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).

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.

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.

Geometry of quantum dynamics in infinite-dimensional Hilbert space

NASA Astrophysics Data System (ADS)

Grabowski, Janusz; Kuś, Marek; Marmo, Giuseppe; Shulman, Tatiana

2018-04-01

We develop a geometric approach to quantum mechanics based on the concept of the Tulczyjew triple. Our approach is genuinely infinite-dimensional, i.e. we do not restrict considerations to finite-dimensional Hilbert spaces, contrary to many other works on the geometry of quantum mechanics, and include a Lagrangian formalism in which self-adjoint (Schrödinger) operators are obtained as Lagrangian submanifolds associated with the Lagrangian. As a byproduct we also obtain results concerning coadjoint orbits of the unitary group in infinite dimensions, embedding of pure states in the unitary group, and self-adjoint extensions of symmetric relations.

A simple method for constructing the inhomogeneous quantum group IGLq(n) and its universal enveloping algebra Uq(igl(n))

NASA Astrophysics Data System (ADS)

Shariati, A.; Aghamohammadi, A.

1995-12-01

We propose a simple and concise method to construct the inhomogeneous quantum group IGLq(n) and its universal enveloping algebra Uq(igl(n)). Our technique is based on embedding an n-dimensional quantum space in an n+1-dimensional one as the set xn+1=1. This is possible only if one considers the multiparametric quantum space whose parameters are fixed in a specific way. The quantum group IGLq(n) is then the subset of GLq(n+1), which leaves the xn+1=1 subset invariant. For the deformed universal enveloping algebra Uq(igl(n)), we will show that it can also be embedded in Uq(gl(n+1)), provided one uses the multiparametric deformation of U(gl(n+1)) with a specific choice of its parameters.

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.

Inhomogeneous Jacobi equation for minimal surfaces and perturbative change in holographic entanglement entropy

NASA Astrophysics Data System (ADS)

Ghosh, Avirup; Mishra, Rohit

2018-04-01

The change in holographic entanglement entropy (HEE) for small fluctuations about pure anti-de Sitter (AdS) is obtained by a perturbative expansion of the area functional in terms of the change in the bulk metric and the embedded extremal surface. However it is known that change in the embedding appears at second order or higher. It was shown that these changes in the embedding can be calculated in the 2 +1 dimensional case by solving a "generalized geodesic deviation equation." We generalize this result to arbitrary dimensions by deriving an inhomogeneous form of the Jacobi equation for minimal surfaces. The solutions of this equation map a minimal surface in a given space time to a minimal surface in a space time which is a perturbation over the initial space time. Using this we perturbatively calculate the changes in HEE up to second order for boosted black brane like perturbations over AdS4.

Consistent Alignment of World Embedding Models

DTIC Science & Technology

2017-03-02

propose a solution that aligns variations of the same model (or different models) in a joint low-dimensional la- tent space leveraging carefully...representations of linguistic enti- ties, most often referred to as embeddings. This includes techniques that rely on matrix factoriza- tion (Levy & Goldberg ...higher, the variation is much higher as well. As we increase the size of the neighborhood, or improve the quality of our sample by only picking the most

Metric Optimization for Surface Analysis in the Laplace-Beltrami Embedding Space

PubMed Central

Lai, Rongjie; Wang, Danny J.J.; Pelletier, Daniel; Mohr, David; Sicotte, Nancy; Toga, Arthur W.

2014-01-01

In this paper we present a novel approach for the intrinsic mapping of anatomical surfaces and its application in brain mapping research. Using the Laplace-Beltrami eigen-system, we represent each surface with an isometry invariant embedding in a high dimensional space. The key idea in our system is that we realize surface deformation in the embedding space via the iterative optimization of a conformal metric without explicitly perturbing the surface or its embedding. By minimizing a distance measure in the embedding space with metric optimization, our method generates a conformal map directly between surfaces with highly uniform metric distortion and the ability of aligning salient geometric features. Besides pairwise surface maps, we also extend the metric optimization approach for group-wise atlas construction and multi-atlas cortical label fusion. In experimental results, we demonstrate the robustness and generality of our method by applying it to map both cortical and hippocampal surfaces in population studies. For cortical labeling, our method achieves excellent performance in a cross-validation experiment with 40 manually labeled surfaces, and successfully models localized brain development in a pediatric study of 80 subjects. For hippocampal mapping, our method produces much more significant results than two popular tools on a multiple sclerosis study of 109 subjects. PMID:24686245

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

Permutation entropy with vector embedding delays

NASA Astrophysics Data System (ADS)

Little, Douglas J.; Kane, Deb M.

2017-12-01

Permutation entropy (PE) is a statistic used widely for the detection of structure within a time series. Embedding delay times at which the PE is reduced are characteristic timescales for which such structure exists. Here, a generalized scheme is investigated where embedding delays are represented by vectors rather than scalars, permitting PE to be calculated over a (D -1 ) -dimensional space, where D is the embedding dimension. This scheme is applied to numerically generated noise, sine wave and logistic map series, and experimental data sets taken from a vertical-cavity surface emitting laser exhibiting temporally localized pulse structures within the round-trip time of the laser cavity. Results are visualized as PE maps as a function of embedding delay, with low PE values indicating combinations of embedding delays where correlation structure is present. It is demonstrated that vector embedding delays enable identification of structure that is ambiguous or masked, when the embedding delay is constrained to scalar form.

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.

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.

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.

Algebra of implicitly defined constraints for gravity as the general form of embedding theory

NASA Astrophysics Data System (ADS)

Paston, S. A.; Semenova, E. N.; Franke, V. A.; Sheykin, A. A.

2017-01-01

We consider the embedding theory, the approach to gravity proposed by Regge and Teitelboim, in which 4D space-time is treated as a surface in high-dimensional flat ambient space. In its general form, which does not contain artificially imposed constraints, this theory can be viewed as an extension of GR. In the present paper we study the canonical description of the embedding theory in this general form. In this case, one of the natural constraints cannot be written explicitly, in contrast to the case where additional Einsteinian constraints are imposed. Nevertheless, it is possible to calculate all Poisson brackets with this constraint. We prove that the algebra of four emerging constraints is closed, i.e., all of them are first-class constraints. The explicit form of this algebra is also obtained.

Strong-field tidal distortions of rotating black holes. III. Embeddings in hyperbolic three-space

NASA Astrophysics Data System (ADS)

Penna, Robert F.; Hughes, Scott A.; O'Sullivan, Stephen

2017-09-01

In previous work, we developed tools for quantifying the tidal distortion of a black hole's event horizon due to an orbiting companion. These tools use techniques which require large mass ratios (companion mass μ much smaller than black hole mass M ), but can be used for arbitrary bound orbits and for any black hole spin. We also showed how to visualize these distorted black holes by embedding their horizons in a global Euclidean three-space, E3. Such visualizations illustrate interesting and important information about horizon dynamics. Unfortunately, we could not visualize black holes with spin parameter a*>√{3 }/2 ≈0.866 : such holes cannot be globally embedded into E3. In this paper, we overcome this difficulty by showing how to embed the horizons of tidally distorted Kerr black holes in a hyperbolic three-space, H3. We use black hole perturbation theory to compute the Gaussian curvatures of tidally distorted event horizons, from which we build a two-dimensional metric of their distorted horizons. We develop a numerical method for embedding the tidally distorted horizons in H3. As an application, we give a sequence of embeddings into H3 of a tidally interacting black hole with spin a*=0.9999 . A small-amplitude, high-frequency oscillation seen in previous work shows up particularly clearly in these embeddings.

Effect of phonon-bath dimensionality on the spectral tuning of single-photon emitters in the Purcell regime

NASA Astrophysics Data System (ADS)

Chassagneux, Yannick; Jeantet, Adrien; Claude, Théo; Voisin, Christophe

2018-05-01

We develop a theoretical frame to investigate the spectral dependence of the brightness of a single-photon source made of a solid-state nanoemitter embedded in a high-quality factor microcavity. This study encompasses the cases of localized excitons embedded in a one-, two-, or three-dimensional matrix. The population evolution is calculated based on a spin-boson model, using the noninteracting blip approximation. We find that the spectral dependence of the single-photon source brightness (hereafter called spectral efficiency) can be expressed analytically through the free-space emission and absorption spectra of the emitter, the vacuum Rabi splitting, and the loss rates of the system. In other words, the free-space spectrum of the emitter encodes all the relevant information on the interaction between the exciton and the phonon bath to obtain the dynamics of the cavity-coupled system. We compute numerically the spectral efficiency for several types of localized emitters differing by the phonon bath dimensionality. In particular, in low-dimensional systems where this interaction is enhanced, a pronounced asymmetric energy exchange between the emitter and the cavity on the phonon sidebands yields a considerable extension of the tuning range of the source through phonon-assisted cavity feeding, possibly surpassing that of a purely resonant system.

Dynamics in the Parameter Space of a Neuron Model

NASA Astrophysics Data System (ADS)

Paulo, C. Rech

2012-06-01

Some two-dimensional parameter-space diagrams are numerically obtained by considering the largest Lyapunov exponent for a four-dimensional thirteen-parameter Hindmarsh—Rose neuron model. Several different parameter planes are considered, and it is shown that depending on the combination of parameters, a typical scenario can be preserved: for some choice of two parameters, the parameter plane presents a comb-shaped chaotic region embedded in a large periodic region. It is also shown that there exist regions close to these comb-shaped chaotic regions, separated by the comb teeth, organizing themselves in period-adding bifurcation cascades.

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.

Computation of multi-dimensional viscous supersonic jet flow

NASA Technical Reports Server (NTRS)

Kim, Y. N.; Buggeln, R. C.; Mcdonald, H.

1986-01-01

A new method has been developed for two- and three-dimensional computations of viscous supersonic flows with embedded subsonic regions adjacent to solid boundaries. The approach employs a reduced form of the Navier-Stokes equations which allows solution as an initial-boundary value problem in space, using an efficient noniterative forward marching algorithm. Numerical instability associated with forward marching algorithms for flows with embedded subsonic regions is avoided by approximation of the reduced form of the Navier-Stokes equations in the subsonic regions of the boundary layers. Supersonic and subsonic portions of the flow field are simultaneously calculated by a consistently split linearized block implicit computational algorithm. The results of computations for a series of test cases relevant to internal supersonic flow is presented and compared with data. Comparison between data and computation are in general excellent thus indicating that the computational technique has great promise as a tool for calculating supersonic flow with embedded subsonic regions. Finally, a User's Manual is presented for the computer code used to perform the calculations.

A Structure-Based Distance Metric for High-Dimensional Space Exploration with Multi-Dimensional Scaling

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

Lee, Hyun Jung; McDonnell, Kevin T.; Zelenyuk, Alla

2014-03-01

Although the Euclidean distance does well in measuring data distances within high-dimensional clusters, it does poorly when it comes to gauging inter-cluster distances. This significantly impacts the quality of global, low-dimensional space embedding procedures such as the popular multi-dimensional scaling (MDS) where one can often observe non-intuitive layouts. We were inspired by the perceptual processes evoked in the method of parallel coordinates which enables users to visually aggregate the data by the patterns the polylines exhibit across the dimension axes. We call the path of such a polyline its structure and suggest a metric that captures this structure directly inmore » high-dimensional space. This allows us to better gauge the distances of spatially distant data constellations and so achieve data aggregations in MDS plots that are more cognizant of existing high-dimensional structure similarities. Our MDS plots also exhibit similar visual relationships as the method of parallel coordinates which is often used alongside to visualize the high-dimensional data in raw form. We then cast our metric into a bi-scale framework which distinguishes far-distances from near-distances. The coarser scale uses the structural similarity metric to separate data aggregates obtained by prior classification or clustering, while the finer scale employs the appropriate Euclidean distance.« less

A Fourier dimensionality reduction model for big data interferometric imaging

NASA Astrophysics Data System (ADS)

Vijay Kartik, S.; Carrillo, Rafael E.; Thiran, Jean-Philippe; Wiaux, Yves

2017-06-01

Data dimensionality reduction in radio interferometry can provide savings of computational resources for image reconstruction through reduced memory footprints and lighter computations per iteration, which is important for the scalability of imaging methods to the big data setting of the next-generation telescopes. This article sheds new light on dimensionality reduction from the perspective of the compressed sensing theory and studies its interplay with imaging algorithms designed in the context of convex optimization. We propose a post-gridding linear data embedding to the space spanned by the left singular vectors of the measurement operator, providing a dimensionality reduction below image size. This embedding preserves the null space of the measurement operator and hence its sampling properties are also preserved in light of the compressed sensing theory. We show that this can be approximated by first computing the dirty image and then applying a weighted subsampled discrete Fourier transform to obtain the final reduced data vector. This Fourier dimensionality reduction model ensures a fast implementation of the full measurement operator, essential for any iterative image reconstruction method. The proposed reduction also preserves the independent and identically distributed Gaussian properties of the original measurement noise. For convex optimization-based imaging algorithms, this is key to justify the use of the standard ℓ2-norm as the data fidelity term. Our simulations confirm that this dimensionality reduction approach can be leveraged by convex optimization algorithms with no loss in imaging quality relative to reconstructing the image from the complete visibility data set. Reconstruction results in simulation settings with no direction dependent effects or calibration errors show promising performance of the proposed dimensionality reduction. Further tests on real data are planned as an extension of the current work. matlab code implementing the proposed reduction method is available on GitHub.

Supervised embedding of textual predictors with applications in clinical diagnostics for pediatric cardiology.

PubMed

Perry, Thomas Ernest; Zha, Hongyuan; Zhou, Ke; Frias, Patricio; Zeng, Dadan; Braunstein, Mark

2014-02-01

Electronic health records possess critical predictive information for machine-learning-based diagnostic aids. However, many traditional machine learning methods fail to simultaneously integrate textual data into the prediction process because of its high dimensionality. In this paper, we present a supervised method using Laplacian Eigenmaps to enable existing machine learning methods to estimate both low-dimensional representations of textual data and accurate predictors based on these low-dimensional representations at the same time. We present a supervised Laplacian Eigenmap method to enhance predictive models by embedding textual predictors into a low-dimensional latent space, which preserves the local similarities among textual data in high-dimensional space. The proposed implementation performs alternating optimization using gradient descent. For the evaluation, we applied our method to over 2000 patient records from a large single-center pediatric cardiology practice to predict if patients were diagnosed with cardiac disease. In our experiments, we consider relatively short textual descriptions because of data availability. We compared our method with latent semantic indexing, latent Dirichlet allocation, and local Fisher discriminant analysis. The results were assessed using four metrics: the area under the receiver operating characteristic curve (AUC), Matthews correlation coefficient (MCC), specificity, and sensitivity. The results indicate that supervised Laplacian Eigenmaps was the highest performing method in our study, achieving 0.782 and 0.374 for AUC and MCC, respectively. Supervised Laplacian Eigenmaps showed an increase of 8.16% in AUC and 20.6% in MCC over the baseline that excluded textual data and a 2.69% and 5.35% increase in AUC and MCC, respectively, over unsupervised Laplacian Eigenmaps. As a solution, we present a supervised Laplacian Eigenmap method to embed textual predictors into a low-dimensional Euclidean space. This method allows many existing machine learning predictors to effectively and efficiently capture the potential of textual predictors, especially those based on short texts.

Nonlinear dimensionality reduction of electroencephalogram (EEG) for Brain Computer interfaces.

PubMed

Teli, Mohammad Nayeem; Anderson, Charles

2009-01-01

Patterns in electroencephalogram (EEG) signals are analyzed for a Brain Computer Interface (BCI). An important aspect of this analysis is the work on transformations of high dimensional EEG data to low dimensional spaces in which we can classify the data according to mental tasks being performed. In this research we investigate how a Neural Network (NN) in an auto-encoder with bottleneck configuration can find such a transformation. We implemented two approximate second-order methods to optimize the weights of these networks, because the more common first-order methods are very slow to converge for networks like these with more than three layers of computational units. The resulting non-linear projections of time embedded EEG signals show interesting separations that are related to tasks. The bottleneck networks do indeed discover nonlinear transformations to low-dimensional spaces that capture much of the information present in EEG signals. However, the resulting low-dimensional representations do not improve classification rates beyond what is possible using Quadratic Discriminant Analysis (QDA) on the original time-lagged EEG.

Tensor Train Neighborhood Preserving Embedding

NASA Astrophysics Data System (ADS)

Wang, Wenqi; Aggarwal, Vaneet; Aeron, Shuchin

2018-05-01

In this paper, we propose a Tensor Train Neighborhood Preserving Embedding (TTNPE) to embed multi-dimensional tensor data into low dimensional tensor subspace. Novel approaches to solve the optimization problem in TTNPE are proposed. For this embedding, we evaluate novel trade-off gain among classification, computation, and dimensionality reduction (storage) for supervised learning. It is shown that compared to the state-of-the-arts tensor embedding methods, TTNPE achieves superior trade-off in classification, computation, and dimensionality reduction in MNIST handwritten digits and Weizmann face datasets.

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.

Visualization of the operational space of edge-localized modes through low-dimensional embedding of probability distributions

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

Shabbir, A., E-mail: aqsa.shabbir@ugent.be; Noterdaeme, J. M.; Max-Planck-Institut für Plasmaphysik, Garching D-85748

2014-11-15

Information visualization aimed at facilitating human perception is an important tool for the interpretation of experiments on the basis of complex multidimensional data characterizing the operational space of fusion devices. This work describes a method for visualizing the operational space on a two-dimensional map and applies it to the discrimination of type I and type III edge-localized modes (ELMs) from a series of carbon-wall ELMy discharges at JET. The approach accounts for stochastic uncertainties that play an important role in fusion data sets, by modeling measurements with probability distributions in a metric space. The method is aimed at contributing tomore » physical understanding of ELMs as well as their control. Furthermore, it is a general method that can be applied to the modeling of various other plasma phenomena as well.« less

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.

Time series analyses of breathing patterns of lung cancer patients using nonlinear dynamical system theory.

PubMed

Tewatia, D K; Tolakanahalli, R P; Paliwal, B R; Tomé, W A

2011-04-07

The underlying requirements for successful implementation of any efficient tumour motion management strategy are regularity and reproducibility of a patient's breathing pattern. The physiological act of breathing is controlled by multiple nonlinear feedback and feed-forward couplings. It would therefore be appropriate to analyse the breathing pattern of lung cancer patients in the light of nonlinear dynamical system theory. The purpose of this paper is to analyse the one-dimensional respiratory time series of lung cancer patients based on nonlinear dynamics and delay coordinate state space embedding. It is very important to select a suitable pair of embedding dimension 'm' and time delay 'τ' when performing a state space reconstruction. Appropriate time delay and embedding dimension were obtained using well-established methods, namely mutual information and the false nearest neighbour method, respectively. Establishing stationarity and determinism in a given scalar time series is a prerequisite to demonstrating that the nonlinear dynamical system that gave rise to the scalar time series exhibits a sensitive dependence on initial conditions, i.e. is chaotic. Hence, once an appropriate state space embedding of the dynamical system has been reconstructed, we show that the time series of the nonlinear dynamical systems under study are both stationary and deterministic in nature. Once both criteria are established, we proceed to calculate the largest Lyapunov exponent (LLE), which is an invariant quantity under time delay embedding. The LLE for all 16 patients is positive, which along with stationarity and determinism establishes the fact that the time series of a lung cancer patient's breathing pattern is not random or irregular, but rather it is deterministic in nature albeit chaotic. These results indicate that chaotic characteristics exist in the respiratory waveform and techniques based on state space dynamics should be employed for tumour motion management.

