is maximal at intermediate auditory noise levels. When the auditory and visual stimuli differ slightly in high noise, the model makes a counterintuitive prediction: as sound quality increases, the proportion of reported words corresponding to the visual stimulus should first increase and then decrease. We confirm this prediction in a behavioral experiment. We conclude that auditory-visual speech perception obeys the same notion of optimality previously observed only for simple multisensory stimuli.

Experimental witness of genuine high-dimensional entanglement

NASA Astrophysics Data System (ADS)

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

2018-06-01

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

The identification of two unusual types of homemade ammunition.

PubMed

Lee, Hsieh-Chang; Meng, Hsien-Hui

2012-07-01

Illegal homemade ammunition is commonly used by criminals to commit crimes in Taiwan. Two unusual types of homemade ammunition that most closely resembling genuine ammunition are studied here. Their genuine counterparts are studied as the control samples for the purpose of comparison. Unfired ammunition is disassembled, and the morphological, dimensional, and compositional features of the bullet and cartridge case are examined. Statistical tests are employed to distinguish the dimensional differences between homemade and genuine ammunitions. Manufacturing marks on head stamps of the cartridge case are carefully examined. Compositional features of propellant powders, primer mixtures, and gunshot residues are also analyzed. The results reveal that the morphological, dimensional, and compositional features of major parts of the ammunition can be employed to differentiate homemade cartridges from genuine ones. Among these features, tool marks on the head stamps left by the bunter can be used to trace the origin of ammunition. © 2012 American Academy of Forensic Sciences.

Intrinsic two-dimensional features as textons

NASA Technical Reports Server (NTRS)

Barth, E.; Zetzsche, C.; Rentschler, I.

1998-01-01

We suggest that intrinsic two-dimensional (i2D) features, computationally defined as the outputs of nonlinear operators that model the activity of end-stopped neurons, play a role in preattentive texture discrimination. We first show that for discriminable textures with identical power spectra the predictions of traditional models depend on the type of nonlinearity and fail for energy measures. We then argue that the concept of intrinsic dimensionality, and the existence of end-stopped neurons, can help us to understand the role of the nonlinearities. Furthermore, we show examples in which models without strong i2D selectivity fail to predict the correct ranking order of perceptual segregation. Our arguments regarding the importance of i2D features resemble the arguments of Julesz and co-workers regarding textons such as terminators and crossings. However, we provide a computational framework that identifies textons with the outputs of nonlinear operators that are selective to i2D features.

Ultra-fast framing camera tube

DOEpatents

Kalibjian, Ralph

1981-01-01

An electronic framing camera tube features focal plane image dissection and synchronized restoration of the dissected electron line images to form two-dimensional framed images. Ultra-fast framing is performed by first streaking a two-dimensional electron image across a narrow slit, thereby dissecting the two-dimensional electron image into sequential electron line images. The dissected electron line images are then restored into a framed image by a restorer deflector operated synchronously with the dissector deflector. The number of framed images on the tube's viewing screen is equal to the number of dissecting slits in the tube. The distinguishing features of this ultra-fast framing camera tube are the focal plane dissecting slits, and the synchronously-operated restorer deflector which restores the dissected electron line images into a two-dimensional framed image. The framing camera tube can produce image frames having high spatial resolution of optical events in the sub-100 picosecond range.

Neural encoding of large-scale three-dimensional space-properties and constraints.

PubMed

Jeffery, Kate J; Wilson, Jonathan J; Casali, Giulio; Hayman, Robin M

2015-01-01

How the brain represents represent large-scale, navigable space has been the topic of intensive investigation for several decades, resulting in the discovery that neurons in a complex network of cortical and subcortical brain regions co-operatively encode distance, direction, place, movement etc. using a variety of different sensory inputs. However, such studies have mainly been conducted in simple laboratory settings in which animals explore small, two-dimensional (i.e., flat) arenas. The real world, by contrast, is complex and three dimensional with hills, valleys, tunnels, branches, and-for species that can swim or fly-large volumetric spaces. Adding an additional dimension to space adds coding challenges, a primary reason for which is that several basic geometric properties are different in three dimensions. This article will explore the consequences of these challenges for the establishment of a functional three-dimensional metric map of space, one of which is that the brains of some species might have evolved to reduce the dimensionality of the representational space and thus sidestep some of these problems.

Scalable posterior approximations for large-scale Bayesian inverse problems via likelihood-informed parameter and state reduction

NASA Astrophysics Data System (ADS)

Cui, Tiangang; Marzouk, Youssef; Willcox, Karen

2016-06-01

Two major bottlenecks to the solution of large-scale Bayesian inverse problems are the scaling of posterior sampling algorithms to high-dimensional parameter spaces and the computational cost of forward model evaluations. Yet incomplete or noisy data, the state variation and parameter dependence of the forward model, and correlations in the prior collectively provide useful structure that can be exploited for dimension reduction in this setting-both in the parameter space of the inverse problem and in the state space of the forward model. To this end, we show how to jointly construct low-dimensional subspaces of the parameter space and the state space in order to accelerate the Bayesian solution of the inverse problem. As a byproduct of state dimension reduction, we also show how to identify low-dimensional subspaces of the data in problems with high-dimensional observations. These subspaces enable approximation of the posterior as a product of two factors: (i) a projection of the posterior onto a low-dimensional parameter subspace, wherein the original likelihood is replaced by an approximation involving a reduced model; and (ii) the marginal prior distribution on the high-dimensional complement of the parameter subspace. We present and compare several strategies for constructing these subspaces using only a limited number of forward and adjoint model simulations. The resulting posterior approximations can rapidly be characterized using standard sampling techniques, e.g., Markov chain Monte Carlo. Two numerical examples demonstrate the accuracy and efficiency of our approach: inversion of an integral equation in atmospheric remote sensing, where the data dimension is very high; and the inference of a heterogeneous transmissivity field in a groundwater system, which involves a partial differential equation forward model with high dimensional state and parameters.

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

PubMed

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

2013-04-01

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

Fast, Distributed Algorithms in Deep Networks

DTIC Science & Technology

2016-05-11

may not have realized how vital she was in making this project a reality is Professor Crainiceanu. Without knowing who you were, you invited me into...objective function. Training is complete when (2) converges, or stated alternatively , when the difference between t and φL can no longer be...the state-of-the art approaches simply rely on random initialization. We propose an alternative 10 (a) Features in 1-dimensional space (b) Features

Extreme Sparse Multinomial Logistic Regression: A Fast and Robust Framework for Hyperspectral Image Classification

NASA Astrophysics Data System (ADS)

Cao, Faxian; Yang, Zhijing; Ren, Jinchang; Ling, Wing-Kuen; Zhao, Huimin; Marshall, Stephen

2017-12-01

Although the sparse multinomial logistic regression (SMLR) has provided a useful tool for sparse classification, it suffers from inefficacy in dealing with high dimensional features and manually set initial regressor values. This has significantly constrained its applications for hyperspectral image (HSI) classification. In order to tackle these two drawbacks, an extreme sparse multinomial logistic regression (ESMLR) is proposed for effective classification of HSI. First, the HSI dataset is projected to a new feature space with randomly generated weight and bias. Second, an optimization model is established by the Lagrange multiplier method and the dual principle to automatically determine a good initial regressor for SMLR via minimizing the training error and the regressor value. Furthermore, the extended multi-attribute profiles (EMAPs) are utilized for extracting both the spectral and spatial features. A combinational linear multiple features learning (MFL) method is proposed to further enhance the features extracted by ESMLR and EMAPs. Finally, the logistic regression via the variable splitting and the augmented Lagrangian (LORSAL) is adopted in the proposed framework for reducing the computational time. Experiments are conducted on two well-known HSI datasets, namely the Indian Pines dataset and the Pavia University dataset, which have shown the fast and robust performance of the proposed ESMLR framework.

Subjective figure reversal in two- and three-dimensional perceptual space.

PubMed

Radilová, J; Radil-Weiss, T

1984-08-01

A permanently illuminated pattern of Mach's truncated pyramid can be perceived according to the experimental instruction given, either as a three-dimensional reversible figure with spontaneously changing convex and concave interpretation (in one experiment), or as a two-dimensional reversible figure-ground pattern (in another experiment). The reversal rate was about twice as slow, without the subjects being aware of it, if it was perceived as a three-dimensional figure compared to the situation when it was perceived as two-dimensional. It may be hypothetized that in the three-dimensional case, the process of perception requires more sequential steps than in the two-dimensional one.

SU-F-R-40: Robustness Test of Computed Tomography Textures of Lung Tissues to Varying Scanning Protocols Using a Realistic Phantom Environment

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

Lee, S; Markel, D; Hegyi, G

2016-06-15

Purpose: The reliability of computed tomography (CT) textures is an important element of radiomics analysis. This study investigates the dependency of lung CT textures on different breathing phases and changes in CT image acquisition protocols in a realistic phantom setting. Methods: We investigated 11 CT texture features for radiation-induced lung disease from 3 categories (first-order, grey level co-ocurrence matrix (GLCM), and Law’s filter). A biomechanical swine lung phantom was scanned at two breathing phases (inhale/exhale) and two scanning protocols set for PET/CT and diagnostic CT scanning. Lung volumes acquired from the CT images were divided into 2-dimensional sub-regions with amore » grid spacing of 31 mm. The distribution of the evaluated texture features from these sub-regions were compared between the two scanning protocols and two breathing phases. The significance of each factor on feature values were tested at 95% significance level using analysis of covariance (ANCOVA) model with interaction terms included. Robustness of a feature to a scanning factor was defined as non-significant dependence on the factor. Results: Three GLCM textures (variance, sum entropy, difference entropy) were robust to breathing changes. Two GLCM (variance, sum entropy) and 3 Law’s filter textures (S5L5, E5L5, W5L5) were robust to scanner changes. Moreover, the two GLCM textures (variance, sum entropy) were consistent across all 4 scanning conditions. First-order features, especially Hounsfield unit intensity features, presented the most drastic variation up to 39%. Conclusion: Amongst the studied features, GLCM and Law’s filter texture features were more robust than first-order features. However, the majority of the features were modified by either breathing phase or scanner changes, suggesting a need for calibration when retrospectively comparing scans obtained at different conditions. Further investigation is necessary to identify the sensitivity of individual image acquisition parameters.« less

Numerical solutions of the semiclassical Boltzmann ellipsoidal-statistical kinetic model equation

PubMed Central

Yang, Jaw-Yen; Yan, Chin-Yuan; Huang, Juan-Chen; Li, Zhihui

2014-01-01

Computations of rarefied gas dynamical flows governed by the semiclassical Boltzmann ellipsoidal-statistical (ES) kinetic model equation using an accurate numerical method are presented. The semiclassical ES model was derived through the maximum entropy principle and conserves not only the mass, momentum and energy, but also contains additional higher order moments that differ from the standard quantum distributions. A different decoding procedure to obtain the necessary parameters for determining the ES distribution is also devised. The numerical method in phase space combines the discrete-ordinate method in momentum space and the high-resolution shock capturing method in physical space. Numerical solutions of two-dimensional Riemann problems for two configurations covering various degrees of rarefaction are presented and various contours of the quantities unique to this new model are illustrated. When the relaxation time becomes very small, the main flow features a display similar to that of ideal quantum gas dynamics, and the present solutions are found to be consistent with existing calculations for classical gas. The effect of a parameter that permits an adjustable Prandtl number in the flow is also studied. PMID:25104904

A Few New 2+1-Dimensional Nonlinear Dynamics and the Representation of Riemann Curvature Tensors

NASA Astrophysics Data System (ADS)

Wang, Yan; Zhang, Yufeng; Zhang, Xiangzhi

2016-09-01

We first introduced a linear stationary equation with a quadratic operator in ∂x and ∂y, then a linear evolution equation is given by N-order polynomials of eigenfunctions. As applications, by taking N=2, we derived a (2+1)-dimensional generalized linear heat equation with two constant parameters associative with a symmetric space. When taking N=3, a pair of generalized Kadomtsev-Petviashvili equations with the same eigenvalues with the case of N=2 are generated. Similarly, a second-order flow associative with a homogeneous space is derived from the integrability condition of the two linear equations, which is a (2+1)-dimensional hyperbolic equation. When N=3, the third second flow associative with the homogeneous space is generated, which is a pair of new generalized Kadomtsev-Petviashvili equations. Finally, as an application of a Hermitian symmetric space, we established a pair of spectral problems to obtain a new (2+1)-dimensional generalized Schrödinger equation, which is expressed by the Riemann curvature tensors.

Model of a Negatively Curved Two-Dimensional Space.

ERIC Educational Resources Information Center

Eckroth, Charles A.

1995-01-01

Describes the construction of models of two-dimensional surfaces with negative curvature that are used to illustrate differences in the triangle sum rule for the various Big Bang Theories of the universe. (JRH)

Geometric Representations of Condition Queries on Three-Dimensional Vector Fields

NASA Technical Reports Server (NTRS)

Henze, Chris

1999-01-01

Condition queries on distributed data ask where particular conditions are satisfied. It is possible to represent condition queries as geometric objects by plotting field data in various spaces derived from the data, and by selecting loci within these derived spaces which signify the desired conditions. Rather simple geometric partitions of derived spaces can represent complex condition queries because much complexity can be encapsulated in the derived space mapping itself A geometric view of condition queries provides a useful conceptual unification, allowing one to intuitively understand many existing vector field feature detection algorithms -- and to design new ones -- as variations on a common theme. A geometric representation of condition queries also provides a simple and coherent basis for computer implementation, reducing a wide variety of existing and potential vector field feature detection techniques to a few simple geometric operations.

Form drag in rivers due to small-scale natural topographic features: 2. Irregular sequences

USGS Publications Warehouse

Kean, J.W.; Smith, J.D.

2006-01-01

The size, shape, and spacing of small-scale topographic features found on the boundaries of natural streams, rivers, and floodplains can be quite variable. Consequently, a procedure for determining the form drag on irregular sequences of different-sized topographic features is essential for calculating near-boundary flows and sediment transport. A method for carrying out such calculations is developed in this paper. This method builds on the work of Kean and Smith (2006), which describes the flow field for the simpler case of a regular sequence of identical topographic features. Both approaches model topographic features as two-dimensional elements with Gaussian-shaped cross sections defined in terms of three parameters. Field measurements of bank topography are used to show that (1) the magnitude of these shape parameters can vary greatly between adjacent topographic features and (2) the variability of these shape parameters follows a lognormal distribution. Simulations using an irregular set of topographic roughness elements show that the drag on an individual element is primarily controlled by the size and shape of the feature immediately upstream and that the spatial average of the boundary shear stress over a large set of randomly ordered elements is relatively insensitive to the sequence of the elements. In addition, a method to transform the topography of irregular surfaces into an equivalently rough surface of regularly spaced, identical topographic elements also is given. The methods described in this paper can be used to improve predictions of flow resistance in rivers as well as quantify bank roughness.

Approximation of Optimal Infinite Dimensional Compensators for Flexible Structures

NASA Technical Reports Server (NTRS)

Gibson, J. S.; Mingori, D. L.; Adamian, A.; Jabbari, F.

1985-01-01

The infinite dimensional compensator for a large class of flexible structures, modeled as distributed systems are discussed, as well as an approximation scheme for designing finite dimensional compensators to approximate the infinite dimensional compensator. The approximation scheme is applied to develop a compensator for a space antenna model based on wrap-rib antennas being built currently. While the present model has been simplified, it retains the salient features of rigid body modes and several distributed components of different characteristics. The control and estimator gains are represented by functional gains, which provide graphical representations of the control and estimator laws. These functional gains also indicate the convergence of the finite dimensional compensators and show which modes the optimal compensator ignores.

Fluid leakage through fractures in an impervious caprock embedded between two geologic aquifers

NASA Astrophysics Data System (ADS)

Selvadurai, A. P. S.

2012-06-01

The paper develops an analytical result for the flow through a single fracture under a hydraulic gradient between the two aquifer regions and takes into account permeability characteristics of all components of the system. Non-dimensional results are presented to illustrate the influence of the permeability mis-match between the two geologic formations and the permeability and geometry of the fracture on the flow rate through the fracture. The analytical result is then used to develop additional results for leakage through a swarm of vertically aligned hydraulically non-interacting fractures and a damaged region containing a densely spaced array of vertically aligned fractures and worm hole type features in the caprock. The work presents a convenient result for the estimation of leakage from storage formations in geoenvironmental applications.

Exploration of the relationship between topology and designability of conformations

NASA Astrophysics Data System (ADS)

Leelananda, Sumudu P.; Towfic, Fadi; Jernigan, Robert L.; Kloczkowski, Andrzej

2011-06-01

Protein structures are evolutionarily more conserved than sequences, and sequences with very low sequence identity frequently share the same fold. This leads to the concept of protein designability. Some folds are more designable and lots of sequences can assume that fold. Elucidating the relationship between protein sequence and the three-dimensional (3D) structure that the sequence folds into is an important problem in computational structural biology. Lattice models have been utilized in numerous studies to model protein folds and predict the designability of certain folds. In this study, all possible compact conformations within a set of two-dimensional and 3D lattice spaces are explored. Complementary interaction graphs are then generated for each conformation and are described using a set of graph features. The full HP sequence space for each lattice model is generated and contact energies are calculated by threading each sequence onto all the possible conformations. Unique conformation giving minimum energy is identified for each sequence and the number of sequences folding to each conformation (designability) is obtained. Machine learning algorithms are used to predict the designability of each conformation. We find that the highly designable structures can be distinguished from other non-designable conformations based on certain graphical geometric features of the interactions. This finding confirms the fact that the topology of a conformation is an important determinant of the extent of its designability and suggests that the interactions themselves are important for determining the designability.

Dimension- and space-based intertrial effects in visual pop-out search: modulation by task demands for focal-attentional processing.

PubMed

Krummenacher, Joseph; Müller, Hermann J; Zehetleitner, Michael; Geyer, Thomas

2009-03-01

Two experiments compared reaction times (RTs) in visual search for singleton feature targets defined, variably across trials, in either the color or the orientation dimension. Experiment 1 required observers to simply discern target presence versus absence (simple-detection task); Experiment 2 required them to respond to a detection-irrelevant form attribute of the target (compound-search task). Experiment 1 revealed a marked dimensional intertrial effect of 34 ms for an target defined in a changed versus a repeated dimension, and an intertrial target distance effect, with an 4-ms increase in RTs (per unit of distance) as the separation of the current relative to the preceding target increased. Conversely, in Experiment 2, the dimension change effect was markedly reduced (11 ms), while the intertrial target distance effect was markedly increased (11 ms per unit of distance). The results suggest that dimension change/repetition effects are modulated by the amount of attentional focusing required by the task, with space-based attention altering the integration of dimension-specific feature contrast signals at the level of the overall-saliency map.

The exaptive excellence of spandrels as a term and prototype

PubMed Central

Gould, Stephen Jay

1997-01-01

In 1979, Lewontin and I borrowed the architectural term “spandrel” (using the pendentives of San Marco in Venice as an example) to designate the class of forms and spaces that arise as necessary byproducts of another decision in design, and not as adaptations for direct utility in themselves. This proposal has generated a large literature featuring two critiques: (i) the terminological claim that the spandrels of San Marco are not true spandrels at all and (ii) the conceptual claim that they are adaptations and not byproducts. The features of the San Marco pendentives that we explicitly defined as spandrel-properties—their necessary number (four) and shape (roughly triangular)—are inevitable architectural byproducts, whatever the structural attributes of the pendentives themselves. The term spandrel may be extended from its particular architectural use for two-dimensional byproducts to the generality of “spaces left over,” a definition that properly includes the San Marco pendentives. Evolutionary biology needs such an explicit term for features arising as byproducts, rather than adaptations, whatever their subsequent exaptive utility. The concept of biological spandrels—including the examples here given of masculinized genitalia in female hyenas, exaptive use of an umbilicus as a brooding chamber by snails, the shoulder hump of the giant Irish deer, and several key features of human mentality—anchors the critique of overreliance upon adaptive scenarios in evolutionary explanation. Causes of historical origin must always be separated from current utilities; their conflation has seriously hampered the evolutionary analysis of form in the history of life. PMID:11038582

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

PubMed

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

2005-01-01

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

The density-matrix renormalization group: a short introduction.

PubMed

Schollwöck, Ulrich

2011-07-13

The density-matrix renormalization group (DMRG) method has established itself over the last decade as the leading method for the simulation of the statics and dynamics of one-dimensional strongly correlated quantum lattice systems. The DMRG is a method that shares features of a renormalization group procedure (which here generates a flow in the space of reduced density operators) and of a variational method that operates on a highly interesting class of quantum states, so-called matrix product states (MPSs). The DMRG method is presented here entirely in the MPS language. While the DMRG generally fails in larger two-dimensional systems, the MPS picture suggests a straightforward generalization to higher dimensions in the framework of tensor network states. The resulting algorithms, however, suffer from difficulties absent in one dimension, apart from a much more unfavourable efficiency, such that their ultimate success remains far from clear at the moment.

Three-dimensional vesicles under shear flow: numerical study of dynamics and phase diagram.

PubMed

Biben, Thierry; Farutin, Alexander; Misbah, Chaouqi

2011-03-01

The study of vesicles under flow, a model system for red blood cells (RBCs), is an essential step in understanding various intricate dynamics exhibited by RBCs in vivo and in vitro. Quantitative three-dimensional analyses of vesicles under flow are presented. The regions of parameters to produce tumbling (TB), tank-treating, vacillating-breathing (VB), and even kayaking (or spinning) modes are determined. New qualitative features are found: (i) a significant widening of the VB mode region in parameter space upon increasing shear rate γ and (ii) a robustness of normalized period of TB and VB with γ. Analytical support is also provided. We make a comparison with existing experimental results. In particular, we find that the phase diagram of the various dynamics depends on three dimensionless control parameters, while a recent experimental work reported that only two are sufficient.

Image Recommendation Algorithm Using Feature-Based Collaborative Filtering

NASA Astrophysics Data System (ADS)

Kim, Deok-Hwan

As the multimedia contents market continues its rapid expansion, the amount of image contents used in mobile phone services, digital libraries, and catalog service is increasing remarkably. In spite of this rapid growth, users experience high levels of frustration when searching for the desired image. Even though new images are profitable to the service providers, traditional collaborative filtering methods cannot recommend them. To solve this problem, in this paper, we propose feature-based collaborative filtering (FBCF) method to reflect the user's most recent preference by representing his purchase sequence in the visual feature space. The proposed approach represents the images that have been purchased in the past as the feature clusters in the multi-dimensional feature space and then selects neighbors by using an inter-cluster distance function between their feature clusters. Various experiments using real image data demonstrate that the proposed approach provides a higher quality recommendation and better performance than do typical collaborative filtering and content-based filtering techniques.

3-D flow and scour near a submerged wing dike: ADCP measurements on the Missouri River

USGS Publications Warehouse

Jamieson, E.C.; Rennie, C.D.; Jacobson, R.B.; Townsend, R.D.

2011-01-01

Detailed mapping of bathymetry and three-dimensional water velocities using a boat-mounted single-beam sonar and acoustic Doppler current profiler (ADCP) was carried out in the vicinity of two submerged wing dikes located in the Lower Missouri River near Columbia, Missouri. During high spring flows the wing dikes become submerged, creating a unique combination of vertical flow separation and overtopping (plunging) flow conditions, causing large-scale three-dimensional turbulent flow structures to form. On three different days and for a range of discharges, sampling transects at 5 and 20 m spacing were completed, covering the area adjacent to and upstream and downstream from two different wing dikes. The objectives of this research are to evaluate whether an ADCP can identify and measure large-scale flow features such as recirculating flow and vortex shedding that develop in the vicinity of a submerged wing dike; and whether or not moving-boat (single-transect) data are sufficient for resolving complex three-dimensional flow fields. Results indicate that spatial averaging from multiple nearby single transects may be more representative of an inherently complex (temporally and spatially variable) three-dimensional flow field than repeated single transects. Results also indicate a correspondence between the location of calculated vortex cores (resolved from the interpolated three-dimensional flow field) and the nearby scour holes, providing new insight into the connections between vertically oriented coherent structures and local scour, with the unique perspective of flow and morphology in a large river.

Rational control of the interlayer space inside two-dimensional titanium carbides for highly efficient uranium removal and imprisonment.

PubMed

Wang, Lin; Tao, Wuqing; Yuan, Liyong; Liu, Zhirong; Huang, Qing; Chai, Zhifang; Gibson, John K; Shi, Weiqun

2017-11-07

Though two-dimensional early transition metal carbides and carbonitrides (MXenes) have attracted extensive interest recently, their superb abilities in various scientific applications always suffer from the very narrow interlayer space inside the multilayered structure. Here we demonstrate an unprecedented large adsorption capacity enhancement of Ti 3 C 2 T x toward radionuclide removal via a hydrated intercalation strategy. By rational control of the interlayer space, the potential for imprisoning the representative actinide U(vi) inside multilayered Ti 3 C 2 T x was also confirmed.

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

PubMed

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

2012-05-30

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

Minimum Free Energy Path of Ligand-Induced Transition in Adenylate Kinase

PubMed Central

Matsunaga, Yasuhiro; Fujisaki, Hiroshi; Terada, Tohru; Furuta, Tadaomi; Moritsugu, Kei; Kidera, Akinori

2012-01-01

Large-scale conformational changes in proteins involve barrier-crossing transitions on the complex free energy surfaces of high-dimensional space. Such rare events cannot be efficiently captured by conventional molecular dynamics simulations. Here we show that, by combining the on-the-fly string method and the multi-state Bennett acceptance ratio (MBAR) method, the free energy profile of a conformational transition pathway in Escherichia coli adenylate kinase can be characterized in a high-dimensional space. The minimum free energy paths of the conformational transitions in adenylate kinase were explored by the on-the-fly string method in 20-dimensional space spanned by the 20 largest-amplitude principal modes, and the free energy and various kinds of average physical quantities along the pathways were successfully evaluated by the MBAR method. The influence of ligand binding on the pathways was characterized in terms of rigid-body motions of the lid-shaped ATP-binding domain (LID) and the AMP-binding (AMPbd) domains. It was found that the LID domain was able to partially close without the ligand, while the closure of the AMPbd domain required the ligand binding. The transition state ensemble of the ligand bound form was identified as those structures characterized by highly specific binding of the ligand to the AMPbd domain, and was validated by unrestrained MD simulations. It was also found that complete closure of the LID domain required the dehydration of solvents around the P-loop. These findings suggest that the interplay of the two different types of domain motion is an essential feature in the conformational transition of the enzyme. PMID:22685395

Built spaces and features associated with user satisfaction in maternity waiting homes in Malawi.

PubMed

McIntosh, Nathalie; Gruits, Patricia; Oppel, Eva; Shao, Amie

2018-07-01

To assess satisfaction with maternity waiting home built spaces and features in women who are at risk for underutilizing maternity waiting homes (i.e. residential facilities that temporarily house near-term pregnant mothers close to healthcare facilities that provide obstetrical care). Specifically we wanted to answer the questions: (1) Are built spaces and features associated with maternity waiting home user satisfaction? (2) Can built spaces and features designed to improve hygiene, comfort, privacy and function improve maternity waiting home user satisfaction? And (3) Which built spaces and features are most important for maternity waiting home user satisfaction? A cross-sectional study comparing satisfaction with standard and non-standard maternity waiting home designs. Between December 2016 and February 2017 we surveyed expectant mothers at two maternity waiting homes that differed in their design of built spaces and features. We used bivariate analyses to assess if built spaces and features were associated with satisfaction. We compared ratings of built spaces and features between the two maternity waiting homes using chi-squares and t-tests to assess if design features to improve hygiene, comfort, privacy and function were associated with higher satisfaction. We used exploratory robust regression analysis to examine the relationship between built spaces and features and maternity waiting home satisfaction. Two maternity waiting homes in Malawi, one that incorporated non-standardized design features to improve hygiene, comfort, privacy, and function (Kasungu maternity waiting home) and the other that had a standard maternity waiting home design (Dowa maternity waiting home). 322 expectant mothers at risk for underutilizing maternity waiting homes (i.e. first-time mothers and those with no pregnancy risk factors) who had stayed at the Kasungu or Dowa maternity waiting homes. There were significant differences in ratings of built spaces and features between the two differently designed maternity waiting homes, with the non-standard design having higher ratings for: adequacy of toilets, and ratings of heating/cooling, air and water quality, sanitation, toilets/showers and kitchen facilities, building maintenance, sleep area, private storage space, comfort level, outdoor spaces and overall satisfaction (p = <.0001 for all). The final regression model showed that built spaces and features that are most important for maternity waiting home user satisfaction are toilets/showers, guardian spaces, safety, building maintenance, sleep area and private storage space (R 2  = 0.28). The design of maternity waiting home built spaces and features is associated with user satisfaction in women at risk for underutilizing maternity waiting homes, especially related to toilets/showers, guardian spaces, safety, building maintenance, sleep area and private storage space. Improving maternity waiting home built spaces and features may offer a promising area for improving maternity waiting home satisfaction and reducing barriers to maternity waiting home use. Copyright © 2018 Elsevier Ltd. All rights reserved.

Improved classification accuracy by feature extraction using genetic algorithms

NASA Astrophysics Data System (ADS)

Patriarche, Julia; Manduca, Armando; Erickson, Bradley J.

2003-05-01

A feature extraction algorithm has been developed for the purposes of improving classification accuracy. The algorithm uses a genetic algorithm / hill-climber hybrid to generate a set of linearly recombined features, which may be of reduced dimensionality compared with the original set. The genetic algorithm performs the global exploration, and a hill climber explores local neighborhoods. Hybridizing the genetic algorithm with a hill climber improves both the rate of convergence, and the final overall cost function value; it also reduces the sensitivity of the genetic algorithm to parameter selection. The genetic algorithm includes the operators: crossover, mutation, and deletion / reactivation - the last of these effects dimensionality reduction. The feature extractor is supervised, and is capable of deriving a separate feature space for each tissue (which are reintegrated during classification). A non-anatomical digital phantom was developed as a gold standard for testing purposes. In tests with the phantom, and with images of multiple sclerosis patients, classification with feature extractor derived features yielded lower error rates than using standard pulse sequences, and with features derived using principal components analysis. Using the multiple sclerosis patient data, the algorithm resulted in a mean 31% reduction in classification error of pure tissues.

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.

A fast image matching algorithm based on key points

NASA Astrophysics Data System (ADS)

Wang, Huilin; Wang, Ying; An, Ru; Yan, Peng

2014-05-01

Image matching is a very important technique in image processing. It has been widely used for object recognition and tracking, image retrieval, three-dimensional vision, change detection, aircraft position estimation, and multi-image registration. Based on the requirements of matching algorithm for craft navigation, such as speed, accuracy and adaptability, a fast key point image matching method is investigated and developed. The main research tasks includes: (1) Developing an improved celerity key point detection approach using self-adapting threshold of Features from Accelerated Segment Test (FAST). A method of calculating self-adapting threshold was introduced for images with different contrast. Hessian matrix was adopted to eliminate insecure edge points in order to obtain key points with higher stability. This approach in detecting key points has characteristics of small amount of computation, high positioning accuracy and strong anti-noise ability; (2) PCA-SIFT is utilized to describe key point. 128 dimensional vector are formed based on the SIFT method for the key points extracted. A low dimensional feature space was established by eigenvectors of all the key points, and each eigenvector was projected onto the feature space to form a low dimensional eigenvector. These key points were re-described by dimension-reduced eigenvectors. After reducing the dimension by the PCA, the descriptor was reduced to 20 dimensions from the original 128. This method can reduce dimensions of searching approximately near neighbors thereby increasing overall speed; (3) Distance ratio between the nearest neighbour and second nearest neighbour searching is regarded as the measurement criterion for initial matching points from which the original point pairs matched are obtained. Based on the analysis of the common methods (e.g. RANSAC (random sample consensus) and Hough transform cluster) used for elimination false matching point pairs, a heuristic local geometric restriction strategy is adopted to discard false matched point pairs further; and (4) Affine transformation model is introduced to correct coordinate difference between real-time image and reference image. This resulted in the matching of the two images. SPOT5 Remote sensing images captured at different date and airborne images captured with different flight attitude were used to test the performance of the method from matching accuracy, operation time and ability to overcome rotation. Results show the effectiveness of the approach.