ESTEEM: A Novel Framework for Qualitatively Evaluating and Visualizing Spatiotemporal Embeddings in Social Media

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

Arendt, Dustin L.; Volkova, Svitlana

Analyzing and visualizing large amounts of social media communications and contrasting short-term conversation changes over time and geo-locations is extremely important for commercial and government applications. Earlier approaches for large-scale text stream summarization used dynamic topic models and trending words. Instead, we rely on text embeddings – low-dimensional word representations in a continuous vector space where similar words are embedded nearby each other. This paper presents ESTEEM,1 a novel tool for visualizing and evaluating spatiotemporal embeddings learned from streaming social media texts. Our tool allows users to monitor and analyze query words and their closest neighbors with an interactive interface.more » We used state-of- the-art techniques to learn embeddings and developed a visualization to represent dynamically changing relations between words in social media over time and other dimensions. This is the first interactive visualization of streaming text representations learned from social media texts that also allows users to contrast differences across multiple dimensions of the data.« less

Correlations of RMT characteristic polynomials and integrability: Hermitean matrices

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

Osipov, Vladimir Al., E-mail: Vladimir.Osipov@uni-due.d; Kanzieper, Eugene, E-mail: Eugene.Kanzieper@hit.ac.i; Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot 76100

Integrable theory is formulated for correlation functions of characteristic polynomials associated with invariant non-Gaussian ensembles of Hermitean random matrices. By embedding the correlation functions of interest into a more general theory of {tau} functions, we (i) identify a zoo of hierarchical relations satisfied by {tau} functions in an abstract infinite-dimensional space and (ii) present a technology to translate these relations into hierarchically structured nonlinear differential equations describing the correlation functions of characteristic polynomials in the physical, spectral space. Implications of this formalism for fermionic, bosonic, and supersymmetric variations of zero-dimensional replica field theories are discussed at length. A particular emphasismore » is placed on the phenomenon of fermionic-bosonic factorisation of random-matrix-theory correlation functions.« less

Hyperspectral target detection using manifold learning and multiple target spectra

DOE PAGES

Ziemann, Amanda K.; Theiler, James; Messinger, David W.

2016-03-31

Imagery collected from satellites and airborne platforms provides an important tool for remotely analyzing the content of a scene. In particular, the ability to remotely detect a specific material within a scene is of critical importance in nonproliferation and other applications. The sensor systems that process hyperspectral images collect the high-dimensional spectral information necessary to perform these detection analyses. For a d-dimensional hyperspectral image, however, where d is the number of spectral bands, it is common for the data to inherently occupy an m-dimensional space with m << d. In the remote sensing community, this has led to recent interestmore » in the use of manifold learning, which seeks to characterize the embedded lower-dimensional, nonlinear manifold that the data discretely approximate. The research presented in this paper focuses on a graph theory and manifold learning approach to target detection, using an adaptive version of locally linear embedding that is biased to separate target pixels from background pixels. Finally, this approach incorporates multiple target signatures for a particular material, accounting for the spectral variability that is often present within a solid material of interest.« less

Engineering Three-dimensional Epithelial Tissues Embedded within Extracellular Matrix.

PubMed

Piotrowski-Daspit, Alexandra S; Nelson, Celeste M

2016-07-10

The architecture of branched organs such as the lungs, kidneys, and mammary glands arises through the developmental process of branching morphogenesis, which is regulated by a variety of soluble and physical signals in the microenvironment. Described here is a method created to study the process of branching morphogenesis by forming engineered three-dimensional (3D) epithelial tissues of defined shape and size that are completely embedded within an extracellular matrix (ECM). This method enables the formation of arrays of identical tissues and enables the control of a variety of environmental factors, including tissue geometry, spacing, and ECM composition. This method can also be combined with widely used techniques such as traction force microscopy (TFM) to gain more information about the interactions between cells and their surrounding ECM. The protocol can be used to investigate a variety of cell and tissue processes beyond branching morphogenesis, including cancer invasion.

Exploring the Free Energy Landscape of Solutes Embedded in Lipid Bilayers.

PubMed

Jämbeck, Joakim P M; Lyubartsev, Alexander P

2013-06-06

Free energy calculations are vital for our understanding of biological processes on an atomistic scale and can offer insight to various mechanisms. However, in some cases, degrees of freedom (DOFs) orthogonal to the reaction coordinate have high energy barriers and/or long equilibration times, which prohibit proper sampling. Here we identify these orthogonal DOFs when studying the transfer of a solute from water to a model membrane. Important DOFs are identified in bulk liquids of different dielectric nature with metadynamics simulations and are used as reaction coordinates for the translocation process, resulting in two- and three-dimensional space of reaction coordinates. The results are in good agreement with experiments and elucidate the pitfalls of using one-dimensional reaction coordinates. The calculations performed here offer the most detailed free energy landscape of solutes embedded in lipid bilayers to date and show that free energy calculations can be used to study complex membrane translocation phenomena.

Quantum metabolism explains the allometric scaling of metabolic rates.

PubMed

Demetrius, Lloyd; Tuszynski, J A

2010-03-06

A general model explaining the origin of allometric laws of physiology is proposed based on coupled energy-transducing oscillator networks embedded in a physical d-dimensional space (d = 1, 2, 3). This approach integrates Mitchell's theory of chemi-osmosis with the Debye model of the thermal properties of solids. We derive a scaling rule that relates the energy generated by redox reactions in cells, the dimensionality of the physical space and the mean cycle time. Two major regimes are found corresponding to classical and quantum behaviour. The classical behaviour leads to allometric isometry while the quantum regime leads to scaling laws relating metabolic rate and body size that cover a broad range of exponents that depend on dimensionality and specific parameter values. The regimes are consistent with a range of behaviours encountered in micelles, plants and animals and provide a conceptual framework for a theory of the metabolic function of living systems.

Inference of boundaries in causal sets

NASA Astrophysics Data System (ADS)

Cunningham, William J.

2018-05-01

We investigate the extrinsic geometry of causal sets in (1+1) -dimensional Minkowski spacetime. The properties of boundaries in an embedding space can be used not only to measure observables, but also to supplement the discrete action in the partition function via discretized Gibbons–Hawking–York boundary terms. We define several ways to represent a causal set using overlapping subsets, which then allows us to distinguish between null and non-null bounding hypersurfaces in an embedding space. We discuss algorithms to differentiate between different types of regions, consider when these distinctions are possible, and then apply the algorithms to several spacetime regions. Numerical results indicate the volumes of timelike boundaries can be measured to within 0.5% accuracy for flat boundaries and within 10% accuracy for highly curved boundaries for medium-sized causal sets with N  =  214 spacetime elements.

Gauging hidden symmetries in two dimensions

NASA Astrophysics Data System (ADS)

Samtleben, Henning; Weidner, Martin

2007-08-01

We initiate the systematic construction of gauged matter-coupled supergravity theories in two dimensions. Subgroups of the affine global symmetry group of toroidally compactified supergravity can be gauged by coupling vector fields with minimal couplings and a particular topological term. The gauge groups typically include hidden symmetries that are not among the target-space isometries of the ungauged theory. The gaugings constructed in this paper are described group-theoretically in terms of a constant embedding tensor subject to a number of constraints which parametrizes the different theories and entirely encodes the gauged Lagrangian. The prime example is the bosonic sector of the maximally supersymmetric theory whose ungauged version admits an affine fraktur e9 global symmetry algebra. The various parameters (related to higher-dimensional p-form fluxes, geometric and non-geometric fluxes, etc.) which characterize the possible gaugings, combine into an embedding tensor transforming in the basic representation of fraktur e9. This yields an infinite-dimensional class of maximally supersymmetric theories in two dimensions. We work out and discuss several examples of higher-dimensional origin which can be systematically analyzed using the different gradings of fraktur e9.

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

Kim, Sang Beom; Dsilva, Carmeline J.; Debenedetti, Pablo G., E-mail: pdebene@princeton.edu

Understanding the mechanisms by which proteins fold from disordered amino-acid chains to spatially ordered structures remains an area of active inquiry. Molecular simulations can provide atomistic details of the folding dynamics which complement experimental findings. Conventional order parameters, such as root-mean-square deviation and radius of gyration, provide structural information but fail to capture the underlying dynamics of the protein folding process. It is therefore advantageous to adopt a method that can systematically analyze simulation data to extract relevant structural as well as dynamical information. The nonlinear dimensionality reduction technique known as diffusion maps automatically embeds the high-dimensional folding trajectories inmore » a lower-dimensional space from which one can more easily visualize folding pathways, assuming the data lie approximately on a lower-dimensional manifold. The eigenvectors that parametrize the low-dimensional space, furthermore, are determined systematically, rather than chosen heuristically, as is done with phenomenological order parameters. We demonstrate that diffusion maps can effectively characterize the folding process of a Trp-cage miniprotein. By embedding molecular dynamics simulation trajectories of Trp-cage folding in diffusion maps space, we identify two folding pathways and intermediate structures that are consistent with the previous studies, demonstrating that this technique can be employed as an effective way of analyzing and constructing protein folding pathways from molecular simulations.« less

Method of Enhancing On-Board State Estimation Using Communication Signals

NASA Technical Reports Server (NTRS)

Anzalone, Evan J. (Inventor); Chuang, Jason C. H. (Inventor)

2015-01-01

A method of enhancing on-board state estimation for a spacecraft utilizes a network of assets to include planetary-based assets and space-based assets. Communication signals transmitted from each of the assets into space are defined by a common protocol. Data is embedded in each communication signal transmitted by the assets. The data includes a time-of-transmission for a corresponding one of the communication signals and a position of a corresponding one of the assets at the time-of-transmission. A spacecraft is equipped to receive the communication signals, has a clock synchronized to the space-wide time reference frame, and has a processor programmed to generate state estimates of the spacecraft. Using its processor, the spacecraft determines a one-dimensional range from itself to at least one of the assets and then updates its state estimates using each one-dimensional range.

Metadynamics in the conformational space nonlinearly dimensionally reduced by Isomap

NASA Astrophysics Data System (ADS)

Spiwok, Vojtěch; Králová, Blanka

2011-12-01

Atomic motions in molecules are not linear. This infers that nonlinear dimensionality reduction methods can outperform linear ones in analysis of collective atomic motions. In addition, nonlinear collective motions can be used as potentially efficient guides for biased simulation techniques. Here we present a simulation with a bias potential acting in the directions of collective motions determined by a nonlinear dimensionality reduction method. Ad hoc generated conformations of trans,trans-1,2,4-trifluorocyclooctane were analyzed by Isomap method to map these 72-dimensional coordinates to three dimensions, as described by Brown and co-workers [J. Chem. Phys. 129, 064118 (2008)]. Metadynamics employing the three-dimensional embeddings as collective variables was applied to explore all relevant conformations of the studied system and to calculate its conformational free energy surface. The method sampled all relevant conformations (boat, boat-chair, and crown) and corresponding transition structures inaccessible by an unbiased simulation. This scheme allows to use essentially any parameter of the system as a collective variable in biased simulations. Moreover, the scheme we used for mapping out-of-sample conformations from the 72D to 3D space can be used as a general purpose mapping for dimensionality reduction, beyond the context of molecular modeling.

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.

Regularized Embedded Multiple Kernel Dimensionality Reduction for Mine Signal Processing.

PubMed

Li, Shuang; Liu, Bing; Zhang, Chen

2016-01-01

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

Low eigenvalues of the entanglement Hamiltonian, localization length, and rare regions in one-dimensional disordered interacting systems

NASA Astrophysics Data System (ADS)

Berkovits, Richard

2018-03-01

The properties of the low-lying eigenvalues of the entanglement Hamiltonian and their relation to the localization length of a disordered interacting one-dimensional many-particle system are studied. The average of the first entanglement Hamiltonian level spacing is proportional to the ground-state localization length and shows the same dependence on the disorder and interaction strength as the localization length. This is the result of the fact that entanglement is limited to distances of order of the localization length. The distribution of the first entanglement level spacing shows a Gaussian-type behavior as expected for level spacings much larger than the disorder broadening. For weakly disordered systems (localization length larger than sample length), the distribution shows an additional peak at low-level spacings. This stems from rare regions in some samples which exhibit metalliclike behavior of large entanglement and large particle-number fluctuations. These intermediate microemulsion metallic regions embedded in the insulating phase are discussed.

New method to design stellarator coils without the winding surface

NASA Astrophysics Data System (ADS)

Zhu, Caoxiang; Hudson, Stuart R.; Song, Yuntao; Wan, Yuanxi

2018-01-01

Finding an easy-to-build coils set has been a critical issue for stellarator design for decades. Conventional approaches assume a toroidal ‘winding’ surface, but a poorly chosen winding surface can unnecessarily constrain the coil optimization algorithm, This article presents a new method to design coils for stellarators. Each discrete coil is represented as an arbitrary, closed, one-dimensional curve embedded in three-dimensional space. A target function to be minimized that includes both physical requirements and engineering constraints is constructed. The derivatives of the target function with respect to the parameters describing the coil geometries and currents are calculated analytically. A numerical code, named flexible optimized coils using space curves (FOCUS), has been developed. Applications to a simple stellarator configuration, W7-X and LHD vacuum fields are presented.

A High Performance Image Data Compression Technique for Space Applications

NASA Technical Reports Server (NTRS)

Yeh, Pen-Shu; Venbrux, Jack

2003-01-01

A highly performing image data compression technique is currently being developed for space science applications under the requirement of high-speed and pushbroom scanning. The technique is also applicable to frame based imaging data. The algorithm combines a two-dimensional transform with a bitplane encoding; this results in an embedded bit string with exact desirable compression rate specified by the user. The compression scheme performs well on a suite of test images acquired from spacecraft instruments. It can also be applied to three-dimensional data cube resulting from hyper-spectral imaging instrument. Flight qualifiable hardware implementations are in development. The implementation is being designed to compress data in excess of 20 Msampledsec and support quantization from 2 to 16 bits. This paper presents the algorithm, its applications and status of development.

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.

Determinism in synthesized chaotic waveforms.

PubMed

Corron, Ned J; Blakely, Jonathan N; Hayes, Scott T; Pethel, Shawn D

2008-03-01

The output of a linear filter driven by a randomly polarized square wave, when viewed backward in time, is shown to exhibit determinism at all times when embedded in a three-dimensional state space. Combined with previous results establishing exponential divergence equivalent to a positive Lyapunov exponent, this result rigorously shows that such reverse-time synthesized waveforms appear equally to have been produced by a deterministic chaotic system.

Localization of a mobile laser scanner via dimensional reduction

NASA Astrophysics Data System (ADS)

Lehtola, Ville V.; Virtanen, Juho-Pekka; Vaaja, Matti T.; Hyyppä, Hannu; Nüchter, Andreas

2016-11-01

We extend the concept of intrinsic localization from a theoretical one-dimensional (1D) solution onto a 2D manifold that is embedded in a 3D space, and then recover the full six degrees of freedom for a mobile laser scanner with a simultaneous localization and mapping algorithm (SLAM). By intrinsic localization, we mean that no reference coordinate system, such as global navigation satellite system (GNSS), nor inertial measurement unit (IMU) are used. Experiments are conducted with a 2D laser scanner mounted on a rolling prototype platform, VILMA. The concept offers potential in being extendable to other wheeled platforms.

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.

Population Coding of Visual Space: Modeling

PubMed Central

Lehky, Sidney R.; Sereno, Anne B.

2011-01-01

We examine how the representation of space is affected by receptive field (RF) characteristics of the encoding population. Spatial responses were defined by overlapping Gaussian RFs. These responses were analyzed using multidimensional scaling to extract the representation of global space implicit in population activity. Spatial representations were based purely on firing rates, which were not labeled with RF characteristics (tuning curve peak location, for example), differentiating this approach from many other population coding models. Because responses were unlabeled, this model represents space using intrinsic coding, extracting relative positions amongst stimuli, rather than extrinsic coding where known RF characteristics provide a reference frame for extracting absolute positions. Two parameters were particularly important: RF diameter and RF dispersion, where dispersion indicates how broadly RF centers are spread out from the fovea. For large RFs, the model was able to form metrically accurate representations of physical space on low-dimensional manifolds embedded within the high-dimensional neural population response space, suggesting that in some cases the neural representation of space may be dimensionally isomorphic with 3D physical space. Smaller RF sizes degraded and distorted the spatial representation, with the smallest RF sizes (present in early visual areas) being unable to recover even a topologically consistent rendition of space on low-dimensional manifolds. Finally, although positional invariance of stimulus responses has long been associated with large RFs in object recognition models, we found RF dispersion rather than RF diameter to be the critical parameter. In fact, at a population level, the modeling suggests that higher ventral stream areas with highly restricted RF dispersion would be unable to achieve positionally-invariant representations beyond this narrow region around fixation. PMID:21344012

Untangling Brain-Wide Dynamics in Consciousness by Cross-Embedding

PubMed Central

Tajima, Satohiro; Yanagawa, Toru; Fujii, Naotaka; Toyoizumi, Taro

2015-01-01

Brain-wide interactions generating complex neural dynamics are considered crucial for emergent cognitive functions. However, the irreducible nature of nonlinear and high-dimensional dynamical interactions challenges conventional reductionist approaches. We introduce a model-free method, based on embedding theorems in nonlinear state-space reconstruction, that permits a simultaneous characterization of complexity in local dynamics, directed interactions between brain areas, and how the complexity is produced by the interactions. We demonstrate this method in large-scale electrophysiological recordings from awake and anesthetized monkeys. The cross-embedding method captures structured interaction underlying cortex-wide dynamics that may be missed by conventional correlation-based analysis, demonstrating a critical role of time-series analysis in characterizing brain state. The method reveals a consciousness-related hierarchy of cortical areas, where dynamical complexity increases along with cross-area information flow. These findings demonstrate the advantages of the cross-embedding method in deciphering large-scale and heterogeneous neuronal systems, suggesting a crucial contribution by sensory-frontoparietal interactions to the emergence of complex brain dynamics during consciousness. PMID:26584045

Mapping higher-order relations between brain structure and function with embedded vector representations of connectomes.

PubMed

Rosenthal, Gideon; Váša, František; Griffa, Alessandra; Hagmann, Patric; Amico, Enrico; Goñi, Joaquín; Avidan, Galia; Sporns, Olaf

2018-06-05

Connectomics generates comprehensive maps of brain networks, represented as nodes and their pairwise connections. The functional roles of nodes are defined by their direct and indirect connectivity with the rest of the network. However, the network context is not directly accessible at the level of individual nodes. Similar problems in language processing have been addressed with algorithms such as word2vec that create embeddings of words and their relations in a meaningful low-dimensional vector space. Here we apply this approach to create embedded vector representations of brain networks or connectome embeddings (CE). CE can characterize correspondence relations among brain regions, and can be used to infer links that are lacking from the original structural diffusion imaging, e.g., inter-hemispheric homotopic connections. Moreover, we construct predictive deep models of functional and structural connectivity, and simulate network-wide lesion effects using the face processing system as our application domain. We suggest that CE offers a novel approach to revealing relations between connectome structure and function.