Application of Linear Discriminant Analysis in Dimensionality Reduction for Hand Motion Classification

NASA Astrophysics Data System (ADS)

Phinyomark, A.; Hu, H.; Phukpattaranont, P.; Limsakul, C.

2012-01-01

The classification of upper-limb movements based on surface electromyography (EMG) signals is an important issue in the control of assistive devices and rehabilitation systems. Increasing the number of EMG channels and features in order to increase the number of control commands can yield a high dimensional feature vector. To cope with the accuracy and computation problems associated with high dimensionality, it is commonplace to apply a processing step that transforms the data to a space of significantly lower dimensions with only a limited loss of useful information. Linear discriminant analysis (LDA) has been successfully applied as an EMG feature projection method. Recently, a number of extended LDA-based algorithms have been proposed, which are more competitive in terms of both classification accuracy and computational costs/times with classical LDA. This paper presents the findings of a comparative study of classical LDA and five extended LDA methods. From a quantitative comparison based on seven multi-feature sets, three extended LDA-based algorithms, consisting of uncorrelated LDA, orthogonal LDA and orthogonal fuzzy neighborhood discriminant analysis, produce better class separability when compared with a baseline system (without feature projection), principle component analysis (PCA), and classical LDA. Based on a 7-dimension time domain and time-scale feature vectors, these methods achieved respectively 95.2% and 93.2% classification accuracy by using a linear discriminant classifier.

A two-dimensional kinematic dynamo model of the ionospheric magnetic field at Venus

NASA Technical Reports Server (NTRS)

Cravens, T. E.; Wu, D.; Shinagawa, H.

1990-01-01

The results of a high-resolution, two-dimensional, time dependent, kinematic dynamo model of the ionospheric magnetic field of Venus are presented. Various one-dimensional models are considered and the two-dimensional model is then detailed. In this model, the two-dimensional magnetic induction equation, the magnetic diffusion-convection equation, is numerically solved using specified plasma velocities. Origins of the vertical velocity profile and of the horizontal velocities are discussed. It is argued that the basic features of the vertical magnetic field profile remain unaltered by horizontal flow effects and also that horizontal plasma flow can strongly affect the magnetic field for altitudes above 300 km.

An unconventional depiction of viewpoint in rock art.

PubMed

Pettigrew, Jack; Scott-Virtue, Lee

2015-01-01

Rock art in Africa sometimes takes advantage of three-dimensional features of the rock wall, such as fissures or protuberances, that can be incorporated into the artistic composition (Lewis-Williams, 2002). More commonly, rock artists choose uniform walls on which two-dimensional depictions may represent three-dimensional figures or objects. In this report we present such a two-dimensional depiction in rock art that we think reveals an intention by the artist to represent an unusual three-dimensional viewpoint, namely, with the two human figures facing into the rock wall, instead of the accustomed Western viewpoint facing out!

Modeling Semantic Emotion Space Using a 3D Hypercube-Projection: An Innovative Analytical Approach for the Psychology of Emotions

PubMed Central

Trnka, Radek; Lačev, Alek; Balcar, Karel; Kuška, Martin; Tavel, Peter

2016-01-01

The widely accepted two-dimensional circumplex model of emotions posits that most instances of human emotional experience can be understood within the two general dimensions of valence and activation. Currently, this model is facing some criticism, because complex emotions in particular are hard to define within only these two general dimensions. The present theory-driven study introduces an innovative analytical approach working in a way other than the conventional, two-dimensional paradigm. The main goal was to map and project semantic emotion space in terms of mutual positions of various emotion prototypical categories. Participants (N = 187; 54.5% females) judged 16 discrete emotions in terms of valence, intensity, controllability and utility. The results revealed that these four dimensional input measures were uncorrelated. This implies that valence, intensity, controllability and utility represented clearly different qualities of discrete emotions in the judgments of the participants. Based on this data, we constructed a 3D hypercube-projection and compared it with various two-dimensional projections. This contrasting enabled us to detect several sources of bias when working with the traditional, two-dimensional analytical approach. Contrasting two-dimensional and three-dimensional projections revealed that the 2D models provided biased insights about how emotions are conceptually related to one another along multiple dimensions. The results of the present study point out the reductionist nature of the two-dimensional paradigm in the psychological theory of emotions and challenge the widely accepted circumplex model. PMID:27148130

A Projection and Density Estimation Method for Knowledge Discovery

PubMed Central

Stanski, Adam; Hellwich, Olaf

2012-01-01

A key ingredient to modern data analysis is probability density estimation. However, it is well known that the curse of dimensionality prevents a proper estimation of densities in high dimensions. The problem is typically circumvented by using a fixed set of assumptions about the data, e.g., by assuming partial independence of features, data on a manifold or a customized kernel. These fixed assumptions limit the applicability of a method. In this paper we propose a framework that uses a flexible set of assumptions instead. It allows to tailor a model to various problems by means of 1d-decompositions. The approach achieves a fast runtime and is not limited by the curse of dimensionality as all estimations are performed in 1d-space. The wide range of applications is demonstrated at two very different real world examples. The first is a data mining software that allows the fully automatic discovery of patterns. The software is publicly available for evaluation. As a second example an image segmentation method is realized. It achieves state of the art performance on a benchmark dataset although it uses only a fraction of the training data and very simple features. PMID:23049675

Numerical Study of a Three Dimensional Interaction between two bow Shock Waves and the Aerodynamic Heating on a Wedge Shaped Nose Cone

NASA Astrophysics Data System (ADS)

Wu, N.; Wang, J. H.; Shen, L.

2017-03-01

This paper presents a numerical investigation on the three-dimensional interaction between two bow shock waves in two environments, i.e. ground high-enthalpy wind tunnel test and real space flight, using Fluent 15.0. The first bow shock wave, also called induced shock wave, which is generated by the leading edge of a hypersonic vehicle. The other bow shock wave can be deemed objective shock wave, which is generated by the cowl clip of hypersonic inlet, and in this paper the inlet is represented by a wedge shaped nose cone. The interaction performances including flow field structures, aerodynamic pressure and heating are analyzed and compared between the ground test and the real space flight. Through the analysis and comparison, we can find the following important phenomena: 1) Three-dimensional complicated flow structures appear in both cases, but only in the real space flight condition, a local two-dimensional type IV interaction appears; 2) The heat flux and pressure in the interaction region are much larger than those in the no-interaction region in both cases, but the peak values of the heat flux and pressure in real space flight are smaller than those in ground test. 3) The interaction region on the objective surface are different in the two cases, and there is a peak value displacement of 3 mm along the stagnation line.

The origin of absorptive features in the two-dimensional electronic spectra of rhodopsin.

PubMed

Farag, Marwa H; Jansen, Thomas L C; Knoester, Jasper

2018-05-09

In rhodopsin, the absorption of a photon causes the isomerization of the 11-cis isomer of the retinal chromophore to its all-trans isomer. This isomerization is known to occur through a conical intersection (CI) and the internal conversion through the CI is known to be vibrationally coherent. Recently measured two-dimensional electronic spectra (2DES) showed dramatic absorptive spectral features at early waiting times associated with the transition through the CI. The common two-state two-mode model Hamiltonian was unable to elucidate the origin of these features. To rationalize the source of these features, we employ a three-state three-mode model Hamiltonian where the hydrogen out-of plane (HOOP) mode and a higher-lying electronic state are included. The 2DES of the retinal chromophore in rhodopsin are calculated and compared with the experiment. Our analysis shows that the source of the observed features in the measured 2DES is the excited state absorption to a higher-lying electronic state and not the HOOP mode.

Spectral-clustering approach to Lagrangian vortex detection.

PubMed

Hadjighasem, Alireza; Karrasch, Daniel; Teramoto, Hiroshi; Haller, George

2016-06-01

One of the ubiquitous features of real-life turbulent flows is the existence and persistence of coherent vortices. Here we show that such coherent vortices can be extracted as clusters of Lagrangian trajectories. We carry out the clustering on a weighted graph, with the weights measuring pairwise distances of fluid trajectories in the extended phase space of positions and time. We then extract coherent vortices from the graph using tools from spectral graph theory. Our method locates all coherent vortices in the flow simultaneously, thereby showing high potential for automated vortex tracking. We illustrate the performance of this technique by identifying coherent Lagrangian vortices in several two- and three-dimensional flows.

OSTA-3 Shuttle payload

NASA Technical Reports Server (NTRS)

Dillman, R. D.; Eav, B. B.; Baldwin, R. R.

1984-01-01

The Office of Space and Terrestrial Applications-3 payload, scheduled for flight on STS Mission 17, consists of four earth-observation experiments. The Feature Identification and Location Experiment-1 will spectrally sense and numerically classify the earth's surface into water, vegetation, bare earth, and ice/snow/cloud-cover, by means of spectra ratio techniques. The Measurement of Atmospheric Pollution from Satellite experiment will measure CO distribution in the middle and upper troposphere. The Imaging Camera-B uses side-looking SAR to create two-dimensional images of the earth's surface. The Large Format Camera/Attitude Reference System will collect metric quality color, color-IR, and black-and-white photographs for topographic mapping.

Hopf solitons in the Nicole model

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

Gillard, Mike; Sutcliffe, Paul

2010-12-15

The Nicole model is a conformal field theory in a three-dimensional space. It has topological soliton solutions classified by the integer-valued Hopf charge, and all currently known solitons are axially symmetric. A volume-preserving flow is used to construct soliton solutions numerically for all Hopf charges from 1 to 8. It is found that the known axially symmetric solutions are unstable for Hopf charges greater than 2 and new lower energy solutions are obtained that include knots and links. A comparison with the Skyrme-Faddeev model suggests many universal features, though there are some differences in the link types obtained in themore » two theories.« less

Unsupervised Deep Hashing With Pseudo Labels for Scalable Image Retrieval.

PubMed

Zhang, Haofeng; Liu, Li; Long, Yang; Shao, Ling

2018-04-01

In order to achieve efficient similarity searching, hash functions are designed to encode images into low-dimensional binary codes with the constraint that similar features will have a short distance in the projected Hamming space. Recently, deep learning-based methods have become more popular, and outperform traditional non-deep methods. However, without label information, most state-of-the-art unsupervised deep hashing (DH) algorithms suffer from severe performance degradation for unsupervised scenarios. One of the main reasons is that the ad-hoc encoding process cannot properly capture the visual feature distribution. In this paper, we propose a novel unsupervised framework that has two main contributions: 1) we convert the unsupervised DH model into supervised by discovering pseudo labels; 2) the framework unifies likelihood maximization, mutual information maximization, and quantization error minimization so that the pseudo labels can maximumly preserve the distribution of visual features. Extensive experiments on three popular data sets demonstrate the advantages of the proposed method, which leads to significant performance improvement over the state-of-the-art unsupervised hashing algorithms.

Merged or monolithic? Using machine-learning to reconstruct the dynamical history of simulated star clusters

NASA Astrophysics Data System (ADS)

Pasquato, Mario; Chung, Chul

2016-05-01

Context. Machine-learning (ML) solves problems by learning patterns from data with limited or no human guidance. In astronomy, ML is mainly applied to large observational datasets, e.g. for morphological galaxy classification. Aims: We apply ML to gravitational N-body simulations of star clusters that are either formed by merging two progenitors or evolved in isolation, planning to later identify globular clusters (GCs) that may have a history of merging from observational data. Methods: We create mock-observations from simulated GCs, from which we measure a set of parameters (also called features in the machine-learning field). After carrying out dimensionality reduction on the feature space, the resulting datapoints are fed in to various classification algorithms. Using repeated random subsampling validation, we check whether the groups identified by the algorithms correspond to the underlying physical distinction between mergers and monolithically evolved simulations. Results: The three algorithms we considered (C5.0 trees, k-nearest neighbour, and support-vector machines) all achieve a test misclassification rate of about 10% without parameter tuning, with support-vector machines slightly outperforming the others. The first principal component of feature space correlates with cluster concentration. If we exclude it from the regression, the performance of the algorithms is only slightly reduced.

Shape component analysis: structure-preserving dimension reduction on biological shape spaces.

PubMed

Lee, Hao-Chih; Liao, Tao; Zhang, Yongjie Jessica; Yang, Ge

2016-03-01

Quantitative shape analysis is required by a wide range of biological studies across diverse scales, ranging from molecules to cells and organisms. In particular, high-throughput and systems-level studies of biological structures and functions have started to produce large volumes of complex high-dimensional shape data. Analysis and understanding of high-dimensional biological shape data require dimension-reduction techniques. We have developed a technique for non-linear dimension reduction of 2D and 3D biological shape representations on their Riemannian spaces. A key feature of this technique is that it preserves distances between different shapes in an embedded low-dimensional shape space. We demonstrate an application of this technique by combining it with non-linear mean-shift clustering on the Riemannian spaces for unsupervised clustering of shapes of cellular organelles and proteins. Source code and data for reproducing results of this article are freely available at https://github.com/ccdlcmu/shape_component_analysis_Matlab The implementation was made in MATLAB and supported on MS Windows, Linux and Mac OS. geyang@andrew.cmu.edu. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Exploring gravitational lensing model variations in the Frontier Fields galaxy clusters

NASA Astrophysics Data System (ADS)

Harris James, Nicholas John; Raney, Catie; Brennan, Sean; Keeton, Charles

2018-01-01

Multiple groups have been working on modeling the mass distributions of the six lensing galaxy clusters in the Hubble Space Telescope Frontier Fields data set. The magnification maps produced from these mass models will be important for the future study of the lensed background galaxies, but there exists significant variation in the different groups’ models and magnification maps. We explore the use of two-dimensional histograms as a tool for visualizing these magnification map variations. Using a number of simple, one- or two-halo singular isothermal sphere models, we explore the features that are produced in 2D histogram model comparisons when parameters such as halo mass, ellipticity, and location are allowed to vary. Our analysis demonstrates the potential of 2D histograms as a means of observing the full range of differences between the Frontier Fields groups’ models.This work has been supported by funding from National Science Foundation grants PHY-1560077 and AST-1211385, and from the Space Telescope Science Institute.

A Moving Mesh Finite Element Algorithm for Singular Problems in Two and Three Space Dimensions

NASA Astrophysics Data System (ADS)

Li, Ruo; Tang, Tao; Zhang, Pingwen

2002-04-01

A framework for adaptive meshes based on the Hamilton-Schoen-Yau theory was proposed by Dvinsky. In a recent work (2001, J. Comput. Phys.170, 562-588), we extended Dvinsky's method to provide an efficient moving mesh algorithm which compared favorably with the previously proposed schemes in terms of simplicity and reliability. In this work, we will further extend the moving mesh methods based on harmonic maps to deal with mesh adaptation in three space dimensions. In obtaining the variational mesh, we will solve an optimization problem with some appropriate constraints, which is in contrast to the traditional method of solving the Euler-Lagrange equation directly. The key idea of this approach is to update the interior and boundary grids simultaneously, rather than considering them separately. Application of the proposed moving mesh scheme is illustrated with some two- and three-dimensional problems with large solution gradients. The numerical experiments show that our methods can accurately resolve detail features of singular problems in 3D.

Nonlinear Network Description for Many-Body Quantum Systems in Continuous Space

NASA Astrophysics Data System (ADS)

Ruggeri, Michele; Moroni, Saverio; Holzmann, Markus

2018-05-01

We show that the recently introduced iterative backflow wave function can be interpreted as a general neural network in continuum space with nonlinear functions in the hidden units. Using this wave function in variational Monte Carlo simulations of liquid 4He in two and three dimensions, we typically find a tenfold increase in accuracy over currently used wave functions. Furthermore, subsequent stages of the iteration procedure define a set of increasingly good wave functions, each with its own variational energy and variance of the local energy: extrapolation to zero variance gives energies in close agreement with the exact values. For two dimensional 4He, we also show that the iterative backflow wave function can describe both the liquid and the solid phase with the same functional form—a feature shared with the shadow wave function, but now joined by much higher accuracy. We also achieve significant progress for liquid 3He in three dimensions, improving previous variational and fixed-node energies.

Feature Screening for Ultrahigh Dimensional Categorical Data with Applications.

PubMed

Huang, Danyang; Li, Runze; Wang, Hansheng

2014-01-01

Ultrahigh dimensional data with both categorical responses and categorical covariates are frequently encountered in the analysis of big data, for which feature screening has become an indispensable statistical tool. We propose a Pearson chi-square based feature screening procedure for categorical response with ultrahigh dimensional categorical covariates. The proposed procedure can be directly applied for detection of important interaction effects. We further show that the proposed procedure possesses screening consistency property in the terminology of Fan and Lv (2008). We investigate the finite sample performance of the proposed procedure by Monte Carlo simulation studies, and illustrate the proposed method by two empirical datasets.

Automatic small bowel tumor diagnosis by using multi-scale wavelet-based analysis in wireless capsule endoscopy images.

PubMed

Barbosa, Daniel C; Roupar, Dalila B; Ramos, Jaime C; Tavares, Adriano C; Lima, Carlos S

2012-01-11

Wireless capsule endoscopy has been introduced as an innovative, non-invasive diagnostic technique for evaluation of the gastrointestinal tract, reaching places where conventional endoscopy is unable to. However, the output of this technique is an 8 hours video, whose analysis by the expert physician is very time consuming. Thus, a computer assisted diagnosis tool to help the physicians to evaluate CE exams faster and more accurately is an important technical challenge and an excellent economical opportunity. The set of features proposed in this paper to code textural information is based on statistical modeling of second order textural measures extracted from co-occurrence matrices. To cope with both joint and marginal non-Gaussianity of second order textural measures, higher order moments are used. These statistical moments are taken from the two-dimensional color-scale feature space, where two different scales are considered. Second and higher order moments of textural measures are computed from the co-occurrence matrices computed from images synthesized by the inverse wavelet transform of the wavelet transform containing only the selected scales for the three color channels. The dimensionality of the data is reduced by using Principal Component Analysis. The proposed textural features are then used as the input of a classifier based on artificial neural networks. Classification performances of 93.1% specificity and 93.9% sensitivity are achieved on real data. These promising results open the path towards a deeper study regarding the applicability of this algorithm in computer aided diagnosis systems to assist physicians in their clinical practice.

Analyzing linear spatial features in ecology.

PubMed

Buettel, Jessie C; Cole, Andrew; Dickey, John M; Brook, Barry W

2018-06-01

The spatial analysis of dimensionless points (e.g., tree locations on a plot map) is common in ecology, for instance using point-process statistics to detect and compare patterns. However, the treatment of one-dimensional linear features (fiber processes) is rarely attempted. Here we appropriate the methods of vector sums and dot products, used regularly in fields like astrophysics, to analyze a data set of mapped linear features (logs) measured in 12 × 1-ha forest plots. For this demonstrative case study, we ask two deceptively simple questions: do trees tend to fall downhill, and if so, does slope gradient matter? Despite noisy data and many potential confounders, we show clearly that topography (slope direction and steepness) of forest plots does matter to treefall. More generally, these results underscore the value of mathematical methods of physics to problems in the spatial analysis of linear features, and the opportunities that interdisciplinary collaboration provides. This work provides scope for a variety of future ecological analyzes of fiber processes in space. © 2018 by the Ecological Society of America.

An efficient semi-implicit method for three-dimensional non-hydrostatic flows in compliant arterial vessels.

PubMed

Fambri, Francesco; Dumbser, Michael; Casulli, Vincenzo

2014-11-01

Blood flow in arterial systems can be described by the three-dimensional Navier-Stokes equations within a time-dependent spatial domain that accounts for the elasticity of the arterial walls. In this article, blood is treated as an incompressible Newtonian fluid that flows through compliant vessels of general cross section. A three-dimensional semi-implicit finite difference and finite volume model is derived so that numerical stability is obtained at a low computational cost on a staggered grid. The key idea of the method consists in a splitting of the pressure into a hydrostatic and a non-hydrostatic part, where first a small quasi-one-dimensional nonlinear system is solved for the hydrostatic pressure and only in a second step the fully three-dimensional non-hydrostatic pressure is computed from a three-dimensional nonlinear system as a correction to the hydrostatic one. The resulting algorithm is robust, efficient, locally and globally mass conservative, and applies to hydrostatic and non-hydrostatic flows in one, two and three space dimensions. These features are illustrated on nontrivial test cases for flows in tubes with circular or elliptical cross section where the exact analytical solution is known. Test cases of steady and pulsatile flows in uniformly curved rigid and elastic tubes are presented. Wherever possible, axial velocity development and secondary flows are shown and compared with previously published results. Copyright © 2014 John Wiley & Sons, Ltd.

A comprehensive three-dimensional cortical map of vowel space.

PubMed

Scharinger, Mathias; Idsardi, William J; Poe, Samantha

2011-12-01

Mammalian cortex is known to contain various kinds of spatial encoding schemes for sensory information including retinotopic, somatosensory, and tonotopic maps. Tonotopic maps are especially interesting for human speech sound processing because they encode linguistically salient acoustic properties. In this study, we mapped the entire vowel space of a language (Turkish) onto cortical locations by using the magnetic N1 (M100), an auditory-evoked component that peaks approximately 100 msec after auditory stimulus onset. We found that dipole locations could be structured into two distinct maps, one for vowels produced with the tongue positioned toward the front of the mouth (front vowels) and one for vowels produced in the back of the mouth (back vowels). Furthermore, we found spatial gradients in lateral-medial, anterior-posterior, and inferior-superior dimensions that encoded the phonetic, categorical distinctions between all the vowels of Turkish. Statistical model comparisons of the dipole locations suggest that the spatial encoding scheme is not entirely based on acoustic bottom-up information but crucially involves featural-phonetic top-down modulation. Thus, multiple areas of excitation along the unidimensional basilar membrane are mapped into higher dimensional representations in auditory cortex.

Guiding exploration in conformational feature space with Lipschitz underestimation for ab-initio protein structure prediction.

PubMed

Hao, Xiaohu; Zhang, Guijun; Zhou, Xiaogen

2018-04-01

Computing conformations which are essential to associate structural and functional information with gene sequences, is challenging due to the high dimensionality and rugged energy surface of the protein conformational space. Consequently, the dimension of the protein conformational space should be reduced to a proper level, and an effective exploring algorithm should be proposed. In this paper, a plug-in method for guiding exploration in conformational feature space with Lipschitz underestimation (LUE) for ab-initio protein structure prediction is proposed. The conformational space is converted into ultrafast shape recognition (USR) feature space firstly. Based on the USR feature space, the conformational space can be further converted into Underestimation space according to Lipschitz estimation theory for guiding exploration. As a consequence of the use of underestimation model, the tight lower bound estimate information can be used for exploration guidance, the invalid sampling areas can be eliminated in advance, and the number of energy function evaluations can be reduced. The proposed method provides a novel technique to solve the exploring problem of protein conformational space. LUE is applied to differential evolution (DE) algorithm, and metropolis Monte Carlo(MMC) algorithm which is available in the Rosetta; When LUE is applied to DE and MMC, it will be screened by the underestimation method prior to energy calculation and selection. Further, LUE is compared with DE and MMC by testing on 15 small-to-medium structurally diverse proteins. Test results show that near-native protein structures with higher accuracy can be obtained more rapidly and efficiently with the use of LUE. Copyright © 2018 Elsevier Ltd. All rights reserved.

USING TWO-DIMENSIONAL HYDRODYNAMIC MODELS AT SCALES OF ECOLOGICAL IMPORTANCE. (R825760)

EPA Science Inventory

Modeling of flow features that are important in assessing stream habitat conditions has been a long-standing interest of stream biologists. Recently, they have begun examining the usefulness of two-dimensional (2-D) hydrodynamic models in attaining this objective. Current modelin...

Well-balanced compressible cut-cell simulation of atmospheric flow.

PubMed

Klein, R; Bates, K R; Nikiforakis, N

2009-11-28

Cut-cell meshes present an attractive alternative to terrain-following coordinates for the representation of topography within atmospheric flow simulations, particularly in regions of steep topographic gradients. In this paper, we present an explicit two-dimensional method for the numerical solution on such meshes of atmospheric flow equations including gravitational sources. This method is fully conservative and allows for time steps determined by the regular grid spacing, avoiding potential stability issues due to arbitrarily small boundary cells. We believe that the scheme is unique in that it is developed within a dimensionally split framework, in which each coordinate direction in the flow is solved independently at each time step. Other notable features of the scheme are: (i) its conceptual and practical simplicity, (ii) its flexibility with regard to the one-dimensional flux approximation scheme employed, and (iii) the well-balancing of the gravitational sources allowing for stable simulation of near-hydrostatic flows. The presented method is applied to a selection of test problems including buoyant bubble rise interacting with geometry and lee-wave generation due to topography.

Evolution of lower hybrid turbulence in the ionosphere

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

Ganguli, G.; Crabtree, C.; Mithaiwala, M.

2015-11-15

Three-dimensional evolution of the lower hybrid turbulence driven by a spatially localized ion ring beam perpendicular to the ambient magnetic field in space plasmas is analyzed. It is shown that the quasi-linear saturation model breaks down when the nonlinear rate of scattering by thermal electron is larger than linear damping rates, which can occur even for low wave amplitudes. The evolution is found to be essentially a three-dimensional phenomenon, which cannot be accurately explained by two-dimensional simulations. An important feature missed in previous studies of this phenomenon is the nonlinear conversion of electrostatic lower hybrid waves into electromagnetic whistler andmore » magnetosonic waves and the consequent energy loss due to radiation from the source region. This can result in unique low-amplitude saturation with extended saturation time. It is shown that when the nonlinear effects are considered the net energy that can be permanently extracted from the ring beam is larger. The results are applied to anticipate the outcome of a planned experiment that will seed lower hybrid turbulence in the ionosphere and monitor its evolution.« less

First Vlasiator results on foreshock ULF wave activity

NASA Astrophysics Data System (ADS)

Palmroth, M.; Eastwood, J. P.; Pokhotelov, D.; Hietala, H.; Kempf, Y.; Hoilijoki, S.; von Alfthan, S.; Vainio, R. O.

2013-12-01

For decades, a certain type of ultra low frequency waves with a period of about 30 seconds have been observed in the Earth's quasi-parallel foreshock. These waves, with a wavelength of about an Earth radius, are compressive and propagate obliquely with respect to the interplanetary magnetic field (IMF). The latter property has caused trouble to scientists as the growth rate for the instability causing the waves is maximized along the magnetic field. So far, these waves have been characterized by single or multi-spacecraft methods and 2-dimensional hybrid-PIC simulations, which have not fully reproduced the wave properties. Vlasiator is a newly developed, global hybrid-Vlasov simulation, which solves ions in the six-dimensional phase space using the Vlasov equation and electrons using magnetohydrodynamics (MHD). The outcome of the simulation is a global reproduction of ion-scale physics in a holistic manner where the generation of physical features can be followed in time and their consequences can be quantitatively characterized. Vlasiator produces the ion distribution functions and the related kinetic physics in unprecedented detail, in the global magnetospheric scale presently with a resolution of 0.13 RE in the ordinary space and 20 km/s in the velocity space. We run two simulations, where we use both a typical Parker-spiral and a radial IMF as an input to the code. The runs are carried out in the ecliptic 2-dimensional plane in the ordinary space, and with three dimensions in the velocity space. We observe the generation of the 30-second ULF waves, and characterize their evolution and physical properties in time, comparing to observations by Cluster spacecraft. We find that Vlasiator reproduces these waves in all reported observational aspects, i.e., they are of the observed size in wavelength and period, they are compressive and propagate obliquely to the IMF. In particular, we investigate the oblique propagation and discuss the issues related to the long-standing question of oblique propagation.

Toward Optimal Manifold Hashing via Discrete Locally Linear Embedding.

PubMed

Rongrong Ji; Hong Liu; Liujuan Cao; Di Liu; Yongjian Wu; Feiyue Huang

2017-11-01

Binary code learning, also known as hashing, has received increasing attention in large-scale visual search. By transforming high-dimensional features to binary codes, the original Euclidean distance is approximated via Hamming distance. More recently, it is advocated that it is the manifold distance, rather than the Euclidean distance, that should be preserved in the Hamming space. However, it retains as an open problem to directly preserve the manifold structure by hashing. In particular, it first needs to build the local linear embedding in the original feature space, and then quantize such embedding to binary codes. Such a two-step coding is problematic and less optimized. Besides, the off-line learning is extremely time and memory consuming, which needs to calculate the similarity matrix of the original data. In this paper, we propose a novel hashing algorithm, termed discrete locality linear embedding hashing (DLLH), which well addresses the above challenges. The DLLH directly reconstructs the manifold structure in the Hamming space, which learns optimal hash codes to maintain the local linear relationship of data points. To learn discrete locally linear embeddingcodes, we further propose a discrete optimization algorithm with an iterative parameters updating scheme. Moreover, an anchor-based acceleration scheme, termed Anchor-DLLH, is further introduced, which approximates the large similarity matrix by the product of two low-rank matrices. Experimental results on three widely used benchmark data sets, i.e., CIFAR10, NUS-WIDE, and YouTube Face, have shown superior performance of the proposed DLLH over the state-of-the-art approaches.

Three-dimensional motor schema based navigation

NASA Technical Reports Server (NTRS)

Arkin, Ronald C.

1989-01-01

Reactive schema-based navigation is possible in space domains by extending the methods developed for ground-based navigation found within the Autonomous Robot Architecture (AuRA). Reformulation of two dimensional motor schemas for three dimensional applications is a straightforward process. The manifold advantages of schema-based control persist, including modular development, amenability to distributed processing, and responsiveness to environmental sensing. Simulation results show the feasibility of this methodology for space docking operations in a cluttered work area.

Cardiac Monitor

NASA Technical Reports Server (NTRS)

1996-01-01

Under contract to Johnson Space Center, the University of Minnesota developed the concept of impedance cardiography as an alternative to thermodilution to access astronaut heart function in flight. NASA then contracted Space Labs, Inc. to construct miniature space units based on this technology. Several companies then launched their own impedance cardiography, including Renaissance Technologies, which manufactures the IQ System. The IQ System is 5 to 17 times cheaper than thermodilution, and features the signal processing technology called TFD (Time Frequency Distribution). TFD provides three- dimensional distribution of the blood circulation force signals, allowing visualization of changes in power, frequency and time.

Oligo kernels for datamining on biological sequences: a case study on prokaryotic translation initiation sites

PubMed Central

Meinicke, Peter; Tech, Maike; Morgenstern, Burkhard; Merkl, Rainer

2004-01-01

Background Kernel-based learning algorithms are among the most advanced machine learning methods and have been successfully applied to a variety of sequence classification tasks within the field of bioinformatics. Conventional kernels utilized so far do not provide an easy interpretation of the learnt representations in terms of positional and compositional variability of the underlying biological signals. Results We propose a kernel-based approach to datamining on biological sequences. With our method it is possible to model and analyze positional variability of oligomers of any length in a natural way. On one hand this is achieved by mapping the sequences to an intuitive but high-dimensional feature space, well-suited for interpretation of the learnt models. On the other hand, by means of the kernel trick we can provide a general learning algorithm for that high-dimensional representation because all required statistics can be computed without performing an explicit feature space mapping of the sequences. By introducing a kernel parameter that controls the degree of position-dependency, our feature space representation can be tailored to the characteristics of the biological problem at hand. A regularized learning scheme enables application even to biological problems for which only small sets of example sequences are available. Our approach includes a visualization method for transparent representation of characteristic sequence features. Thereby importance of features can be measured in terms of discriminative strength with respect to classification of the underlying sequences. To demonstrate and validate our concept on a biochemically well-defined case, we analyze E. coli translation initiation sites in order to show that we can find biologically relevant signals. For that case, our results clearly show that the Shine-Dalgarno sequence is the most important signal upstream a start codon. The variability in position and composition we found for that signal is in accordance with previous biological knowledge. We also find evidence for signals downstream of the start codon, previously introduced as transcriptional enhancers. These signals are mainly characterized by occurrences of adenine in a region of about 4 nucleotides next to the start codon. Conclusions We showed that the oligo kernel can provide a valuable tool for the analysis of relevant signals in biological sequences. In the case of translation initiation sites we could clearly deduce the most discriminative motifs and their positional variation from example sequences. Attractive features of our approach are its flexibility with respect to oligomer length and position conservation. By means of these two parameters oligo kernels can easily be adapted to different biological problems. PMID:15511290

Optical sectioning for optical scanning holography using phase-space filtering with Wigner distribution functions.

PubMed

Kim, Hwi; Min, Sung-Wook; Lee, Byoungho; Poon, Ting-Chung

2008-07-01

We propose a novel optical sectioning method for optical scanning holography, which is performed in phase space by using Wigner distribution functions together with the fractional Fourier transform. The principle of phase-space optical sectioning for one-dimensional signals, such as slit objects, and two-dimensional signals, such as rectangular objects, is first discussed. Computer simulation results are then presented to substantiate the proposed idea.

Killing Forms on the Five-Dimensional Einstein-Sasaki Y(p, q) Spaces

NASA Astrophysics Data System (ADS)

Visinescu, Mihai

2012-12-01

We present the complete set of Killing-Yano tensors on the five-dimensional Einstein-Sasaki Y(p, q) spaces. Two new Killing-Yano tensors are identified, associated with the complex volume form of the Calabi-Yau metric cone. The corresponding hidden symmetries are not anomalous and the geodesic equations are superintegrable.

Resolving runaway electron distributions in space, time, and energy

DOE PAGES

Paz-Soldan, Carlos; Cooper, C. M.; Aleynikov, P.; ...

2018-05-01

Areas of agreement and disagreement with present-day models of RE evolution are revealed by measuring MeV-level bremsstrahlung radiation from runaway electrons (REs) with a pinhole camera. Spatially-resolved measurements localize the RE beam, reveal energy-dependent RE transport, and can be used to perform full two-dimensional (energy and pitch-angle) inversions of the RE phase space distribution. Energy-resolved measurements find qualitative agreement with modeling on the role of collisional and synchrotron damping in modifying the RE distribution shape. Measurements are consistent with predictions of phase-space attractors that accumulate REs, with non-monotonic features observed in the distribution. Temporally-resolved measurements find qualitative agreement with modelingmore » on the impact of collisional and synchrotron damping in varying the RE growth and decay rate. Anomalous RE loss is observed and found to be largest at low energy. As a result, possible roles for kinetic instability or spatial transport to resolve these anomalies are discussed.« less

Resolving runaway electron distributions in space, time, and energy

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

Paz-Soldan, Carlos; Cooper, C. M.; Aleynikov, P.

Areas of agreement and disagreement with present-day models of RE evolution are revealed by measuring MeV-level bremsstrahlung radiation from runaway electrons (REs) with a pinhole camera. Spatially-resolved measurements localize the RE beam, reveal energy-dependent RE transport, and can be used to perform full two-dimensional (energy and pitch-angle) inversions of the RE phase space distribution. Energy-resolved measurements find qualitative agreement with modeling on the role of collisional and synchrotron damping in modifying the RE distribution shape. Measurements are consistent with predictions of phase-space attractors that accumulate REs, with non-monotonic features observed in the distribution. Temporally-resolved measurements find qualitative agreement with modelingmore » on the impact of collisional and synchrotron damping in varying the RE growth and decay rate. Anomalous RE loss is observed and found to be largest at low energy. As a result, possible roles for kinetic instability or spatial transport to resolve these anomalies are discussed.« less

An exact solution of the Currie-Hill equations in 1 + 1 dimensional Minkowski space

NASA Astrophysics Data System (ADS)

Balog, János

2014-11-01

We present an exact two-particle solution of the Currie-Hill equations of Predictive Relativistic Mechanics in 1 + 1 dimensional Minkowski space. The instantaneous accelerations are given in terms of elementary functions depending on the relative particle position and velocities. The general solution of the equations of motion is given and by studying the global phase space of this system it is shown that this is a subspace of the full kinematic phase space.

Direct Manipulation in Virtual Reality

NASA Technical Reports Server (NTRS)

Bryson, Steve

2003-01-01

Virtual Reality interfaces offer several advantages for scientific visualization such as the ability to perceive three-dimensional data structures in a natural way. The focus of this chapter is direct manipulation, the ability for a user in virtual reality to control objects in the virtual environment in a direct and natural way, much as objects are manipulated in the real world. Direct manipulation provides many advantages for the exploration of complex, multi-dimensional data sets, by allowing the investigator the ability to intuitively explore the data environment. Because direct manipulation is essentially a control interface, it is better suited for the exploration and analysis of a data set than for the publishing or communication of features found in that data set. Thus direct manipulation is most relevant to the analysis of complex data that fills a volume of three-dimensional space, such as a fluid flow data set. Direct manipulation allows the intuitive exploration of that data, which facilitates the discovery of data features that would be difficult to find using more conventional visualization methods. Using a direct manipulation interface in virtual reality, an investigator can, for example, move a data probe about in space, watching the results and getting a sense of how the data varies within its spatial volume.

A Method of Three-Dimensional Recording of Mandibular Movement Based on Two-Dimensional Image Feature Extraction

PubMed Central

Li, Zhongke; Yang, Huifang; Lü, Peijun; Wang, Yong; Sun, Yuchun

2015-01-01

Background and Objective To develop a real-time recording system based on computer binocular vision and two-dimensional image feature extraction to accurately record mandibular movement in three dimensions. Methods A computer-based binocular vision device with two digital cameras was used in conjunction with a fixed head retention bracket to track occlusal movement. Software was developed for extracting target spatial coordinates in real time based on two-dimensional image feature recognition. A plaster model of a subject’s upper and lower dentition were made using conventional methods. A mandibular occlusal splint was made on the plaster model, and then the occlusal surface was removed. Temporal denture base resin was used to make a 3-cm handle extending outside the mouth connecting the anterior labial surface of the occlusal splint with a detection target with intersecting lines designed for spatial coordinate extraction. The subject's head was firmly fixed in place, and the occlusal splint was fully seated on the mandibular dentition. The subject was then asked to make various mouth movements while the mandibular movement target locus point set was recorded. Comparisons between the coordinate values and the actual values of the 30 intersections on the detection target were then analyzed using paired t-tests. Results The three-dimensional trajectory curve shapes of the mandibular movements were consistent with the respective subject movements. Mean XYZ coordinate values and paired t-test results were as follows: X axis: -0.0037 ± 0.02953, P = 0.502; Y axis: 0.0037 ± 0.05242, P = 0.704; and Z axis: 0.0007 ± 0.06040, P = 0.952. The t-test result showed that the coordinate values of the 30 cross points were considered statistically no significant. (P<0.05) Conclusions Use of a real-time recording system of three-dimensional mandibular movement based on computer binocular vision and two-dimensional image feature recognition technology produced a recording accuracy of approximately ± 0.1 mm, and is therefore suitable for clinical application. Certainly, further research is necessary to confirm the clinical applications of the method. PMID:26375800

Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction.

PubMed

Ma, Xiaolei; Dai, Zhuang; He, Zhengbing; Ma, Jihui; Wang, Yong; Wang, Yunpeng

2017-04-10

This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network. The results show that the proposed method outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time. The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks.

Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction

PubMed Central

Ma, Xiaolei; Dai, Zhuang; He, Zhengbing; Ma, Jihui; Wang, Yong; Wang, Yunpeng

2017-01-01

This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network. The results show that the proposed method outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time. The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks. PMID:28394270

Human red blood cell recognition enhancement with three-dimensional morphological features obtained by digital holographic imaging

NASA Astrophysics Data System (ADS)

Jaferzadeh, Keyvan; Moon, Inkyu

2016-12-01

The classification of erythrocytes plays an important role in the field of hematological diagnosis, specifically blood disorders. Since the biconcave shape of red blood cell (RBC) is altered during the different stages of hematological disorders, we believe that the three-dimensional (3-D) morphological features of erythrocyte provide better classification results than conventional two-dimensional (2-D) features. Therefore, we introduce a set of 3-D features related to the morphological and chemical properties of RBC profile and try to evaluate the discrimination power of these features against 2-D features with a neural network classifier. The 3-D features include erythrocyte surface area, volume, average cell thickness, sphericity index, sphericity coefficient and functionality factor, MCH and MCHSD, and two newly introduced features extracted from the ring section of RBC at the single-cell level. In contrast, the 2-D features are RBC projected surface area, perimeter, radius, elongation, and projected surface area to perimeter ratio. All features are obtained from images visualized by off-axis digital holographic microscopy with a numerical reconstruction algorithm, and four categories of biconcave (doughnut shape), flat-disc, stomatocyte, and echinospherocyte RBCs are interested. Our experimental results demonstrate that the 3-D features can be more useful in RBC classification than the 2-D features. Finally, we choose the best feature set of the 2-D and 3-D features by sequential forward feature selection technique, which yields better discrimination results. We believe that the final feature set evaluated with a neural network classification strategy can improve the RBC classification accuracy.

Nebula: reconstruction and visualization of scattering data in reciprocal space.

PubMed

Reiten, Andreas; Chernyshov, Dmitry; Mathiesen, Ragnvald H

2015-04-01

Two-dimensional solid-state X-ray detectors can now operate at considerable data throughput rates that allow full three-dimensional sampling of scattering data from extended volumes of reciprocal space within second to minute time-scales. For such experiments, simultaneous analysis and visualization allows for remeasurements and a more dynamic measurement strategy. A new software, Nebula , is presented. It efficiently reconstructs X-ray scattering data, generates three-dimensional reciprocal space data sets that can be visualized interactively, and aims to enable real-time processing in high-throughput measurements by employing parallel computing on commodity hardware.

Nebula: reconstruction and visualization of scattering data in reciprocal space

PubMed Central

Reiten, Andreas; Chernyshov, Dmitry; Mathiesen, Ragnvald H.

2015-01-01

Two-dimensional solid-state X-ray detectors can now operate at considerable data throughput rates that allow full three-dimensional sampling of scattering data from extended volumes of reciprocal space within second to minute time­scales. For such experiments, simultaneous analysis and visualization allows for remeasurements and a more dynamic measurement strategy. A new software, Nebula, is presented. It efficiently reconstructs X-ray scattering data, generates three-dimensional reciprocal space data sets that can be visualized interactively, and aims to enable real-time processing in high-throughput measurements by employing parallel computing on commodity hardware. PMID:25844083

Topological analysis of group fragmentation in multiagent systems

NASA Astrophysics Data System (ADS)

DeLellis, Pietro; Porfiri, Maurizio; Bollt, Erik M.

2013-02-01

In social animals, the presence of conflicts of interest or multiple leaders can promote the emergence of two or more subgroups. Such subgroups are easily recognizable by human observers, yet a quantitative and objective measure of group fragmentation is currently lacking. In this paper, we explore the feasibility of detecting group fragmentation by embedding the raw data from the individuals' motions on a low-dimensional manifold and analyzing the topological features of this manifold. To perform the embedding, we employ the isomap algorithm, which is a data-driven machine learning tool extensively used in computer vision. We implement this procedure on a data set generated by a modified à la Vicsek model, where agents are partitioned into two or more subsets and an independent leader is assigned to each subset. The dimensionality of the embedding manifold is shown to be a measure of the number of emerging subgroups in the selected observation window and a cluster analysis is proposed to aid the interpretation of these findings. To explore the feasibility of using this approach to characterize group fragmentation in real time and thus reduce the computational cost in data processing and storage, we propose an interpolation method based on an inverse mapping from the embedding space to the original space. The effectiveness of the interpolation technique is illustrated on a test-bed example with potential impact on the regulation of collective behavior of animal groups using robotic stimuli.

Spin-wave energy dispersion of a frustrated spin-½ Heisenberg antiferromagnet on a stacked square lattice.

PubMed

Majumdar, Kingshuk

2011-03-23

The effects of interlayer coupling and spatial anisotropy on the spin-wave excitation spectra of a three-dimensional spatially anisotropic, frustrated spin-½ Heisenberg antiferromagnet (HAFM) are investigated for the two ordered phases using second-order spin-wave expansion. We show that the second-order corrections to the spin-wave energies are significant and find that the energy spectra of the three-dimensional HAFM have similar qualitative features to the energy spectra of the two-dimensional HAFM on a square lattice. We also discuss the features that can provide experimental measures for the strength of the interlayer coupling, spatial anisotropy parameter, and magnetic frustration.

Characteristics of angular cross correlations studied by light scattering from two-dimensional microsphere films

NASA Astrophysics Data System (ADS)

Schroer, M. A.; Gutt, C.; Grübel, G.

2014-07-01

Recently the analysis of scattering patterns by angular cross-correlation analysis (CCA) was introduced to reveal the orientational order in disordered samples with special focus to future applications on x-ray free-electron laser facilities. We apply this CCA approach to ultra-small-angle light-scattering data obtained from two-dimensional monolayers of microspheres. The films were studied in addition by optical microscopy. This combined approach allows to calculate the cross-correlations of the scattering patterns, characterized by the orientational correlation function Ψl(q), as well as to obtain the real-space structure of the monolayers. We show that CCA is sensitive to the orientational order of monolayers formed by the microspheres which are not directly visible from the scattering patterns. By mixing microspheres of different radii the sizes of ordered monolayer domains is reduced. For these samples it is shown that Ψl(q) quantitatively describes the degree of hexagonal order of the two-dimensional films. The experimental CCA results are compared with calculations based on the microscopy images. Both techniques show qualitatively similar features. Differences can be attributed to the wave-front distortion of the laser beam in the experiment. This effect is discussed by investigating the effect of different wave fronts on the cross-correlation analysis results. The so-determined characteristics of the cross-correlation analysis will be also relevant for future x-ray-based studies.

The features of the Cosmic Web unveiled by the flip-flop field

NASA Astrophysics Data System (ADS)

Shandarin, Sergei F.; Medvedev, Mikhail V.

2017-07-01

Currently the dark matter environment is widely accepted as a framework for understanding of the observed structure in the universe. N-body simulations are indispensable for the analysis of the formation and evolution of the dark matter web. Two primary fields - density and velocity fields - are used in most of studies. Dark matter provides two additional fields that are unique for collisionless media only. They are the multistream field in Eulerian space and flip-flop field in Lagrangian space. The flip-flop field represents the number of sign reversals of an elementary volume of each collisionless fluid element. This field can be estimated by counting the sign reversals of the Jacobian at each particle at every time step of the simulation. The Jacobian is evaluated by numerical differentiation of the Lagrangian submanifold, I.e. the three-dimensional dark matter sheet in the six-dimensional space formed by three Lagrangian and three Eulerian coordinates. We present the results of the statistical study of the evolution of the flip-flop field from z = 50 to the present time z = 0. A number of statistical characteristics show that the pattern of the flip-flop field remains remarkably stable from z ≈ 30 to the present time. As a result the flip-flop field evaluated at z = 0 stores a wealth of information about the dynamical history of the dark matter web. In particular one of the most intriguing properties of the flip-flop is a unique capability to preserve the information about the merging history of haloes.

Using Gaussian windows to explore a multivariate data set

NASA Technical Reports Server (NTRS)

Jaeckel, Louis A.

1991-01-01

In an earlier paper, I recounted an exploratory analysis, using Gaussian windows, of a data set derived from the Infrared Astronomical Satellite. Here, my goals are to develop strategies for finding structural features in a data set in a many-dimensional space, and to find ways to describe the shape of such a data set. After a brief review of Gaussian windows, I describe the current implementation of the method. I give some ways of describing features that we might find in the data, such as clusters and saddle points, and also extended structures such as a 'bar', which is an essentially one-dimensional concentration of data points. I then define a distance function, which I use to determine which data points are 'associated' with a feature. Data points not associated with any feature are called 'outliers'. I then explore the data set, giving the strategies that I used and quantitative descriptions of the features that I found, including clusters, bars, and a saddle point. I tried to use strategies and procedures that could, in principle, be used in any number of dimensions.

A solution for two-dimensional mazes with use of chaotic dynamics in a recurrent neural network model.

PubMed

Suemitsu, Yoshikazu; Nara, Shigetoshi

2004-09-01

Chaotic dynamics introduced into a neural network model is applied to solving two-dimensional mazes, which are ill-posed problems. A moving object moves from the position at t to t + 1 by simply defined motion function calculated from firing patterns of the neural network model at each time step t. We have embedded several prototype attractors that correspond to the simple motion of the object orienting toward several directions in two-dimensional space in our neural network model. Introducing chaotic dynamics into the network gives outputs sampled from intermediate state points between embedded attractors in a state space, and these dynamics enable the object to move in various directions. System parameter switching between a chaotic and an attractor regime in the state space of the neural network enables the object to move to a set target in a two-dimensional maze. Results of computer simulations show that the success rate for this method over 300 trials is higher than that of random walk. To investigate why the proposed method gives better performance, we calculate and discuss statistical data with respect to dynamical structure.

Collaborative activity between parietal and dorso-lateral prefrontal cortex in dynamic spatial working memory revealed by fMRI.

PubMed

Diwadkar, V A; Carpenter, P A; Just, M A

2000-07-01

Functional MRI was used to determine how the constituents of the cortical network subserving dynamic spatial working memory respond to two types of increases in task complexity. Participants mentally maintained the most recent location of either one or three objects as the three objects moved discretely in either a two- or three-dimensional array. Cortical activation in the dorsolateral prefrontal (DLPFC) and the parietal cortex increased as a function of the number of object locations to be maintained and the dimensionality of the display. An analysis of the response characteristics of the individual voxels showed that a large proportion were activated only when both the variables imposed the higher level of demand. A smaller proportion were activated specifically in response to increases in task demand associated with each of the independent variables. A second experiment revealed the same effect of dimensionality in the parietal cortex when the movement of objects was signaled auditorily rather than visually, indicating that the additional representational demands induced by 3-D space are independent of input modality. The comodulation of activation in the prefrontal and parietal areas by the amount of computational demand suggests that the collaboration between areas is a basic feature underlying much of the functionality of spatial working memory. Copyright 2000 Academic Press.

An optical flow-based state-space model of the vocal folds.

PubMed

Granados, Alba; Brunskog, Jonas

2017-06-01

High-speed movies of the vocal fold vibration are valuable data to reveal vocal fold features for voice pathology diagnosis. This work presents a suitable Bayesian model and a purely theoretical discussion for further development of a framework for continuum biomechanical features estimation. A linear and Gaussian nonstationary state-space model is proposed and thoroughly discussed. The evolution model is based on a self-sustained three-dimensional finite element model of the vocal folds, and the observation model involves a dense optical flow algorithm. The results show that the method is able to capture different deformation patterns between the computed optical flow and the finite element deformation, controlled by the choice of the model tissue parameters.

Detecting multiple moving objects in crowded environments with coherent motion regions

DOEpatents

Cheriyadat, Anil M.; Radke, Richard J.

2013-06-11

Coherent motion regions extend in time as well as space, enforcing consistency in detected objects over long time periods and making the algorithm robust to noisy or short point tracks. As a result of enforcing the constraint that selected coherent motion regions contain disjoint sets of tracks defined in a three-dimensional space including a time dimension. An algorithm operates directly on raw, unconditioned low-level feature point tracks, and minimizes a global measure of the coherent motion regions. At least one discrete moving object is identified in a time series of video images based on the trajectory similarity factors, which is a measure of a maximum distance between a pair of feature point tracks.

Evidence of tampering in watermark identification

NASA Astrophysics Data System (ADS)

McLauchlan, Lifford; Mehrübeoglu, Mehrübe

2009-08-01

In this work, watermarks are embedded in digital images in the discrete wavelet transform (DWT) domain. Principal component analysis (PCA) is performed on the DWT coefficients. Next higher order statistics based on the principal components and the eigenvalues are determined for different sets of images. Feature sets are analyzed for different types of attacks in m dimensional space. The results demonstrate the separability of the features for the tampered digital copies. Different feature sets are studied to determine more effective tamper evident feature sets. The digital forensics, the probable manipulation(s) or modification(s) performed on the digital information can be identified using the described technique.

Dimensional control of die castings

NASA Astrophysics Data System (ADS)

Karve, Aniruddha Ajit

The demand for net shape die castings, which require little or no machining, is steadily increasing. Stringent customer requirements are forcing die casters to deliver high quality castings in increasingly short lead times. Dimensional conformance to customer specifications is an inherent part of die casting quality. The dimensional attributes of a die casting are essentially dependent upon many factors--the quality of the die and the degree of control over the process variables being the two major sources of dimensional error in die castings. This study focused on investigating the nature and the causes of dimensional error in die castings. The two major components of dimensional error i.e., dimensional variability and die allowance were studied. The major effort of this study was to qualitatively and quantitatively study the effects of casting geometry and process variables on die casting dimensional variability and die allowance. This was accomplished by detailed dimensional data collection at production die casting sites. Robust feature characterization schemes were developed to describe complex casting geometry in quantitative terms. Empirical modeling was utilized to quantify the effects of the casting variables on dimensional variability and die allowance for die casting features. A number of casting geometry and process variables were found to affect dimensional variability in die castings. The dimensional variability was evaluated by comparisons with current published dimensional tolerance standards. The casting geometry was found to play a significant role in influencing the die allowance of the features measured. The predictive models developed for dimensional variability and die allowance were evaluated to test their effectiveness. Finally, the relative impact of all the components of dimensional error in die castings was put into perspective, and general guidelines for effective dimensional control in the die casting plant were laid out. The results of this study will contribute to enhancement of dimensional quality and lead time compression in the die casting industry, thus making it competitive with other net shape manufacturing processes.

Physics in space-time with scale-dependent metrics

NASA Astrophysics Data System (ADS)

Balankin, Alexander S.

2013-10-01

We construct three-dimensional space Rγ3 with the scale-dependent metric and the corresponding Minkowski space-time Mγ,β4 with the scale-dependent fractal (DH) and spectral (DS) dimensions. The local derivatives based on scale-dependent metrics are defined and differential vector calculus in Rγ3 is developed. We state that Mγ,β4 provides a unified phenomenological framework for dimensional flow observed in quite different models of quantum gravity. Nevertheless, the main attention is focused on the special case of flat space-time M1/3,14 with the scale-dependent Cantor-dust-like distribution of admissible states, such that DH increases from DH=2 on the scale ≪ℓ0 to DH=4 in the infrared limit ≫ℓ0, where ℓ0 is the characteristic length (e.g. the Planck length, or characteristic size of multi-fractal features in heterogeneous medium), whereas DS≡4 in all scales. Possible applications of approach based on the scale-dependent metric to systems of different nature are briefly discussed.

Computer modelling of grain microstructure in three dimensions

NASA Astrophysics Data System (ADS)

Narayan, K. Lakshmi

We present a program that generates the two-dimensional micrographs of a three dimensional grain microstructure. The code utilizes a novel scanning, pixel mapping technique to secure statistical distributions of surface areas, grain sizes, aspect ratios, perimeters, number of nearest neighbors and volumes of the randomly nucleated particles. The program can be used for comparing the existing theories of grain growth, and interpretation of two-dimensional microstructure of three-dimensional samples. Special features have been included to minimize the computation time and resource requirements.

Neural correlates of processing facial identity based on features versus their spacing.

PubMed

Maurer, D; O'Craven, K M; Le Grand, R; Mondloch, C J; Springer, M V; Lewis, T L; Grady, C L

2007-04-08

Adults' expertise in recognizing facial identity involves encoding subtle differences among faces in the shape of individual facial features (featural processing) and in the spacing among features (a type of configural processing called sensitivity to second-order relations). We used fMRI to investigate the neural mechanisms that differentiate these two types of processing. Participants made same/different judgments about pairs of faces that differed only in the shape of the eyes and mouth, with minimal differences in spacing (featural blocks), or pairs of faces that had identical features but differed in the positions of those features (spacing blocks). From a localizer scan with faces, objects, and houses, we identified regions with comparatively more activity for faces, including the fusiform face area (FFA) in the right fusiform gyrus, other extrastriate regions, and prefrontal cortices. Contrasts between the featural and spacing conditions revealed distributed patterns of activity differentiating the two conditions. A region of the right fusiform gyrus (near but not overlapping the localized FFA) showed greater activity during the spacing task, along with multiple areas of right frontal cortex, whereas left prefrontal activity increased for featural processing. These patterns of activity were not related to differences in performance between the two tasks. The results indicate that the processing of facial features is distinct from the processing of second-order relations in faces, and that these functions are mediated by separate and lateralized networks involving the right fusiform gyrus, although the FFA as defined from a localizer scan is not differentially involved.

Stereo imaging with spaceborne radars

NASA Technical Reports Server (NTRS)

Leberl, F.; Kobrick, M.

1983-01-01

Stereo viewing is a valuable tool in photointerpretation and is used for the quantitative reconstruction of the three dimensional shape of a topographical surface. Stereo viewing refers to a visual perception of space by presenting an overlapping image pair to an observer so that a three dimensional model is formed in the brain. Some of the observer's function is performed by machine correlation of the overlapping images - so called automated stereo correlation. The direct perception of space with two eyes is often called natural binocular vision; techniques of generating three dimensional models of the surface from two sets of monocular image measurements is the topic of stereology.

Pythagoras's theorem on a two-dimensional lattice from a `natural' Dirac operator and Connes's distance formula

NASA Astrophysics Data System (ADS)

Dai, Jian; Song, Xing-Chang

2001-07-01

One of the key ingredients of Connes's noncommutative geometry is a generalized Dirac operator which induces a metric (Connes's distance) on the pure state space. We generalize such a Dirac operator devised by Dimakis et al, whose Connes distance recovers the linear distance on an one-dimensional lattice, to the two-dimensional case. This Dirac operator has the local eigenvalue property and induces a Euclidean distance on this two-dimensional lattice, which is referred to as `natural'. This kind of Dirac operator can be easily generalized into any higher-dimensional lattices.

Electronic Transport and Possible Superconductivity at Van Hove Singularities in Carbon Nanotubes.

PubMed

Yang, Y; Fedorov, G; Shafranjuk, S E; Klapwijk, T M; Cooper, B K; Lewis, R M; Lobb, C J; Barbara, P

2015-12-09

Van Hove singularities (VHSs) are a hallmark of reduced dimensionality, leading to a divergent density of states in one and two dimensions and predictions of new electronic properties when the Fermi energy is close to these divergences. In carbon nanotubes, VHSs mark the onset of new subbands. They are elusive in standard electronic transport characterization measurements because they do not typically appear as notable features and therefore their effect on the nanotube conductance is largely unexplored. Here we report conductance measurements of carbon nanotubes where VHSs are clearly revealed by interference patterns of the electronic wave functions, showing both a sharp increase of quantum capacitance, and a sharp reduction of energy level spacing, consistent with an upsurge of density of states. At VHSs, we also measure an anomalous increase of conductance below a temperature of about 30 K. We argue that this transport feature is consistent with the formation of Cooper pairs in the nanotube.

Musings about beauty.

PubMed

Kintsch, Walter

2012-01-01

In this essay, I explore how cognitive science could illuminate the concept of beauty. Two results from the extensive literature on aesthetics guide my discussion. As the term "beauty" is overextended in general usage, I choose as my starting point the notion of "perfect form." Aesthetic theorists are in reasonable agreement about the criteria for perfect form. What do these criteria imply for mental representations that are experienced as beautiful? Complexity theory can be used to specify constraints on mental representations abstractly formulated as vectors in a high-dimensional space. A central feature of the proposed model is that perfect form depends both on features of the objects or events perceived and on the nature of the encoding strategies or model of the observer. A simple example illustrates the proposed calculations. A number of interesting implications that arise as a consequence of reformulating beauty in this way are noted. Copyright © 2012 Cognitive Science Society, Inc.

Quasiparticle interference of Fermi arc states in the type-II Weyl semimetal candidate WT e2

NASA Astrophysics Data System (ADS)

Yuan, Yuan; Yang, Xing; Peng, Lang; Wang, Zhi-Jun; Li, Jian; Yi, Chang-Jiang; Xian, Jing-Jing; Shi, You-Guo; Fu, Ying-Shuang

2018-04-01

Weyl semimetals possess linear dispersions through pairs of Weyl nodes in three-dimensional momentum spaces, whose hallmark arclike surface states are connected to Weyl nodes with different chirality. WT e2 was recently predicted to be a new type of Weyl semimetal. Here, we study the quasiparticle interference (QPI) of its Fermi arc surface states by combined spectroscopic-imaging scanning tunneling spectroscopy and density functional theory calculations. We observed the electron scattering on two types of WT e2 surfaces unambiguously. Its scattering signal can be ascribed mainly to trivial surface states. We also address the QPI feature of nontrivial surface states from theoretical calculations. The experimental QPI patterns show some features that are likely related to the nontrivial Fermi arc states, whose existence is, however, not conclusive. Our study provides an indispensable clue for studying the Weyl semimetal phase in WT e2 .

On six-dimensional pseudo-Riemannian almost g.o. spaces

NASA Astrophysics Data System (ADS)

Dušek, Zdeněk; Kowalski, Oldřich

2007-09-01

We modify the "Kaplan example" (a six-dimensional nilpotent Lie group which is a Riemannian g.o. space) and we obtain two pseudo-Riemannian homogeneous spaces with noncompact isotropy group. These examples have the property that all geodesics are homogeneous up to a set of measure zero. We also show that the (incomplete) geodesic graphs are strongly discontinuous at the boundary, i.e., the limits along certain curves are always infinite.

AGT/ℤ2

NASA Astrophysics Data System (ADS)

Le Floch, Bruno; Turiaci, Gustavo J.

2017-12-01

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

Application of Fourier analysis to multispectral/spatial recognition

NASA Technical Reports Server (NTRS)

Hornung, R. J.; Smith, J. A.