Correlation coefficient based supervised locally linear embedding for pulmonary nodule recognition.

PubMed

Wu, Panpan; Xia, Kewen; Yu, Hengyong

2016-11-01

Dimensionality reduction techniques are developed to suppress the negative effects of high dimensional feature space of lung CT images on classification performance in computer aided detection (CAD) systems for pulmonary nodule detection. An improved supervised locally linear embedding (SLLE) algorithm is proposed based on the concept of correlation coefficient. The Spearman's rank correlation coefficient is introduced to adjust the distance metric in the SLLE algorithm to ensure that more suitable neighborhood points could be identified, and thus to enhance the discriminating power of embedded data. The proposed Spearman's rank correlation coefficient based SLLE (SC(2)SLLE) is implemented and validated in our pilot CAD system using a clinical dataset collected from the publicly available lung image database consortium and image database resource initiative (LICD-IDRI). Particularly, a representative CAD system for solitary pulmonary nodule detection is designed and implemented. After a sequential medical image processing steps, 64 nodules and 140 non-nodules are extracted, and 34 representative features are calculated. The SC(2)SLLE, as well as SLLE and LLE algorithm, are applied to reduce the dimensionality. Several quantitative measurements are also used to evaluate and compare the performances. Using a 5-fold cross-validation methodology, the proposed algorithm achieves 87.65% accuracy, 79.23% sensitivity, 91.43% specificity, and 8.57% false positive rate, on average. Experimental results indicate that the proposed algorithm outperforms the original locally linear embedding and SLLE coupled with the support vector machine (SVM) classifier. Based on the preliminary results from a limited number of nodules in our dataset, this study demonstrates the great potential to improve the performance of a CAD system for nodule detection using the proposed SC(2)SLLE. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

An embedded real-time red peach detection system based on an OV7670 camera, ARM cortex-M4 processor and 3D look-up tables.

PubMed

Teixidó, Mercè; Font, Davinia; Pallejà, Tomàs; Tresanchez, Marcel; Nogués, Miquel; Palacín, Jordi

2012-10-22

This work proposes the development of an embedded real-time fruit detection system for future automatic fruit harvesting. The proposed embedded system is based on an ARM Cortex-M4 (STM32F407VGT6) processor and an Omnivision OV7670 color camera. The future goal of this embedded vision system will be to control a robotized arm to automatically select and pick some fruit directly from the tree. The complete embedded system has been designed to be placed directly in the gripper tool of the future robotized harvesting arm. The embedded system will be able to perform real-time fruit detection and tracking by using a three-dimensional look-up-table (LUT) defined in the RGB color space and optimized for fruit picking. Additionally, two different methodologies for creating optimized 3D LUTs based on existing linear color models and fruit histograms were implemented in this work and compared for the case of red peaches. The resulting system is able to acquire general and zoomed orchard images and to update the relative tracking information of a red peach in the tree ten times per second.

An Embedded Real-Time Red Peach Detection System Based on an OV7670 Camera, ARM Cortex-M4 Processor and 3D Look-Up Tables

PubMed Central

Teixidó, Mercè; Font, Davinia; Pallejà, Tomàs; Tresanchez, Marcel; Nogués, Miquel; Palacín, Jordi

2012-01-01

This work proposes the development of an embedded real-time fruit detection system for future automatic fruit harvesting. The proposed embedded system is based on an ARM Cortex-M4 (STM32F407VGT6) processor and an Omnivision OV7670 color camera. The future goal of this embedded vision system will be to control a robotized arm to automatically select and pick some fruit directly from the tree. The complete embedded system has been designed to be placed directly in the gripper tool of the future robotized harvesting arm. The embedded system will be able to perform real-time fruit detection and tracking by using a three-dimensional look-up-table (LUT) defined in the RGB color space and optimized for fruit picking. Additionally, two different methodologies for creating optimized 3D LUTs based on existing linear color models and fruit histograms were implemented in this work and compared for the case of red peaches. The resulting system is able to acquire general and zoomed orchard images and to update the relative tracking information of a red peach in the tree ten times per second. PMID:23202040

Existence and construction of Galilean invariant z ≠2 theories

NASA Astrophysics Data System (ADS)

Grinstein, Benjamín; Pal, Sridip

2018-06-01

We prove a no-go theorem for the construction of a Galilean boost invariant and z ≠2 anisotropic scale invariant field theory with a finite dimensional basis of fields. Two point correlators in such theories, we show, grow unboundedly with spatial separation. Correlators of theories with an infinite dimensional basis of fields, for example, labeled by a continuous parameter, do not necessarily exhibit this bad behavior. Hence, such theories behave effectively as if in one extra dimension. Embedding the symmetry algebra into the conformal algebra of one higher dimension also reveals the existence of an internal continuous parameter. Consideration of isometries shows that the nonrelativistic holographic picture assumes a canonical form, where the bulk gravitational theory lives in a space-time with one extra dimension. This can be contrasted with the original proposal by Balasubramanian and McGreevy, and by Son, where the metric of a (d +2 )-dimensional space-time is proposed to be dual of a d -dimensional field theory. We provide explicit examples of theories living at fixed point with anisotropic scaling exponent z =2/ℓ ℓ+1 , ℓ∈Z .

Quantum decimation in Hilbert space: Coarse graining without structure

NASA Astrophysics Data System (ADS)

Singh, Ashmeet; Carroll, Sean M.

2018-03-01

We present a technique to coarse grain quantum states in a finite-dimensional Hilbert space. Our method is distinguished from other approaches by not relying on structures such as a preferred factorization of Hilbert space or a preferred set of operators (local or otherwise) in an associated algebra. Rather, we use the data corresponding to a given set of states, either specified independently or constructed from a single state evolving in time. Our technique is based on principle component analysis (PCA), and the resulting coarse-grained quantum states live in a lower-dimensional Hilbert space whose basis is defined using the underlying (isometric embedding) transformation of the set of fine-grained states we wish to coarse grain. Physically, the transformation can be interpreted to be an "entanglement coarse-graining" scheme that retains most of the global, useful entanglement structure of each state, while needing fewer degrees of freedom for its reconstruction. This scheme could be useful for efficiently describing collections of states whose number is much smaller than the dimension of Hilbert space, or a single state evolving over time.

Contribution to the optimal shape design of two-dimensional internal flows with embedded shocks

NASA Technical Reports Server (NTRS)

Iollo, Angelo; Salas, Manuel D.

1995-01-01

We explore the practicability of optimal shape design for flows modeled by the Euler equations. We define a functional whose minimum represents the optimality condition. The gradient of the functional with respect to the geometry is calculated with the Lagrange multipliers, which are determined by solving a co-state equation. The optimization problem is then examined by comparing the performance of several gradient-based optimization algorithms. In this formulation, the flow field can be computed to an arbitrary order of accuracy. Finally, some results for internal flows with embedded shocks are presented, including a case for which the solution to the inverse problem does not belong to the design space.

Multiview Locally Linear Embedding for Effective Medical Image Retrieval

PubMed Central

Shen, Hualei; Tao, Dacheng; Ma, Dianfu

2013-01-01

Content-based medical image retrieval continues to gain attention for its potential to assist radiological image interpretation and decision making. Many approaches have been proposed to improve the performance of medical image retrieval system, among which visual features such as SIFT, LBP, and intensity histogram play a critical role. Typically, these features are concatenated into a long vector to represent medical images, and thus traditional dimension reduction techniques such as locally linear embedding (LLE), principal component analysis (PCA), or laplacian eigenmaps (LE) can be employed to reduce the “curse of dimensionality”. Though these approaches show promising performance for medical image retrieval, the feature-concatenating method ignores the fact that different features have distinct physical meanings. In this paper, we propose a new method called multiview locally linear embedding (MLLE) for medical image retrieval. Following the patch alignment framework, MLLE preserves the geometric structure of the local patch in each feature space according to the LLE criterion. To explore complementary properties among a range of features, MLLE assigns different weights to local patches from different feature spaces. Finally, MLLE employs global coordinate alignment and alternating optimization techniques to learn a smooth low-dimensional embedding from different features. To justify the effectiveness of MLLE for medical image retrieval, we compare it with conventional spectral embedding methods. We conduct experiments on a subset of the IRMA medical image data set. Evaluation results show that MLLE outperforms state-of-the-art dimension reduction methods. PMID:24349277

Enhanced multi-protocol analysis via intelligent supervised embedding (EMPrAvISE): detecting prostate cancer on multi-parametric MRI

NASA Astrophysics Data System (ADS)

Viswanath, Satish; Bloch, B. Nicholas; Chappelow, Jonathan; Patel, Pratik; Rofsky, Neil; Lenkinski, Robert; Genega, Elizabeth; Madabhushi, Anant

2011-03-01

Currently, there is significant interest in developing methods for quantitative integration of multi-parametric (structural, functional) imaging data with the objective of building automated meta-classifiers to improve disease detection, diagnosis, and prognosis. Such techniques are required to address the differences in dimensionalities and scales of individual protocols, while deriving an integrated multi-parametric data representation which best captures all disease-pertinent information available. In this paper, we present a scheme called Enhanced Multi-Protocol Analysis via Intelligent Supervised Embedding (EMPrAvISE); a powerful, generalizable framework applicable to a variety of domains for multi-parametric data representation and fusion. Our scheme utilizes an ensemble of embeddings (via dimensionality reduction, DR); thereby exploiting the variance amongst multiple uncorrelated embeddings in a manner similar to ensemble classifier schemes (e.g. Bagging, Boosting). We apply this framework to the problem of prostate cancer (CaP) detection on 12 3 Tesla pre-operative in vivo multi-parametric (T2-weighted, Dynamic Contrast Enhanced, and Diffusion-weighted) magnetic resonance imaging (MRI) studies, in turn comprising a total of 39 2D planar MR images. We first align the different imaging protocols via automated image registration, followed by quantification of image attributes from individual protocols. Multiple embeddings are generated from the resultant high-dimensional feature space which are then combined intelligently to yield a single stable solution. Our scheme is employed in conjunction with graph embedding (for DR) and probabilistic boosting trees (PBTs) to detect CaP on multi-parametric MRI. Finally, a probabilistic pairwise Markov Random Field algorithm is used to apply spatial constraints to the result of the PBT classifier, yielding a per-voxel classification of CaP presence. Per-voxel evaluation of detection results against ground truth for CaP extent on MRI (obtained by spatially registering pre-operative MRI with available whole-mount histological specimens) reveals that EMPrAvISE yields a statistically significant improvement (AUC=0.77) over classifiers constructed from individual protocols (AUC=0.62, 0.62, 0.65, for T2w, DCE, DWI respectively) as well as one trained using multi-parametric feature concatenation (AUC=0.67).

An extinction/reignition dynamic method for turbulent combustion

NASA Astrophysics Data System (ADS)

Knaus, Robert; Pantano, Carlos

2011-11-01

Quasi-randomly distributed locations of high strain in turbulent combustion can cause a nonpremixed or partially premixed flame to develop local regions of extinction called ``flame holes''. The presence and extent of these holes can increase certain pollutants and reduce the amount of fuel burned. Accurately modeling the dynamics of these interacting regions can improve the accuracy of combustion simulations by effectively incorporating finite-rate chemistry effects. In the proposed method, the flame hole state is characterized by a progress variable that nominally exists on the stoichiometric surface. The evolution of this field is governed by a partial-differential equation embedded in the time-dependent two-manifold of the flame surface. This equation includes advection, propagation, and flame hole formation (flame hole healing or collapse is accounted by propagation naturally). We present a computational algorithm that solves this equation by embedding it in the usual three-dimensional space. A piece-wise parabolic WENO scheme combined with a compression algorithm are used to evolve the flame hole progress variable. A key aspect of the method is the extension of the surface data to the three-dimensional space in an efficient manner. We present results of this method applied to canonical turbulent combusting flows where the flame holes interact and describe their statistics.

Supergravitational conformal Galileons

NASA Astrophysics Data System (ADS)

Deen, Rehan; Ovrut, Burt

2017-08-01

The worldvolume actions of 3+1 dimensional bosonic branes embedded in a five-dimensional bulk space can lead to important effective field theories, such as the DBI conformal Galileons, and may, when the Null Energy Condition is violated, play an essential role in cosmological theories of the early universe. These include Galileon Genesis and "bouncing" cosmology, where a pre-Big Bang contracting phase bounces smoothly to the presently observed expanding universe. Perhaps the most natural arena for such branes to arise is within the context of superstring and M -theory vacua. Here, not only are branes required for the consistency of the theory, but, in many cases, the exact spectrum of particle physics occurs at low energy. However, such theories have the additional constraint that they must be N = 1 supersymmetric. This motivates us to compute the worldvolume actions of N = 1 supersymmetric three-branes, first in flat superspace and then to generalize them to N = 1 supergravitation. In this paper, for simplicity, we begin the process, not within the context of a superstring vacuum but, rather, for the conformal Galileons arising on a co-dimension one brane embedded in a maximally symmetric AdS 5 bulk space. We proceed to N = 1 supersymmetrize the associated worldvolume theory and then generalize the results to N = 1 supergravity, opening the door to possible new cosmological scenarios

Manifold learning in machine vision and robotics

NASA Astrophysics Data System (ADS)

Bernstein, Alexander

2017-02-01

Smart algorithms are used in Machine vision and Robotics to organize or extract high-level information from the available data. Nowadays, Machine learning is an essential and ubiquitous tool to automate extraction patterns or regularities from data (images in Machine vision; camera, laser, and sonar sensors data in Robotics) in order to solve various subject-oriented tasks such as understanding and classification of images content, navigation of mobile autonomous robot in uncertain environments, robot manipulation in medical robotics and computer-assisted surgery, and other. Usually such data have high dimensionality, however, due to various dependencies between their components and constraints caused by physical reasons, all "feasible and usable data" occupy only a very small part in high dimensional "observation space" with smaller intrinsic dimensionality. Generally accepted model of such data is manifold model in accordance with which the data lie on or near an unknown manifold (surface) of lower dimensionality embedded in an ambient high dimensional observation space; real-world high-dimensional data obtained from "natural" sources meet, as a rule, this model. The use of Manifold learning technique in Machine vision and Robotics, which discovers a low-dimensional structure of high dimensional data and results in effective algorithms for solving of a large number of various subject-oriented tasks, is the content of the conference plenary speech some topics of which are in the paper.

OBSERVING LYAPUNOV EXPONENTS OF INFINITE-DIMENSIONAL DYNAMICAL SYSTEMS

PubMed Central

OTT, WILLIAM; RIVAS, MAURICIO A.; WEST, JAMES

2016-01-01

Can Lyapunov exponents of infinite-dimensional dynamical systems be observed by projecting the dynamics into ℝN using a ‘typical’ nonlinear projection map? We answer this question affirmatively by developing embedding theorems for compact invariant sets associated with C1 maps on Hilbert spaces. Examples of such discrete-time dynamical systems include time-T maps and Poincaré return maps generated by the solution semigroups of evolution partial differential equations. We make every effort to place hypotheses on the projected dynamics rather than on the underlying infinite-dimensional dynamical system. In so doing, we adopt an empirical approach and formulate checkable conditions under which a Lyapunov exponent computed from experimental data will be a Lyapunov exponent of the infinite-dimensional dynamical system under study (provided the nonlinear projection map producing the data is typical in the sense of prevalence). PMID:28066028

OBSERVING LYAPUNOV EXPONENTS OF INFINITE-DIMENSIONAL DYNAMICAL SYSTEMS.

PubMed

Ott, William; Rivas, Mauricio A; West, James

2015-12-01

Can Lyapunov exponents of infinite-dimensional dynamical systems be observed by projecting the dynamics into ℝ N using a 'typical' nonlinear projection map? We answer this question affirmatively by developing embedding theorems for compact invariant sets associated with C 1 maps on Hilbert spaces. Examples of such discrete-time dynamical systems include time- T maps and Poincaré return maps generated by the solution semigroups of evolution partial differential equations. We make every effort to place hypotheses on the projected dynamics rather than on the underlying infinite-dimensional dynamical system. In so doing, we adopt an empirical approach and formulate checkable conditions under which a Lyapunov exponent computed from experimental data will be a Lyapunov exponent of the infinite-dimensional dynamical system under study (provided the nonlinear projection map producing the data is typical in the sense of prevalence).

New method to design stellarator coils without the winding surface

DOE PAGES

Zhu, Caoxiang; Hudson, Stuart R.; Song, Yuntao; ...

2017-11-06

Finding an easy-to-build coils set has been a critical issue for stellarator design for decades. Conventional approaches assume a toroidal 'winding' surface, but a poorly chosen winding surface can unnecessarily constrain the coil optimization algorithm, This article presents a new method to design coils for stellarators. Each discrete coil is represented as an arbitrary, closed, one-dimensional curve embedded in three-dimensional space. A target function to be minimized that includes both physical requirements and engineering constraints is constructed. The derivatives of the target function with respect to the parameters describing the coil geometries and currents are calculated analytically. A numerical code,more » named flexible optimized coils using space curves (FOCUS), has been developed. Furthermore, applications to a simple stellarator configuration, W7-X and LHD vacuum fields are presented.« less

New method to design stellarator coils without the winding surface

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

Zhu, Caoxiang; Hudson, Stuart R.; Song, Yuntao

Finding an easy-to-build coils set has been a critical issue for stellarator design for decades. Conventional approaches assume a toroidal 'winding' surface, but a poorly chosen winding surface can unnecessarily constrain the coil optimization algorithm, This article presents a new method to design coils for stellarators. Each discrete coil is represented as an arbitrary, closed, one-dimensional curve embedded in three-dimensional space. A target function to be minimized that includes both physical requirements and engineering constraints is constructed. The derivatives of the target function with respect to the parameters describing the coil geometries and currents are calculated analytically. A numerical code,more » named flexible optimized coils using space curves (FOCUS), has been developed. Furthermore, applications to a simple stellarator configuration, W7-X and LHD vacuum fields are presented.« less

Metadynamics in the conformational space nonlinearly dimensionally reduced by Isomap.

PubMed

Spiwok, Vojtěch; Králová, Blanka

2011-12-14

Atomic motions in molecules are not linear. This infers that nonlinear dimensionality reduction methods can outperform linear ones in analysis of collective atomic motions. In addition, nonlinear collective motions can be used as potentially efficient guides for biased simulation techniques. Here we present a simulation with a bias potential acting in the directions of collective motions determined by a nonlinear dimensionality reduction method. Ad hoc generated conformations of trans,trans-1,2,4-trifluorocyclooctane were analyzed by Isomap method to map these 72-dimensional coordinates to three dimensions, as described by Brown and co-workers [J. Chem. Phys. 129, 064118 (2008)]. Metadynamics employing the three-dimensional embeddings as collective variables was applied to explore all relevant conformations of the studied system and to calculate its conformational free energy surface. The method sampled all relevant conformations (boat, boat-chair, and crown) and corresponding transition structures inaccessible by an unbiased simulation. This scheme allows to use essentially any parameter of the system as a collective variable in biased simulations. Moreover, the scheme we used for mapping out-of-sample conformations from the 72D to 3D space can be used as a general purpose mapping for dimensionality reduction, beyond the context of molecular modeling. © 2011 American Institute of Physics

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.