1973-01-01

One approach for investigating spectral response from materials is to consider spatial features of the response. This might be accomplished by considering the Fourier spectrum of the spatial response. The Fourier Transform may be used in a one-dimensional to multidimensional analysis of more than one channel of data. The two-dimensional transform represents the Fraunhofer diffraction pattern of the image in optics and has certain invariant features. Physically the diffraction pattern contains spatial features which are possibly unique to a given configuration or classification type. Different sampling strategies may be used to either enhance geometrical differences or extract additional features.

Cascaded chirped narrow bandpass filter with flat-top based on two-dimensional photonic crystals.

PubMed

Zhuang, Yuyang; Chen, Heming; Ji, Ke

2017-05-10

We propose a structure of a cascaded chirped narrow bandpass filter with a flat-top based on two-dimensional (2D) photonic crystals (PhCs). The filter discussed here consists of three filter units, each with a resonator and two reflectors. Coupled mode theory and transfer matrix method are methodologies applied in the analysis of the features. The calculations show that the bandwidth of the filter can be adjusted by changing the distances between resonators and reflectors, and based on this, a flat-top response can be achieved by chirped-cascading the filter units. According to the theoretical model, we design a narrow bandpass filter based on 2D PhCs with a triangular lattice of air holes, the parameters of which are calculated using the finite element method. The simulation results show that the filter has a center frequency of 193.40 THz, an insertion loss of 0.18 dB, a flat bandwidth of 40 GHz, and ripples of about 0.2 dB in the passband. The filter is suitable for dense-wavelength-division-multiplexed optical communication systems with 100 GHz channel spacing.

Score-level fusion of two-dimensional and three-dimensional palmprint for personal recognition systems

NASA Astrophysics Data System (ADS)

Chaa, Mourad; Boukezzoula, Naceur-Eddine; Attia, Abdelouahab

2017-01-01

Two types of scores extracted from two-dimensional (2-D) and three-dimensional (3-D) palmprint for personal recognition systems are merged, introducing a local image descriptor for 2-D palmprint-based recognition systems, named bank of binarized statistical image features (B-BSIF). The main idea of B-BSIF is that the extracted histograms from the binarized statistical image features (BSIF) code images (the results of applying the different BSIF descriptor size with the length 12) are concatenated into one to produce a large feature vector. 3-D palmprint contains the depth information of the palm surface. The self-quotient image (SQI) algorithm is applied for reconstructing illumination-invariant 3-D palmprint images. To extract discriminative Gabor features from SQI images, Gabor wavelets are defined and used. Indeed, the dimensionality reduction methods have shown their ability in biometrics systems. Given this, a principal component analysis (PCA)+linear discriminant analysis (LDA) technique is employed. For the matching process, the cosine Mahalanobis distance is applied. Extensive experiments were conducted on a 2-D and 3-D palmprint database with 10,400 range images from 260 individuals. Then, a comparison was made between the proposed algorithm and other existing methods in the literature. Results clearly show that the proposed framework provides a higher correct recognition rate. Furthermore, the best results were obtained by merging the score of B-BSIF descriptor with the score of the SQI+Gabor wavelets+PCA+LDA method, yielding an equal error rate of 0.00% and a recognition rate of rank-1=100.00%.

Inter- and Intra-Dimensional Dependencies in Implicit Phonotactic Learning

ERIC Educational Resources Information Center

Moreton, Elliott

2012-01-01

Is phonological learning subject to the same inductive biases as learning in other domains? Previous studies of non-linguistic learning found that intra-dimensional dependencies (between two instances of the same feature) were learned more easily than inter-dimensional ones. This study compares implicit learning of intra- and inter-dimensional…

Asymptotic Behaviour of Solitons with a Double Spectral Parameter for the Bogomolny Equation in (2+1)-Dimensional Anti de Sitter Space

NASA Astrophysics Data System (ADS)

Ji, Xue-Feng; Zhou, Zi-Xiang

2005-07-01

The asymptotic behaviour of the solitons with a double spectral parameter for the Bogomolny equation in (2+1)-dimensional anti de Sitter space is obtained. The asymptotic solution has two ridges close to each other which locates beside the geodesic of the Poincaré half-plane.

Discriminative clustering on manifold for adaptive transductive classification.

PubMed

Zhang, Zhao; Jia, Lei; Zhang, Min; Li, Bing; Zhang, Li; Li, Fanzhang

2017-10-01

In this paper, we mainly propose a novel adaptive transductive label propagation approach by joint discriminative clustering on manifolds for representing and classifying high-dimensional data. Our framework seamlessly combines the unsupervised manifold learning, discriminative clustering and adaptive classification into a unified model. Also, our method incorporates the adaptive graph weight construction with label propagation. Specifically, our method is capable of propagating label information using adaptive weights over low-dimensional manifold features, which is different from most existing studies that usually predict the labels and construct the weights in the original Euclidean space. For transductive classification by our formulation, we first perform the joint discriminative K-means clustering and manifold learning to capture the low-dimensional nonlinear manifolds. Then, we construct the adaptive weights over the learnt manifold features, where the adaptive weights are calculated through performing the joint minimization of the reconstruction errors over features and soft labels so that the graph weights can be joint-optimal for data representation and classification. Using the adaptive weights, we can easily estimate the unknown labels of samples. After that, our method returns the updated weights for further updating the manifold features. Extensive simulations on image classification and segmentation show that our proposed algorithm can deliver the state-of-the-art performance on several public datasets. Copyright © 2017 Elsevier Ltd. All rights reserved.

Tachyon Condensation and Brane Annihilation in Bose-Einstein Condensates: Spontaneous Symmetry Breaking in Restricted Lower-Dimensional Subspace

NASA Astrophysics Data System (ADS)

Takeuchi, Hiromitsu; Kasamatsu, Kenichi; Tsubota, Makoto; Nitta, Muneto

2013-05-01

In brane cosmology, the Big Bang is hypothesized to occur by the annihilation of the brane-anti-brane pair in a collision, where the branes are three-dimensional objects in a higher-dimensional Universe. Spontaneous symmetry breaking accompanied by the formation of lower-dimensional topological defects, e.g. cosmic strings, is triggered by the so-called `tachyon condensation', where the existence of tachyons is attributable to the instability of the brane-anti-brane system. Here, we discuss the closest analogue of the tachyon condensation in atomic Bose-Einstein condensates. We consider annihilation of domain walls, namely branes, in strongly segregated two-component condensates, where one component is sandwiched by two domains of the other component. In this system, the process of the brane annihilation can be projected effectively as ferromagnetic ordering dynamics onto a two-dimensional space. Based on this correspondence, three-dimensional formation of vortices from a domain-wall annihilation is considered to be a kink formation due to spontaneous symmetry breaking in the two-dimensional space. We also discuss a mechanism to create a `vorton' when the sandwiched component has a vortex string bridged between the branes. We hope that this study motivates experimental researches to realize this exotic phenomenon of spontaneous symmetry breaking in superfluid systems.

A time-implicit numerical method and benchmarks for the relativistic Vlasov–Ampere equations

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

Carrie, Michael; Shadwick, B. A.

2016-01-04

Here, we present a time-implicit numerical method to solve the relativistic Vlasov–Ampere system of equations on a two dimensional phase space grid. The time-splitting algorithm we use allows the generalization of the work presented here to higher dimensions keeping the linear aspect of the resulting discrete set of equations. The implicit method is benchmarked against linear theory results for the relativistic Landau damping for which analytical expressions using the Maxwell-Juttner distribution function are derived. We note that, independently from the shape of the distribution function, the relativistic treatment features collective behaviors that do not exist in the non relativistic case.more » The numerical study of the relativistic two-stream instability completes the set of benchmarking tests.« less

A time-implicit numerical method and benchmarks for the relativistic Vlasov–Ampere equations

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

Carrié, Michael, E-mail: mcarrie2@unl.edu; Shadwick, B. A., E-mail: shadwick@mailaps.org

2016-01-15

We present a time-implicit numerical method to solve the relativistic Vlasov–Ampere system of equations on a two dimensional phase space grid. The time-splitting algorithm we use allows the generalization of the work presented here to higher dimensions keeping the linear aspect of the resulting discrete set of equations. The implicit method is benchmarked against linear theory results for the relativistic Landau damping for which analytical expressions using the Maxwell-Jüttner distribution function are derived. We note that, independently from the shape of the distribution function, the relativistic treatment features collective behaviours that do not exist in the nonrelativistic case. The numericalmore » study of the relativistic two-stream instability completes the set of benchmarking tests.« less

Real-space collapse of a polariton condensate

PubMed Central

Dominici, L.; Petrov, M.; Matuszewski, M.; Ballarini, D.; De Giorgi, M.; Colas, D.; Cancellieri, E.; Silva Fernández, B.; Bramati, A.; Gigli, G.; Kavokin, A.; Laussy, F.; Sanvitto, D.

2015-01-01

Microcavity polaritons are two-dimensional bosonic fluids with strong nonlinearities, composed of coupled photonic and electronic excitations. In their condensed form, they display quantum hydrodynamic features similar to atomic Bose–Einstein condensates, such as long-range coherence, superfluidity and quantized vorticity. Here we report the unique phenomenology that is observed when a pulse of light impacts the polariton vacuum: the fluid which is suddenly created does not splash but instead coheres into a very bright spot. The real-space collapse into a sharp peak is at odd with the repulsive interactions of polaritons and their positive mass, suggesting that an unconventional mechanism is at play. Our modelling devises a possible explanation in the self-trapping due to a local heating of the crystal lattice, that can be described as a collective polaron formed by a polariton condensate. These observations hint at the polariton fluid dynamics in conditions of extreme intensities and ultrafast times. PMID:26634817

Confocal Imaging of porous media

NASA Astrophysics Data System (ADS)

Shah, S.; Crawshaw, D.; Boek, D.

2012-12-01

Carbonate rocks, which hold approximately 50% of the world's oil and gas reserves, have a very complicated and heterogeneous structure in comparison with sandstone reservoir rock. We present advances with different techniques to image, reconstruct, and characterize statistically the micro-geometry of carbonate pores. The main goal here is to develop a technique to obtain two dimensional and three dimensional images using Confocal Laser Scanning Microscopy. CLSM is used in epi-fluorescent imaging mode, allowing for the very high optical resolution of features well below 1μm size. Images of pore structures were captured using CLSM imaging where spaces in the carbonate samples were impregnated with a fluorescent, dyed epoxy-resin, and scanned in the x-y plane by a laser probe. We discuss the sample preparation in detail for Confocal Imaging to obtain sub-micron resolution images of heterogeneous carbonate rocks. We also discuss the technical and practical aspects of this imaging technique, including its advantages and limitation. We present several examples of this application, including studying pore geometry in carbonates, characterizing sub-resolution porosity in two dimensional images. We then describe approaches to extract statistical information about porosity using image processing and spatial correlation function. We have managed to obtain very low depth information in z -axis (~ 50μm) to develop three dimensional images of carbonate rocks with the current capabilities and limitation of CLSM technique. Hence, we have planned a novel technique to obtain higher depth information to obtain high three dimensional images with sub-micron resolution possible in the lateral and axial planes.

Construction of high-dimensional universal quantum logic gates using a Λ system coupled with a whispering-gallery-mode microresonator.

PubMed

He, Ling Yan; Wang, Tie-Jun; Wang, Chuan

2016-07-11

High-dimensional quantum system provides a higher capacity of quantum channel, which exhibits potential applications in quantum information processing. However, high-dimensional universal quantum logic gates is difficult to achieve directly with only high-dimensional interaction between two quantum systems and requires a large number of two-dimensional gates to build even a small high-dimensional quantum circuits. In this paper, we propose a scheme to implement a general controlled-flip (CF) gate where the high-dimensional single photon serve as the target qudit and stationary qubits work as the control logic qudit, by employing a three-level Λ-type system coupled with a whispering-gallery-mode microresonator. In our scheme, the required number of interaction times between the photon and solid state system reduce greatly compared with the traditional method which decomposes the high-dimensional Hilbert space into 2-dimensional quantum space, and it is on a shorter temporal scale for the experimental realization. Moreover, we discuss the performance and feasibility of our hybrid CF gate, concluding that it can be easily extended to a 2n-dimensional case and it is feasible with current technology.

Optimizing random searches on three-dimensional lattices

NASA Astrophysics Data System (ADS)

Yang, Benhao; Yang, Shunkun; Zhang, Jiaquan; Li, Daqing

2018-07-01

Search is a universal behavior related to many types of intelligent individuals. While most studies have focused on search in two or infinite-dimensional space, it is still missing how search can be optimized in three-dimensional space. Here we study random searches on three-dimensional (3d) square lattices with periodic boundary conditions, and explore the optimal search strategy with a power-law step length distribution, p(l) ∼l-μ, known as Lévy flights. We find that compared to random searches on two-dimensional (2d) lattices, the optimal exponent μopt on 3d lattices is relatively smaller in non-destructive case and remains similar in destructive case. We also find μopt decreases as the lattice length in z direction increases under high target density. Our findings may help us to understand the role of spatial dimension in search behaviors.

Data center thermal management

DOEpatents

Hamann, Hendrik F.; Li, Hongfei

2016-02-09

Historical high-spatial-resolution temperature data and dynamic temperature sensor measurement data may be used to predict temperature. A first formulation may be derived based on the historical high-spatial-resolution temperature data for determining a temperature at any point in 3-dimensional space. The dynamic temperature sensor measurement data may be calibrated based on the historical high-spatial-resolution temperature data at a corresponding historical time. Sensor temperature data at a plurality of sensor locations may be predicted for a future time based on the calibrated dynamic temperature sensor measurement data. A three-dimensional temperature spatial distribution associated with the future time may be generated based on the forecasted sensor temperature data and the first formulation. The three-dimensional temperature spatial distribution associated with the future time may be projected to a two-dimensional temperature distribution, and temperature in the future time for a selected space location may be forecasted dynamically based on said two-dimensional temperature distribution.

Tool Wear Prediction in Ti-6Al-4V Machining through Multiple Sensor Monitoring and PCA Features Pattern Recognition.

PubMed

Caggiano, Alessandra

2018-03-09

Machining of titanium alloys is characterised by extremely rapid tool wear due to the high cutting temperature and the strong adhesion at the tool-chip and tool-workpiece interface, caused by the low thermal conductivity and high chemical reactivity of Ti alloys. With the aim to monitor the tool conditions during dry turning of Ti-6Al-4V alloy, a machine learning procedure based on the acquisition and processing of cutting force, acoustic emission and vibration sensor signals during turning is implemented. A number of sensorial features are extracted from the acquired sensor signals in order to feed machine learning paradigms based on artificial neural networks. To reduce the large dimensionality of the sensorial features, an advanced feature extraction methodology based on Principal Component Analysis (PCA) is proposed. PCA allowed to identify a smaller number of features ( k = 2 features), the principal component scores, obtained through linear projection of the original d features into a new space with reduced dimensionality k = 2, sufficient to describe the variance of the data. By feeding artificial neural networks with the PCA features, an accurate diagnosis of tool flank wear ( VB max ) was achieved, with predicted values very close to the measured tool wear values.

Tool Wear Prediction in Ti-6Al-4V Machining through Multiple Sensor Monitoring and PCA Features Pattern Recognition

PubMed Central

2018-01-01

Machining of titanium alloys is characterised by extremely rapid tool wear due to the high cutting temperature and the strong adhesion at the tool-chip and tool-workpiece interface, caused by the low thermal conductivity and high chemical reactivity of Ti alloys. With the aim to monitor the tool conditions during dry turning of Ti-6Al-4V alloy, a machine learning procedure based on the acquisition and processing of cutting force, acoustic emission and vibration sensor signals during turning is implemented. A number of sensorial features are extracted from the acquired sensor signals in order to feed machine learning paradigms based on artificial neural networks. To reduce the large dimensionality of the sensorial features, an advanced feature extraction methodology based on Principal Component Analysis (PCA) is proposed. PCA allowed to identify a smaller number of features (k = 2 features), the principal component scores, obtained through linear projection of the original d features into a new space with reduced dimensionality k = 2, sufficient to describe the variance of the data. By feeding artificial neural networks with the PCA features, an accurate diagnosis of tool flank wear (VBmax) was achieved, with predicted values very close to the measured tool wear values. PMID:29522443

Observation of entanglement witnesses for orbital angular momentum states

NASA Astrophysics Data System (ADS)

Agnew, M.; Leach, J.; Boyd, R. W.

2012-06-01

Entanglement witnesses provide an efficient means of determining the level of entanglement of a system using the minimum number of measurements. Here we demonstrate the observation of two-dimensional entanglement witnesses in the high-dimensional basis of orbital angular momentum (OAM). In this case, the number of potentially entangled subspaces scales as d(d - 1)/2, where d is the dimension of the space. The choice of OAM as a basis is relevant as each subspace is not necessarily maximally entangled, thus providing the necessary state for certain tests of nonlocality. The expectation value of the witness gives an estimate of the state of each two-dimensional subspace belonging to the d-dimensional Hilbert space. These measurements demonstrate the degree of entanglement and therefore the suitability of the resulting subspaces for quantum information applications.

Sample-independent approach to normalize two-dimensional data for orthogonality evaluation using whole separation space scaling.

PubMed

Jáčová, Jaroslava; Gardlo, Alžběta; Friedecký, David; Adam, Tomáš; Dimandja, Jean-Marie D

2017-08-18

Orthogonality is a key parameter that is used to evaluate the separation power of chromatography-based two-dimensional systems. It is necessary to scale the separation data before the assessment of the orthogonality. Current scaling approaches are sample-dependent, and the extent of the retention space that is converted into a normalized retention space is set according to the retention times of the first and last analytes contained in a unique sample to elute. The presence or absence of a highly retained analyte in a sample can thus significantly influence the amount of information (in terms of the total amount of separation space) contained in the normalized retention space considered for the calculation of the orthogonality. We propose a Whole Separation Space Scaling (WOSEL) approach that accounts for the whole separation space delineated by the analytical method, and not the sample. This approach enables an orthogonality-based evaluation of the efficiency of the analytical system that is independent of the sample selected. The WOSEL method was compared to two currently used orthogonality approaches through the evaluation of in silico-generated chromatograms and real separations of human biofluids and petroleum samples. WOSEL exhibits sample-to-sample stability values of 3.8% on real samples, compared to 7.0% and 10.1% for the two other methods, respectively. Using real analyses, we also demonstrate that some previously developed approaches can provide misleading conclusions on the overall orthogonality of a two-dimensional chromatographic system. Copyright © 2017 Elsevier B.V. All rights reserved.

Multidimensionally encoded magnetic resonance imaging.

PubMed

Lin, Fa-Hsuan

2013-07-01

Magnetic resonance imaging (MRI) typically achieves spatial encoding by measuring the projection of a q-dimensional object over q-dimensional spatial bases created by linear spatial encoding magnetic fields (SEMs). Recently, imaging strategies using nonlinear SEMs have demonstrated potential advantages for reconstructing images with higher spatiotemporal resolution and reducing peripheral nerve stimulation. In practice, nonlinear SEMs and linear SEMs can be used jointly to further improve the image reconstruction performance. Here, we propose the multidimensionally encoded (MDE) MRI to map a q-dimensional object onto a p-dimensional encoding space where p > q. MDE MRI is a theoretical framework linking imaging strategies using linear and nonlinear SEMs. Using a system of eight surface SEM coils with an eight-channel radiofrequency coil array, we demonstrate the five-dimensional MDE MRI for a two-dimensional object as a further generalization of PatLoc imaging and O-space imaging. We also present a method of optimizing spatial bases in MDE MRI. Results show that MDE MRI with a higher dimensional encoding space can reconstruct images more efficiently and with a smaller reconstruction error when the k-space sampling distribution and the number of samples are controlled. Copyright © 2012 Wiley Periodicals, Inc.

A Fast Exact k-Nearest Neighbors Algorithm for High Dimensional Search Using k-Means Clustering and Triangle Inequality.

PubMed

Wang, Xueyi

2012-02-08

The k-nearest neighbors (k-NN) algorithm is a widely used machine learning method that finds nearest neighbors of a test object in a feature space. We present a new exact k-NN algorithm called kMkNN (k-Means for k-Nearest Neighbors) that uses the k-means clustering and the triangle inequality to accelerate the searching for nearest neighbors in a high dimensional space. The kMkNN algorithm has two stages. In the buildup stage, instead of using complex tree structures such as metric trees, kd-trees, or ball-tree, kMkNN uses a simple k-means clustering method to preprocess the training dataset. In the searching stage, given a query object, kMkNN finds nearest training objects starting from the nearest cluster to the query object and uses the triangle inequality to reduce the distance calculations. Experiments show that the performance of kMkNN is surprisingly good compared to the traditional k-NN algorithm and tree-based k-NN algorithms such as kd-trees and ball-trees. On a collection of 20 datasets with up to 10(6) records and 10(4) dimensions, kMkNN shows a 2-to 80-fold reduction of distance calculations and a 2- to 60-fold speedup over the traditional k-NN algorithm for 16 datasets. Furthermore, kMkNN performs significant better than a kd-tree based k-NN algorithm for all datasets and performs better than a ball-tree based k-NN algorithm for most datasets. The results show that kMkNN is effective for searching nearest neighbors in high dimensional spaces.

Two dimensional vernier

NASA Technical Reports Server (NTRS)

Juday, Richard D. (Inventor)

1992-01-01

A two-dimensional vernier scale is disclosed utilizing a cartesian grid on one plate member with a polar grid on an overlying transparent plate member. The polar grid has multiple concentric circles at a fractional spacing of the spacing of the cartesian grid lines. By locating the center of the polar grid on a location on the cartesian grid, interpolation can be made of both the X and Y fractional relationship to the cartesian grid by noting which circles coincide with a cartesian grid line for the X and Y direction.

Symplectic multiparticle tracking model for self-consistent space-charge simulation

DOE PAGES

Qiang, Ji

2017-01-23

Symplectic tracking is important in accelerator beam dynamics simulation. So far, to the best of our knowledge, there is no self-consistent symplectic space-charge tracking model available in the accelerator community. In this paper, we present a two-dimensional and a three-dimensional symplectic multiparticle spectral model for space-charge tracking simulation. This model includes both the effect from external fields and the effect of self-consistent space-charge fields using a split-operator method. Such a model preserves the phase space structure and shows much less numerical emittance growth than the particle-in-cell model in the illustrative examples.

Symplectic multiparticle tracking model for self-consistent space-charge simulation

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

Qiang, Ji

Symplectic tracking is important in accelerator beam dynamics simulation. So far, to the best of our knowledge, there is no self-consistent symplectic space-charge tracking model available in the accelerator community. In this paper, we present a two-dimensional and a three-dimensional symplectic multiparticle spectral model for space-charge tracking simulation. This model includes both the effect from external fields and the effect of self-consistent space-charge fields using a split-operator method. Such a model preserves the phase space structure and shows much less numerical emittance growth than the particle-in-cell model in the illustrative examples.

Quantitative analysis of fetal facial morphology using 3D ultrasound and statistical shape modeling: a feasibility study.

PubMed

Dall'Asta, Andrea; Schievano, Silvia; Bruse, Jan L; Paramasivam, Gowrishankar; Kaihura, Christine Tita; Dunaway, David; Lees, Christoph C

2017-07-01

The antenatal detection of facial dysmorphism using 3-dimensional ultrasound may raise the suspicion of an underlying genetic condition but infrequently leads to a definitive antenatal diagnosis. Despite advances in array and noninvasive prenatal testing, not all genetic conditions can be ascertained from such testing. The aim of this study was to investigate the feasibility of quantitative assessment of fetal face features using prenatal 3-dimensional ultrasound volumes and statistical shape modeling. STUDY DESIGN: Thirteen normal and 7 abnormal stored 3-dimensional ultrasound fetal face volumes were analyzed, at a median gestation of 29 +4  weeks (25 +0 to 36 +1 ). The 20 3-dimensional surface meshes generated were aligned and served as input for a statistical shape model, which computed the mean 3-dimensional face shape and 3-dimensional shape variations using principal component analysis. Ten shape modes explained more than 90% of the total shape variability in the population. While the first mode accounted for overall size differences, the second highlighted shape feature changes from an overall proportionate toward a more asymmetric face shape with a wide prominent forehead and an undersized, posteriorly positioned chin. Analysis of the Mahalanobis distance in principal component analysis shape space suggested differences between normal and abnormal fetuses (median and interquartile range distance values, 7.31 ± 5.54 for the normal group vs 13.27 ± 9.82 for the abnormal group) (P = .056). This feasibility study demonstrates that objective characterization and quantification of fetal facial morphology is possible from 3-dimensional ultrasound. This technique has the potential to assist in utero diagnosis, particularly of rare conditions in which facial dysmorphology is a feature. Copyright © 2017 Elsevier Inc. All rights reserved.

Supersymmetry and the rotation group

NASA Astrophysics Data System (ADS)

McKeon, D. G. C.

2018-04-01

A model invariant under a supersymmetric extension of the rotation group 0(3) is mapped, using a stereographic projection, from the spherical surface S2 to two-dimensional Euclidean space. The resulting model is not translation invariant. This has the consequence that fields that are supersymmetric partners no longer have a degenerate mass. This degeneracy is restored once the radius of S2 goes to infinity, and the resulting supersymmetry transformation for the fields is now mass dependent. An analogous model on the surface S4 is introduced and its projection onto four-dimensional Euclidean space is examined. This model in turn suggests a supersymmetric model on (3 + 1)-dimensional Minkowski space.

Automatic segmentation of brain MRI in high-dimensional local and non-local feature space based on sparse representation.

PubMed

Khalilzadeh, Mohammad Mahdi; Fatemizadeh, Emad; Behnam, Hamid

2013-06-01

Automatic extraction of the varying regions of magnetic resonance images is required as a prior step in a diagnostic intelligent system. The sparsest representation and high-dimensional feature are provided based on learned dictionary. The classification is done by employing the technique that computes the reconstruction error locally and non-locally of each pixel. The acquired results from the real and simulated images are superior to the best MRI segmentation method with regard to the stability advantages. In addition, it is segmented exactly through a formula taken from the distance and sparse factors. Also, it is done automatically taking sparse factor in unsupervised clustering methods whose results have been improved. Copyright © 2013 Elsevier Inc. All rights reserved.

Conformal Yano-Killing Tensors for Space-times with Cosmological Constant

NASA Astrophysics Data System (ADS)

Czajka, P.; Jezierski, J.

We present a new method for constructing conformal Yano-Killing tensors in five-di\\-men\\-sio\\-nal Anti-de Sitter space-time. The found tensors are represented in two different coordinate systems. We also discuss, in terms of CYK tensors, global charges which are well defined for asymptotically (five-dimensional) Anti-de Sitter space-time. Additionally in Appendix we present our own derivation of conformal Killing one-forms in four-dimensional Anti-de Sitter space-time as an application of the Theorem presented in the paper.

Recent developments of advanced structures for space optics at Astrium, Germany

NASA Astrophysics Data System (ADS)

Stute, Thomas; Wulz, Georg; Scheulen, Dietmar

2003-12-01

The mechanical division of EADS Astrium GmbH, Friedrichshafen Germany, the former Dornier Satellitensystem GmbH is currently engaged with the development, manufacturing and testing of three different advanced dimensionally stable composite and ceramic material structures for satellite borne optics: -CFRP Camera Structure -Planck Telescope Reflectors -NIRSpec Optical Bench Breadboard for James Web Space Telescope The paper gives an overview over the requirements and the main structural features how these requirements are met. Special production aspects and available test results are reported.

AUTOGEN

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

2003-05-29

AUTOGEN computes collision-free sequences of robot motion instructions to permit traversal of three-dimensional space curves. Order and direction of curve traversal and orientation of end effector are constraided by a set of manufacturing rules. Input can be provided as a collection of solid models or in terms of wireframe objects and structural cross-section definitions. Entity juxtaposition can be inferred, with appropriate structural features automatically provided. Process control is asserted as a function of position and orientation along each space curve, and is currently implemented for welding processes.

A series of Cadmium(II) complexes with 2-substituted terephthalate building block and N-Donor co-ligands: Structural diversity and fluorescence properties

NASA Astrophysics Data System (ADS)

Ren, Yixia; Zhou, Shanhong; Wang, Zhixiang; Zhang, Meili; Wang, Jijiang; Cao, Jia

2017-11-01

Four new Cd(II) complexes have been prepared based on 1,2,4-trimellitic acid (H3tma) and monosodium 2-sulfoterephthalate (2-NaH2stp), formulated as [Cd2(Htma)2 (dpp)2(H2O)] (1), [Cd3 (tma)2 (2,4-bipy)4(H2O)2] (2), [Cd (2-Hstp) (2,2'-bipy)2]·2H2O (3) and [Cd (2-Hstp) (2,4-bipy) (H2O)2] (4) (dpp = dipyrido [3,2-a:2‧,3'-c] phenazine, 2,4-bipy = 2,4-bipyridine, 2,2'-bipy = 2,2'- bipyridine) by hydrothermal method. X-ray diffraction structural analyses show all these complexes crystallized in triclinic crystal system of Pī space group, but their structures are diverse. Complex 1 exhibits an infinite one-dimensional chain featuring the left- and right-handed stranded chains interweaved each other. For 2, the two-dimensional network is constructed by one-dimensional ladder-like chain linked by Cd2 ions. In complex 3, the cadmium ion is surrounded with one 2-Hstp2- anion and two 2,2'-bipy molecules. Complex 4 is also a discrete structure based on a metallic dimer unit. In all these complexes, the N-donor co-ligands take the important roles in the assembly of three-dimensional supramolecular structures. The fluorescence properties of complexes 1-4 could be assigned to the π - π* transition of organic ligands.

Intrinsic two-dimensional states on the pristine surface of tellurium

NASA Astrophysics Data System (ADS)

Li, Pengke; Appelbaum, Ian

2018-05-01

Atomic chains configured in a helical geometry have fascinating properties, including phases hosting localized bound states in their electronic structure. We show how the zero-dimensional state—bound to the edge of a single one-dimensional helical chain of tellurium atoms—evolves into two-dimensional bands on the c -axis surface of the three-dimensional trigonal bulk. We give an effective Hamiltonian description of its dispersion in k space by exploiting confinement to a virtual bilayer, and elaborate on the diminished role of spin-orbit coupling. These intrinsic gap-penetrating surface bands were neglected in the interpretation of seminal experiments, where two-dimensional transport was otherwise attributed to extrinsic accumulation layers.

A Spatiotemporal-Chaos-Based Encryption Having Overall Properties Considerably Better than Advanced Encryption Standard

NASA Astrophysics Data System (ADS)

Wang, Shi-Hong; Ye, Wei-Ping; Lü, Hua-Ping; Kuang, Jin-Yu; Li, Jing-Hua; Luo, Yun-Lun; Hu, Gang

2003-07-01

Spatiotemporal chaos of a two-dimensional one-way coupled map lattice is used for chaotic cryptography. The chaotic outputs of many space units are used for encryption simultaneously. This system shows satisfactory cryptographic properties of high security, fast encryption (decryption) speed, and robustness against noise disturbances in communication channel. The overall features of this spatiotemporal-chaos-based cryptosystem are better than chaotic cryptosystems known so far, and also than currently used conventional cryptosystems, such as the Advanced Encryption Standard (AES). The project supported by National Natural Science Foundation of China under Grant No. 10175010 and the Special Funds for Major State Basic Research Projects under Grant No. G2000077304

Depth measurements through controlled aberrations of projected patterns.

PubMed

Birch, Gabriel C; Tyo, J Scott; Schwiegerling, Jim

2012-03-12

Three-dimensional displays have become increasingly present in consumer markets. However, the ability to capture three-dimensional images in space confined environments and without major modifications to current cameras is uncommon. Our goal is to create a simple modification to a conventional camera that allows for three dimensional reconstruction. We require such an imaging system have imaging and illumination paths coincident. Furthermore, we require that any three-dimensional modification to a camera also permits full resolution 2D image capture.Here we present a method of extracting depth information with a single camera and aberrated projected pattern. A commercial digital camera is used in conjunction with a projector system with astigmatic focus to capture images of a scene. By using an astigmatic projected pattern we can create two different focus depths for horizontal and vertical features of a projected pattern, thereby encoding depth. By designing an aberrated projected pattern, we are able to exploit this differential focus in post-processing designed to exploit the projected pattern and optical system. We are able to correlate the distance of an object at a particular transverse position from the camera to ratios of particular wavelet coefficients.We present our information regarding construction, calibration, and images produced by this system. The nature of linking a projected pattern design and image processing algorithms will be discussed.

Cosmological perturbations in the (1 + 3 + 6)-dimensional space-times

NASA Astrophysics Data System (ADS)

Tomita, K.

2014-12-01

Cosmological perturbations in the (1+3+6)-dimensional space-times including photon gas without viscous processes are studied on the basis of Abbott et al.'s formalism [R. B. Abbott, B. Bednarz, and S. D. Ellis, Phys. Rev. D 33, 2147 (1986)]. Space-times consist of outer space (the 3-dimensional expanding section) and inner space (the 6-dimensional section). The inner space expands initially and later contracts. Abbott et al. derived only power-type solutions, which appear at the final stage of the space-times, in the small wave-number limit. In this paper, we derive not only small wave-number solutions, but also large wave-number solutions. It is found that the latter solutions depend on the two wave-numbers k_r and k_R (which are defined in the outer and inner spaces, respectively), and that the k_r-dependent and k_R-dependent parts dominate the total perturbations when (k_r/r(t))/(k_R/R(t)) ≫ 1 or ≪ 1, respectively, where r(t) and R(t) are the scale-factors in the outer and inner spaces. By comparing the behaviors of these perturbations, moreover, changes in the spectrum of perturbations in the outer space with time are discussed.

Self-dual Skyrmions on the spheres S2 N +1

NASA Astrophysics Data System (ADS)

Amari, Y.; Ferreira, L. A.

2018-04-01

We construct self-dual sectors for scalar field theories on a (2 N +2 )-dimensional Minkowski space-time with the target space being the 2 N +1 -dimensional sphere S2 N +1. The construction of such self-dual sectors is made possible by the introduction of an extra functional in the action that renders the static energy and the self-duality equations conformally invariant on the (2 N +1 )-dimensional spatial submanifold. The conformal and target-space symmetries are used to build an ansatz that leads to an infinite number of exact self-dual solutions with arbitrary values of the topological charge. The five-dimensional case is discussed in detail, where it is shown that two types of theories admit self-dual sectors. Our work generalizes the known results in the three-dimensional case that lead to an infinite set of self-dual Skyrmion solutions.

Out-of-Sample Extrapolation utilizing Semi-Supervised Manifold Learning (OSE-SSL): Content Based Image Retrieval for Histopathology Images

PubMed Central

Sparks, Rachel; Madabhushi, Anant

2016-01-01

Content-based image retrieval (CBIR) retrieves database images most similar to the query image by (1) extracting quantitative image descriptors and (2) calculating similarity between database and query image descriptors. Recently, manifold learning (ML) has been used to perform CBIR in a low dimensional representation of the high dimensional image descriptor space to avoid the curse of dimensionality. ML schemes are computationally expensive, requiring an eigenvalue decomposition (EVD) for every new query image to learn its low dimensional representation. We present out-of-sample extrapolation utilizing semi-supervised ML (OSE-SSL) to learn the low dimensional representation without recomputing the EVD for each query image. OSE-SSL incorporates semantic information, partial class label, into a ML scheme such that the low dimensional representation co-localizes semantically similar images. In the context of prostate histopathology, gland morphology is an integral component of the Gleason score which enables discrimination between prostate cancer aggressiveness. Images are represented by shape features extracted from the prostate gland. CBIR with OSE-SSL for prostate histology obtained from 58 patient studies, yielded an area under the precision recall curve (AUPRC) of 0.53 ± 0.03 comparatively a CBIR with Principal Component Analysis (PCA) to learn a low dimensional space yielded an AUPRC of 0.44 ± 0.01. PMID:27264985

Reverse engineering the face space: Discovering the critical features for face identification.

PubMed

Abudarham, Naphtali; Yovel, Galit

2016-01-01

How do we identify people? What are the critical facial features that define an identity and determine whether two faces belong to the same person or different people? To answer these questions, we applied the face space framework, according to which faces are represented as points in a multidimensional feature space, such that face space distances are correlated with perceptual similarities between faces. In particular, we developed a novel method that allowed us to reveal the critical dimensions (i.e., critical features) of the face space. To that end, we constructed a concrete face space, which included 20 facial features of natural face images, and asked human observers to evaluate feature values (e.g., how thick are the lips). Next, we systematically and quantitatively changed facial features, and measured the perceptual effects of these manipulations. We found that critical features were those for which participants have high perceptual sensitivity (PS) for detecting differences across identities (e.g., which of two faces has thicker lips). Furthermore, these high PS features vary minimally across different views of the same identity, suggesting high PS features support face recognition across different images of the same face. The methods described here set an infrastructure for discovering the critical features of other face categories not studied here (e.g., Asians, familiar) as well as other aspects of face processing, such as attractiveness or trait inferences.

Band-gap tuning and optical response of two-dimensional Si x C 1 - x : A first-principles real-space study of disordered two-dimensional materials

DOE PAGES

Sadhukhan, Banasree; Singh, Prashant; Nayak, Arabinda; ...

2017-08-09

We present a real-space formulation for calculating the electronic structure and optical conductivity of random alloys based on Kubo-Greenwood formalism interfaced with augmented space recursion technique formulated with the tight-binding linear muffin-tin orbital basis with the van Leeuwen–Baerends corrected exchange potential. This approach has been used to quantitatively analyze the effect of chemical disorder on the configuration averaged electronic properties and optical response of two-dimensional honeycomb siliphene Si xC 1–x beyond the usual Dirac-cone approximation. We predicted the quantitative effect of disorder on both the electronic structure and optical response over a wide energy range, and the results are discussedmore » in the light of the available experimental and other theoretical data. As a result, our proposed formalism may open up a facile way for planned band-gap engineering in optoelectronic applications.« less

Band-gap tuning and optical response of two-dimensional Si x C 1 - x : A first-principles real-space study of disordered two-dimensional materials

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

Sadhukhan, Banasree; Singh, Prashant; Nayak, Arabinda

We present a real-space formulation for calculating the electronic structure and optical conductivity of random alloys based on Kubo-Greenwood formalism interfaced with augmented space recursion technique formulated with the tight-binding linear muffin-tin orbital basis with the van Leeuwen–Baerends corrected exchange potential. This approach has been used to quantitatively analyze the effect of chemical disorder on the configuration averaged electronic properties and optical response of two-dimensional honeycomb siliphene Si xC 1–x beyond the usual Dirac-cone approximation. We predicted the quantitative effect of disorder on both the electronic structure and optical response over a wide energy range, and the results are discussedmore » in the light of the available experimental and other theoretical data. As a result, our proposed formalism may open up a facile way for planned band-gap engineering in optoelectronic applications.« less

Unidirectional Wave Vector Manipulation in Two-Dimensional Space with an All Passive Acoustic Parity-Time-Symmetric Metamaterials Crystal

NASA Astrophysics Data System (ADS)

Liu, Tuo; Zhu, Xuefeng; Chen, Fei; Liang, Shanjun; Zhu, Jie

2018-03-01

Exploring the concept of non-Hermitian Hamiltonians respecting parity-time symmetry with classical wave systems is of great interest as it enables the experimental investigation of parity-time-symmetric systems through the quantum-classical analogue. Here, we demonstrate unidirectional wave vector manipulation in two-dimensional space, with an all passive acoustic parity-time-symmetric metamaterials crystal. The metamaterials crystal is constructed through interleaving groove- and holey-structured acoustic metamaterials to provide an intrinsic parity-time-symmetric potential that is two-dimensionally extended and curved, which allows the flexible manipulation of unpaired wave vectors. At the transition point from the unbroken to broken parity-time symmetry phase, the unidirectional sound focusing effect (along with reflectionless acoustic transparency in the opposite direction) is experimentally realized over the spectrum. This demonstration confirms the capability of passive acoustic systems to carry the experimental studies on general parity-time symmetry physics and further reveals the unique functionalities enabled by the judiciously tailored unidirectional wave vectors in space.

Intertwined Hamiltonians in two-dimensional curved spaces

NASA Astrophysics Data System (ADS)

Aghababaei Samani, Keivan; Zarei, Mina

2005-04-01

The problem of intertwined Hamiltonians in two-dimensional curved spaces is investigated. Explicit results are obtained for Euclidean plane, Minkowski plane, Poincaré half plane (AdS2), de Sitter plane (dS2), sphere, and torus. It is shown that the intertwining operator is related to the Killing vector fields and the isometry group of corresponding space. It is shown that the intertwined potentials are closely connected to the integral curves of the Killing vector fields. Two problems are considered as applications of the formalism presented in the paper. The first one is the problem of Hamiltonians with equispaced energy levels and the second one is the problem of Hamiltonians whose spectrum is like the spectrum of a free particle.

Overhead View of Area Surrounding Pathfinder

NASA Technical Reports Server (NTRS)

1997-01-01

Overhead view of the area surrounding the Pathfinder lander illustrating the Sojourner traverse. Red rectangles are rover positions at the end of sols 1-30. Locations of soil mechanics experiments, wheel abrasion experiments, and APXS measurements are shown. The A numbers refer to APXS measurements as discussed in the paper by Rieder et al. (p. 1770, Science Magazine, see image note). Coordinates are given in the LL frame.

The photorealistic, interactive, three-dimensional virtual reality (VR) terrain models were created from IMP images using a software package developed for Pathfinder by C. Stoker et al. as a participating science project. By matching features in the left and right camera, an automated machine vision algorithm produced dense range maps of the nearfield, which were projected into a three-dimensional model as a connected polygonal mesh. Distance and angle measurements can be made on features viewed in the model using a mouse-driven three-dimensional cursor and a point-and-click interface. The VR model also incorporates graphical representations of the lander and rover and the sequence and spatial locations at which rover data were taken. As the rover moved, graphical models of the rover were added for each position that could be uniquely determined using stereo images of the rover taken by the IMP. Images taken by the rover were projected into the model as two-dimensional 'billboards' to show the proper perspective of these images.

NOTE: original caption as published in Science Magazine

Mars Pathfinder is the second in NASA's Discovery program of low-cost spacecraft with highly focused science goals. The Jet Propulsion Laboratory, Pasadena, CA, developed and manages the Mars Pathfinder mission for NASA's Office of Space Science, Washington, D.C. JPL is a division of the California Institute of Technology (Caltech).

Paradigm shift regarding the transversalis fascia, preperitoneal space, and Retzius' space.

PubMed

Asakage, N

2018-06-01

There has been confusion in the anatomical recognition when performing inguinal hernia operations in Japan. From now on, a paradigm shift from the concept of two-dimensional layer structure to the three-dimensional space recognition is necessary to promote an understanding of anatomy. Along with the formation of the abdominal wall, the extraperitoneal space is formed by the transversalis fascia and preperitoneal space. The transversalis fascia is a somatic vascular fascia originating from an arteriovenous fascia. It is a dense areolar tissue layer at the outermost of the extraperitoneal space that runs under the diaphragm and widely lines the body wall muscle. The umbilical funiculus is taken into the abdominal wall and transformed into the preperitoneal space that is a local three-dimensional cavity enveloping preperitoneal fasciae composed of the renal fascia, vesicohypogastric fascia, and testiculoeferential fascia. The Retzius' space is an artificial cavity formed at the boundary between the transversalis fascia and preperitoneal space. In the underlay mesh repair, the mesh expands in the range spanning across the Retzius' space and preperitoneal space.

Dimensional Reduction for the General Markov Model on Phylogenetic Trees.

PubMed

Sumner, Jeremy G

2017-03-01

We present a method of dimensional reduction for the general Markov model of sequence evolution on a phylogenetic tree. We show that taking certain linear combinations of the associated random variables (site pattern counts) reduces the dimensionality of the model from exponential in the number of extant taxa, to quadratic in the number of taxa, while retaining the ability to statistically identify phylogenetic divergence events. A key feature is the identification of an invariant subspace which depends only bilinearly on the model parameters, in contrast to the usual multi-linear dependence in the full space. We discuss potential applications including the computation of split (edge) weights on phylogenetic trees from observed sequence data.

Using patient data similarities to predict radiation pneumonitis via a self-organizing map

NASA Astrophysics Data System (ADS)

Chen, Shifeng; Zhou, Sumin; Yin, Fang-Fang; Marks, Lawrence B.; Das, Shiva K.

2008-01-01

This work investigates the use of the self-organizing map (SOM) technique for predicting lung radiation pneumonitis (RP) risk. SOM is an effective method for projecting and visualizing high-dimensional data in a low-dimensional space (map). By projecting patients with similar data (dose and non-dose factors) onto the same region of the map, commonalities in their outcomes can be visualized and categorized. Once built, the SOM may be used to predict pneumonitis risk by identifying the region of the map that is most similar to a patient's characteristics. Two SOM models were developed from a database of 219 lung cancer patients treated with radiation therapy (34 clinically diagnosed with Grade 2+ pneumonitis). The models were: SOMall built from all dose and non-dose factors and, for comparison, SOMdose built from dose factors alone. Both models were tested using ten-fold cross validation and Receiver Operating Characteristics (ROC) analysis. Models SOMall and SOMdose yielded ten-fold cross-validated ROC areas of 0.73 (sensitivity/specificity = 71%/68%) and 0.67 (sensitivity/specificity = 63%/66%), respectively. The significant difference between the cross-validated ROC areas of these two models (p < 0.05) implies that non-dose features add important information toward predicting RP risk. Among the input features selected by model SOMall, the two with highest impact for increasing RP risk were: (a) higher mean lung dose and (b) chemotherapy prior to radiation therapy. The SOM model developed here may not be extrapolated to treatment techniques outside that used in our database, such as several-field lung intensity modulated radiation therapy or gated radiation therapy.

Design and implementation of an array of micro-electrochemical detectors for two-dimensional liquid chromatography--proof of principle.

PubMed

Abia, Jude A; Putnam, Joel; Mriziq, Khaled; Guiochon, Georges A

2010-03-05

Simultaneous two-dimensional liquid chromatography (2D-LC) is an implementation of two-dimensional liquid chromatography which has the potential to provide very fast, yet highly efficient separations. It is based on the use of time x space and space x space separation systems. The basic principle of this instrument has been validated long ago by the success of two-dimensional thin layer chromatography. The construction of a pressurized wide and flat column (100 mm x 100 mm x 1 mm) operated under an inlet pressure of up to 50 bar was described previously. However, to become a modern analytical method, simultaneous 2D-LC requires the development of detectors suitable for the monitoring of the composition of the eluent of this pressurized planar, wide column. An array of five equidistant micro-electrochemical sensors was built for this purpose and tested. Each sensor is a three-electrode system, with the working electrode being a 25 microm polished platinum micro-electrode. The auxiliary electrode is a thin platinum wire and the reference electrode an Ag/AgCl (3M sat. KCl) electrode. In this first implementation, proof of principle is demonstrated, but the final instrument will require a much larger array. 2010 Elsevier B.V. All rights reserved.

Usefulness of three-dimensional echocardiography in the assessment of valvular involvement in Loeffler endocarditis.

PubMed

Hernandez, Carlos M; Arisha, Mohammed J; Ahmad, Amier; Oates, Ethan; Nanda, Navin C; Nanda, Anil; Wasan, Anita; Caleti, Beda E; Bernal, Cinthia L P; Gallardo, Sergio M

2017-07-01

Loeffler endocarditis is a complication of hypereosinophilic syndrome resulting from eosinophilic infiltration of heart tissue. We report a case of Loeffler endocarditis in which three-dimensional transthoracic and transesophageal echocardiography provided additional information to what was found by two-dimensional transthoracic echocardiography alone. Our case illustrates the usefulness of combined two- and three-dimensional echocardiography in the assessment of Loeffler endocarditis. In addition, a summary of the features of hypereosinophilic syndrome and Loeffler endocarditis is provided in tabular form. © 2017, Wiley Periodicals, Inc.

On the evolution of the Universe

NASA Astrophysics Data System (ADS)

Kondratenko, P. O.

2014-12-01

In this paper a model of creation and evolution of the universe in which the laws of physics are performed. The model implies that our Universe is a part of a Super-Universe as a separate layer in the fiber space, and the information communication exists between adjacent layers through the single point. During the formation of Super-Universe it was filled first a one-dimensional World of Field-time, then a two-dimensional (1+1) World was filled with energy and Planck's particles which carry the electric and magnetic charges. Completion of two-dimensional world filling leads to a "transfusion" of energy into the neighboring three-dimensional World which presents a world of known quarks which have the fractional electric charges, color charges, and spins. The next step is a "transfusion" of energy into the four-dimensional (3+1) World and the birth of the particles of this World. Evolution of this World has a completion by the brane creation of five-dimensional World. This evolution is accompanying by the birth of the entire set of stable and unstable heavy nuclei and atoms. A filling of each new layer at the fiber space does not bring the entropy into this space (i.e. cold and completely deterministic start of evolution). The proposed model supports the anthropic principle in the Universe.

Tungsten fragmentation in nuclear reactions induced by high-energy cosmic-ray protons

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

Chechenin, N. G., E-mail: chechenin@sinp.msu.ru; Chuvilskaya, T. V.; Shirokova, A. A.

2015-01-15

Tungsten fragmentation arising in nuclear reactions induced by cosmic-ray protons in space-vehicle electronics is considered. In modern technologies of integrated circuits featuring a three-dimensional layered architecture, tungsten is frequently used as a material for interlayer conducting connections. Within the preequilibrium model, tungsten-fragmentation features, including the cross sections for the elastic and inelastic scattering of protons of energy between 30 and 240 MeV; the yields of isotopes and isobars; their energy, charge, and mass distributions; and recoil energy spectra, are calculated on the basis of the TALYS and EMPIRE-II-19 codes. It is shown that tungsten fragmentation affects substantially forecasts of failuresmore » of space-vehicle electronics.« less

New vanadium tellurites: Syntheses, structures, optical properties of noncentrosymmetric VTeO{sub 4}(OH), centrosymmetric Ba{sub 2}V{sub 4}O{sub 8}(Te{sub 3}O{sub 10})

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

Liang, Ming-Li; State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou 350002; Marsh, Matthew

Two new vanadium tellurites, VTeO{sub 4}(OH) (1) and Ba{sub 2}V{sub 4}O{sub 8}(Te{sub 3}O{sub 10}) (2), have been synthesized successfully with the use of hydrothermal reactions. The crystal structures of the two compounds were determined by single-crystal X-ray diffraction. Compound 1 crystallizes in the polar space group Pca2{sub 1} (No. 29) while compound 2 crystallizes in the centrosymmetric space group C2/c (No. 15). The topography of compound 1 reveals a two-dimensional, layered structure comprised of VO{sub 6} octahedral chains and TeO{sub 3}(OH) zig-zag chains. Compound 2, on the contrary, features a three-dimensional [V{sub 4}O{sub 8}(Te{sub 3}O{sub 10})]{sup 4-} anionic framework withmore » Ba{sup 2+} ions filled into the 10-member ring helical tunnels. The [V{sub 4}O{sub 8}(Te{sub 3}O{sub 10})]{sup 4-} anionic network is the first 3D vanadium tellurite framework to be discovered in the alkaline-earth vanadium tellurite system. Powder second harmonic generation (SHG) measurements indicate that compound 1 shows a weak SHG response of about 0.3×KDP (KH{sub 2}PO{sub 4}) under 1064 nm laser radiation. Infrared spectroscopy, elemental analysis, thermal analysis, and dipole moment calculations have also been carried out. - Graphical abstract: VTeO{sub 4}(OH) (1) crystallizes in the noncentrosymmetric space group Pca2{sub 1} (No. 29) while Ba{sub 2}V{sub 4}O{sub 8}(Te{sub 3}O{sub 10}) (2) crystallizes in the centrosymmetric space group C2/c (No. 15). - Highlights: • VTeO{sub 4}(OH) (1) and Ba{sub 2}V{sub 4}O{sub 8}(Te{sub 3}O{sub 10}) (2) have been synthesized successfully with the use of hydrothermal reactions. • VTeO{sub 4}(OH) (1) crystallizes in the noncentrosymmetric space group Pca2{sub 1} and displays a weak SHG response. • VTeO{sub 4}(OH) (1) represents only the fourth SHG-active material found in vanadium tellurite systems. • Ba{sub 2}V{sub 4}O{sub 8}(Te{sub 3}O{sub 10}) (2) exhibits a novel three-dimensional [V{sub 4}O{sub 8}(Te{sub 3}O{sub 10})]{sup 4-} anionic framework.« less

Advanced Microwave Precipitation Radiometer (AMPR) for remote observation of precipitation

NASA Technical Reports Server (NTRS)

Galliano, J. A.; Platt, R. H.

1990-01-01

The design, development, and tests of the Advanced Microwave Precipitation Radiometer (AMPR) operating in the 10 to 85 GHz range specifically for precipitation retrieval and mesoscale storm system studies from a high altitude aircraft platform (i.e., ER-2) are described. The primary goals of AMPR are the exploitation of the scattering signal of precipitation at frequencies near 10, 19, 37, and 85 GHz together to unambiguously retrieve precipitation and storm structure and intensity information in support of proposed and planned space sensors in geostationary and low earth orbit, as well as storm-related field experiments. The development of AMPR will have an important impact on the interpretation of microwave radiances for rain retrievals over both land and ocean for the following reasons: (1) A scanning instrument, such as AMPR, will allow the unambiguous detection and analysis of features in two dimensional space, allowing an improved interpretation of signals in terms of cloud features, and microphysical and radiative processes; (2) AMPR will offer more accurate comparisons with ground-based radar data by feature matching since the navigation of the ER-2 platform can be expected to drift 3 to 4 km per hour of flight time; and (3) AMPR will allow underflights of the SSM/I satellite instrument with enough spatial coverage at the same frequencies to make meaningful comparisons of the data for precipitation studies.

Functional feature embedded space mapping of fMRI data.

PubMed

Hu, Jin; Tian, Jie; Yang, Lei

2006-01-01

We have proposed a new method for fMRI data analysis which is called Functional Feature Embedded Space Mapping (FFESM). Our work mainly focuses on the experimental design with periodic stimuli which can be described by a number of Fourier coefficients in the frequency domain. A nonlinear dimension reduction technique Isomap is applied to the high dimensional features obtained from frequency domain of the fMRI data for the first time. Finally, the presence of activated time series is identified by the clustering method in which the information theoretic criterion of minimum description length (MDL) is used to estimate the number of clusters. The feasibility of our algorithm is demonstrated by real human experiments. Although we focus on analyzing periodic fMRI data, the approach can be extended to analyze non-periodic fMRI data (event-related fMRI) by replacing the Fourier analysis with a wavelet analysis.

Particle systems for adaptive, isotropic meshing of CAD models

PubMed Central

Levine, Joshua A.; Whitaker, Ross T.

2012-01-01

We present a particle-based approach for generating adaptive triangular surface and tetrahedral volume meshes from computer-aided design models. Input shapes are treated as a collection of smooth, parametric surface patches that can meet non-smoothly on boundaries. Our approach uses a hierarchical sampling scheme that places particles on features in order of increasing dimensionality. These particles reach a good distribution by minimizing an energy computed in 3D world space, with movements occurring in the parametric space of each surface patch. Rather than using a pre-computed measure of feature size, our system automatically adapts to both curvature as well as a notion of topological separation. It also enforces a measure of smoothness on these constraints to construct a sizing field that acts as a proxy to piecewise-smooth feature size. We evaluate our technique with comparisons against other popular triangular meshing techniques for this domain. PMID:23162181

All symmetric space solutions of eleven-dimensional supergravity

NASA Astrophysics Data System (ADS)

Wulff, Linus

2017-06-01

We find all symmetric space solutions of eleven-dimensional supergravity completing an earlier classification by Figueroa-O’Farrill. They come in two types: AdS solutions and pp-wave solutions. We analyze the supersymmetry conditions and show that out of the 99 AdS geometries the only supersymmetric ones are the well known backgrounds arising as near-horizon limits of (intersecting) branes and preserving 32, 16 or 8 supersymmetries. The general form of the superisometry algebra for symmetric space backgrounds is also derived.

Discrete elliptic solitons in two-dimensional waveguide arrays

NASA Astrophysics Data System (ADS)

Ye, Fangwei; Dong, Liangwei; Wang, Jiandong; Cai, Tian; Li, Yong-Ping

2005-04-01

The fundamental properties of discrete elliptic solitons (DESs) in the two-dimensional waveguide arrays were studied. The DESs show nontrivial spatial structures in their parameters space due to the introduction of the new freedom of ellipticity, and their stability is closely linked to their propagation directions in the transverse plane.

Mixing Categories and Modal Logics in the Quantum Setting

NASA Astrophysics Data System (ADS)

Cinà, Giovanni

The study of the foundations of Quantum Mechanics, especially after the advent of Quantum Computation and Information, has benefited from the application of category-theoretic tools and modal logics to the analysis of Quantum processes: we witness a wealth of theoretical frameworks casted in either of the two languages. This paper explores the interplay of the two formalisms in the peculiar context of Quantum Theory. After a review of some influential abstract frameworks, we show how different modal logic frames can be extracted from the category of finite dimensional Hilbert spaces, connecting the Categorical Quantum Mechanics approach to some modal logics that have been proposed for Quantum Computing. We then apply a general version of the same technique to two other categorical frameworks, the `topos approach' of Doering and Isham and the sheaf-theoretic work on contextuality by Abramsky and Brandenburger, suggesting how some key features can be expressed with modal languages.

A recursive algorithm for the three-dimensional imaging of brain electric activity: Shrinking LORETA-FOCUSS.

PubMed

Liu, Hesheng; Gao, Xiaorong; Schimpf, Paul H; Yang, Fusheng; Gao, Shangkai

2004-10-01

Estimation of intracranial electric activity from the scalp electroencephalogram (EEG) requires a solution to the EEG inverse problem, which is known as an ill-conditioned problem. In order to yield a unique solution, weighted minimum norm least square (MNLS) inverse methods are generally used. This paper proposes a recursive algorithm, termed Shrinking LORETA-FOCUSS, which combines and expands upon the central features of two well-known weighted MNLS methods: LORETA and FOCUSS. This recursive algorithm makes iterative adjustments to the solution space as well as the weighting matrix, thereby dramatically reducing the computation load, and increasing local source resolution. Simulations are conducted on a 3-shell spherical head model registered to the Talairach human brain atlas. A comparative study of four different inverse methods, standard Weighted Minimum Norm, L1-norm, LORETA-FOCUSS and Shrinking LORETA-FOCUSS are presented. The results demonstrate that Shrinking LORETA-FOCUSS is able to reconstruct a three-dimensional source distribution with smaller localization and energy errors compared to the other methods.

A knowledge-based object recognition system for applications in the space station

NASA Technical Reports Server (NTRS)

Dhawan, Atam P.

1988-01-01

A knowledge-based three-dimensional (3D) object recognition system is being developed. The system uses primitive-based hierarchical relational and structural matching for the recognition of 3D objects in the two-dimensional (2D) image for interpretation of the 3D scene. At present, the pre-processing, low-level preliminary segmentation, rule-based segmentation, and the feature extraction are completed. The data structure of the primitive viewing knowledge-base (PVKB) is also completed. Algorithms and programs based on attribute-trees matching for decomposing the segmented data into valid primitives were developed. The frame-based structural and relational descriptions of some objects were created and stored in a knowledge-base. This knowledge-base of the frame-based descriptions were developed on the MICROVAX-AI microcomputer in LISP environment. The simulated 3D scene of simple non-overlapping objects as well as real camera data of images of 3D objects of low-complexity have been successfully interpreted.

Chaotic behaviour of the short-term variations in ozone column observed in Arctic

NASA Astrophysics Data System (ADS)

Petkov, Boyan H.; Vitale, Vito; Mazzola, Mauro; Lanconelli, Christian; Lupi, Angelo

2015-09-01

The diurnal variations observed in the ozone column at Ny-Ålesund, Svalbard during different periods of 2009, 2010 and 2011 have been examined to test the hypothesis that they could be a result of a chaotic process. It was found that each of the attractors, reconstructed by applying the time delay technique and corresponding to any of the three time series can be embedded by 6-dimensional space. Recurrence plots, depicted to characterise the attractor features revealed structures typical for a chaotic system. In addition, the two positive Lyapunov exponents found for the three attractors, the fractal Hausdorff dimension presented by the Kaplan-Yorke estimator and the feasibility to predict the short-term ozone column variations within 10-20 h, knowing the past behaviour make the assumption about their chaotic character more realistic. The similarities of the estimated parameters in all three cases allow us to hypothesise that the three time series under study likely present one-dimensional projections of the same chaotic system taken at different time intervals.

Polarization-independent broadband meta-holograms via polarization-dependent nanoholes.

PubMed

Zhang, Xiaohu; Li, Xiong; Jin, Jinjin; Pu, Mingbo; Ma, Xiaoliang; Luo, Jun; Guo, Yinghui; Wang, Changtao; Luo, Xiangang

2018-05-17

Composed of ultrathin metal or dielectric nanostructures, metasurfaces can manipulate the phase, amplitude and polarization of electromagnetic waves at a subwavelength scale, which is promising for flat optical devices. In general, metasurfaces composed of space-variant anisotropic units are sensitive to the incident polarization due to the inherent polarization dependent geometric phase. Here, we implement polarization-independent broadband metasurface holograms constructed by polarization-dependent anisotropic elliptical nanoholes by elaborate design of complex amplitude holograms. The fabricated meta-hologram exhibits a polarization insensitive feature with an acceptable image quality. We verify the feasibility of the design algorithm for three-dimensional (3D) meta-holograms with simulation and the feasibility for two-dimensional (2D) meta-holograms is experimentally demonstrated at a broadband wavelength range from 405 nm to 632.8 nm. The effective polarization-independent broadband complex wavefront control with anisotropic elliptical nanoholes proposed in this paper greatly promotes the practical applications of the metasurface in technologies associated with wavefront manipulation, such as flat lens, colorful holographic displays and optical storage.

Clustering of Multi-Temporal Fully Polarimetric L-Band SAR Data for Agricultural Land Cover Mapping

NASA Astrophysics Data System (ADS)

Tamiminia, H.; Homayouni, S.; Safari, A.