Computational techniques to enable visualizing shapes of objects of extra spatial dimensions

NASA Astrophysics Data System (ADS)

Black, Don Vaughn, II

Envisioning extra dimensions beyond the three of common experience is a daunting challenge for three dimensional observers. Intuition relies on experience gained in a three dimensional environment. Gaining experience with virtual four dimensional objects and virtual three manifolds in four-space on a personal computer may provide the basis for an intuitive grasp of four dimensions. In order to enable such a capability for ourselves, it is first necessary to devise and implement a computationally tractable method to visualize, explore, and manipulate objects of dimension beyond three on the personal computer. A technology is described in this dissertation to convert a representation of higher dimensional models into a format that may be displayed in realtime on graphics cards available on many off-the-shelf personal computers. As a result, an opportunity has been created to experience the shape of four dimensional objects on the desktop computer. The ultimate goal has been to provide the user a tangible and memorable experience with mathematical models of four dimensional objects such that the user can see the model from any user selected vantage point. By use of a 4D GUI, an arbitrary convex hull or 3D silhouette of the 4D model can be rotated, panned, scrolled, and zoomed until a suitable dimensionally reduced view or Aspect is obtained. The 4D GUI then allows the user to manipulate a 3-flat hyperplane cutting tool to slice the model at an arbitrary orientation and position to extract or "pluck" an embedded 3D slice or "aspect" from the embedding four-space. This plucked 3D aspect can be viewed from all angles via a conventional 3D viewer using three multiple POV viewports, and optionally exported to a third party CAD viewer for further manipulation. Plucking and Manipulating the Aspect provides a tangible experience for the end-user in the same manner as any 3D Computer Aided Design viewing and manipulation tool does for the engineer or a 3D video game provides for the nascent student.

Control of extreme events in the bubbling onset of wave turbulence.

PubMed

Galuzio, P P; Viana, R L; Lopes, S R

2014-04-01

We show the existence of an intermittent transition from temporal chaos to turbulence in a spatially extended dynamical system, namely, the forced and damped one-dimensional nonlinear Schrödinger equation. For some values of the forcing parameter, the system dynamics intermittently switches between ordered states and turbulent states, which may be seen as extreme events in some contexts. In a Fourier phase space, the intermittency takes place due to the loss of transversal stability of unstable periodic orbits embedded in a low-dimensional subspace. We mapped these transversely unstable regions and perturbed the system in order to significantly reduce the occurrence of extreme events of turbulence.

Spherical hashing: binary code embedding with hyperspheres.

PubMed

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

2015-11-01

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

Higher (odd) dimensional quantum Hall effect and extended dimensional hierarchy

NASA Astrophysics Data System (ADS)

Hasebe, Kazuki

2017-07-01

We demonstrate dimensional ladder of higher dimensional quantum Hall effects by exploiting quantum Hall effects on arbitrary odd dimensional spheres. Non-relativistic and relativistic Landau models are analyzed on S 2 k - 1 in the SO (2 k - 1) monopole background. The total sub-band degeneracy of the odd dimensional lowest Landau level is shown to be equal to the winding number from the base-manifold S 2 k - 1 to the one-dimension higher SO (2 k) gauge group. Based on the chiral Hopf maps, we clarify the underlying quantum Nambu geometry for odd dimensional quantum Hall effect and the resulting quantum geometry is naturally embedded also in one-dimension higher quantum geometry. An origin of such dimensional ladder connecting even and odd dimensional quantum Hall effects is illuminated from a viewpoint of the spectral flow of Atiyah-Patodi-Singer index theorem in differential topology. We also present a BF topological field theory as an effective field theory in which membranes with different dimensions undergo non-trivial linking in odd dimensional space. Finally, an extended version of the dimensional hierarchy for higher dimensional quantum Hall liquids is proposed, and its relationship to quantum anomaly and D-brane physics is discussed.

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.

Local matrix learning in clustering and applications for manifold visualization.

PubMed

Arnonkijpanich, Banchar; Hasenfuss, Alexander; Hammer, Barbara

2010-05-01

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

IIB supergravity and the E 6(6) covariant vector-tensor hierarchy

DOE PAGES

Ciceri, Franz; de Wit, Bernard; Varela, Oscar

2015-04-20

IIB supergravity is reformulated with a manifest local USp(8) invariance that makes the embedding of five-dimensional maximal supergravities transparent. In this formulation the ten-dimensional theory exhibits all the 27 one-form fields and 22 of the 27 two-form fields that are required by the vector-tensor hierarchy of the five-dimensional theory. The missing 5 two-form fields must transform in the same representation as a descendant of the ten-dimensional ‘dual graviton’. The invariant E 6(6) symmetric tensor that appears in the vector-tensor hierarchy is reproduced. Generalized vielbeine are derived from the supersymmetry transformations of the vector fields, as well as consistent expressions formore » the USp(8) covariant fermion fields. Implications are further discussed for the consistency of the truncation of IIB supergravity compactified on the five-sphere to maximal gauged supergravity in five space-time dimensions with an SO(6) gauge group.« less

Visualizing curved spacetime

NASA Astrophysics Data System (ADS)

Jonsson, Rickard M.

2005-03-01

I present a way to visualize the concept of curved spacetime. The result is a curved surface with local coordinate systems (Minkowski systems) living on it, giving the local directions of space and time. Relative to these systems, special relativity holds. The method can be used to visualize gravitational time dilation, the horizon of black holes, and cosmological models. The idea underlying the illustrations is first to specify a field of timelike four-velocities uμ. Then, at every point, one performs a coordinate transformation to a local Minkowski system comoving with the given four-velocity. In the local system, the sign of the spatial part of the metric is flipped to create a new metric of Euclidean signature. The new positive definite metric, called the absolute metric, can be covariantly related to the original Lorentzian metric. For the special case of a two-dimensional original metric, the absolute metric may be embedded in three-dimensional Euclidean space as a curved surface.

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.

Decoupling function and anatomy in atlases of functional connectivity patterns: language mapping in tumor patients.

PubMed

Langs, Georg; Sweet, Andrew; Lashkari, Danial; Tie, Yanmei; Rigolo, Laura; Golby, Alexandra J; Golland, Polina

2014-12-01

In this paper we construct an atlas that summarizes functional connectivity characteristics of a cognitive process from a population of individuals. The atlas encodes functional connectivity structure in a low-dimensional embedding space that is derived from a diffusion process on a graph that represents correlations of fMRI time courses. The functional atlas is decoupled from the anatomical space, and thus can represent functional networks with variable spatial distribution in a population. In practice the atlas is represented by a common prior distribution for the embedded fMRI signals of all subjects. We derive an algorithm for fitting this generative model to the observed data in a population. Our results in a language fMRI study demonstrate that the method identifies coherent and functionally equivalent regions across subjects. The method also successfully maps functional networks from a healthy population used as a training set to individuals whose language networks are affected by tumors. Copyright © 2014. Published by Elsevier Inc.

Nonlinear ion acoustic waves scattered by vortexes

NASA Astrophysics Data System (ADS)

Ohno, Yuji; Yoshida, Zensho

2016-09-01

The Kadomtsev-Petviashvili (KP) hierarchy is the archetype of infinite-dimensional integrable systems, which describes nonlinear ion acoustic waves in two-dimensional space. This remarkably ordered system resides on a singular submanifold (leaf) embedded in a larger phase space of more general ion acoustic waves (low-frequency electrostatic perturbations). The KP hierarchy is characterized not only by small amplitudes but also by irrotational (zero-vorticity) velocity fields. In fact, the KP equation is derived by eliminating vorticity at every order of the reductive perturbation. Here, we modify the scaling of the velocity field so as to introduce a vortex term. The newly derived system of equations consists of a generalized three-dimensional KP equation and a two-dimensional vortex equation. The former describes 'scattering' of vortex-free waves by ambient vortexes that are determined by the latter. We say that the vortexes are 'ambient' because they do not receive reciprocal reactions from the waves (i.e., the vortex equation is independent of the wave fields). This model describes a minimal departure from the integrable KP system. By the Painlevé test, we delineate how the vorticity term violates integrability, bringing about an essential three-dimensionality to the solutions. By numerical simulation, we show how the solitons are scattered by vortexes and become chaotic.

A manifold learning approach to target detection in high-resolution hyperspectral imagery

NASA Astrophysics Data System (ADS)

Ziemann, Amanda K.

Imagery collected from airborne platforms and satellites provide an important medium for remotely analyzing the content in a scene. In particular, the ability to detect a specific material within a scene is of high importance to both civilian and defense applications. This may include identifying "targets" such as vehicles, buildings, or boats. Sensors that process hyperspectral images provide the high-dimensional spectral information necessary to perform such analyses. However, for a d-dimensional hyperspectral image, it is typical for the data to inherently occupy an m-dimensional space, with m << d. In the remote sensing community, this has led to a recent increase in the use of manifold learning, which aims to characterize the embedded lower-dimensional, non-linear manifold upon which the hyperspectral data inherently lie. Classic hyperspectral data models include statistical, linear subspace, and linear mixture models, but these can place restrictive assumptions on the distribution of the data; this is particularly true when implementing traditional target detection approaches, and the limitations of these models are well-documented. With manifold learning based approaches, the only assumption is that the data reside on an underlying manifold that can be discretely modeled by a graph. The research presented here focuses on the use of graph theory and manifold learning in hyperspectral imagery. Early work explored various graph-building techniques with application to the background model of the Topological Anomaly Detection (TAD) algorithm, which is a graph theory based approach to anomaly detection. This led towards a focus on target detection, and in the development of a specific graph-based model of the data and subsequent dimensionality reduction using manifold learning. An adaptive graph is built on the data, and then used to implement an adaptive version of locally linear embedding (LLE). We artificially induce a target manifold and incorporate it into the adaptive LLE transformation; the artificial target manifold helps to guide the separation of the target data from the background data in the new, lower-dimensional manifold coordinates. Then, target detection is performed in the manifold space.

On the stability of dyons and dyonic black holes in Einstein-Yang-Mills theory

NASA Astrophysics Data System (ADS)

Nolan, Brien C.; Winstanley, Elizabeth

2016-02-01

We investigate the stability of four-dimensional dyonic soliton and black hole solutions of {su}(2) Einstein-Yang-Mills theory in anti-de Sitter space. We prove that, in a neighbourhood of the embedded trivial (Schwarzschild-)anti-de Sitter solution, there exist non-trivial dyonic soliton and black hole solutions of the field equations which are stable under linear, spherically symmetric, perturbations of the metric and non-Abelian gauge field.

Superspace models for S-3

NASA Astrophysics Data System (ADS)

McKeon, D. G. C.

2003-11-01

The simplest supersymmetric extension of the group SO(4) is discussed. The superalgebra is realized in a superspace whose Bosonic subspace is the surface of a sphere S-3 embedded in four-dimensional Euclidean space. By using Fermionic coordinates in this superspace, which are chiral symplectic Majorana spinors, it proves possible to devise superfield models involving a complex scalar, a pair of chiral symplectic Majorana spinors, and a complex auxiliary scalar. Kinetic terms involve operators that are isometry generators on S-3.

Supergravitational conformal Galileons

DOE PAGES

Deen, Rehan; Ovrut, Burt

2017-08-04

The worldvolume actions of 3+1 dimensional bosonic branes embedded in a five-dimensional bulk space can lead to important effective field theories, such as the DBI conformal Galileons, and may, when the Null Energy Condition is violated, play an essential role in cosmological theories of the early universe. These include Galileon Genesis and “bouncing” cosmology, where a pre-Big Bang contracting phase bounces smoothly to the presently observed expanding universe. Perhaps the most natural arena for such branes to arise is within the context of superstring and M -theory vacua. Here, not only are branes required for the consistency of the theory,more » but, in many cases, the exact spectrum of particle physics occurs at low energy. However, such theories have the additional constraint that they must be N = 1 supersymmetric. This motivates us to compute the worldvolume actions of N = 1 supersymmetric three-branes, first in flat superspace and then to generalize them to N = 1 supergravitation. In this paper, for simplicity, we begin the process, not within the context of a superstring vacuum but, rather, for the conformal Galileons arising on a co-dimension one brane embedded in a maximally symmetric AdS 5 bulk space. We proceed to N = 1 supersymmetrize the associated worldvolume theory and then generalize the results to N = 1 supergravity, opening the door to possible new cosmological scenarios« less

Supergravitational conformal Galileons

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

Deen, Rehan; Ovrut, Burt

The worldvolume actions of 3+1 dimensional bosonic branes embedded in a five-dimensional bulk space can lead to important effective field theories, such as the DBI conformal Galileons, and may, when the Null Energy Condition is violated, play an essential role in cosmological theories of the early universe. These include Galileon Genesis and “bouncing” cosmology, where a pre-Big Bang contracting phase bounces smoothly to the presently observed expanding universe. Perhaps the most natural arena for such branes to arise is within the context of superstring and M -theory vacua. Here, not only are branes required for the consistency of the theory,more » but, in many cases, the exact spectrum of particle physics occurs at low energy. However, such theories have the additional constraint that they must be N = 1 supersymmetric. This motivates us to compute the worldvolume actions of N = 1 supersymmetric three-branes, first in flat superspace and then to generalize them to N = 1 supergravitation. In this paper, for simplicity, we begin the process, not within the context of a superstring vacuum but, rather, for the conformal Galileons arising on a co-dimension one brane embedded in a maximally symmetric AdS 5 bulk space. We proceed to N = 1 supersymmetrize the associated worldvolume theory and then generalize the results to N = 1 supergravity, opening the door to possible new cosmological scenarios« less

The GALAH survey: chemical tagging of star clusters and new members in the Pleiades

NASA Astrophysics Data System (ADS)

Kos, Janez; Bland-Hawthorn, Joss; Freeman, Ken; Buder, Sven; Traven, Gregor; De Silva, Gayandhi M.; Sharma, Sanjib; Asplund, Martin; Duong, Ly; Lin, Jane; Lind, Karin; Martell, Sarah; Simpson, Jeffrey D.; Stello, Dennis; Zucker, Daniel B.; Zwitter, Tomaž; Anguiano, Borja; Da Costa, Gary; D'Orazi, Valentina; Horner, Jonathan; Kafle, Prajwal R.; Lewis, Geraint; Munari, Ulisse; Nataf, David M.; Ness, Melissa; Reid, Warren; Schlesinger, Katie; Ting, Yuan-Sen; Wyse, Rosemary

2018-02-01

The technique of chemical tagging uses the elemental abundances of stellar atmospheres to 'reconstruct' chemically homogeneous star clusters that have long since dispersed. The GALAH spectroscopic survey - which aims to observe one million stars using the Anglo-Australian Telescope - allows us to measure up to 30 elements or dimensions in the stellar chemical abundance space, many of which are not independent. How to find clustering reliably in a noisy high-dimensional space is a difficult problem that remains largely unsolved. Here, we explore t-distributed stochastic neighbour embedding (t-SNE) - which identifies an optimal mapping of a high-dimensional space into fewer dimensions - whilst conserving the original clustering information. Typically, the projection is made to a 2D space to aid recognition of clusters by eye. We show that this method is a reliable tool for chemical tagging because it can: (i) resolve clustering in chemical space alone, (ii) recover known open and globular clusters with high efficiency and low contamination, and (iii) relate field stars to known clusters. t-SNE also provides a useful visualization of a high-dimensional space. We demonstrate the method on a data set of 13 abundances measured in the spectra of 187 000 stars by the GALAH survey. We recover seven of the nine observed clusters (six globular and three open clusters) in chemical space with minimal contamination from field stars and low numbers of outliers. With chemical tagging, we also identify two Pleiades supercluster members (which we confirm kinematically), one as far as 6° - one tidal radius away from the cluster centre.

The geometry of structural equilibrium

PubMed Central

2017-01-01

Building on a long tradition from Maxwell, Rankine, Klein and others, this paper puts forward a geometrical description of structural equilibrium which contains a procedure for the graphic analysis of stress resultants within general three-dimensional frames. The method is a natural generalization of Rankine’s reciprocal diagrams for three-dimensional trusses. The vertices and edges of dual abstract 4-polytopes are embedded within dual four-dimensional vector spaces, wherein the oriented area of generalized polygons give all six components (axial and shear forces with torsion and bending moments) of the stress resultants. The relevant quantities may be readily calculated using four-dimensional Clifford algebra. As well as giving access to frame analysis and design, the description resolves a number of long-standing problems with the incompleteness of Rankine’s description of three-dimensional trusses. Examples are given of how the procedure may be applied to structures of engineering interest, including an outline of a two-stage procedure for addressing the equilibrium of loaded gridshell rooves. PMID:28405361

Geometrical structure of Neural Networks: Geodesics, Jeffrey's Prior and Hyper-ribbons

NASA Astrophysics Data System (ADS)

Hayden, Lorien; Alemi, Alex; Sethna, James

2014-03-01

Neural networks are learning algorithms which are employed in a host of Machine Learning problems including speech recognition, object classification and data mining. In practice, neural networks learn a low dimensional representation of high dimensional data and define a model manifold which is an embedding of this low dimensional structure in the higher dimensional space. In this work, we explore the geometrical structure of a neural network model manifold. A Stacked Denoising Autoencoder and a Deep Belief Network are trained on handwritten digits from the MNIST database. Construction of geodesics along the surface and of slices taken from the high dimensional manifolds reveal a hierarchy of widths corresponding to a hyper-ribbon structure. This property indicates that neural networks fall into the class of sloppy models, in which certain parameter combinations dominate the behavior. Employing this information could prove valuable in designing both neural network architectures and training algorithms. This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No . DGE-1144153.

Si Nanocrystal-Embedded SiO x nanofoils: Two-Dimensional Nanotechnology-Enabled High Performance Li Storage Materials.

PubMed

Yoo, Hyundong; Park, Eunjun; Bae, Juhye; Lee, Jaewoo; Chung, Dong Jae; Jo, Yong Nam; Park, Min-Sik; Kim, Jung Ho; Dou, Shi Xue; Kim, Young-Jun; Kim, Hansu

2018-05-02

Silicon (Si) based materials are highly desirable to replace currently used graphite anode for lithium ion batteries. Nevertheless, its usage is still a big challenge due to poor battery performance and scale-up issue. In addition, two-dimensional (2D) architectures, which remain unresolved so far, would give them more interesting and unexpected properties. Herein, we report a facile, cost-effective, and scalable approach to synthesize Si nanocrystals embedded 2D SiO x nanofoils for next-generation lithium ion batteries through a solution-evaporation-induced interfacial sol-gel reaction of hydrogen silsesquioxane (HSiO 1.5 , HSQ). The unique nature of the thus-prepared centimeter scale 2D nanofoil with a large surface area enables ultrafast Li + insertion and extraction, with a reversible capacity of more than 650 mAh g -1 , even at a high current density of 50 C (50 A g -1 ). Moreover, the 2D nanostructured Si/SiO x nanofoils show excellent cycling performance up to 200 cycles and maintain their initial dimensional stability. This superior performance stems from the peculiar nanoarchitecture of 2D Si/SiO x nanofoils, which provides short diffusion paths for lithium ions and abundant free space to effectively accommodate the huge volume changes of Si during cycling.