2015-12-01

Recently, the unique capabilities of Polarimetric Synthetic Aperture Radar (PolSAR) sensors make them an important and efficient tool for natural resources and environmental applications, such as land cover and crop classification. The aim of this paper is to classify multi-temporal full polarimetric SAR data using kernel-based fuzzy C-means clustering method, over an agricultural region. This method starts with transforming input data into the higher dimensional space using kernel functions and then clustering them in the feature space. Feature space, due to its inherent properties, has the ability to take in account the nonlinear and complex nature of polarimetric data. Several SAR polarimetric features extracted using target decomposition algorithms. Features from Cloude-Pottier, Freeman-Durden and Yamaguchi algorithms used as inputs for the clustering. This method was applied to multi-temporal UAVSAR L-band images acquired over an agricultural area near Winnipeg, Canada, during June and July in 2012. The results demonstrate the efficiency of this approach with respect to the classical methods. In addition, using multi-temporal data in the clustering process helped to investigate the phenological cycle of plants and significantly improved the performance of agricultural land cover mapping.

Numerical study of chemically reacting viscous flow relevant to pulsed detonation engines

NASA Astrophysics Data System (ADS)

Yi, Tae-Hyeong

2005-11-01

A computational fluid dynamics code for two-dimensional, multi-species, laminar Navier-Stokes equations is developed to simulate a recently proposed engine concept for a pulsed detonation based propulsion system and to investigate the feasibility of the engine of the concept. The governing equations that include transport phenomena such as viscosity, thermal conduction and diffusion are coupled with chemical reactions. The gas is assumed to be thermally perfect and in chemically non-equilibrium. The stiffness due to coupling the fluid dynamics and the chemical kinetics is properly taken care of by using a time-operator splitting method and a variable coefficient ordinary differential equation solver. A second-order Roe scheme with a minmod limiter is explicitly used for space descretization, while a second-order, two-step Runge-Kutta method is used for time descretization. In space integration, a finite volume method and a cell-centered scheme are employed. The first-order derivatives in the equations of transport properties are discretized by a central differencing with Green's theorem. Detailed chemistry is involved in this study. Two chemical reaction mechanisms are extracted from GRI-Mech, which are forty elementary reactions with thirteen species for a hydrogen-air mixture and twenty-seven reactions with eight species for a hydrogen-oxygen mixture. The code is ported to a high-performance parallel machine with Message-Passing Interface. Code validation is performed with chemical kinetic modeling for a stoichiometric hydrogen-air mixture, an one-dimensional detonation tube, a two-dimensional, inviscid flow over a wedge and a viscous flow over a flat plate. Detonation is initiated using a numerically simulated arc-ignition or shock-induced ignition system. Various freestream conditions are utilized to study the propagation of the detonation in the proposed concept of the engine. Investigation of the detonation propagation is performed for a pulsed detonation rocket and a supersonic combustion chamber. For a pulsed detonation rocket case, the detonation tube is embedded in a mixing chamber where an initiator is added to the main detonation chamber. Propagating detonation waves in a supersonic combustion chamber is investigated for one- and two-dimensional cases. The detonation initiated by an arc and a shock wave is studied in the inviscid and viscous flow, respectively. Various features including a detonation-shock interaction, a detonation diffraction, a base flow and a vortex are observed.

The Euclidean scalar Green function in the five-dimensional Kaluza-Klein magnetic monopole space-time

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

Bezerra de Mello, E.R.

2006-01-15

In this paper we present, in a integral form, the Euclidean Green function associated with a massless scalar field in the five-dimensional Kaluza-Klein magnetic monopole superposed to a global monopole, admitting a nontrivial coupling between the field with the geometry. This Green function is expressed as the sum of two contributions: the first one related with uncharged component of the field, is similar to the Green function associated with a scalar field in a four-dimensional global monopole space-time. The second contains the information of all the other components. Using this Green function it is possible to study the vacuum polarizationmore » effects on this space-time. Explicitly we calculate the renormalized vacuum expectation value <{phi}{sup *}(x){phi}(x)>{sub Ren}, which by its turn is also expressed as the sum of two contributions.« less

Performance Evaluation of Multimodal Multifeature Authentication System Using KNN Classification.

PubMed

Rajagopal, Gayathri; Palaniswamy, Ramamoorthy

2015-01-01

This research proposes a multimodal multifeature biometric system for human recognition using two traits, that is, palmprint and iris. The purpose of this research is to analyse integration of multimodal and multifeature biometric system using feature level fusion to achieve better performance. The main aim of the proposed system is to increase the recognition accuracy using feature level fusion. The features at the feature level fusion are raw biometric data which contains rich information when compared to decision and matching score level fusion. Hence information fused at the feature level is expected to obtain improved recognition accuracy. However, information fused at feature level has the problem of curse in dimensionality; here PCA (principal component analysis) is used to diminish the dimensionality of the feature sets as they are high dimensional. The proposed multimodal results were compared with other multimodal and monomodal approaches. Out of these comparisons, the multimodal multifeature palmprint iris fusion offers significant improvements in the accuracy of the suggested multimodal biometric system. The proposed algorithm is tested using created virtual multimodal database using UPOL iris database and PolyU palmprint database.

Performance Evaluation of Multimodal Multifeature Authentication System Using KNN Classification

PubMed Central

Rajagopal, Gayathri; Palaniswamy, Ramamoorthy

2015-01-01

This research proposes a multimodal multifeature biometric system for human recognition using two traits, that is, palmprint and iris. The purpose of this research is to analyse integration of multimodal and multifeature biometric system using feature level fusion to achieve better performance. The main aim of the proposed system is to increase the recognition accuracy using feature level fusion. The features at the feature level fusion are raw biometric data which contains rich information when compared to decision and matching score level fusion. Hence information fused at the feature level is expected to obtain improved recognition accuracy. However, information fused at feature level has the problem of curse in dimensionality; here PCA (principal component analysis) is used to diminish the dimensionality of the feature sets as they are high dimensional. The proposed multimodal results were compared with other multimodal and monomodal approaches. Out of these comparisons, the multimodal multifeature palmprint iris fusion offers significant improvements in the accuracy of the suggested multimodal biometric system. The proposed algorithm is tested using created virtual multimodal database using UPOL iris database and PolyU palmprint database. PMID:26640813

Facial recognition using multisensor images based on localized kernel eigen spaces.

PubMed

Gundimada, Satyanadh; Asari, Vijayan K

2009-06-01

A feature selection technique along with an information fusion procedure for improving the recognition accuracy of a visual and thermal image-based facial recognition system is presented in this paper. A novel modular kernel eigenspaces approach is developed and implemented on the phase congruency feature maps extracted from the visual and thermal images individually. Smaller sub-regions from a predefined neighborhood within the phase congruency images of the training samples are merged to obtain a large set of features. These features are then projected into higher dimensional spaces using kernel methods. The proposed localized nonlinear feature selection procedure helps to overcome the bottlenecks of illumination variations, partial occlusions, expression variations and variations due to temperature changes that affect the visual and thermal face recognition techniques. AR and Equinox databases are used for experimentation and evaluation of the proposed technique. The proposed feature selection procedure has greatly improved the recognition accuracy for both the visual and thermal images when compared to conventional techniques. Also, a decision level fusion methodology is presented which along with the feature selection procedure has outperformed various other face recognition techniques in terms of recognition accuracy.

Comparison of image features calculated in different dimensions for computer-aided diagnosis of lung nodules

NASA Astrophysics Data System (ADS)

Xu, Ye; Lee, Michael C.; Boroczky, Lilla; Cann, Aaron D.; Borczuk, Alain C.; Kawut, Steven M.; Powell, Charles A.

2009-02-01

Features calculated from different dimensions of images capture quantitative information of the lung nodules through one or multiple image slices. Previously published computer-aided diagnosis (CADx) systems have used either twodimensional (2D) or three-dimensional (3D) features, though there has been little systematic analysis of the relevance of the different dimensions and of the impact of combining different dimensions. The aim of this study is to determine the importance of combining features calculated in different dimensions. We have performed CADx experiments on 125 pulmonary nodules imaged using multi-detector row CT (MDCT). The CADx system computed 192 2D, 2.5D, and 3D image features of the lesions. Leave-one-out experiments were performed using five different combinations of features from different dimensions: 2D, 3D, 2.5D, 2D+3D, and 2D+3D+2.5D. The experiments were performed ten times for each group. Accuracy, sensitivity and specificity were used to evaluate the performance. Wilcoxon signed-rank tests were applied to compare the classification results from these five different combinations of features. Our results showed that 3D image features generate the best result compared with other combinations of features. This suggests one approach to potentially reducing the dimensionality of the CADx data space and the computational complexity of the system while maintaining diagnostic accuracy.

Reduction of respiratory ghosting motion artifacts in conventional two-dimensional multi-slice Cartesian turbo spin-echo: which k-space filling order is the best?

PubMed

Inoue, Yuuji; Yoneyama, Masami; Nakamura, Masanobu; Takemura, Atsushi

2018-06-01

The two-dimensional Cartesian turbo spin-echo (TSE) sequence is widely used in routine clinical studies, but it is sensitive to respiratory motion. We investigated the k-space orders in Cartesian TSE that can effectively reduce motion artifacts. The purpose of this study was to demonstrate the relationship between k-space order and degree of motion artifacts using a moving phantom. We compared the degree of motion artifacts between linear and asymmetric k-space orders. The actual spacing of ghost artifacts in the asymmetric order was doubled compared with that in the linear order in the free-breathing situation. The asymmetric order clearly showed less sensitivity to incomplete breath-hold at the latter half of the imaging period. Because of the actual number of partitions of the k-space and the temporal filling order, the asymmetric k-space order of Cartesian TSE was superior to the linear k-space order for reduction of ghosting motion artifacts.

Accurate facade feature extraction method for buildings from three-dimensional point cloud data considering structural information

NASA Astrophysics Data System (ADS)

Wang, Yongzhi; Ma, Yuqing; Zhu, A.-xing; Zhao, Hui; Liao, Lixia

2018-05-01

Facade features represent segmentations of building surfaces and can serve as a building framework. Extracting facade features from three-dimensional (3D) point cloud data (3D PCD) is an efficient method for 3D building modeling. By combining the advantages of 3D PCD and two-dimensional optical images, this study describes the creation of a highly accurate building facade feature extraction method from 3D PCD with a focus on structural information. The new extraction method involves three major steps: image feature extraction, exploration of the mapping method between the image features and 3D PCD, and optimization of the initial 3D PCD facade features considering structural information. Results show that the new method can extract the 3D PCD facade features of buildings more accurately and continuously. The new method is validated using a case study. In addition, the effectiveness of the new method is demonstrated by comparing it with the range image-extraction method and the optical image-extraction method in the absence of structural information. The 3D PCD facade features extracted by the new method can be applied in many fields, such as 3D building modeling and building information modeling.

Two-stage Framework for a Topology-Based Projection and Visualization of Classified Document Collections

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

Oesterling, Patrick; Scheuermann, Gerik; Teresniak, Sven

During the last decades, electronic textual information has become the world's largest and most important information source available. People have added a variety of daily newspapers, books, scientific and governmental publications, blogs and private messages to this wellspring of endless information and knowledge. Since neither the existing nor the new information can be read in its entirety, computers are used to extract and visualize meaningful or interesting topics and documents from this huge information clutter. In this paper, we extend, improve and combine existing individual approaches into an overall framework that supports topological analysis of high dimensional document point cloudsmore » given by the well-known tf-idf document-term weighting method. We show that traditional distance-based approaches fail in very high dimensional spaces, and we describe an improved two-stage method for topology-based projections from the original high dimensional information space to both two dimensional (2-D) and three dimensional (3-D) visualizations. To show the accuracy and usability of this framework, we compare it to methods introduced recently and apply it to complex document and patent collections.« less

Static stability of a three-dimensional space truss. M.S. Thesis - Case Western Reserve Univ., 1994

NASA Technical Reports Server (NTRS)

Shaker, John F.

1995-01-01

In order to deploy large flexible space structures it is necessary to develop support systems that are strong and lightweight. The most recent example of this aerospace design need is vividly evident in the space station solar array assembly. In order to accommodate both weight limitations and strength performance criteria, ABLE Engineering has developed the Folding Articulating Square Truss (FASTMast) support structure. The FASTMast is a space truss/mechanism hybrid that can provide system support while adhering to stringent packaging demands. However, due to its slender nature and anticipated loading, stability characterization is a critical part of the design process. Furthermore, the dire consequences surely to result from a catastrophic instability quickly provide the motivation for careful examination of this problem. The fundamental components of the space station solar array system are the (1) solar array blanket system, (2) FASTMast support structure, and (3) mast canister assembly. The FASTMast once fully deployed from the canister will provide support to the solar array blankets. A unique feature of this structure is that the system responds linearly within a certain range of operating loads and nonlinearly when that range is exceeded. The source of nonlinear behavior in this case is due to a changing stiffness state resulting from an inability of diagonal members to resist applied loads. The principal objective of this study was to establish the failure modes involving instability of the FASTMast structure. Also of great interest during this effort was to establish a reliable analytical approach capable of effectively predicting critical values at which the mast becomes unstable. Due to the dual nature of structural response inherent to this problem, both linear and nonlinear analyses are required to characterize the mast in terms of stability. The approach employed herein is one that can be considered systematic in nature. The analysis begins with one and two-dimensional failure models of the system and its important components. From knowledge gained through preliminary analyses a foundation is developed for three-dimensional analyses of the FASTMast structure. The three-dimensional finite element (FE) analysis presented here involves a FASTMast system one-tenth the size of the actual flight unit. Although this study does not yield failure analysis results that apply directly to the flight article, it does establish a method by which the full-scale mast can be evaluated.

Static stability of a three-dimensional space truss

NASA Astrophysics Data System (ADS)

Shaker, John F.

1995-05-01

In order to deploy large flexible space structures it is necessary to develop support systems that are strong and lightweight. The most recent example of this aerospace design need is vividly evident in the space station solar array assembly. In order to accommodate both weight limitations and strength performance criteria, ABLE Engineering has developed the Folding Articulating Square Truss (FASTMast) support structure. The FASTMast is a space truss/mechanism hybrid that can provide system support while adhering to stringent packaging demands. However, due to its slender nature and anticipated loading, stability characterization is a critical part of the design process. Furthermore, the dire consequences surely to result from a catastrophic instability quickly provide the motivation for careful examination of this problem. The fundamental components of the space station solar array system are the (1) solar array blanket system, (2) FASTMast support structure, and (3) mast canister assembly. The FASTMast once fully deployed from the canister will provide support to the solar array blankets. A unique feature of this structure is that the system responds linearly within a certain range of operating loads and nonlinearly when that range is exceeded. The source of nonlinear behavior in this case is due to a changing stiffness state resulting from an inability of diagonal members to resist applied loads. The principal objective of this study was to establish the failure modes involving instability of the FASTMast structure. Also of great interest during this effort was to establish a reliable analytical approach capable of effectively predicting critical values at which the mast becomes unstable. Due to the dual nature of structural response inherent to this problem, both linear and nonlinear analyses are required to characterize the mast in terms of stability. The approach employed herein is one that can be considered systematic in nature. The analysis begins with one and two-dimensional failure models of the system and its important components. From knowledge gained through preliminary analyses a foundation is developed for three-dimensional analyses of the FASTMast structure. The three-dimensional finite element (FE) analysis presented here involves a FASTMast system one-tenth the size of the actual flight unit. Although this study does not yield failure analysis results that apply directly to the flight article, it does establish a method by which the full-scale mast can be evaluated.

Comparative Study of SVM Methods Combined with Voxel Selection for Object Category Classification on fMRI Data

PubMed Central

Song, Sutao; Zhan, Zhichao; Long, Zhiying; Zhang, Jiacai; Yao, Li

2011-01-01

Background Support vector machine (SVM) has been widely used as accurate and reliable method to decipher brain patterns from functional MRI (fMRI) data. Previous studies have not found a clear benefit for non-linear (polynomial kernel) SVM versus linear one. Here, a more effective non-linear SVM using radial basis function (RBF) kernel is compared with linear SVM. Different from traditional studies which focused either merely on the evaluation of different types of SVM or the voxel selection methods, we aimed to investigate the overall performance of linear and RBF SVM for fMRI classification together with voxel selection schemes on classification accuracy and time-consuming. Methodology/Principal Findings Six different voxel selection methods were employed to decide which voxels of fMRI data would be included in SVM classifiers with linear and RBF kernels in classifying 4-category objects. Then the overall performances of voxel selection and classification methods were compared. Results showed that: (1) Voxel selection had an important impact on the classification accuracy of the classifiers: in a relative low dimensional feature space, RBF SVM outperformed linear SVM significantly; in a relative high dimensional space, linear SVM performed better than its counterpart; (2) Considering the classification accuracy and time-consuming holistically, linear SVM with relative more voxels as features and RBF SVM with small set of voxels (after PCA) could achieve the better accuracy and cost shorter time. Conclusions/Significance The present work provides the first empirical result of linear and RBF SVM in classification of fMRI data, combined with voxel selection methods. Based on the findings, if only classification accuracy was concerned, RBF SVM with appropriate small voxels and linear SVM with relative more voxels were two suggested solutions; if users concerned more about the computational time, RBF SVM with relative small set of voxels when part of the principal components were kept as features was a better choice. PMID:21359184

Comparative study of SVM methods combined with voxel selection for object category classification on fMRI data.

PubMed

Song, Sutao; Zhan, Zhichao; Long, Zhiying; Zhang, Jiacai; Yao, Li

2011-02-16

Support vector machine (SVM) has been widely used as accurate and reliable method to decipher brain patterns from functional MRI (fMRI) data. Previous studies have not found a clear benefit for non-linear (polynomial kernel) SVM versus linear one. Here, a more effective non-linear SVM using radial basis function (RBF) kernel is compared with linear SVM. Different from traditional studies which focused either merely on the evaluation of different types of SVM or the voxel selection methods, we aimed to investigate the overall performance of linear and RBF SVM for fMRI classification together with voxel selection schemes on classification accuracy and time-consuming. Six different voxel selection methods were employed to decide which voxels of fMRI data would be included in SVM classifiers with linear and RBF kernels in classifying 4-category objects. Then the overall performances of voxel selection and classification methods were compared. Results showed that: (1) Voxel selection had an important impact on the classification accuracy of the classifiers: in a relative low dimensional feature space, RBF SVM outperformed linear SVM significantly; in a relative high dimensional space, linear SVM performed better than its counterpart; (2) Considering the classification accuracy and time-consuming holistically, linear SVM with relative more voxels as features and RBF SVM with small set of voxels (after PCA) could achieve the better accuracy and cost shorter time. The present work provides the first empirical result of linear and RBF SVM in classification of fMRI data, combined with voxel selection methods. Based on the findings, if only classification accuracy was concerned, RBF SVM with appropriate small voxels and linear SVM with relative more voxels were two suggested solutions; if users concerned more about the computational time, RBF SVM with relative small set of voxels when part of the principal components were kept as features was a better choice.

Distinctive Features of Spatial Perspective-Taking in the Elderly

ERIC Educational Resources Information Center

Watanabe, Masayuki

2011-01-01

This study aimed to ascertain the characteristics of spatial perspective-taking ability--assumed to be a form of imaginary body movement in three-dimensional space--in the elderly. A new task was devised to evaluate the development of this function: 20 children, 20 university students, and 20 elderly people (each group comprising 10 men and 10…

Data Mining and Machine Learning Models for Predicting Drug Likeness and Their Disease or Organ Category.

PubMed

Yosipof, Abraham; Guedes, Rita C; García-Sosa, Alfonso T

2018-01-01

Data mining approaches can uncover underlying patterns in chemical and pharmacological property space decisive for drug discovery and development. Two of the most common approaches are visualization and machine learning methods. Visualization methods use dimensionality reduction techniques in order to reduce multi-dimension data into 2D or 3D representations with a minimal loss of information. Machine learning attempts to find correlations between specific activities or classifications for a set of compounds and their features by means of recurring mathematical models. Both models take advantage of the different and deep relationships that can exist between features of compounds, and helpfully provide classification of compounds based on such features or in case of visualization methods uncover underlying patterns in the feature space. Drug-likeness has been studied from several viewpoints, but here we provide the first implementation in chemoinformatics of the t-Distributed Stochastic Neighbor Embedding (t-SNE) method for the visualization and the representation of chemical space, and the use of different machine learning methods separately and together to form a new ensemble learning method called AL Boost. The models obtained from AL Boost synergistically combine decision tree, random forests (RF), support vector machine (SVM), artificial neural network (ANN), k nearest neighbors (kNN), and logistic regression models. In this work, we show that together they form a predictive model that not only improves the predictive force but also decreases bias. This resulted in a corrected classification rate of over 0.81, as well as higher sensitivity and specificity rates for the models. In addition, separation and good models were also achieved for disease categories such as antineoplastic compounds and nervous system diseases, among others. Such models can be used to guide decision on the feature landscape of compounds and their likeness to either drugs or other characteristics, such as specific or multiple disease-category(ies) or organ(s) of action of a molecule.

Data-driven clustering of rain events: microphysics information derived from macro-scale observations

NASA Astrophysics Data System (ADS)

Djallel Dilmi, Mohamed; Mallet, Cécile; Barthes, Laurent; Chazottes, Aymeric

2017-04-01

Rain time series records are generally studied using rainfall rate or accumulation parameters, which are estimated for a fixed duration (typically 1 min, 1 h or 1 day). In this study we use the concept of rain events. The aim of the first part of this paper is to establish a parsimonious characterization of rain events, using a minimal set of variables selected among those normally used for the characterization of these events. A methodology is proposed, based on the combined use of a genetic algorithm (GA) and self-organizing maps (SOMs). It can be advantageous to use an SOM, since it allows a high-dimensional data space to be mapped onto a two-dimensional space while preserving, in an unsupervised manner, most of the information contained in the initial space topology. The 2-D maps obtained in this way allow the relationships between variables to be determined and redundant variables to be removed, thus leading to a minimal subset of variables. We verify that such 2-D maps make it possible to determine the characteristics of all events, on the basis of only five features (the event duration, the peak rain rate, the rain event depth, the standard deviation of the rain rate event and the absolute rain rate variation of the order of 0.5). From this minimal subset of variables, hierarchical cluster analyses were carried out. We show that clustering into two classes allows the conventional convective and stratiform classes to be determined, whereas classification into five classes allows this convective-stratiform classification to be further refined. Finally, our study made it possible to reveal the presence of some specific relationships between these five classes and the microphysics of their associated rain events.

Diagnostic analysis of two-dimensional monthly average ozone balance with Chapman chemistry

NASA Technical Reports Server (NTRS)

Stolarski, Richard S.; Jackman, Charles H.; Kaye, Jack A.

1986-01-01

Chapman chemistry has been used in a two-dimensional model to simulate ozone balance phenomenology. The similarity between regions of ozone production and loss calculated using Chapman chemistry and those computed using LIMS and SAMS data with a photochemical equilibrium model indicate that such simplified chemistry is useful in studying gross features in stratospheric ozone balance. Net ozone production or loss rates are brought about by departures from the photochemical equilibrium (PCE) condition. If transport drives ozone above its PCE condition, then photochemical loss dominates production. If transport drives ozone below its PCE condition, then photochemical production dominates loss. Gross features of ozone loss/production (L/P) inferred for the real atmosphere from data are also simulated using only eddy diffusion. This indicates that one must be careful in assigning a transport scheme for a two-dimensional model that mimics only behavior of the observed ozone L/P.

3D/2D image registration using weighted histogram of gradient directions

NASA Astrophysics Data System (ADS)

Ghafurian, Soheil; Hacihaliloglu, Ilker; Metaxas, Dimitris N.; Tan, Virak; Li, Kang

2015-03-01

Three dimensional (3D) to two dimensional (2D) image registration is crucial in many medical applications such as image-guided evaluation of musculoskeletal disorders. One of the key problems is to estimate the 3D CT- reconstructed bone model positions (translation and rotation) which maximize the similarity between the digitally reconstructed radiographs (DRRs) and the 2D fluoroscopic images using a registration method. This problem is computational-intensive due to a large search space and the complicated DRR generation process. Also, finding a similarity measure which converges to the global optimum instead of local optima adds to the challenge. To circumvent these issues, most existing registration methods need a manual initialization, which requires user interaction and is prone to human error. In this paper, we introduce a novel feature-based registration method using the weighted histogram of gradient directions of images. This method simplifies the computation by searching the parameter space (rotation and translation) sequentially rather than simultaneously. In our numeric simulation experiments, the proposed registration algorithm was able to achieve sub-millimeter and sub-degree accuracies. Moreover, our method is robust to the initial guess. It can tolerate up to +/-90°rotation offset from the global optimal solution, which minimizes the need for human interaction to initialize the algorithm.

Tubular Crystals and Helical Arrays: Structural Determination of HIV-1 Capsid Assemblies Using Iterative Helical Real-Space Reconstruction

PubMed Central

Zhang, Peijun; Meng, Xin; Zhao, Gongpu

2013-01-01

Helical structures are important in many different life forms and are well-suited for structural studies by cryo-EM. A unique feature of helical objects is that a single projection image contains all the views needed to perform a three-dimensional (3D) crystallographic reconstruction. Here, we use HIV-1 capsid assemblies to illustrate the detailed approaches to obtain 3D density maps from helical objects. Mature HIV-1 particles contain a conical- or tubular-shaped capsid that encloses the viral RNA genome and performs essential functions in the virus life cycle. The capsid is composed of capsid protein (CA) oligomers which are helically arranged on the surface. The N-terminal domain (NTD) of CA is connected to its C-terminal domain (CTD) through a flexible hinge. Structural analysis of two- and three-dimensional crystals provided molecular models of the capsid protein (CA) and its oligomer forms. We determined the 3D density map of helically assembled HIV-1 CA hexamers at 16 Å resolution using an iterative helical real-space reconstruction method. Docking of atomic models of CA-NTD and CA-CTD dimer into the electron density map indicated that the CTD dimer interface is retained in the assembled CA. Furthermore, molecular docking revealed an additional, novel CTD trimer interface. PMID:23132072

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

NASA Astrophysics Data System (ADS)

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

2010-03-01

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

A novel multi-detection technique for three-dimensional reciprocal-space mapping in grazing-incidence X-ray diffraction.

PubMed

Schmidbauer, M; Schäfer, P; Besedin, S; Grigoriev, D; Köhler, R; Hanke, M

2008-11-01

A new scattering technique in grazing-incidence X-ray diffraction geometry is described which enables three-dimensional mapping of reciprocal space by a single rocking scan of the sample. This is achieved by using a two-dimensional detector. The new set-up is discussed in terms of angular resolution and dynamic range of scattered intensity. As an example the diffuse scattering from a strained multilayer of self-assembled (In,Ga)As quantum dots grown on GaAs substrate is presented.

Dynamical features and electric field strengths of double layers driven by currents. [in auroras

NASA Technical Reports Server (NTRS)

Singh, N.; Thiemann, H.; Schunk, R. W.

1985-01-01

In recent years, a number of papers have been concerned with 'ion-acoustic' double layers. In the present investigation, results from numerical simulations are presented to show that the shapes and forms of current-driven double layers evolve dynamically with the fluctuations in the current through the plasma. It is shown that double layers with a potential dip can form even without the excitation of ion-acoustic modes. Double layers in two-and one-half-dimensional simulations are discussed, taking into account the simulation technique, the spatial and temporal features of plasma, and the dynamical behavior of the parallel potential distribution. Attention is also given to double layers in one-dimensional simulations, and electrical field strengths predicted by two-and one-half-dimensional simulations.

Charged black holes in compactified spacetimes

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

Karlovini, Max; Unge, Rikard von

2005-11-15

We construct and investigate a compactified version of the four-dimensional Reissner-Nordstroem-Taub-NUT solution, generalizing the compactified Schwarzschild black hole that has been previously studied by several workers. Our approach to compactification is based on dimensional reduction with respect to the stationary Killing vector, resulting in three-dimensional gravity coupled to a nonlinear sigma model. Knowing that the original noncompactified solution corresponds to a target space geodesic, the problem can be linearized much in the same way as in the case of no electric or Taub-NUT charge. An interesting feature of the solution family is that, for nonzero electric charge but vanishing Taub-NUTmore » charge, the solution has a curvature singularity on a torus that surrounds the event horizon, but this singularity is removed when the Taub-NUT charge is switched on. We also treat the Schwarzschild case in a more complete way than has been done previously. In particular, the asymptotic solution (the Levi-Civita solution with the height coordinate made periodic) has to our knowledge only been calculated up to a determination of the mass parameter. The periodic Levi-Civita solution contains three essential parameters, however, and the remaining two are explicitly calculated here.« less

STARS: A general-purpose finite element computer program for analysis of engineering structures

NASA Technical Reports Server (NTRS)

Gupta, K. K.

1984-01-01

STARS (Structural Analysis Routines) is primarily an interactive, graphics-oriented, finite-element computer program for analyzing the static, stability, free vibration, and dynamic responses of damped and undamped structures, including rotating systems. The element library consists of one-dimensional (1-D) line elements, two-dimensional (2-D) triangular and quadrilateral shell elements, and three-dimensional (3-D) tetrahedral and hexahedral solid elements. These elements enable the solution of structural problems that include truss, beam, space frame, plane, plate, shell, and solid structures, or any combination thereof. Zero, finite, and interdependent deflection boundary conditions can be implemented by the program. The associated dynamic response analysis capability provides for initial deformation and velocity inputs, whereas the transient excitation may be either forces or accelerations. An effective in-core or out-of-core solution strategy is automatically employed by the program, depending on the size of the problem. Data input may be at random within a data set, and the program offers certain automatic data-generation features. Input data are formatted as an optimal combination of free and fixed formats. Interactive graphics capabilities enable convenient display of nodal deformations, mode shapes, and element stresses.

Effect of Proximity of Features on the Damage Threshold During Submicron Additive Manufacturing Via Two-Photon Polymerization

DOE PAGES

Saha, Sourabh K.; Divin, Chuck; Cuadra, Jefferson A.; ...

2017-05-12

Two-photon polymerization (TPP) is a laser writing process that enables fabrication of millimeter scale three-dimensional (3D) structures with submicron features. In TPP, writing is achieved via nonlinear two-photon absorption that occurs at high laser intensities. Thus, it is essential to carefully select the incident power to prevent laser damage during polymerization. Currently, the feasible range of laser power is identified by writing small test patterns at varying power levels. Here in this paper, we demonstrate that the results of these tests cannot be generalized, because the damage threshold power depends on the proximity of features and reduces by as muchmore » as 47% for overlapping features. We have identified that this reduction occurs primarily due to an increase in the single-photon absorptivity of the resin after curing. We have captured the damage from proximity effects via X-ray 3D computed tomography (CT) images of a non-homogenous part that has varying feature density. Part damage manifests as internal spherical voids that arise due to boiling of the resist. We have empirically quantified this proximity effect by identifying the damage threshold power at different writing speeds and feature overlap spacings. In addition, we present a first-order analytical model that captures the scaling of this proximity effect. Based on this model and the experiments, we have identified that the proximity effect is more significant at high writing speeds; therefore, it adversely affects the scalability of manufacturing. The scaling laws and the empirical data generated here can be used to select the appropriate TPP writing parameters.« less

Effect of Proximity of Features on the Damage Threshold During Submicron Additive Manufacturing Via Two-Photon Polymerization

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

Saha, Sourabh K.; Divin, Chuck; Cuadra, Jefferson A.

Two-photon polymerization (TPP) is a laser writing process that enables fabrication of millimeter scale three-dimensional (3D) structures with submicron features. In TPP, writing is achieved via nonlinear two-photon absorption that occurs at high laser intensities. Thus, it is essential to carefully select the incident power to prevent laser damage during polymerization. Currently, the feasible range of laser power is identified by writing small test patterns at varying power levels. Here in this paper, we demonstrate that the results of these tests cannot be generalized, because the damage threshold power depends on the proximity of features and reduces by as muchmore » as 47% for overlapping features. We have identified that this reduction occurs primarily due to an increase in the single-photon absorptivity of the resin after curing. We have captured the damage from proximity effects via X-ray 3D computed tomography (CT) images of a non-homogenous part that has varying feature density. Part damage manifests as internal spherical voids that arise due to boiling of the resist. We have empirically quantified this proximity effect by identifying the damage threshold power at different writing speeds and feature overlap spacings. In addition, we present a first-order analytical model that captures the scaling of this proximity effect. Based on this model and the experiments, we have identified that the proximity effect is more significant at high writing speeds; therefore, it adversely affects the scalability of manufacturing. The scaling laws and the empirical data generated here can be used to select the appropriate TPP writing parameters.« less

Ensemble of sparse classifiers for high-dimensional biological data.

PubMed

Kim, Sunghan; Scalzo, Fabien; Telesca, Donatello; Hu, Xiao

2015-01-01

Biological data are often high in dimension while the number of samples is small. In such cases, the performance of classification can be improved by reducing the dimension of data, which is referred to as feature selection. Recently, a novel feature selection method has been proposed utilising the sparsity of high-dimensional biological data where a small subset of features accounts for most variance of the dataset. In this study we propose a new classification method for high-dimensional biological data, which performs both feature selection and classification within a single framework. Our proposed method utilises a sparse linear solution technique and the bootstrap aggregating algorithm. We tested its performance on four public mass spectrometry cancer datasets along with two other conventional classification techniques such as Support Vector Machines and Adaptive Boosting. The results demonstrate that our proposed method performs more accurate classification across various cancer datasets than those conventional classification techniques.

Low-latitude Ionospheric Research using the CIRCE Mission

NASA Astrophysics Data System (ADS)

Dymond, K.; Nicholas, A. C.; Budzien, S. A.; Stephan, A. W.

2016-12-01

The Coordinated Ionospheric Reconstruction Cubesat Experiment (CIRCE) is a dual-satellite mission consisting of two 6U CubeSats actively maintaining a lead-follow configuration in the same orbit with a launch planned for the 2018-2019 time frame. These nano-satellites will each feature two 1U ultraviolet photometers, observing the 135.6 nm emission of atomic oxygen at nighttime. The primary objective is to characterize the two-dimensional distribution of electrons in the Equatorial Ionization Anomaly (EIA). The methodology used to reconstruct the nighttime ionosphere employs continuous UV photometry from four distinct viewing angles in combination with an additional data source, such as in situ plasma density measurements or a wide-band beacon data, with advanced image space reconstruction algorithm tomography techniques. The COSMIC/FORMOSAT-3 (CF3) constellation featured six Tiny Ionospheric Photometers, a compact UV sensor design which served as the pathfinder for the CIRCE instruments. The TIP instruments on the CF3 satellites demonstrated detection of ionospheric bubbles before they had penetrated the peak of the F-region ionosphere. We present our mission concept, simulations illustrating the imaging capability of the sensor suite, and a range of science questions addressable using such a system.

Backshort-Under-Grid arrays for infrared astronomy

NASA Astrophysics Data System (ADS)

Allen, C. A.; Benford, D. J.; Chervenak, J. A.; Chuss, D. T.; Miller, T. M.; Moseley, S. H.; Staguhn, J. G.; Wollack, E. J.

2006-04-01

We are developing a kilopixel, filled bolometer array for space infrared astronomy. The array consists of three individual components, to be merged into a single, working unit; (1) a transition edge sensor bolometer array, operating in the milliKelvin regime, (2) a quarter-wave backshort grid, and (3) superconducting quantum interference device multiplexer readout. The detector array is designed as a filled, square grid of suspended, silicon bolometers with superconducting sensors. The backshort arrays are fabricated separately and will be positioned in the cavities created behind each detector during fabrication. The grids have a unique interlocking feature machined into the walls for positioning and mechanical stability. The spacing of the backshort beneath the detector grid can be set from ˜30 300 μm, by independently adjusting two process parameters during fabrication. The ultimate goal is to develop a large-format array architecture with background-limited sensitivity, suitable for a wide range of wavelengths and applications, to be directly bump bonded to a multiplexer circuit. We have produced prototype two-dimensional arrays having 8×8 detector elements. We present detector design, fabrication overview, and assembly technologies.

Semiconducting Ba 3Sn 3Sb 4 and Metallic Ba 7–xSn 11Sb 15–y ( x = 0.4, y = 0.6) Zintl Phases

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

Chen, Haijie; Narayan, Awadhesh; Stoumpos, Constantinos C.

In this paper, we report the discovery of two ternary Zintl phases Ba 3Sn 3Sb 4 and Ba 7-xSn 11Sb 15-y, (x = 0.4, y = 0.6). Ba 3Sn 3Sb 4 adopts the monoclinic space group P2 1/c with a = 14.669(3) Å, b = 6.9649(14) Å, c = 13.629(3) Å, and β = 104.98(3)°. It features a unique corrugated two-dimensional (2D) structure consisting of [Sn 3Sb 4] 6- layers extending along the ab plane with Ba 2+ atoms sandwiched between them. The non-stoichiometric Ba 6.6Sn 11Sb 14.4 has a complex one-dimensional (1D) structure adopting the orthorhombic space group Pnma,more » with unit cell parameters a = 37.964(8) Å, b = 4.4090(9) Å and c = 24.682(5) Å. It consists of large double Sn-Sb ribbons separated by Ba 2+ atoms. Ba3Sn3Sb4 is an n-type semiconductor which has a narrow energy gap of ~0.18 eV and a room temperature carrier concentration of ~4.2 × 10 18 cm -3. Lastly, Ba 6.6Sn 11Sb 14.4 is determined to be a metal with electrons being the dominant carriers.« less

Semiconducting Ba 3Sn 3Sb 4 and Metallic Ba 7–xSn 11Sb 15–y ( x = 0.4, y = 0.6) Zintl Phases

DOE PAGES

Chen, Haijie; Narayan, Awadhesh; Stoumpos, Constantinos C.; ...

2017-11-08

In this paper, we report the discovery of two ternary Zintl phases Ba 3Sn 3Sb 4 and Ba 7-xSn 11Sb 15-y, (x = 0.4, y = 0.6). Ba 3Sn 3Sb 4 adopts the monoclinic space group P2 1/c with a = 14.669(3) Å, b = 6.9649(14) Å, c = 13.629(3) Å, and β = 104.98(3)°. It features a unique corrugated two-dimensional (2D) structure consisting of [Sn 3Sb 4] 6- layers extending along the ab plane with Ba 2+ atoms sandwiched between them. The non-stoichiometric Ba 6.6Sn 11Sb 14.4 has a complex one-dimensional (1D) structure adopting the orthorhombic space group Pnma,more » with unit cell parameters a = 37.964(8) Å, b = 4.4090(9) Å and c = 24.682(5) Å. It consists of large double Sn-Sb ribbons separated by Ba 2+ atoms. Ba3Sn3Sb4 is an n-type semiconductor which has a narrow energy gap of ~0.18 eV and a room temperature carrier concentration of ~4.2 × 10 18 cm -3. Lastly, Ba 6.6Sn 11Sb 14.4 is determined to be a metal with electrons being the dominant carriers.« less

Aerodynamic Analyses Requiring Advanced Computers, part 2

NASA Technical Reports Server (NTRS)

1975-01-01

Papers given at the conference present the results of theoretical research on aerodynamic flow problems requiring the use of advanced computers. Topics discussed include two-dimensional configurations, three-dimensional configurations, transonic aircraft, and the space shuttle.

Space Product Development (SPD)

NASA Image and Video Library

2003-06-01

Echocardiography uses sound waves to image the heart and other organs. Developing a compact version of the latest technology improved the ease of monitoring crew member health, a critical task during long space flights. NASA researchers plan to adapt the three-dimensional (3-D) echocardiogram for space flight. The two-dimensional (2-D) echocardiogram utilized in orbit on the International Space Station (ISS) was effective, but difficult to use with precision. A heart image from a 2-D echocardiogram (left) is of a better quality than that from a 3-D device (right), but the 3-D imaging procedure is more user-friendly.

Entanglement, holography and causal diamonds

NASA Astrophysics Data System (ADS)

de Boer, Jan; Haehl, Felix M.; Heller, Michal P.; Myers, Robert C.

2016-08-01

We argue that the degrees of freedom in a d-dimensional CFT can be reorganized in an insightful way by studying observables on the moduli space of causal diamonds (or equivalently, the space of pairs of timelike separated points). This 2 d-dimensional space naturally captures some of the fundamental nonlocality and causal structure inherent in the entanglement of CFT states. For any primary CFT operator, we construct an observable on this space, which is defined by smearing the associated one-point function over causal diamonds. Known examples of such quantities are the entanglement entropy of vacuum excitations and its higher spin generalizations. We show that in holographic CFTs, these observables are given by suitably defined integrals of dual bulk fields over the corresponding Ryu-Takayanagi minimal surfaces. Furthermore, we explain connections to the operator product expansion and the first law of entanglemententropy from this unifying point of view. We demonstrate that for small perturbations of the vacuum, our observables obey linear two-derivative equations of motion on the space of causal diamonds. In two dimensions, the latter is given by a product of two copies of a two-dimensional de Sitter space. For a class of universal states, we show that the entanglement entropy and its spin-three generalization obey nonlinear equations of motion with local interactions on this moduli space, which can be identified with Liouville and Toda equations, respectively. This suggests the possibility of extending the definition of our new observables beyond the linear level more generally and in such a way that they give rise to new dynamically interacting theories on the moduli space of causal diamonds. Various challenges one has to face in order to implement this idea are discussed.

Chiral primordial blue tensor spectra from the axion-gauge couplings

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

Obata, Ippei, E-mail: obata@tap.scphys.kyoto-u.ac.jp

We suggest the new feature of primordial gravitational waves sourced by the axion-gauge couplings, whose forms are motivated by the dimensional reduction of the form field in the string theory. In our inflationary model, as an inflaton we adopt two types of axion, dubbed the model-independent axion and the model-dependent axion, which couple with two gauge groups with different sign combination each other. Due to these forms both polarization modes of gauge fields are amplified and enhance both helicies of tensor modes during inflation. We point out the possibility that a primordial blue-tilted tensor power spectra with small chirality aremore » provided by the combination of these axion-gauge couplings, intriguingly both amplitudes and chirality are potentially testable by future space-based gravitational wave interferometers such as DECIGO and BBO project.« less

Entanglement and the three-dimensionality of the Bloch ball

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

Masanes, Ll., E-mail: ll.masanes@gmail.com; Müller, M. P.; Pérez-García, D.

2014-12-15

We consider a very natural generalization of quantum theory by letting the dimension of the Bloch ball be not necessarily three. We analyze bipartite state spaces where each of the components has a d-dimensional Euclidean ball as state space. In addition to this, we impose two very natural assumptions: the continuity and reversibility of dynamics and the possibility of characterizing bipartite states by local measurements. We classify all these bipartite state spaces and prove that, except for the quantum two-qubit state space, none of them contains entangled states. Equivalently, in any of these non-quantum theories, interacting dynamics is impossible. Thismore » result reveals that “existence of entanglement” is the requirement with minimal logical content which singles out quantum theory from our family of theories.« less

Maximal violation of a bipartite three-setting, two-outcome Bell inequality using infinite-dimensional quantum systems

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

Pal, Karoly F.; Vertesi, Tamas

2010-08-15

The I{sub 3322} inequality is the simplest bipartite two-outcome Bell inequality beyond the Clauser-Horne-Shimony-Holt (CHSH) inequality, consisting of three two-outcome measurements per party. In the case of the CHSH inequality the maximal quantum violation can already be attained with local two-dimensional quantum systems; however, there is no such evidence for the I{sub 3322} inequality. In this paper a family of measurement operators and states is given which enables us to attain the maximum quantum value in an infinite-dimensional Hilbert space. Further, it is conjectured that our construction is optimal in the sense that measuring finite-dimensional quantum systems is not enoughmore » to achieve the true quantum maximum. We also describe an efficient iterative algorithm for computing quantum maximum of an arbitrary two-outcome Bell inequality in any given Hilbert space dimension. This algorithm played a key role in obtaining our results for the I{sub 3322} inequality, and we also applied it to improve on our previous results concerning the maximum quantum violation of several bipartite two-outcome Bell inequalities with up to five settings per party.« less

Mof-Tree: A Spatial Access Method To Manipulate Multiple Overlapping Features.

ERIC Educational Resources Information Center

Manolopoulos, Yannis; Nardelli, Enrico; Papadopoulos, Apostolos; Proietti, Guido

1997-01-01

Investigates the manipulation of large sets of two-dimensional data representing multiple overlapping features, and presents a new access method, the MOF-tree. Analyzes storage requirements and time with respect to window query operations involving multiple features. Examines both the pointer-based and pointerless MOF-tree representations.…

Decorrelation of the true and estimated classifier errors in high-dimensional settings.

PubMed

Hanczar, Blaise; Hua, Jianping; Dougherty, Edward R

2007-01-01

The aim of many microarray experiments is to build discriminatory diagnosis and prognosis models. Given the huge number of features and the small number of examples, model validity which refers to the precision of error estimation is a critical issue. Previous studies have addressed this issue via the deviation distribution (estimated error minus true error), in particular, the deterioration of cross-validation precision in high-dimensional settings where feature selection is used to mitigate the peaking phenomenon (overfitting). Because classifier design is based upon random samples, both the true and estimated errors are sample-dependent random variables, and one would expect a loss of precision if the estimated and true errors are not well correlated, so that natural questions arise as to the degree of correlation and the manner in which lack of correlation impacts error estimation. We demonstrate the effect of correlation on error precision via a decomposition of the variance of the deviation distribution, observe that the correlation is often severely decreased in high-dimensional settings, and show that the effect of high dimensionality on error estimation tends to result more from its decorrelating effects than from its impact on the variance of the estimated error. We consider the correlation between the true and estimated errors under different experimental conditions using both synthetic and real data, several feature-selection methods, different classification rules, and three error estimators commonly used (leave-one-out cross-validation, k-fold cross-validation, and .632 bootstrap). Moreover, three scenarios are considered: (1) feature selection, (2) known-feature set, and (3) all features. Only the first is of practical interest; however, the other two are needed for comparison purposes. We will observe that the true and estimated errors tend to be much more correlated in the case of a known feature set than with either feature selection or using all features, with the better correlation between the latter two showing no general trend, but differing for different models.

Use of Probability Distribution Functions for Discriminating Between Cloud and Aerosol in Lidar Backscatter Data

NASA Technical Reports Server (NTRS)

Liu, Zhaoyan; Vaughan, Mark A.; Winker, Davd M.; Hostetler, Chris A.; Poole, Lamont R.; Hlavka, Dennis; Hart, William; McGill, Mathew

2004-01-01

In this paper we describe the algorithm hat will be used during the upcoming Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission for discriminating between clouds and aerosols detected in two wavelength backscatter lidar profiles. We first analyze single-test and multiple-test classification approaches based on one-dimensional and multiple-dimensional probability density functions (PDFs) in the context of a two-class feature identification scheme. From these studies we derive an operational algorithm based on a set of 3-dimensional probability distribution functions characteristic of clouds and aerosols. A dataset acquired by the Cloud Physics Lidar (CPL) is used to test the algorithm. Comparisons are conducted between the CALIPSO algorithm results and the CPL data product. The results obtained show generally good agreement between the two methods. However, of a total of 228,264 layers analyzed, approximately 5.7% are classified as different types by the CALIPSO and CPL algorithm. This disparity is shown to be due largely to the misclassification of clouds as aerosols by the CPL algorithm. The use of 3-dimensional PDFs in the CALIPSO algorithm is found to significantly reduce this type of error. Dust presents a special case. Because the intrinsic scattering properties of dust layers can be very similar to those of clouds, additional algorithm testing was performed using an optically dense layer of Saharan dust measured during the Lidar In-space Technology Experiment (LITE). In general, the method is shown to distinguish reliably between dust layers and clouds. The relatively few erroneous classifications occurred most often in the LITE data, in those regions of the Saharan dust layer where the optical thickness was the highest.

Vectorized and multitasked solution of the few-group neutron diffusion equations

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

Zee, S.K.; Turinsky, P.J.; Shayer, Z.

1989-03-01

A numerical algorithm with parallelism was used to solve the two-group, multidimensional neutron diffusion equations on computers characterized by shared memory, vector pipeline, and multi-CPU architecture features. Specifically, solutions were obtained on the Cray X/MP-48, the IBM-3090 with vector facilities, and the FPS-164. The material-centered mesh finite difference method approximation and outer-inner iteration method were employed. Parallelism was introduced in the inner iterations using the cyclic line successive overrelaxation iterative method and solving in parallel across lines. The outer iterations were completed using the Chebyshev semi-iterative method that allows parallelism to be introduced in both space and energy groups. Formore » the three-dimensional model, power, soluble boron, and transient fission product feedbacks were included. Concentrating on the pressurized water reactor (PWR), the thermal-hydraulic calculation of moderator density assumed single-phase flow and a closed flow channel, allowing parallelism to be introduced in the solution across the radial plane. Using a pinwise detail, quarter-core model of a typical PWR in cycle 1, for the two-dimensional model without feedback the measured million floating point operations per second (MFLOPS)/vector speedups were 83/11.7. 18/2.2, and 2.4/5.6 on the Cray, IBM, and FPS without multitasking, respectively. Lower performance was observed with a coarser mesh, i.e., shorter vector length, due to vector pipeline start-up. For an 18 x 18 x 30 (x-y-z) three-dimensional model with feedback of the same core, MFLOPS/vector speedups of --61/6.7 and an execution time of 0.8 CPU seconds on the Cray without multitasking were measured. Finally, using two CPUs and the vector pipelines of the Cray, a multitasking efficiency of 81% was noted for the three-dimensional model.« less

A new approach to increase the two-dimensional detection probability of CSI algorithm for WAS-GMTI mode

NASA Astrophysics Data System (ADS)

Yan, H.; Zheng, M. J.; Zhu, D. Y.; Wang, H. T.; Chang, W. S.

2015-07-01

When using clutter suppression interferometry (CSI) algorithm to perform signal processing in a three-channel wide-area surveillance radar system, the primary concern is to effectively suppress the ground clutter. However, a portion of moving target's energy is also lost in the process of channel cancellation, which is often neglected in conventional applications. In this paper, we firstly investigate the two-dimensional (radial velocity dimension and squint angle dimension) residual amplitude of moving targets after channel cancellation with CSI algorithm. Then, a new approach is proposed to increase the two-dimensional detection probability of moving targets by reserving the maximum value of the three channel cancellation results in non-uniformly spaced channel system. Besides, theoretical expression of the false alarm probability with the proposed approach is derived in the paper. Compared with the conventional approaches in uniformly spaced channel system, simulation results validate the effectiveness of the proposed approach. To our knowledge, it is the first time that the two-dimensional detection probability of CSI algorithm is studied.

[Features of PHITS and its application to medical physics].

PubMed

Hashimoto, Shintaro; Niita, Koji; Matsuda, Norihiro; Iwamoto, Yosuke; Iwase, Hiroshi; Sato, Tatsuhiko; Noda, Shusaku; Ogawa, Tatsuhiko; Nakashima, Hiroshi; Fukahori, Tokio; Furuta, Takuya; Chiba, Satoshi

2013-01-01

PHITS is a general purpose Monte Carlo particle transport simulation code to analyze the transport in three-dimensional phase space and collisions of nearly all particles, including heavy ions, over wide energy range up to 100 GeV/u. Various quantities, such as particle fluence and deposition energies in materials, can be deduced using estimator functions "tally". Recently, a microdosimetric tally function was also developed to apply PHITS to medical physics. Owing to these features, PHITS has been used for medical applications, such as radiation therapy and protection.

Model-Free Conditional Independence Feature Screening For Ultrahigh Dimensional Data.

PubMed

Wang, Luheng; Liu, Jingyuan; Li, Yong; Li, Runze

2017-03-01

Feature screening plays an important role in ultrahigh dimensional data analysis. This paper is concerned with conditional feature screening when one is interested in detecting the association between the response and ultrahigh dimensional predictors (e.g., genetic makers) given a low-dimensional exposure variable (such as clinical variables or environmental variables). To this end, we first propose a new index to measure conditional independence, and further develop a conditional screening procedure based on the newly proposed index. We systematically study the theoretical property of the proposed procedure and establish the sure screening and ranking consistency properties under some very mild conditions. The newly proposed screening procedure enjoys some appealing properties. (a) It is model-free in that its implementation does not require a specification on the model structure; (b) it is robust to heavy-tailed distributions or outliers in both directions of response and predictors; and (c) it can deal with both feature screening and the conditional screening in a unified way. We study the finite sample performance of the proposed procedure by Monte Carlo simulations and further illustrate the proposed method through two real data examples.

Classification of Microarray Data Using Kernel Fuzzy Inference System

PubMed Central

Kumar Rath, Santanu

2014-01-01

The DNA microarray classification technique has gained more popularity in both research and practice. In real data analysis, such as microarray data, the dataset contains a huge number of insignificant and irrelevant features that tend to lose useful information. Classes with high relevance and feature sets with high significance are generally referred for the selected features, which determine the samples classification into their respective classes. In this paper, kernel fuzzy inference system (K-FIS) algorithm is applied to classify the microarray data (leukemia) using t-test as a feature selection method. Kernel functions are used to map original data points into a higher-dimensional (possibly infinite-dimensional) feature space defined by a (usually nonlinear) function ϕ through a mathematical process called the kernel trick. This paper also presents a comparative study for classification using K-FIS along with support vector machine (SVM) for different set of features (genes). Performance parameters available in the literature such as precision, recall, specificity, F-measure, ROC curve, and accuracy are considered to analyze the efficiency of the classification model. From the proposed approach, it is apparent that K-FIS model obtains similar results when compared with SVM model. This is an indication that the proposed approach relies on kernel function. PMID:27433543

Electrostatic protection of the solar power satellite and rectenna. Part 1: Protection of the solar power satellite

NASA Technical Reports Server (NTRS)

1980-01-01

Several features of the interactions of the Solar Power Satellite (SPS) with its space environment are examined theoretically. The voltages produced at various surfaces due to space plasmas and the plasma leakage currents through the kapton and sapphire solar cell blankets are calculated. At geosynchronous orbit, this parasitic power loss is only 0.7%, and is easily compensated by oversizing. At low Earth orbit, the power loss is potentially much larger (3%), and anomalous arcing is expected for the EOTV high voltage negative surfaces. Preliminary results of a three dimensional self consistent plasma and electric field computer program are presented, confirming the validity of the predictions made from the one dimensional models. Lastly, magnetic shielding of the satellite is considered to reduce the power drain and to protect the solar cells from energetic electron and plasma ion bombardment. It is concluded that minor modifications can allow the SPS to operate safely and efficiently in its space environment. Subsequent design changes will substantially alter the basic conclusions.

Moduli stabilising in heterotic nearly Kähler compactifications

NASA Astrophysics Data System (ADS)

Klaput, Michael; Lukas, Andre; Matti, Cyril; Svanes, Eirik E.

2013-01-01

We study heterotic string compactifications on nearly Kähler homogeneous spaces, including the gauge field effects which arise at order α'. Using Abelian gauge fields, we are able to solve the Bianchi identity and supersymmetry conditions to this order. The four-dimensional external space-time consists of a domain wall solution with moduli fields varying along the transverse direction. We find that the inclusion of α' corrections improves the moduli stabilization features of this solution. In this case, one of the dilaton and the volume modulus asymptotes to a constant value away from the domain wall. It is further shown that the inclusion of non-perturbative effects can stabilize the remaining modulus and "lift" the domain wall to an AdS vacuum. The coset SU(3)/U(1)2 is used as an explicit example to demonstrate the validity of this AdS vacuum. Our results show that heterotic nearly Kähler compactifications can lead to maximally symmetric four-dimensional space-times at the non-perturbative level.

Improved black-blood imaging using DANTE-SPACE for simultaneous carotid and intracranial vessel wall evaluation.

PubMed

Xie, Yibin; Yang, Qi; Xie, Guoxi; Pang, Jianing; Fan, Zhaoyang; Li, Debiao

2016-06-01

The purpose of this study was to develop a three-dimensional black blood imaging method for simultaneously evaluating the carotid and intracranial arterial vessel walls with high spatial resolution and excellent blood suppression with and without contrast enhancement. The delay alternating with nutation for tailored excitation (DANTE) preparation module was incorporated into three-dimensional variable flip angle turbo spin echo (SPACE) sequence to improve blood signal suppression. Simulations and phantom studies were performed to quantify image contrast variations induced by DANTE. DANTE-SPACE, SPACE, and two-dimensional turbo spin echo were compared for apparent signal-to-noise ratio, contrast-to-noise ratio, and morphometric measurements in 14 healthy subjects. Preliminary clinical validation was performed in six symptomatic patients. Apparent residual luminal blood was observed in five (pre-contrast) and nine (post-contrast) subjects with SPACE and only two (post-contrast) subjects with DANTE-SPACE. DANTE-SPACE showed 31% (pre-contrast) and 100% (post-contrast) improvement in wall-to-blood contrast-to-noise ratio over SPACE. Vessel wall area measured from SPACE was significantly larger than that from DANTE-SPACE due to possible residual blood signal contamination. DANTE-SPACE showed the potential to detect vessel wall dissection and identify plaque components in patients. DANTE-SPACE significantly improved arterial and venous blood suppression compared with SPACE. Simultaneous high-resolution carotid and intracranial vessel wall imaging to potentially identify plaque components was feasible with a scan time under 6 min. Magn Reson Med 75:2286-2294, 2016. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Linear canonical transformations of coherent and squeezed states in the Wigner phase space. III - Two-mode states

NASA Technical Reports Server (NTRS)

Han, D.; Kim, Y. S.; Noz, Marilyn E.

1990-01-01

It is shown that the basic symmetry of two-mode squeezed states is governed by the group SP(4) in the Wigner phase space which is locally isomorphic to the (3 + 2)-dimensional Lorentz group. This symmetry, in the Schroedinger picture, appears as Dirac's two-oscillator representation of O(3,2). It is shown that the SU(2) and SU(1,1) interferometers exhibit the symmetry of this higher-dimensional Lorentz group. The mathematics of two-mode squeezed states is shown to be applicable to other branches of physics including thermally excited states in statistical mechanics and relativistic extended hadrons in the quark model.

Role of dimensionality in Axelrod's model for the dissemination of culture

NASA Astrophysics Data System (ADS)

Klemm, Konstantin; Eguíluz, Víctor M.; Toral, Raúl; Miguel, Maxi San

2003-09-01

We analyze a model of social interaction in one- and two-dimensional lattices for a moderate number of features. We introduce an order parameter as a function of the overlap between neighboring sites. In a one-dimensional chain, we observe that the dynamics is consistent with a second-order transition, where the order parameter changes continuously and the average domain diverges at the transition point. However, in a two-dimensional lattice the order parameter is discontinuous at the transition point characteristic of a first-order transition between an ordered and a disordered state.

One dimensional coordination polymers: Synthesis, crystal structures and spectroscopic properties

NASA Astrophysics Data System (ADS)

Karaağaç, Dursun; Kürkçüoğlu, Güneş Süheyla; Şenyel, Mustafa; Şahin, Onur

2016-11-01

Two new one dimensional (1D) cyanide complexes, namely [M(4-aepy)2(H2O)2][Pt(CN)4], (4-aepy = 4-(2-aminoethyl)pyridine M = Cu(II) (1) or Zn(II) (2)), have been synthesized and characterized by vibrational (FT-IR and Raman) spectroscopy, single crystal X-ray diffraction, thermal and elemental analyses techniques. The crystallographic analyses reveal that 1 and 2 are isomorphous and isostructural, and crystallize in the monoclinic system and C2 space group. The Pt(II) ions are coordinated by four cyanide-carbon atoms in the square-planar geometry and the [Pt(CN)4]2- ions act as a counter ion. The M(II) ions display an N4O2 coordination sphere with a distorted octahedral geometry, the nitrogen donors belonging to four molecules of the organic 4-aepy that act as unidentate ligands and two oxygen atoms from aqua ligands. The crystal structures of 1 and 2 are similar each other and linked via intermolecular hydrogen bonding, Pt⋯π interactions to form 3D supramolecular network. Vibration assignments of all the observed bands are given and the spectral features also supported to the crystal structures of the complexes.

Images as embedding maps and minimal surfaces: Movies, color, and volumetric medical images

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

Kimmel, R.; Malladi, R.; Sochen, N.

A general geometrical framework for image processing is presented. The authors consider intensity images as surfaces in the (x,I) space. The image is thereby a two dimensional surface in three dimensional space for gray level images. The new formulation unifies many classical schemes, algorithms, and measures via choices of parameters in a {open_quote}master{close_quotes} geometrical measure. More important, it is a simple and efficient tool for the design of natural schemes for image enhancement, segmentation, and scale space. Here the authors give the basic motivation and apply the scheme to enhance images. They present the concept of an image as amore » surface in dimensions higher than the three dimensional intuitive space. This will help them handle movies, color, and volumetric medical images.« less

Measurement of Galactic Logarithmic Spiral Arm Pitch Angle Using Two-dimensional Fast Fourier Transform Decomposition

NASA Astrophysics Data System (ADS)

Davis, Benjamin L.; Berrier, Joel C.; Shields, Douglas W.; Kennefick, Julia; Kennefick, Daniel; Seigar, Marc S.; Lacy, Claud H. S.; Puerari, Ivânio

2012-04-01

A logarithmic spiral is a prominent feature appearing in a majority of observed galaxies. This feature has long been associated with the traditional Hubble classification scheme, but historical quotes of pitch angle of spiral galaxies have been almost exclusively qualitative. We have developed a methodology, utilizing two-dimensional fast Fourier transformations of images of spiral galaxies, in order to isolate and measure the pitch angles of their spiral arms. Our technique provides a quantitative way to measure this morphological feature. This will allow comparison of spiral galaxy pitch angle to other galactic parameters and test spiral arm genesis theories. In this work, we detail our image processing and analysis of spiral galaxy images and discuss the robustness of our analysis techniques.

Non-classical photon correlation in a two-dimensional photonic lattice.

PubMed

Gao, Jun; Qiao, Lu-Feng; Lin, Xiao-Feng; Jiao, Zhi-Qiang; Feng, Zhen; Zhou, Zheng; Gao, Zhen-Wei; Xu, Xiao-Yun; Chen, Yuan; Tang, Hao; Jin, Xian-Min

2016-06-13

Quantum interference and quantum correlation, as two main features of quantum optics, play an essential role in quantum information applications, such as multi-particle quantum walk and boson sampling. While many experimental demonstrations have been done in one-dimensional waveguide arrays, it remains unexplored in higher dimensions due to tight requirement of manipulating and detecting photons in large-scale. Here, we experimentally observe non-classical correlation of two identical photons in a fully coupled two-dimensional structure, i.e. photonic lattice manufactured by three-dimensional femtosecond laser writing. Photon interference consists of 36 Hong-Ou-Mandel interference and 9 bunching. The overlap between measured and simulated distribution is up to 0.890 ± 0.001. Clear photon correlation is observed in the two-dimensional photonic lattice. Combining with controllably engineered disorder, our results open new perspectives towards large-scale implementation of quantum simulation on integrated photonic chips.