Classification of fMRI resting-state maps using machine learning techniques: A comparative study

NASA Astrophysics Data System (ADS)

Gallos, Ioannis; Siettos, Constantinos

2017-11-01

We compare the efficiency of Principal Component Analysis (PCA) and nonlinear learning manifold algorithms (ISOMAP and Diffusion maps) for classifying brain maps between groups of schizophrenia patients and healthy from fMRI scans during a resting-state experiment. After a standard pre-processing pipeline, we applied spatial Independent component analysis (ICA) to reduce (a) noise and (b) spatial-temporal dimensionality of fMRI maps. On the cross-correlation matrix of the ICA components, we applied PCA, ISOMAP and Diffusion Maps to find an embedded low-dimensional space. Finally, support-vector-machines (SVM) and k-NN algorithms were used to evaluate the performance of the algorithms in classifying between the two groups.

Minimum curvilinearity to enhance topological prediction of protein interactions by network embedding

PubMed Central

Cannistraci, Carlo Vittorio; Alanis-Lobato, Gregorio; Ravasi, Timothy

2013-01-01

Motivation: Most functions within the cell emerge thanks to protein–protein interactions (PPIs), yet experimental determination of PPIs is both expensive and time-consuming. PPI networks present significant levels of noise and incompleteness. Predicting interactions using only PPI-network topology (topological prediction) is difficult but essential when prior biological knowledge is absent or unreliable. Methods: Network embedding emphasizes the relations between network proteins embedded in a low-dimensional space, in which protein pairs that are closer to each other represent good candidate interactions. To achieve network denoising, which boosts prediction performance, we first applied minimum curvilinear embedding (MCE), and then adopted shortest path (SP) in the reduced space to assign likelihood scores to candidate interactions. Furthermore, we introduce (i) a new valid variation of MCE, named non-centred MCE (ncMCE); (ii) two automatic strategies for selecting the appropriate embedding dimension; and (iii) two new randomized procedures for evaluating predictions. Results: We compared our method against several unsupervised and supervisedly tuned embedding approaches and node neighbourhood techniques. Despite its computational simplicity, ncMCE-SP was the overall leader, outperforming the current methods in topological link prediction. Conclusion: Minimum curvilinearity is a valuable non-linear framework that we successfully applied to the embedding of protein networks for the unsupervised prediction of novel PPIs. The rationale for our approach is that biological and evolutionary information is imprinted in the non-linear patterns hidden behind the protein network topology, and can be exploited for predicting new protein links. The predicted PPIs represent good candidates for testing in high-throughput experiments or for exploitation in systems biology tools such as those used for network-based inference and prediction of disease-related functional modules. Availability: https://sites.google.com/site/carlovittoriocannistraci/home Contact: kalokagathos.agon@gmail.com or timothy.ravasi@kaust.edu.sa Supplementary information: Supplementary data are available at Bioinformatics online. PMID:23812985

Two diverse models of embedding class one

NASA Astrophysics Data System (ADS)

Kuhfittig, Peter K. F.

2018-05-01

Embedding theorems have continued to be a topic of interest in the general theory of relativity since these help connect the classical theory to higher-dimensional manifolds. This paper deals with spacetimes of embedding class one, i.e., spacetimes that can be embedded in a five-dimensional flat spacetime. These ideas are applied to two diverse models, a complete solution for a charged wormhole admitting a one-parameter group of conformal motions and a new model to explain the flat rotation curves in spiral galaxies without the need for dark matter.

Variational submanifolds of Euclidean spaces

NASA Astrophysics Data System (ADS)

Krupka, D.; Urban, Z.; Volná, J.

2018-03-01

Systems of ordinary differential equations (or dynamical forms in Lagrangian mechanics), induced by embeddings of smooth fibered manifolds over one-dimensional basis, are considered in the class of variational equations. For a given non-variational system, conditions assuring variationality (the Helmholtz conditions) of the induced system with respect to a submanifold of a Euclidean space are studied, and the problem of existence of these "variational submanifolds" is formulated in general and solved for second-order systems. The variational sequence theory on sheaves of differential forms is employed as a main tool for the analysis of local and global aspects (variationality and variational triviality). The theory is illustrated by examples of holonomic constraints (submanifolds of a configuration Euclidean space) which are variational submanifolds in geometry and mechanics.

DEEP ATTRACTOR NETWORK FOR SINGLE-MICROPHONE SPEAKER SEPARATION.

PubMed

Chen, Zhuo; Luo, Yi; Mesgarani, Nima

2017-03-01

Despite the overwhelming success of deep learning in various speech processing tasks, the problem of separating simultaneous speakers in a mixture remains challenging. Two major difficulties in such systems are the arbitrary source permutation and unknown number of sources in the mixture. We propose a novel deep learning framework for single channel speech separation by creating attractor points in high dimensional embedding space of the acoustic signals which pull together the time-frequency bins corresponding to each source. Attractor points in this study are created by finding the centroids of the sources in the embedding space, which are subsequently used to determine the similarity of each bin in the mixture to each source. The network is then trained to minimize the reconstruction error of each source by optimizing the embeddings. The proposed model is different from prior works in that it implements an end-to-end training, and it does not depend on the number of sources in the mixture. Two strategies are explored in the test time, K-means and fixed attractor points, where the latter requires no post-processing and can be implemented in real-time. We evaluated our system on Wall Street Journal dataset and show 5.49% improvement over the previous state-of-the-art methods.

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.

Learning structure-property relationship in crystalline materials: A study of lanthanide-transition metal alloys

NASA Astrophysics Data System (ADS)

Pham, Tien-Lam; Nguyen, Nguyen-Duong; Nguyen, Van-Doan; Kino, Hiori; Miyake, Takashi; Dam, Hieu-Chi

2018-05-01

We have developed a descriptor named Orbital Field Matrix (OFM) for representing material structures in datasets of multi-element materials. The descriptor is based on the information regarding atomic valence shell electrons and their coordination. In this work, we develop an extension of OFM called OFM1. We have shown that these descriptors are highly applicable in predicting the physical properties of materials and in providing insights on the materials space by mapping into a low embedded dimensional space. Our experiments with transition metal/lanthanide metal alloys show that the local magnetic moments and formation energies can be accurately reproduced using simple nearest-neighbor regression, thus confirming the relevance of our descriptors. Using kernel ridge regressions, we could accurately reproduce formation energies and local magnetic moments calculated based on first-principles, with mean absolute errors of 0.03 μB and 0.10 eV/atom, respectively. We show that meaningful low-dimensional representations can be extracted from the original descriptor using descriptive learning algorithms. Intuitive prehension on the materials space, qualitative evaluation on the similarities in local structures or crystalline materials, and inference in the designing of new materials by element substitution can be performed effectively based on these low-dimensional representations.

Origin of gauge invariance in string theory

NASA Technical Reports Server (NTRS)

Horowitz, G. T.; Strominger, A.

1986-01-01

A first quantization of the space-time embedding Chi exp mu and the world-sheet metric rho of the open bosonic string. The world-sheet metric rho decouples from S-matrix elements in 26 dimensions. This formulation of the theory naturally includes 26-dimensional gauge transformations. The gauge invariance of S-matrix elements is a direct consequence of the decoupling of rho. Second quantization leads to a string field Phi(Chi exp mu, rho) with a gauge-covariant equation of motion.

Self-Avoiding Walks Over Adaptive Triangular Grids

NASA Technical Reports Server (NTRS)

Heber, Gerd; Biswas, Rupak; Gao, Guang R.; Saini, Subhash (Technical Monitor)

1999-01-01

Space-filling curves is a popular approach based on a geometric embedding for linearizing computational meshes. We present a new O(n log n) combinatorial algorithm for constructing a self avoiding walk through a two dimensional mesh containing n triangles. We show that for hierarchical adaptive meshes, the algorithm can be locally adapted and easily parallelized by taking advantage of the regularity of the refinement rules. The proposed approach should be very useful in the runtime partitioning and load balancing of adaptive unstructured grids.

Equivariant Verlinde Formula from Fivebranes and Vortices

NASA Astrophysics Data System (ADS)

Gukov, Sergei; Pei, Du

2017-10-01

We study complex Chern-Simons theory on a Seifert manifold M 3 by embedding it into string theory. We show that complex Chern-Simons theory on M 3 is equivalent to a topologically twisted supersymmetric theory and its partition function can be naturally regularized by turning on a mass parameter. We find that the dimensional reduction of this theory to 2d gives the low energy dynamics of vortices in four-dimensional gauge theory, the fact apparently overlooked in the vortex literature. We also generalize the relations between (1) the Verlinde algebra, (2) quantum cohomology of the Grassmannian, (3) Chern-Simons theory on {Σ× S^1} and (4) index of a spin c Dirac operator on the moduli space of flat connections to a new set of relations between (1) the "equivariant Verlinde algebra" for a complex group, (2) the equivariant quantum K-theory of the vortex moduli space, (3) complex Chern-Simons theory on {Σ × S^1} and (4) the equivariant index of a spin c Dirac operator on the moduli space of Higgs bundles.

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).

Low-dimensional approximation searching strategy for transfer entropy from non-uniform embedding

PubMed Central

2018-01-01

Transfer entropy from non-uniform embedding is a popular tool for the inference of causal relationships among dynamical subsystems. In this study we present an approach that makes use of low-dimensional conditional mutual information quantities to decompose the original high-dimensional conditional mutual information in the searching procedure of non-uniform embedding for significant variables at different lags. We perform a series of simulation experiments to assess the sensitivity and specificity of our proposed method to demonstrate its advantage compared to previous algorithms. The results provide concrete evidence that low-dimensional approximations can help to improve the statistical accuracy of transfer entropy in multivariate causality analysis and yield a better performance over other methods. The proposed method is especially efficient as the data length grows. PMID:29547669

Dynamical Connections in a Turbulent Fluid: Experiment and Simulation

NASA Astrophysics Data System (ADS)

Kageorge, Logan; Suri, Balachandra; Tithof, Jeff; Grigoriev, Roman; Schatz, Michael

2017-11-01

Embedded in the state space of a turbulent flow there exist invariant solutions to the Navier-Stokes equation called Exact Coherent Structures (ECS). Recent studies have demonstrated that the geometry of the ECS locally describes the evolution of the turbulent flow. Theory suggests that global connections may serve to guide the flow from the neighborhood of one ECS to that of another. We present here a numerical model of a Kolmogorov-like two-dimensional flow in which such connections have been calculated. Moreover, we present an experimental quasi-two-dimensional realization of this flow in which these connections prove dynamically relevant. This work is supported by the National Science Foundation (NSF CMMI 12-34436) and DARPA (HR0011-16-2-0033 subcontract to Georgia Tech).

Glassy phase in quenched disordered crystalline membranes

NASA Astrophysics Data System (ADS)

Coquand, O.; Essafi, K.; Kownacki, J.-P.; Mouhanna, D.

2018-03-01

We investigate the flat phase of D -dimensional crystalline membranes embedded in a d -dimensional space and submitted to both metric and curvature quenched disorders using a nonperturbative renormalization group approach. We identify a second-order phase transition controlled by a finite-temperature, finite-disorder fixed point unreachable within the leading order of ɛ =4 -D and 1 /d expansions. This critical point divides the flow diagram into two basins of attraction: that associated with the finite-temperature fixed point controlling the long-distance behavior of disorder-free membranes and that associated with the zero-temperature, finite-disorder fixed point. Our work thus strongly suggests the existence of a whole low-temperature glassy phase for quenched disordered crystalline membranes and, possibly, for graphene and graphene-like compounds.

Big Data Clustering via Community Detection and Hyperbolic Network Embedding in IoT Applications.

PubMed

Karyotis, Vasileios; Tsitseklis, Konstantinos; Sotiropoulos, Konstantinos; Papavassiliou, Symeon

2018-04-15

In this paper, we present a novel data clustering framework for big sensory data produced by IoT applications. Based on a network representation of the relations among multi-dimensional data, data clustering is mapped to node clustering over the produced data graphs. To address the potential very large scale of such datasets/graphs that test the limits of state-of-the-art approaches, we map the problem of data clustering to a community detection one over the corresponding data graphs. Specifically, we propose a novel computational approach for enhancing the traditional Girvan-Newman (GN) community detection algorithm via hyperbolic network embedding. The data dependency graph is embedded in the hyperbolic space via Rigel embedding, allowing more efficient computation of edge-betweenness centrality needed in the GN algorithm. This allows for more efficient clustering of the nodes of the data graph in terms of modularity, without sacrificing considerable accuracy. In order to study the operation of our approach with respect to enhancing GN community detection, we employ various representative types of artificial complex networks, such as scale-free, small-world and random geometric topologies, and frequently-employed benchmark datasets for demonstrating its efficacy in terms of data clustering via community detection. Furthermore, we provide a proof-of-concept evaluation by applying the proposed framework over multi-dimensional datasets obtained from an operational smart-city/building IoT infrastructure provided by the Federated Interoperable Semantic IoT/cloud Testbeds and Applications (FIESTA-IoT) testbed federation. It is shown that the proposed framework can be indeed used for community detection/data clustering and exploited in various other IoT applications, such as performing more energy-efficient smart-city/building sensing.

Big Data Clustering via Community Detection and Hyperbolic Network Embedding in IoT Applications

PubMed Central

Sotiropoulos, Konstantinos

2018-01-01

In this paper, we present a novel data clustering framework for big sensory data produced by IoT applications. Based on a network representation of the relations among multi-dimensional data, data clustering is mapped to node clustering over the produced data graphs. To address the potential very large scale of such datasets/graphs that test the limits of state-of-the-art approaches, we map the problem of data clustering to a community detection one over the corresponding data graphs. Specifically, we propose a novel computational approach for enhancing the traditional Girvan–Newman (GN) community detection algorithm via hyperbolic network embedding. The data dependency graph is embedded in the hyperbolic space via Rigel embedding, allowing more efficient computation of edge-betweenness centrality needed in the GN algorithm. This allows for more efficient clustering of the nodes of the data graph in terms of modularity, without sacrificing considerable accuracy. In order to study the operation of our approach with respect to enhancing GN community detection, we employ various representative types of artificial complex networks, such as scale-free, small-world and random geometric topologies, and frequently-employed benchmark datasets for demonstrating its efficacy in terms of data clustering via community detection. Furthermore, we provide a proof-of-concept evaluation by applying the proposed framework over multi-dimensional datasets obtained from an operational smart-city/building IoT infrastructure provided by the Federated Interoperable Semantic IoT/cloud Testbeds and Applications (FIESTA-IoT) testbed federation. It is shown that the proposed framework can be indeed used for community detection/data clustering and exploited in various other IoT applications, such as performing more energy-efficient smart-city/building sensing. PMID:29662043

A General Exponential Framework for Dimensionality Reduction.

PubMed

Wang, Su-Jing; Yan, Shuicheng; Yang, Jian; Zhou, Chun-Guang; Fu, Xiaolan

2014-02-01

As a general framework, Laplacian embedding, based on a pairwise similarity matrix, infers low dimensional representations from high dimensional data. However, it generally suffers from three issues: 1) algorithmic performance is sensitive to the size of neighbors; 2) the algorithm encounters the well known small sample size (SSS) problem; and 3) the algorithm de-emphasizes small distance pairs. To address these issues, here we propose exponential embedding using matrix exponential and provide a general framework for dimensionality reduction. In the framework, the matrix exponential can be roughly interpreted by the random walk over the feature similarity matrix, and thus is more robust. The positive definite property of matrix exponential deals with the SSS problem. The behavior of the decay function of exponential embedding is more significant in emphasizing small distance pairs. Under this framework, we apply matrix exponential to extend many popular Laplacian embedding algorithms, e.g., locality preserving projections, unsupervised discriminant projections, and marginal fisher analysis. Experiments conducted on the synthesized data, UCI, and the Georgia Tech face database show that the proposed new framework can well address the issues mentioned above.

Influence of pinches on magnetic reconnection in turbulent space plasmas

NASA Astrophysics Data System (ADS)

Olshevsky, Vyacheslav; Lapenta, Giovanni; Markidis, Stefano; Divin, Andrey

A generally accepted scenario of magnetic reconnection in space plasmas is the breakage of magnetic field lines in X-points. In laboratory, reconnection is widely studied in pinches, current channels embedded into twisted magnetic fields. No model of magnetic reconnection in space plasmas considers both null-points and pinches as peers. We have performed a particle-in-cell simulation of magnetic reconnection in a three-dimensional configuration where null-points are present nitially, and Z-pinches are formed during the simulation. The X-points are relatively stable, and no substantial energy dissipation is associated with them. On contrary, turbulent magnetic reconnection in the pinches causes the magnetic energy to decay at a rate of approximately 1.5 percent per ion gyro period. Current channels and twisted magnetic fields are ubiquitous in turbulent space plasmas, so pinches can be responsible for the observed high magnetic reconnection rates.

Multi-stream portrait of the Cosmic web

NASA Astrophysics Data System (ADS)

Ramachandra, Nesar; Shandarin, Sergei

2016-03-01

We report the results of the first study of the multi-stream environment of dark matter haloes in cosmological N-body simulations in the ΛCDM cosmology. The full dynamical state of dark matter can be described as a three-dimensional sub-manifold in six-dimensional phase space - the dark matter sheet. In our study we use a Lagrangian sub-manifold x = x (q , t) (where x and q are co-moving Eulerian and Lagrangian coordinates respectively), which is dynamically equivalent to the dark matter sheet but is more convenient for numerical analysis. Our major results can be summarized as follows. At the resolution of the simulation, the cosmic web represents a hierarchical structure: each halo is embedded in the filamentary framework of the web predominantly at the filament crossings, and each filament is embedded in the wall like fabric of the web at the wall crossings. Locally, each halo or sub-halo is a peak in the number of streams field. The number of streams in the neighbouring filaments is higher than in the neighbouring walls. The walls are regions where number of streams is equal to three or a few. Voids are uniquely defined by the local condition requiring to be a single-stream flow region.

One Shot Detection with Laplacian Object and Fast Matrix Cosine Similarity.

PubMed

Biswas, Sujoy Kumar; Milanfar, Peyman

2016-03-01

One shot, generic object detection involves searching for a single query object in a larger target image. Relevant approaches have benefited from features that typically model the local similarity patterns. In this paper, we combine local similarity (encoded by local descriptors) with a global context (i.e., a graph structure) of pairwise affinities among the local descriptors, embedding the query descriptors into a low dimensional but discriminatory subspace. Unlike principal components that preserve global structure of feature space, we actually seek a linear approximation to the Laplacian eigenmap that permits us a locality preserving embedding of high dimensional region descriptors. Our second contribution is an accelerated but exact computation of matrix cosine similarity as the decision rule for detection, obviating the computationally expensive sliding window search. We leverage the power of Fourier transform combined with integral image to achieve superior runtime efficiency that allows us to test multiple hypotheses (for pose estimation) within a reasonably short time. Our approach to one shot detection is training-free, and experiments on the standard data sets confirm the efficacy of our model. Besides, low computation cost of the proposed (codebook-free) object detector facilitates rather straightforward query detection in large data sets including movie videos.

Brane world in non-Riemannian geometry

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

Maier, R.; Falciano, F. T.

2011-03-15

We carefully investigate the modified Einstein's field equation in a 4-dimensional (3-brane) arbitrary manifold embedded in a 5-dimensional non-Riemannian bulk spacetime with a noncompact extra dimension. In this context the Israel-Darmois matching conditions are extended assuming that the torsion in the bulk is continuous. The discontinuity in the torsion first derivatives are related to the matter distribution through the field equation. In addition, we develop a model that describes a flat FLRW model embedded in a 5-dimensional de Sitter or anti-de Sitter, where a 5-dimensional cosmological constant emerges from the torsion.

Embedded 3D shape measurement system based on a novel spatio-temporal coding method

NASA Astrophysics Data System (ADS)

Xu, Bin; Tian, Jindong; Tian, Yong; Li, Dong

2016-11-01

Structured light measurement has been wildly used since 1970s in industrial component detection, reverse engineering, 3D molding, robot navigation, medical and many other fields. In order to satisfy the demand for high speed, high precision and high resolution 3-D measurement for embedded system, a new patterns combining binary and gray coding principle in space are designed and projected onto the object surface orderly. Each pixel corresponds to the designed sequence of gray values in time - domain, which is treated as a feature vector. The unique gray vector is then dimensionally reduced to a scalar which could be used as characteristic information for binocular matching. In this method, the number of projected structured light patterns is reduced, and the time-consuming phase unwrapping in traditional phase shift methods is avoided. This algorithm is eventually implemented on DM3730 embedded system for 3-D measuring, which consists of an ARM and a DSP core and has a strong capability of digital signal processing. Experimental results demonstrated the feasibility of the proposed method.

Effect of Age on Complexity and Causality of the Cardiovascular Control: Comparison between Model-Based and Model-Free Approaches

PubMed Central

Porta, Alberto; Faes, Luca; Bari, Vlasta; Marchi, Andrea; Bassani, Tito; Nollo, Giandomenico; Perseguini, Natália Maria; Milan, Juliana; Minatel, Vinícius; Borghi-Silva, Audrey; Takahashi, Anielle C. M.; Catai, Aparecida M.

2014-01-01

The proposed approach evaluates complexity of the cardiovascular control and causality among cardiovascular regulatory mechanisms from spontaneous variability of heart period (HP), systolic arterial pressure (SAP) and respiration (RESP). It relies on construction of a multivariate embedding space, optimization of the embedding dimension and a procedure allowing the selection of the components most suitable to form the multivariate embedding space. Moreover, it allows the comparison between linear model-based (MB) and nonlinear model-free (MF) techniques and between MF approaches exploiting local predictability (LP) and conditional entropy (CE). The framework was applied to study age-related modifications of complexity and causality in healthy humans in supine resting (REST) and during standing (STAND). We found that: 1) MF approaches are more efficient than the MB method when nonlinear components are present, while the reverse situation holds in presence of high dimensional embedding spaces; 2) the CE method is the least powerful in detecting age-related trends; 3) the association of HP complexity on age suggests an impairment of cardiac regulation and response to STAND; 4) the relation of SAP complexity on age indicates a gradual increase of sympathetic activity and a reduced responsiveness of vasomotor control to STAND; 5) the association from SAP to HP on age during STAND reveals a progressive inefficiency of baroreflex; 6) the reduced connection from HP to SAP with age might be linked to the progressive exploitation of Frank-Starling mechanism at REST and to the progressive increase of peripheral resistances during STAND; 7) at REST the diminished association from RESP to HP with age suggests a vagal withdrawal and a gradual uncoupling between respiratory activity and heart; 8) the weakened connection from RESP to SAP with age might be related to the progressive increase of left ventricular thickness and vascular stiffness and to the gradual decrease of respiratory sinus arrhythmia. PMID:24586796

Five-dimensional gauge theory and compactification on a torus

NASA Astrophysics Data System (ADS)

Haghighat, Babak; Vandoren, Stefan

2011-09-01

We study five-dimensional minimally supersymmetric gauge theory compactified on a torus down to three dimensions, and its embedding into string/M-theory using geometric engineering. The moduli space on the Coulomb branch is hyperkähler equipped with a metric with modular transformation properties. We determine the one-loop corrections to the metric and show that they can be interpreted as worldsheet and D1-brane instantons in type IIB string theory. Furthermore, we analyze instanton corrections coming from the solitonic BPS magnetic string wrapped over the torus. In particular, we show how to compute the path-integral for the zero-modes from the partition function of the M5 brane, or, using a 2d/4d correspondence, from the partition function of N=4 SYM theory on a Hirzebruch surface.

Graph embedding and extensions: a general framework for dimensionality reduction.

PubMed

Yan, Shuicheng; Xu, Dong; Zhang, Benyu; Zhang, Hong-Jiang; Yang, Qiang; Lin, Stephen

2007-01-01

Over the past few decades, a large family of algorithms - supervised or unsupervised; stemming from statistics or geometry theory - has been designed to provide different solutions to the problem of dimensionality reduction. Despite the different motivations of these algorithms, we present in this paper a general formulation known as graph embedding to unify them within a common framework. In graph embedding, each algorithm can be considered as the direct graph embedding or its linear/kernel/tensor extension of a specific intrinsic graph that describes certain desired statistical or geometric properties of a data set, with constraints from scale normalization or a penalty graph that characterizes a statistical or geometric property that should be avoided. Furthermore, the graph embedding framework can be used as a general platform for developing new dimensionality reduction algorithms. By utilizing this framework as a tool, we propose a new supervised dimensionality reduction algorithm called Marginal Fisher Analysis in which the intrinsic graph characterizes the intraclass compactness and connects each data point with its neighboring points of the same class, while the penalty graph connects the marginal points and characterizes the interclass separability. We show that MFA effectively overcomes the limitations of the traditional Linear Discriminant Analysis algorithm due to data distribution assumptions and available projection directions. Real face recognition experiments show the superiority of our proposed MFA in comparison to LDA, also for corresponding kernel and tensor extensions.

Cross diffusion and exponential space dependent heat source impacts in radiated three-dimensional (3D) flow of Casson fluid by heated surface

NASA Astrophysics Data System (ADS)

Zaigham Zia, Q. M.; Ullah, Ikram; Waqas, M.; Alsaedi, A.; Hayat, T.

2018-03-01

This research intends to elaborate Soret-Dufour characteristics in mixed convective radiated Casson liquid flow by exponentially heated surface. Novel features of exponential space dependent heat source are introduced. Appropriate variables are implemented for conversion of partial differential frameworks into a sets of ordinary differential expressions. Homotopic scheme is employed for construction of analytic solutions. Behavior of various embedding variables on velocity, temperature and concentration distributions are plotted graphically and analyzed in detail. Besides, skin friction coefficients and heat and mass transfer rates are also computed and interpreted. The results signify the pronounced characteristics of temperature corresponding to convective and radiation variables. Concentration bears opposite response for Soret and Dufour variables.

Embedded sparse representation of fMRI data via group-wise dictionary optimization

NASA Astrophysics Data System (ADS)

Zhu, Dajiang; Lin, Binbin; Faskowitz, Joshua; Ye, Jieping; Thompson, Paul M.

2016-03-01

Sparse learning enables dimension reduction and efficient modeling of high dimensional signals and images, but it may need to be tailored to best suit specific applications and datasets. Here we used sparse learning to efficiently represent functional magnetic resonance imaging (fMRI) data from the human brain. We propose a novel embedded sparse representation (ESR), to identify the most consistent dictionary atoms across different brain datasets via an iterative group-wise dictionary optimization procedure. In this framework, we introduced additional criteria to make the learned dictionary atoms more consistent across different subjects. We successfully identified four common dictionary atoms that follow the external task stimuli with very high accuracy. After projecting the corresponding coefficient vectors back into the 3-D brain volume space, the spatial patterns are also consistent with traditional fMRI analysis results. Our framework reveals common features of brain activation in a population, as a new, efficient fMRI analysis method.

Coupling Between Microstrip Lines With Finite Width Ground Plane Embedded in Thin Film Circuits

NASA Technical Reports Server (NTRS)

Ponchak, George E.; Dalton, Edan; Tentzeris, Manos M.; Papapolymerou, John

2003-01-01

Three-dimensional (3D) interconnects built upon multiple layers of polyimide are required for constructing 3D circuits on CMOS (low resistivity) Si wafers, GaAs, and ceramic substrates. Thin film microstrip lines (TFMS) with finite width ground planes embedded in the polyimide are often used. However, the closely spaced TFMS lines a r e susceptible to high levels of coupling, which degrades circuit performance. In this paper, Finite Difference Time Domain (FDTD) analysis and experimental measurements a r e used to show that the ground planes must be connected by via holes to reduce coupling in both the forward and backward directions. Furthermore, it is shown that coupled microstrip lines establish a slotline type mode between the two ground planes and a dielectric waveguide type mode, and that the via holes recommended here eliminate these two modes.

Terahertz Computed Tomography of NASA Thermal Protection System Materials

NASA Technical Reports Server (NTRS)

Roth, D. J.; Reyes-Rodriguez, S.; Zimdars, D. A.; Rauser, R. W.; Ussery, W. W.

2011-01-01

A terahertz axial computed tomography system has been developed that uses time domain measurements in order to form cross-sectional image slices and three-dimensional volume renderings of terahertz-transparent materials. The system can inspect samples as large as 0.0283 cubic meters (1 cubic foot) with no safety concerns as for x-ray computed tomography. In this study, the system is evaluated for its ability to detect and characterize flat bottom holes, drilled holes, and embedded voids in foam materials utilized as thermal protection on the external fuel tanks for the Space Shuttle. X-ray micro-computed tomography was also performed on the samples to compare against the terahertz computed tomography results and better define embedded voids. Limits of detectability based on depth and size for the samples used in this study are loosely defined. Image sharpness and morphology characterization ability for terahertz computed tomography are qualitatively described.

Embedding and Publishing Interactive, 3-Dimensional, Scientific Figures in Portable Document Format (PDF) Files

PubMed Central

Barnes, David G.; Vidiassov, Michail; Ruthensteiner, Bernhard; Fluke, Christopher J.; Quayle, Michelle R.; McHenry, Colin R.

2013-01-01

With the latest release of the S2PLOT graphics library, embedding interactive, 3-dimensional (3-d) scientific figures in Adobe Portable Document Format (PDF) files is simple, and can be accomplished without commercial software. In this paper, we motivate the need for embedding 3-d figures in scholarly articles. We explain how 3-d figures can be created using the S2PLOT graphics library, exported to Product Representation Compact (PRC) format, and included as fully interactive, 3-d figures in PDF files using the movie15 LaTeX package. We present new examples of 3-d PDF figures, explain how they have been made, validate them, and comment on their advantages over traditional, static 2-dimensional (2-d) figures. With the judicious use of 3-d rather than 2-d figures, scientists can now publish, share and archive more useful, flexible and faithful representations of their study outcomes. The article you are reading does not have embedded 3-d figures. The full paper, with embedded 3-d figures, is recommended and is available as a supplementary download from PLoS ONE (File S2). PMID:24086243

Embedding and publishing interactive, 3-dimensional, scientific figures in Portable Document Format (PDF) files.

PubMed

Barnes, David G; Vidiassov, Michail; Ruthensteiner, Bernhard; Fluke, Christopher J; Quayle, Michelle R; McHenry, Colin R

2013-01-01

With the latest release of the S2PLOT graphics library, embedding interactive, 3-dimensional (3-d) scientific figures in Adobe Portable Document Format (PDF) files is simple, and can be accomplished without commercial software. In this paper, we motivate the need for embedding 3-d figures in scholarly articles. We explain how 3-d figures can be created using the S2PLOT graphics library, exported to Product Representation Compact (PRC) format, and included as fully interactive, 3-d figures in PDF files using the movie15 LaTeX package. We present new examples of 3-d PDF figures, explain how they have been made, validate them, and comment on their advantages over traditional, static 2-dimensional (2-d) figures. With the judicious use of 3-d rather than 2-d figures, scientists can now publish, share and archive more useful, flexible and faithful representations of their study outcomes. The article you are reading does not have embedded 3-d figures. The full paper, with embedded 3-d figures, is recommended and is available as a supplementary download from PLoS ONE (File S2).

Detection of chaotic dynamics in human gait signals from mobile devices

NASA Astrophysics Data System (ADS)

DelMarco, Stephen; Deng, Yunbin

2017-05-01

The ubiquity of mobile devices offers the opportunity to exploit device-generated signal data for biometric identification, health monitoring, and activity recognition. In particular, mobile devices contain an Inertial Measurement Unit (IMU) that produces acceleration and rotational rate information from the IMU accelerometers and gyros. These signals reflect motion properties of the human carrier. It is well-known that the complexity of bio-dynamical systems gives rise to chaotic dynamics. Knowledge of chaotic properties of these systems has shown utility, for example, in detecting abnormal medical conditions and neurological disorders. Chaotic dynamics has been found, in the lab, in bio-dynamical systems data such as electrocardiogram (heart), electroencephalogram (brain), and gait data. In this paper, we investigate the following question: can we detect chaotic dynamics in human gait as measured by IMU acceleration and gyro data from mobile phones? To detect chaotic dynamics, we perform recurrence analysis on real gyro and accelerometer signal data obtained from mobile devices. We apply the delay coordinate embedding approach from Takens' theorem to reconstruct the phase space trajectory of the multi-dimensional gait dynamical system. We use mutual information properties of the signal to estimate the appropriate delay value, and the false nearest neighbor approach to determine the phase space embedding dimension. We use a correlation dimension-based approach together with estimation of the largest Lyapunov exponent to make the chaotic dynamics detection decision. We investigate the ability to detect chaotic dynamics for the different one-dimensional IMU signals, across human subject and walking modes, and as a function of different phone locations on the human carrier.

Design and Implementation of a Self-Directed Stereochemistry Lesson Using Embedded Virtual Three-Dimensional Images in a Portable Document Format

ERIC Educational Resources Information Center

Cody, Jeremy A.; Craig, Paul A.; Loudermilk, Adam D.; Yacci, Paul M.; Frisco, Sarah L.; Milillo, Jennifer R.

2012-01-01

A novel stereochemistry lesson was prepared that incorporated both handheld molecular models and embedded virtual three-dimensional (3D) images. The images are fully interactive and eye-catching for the students; methods for preparing 3D molecular images in Adobe Acrobat are included. The lesson was designed and implemented to showcase the 3D…

Dimensionality of visual complexity in computer graphics scenes

NASA Astrophysics Data System (ADS)

Ramanarayanan, Ganesh; Bala, Kavita; Ferwerda, James A.; Walter, Bruce

2008-02-01

How do human observers perceive visual complexity in images? This problem is especially relevant for computer graphics, where a better understanding of visual complexity can aid in the development of more advanced rendering algorithms. In this paper, we describe a study of the dimensionality of visual complexity in computer graphics scenes. We conducted an experiment where subjects judged the relative complexity of 21 high-resolution scenes, rendered with photorealistic methods. Scenes were gathered from web archives and varied in theme, number and layout of objects, material properties, and lighting. We analyzed the subject responses using multidimensional scaling of pooled subject responses. This analysis embedded the stimulus images in a two-dimensional space, with axes that roughly corresponded to "numerosity" and "material / lighting complexity". In a follow-up analysis, we derived a one-dimensional complexity ordering of the stimulus images. We compared this ordering with several computable complexity metrics, such as scene polygon count and JPEG compression size, and did not find them to be very correlated. Understanding the differences between these measures can lead to the design of more efficient rendering algorithms in computer graphics.

The Atlas of Chinese World Wide Web Ecosystem Shaped by the Collective Attention Flows.

PubMed

Lou, Xiaodan; Li, Yong; Gu, Weiwei; Zhang, Jiang

2016-01-01

The web can be regarded as an ecosystem of digital resources connected and shaped by collective successive behaviors of users. Knowing how people allocate limited attention on different resources is of great importance. To answer this, we embed the most popular Chinese web sites into a high dimensional Euclidean space based on the open flow network model of a large number of Chinese users' collective attention flows, which both considers the connection topology of hyperlinks between the sites and the collective behaviors of the users. With these tools, we rank the web sites and compare their centralities based on flow distances with other metrics. We also study the patterns of attention flow allocation, and find that a large number of web sites concentrate on the central area of the embedding space, and only a small fraction of web sites disperse in the periphery. The entire embedding space can be separated into 3 regions(core, interim, and periphery). The sites in the core (1%) occupy a majority of the attention flows (40%), and the sites (34%) in the interim attract 40%, whereas other sites (65%) only take 20% flows. What's more, we clustered the web sites into 4 groups according to their positions in the space, and found that similar web sites in contents and topics are grouped together. In short, by incorporating the open flow network model, we can clearly see how collective attention allocates and flows on different web sites, and how web sites connected each other.

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

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.

A complex systems analysis of stick-slip dynamics of a laboratory fault

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

Walker, David M.; Tordesillas, Antoinette, E-mail: atordesi@unimelb.edu.au; Small, Michael

2014-03-15

We study the stick-slip behavior of a granular bed of photoelastic disks sheared by a rough slider pulled along the surface. Time series of a proxy for granular friction are examined using complex systems methods to characterize the observed stick-slip dynamics of this laboratory fault. Nonlinear surrogate time series methods show that the stick-slip behavior appears more complex than a periodic dynamics description. Phase space embedding methods show that the dynamics can be locally captured within a four to six dimensional subspace. These slider time series also provide an experimental test for recent complex network methods. Phase space networks, constructedmore » by connecting nearby phase space points, proved useful in capturing the key features of the dynamics. In particular, network communities could be associated to slip events and the ranking of small network subgraphs exhibited a heretofore unreported ordering.« less

Homogeneous, anisotropic three-manifolds of topologically massive gravity

NASA Astrophysics Data System (ADS)

Nutku, Y.; Baekler, P.

1989-10-01

We present a new class of exact solutions of Deser, Jackiw, and Templeton's theory (DJT) of topologically massive gravity which consists of homogeneous, anisotropic manifolds. In these solutions the coframe is given by the left-invariant 1-forms of 3-dimensional Lie algebras up to constant scale factors. These factors are fixed in terms of the DJT coupling constant μ which is the constant of proportionality between the Einstein and Cotton tensors in 3-dimensions. Differences between the scale factors result in anisotropy which is a common feature of topologically massive 3-manifolds. We have found that only Bianchi Types VI, VIII, and IX lead to nontrivial solutions. Among these, a Bianchi Type IX, squashed 3-sphere solution of the Euclideanized DJT theory has finite action. Bianchi Type VIII, IX solutions can variously be embedded in the de Sitter/anti-de Sitter space. That is, some DJT 3-manifolds that we shall present here can be regarded as the basic constituent of anti-de Sitter space which is the ground state solution in higher dimensional generalization of Einstein's general relativity.

Homogeneous, anisotropic three-manifolds of topologically massive gravity

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

Nutku, Y.; Baekler, P.