The Cutplane - A tool for interactive solid modeling

NASA Technical Reports Server (NTRS)

Edwards, Laurence; Kessler, William; Leifer, Larry

1988-01-01

A geometric modeling system which incorporates a new concept for intuitively and unambiguously specifying and manipulating points or features in three dimensional space is presented. The central concept, the Cutplane, consists of a plane that moves through space under control of a mouse or similar input device. The intersection of the plane and any object is highlighted, and only this highlighted section can be selected for manipulation. Selection is accomplished with a crosshair that is constrained to remain within the plane, so that the relationship between the crosshair and the feature of interest is immediately evident. Although the idea of a section view is not new, previously it has been used as a way to reveal hidden structure, not as a means of manipulating objects or indicating spatial position, as is proposed here.

Entropic potential field formed for a linear-motor protein near a filament: Statistical-mechanical analyses using simple models.

PubMed

Amano, Ken-Ichi; Yoshidome, Takashi; Iwaki, Mitsuhiro; Suzuki, Makoto; Kinoshita, Masahiro

2010-07-28

We report a new progress in elucidating the mechanism of the unidirectional movement of a linear-motor protein (e.g., myosin) along a filament (e.g., F-actin). The basic concept emphasized here is that a potential field is entropically formed for the protein on the filament immersed in solvent due to the effect of the translational displacement of solvent molecules. The entropic potential field is strongly dependent on geometric features of the protein and the filament, their overall shapes as well as details of the polyatomic structures. The features and the corresponding field are judiciously adjusted by the binding of adenosine triphosphate (ATP) to the protein, hydrolysis of ATP into adenosine diphosphate (ADP)+Pi, and release of Pi and ADP. As the first step, we propose the following physical picture: The potential field formed along the filament for the protein without the binding of ATP or ADP+Pi to it is largely different from that for the protein with the binding, and the directed movement is realized by repeated switches from one of the fields to the other. To illustrate the picture, we analyze the spatial distribution of the entropic potential between a large solute and a large body using the three-dimensional integral equation theory. The solute is modeled as a large hard sphere. Two model filaments are considered as the body: model 1 is a set of one-dimensionally connected large hard spheres and model 2 is a double helical structure formed by two sets of connected large hard spheres. The solute and the filament are immersed in small hard spheres forming the solvent. The major findings are as follows. The solute is strongly confined within a narrow space in contact with the filament. Within the space there are locations with sharply deep local potential minima along the filament, and the distance between two adjacent locations is equal to the diameter of the large spheres constituting the filament. The potential minima form a ringlike domain in model 1 while they form a pointlike one in model 2. We then examine the effects of geometric features of the solute on the amplitudes and asymmetry of the entropic potential field acting on the solute along the filament. A large aspherical solute with a cleft near the solute-filament interface, which mimics the myosin motor domain, is considered in the examination. Thus, the two fields in our physical picture described above are qualitatively reproduced. The factors to be taken into account in further studies are also discussed.

Momentum-space cigar geometry in topological phases

NASA Astrophysics Data System (ADS)

Palumbo, Giandomenico

2018-01-01

In this paper, we stress the importance of momentum-space geometry in the understanding of two-dimensional topological phases of matter. We focus, for simplicity, on the gapped boundary of three-dimensional topological insulators in class AII, which are described by a massive Dirac Hamiltonian and characterized by an half-integer Chern number. The gap is induced by introducing a magnetic perturbation, such as an external Zeeman field or a ferromagnet on the surface. The quantum Bures metric acquires a central role in our discussion and identifies a cigar geometry. We first derive the Chern number from the cigar geometry and we then show that the quantum metric can be seen as a solution of two-dimensional non-Abelian BF theory in momentum space. The gauge connection for this model is associated to the Maxwell algebra, which takes into account the Lorentz symmetries related to the Dirac theory and the momentum-space magnetic translations connected to the magnetic perturbation. The Witten black-hole metric is a solution of this gauge theory and coincides with the Bures metric. This allows us to calculate the corresponding momentum-space entanglement entropy that surprisingly carries information about the real-space conformal field theory describing the defect lines that can be created on the gapped boundary.

Continuum modeling of three-dimensional truss-like space structures

NASA Technical Reports Server (NTRS)

Nayfeh, A. H.; Hefzy, M. S.

1978-01-01

A mathematical and computational analysis capability has been developed for calculating the effective mechanical properties of three-dimensional periodic truss-like structures. Two models are studied in detail. The first, called the octetruss model, is a three-dimensional extension of a two-dimensional model, and the second is a cubic model. Symmetry considerations are employed as a first step to show that the specific octetruss model has four independent constants and that the cubic model has two. The actual values of these constants are determined by averaging the contributions of each rod element to the overall structure stiffness. The individual rod member contribution to the overall stiffness is obtained by a three-dimensional coordinate transformation. The analysis shows that the effective three-dimensional elastic properties of both models are relatively close to each other.

Prediction of high-dimensional states subject to respiratory motion: a manifold learning approach

NASA Astrophysics Data System (ADS)

Liu, Wenyang; Sawant, Amit; Ruan, Dan

2016-07-01

The development of high-dimensional imaging systems in image-guided radiotherapy provides important pathways to the ultimate goal of real-time full volumetric motion monitoring. Effective motion management during radiation treatment usually requires prediction to account for system latency and extra signal/image processing time. It is challenging to predict high-dimensional respiratory motion due to the complexity of the motion pattern combined with the curse of dimensionality. Linear dimension reduction methods such as PCA have been used to construct a linear subspace from the high-dimensional data, followed by efficient predictions on the lower-dimensional subspace. In this study, we extend such rationale to a more general manifold and propose a framework for high-dimensional motion prediction with manifold learning, which allows one to learn more descriptive features compared to linear methods with comparable dimensions. Specifically, a kernel PCA is used to construct a proper low-dimensional feature manifold, where accurate and efficient prediction can be performed. A fixed-point iterative pre-image estimation method is used to recover the predicted value in the original state space. We evaluated and compared the proposed method with a PCA-based approach on level-set surfaces reconstructed from point clouds captured by a 3D photogrammetry system. The prediction accuracy was evaluated in terms of root-mean-squared-error. Our proposed method achieved consistent higher prediction accuracy (sub-millimeter) for both 200 ms and 600 ms lookahead lengths compared to the PCA-based approach, and the performance gain was statistically significant.

Continuous-Flow Electrophoresis of DNA and Proteins in a Two-Dimensional Capillary-Well Sieve.

PubMed

Duan, Lian; Cao, Zhen; Yobas, Levent

2017-09-19

Continuous-flow electrophoresis of macromolecules is demonstrated using an integrated capillary-well sieve arranged into a two-dimensional anisotropic array on silicon. The periodic array features thousands of entropic barriers, each resulting from an abrupt interface between a 2 μm deep well (channel) and a 70 nm capillary. These entropic barriers owing to two-dimensional confinement within the capillaries are vastly steep in relation to those arising from slits featuring one-dimensional confinement. Thus, the sieving mechanisms can sustain relatively large electric field strengths over a relatively small array area. The sieve rapidly sorts anionic macromolecules, including DNA chains and proteins in native or denatured states, into distinct trajectories according to size or charge under electric field vectors orthogonally applied. The baseline separation is achieved in less than 1 min within a horizontal migration length of ∼1.5 mm. The capillaries are self-enclosed conduits in cylindrical profile featuring a uniform diameter and realized through an approach that avoids advanced patterning techniques. The approach exploits a thermal reflow of a layer of doped glass for shape transformation into cylindrical capillaries and for controllably shrinking the capillary diameter. Lastly, atomic layer deposition of alumina is introduced for the first time to fine-tune the capillary diameter as well as to neutralize the surface charge, thereby suppressing undesired electroosmotic flows.

Localization and tracking of moving objects in two-dimensional space by echolocation.

PubMed

Matsuo, Ikuo

2013-02-01

Bats use frequency-modulated echolocation to identify and capture moving objects in real three-dimensional space. Experimental evidence indicates that bats are capable of locating static objects with a range accuracy of less than 1 μs. A previously introduced model estimates ranges of multiple, static objects using linear frequency modulation (LFM) sound and Gaussian chirplets with a carrier frequency compatible with bat emission sweep rates. The delay time for a single object was estimated with an accuracy of about 1.3 μs by measuring the echo at a low signal-to-noise ratio (SNR). The range accuracy was dependent not only on the SNR but also the Doppler shift, which was dependent on the movements. However, it was unclear whether this model could estimate the moving object range at each timepoint. In this study, echoes were measured from the rotating pole at two receiving points by intermittently emitting LFM sounds. The model was shown to localize moving objects in two-dimensional space by accurately estimating the object's range at each timepoint.

Band-gap tuning and optical response of two-dimensional SixC1 -x : A first-principles real-space study of disordered two-dimensional materials

NASA Astrophysics Data System (ADS)

Sadhukhan, Banasree; Singh, Prashant; Nayak, Arabinda; Datta, Sujoy; Johnson, Duane D.; Mookerjee, Abhijit

2017-08-01

We present a real-space formulation for calculating the electronic structure and optical conductivity of random alloys based on Kubo-Greenwood formalism interfaced with augmented space recursion technique [Mookerjee, J. Phys. C 6, 1340 (1973), 10.1088/0022-3719/6/8/003] formulated with the tight-binding linear muffin-tin orbital basis with the van Leeuwen-Baerends corrected exchange potential [Singh, Harbola, Hemanadhan, Mookerjee, and Johnson, Phys. Rev. B 93, 085204 (2016), 10.1103/PhysRevB.93.085204]. This approach has been used to quantitatively analyze the effect of chemical disorder on the configuration averaged electronic properties and optical response of two-dimensional honeycomb siliphene SixC1 -x beyond the usual Dirac-cone approximation. We predicted the quantitative effect of disorder on both the electronic structure and optical response over a wide energy range, and the results are discussed in the light of the available experimental and other theoretical data. Our proposed formalism may open up a facile way for planned band-gap engineering in optoelectronic applications.

Turbulence in Three Dimensional Simulations of Magnetopause Reconnection

NASA Astrophysics Data System (ADS)

Drake, J. F.; Price, L.; Swisdak, M.; Burch, J. L.; Cassak, P.; Dahlin, J. T.; Ergun, R.

2017-12-01

We present two- and three-dimensional particle-in-cell simulations of the 16 October 2015 MMS magnetopause reconnection event. While the two-dimensional simulation is laminar, turbulence develops at both the x-line and along the magnetic separatrices in the three-dimensional simulation. This turbulence is electromagnetic in nature, is characterized by a wavevector k given by kρ e ˜(m_e/m_i)0.25 with ρ e the electron Larmor radius, and appears to have the ion pressure gradient as its source of free energy. Taken together, these results suggest the instability is a variant of the lower-hybrid drift instability. The turbulence produces electric field fluctuations in the out-of-plane direction (the direction of the reconnection electric field) with an amplitude of around ± 10 mV/m, which is much greater than the reconnection electric field of around 0.1 mV/m. Such large values of the out-of-plane electric field have been identified in the MMS data. The turbulence in the simulation controls the scale lengths of the density profile and current layers in asymmetric reconnection, driving them closer to √ {ρ eρ_i } than the ρ e or de scalings seen in 2D reconnection simulations, where de is the electron inertial length. The turbulence is strong enough to make the magnetic field around the reconnection island chaotic and produces both anomalous resistivity and anomalous viscosity. Each contribute significantly to breaking the frozen-in condition in the electron diffusion region. The crescent-shaped features in velocity space seen both in MMS observations and in two-dimensional simulations survive, even in the turbulent environment of the three-dimensional system. We compare and contrast these results to a three-dimensional simulation of the 8 December 2015 MMS magnetopause reconnection event in which the reconnecting and out-of-plane guide fields are comparable. LHDI is still present in this event, although its appearance is modified by the presence of the guide field. The crescents also survive although, as is also observed by MMS, their intensity decreases. Nevertheless, the turbulence that develops remains strong.

Thermodynamic and Kinetic Properties of Shocks in Two-Dimensional Yukawa Systems [Thermodynamic and Kinetic Properties of Shocks in 2D Yukawa Systems

DOE PAGES

Marciante, Mathieu; Murillo, Michael Sean

2017-01-10

Particle-level simulations of shocked plasmas are carried out to examine kinetic properties not captured by hydrodynamic models. In particular, molecular dynamics simulations of 2D Yukawa plasmas with variable couplings and screening lengths are used to examine shock features unique to plasmas, including the presence of dispersive shock structures for weak shocks. A phase-space analysis reveals several kinetic properties, including anisotropic velocity distributions, non-Maxwellian tails, and the presence of fast particles ahead of the shock, even for moderately low Mach numbers. As a result, we also examine the thermodynamics (Rankine-Hugoniot relations) of recent experiments and find no anomalies in their equationsmore » of state.« less

Edge Singularities and Quasilong-Range Order in Nonequilibrium Steady States.

PubMed

De Nardis, Jacopo; Panfil, Miłosz

2018-05-25

The singularities of the dynamical response function are one of the most remarkable effects in many-body interacting systems. However in one dimension these divergences only exist strictly at zero temperature, making their observation very difficult in most cold atomic experimental settings. Moreover the presence of a finite temperature destroys another feature of one-dimensional quantum liquids: the real space quasilong-range order in which the spatial correlation functions exhibit power-law decay. We consider a nonequilibrium protocol where two interacting Bose gases are prepared either at different temperatures or chemical potentials and then joined. We show that the nonequilibrium steady state emerging at large times around the junction displays edge singularities in the response function and quasilong-range order.

Thermodynamic and Kinetic Properties of Shocks in Two-Dimensional Yukawa Systems [Thermodynamic and Kinetic Properties of Shocks in 2D Yukawa Systems

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

Marciante, Mathieu; Murillo, Michael Sean

Particle-level simulations of shocked plasmas are carried out to examine kinetic properties not captured by hydrodynamic models. In particular, molecular dynamics simulations of 2D Yukawa plasmas with variable couplings and screening lengths are used to examine shock features unique to plasmas, including the presence of dispersive shock structures for weak shocks. A phase-space analysis reveals several kinetic properties, including anisotropic velocity distributions, non-Maxwellian tails, and the presence of fast particles ahead of the shock, even for moderately low Mach numbers. As a result, we also examine the thermodynamics (Rankine-Hugoniot relations) of recent experiments and find no anomalies in their equationsmore » of state.« less

Summary of the thermal evaluation of LWBR (LWBR Development Program)

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

Lerner, S.; McWilliams, K.D.; Stout, J.W.

1980-03-01

This report describes the thermal evaluation of the core for the Shippingport Light Water Breeder Reactor. This core contains unique thermal-hydraulic features such as (1) close rod-to-rod proximity, (2) an open-lattice array of fuel rods with two different diameters and rod-to-rod spacings in the same flow region, (3) triplate orifices located at both the entrance and exit of fuel modules and (4) a hydraulically-balanced movable-fuel system coupled with (5) axial-and-radial fuel zoning for reactivity control. Performance studies used reactor thermal principles such as the hot-and-nominal channel concept and related nuclear/engineering design allowances. These were applied to models of three-dimensional roddedmore » arrays comprising the core fuel regions.« less

Physics based model for online fault detection in autonomous cryogenic loading system

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

Kashani, Ali; Ponizhovskaya, Ekaterina; Luchinsky, Dmitry

2014-01-29

We report the progress in the development of the chilldown model for a rapid cryogenic loading system developed at NASA-Kennedy Space Center. The nontrivial characteristic feature of the analyzed chilldown regime is its active control by dump valves. The two-phase flow model of the chilldown is approximated as one-dimensional homogeneous fluid flow with no slip condition for the interphase velocity. The model is built using commercial SINDA/FLUINT software. The results of numerical predictions are in good agreement with the experimental time traces. The obtained results pave the way to the application of the SINDA/FLUINT model as a verification tool formore » the design and algorithm development required for autonomous loading operation.« less

Edge Singularities and Quasilong-Range Order in Nonequilibrium Steady States

NASA Astrophysics Data System (ADS)

De Nardis, Jacopo; Panfil, Miłosz

2018-05-01

The singularities of the dynamical response function are one of the most remarkable effects in many-body interacting systems. However in one dimension these divergences only exist strictly at zero temperature, making their observation very difficult in most cold atomic experimental settings. Moreover the presence of a finite temperature destroys another feature of one-dimensional quantum liquids: the real space quasilong-range order in which the spatial correlation functions exhibit power-law decay. We consider a nonequilibrium protocol where two interacting Bose gases are prepared either at different temperatures or chemical potentials and then joined. We show that the nonequilibrium steady state emerging at large times around the junction displays edge singularities in the response function and quasilong-range order.

Towards a voxel-based geographic automata for the simulation of geospatial processes

NASA Astrophysics Data System (ADS)

Jjumba, Anthony; Dragićević, Suzana

2016-07-01

Many geographic processes evolve in a three dimensional space and time continuum. However, when they are represented with the aid of geographic information systems (GIS) or geosimulation models they are modelled in a framework of two-dimensional space with an added temporal component. The objective of this study is to propose the design and implementation of voxel-based automata as a methodological approach for representing spatial processes evolving in the four-dimensional (4D) space-time domain. Similar to geographic automata models which are developed to capture and forecast geospatial processes that change in a two-dimensional spatial framework using cells (raster geospatial data), voxel automata rely on the automata theory and use three-dimensional volumetric units (voxels). Transition rules have been developed to represent various spatial processes which range from the movement of an object in 3D to the diffusion of airborne particles and landslide simulation. In addition, the proposed 4D models demonstrate that complex processes can be readily reproduced from simple transition functions without complex methodological approaches. The voxel-based automata approach provides a unique basis to model geospatial processes in 4D for the purpose of improving representation, analysis and understanding their spatiotemporal dynamics. This study contributes to the advancement of the concepts and framework of 4D GIS.

Order and chaos in the one-dimensional ϕ4 model: N-dependence and the Second Law of Thermodynamics

NASA Astrophysics Data System (ADS)

Hoover, William Graham; Aoki, Kenichiro

2017-08-01

We revisit the equilibrium one-dimensional ϕ4 model from the dynamical systems point of view. We find an infinite number of periodic orbits which are computationally stable. At the same time some of the orbits are found to exhibit positive Lyapunov exponents! The periodic orbits confine every particle in a periodic chain to trace out either the same or a mirror-image trajectory in its two-dimensional phase space. These ;computationally stable; sets of pairs of single-particle orbits are either symmetric or antisymmetric to the very last computational bit. In such a periodic chain the odd-numbered and even-numbered particles' coordinates and momenta are either identical or differ only in sign. ;Positive Lyapunov exponents; can and do result if an infinitesimal perturbation breaking a perfect two-dimensional antisymmetry is introduced so that the motion expands into a four-dimensional phase space. In that extended space a positive exponent results. We formulate a standard initial condition for the investigation of the microcanonical chaotic number dependence of the model. We speculate on the uniqueness of the model's chaotic sea and on the connection of such collections of deterministic and time-reversible states to the Second Law of Thermodynamics.

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

Dehghani, M.H.; Department of Physics, University of Waterloo, 200 University Avenue West, Waterloo, Ontario, N2L 3G1; Perimeter Institute for Theoretical Physics, 35 Caroline Street North, Waterloo, Ontario

We investigate the existence of Taub-NUT (Newman-Unti-Tamburino) and Taub-bolt solutions in Gauss-Bonnet gravity and obtain the general form of these solutions in d dimensions. We find that for all nonextremal NUT solutions of Einstein gravity having no curvature singularity at r=N, there exist NUT solutions in Gauss-Bonnet gravity that contain these solutions in the limit that the Gauss-Bonnet parameter {alpha} goes to zero. Furthermore there are no NUT solutions in Gauss-Bonnet gravity that yield nonextremal NUT solutions to Einstein gravity having a curvature singularity at r=N in the limit {alpha}{yields}0. Indeed, we have nonextreme NUT solutions in 2+2k dimensions withmore » nontrivial fibration only when the 2k-dimensional base space is chosen to be CP{sup 2k}. We also find that the Gauss-Bonnet gravity has extremal NUT solutions whenever the base space is a product of 2-torii with at most a two-dimensional factor space of positive curvature. Indeed, when the base space has at most one positively curved two-dimensional space as one of its factor spaces, then Gauss-Bonnet gravity admits extreme NUT solutions, even though there a curvature singularity exists at r=N. We also find that one can have bolt solutions in Gauss-Bonnet gravity with any base space with factor spaces of zero or positive constant curvature. The only case for which one does not have bolt solutions is in the absence of a cosmological term with zero curvature base space.« less

A three-dimensional quality-guided phase unwrapping method for MR elastography

NASA Astrophysics Data System (ADS)

Wang, Huifang; Weaver, John B.; Perreard, Irina I.; Doyley, Marvin M.; Paulsen, Keith D.

2011-07-01

Magnetic resonance elastography (MRE) uses accumulated phases that are acquired at multiple, uniformly spaced relative phase offsets, to estimate harmonic motion information. Heavily wrapped phase occurs when the motion is large and unwrapping procedures are necessary to estimate the displacements required by MRE. Two unwrapping methods were developed and compared in this paper. The first method is a sequentially applied approach. The three-dimensional MRE phase image block for each slice was processed by two-dimensional unwrapping followed by a one-dimensional phase unwrapping approach along the phase-offset direction. This unwrapping approach generally works well for low noise data. However, there are still cases where the two-dimensional unwrapping method fails when noise is high. In this case, the baseline of the corrupted regions within an unwrapped image will not be consistent. Instead of separating the two-dimensional and one-dimensional unwrapping in a sequential approach, an interleaved three-dimensional quality-guided unwrapping method was developed to combine both the two-dimensional phase image continuity and one-dimensional harmonic motion information. The quality of one-dimensional harmonic motion unwrapping was used to guide the three-dimensional unwrapping procedures and it resulted in stronger guidance than in the sequential method. In this work, in vivo results generated by the two methods were compared.

Detection of Subtle Context-Dependent Model Inaccuracies in High-Dimensional Robot Domains.

PubMed

Mendoza, Juan Pablo; Simmons, Reid; Veloso, Manuela

2016-12-01

Autonomous robots often rely on models of their sensing and actions for intelligent decision making. However, when operating in unconstrained environments, the complexity of the world makes it infeasible to create models that are accurate in every situation. This article addresses the problem of using potentially large and high-dimensional sets of robot execution data to detect situations in which a robot model is inaccurate-that is, detecting context-dependent model inaccuracies in a high-dimensional context space. To find inaccuracies tractably, the robot conducts an informed search through low-dimensional projections of execution data to find parametric Regions of Inaccurate Modeling (RIMs). Empirical evidence from two robot domains shows that this approach significantly enhances the detection power of existing RIM-detection algorithms in high-dimensional spaces.

A new method for recognizing quadric surfaces from range data and its application to telerobotics and automation

NASA Technical Reports Server (NTRS)

Alvertos, Nicolas; Dcunha, Ivan

1993-01-01

The problem of recognizing and positioning of objects in three-dimensional space is important for robotics and navigation applications. In recent years, digital range data, also referred to as range images or depth maps, have been available for the analysis of three-dimensional objects owing to the development of several active range finding techniques. The distinct advantage of range images is the explicitness of the surface information available. Many industrial and navigational robotics tasks will be more easily accomplished if such explicit information can be efficiently interpreted. In this research, a new technique based on analytic geometry for the recognition and description of three-dimensional quadric surfaces from range images is presented. Beginning with the explicit representation of quadrics, a set of ten coefficients are determined for various three-dimensional surfaces. For each quadric surface, a unique set of two-dimensional curves which serve as a feature set is obtained from the various angles at which the object is intersected with a plane. Based on a discriminant method, each of the curves is classified as a parabola, circle, ellipse, hyperbola, or a line. Each quadric surface is shown to be uniquely characterized by a set of these two-dimensional curves, thus allowing discrimination from the others. Before the recognition process can be implemented, the range data have to undergo a set of pre-processing operations, thereby making it more presentable to classification algorithms. One such pre-processing step is to study the effect of median filtering on raw range images. Utilizing a variety of surface curvature techniques, reliable sets of image data that approximate the shape of a quadric surface are determined. Since the initial orientation of the surfaces is unknown, a new technique is developed wherein all the rotation parameters are determined and subsequently eliminated. This approach enables us to position the quadric surfaces in a desired coordinate system. Experiments were conducted on raw range images of spheres, cylinders, and cones. Experiments were also performed on simulated data for surfaces such as hyperboloids of one and two sheets, elliptical and hyperbolic paraboloids, elliptical and hyperbolic cylinders, ellipsoids and the quadric cones. Both the real and simulated data yielded excellent results. Our approach is found to be more accurate and computationally inexpensive as compared to traditional approaches, such as the three-dimensional discriminant approach which involves evaluation of the rank of a matrix. Finally, we have proposed one other new approach, which involves the formulation of a mapping between the explicit and implicit forms of representing quadric surfaces. This approach, when fully realized, will yield a three-dimensional discriminant, which will recognize quadric surfaces based upon their component surfaces patches. This approach is faster than prior approaches and at the same time is invariant to pose and orientation of the surfaces in three-dimensional space.

A new method for recognizing quadric surfaces from range data and its application to telerobotics and automation

NASA Astrophysics Data System (ADS)

Alvertos, Nicolas; Dcunha, Ivan

1993-03-01

The problem of recognizing and positioning of objects in three-dimensional space is important for robotics and navigation applications. In recent years, digital range data, also referred to as range images or depth maps, have been available for the analysis of three-dimensional objects owing to the development of several active range finding techniques. The distinct advantage of range images is the explicitness of the surface information available. Many industrial and navigational robotics tasks will be more easily accomplished if such explicit information can be efficiently interpreted. In this research, a new technique based on analytic geometry for the recognition and description of three-dimensional quadric surfaces from range images is presented. Beginning with the explicit representation of quadrics, a set of ten coefficients are determined for various three-dimensional surfaces. For each quadric surface, a unique set of two-dimensional curves which serve as a feature set is obtained from the various angles at which the object is intersected with a plane. Based on a discriminant method, each of the curves is classified as a parabola, circle, ellipse, hyperbola, or a line. Each quadric surface is shown to be uniquely characterized by a set of these two-dimensional curves, thus allowing discrimination from the others. Before the recognition process can be implemented, the range data have to undergo a set of pre-processing operations, thereby making it more presentable to classification algorithms. One such pre-processing step is to study the effect of median filtering on raw range images. Utilizing a variety of surface curvature techniques, reliable sets of image data that approximate the shape of a quadric surface are determined. Since the initial orientation of the surfaces is unknown, a new technique is developed wherein all the rotation parameters are determined and subsequently eliminated. This approach enables us to position the quadric surfaces in a desired coordinate system. Experiments were conducted on raw range images of spheres, cylinders, and cones. Experiments were also performed on simulated data for surfaces such as hyperboloids of one and two sheets, elliptical and hyperbolic paraboloids, elliptical and hyperbolic cylinders, ellipsoids and the quadric cones. Both the real and simulated data yielded excellent results. Our approach is found to be more accurate and computationally inexpensive as compared to traditional approaches, such as the three-dimensional discriminant approach which involves evaluation of the rank of a matrix.

Experimental investigation on flow past nine cylinders in a square configuration

NASA Astrophysics Data System (ADS)

Ma, Lili; Gao, Yangyang; Guo, Zhen; Wang, Lizhong

2018-04-01

An experimental investigation on flow past nine cylinders in a square configuration was carried out using the particle image velocimetry technique and load cell in a water channel. The center-to-center spacing ratio L/D was in the range of 1.5-3.0 and the Reynolds number Re was varied from 1500 to 5000. The effects of spacing ratio and Reynolds number on the instantaneous time-averaged flow fields and force coefficients are investigated. The results show that three distinct flow regimes are categorized with variation of the spacing ratios and Reynolds numbers, namely, shielding flow regime, transition flow regime and vortex shedding flow regime. Depending on the interferences of shear layers around the nine cylinders, each flow regime is further divided into two types of flow patterns. An interesting feature of bistable flow pattern with different flow modes is observed at small spacing ratio L/D = 1.5. The non-dimensional vortex shedding frequencies appear to be more associated with the individual shear layers rather than the multiple cylinders. Moreover, force analysis, streamline topologies and Reynolds stress contours are presented to elucidate the effects of spacing ratio and Reynolds number on the complex wake interference among the nine cylinders. The flow characteristics and force coefficients are found to be more sensitive to L/D rather than Re.

On the background independence of two-dimensional topological gravity

NASA Astrophysics Data System (ADS)

Imbimbo, Camillo

1995-04-01

We formulate two-dimensional topological gravity in a background covariant Lagrangian framework. We derive the Ward identities which characterize the dependence of physical correlators on the background world-sheet metric defining the gauge-slice. We point out the existence of an "anomaly" in Ward identitites involving correlators of observables with higher ghost number. This "anomaly" represents an obstruction for physical correlators to be globally defined forms on moduli space which could be integrated in a background independent way. Starting from the anomalous Ward identities, we derive "descent" equations whose solutions are cocycles of the Lie algebra of the diffeomorphism group with values in the space of local forms on the moduli space. We solve the descent equations and provide explicit formulas for the cocycles, which allow for the definition of background independent integrals of physical correlators on the moduli space.

Three ways to solve critical ϕ4 theory on 4 ‑ 𝜖 dimensional real projective space: Perturbation, bootstrap, and Schwinger-Dyson equation

NASA Astrophysics Data System (ADS)

Hasegawa, Chika; Nakayama, Yu

2018-03-01

In this paper, we solve the two-point function of the lowest dimensional scalar operator in the critical ϕ4 theory on 4 ‑ 𝜖 dimensional real projective space in three different methods. The first is to use the conventional perturbation theory, and the second is to impose the cross-cap bootstrap equation, and the third is to solve the Schwinger-Dyson equation under the assumption of conformal invariance. We find that the three methods lead to mutually consistent results but each has its own advantage.

Local P violation effects and thermalization in QCD: Views from quantum field theory and holography

NASA Astrophysics Data System (ADS)

Zhitnitsky, Ariel R.

2012-07-01

We argue that the local violation of P and CP invariance in heavy ion collisions and the universal thermal aspects observed in high energy collisions are in fact two sides of the same coin, and both are related to quantum anomalies of QCD. We argue that the low energy relations representing the quantum anomalies of QCD are saturated by coherent low-dimensional vacuum configurations as observed in Monte Carlo lattice studies. The thermal spectrum and approximate universality of the temperature with no dependence on energy of colliding particles in this framework is due to the fact that the emission results from the distortion of these low-dimensional vacuum sheets rather than from the colliding particles themselves. The emergence of the long-range correlations of P odd domains (a feature which is apparently required for explanation of the asymmetry observed at RHIC and LHC) is also a result of the same distortion of the QCD vacuum configurations. We formulate the corresponding physics using the effective low energy effective Lagrangian. We also formulate the same physics in terms of the dual holographic picture when low-dimensional sheets of topological charge embedded in 4d space, as observed in Monte Carlo simulations, are identified with D2 branes. Finally, we argue that study of these long-range correlations in heavy ion collisions could serve as a perfect test of a proposal that the observed dark energy in present epoch is a result of a tiny deviation of the QCD vacuum energy in expanding universe from its conventional value in Minkowski space-time.