1989-10-01

We present a new class of exact solutions of Deser, Jackiw, and Templeton's theory (DJT) of topologically massive gravity which consists of homogeneous, anisotropic manifolds. In these solutions the coframe is given by the left-invariant 1-forms of 3-dimensional Lie algebras up to constant scale factors. These factors are fixed in terms of the DJT coupling constant {mu}m which is the constant of proportionality between the Einstein and Cotton tensors in 3-dimensions. Differences between the scale factors result in anisotropy which is a common feature of topologically massive 3-manifolds. We have found that only Bianchi Types VI, VIII, and IX leadmore » to nontrivial solutions. Among these, a Bianchi Type IX, squashed 3-sphere solution of the Euclideanized DJT theory has finite action, Bianchi Type VIII, IX solutions can variously be embedded in the de Sitter/anti-de Sitter space. That is, some DJT 3-manifolds that we shall present here can be regarded as the basic constitent of anti-de Sitter space which is the ground state solution in higher dimensional generalizations of Einstein's general relativity. {copyright} 1989 Academic Press, Inc.« less

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.

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.

Three dimensional modelling of earthquake rupture cycles on frictional faults

NASA Astrophysics Data System (ADS)

Simpson, Guy; May, Dave

2017-04-01

We are developing an efficient MPI-parallel numerical method to simulate earthquake sequences on preexisting faults embedding within a three dimensional viscoelastic half-space. We solve the velocity form of the elasto(visco)dynamic equations using a continuous Galerkin Finite Element Method on an unstructured pentahedral mesh, which thus permits local spatial refinement in the vicinity of the fault. Friction sliding is coupled to the viscoelastic solid via rate- and state-dependent friction laws using the split-node technique. Our coupled formulation employs a picard-type non-linear solver with a fully implicit, first order accurate time integrator that utilises an adaptive time step that efficiently evolves the system through multiple seismic cycles. The implementation leverages advanced parallel solvers, preconditioners and linear algebra from the Portable Extensible Toolkit for Scientific Computing (PETSc) library. The model can treat heterogeneous frictional properties and stress states on the fault and surrounding solid as well as non-planar fault geometries. Preliminary tests show that the model successfully reproduces dynamic rupture on a vertical strike-slip fault in a half-space governed by rate-state friction with the ageing law.

Efimov effect in D spatial dimensions in A A B systems

NASA Astrophysics Data System (ADS)

Rosa, D. S.; Frederico, T.; Krein, G.; Yamashita, M. T.

2018-05-01

The existence of the Efimov effect is drastically affected by the dimensionality of the space in which the system is embedded. The effective spatial dimension containing an atomic cloud can be continuously modified by compressing it in one or two directions. In the present Rapid Communication we determine the dimensionality D for which the Efimov effect can exist for different values of the mass ratio A =mB/mA for a general A A B system formed by two identical bosons A and a third particle B in the two-body unitary limit. In addition, we provide a prediction for the Efimov discrete scaling factor exp(π /s ) as a function of a wide range of values of A and D , which can be tested in experiments that can be realized with currently available technology.

Toward in situ x-ray diffraction imaging at the nanometer scale

NASA Astrophysics Data System (ADS)

Zatsepin, Nadia A.; Dilanian, Ruben A.; Nikulin, Andrei Y.; Gable, Brian M.; Muddle, Barry C.; Sakata, Osami

2008-08-01

We present the results of preliminary investigations determining the sensitivity and applicability of a novel x-ray diffraction based nanoscale imaging technique, including simulations and experiments. The ultimate aim of this nascent technique is non-destructive, bulk-material characterization on the nanometer scale, involving three dimensional image reconstructions of embedded nanoparticles and in situ sample characterization. The approach is insensitive to x-ray coherence, making it applicable to synchrotron and laboratory hard x-ray sources, opening the possibility of unprecedented nanometer resolution with the latter. The technique is being developed with a focus on analyzing a technologically important light metal alloy, Al-xCu (where x is 2.0-5.0 %wt). The mono- and polycrystalline samples contain crystallographically oriented, weakly diffracting Al2Cu nanoprecipitates in a sparse, spatially random dispersion within the Al matrix. By employing a triple-axis diffractometer in the non-dispersive setup we collected two-dimensional reciprocal space maps of synchrotron x-rays diffracted from the Al2Cu nanoparticles. The intensity profiles of the diffraction peaks confirmed the sensitivity of the technique to the presence and orientation of the nanoparticles. This is a fundamental step towards in situ observation of such extremely sparse, weakly diffracting nanoprecipitates embedded in light metal alloys at early stages of their growth.

Signal separation by nonlinear projections: The fetal electrocardiogram

NASA Astrophysics Data System (ADS)

Schreiber, Thomas; Kaplan, Daniel T.

1996-05-01

We apply a locally linear projection technique which has been developed for noise reduction in deterministically chaotic signals to extract the fetal component from scalar maternal electrocardiographic (ECG) recordings. Although we do not expect the maternal ECG to be deterministic chaotic, typical signals are effectively confined to a lower-dimensional manifold when embedded in delay space. The method is capable of extracting fetal heart rate even when the fetal component and the noise are of comparable amplitude. If the noise is small, more details of the fetal ECG, like P and T waves, can be recovered.

String-inspired special grand unification

NASA Astrophysics Data System (ADS)

Yamatsu, Naoki

2017-10-01

We discuss a grand unified theory (GUT) based on an SO(32) GUT gauge group broken to its subgroups including a special subgroup. In the SO(32) GUT on the six-dimensional (6D) orbifold space M^4× T^2/\\mathbb{Z}_2, one generation of the standard model fermions can be embedded into a 6D bulk Weyl fermion in the SO(32) vector representation. We show that for a three-generation model, all the 6D and 4D gauge anomalies in the bulk and on the fixed points are canceled out without exotic chiral fermions at low energies.

Simultaneous Control of Multispecies Particle Transport and Segregation in Driven Lattices

NASA Astrophysics Data System (ADS)

Mukhopadhyay, Aritra K.; Liebchen, Benno; Schmelcher, Peter

2018-05-01

We provide a generic scheme to separate the particles of a mixture by their physical properties like mass, friction, or size. The scheme employs a periodically shaken two-dimensional dissipative lattice and hinges on a simultaneous transport of particles in species-specific directions. This selective transport is achieved by controlling the late-time nonlinear particle dynamics, via the attractors embedded in the phase space and their bifurcations. To illustrate the spectrum of possible applications of the scheme, we exemplarily demonstrate the separation of polydisperse colloids and mixtures of cold thermal alkali atoms in optical lattices.

Hybrid Techniques for Quantum Circuit Simulation

DTIC Science & Technology

2014-02-01

Detailed theorems and proofs describing these results are included in our published manuscript [10]. Embedding of stabilizer geometry in the Hilbert ...space. We also describe how the discrete embedding of stabilizer geometry in Hilbert space complicates several natural geometric tasks. As described...the Hilbert space in which they are embedded, and that they are arranged in a fairly uniform pattern. These factors suggest that, if one seeks a

Extending topological surgery to natural processes and dynamical systems.

PubMed

Antoniou, Stathis; Lambropoulou, Sofia

2017-01-01

Topological surgery is a mathematical technique used for creating new manifolds out of known ones. We observe that it occurs in natural phenomena where a sphere of dimension 0 or 1 is selected, forces are applied and the manifold in which they occur changes type. For example, 1-dimensional surgery happens during chromosomal crossover, DNA recombination and when cosmic magnetic lines reconnect, while 2-dimensional surgery happens in the formation of tornadoes, in the phenomenon of Falaco solitons, in drop coalescence and in the cell mitosis. Inspired by such phenomena, we introduce new theoretical concepts which enhance topological surgery with the observed forces and dynamics. To do this, we first extend the formal definition to a continuous process caused by local forces. Next, for modeling phenomena which do not happen on arcs or surfaces but are 2-dimensional or 3-dimensional, we fill in the interior space by defining the notion of solid topological surgery. We further introduce the notion of embedded surgery in S3 for modeling phenomena which involve more intrinsically the ambient space, such as the appearance of knotting in DNA and phenomena where the causes and effect of the process lies beyond the initial manifold, such as the formation of black holes. Finally, we connect these new theoretical concepts with a dynamical system and we present it as a model for both 2-dimensional 0-surgery and natural phenomena exhibiting a 'hole drilling' behavior. We hope that through this study, topology and dynamics of many natural phenomena, as well as topological surgery itself, will be better understood.

Extending topological surgery to natural processes and dynamical systems

PubMed Central

Antoniou, Stathis; Lambropoulou, Sofia

2017-01-01

Topological surgery is a mathematical technique used for creating new manifolds out of known ones. We observe that it occurs in natural phenomena where a sphere of dimension 0 or 1 is selected, forces are applied and the manifold in which they occur changes type. For example, 1-dimensional surgery happens during chromosomal crossover, DNA recombination and when cosmic magnetic lines reconnect, while 2-dimensional surgery happens in the formation of tornadoes, in the phenomenon of Falaco solitons, in drop coalescence and in the cell mitosis. Inspired by such phenomena, we introduce new theoretical concepts which enhance topological surgery with the observed forces and dynamics. To do this, we first extend the formal definition to a continuous process caused by local forces. Next, for modeling phenomena which do not happen on arcs or surfaces but are 2-dimensional or 3-dimensional, we fill in the interior space by defining the notion of solid topological surgery. We further introduce the notion of embedded surgery in S3 for modeling phenomena which involve more intrinsically the ambient space, such as the appearance of knotting in DNA and phenomena where the causes and effect of the process lies beyond the initial manifold, such as the formation of black holes. Finally, we connect these new theoretical concepts with a dynamical system and we present it as a model for both 2-dimensional 0-surgery and natural phenomena exhibiting a ‘hole drilling’ behavior. We hope that through this study, topology and dynamics of many natural phenomena, as well as topological surgery itself, will be better understood. PMID:28915271

Comparative analysis of nonlinear dimensionality reduction techniques for breast MRI segmentation

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

Akhbardeh, Alireza; Jacobs, Michael A.; Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205

2012-04-15

Purpose: Visualization of anatomical structures using radiological imaging methods is an important tool in medicine to differentiate normal from pathological tissue and can generate large amounts of data for a radiologist to read. Integrating these large data sets is difficult and time-consuming. A new approach uses both supervised and unsupervised advanced machine learning techniques to visualize and segment radiological data. This study describes the application of a novel hybrid scheme, based on combining wavelet transform and nonlinear dimensionality reduction (NLDR) methods, to breast magnetic resonance imaging (MRI) data using three well-established NLDR techniques, namely, ISOMAP, local linear embedding (LLE), andmore » diffusion maps (DfM), to perform a comparative performance analysis. Methods: Twenty-five breast lesion subjects were scanned using a 3T scanner. MRI sequences used were T1-weighted, T2-weighted, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) imaging. The hybrid scheme consisted of two steps: preprocessing and postprocessing of the data. The preprocessing step was applied for B{sub 1} inhomogeneity correction, image registration, and wavelet-based image compression to match and denoise the data. In the postprocessing step, MRI parameters were considered data dimensions and the NLDR-based hybrid approach was applied to integrate the MRI parameters into a single image, termed the embedded image. This was achieved by mapping all pixel intensities from the higher dimension to a lower dimensional (embedded) space. For validation, the authors compared the hybrid NLDR with linear methods of principal component analysis (PCA) and multidimensional scaling (MDS) using synthetic data. For the clinical application, the authors used breast MRI data, comparison was performed using the postcontrast DCE MRI image and evaluating the congruence of the segmented lesions. Results: The NLDR-based hybrid approach was able to define and segment both synthetic and clinical data. In the synthetic data, the authors demonstrated the performance of the NLDR method compared with conventional linear DR methods. The NLDR approach enabled successful segmentation of the structures, whereas, in most cases, PCA and MDS failed. The NLDR approach was able to segment different breast tissue types with a high accuracy and the embedded image of the breast MRI data demonstrated fuzzy boundaries between the different types of breast tissue, i.e., fatty, glandular, and tissue with lesions (>86%). Conclusions: The proposed hybrid NLDR methods were able to segment clinical breast data with a high accuracy and construct an embedded image that visualized the contribution of different radiological parameters.« less

Instantaneous brain dynamics mapped to a continuous state space.

PubMed

Billings, Jacob C W; Medda, Alessio; Shakil, Sadia; Shen, Xiaohong; Kashyap, Amrit; Chen, Shiyang; Abbas, Anzar; Zhang, Xiaodi; Nezafati, Maysam; Pan, Wen-Ju; Berman, Gordon J; Keilholz, Shella D

2017-11-15

Measures of whole-brain activity, from techniques such as functional Magnetic Resonance Imaging, provide a means to observe the brain's dynamical operations. However, interpretation of whole-brain dynamics has been stymied by the inherently high-dimensional structure of brain activity. The present research addresses this challenge through a series of scale transformations in the spectral, spatial, and relational domains. Instantaneous multispectral dynamics are first developed from input data via a wavelet filter bank. Voxel-level signals are then projected onto a representative set of spatially independent components. The correlation distance over the instantaneous wavelet-ICA state vectors is a graph that may be embedded onto a lower-dimensional space to assist the interpretation of state-space dynamics. Applying this procedure to a large sample of resting-state and task-active data (acquired through the Human Connectome Project), we segment the empirical state space into a continuum of stimulus-dependent brain states. Upon observing the local neighborhood of brain-states adopted subsequent to each stimulus, we may conclude that resting brain activity includes brain states that are, at times, similar to those adopted during tasks, but that are at other times distinct from task-active brain states. As task-active brain states often populate a local neighborhood, back-projection of segments of the dynamical state space onto the brain's surface reveals the patterns of brain activity that support many experimentally-defined states. Copyright © 2017 Elsevier Inc. All rights reserved.

Assessment of Schrodinger Eigenmaps for target detection

NASA Astrophysics Data System (ADS)

Dorado Munoz, Leidy P.; Messinger, David W.; Czaja, Wojtek

2014-06-01

Non-linear dimensionality reduction methods have been widely applied to hyperspectral imagery due to its structure as the information can be represented in a lower dimension without losing information, and because the non-linear methods preserve the local geometry of the data while the dimension is reduced. One of these methods is Laplacian Eigenmaps (LE), which assumes that the data lies on a low dimensional manifold embedded in a high dimensional space. LE builds a nearest neighbor graph, computes its Laplacian and performs the eigendecomposition of the Laplacian. These eigenfunctions constitute a basis for the lower dimensional space in which the geometry of the manifold is preserved. In addition to the reduction problem, LE has been widely used in tasks such as segmentation, clustering, and classification. In this regard, a new Schrodinger Eigenmaps (SE) method was developed and presented as a semi-supervised classification scheme in order to improve the classification performance and take advantage of the labeled data. SE is an algorithm built upon LE, where the former Laplacian operator is replaced by the Schrodinger operator. The Schrodinger operator includes a potential term V, that, taking advantage of the additional information such as labeled data, allows clustering of similar points. In this paper, we explore the idea of using SE in target detection. In this way, we present a framework where the potential term V is defined as a barrier potential: a diagonal matrix encoding the spatial position of the target, and the detection performance is evaluated by using different targets and different hyperspectral scenes.

Exploring nonlinear feature space dimension reduction and data representation in breast Cadx with Laplacian eigenmaps and t-SNE.

PubMed

Jamieson, Andrew R; Giger, Maryellen L; Drukker, Karen; Li, Hui; Yuan, Yading; Bhooshan, Neha

2010-01-01

In this preliminary study, recently developed unsupervised nonlinear dimension reduction (DR) and data representation techniques were applied to computer-extracted breast lesion feature spaces across three separate imaging modalities: Ultrasound (U.S.) with 1126 cases, dynamic contrast enhanced magnetic resonance imaging with 356 cases, and full-field digital mammography with 245 cases. Two methods for nonlinear DR were explored: Laplacian eigenmaps [M. Belkin and P. Niyogi, "Laplacian eigenmaps for dimensionality reduction and data representation," Neural Comput. 15, 1373-1396 (2003)] and t-distributed stochastic neighbor embedding (t-SNE) [L. van der Maaten and G. Hinton, "Visualizing data using t-SNE," J. Mach. Learn. Res. 9, 2579-2605 (2008)]. These methods attempt to map originally high dimensional feature spaces to more human interpretable lower dimensional spaces while preserving both local and global information. The properties of these methods as applied to breast computer-aided diagnosis (CADx) were evaluated in the context of malignancy classification performance as well as in the visual inspection of the sparseness within the two-dimensional and three-dimensional mappings. Classification performance was estimated by using the reduced dimension mapped feature output as input into both linear and nonlinear classifiers: Markov chain Monte Carlo based Bayesian artificial neural network (MCMC-BANN) and linear discriminant analysis. The new techniques were compared to previously developed breast CADx methodologies, including automatic relevance determination and linear stepwise (LSW) feature selection, as well as a linear DR method based on principal component analysis. Using ROC analysis and 0.632+bootstrap validation, 95% empirical confidence intervals were computed for the each classifier's AUC performance. In the large U.S. data set, sample high performance results include, AUC0.632+ = 0.88 with 95% empirical bootstrap interval [0.787;0.895] for 13 ARD selected features and AUC0.632+ = 0.87 with interval [0.817;0.906] for four LSW selected features compared to 4D t-SNE mapping (from the original 81D feature space) giving AUC0.632+ = 0.90 with interval [0.847;0.919], all using the MCMC-BANN. Preliminary results appear to indicate capability for the new methods to match or exceed classification performance of current advanced breast lesion CADx algorithms. While not appropriate as a complete replacement of feature selection in CADx problems, DR techniques offer a complementary approach, which can aid elucidation of additional properties associated with the data. Specifically, the new techniques were shown to possess the added benefit of delivering sparse lower dimensional representations for visual interpretation, revealing intricate data structure of the feature space.

Fracture Mechanics Method for Word Embedding Generation of Neural Probabilistic Linguistic Model.

PubMed

Bi, Size; Liang, Xiao; Huang, Ting-Lei

2016-01-01

Word embedding, a lexical vector representation generated via the neural linguistic model (NLM), is empirically demonstrated to be appropriate for improvement of the performance of traditional language model. However, the supreme dimensionality that is inherent in NLM contributes to the problems of hyperparameters and long-time training in modeling. Here, we propose a force-directed method to improve such problems for simplifying the generation of word embedding. In this framework, each word is assumed as a point in the real world; thus it can approximately simulate the physical movement following certain mechanics. To simulate the variation of meaning in phrases, we use the fracture mechanics to do the formation and breakdown of meaning combined by a 2-gram word group. With the experiments on the natural linguistic tasks of part-of-speech tagging, named entity recognition and semantic role labeling, the result demonstrated that the 2-dimensional word embedding can rival the word embeddings generated by classic NLMs, in terms of accuracy, recall, and text visualization.

Manifold Embedding and Semantic Segmentation for Intraoperative Guidance With Hyperspectral Brain Imaging.

PubMed

Ravi, Daniele; Fabelo, Himar; Callic, Gustavo Marrero; Yang, Guang-Zhong

2017-09-01

Recent advances in hyperspectral imaging have made it a promising solution for intra-operative tissue characterization, with the advantages of being non-contact, non-ionizing, and non-invasive. Working with hyperspectral images in vivo, however, is not straightforward as the high dimensionality of the data makes real-time processing challenging. In this paper, a novel dimensionality reduction scheme and a new processing pipeline are introduced to obtain a detailed tumor classification map for intra-operative margin definition during brain surgery. However, existing approaches to dimensionality reduction based on manifold embedding can be time consuming and may not guarantee a consistent result, thus hindering final tissue classification. The proposed framework aims to overcome these problems through a process divided into two steps: dimensionality reduction based on an extension of the T-distributed stochastic neighbor approach is first performed and then a semantic segmentation technique is applied to the embedded results by using a Semantic Texton Forest for tissue classification. Detailed in vivo validation of the proposed method has been performed to demonstrate the potential clinical value of the system.

Semi-Supervised Tensor-Based Graph Embedding Learning and Its Application to Visual Discriminant Tracking.

PubMed

Hu, Weiming; Gao, Jin; Xing, Junliang; Zhang, Chao; Maybank, Stephen

2017-01-01

An appearance model adaptable to changes in object appearance is critical in visual object tracking. In this paper, we treat an image patch as a two-order tensor which preserves the original image structure. We design two graphs for characterizing the intrinsic local geometrical structure of the tensor samples of the object and the background. Graph embedding is used to reduce the dimensions of the tensors while preserving the structure of the graphs. Then, a discriminant embedding space is constructed. We prove two propositions for finding the transformation matrices which are used to map the original tensor samples to the tensor-based graph embedding space. In order to encode more discriminant information in the embedding space, we propose a transfer-learning- based semi-supervised strategy to iteratively adjust the embedding space into which discriminative information obtained from earlier times is transferred. We apply the proposed semi-supervised tensor-based graph embedding learning algorithm to visual tracking. The new tracking algorithm captures an object's appearance characteristics during tracking and uses a particle filter to estimate the optimal object state. Experimental results on the CVPR 2013 benchmark dataset demonstrate the effectiveness of the proposed tracking algorithm.

Glass-embedded two-dimensional silicon photonic crystal devices with a broad bandwidth waveguide and a high quality nanocavity.

PubMed

Jeon, Seung-Woo; Han, Jin-Kyu; Song, Bong-Shik; Noda, Susumu

2010-08-30

To enhance the mechanical stability of a two-dimensional photonic crystal slab structure and maintain its excellent performance, we designed a glass-embedded silicon photonic crystal device consisting of a broad bandwidth waveguide and a nanocavity with a high quality (Q) factor, and then fabricated the structure using spin-on glass (SOG). Furthermore, we showed that the refractive index of the SOG could be tuned from 1.37 to 1.57 by varying the curing temperature of the SOG. Finally, we demonstrated a glass-embedded heterostructured cavity with an ultrahigh Q factor of 160,000 by adjusting the refractive index of the SOG.

Structural and functional properties of spatially embedded scale-free networks.

PubMed

Emmerich, Thorsten; Bunde, Armin; Havlin, Shlomo

2014-06-01

Scale-free networks have been studied mostly as non-spatially embedded systems. However, in many realistic cases, they are spatially embedded and these constraints should be considered. Here, we study the structural and functional properties of a model of scale-free (SF) spatially embedded networks. In our model, both the degree and the length of links follow power law distributions as found in many real networks. We show that not all SF networks can be embedded in space and that the largest degree of a node in the network is usually smaller than in nonembedded SF networks. Moreover, the spatial constraints (each node has only few neighboring nodes) introduce degree-degree anticorrelations (disassortativity) since two high degree nodes cannot stay close in space. We also find significant effects of space embedding on the hopping distances (chemical distance) and the vulnerability of the networks.

Taub-Nut Crystal

NASA Astrophysics Data System (ADS)

Imazato, Harunobu; Mizoguchi, Shun'ya; Yata, Masaya

We consider the Gibbons-Hawking metric for a three-dimensional periodic array of multi-Taub-NUT centers, containing not only centers with a positive NUT charge but also ones with a negative NUT charge. The latter are regarded as representing the asymptotic form of the Atiyah-Hitchin metric. The periodic arrays of Taub-NUT centers have close parallels with ionic crystals, where the Gibbons-Hawking potential plays the role of the Coulomb static potential of the ions, and are similarly classified according to their space groups. After a periodic identification and a Z2 projection, the array is transformed by T-duality to a system of NS5-branes with the SU(2) structure, and a further standard embedding yields, though singular, a half-BPS heterotic 5-brane background with warped compact transverse dimensions. A discussion is given on the possibility of probing the singular geometry by two-dimensional gauge theories.

Nonlinear vs. linear biasing in Trp-cage folding simulations

NASA Astrophysics Data System (ADS)

Spiwok, Vojtěch; Oborský, Pavel; Pazúriková, Jana; Křenek, Aleš; Králová, Blanka

2015-03-01

Biased simulations have great potential for the study of slow processes, including protein folding. Atomic motions in molecules are nonlinear, which suggests that simulations with enhanced sampling of collective motions traced by nonlinear dimensionality reduction methods may perform better than linear ones. In this study, we compare an unbiased folding simulation of the Trp-cage miniprotein with metadynamics simulations using both linear (principle component analysis) and nonlinear (Isomap) low dimensional embeddings as collective variables. Folding of the mini-protein was successfully simulated in 200 ns simulation with linear biasing and non-linear motion biasing. The folded state was correctly predicted as the free energy minimum in both simulations. We found that the advantage of linear motion biasing is that it can sample a larger conformational space, whereas the advantage of nonlinear motion biasing lies in slightly better resolution of the resulting free energy surface. In terms of sampling efficiency, both methods are comparable.

Nonlinear vs. linear biasing in Trp-cage folding simulations.

PubMed

Spiwok, Vojtěch; Oborský, Pavel; Pazúriková, Jana; Křenek, Aleš; Králová, Blanka

2015-03-21

Biased simulations have great potential for the study of slow processes, including protein folding. Atomic motions in molecules are nonlinear, which suggests that simulations with enhanced sampling of collective motions traced by nonlinear dimensionality reduction methods may perform better than linear ones. In this study, we compare an unbiased folding simulation of the Trp-cage miniprotein with metadynamics simulations using both linear (principle component analysis) and nonlinear (Isomap) low dimensional embeddings as collective variables. Folding of the mini-protein was successfully simulated in 200 ns simulation with linear biasing and non-linear motion biasing. The folded state was correctly predicted as the free energy minimum in both simulations. We found that the advantage of linear motion biasing is that it can sample a larger conformational space, whereas the advantage of nonlinear motion biasing lies in slightly better resolution of the resulting free energy surface. In terms of sampling efficiency, both methods are comparable.

The Atlas of Chinese World Wide Web Ecosystem Shaped by the Collective Attention Flows

PubMed Central

Lou, Xiaodan; Li, Yong; Gu, Weiwei; Zhang, Jiang

2016-01-01

The web can be regarded as an ecosystem of digital resources connected and shaped by collective successive behaviors of users. Knowing how people allocate limited attention on different resources is of great importance. To answer this, we embed the most popular Chinese web sites into a high dimensional Euclidean space based on the open flow network model of a large number of Chinese users’ collective attention flows, which both considers the connection topology of hyperlinks between the sites and the collective behaviors of the users. With these tools, we rank the web sites and compare their centralities based on flow distances with other metrics. We also study the patterns of attention flow allocation, and find that a large number of web sites concentrate on the central area of the embedding space, and only a small fraction of web sites disperse in the periphery. The entire embedding space can be separated into 3 regions(core, interim, and periphery). The sites in the core (1%) occupy a majority of the attention flows (40%), and the sites (34%) in the interim attract 40%, whereas other sites (65%) only take 20% flows. What’s more, we clustered the web sites into 4 groups according to their positions in the space, and found that similar web sites in contents and topics are grouped together. In short, by incorporating the open flow network model, we can clearly see how collective attention allocates and flows on different web sites, and how web sites connected each other. PMID:27812133

Broadband IR polarizing beam splitter using a subwavelength-structured one-dimensional photonic-crystal layer embedded in a high-index prism.

PubMed

Khanfar, H K; Azzam, R M A

2009-09-20

An iterative procedure for the design of a polarizing beam splitter (PBS) that uses a form-birefringent, subwavelength-structured, one-dimensional photonic-crystal layer (SWS 1-D PCL) embedded in a high-index cubical prism is presented. The PBS is based on index matching and total transmission for the p polarization and total internal reflection for the s polarization at the prism-PCL interface at 45 degrees angle of incidence. A high extinction ratio in reflection (>50 dB) over the 4-12 microm IR spectral range is achieved using a SWS 1-D PCL of ZnTe embedded in a ZnS cube within an external field of view of +/-6.6 degrees and in the presence of grating filling factor errors of up to +/-10%. Comparable results, but with wider field of view, are also obtained with a Ge PCL embedded in a Si prism.

Modeling local extinction in turbulent combustion using an embedding method

NASA Astrophysics Data System (ADS)

Knaus, Robert; Pantano, Carlos

2012-11-01

Local regions of extinction in diffusion flames, called ``flame holes,'' can reduce the efficiency of combustion and increase the production of certain pollutants. At sufficiently high speeds, a flame may also be lifted from the rim of the burner to a downstream location that may be stable. These two phenomena share a common underlying mechanism of propagation related to edge-flame dynamics where chemistry and fluid mechanics are equally important. We present a formulation that describes the formation, propagation, and growth of flames holes on the stoichiometric surface using edge flame dynamics. The boundary separating the flame from the quenched region is modeled using a progress variable defined on the moving stoichiometric surface that is embedded in the three-dimensional space using an extension algorithm. This Cartesian problem is solved using a high-order finite-volume WENO method extended to this nonconservative problem. This algorithm can track the dynamics of flame holes in a turbulent reacting-shear layer and model flame liftoff without requiring full chemistry calculations.

Parameter estimation in nonlinear distributed systems - Approximation theory and convergence results

NASA Technical Reports Server (NTRS)

Banks, H. T.; Reich, Simeon; Rosen, I. G.

1988-01-01

An abstract approximation framework and convergence theory is described for Galerkin approximations applied to inverse problems involving nonlinear distributed parameter systems. Parameter estimation problems are considered and formulated as the minimization of a least-squares-like performance index over a compact admissible parameter set subject to state constraints given by an inhomogeneous nonlinear distributed system. The theory applies to systems whose dynamics can be described by either time-independent or nonstationary strongly maximal monotonic operators defined on a reflexive Banach space which is densely and continuously embedded in a Hilbert space. It is demonstrated that if readily verifiable conditions on the system's dependence on the unknown parameters are satisfied, and the usual Galerkin approximation assumption holds, then solutions to the approximating problems exist and approximate a solution to the original infinite-dimensional identification problem.

Fault Diagnosis for Rolling Bearings under Variable Conditions Based on Visual Cognition

PubMed Central

Cheng, Yujie; Zhou, Bo; Lu, Chen; Yang, Chao

2017-01-01

Fault diagnosis for rolling bearings has attracted increasing attention in recent years. However, few studies have focused on fault diagnosis for rolling bearings under variable conditions. This paper introduces a fault diagnosis method for rolling bearings under variable conditions based on visual cognition. The proposed method includes the following steps. First, the vibration signal data are transformed into a recurrence plot (RP), which is a two-dimensional image. Then, inspired by the visual invariance characteristic of the human visual system (HVS), we utilize speed up robust feature to extract fault features from the two-dimensional RP and generate a 64-dimensional feature vector, which is invariant to image translation, rotation, scaling variation, etc. Third, based on the manifold perception characteristic of HVS, isometric mapping, a manifold learning method that can reflect the intrinsic manifold embedded in the high-dimensional space, is employed to obtain a low-dimensional feature vector. Finally, a classical classification method, support vector machine, is utilized to realize fault diagnosis. Verification data were collected from Case Western Reserve University Bearing Data Center, and the experimental result indicates that the proposed fault diagnosis method based on visual cognition is highly effective for rolling bearings under variable conditions, thus providing a promising approach from the cognitive computing field. PMID:28772943

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.

A New Approach to Galaxy Morphology. I. Analysis of the Sloan Digital Sky Survey Early Data Release

NASA Astrophysics Data System (ADS)

Abraham, Roberto G.; van den Bergh, Sidney; Nair, Preethi

2003-05-01

In this paper we present a new statistic for quantifying galaxy morphology based on measurements of the Gini coefficient of galaxy light distributions. This statistic is easy to measure and is commonly used in econometrics to measure how wealth is distributed in human populations. When applied to galaxy images, the Gini coefficient provides a quantitative measure of the inequality with which a galaxy's light is distributed among its constituent pixels. We measure the Gini coefficient of local galaxies in the Early Data Release of the Sloan Digital Sky Survey and demonstrate that this quantity is closely correlated with measurements of central concentration, but with significant scatter. This scatter is almost entirely due to variations in the mean surface brightness of galaxies. By exploring the distribution of galaxies in the three-dimensional parameter space defined by the Gini coefficient, central concentration, and mean surface brightness, we show that all nearby galaxies lie on a well-defined two-dimensional surface (a slightly warped plane) embedded within a three-dimensional parameter space. By associating each galaxy sample with the equation of this plane, we can encode the morphological composition of the entire SDSS g*-band sample using the following three numbers: {22.451, 5.366, 7.010}. The i*-band sample is encoded as {22.149, 5.373, and 7.627}.

A framework for optimal kernel-based manifold embedding of medical image data.

PubMed

Zimmer, Veronika A; Lekadir, Karim; Hoogendoorn, Corné; Frangi, Alejandro F; Piella, Gemma

2015-04-01

Kernel-based dimensionality reduction is a widely used technique in medical image analysis. To fully unravel the underlying nonlinear manifold the selection of an adequate kernel function and of its free parameters is critical. In practice, however, the kernel function is generally chosen as Gaussian or polynomial and such standard kernels might not always be optimal for a given image dataset or application. In this paper, we present a study on the effect of the kernel functions in nonlinear manifold embedding of medical image data. To this end, we first carry out a literature review on existing advanced kernels developed in the statistics, machine learning, and signal processing communities. In addition, we implement kernel-based formulations of well-known nonlinear dimensional reduction techniques such as Isomap and Locally Linear Embedding, thus obtaining a unified framework for manifold embedding using kernels. Subsequently, we present a method to automatically choose a kernel function and its associated parameters from a pool of kernel candidates, with the aim to generate the most optimal manifold embeddings. Furthermore, we show how the calculated selection measures can be extended to take into account the spatial relationships in images, or used to combine several kernels to further improve the embedding results. Experiments are then carried out on various synthetic and phantom datasets for numerical assessment of the methods. Furthermore, the workflow is applied to real data that include brain manifolds and multispectral images to demonstrate the importance of the kernel selection in the analysis of high-dimensional medical images. Copyright © 2014 Elsevier Ltd. All rights reserved.

Band structure analysis of leaky Bloch waves in 2D phononic crystal plates.

PubMed

Mazzotti, Matteo; Miniaci, Marco; Bartoli, Ivan

2017-02-01

A hybrid Finite Element-Plane Wave Expansion method is presented for the band structure analysis of phononic crystal plates with two dimensional lattice that are in contact with acoustic half-spaces. The method enables the computation of both real (propagative) and imaginary (attenuation) components of the Bloch wavenumber at any given frequency. Three numerical applications are presented: a benchmark dispersion analysis for an oil-loaded Titanium isotropic plate, the band structure analysis of a water-loaded Tungsten slab with square cylindrical cavities and a phononic crystal plate composed of Aurum cylinders embedded in an epoxy matrix. Copyright © 2016 Elsevier B.V. All rights reserved.

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.

Fault Diagnosis for Rotating Machinery: A Method based on Image Processing

PubMed Central

Lu, Chen; Wang, Yang; Ragulskis, Minvydas; Cheng, Yujie

2016-01-01

Rotating machinery is one of the most typical types of mechanical equipment and plays a significant role in industrial applications. Condition monitoring and fault diagnosis of rotating machinery has gained wide attention for its significance in preventing catastrophic accident and guaranteeing sufficient maintenance. With the development of science and technology, fault diagnosis methods based on multi-disciplines are becoming the focus in the field of fault diagnosis of rotating machinery. This paper presents a multi-discipline method based on image-processing for fault diagnosis of rotating machinery. Different from traditional analysis method in one-dimensional space, this study employs computing method in the field of image processing to realize automatic feature extraction and fault diagnosis in a two-dimensional space. The proposed method mainly includes the following steps. First, the vibration signal is transformed into a bi-spectrum contour map utilizing bi-spectrum technology, which provides a basis for the following image-based feature extraction. Then, an emerging approach in the field of image processing for feature extraction, speeded-up robust features, is employed to automatically exact fault features from the transformed bi-spectrum contour map and finally form a high-dimensional feature vector. To reduce the dimensionality of the feature vector, thus highlighting main fault features and reducing subsequent computing resources, t-Distributed Stochastic Neighbor Embedding is adopt to reduce the dimensionality of the feature vector. At last, probabilistic neural network is introduced for fault identification. Two typical rotating machinery, axial piston hydraulic pump and self-priming centrifugal pumps, are selected to demonstrate the effectiveness of the proposed method. Results show that the proposed method based on image-processing achieves a high accuracy, thus providing a highly effective means to fault diagnosis for rotating machinery. PMID:27711246

Fault Diagnosis for Rotating Machinery: A Method based on Image Processing.

PubMed

Lu, Chen; Wang, Yang; Ragulskis, Minvydas; Cheng, Yujie

2016-01-01

Rotating machinery is one of the most typical types of mechanical equipment and plays a significant role in industrial applications. Condition monitoring and fault diagnosis of rotating machinery has gained wide attention for its significance in preventing catastrophic accident and guaranteeing sufficient maintenance. With the development of science and technology, fault diagnosis methods based on multi-disciplines are becoming the focus in the field of fault diagnosis of rotating machinery. This paper presents a multi-discipline method based on image-processing for fault diagnosis of rotating machinery. Different from traditional analysis method in one-dimensional space, this study employs computing method in the field of image processing to realize automatic feature extraction and fault diagnosis in a two-dimensional space. The proposed method mainly includes the following steps. First, the vibration signal is transformed into a bi-spectrum contour map utilizing bi-spectrum technology, which provides a basis for the following image-based feature extraction. Then, an emerging approach in the field of image processing for feature extraction, speeded-up robust features, is employed to automatically exact fault features from the transformed bi-spectrum contour map and finally form a high-dimensional feature vector. To reduce the dimensionality of the feature vector, thus highlighting main fault features and reducing subsequent computing resources, t-Distributed Stochastic Neighbor Embedding is adopt to reduce the dimensionality of the feature vector. At last, probabilistic neural network is introduced for fault identification. Two typical rotating machinery, axial piston hydraulic pump and self-priming centrifugal pumps, are selected to demonstrate the effectiveness of the proposed method. Results show that the proposed method based on image-processing achieves a high accuracy, thus providing a highly effective means to fault diagnosis for rotating machinery.

Global Three-Dimensional Simulation of Earth's Dayside Reconnection Using a Two-Way Coupled Magnetohydrodynamics With Embedded Particle-in-Cell Model: Initial Results

NASA Astrophysics Data System (ADS)

Chen, Yuxi; Tóth, Gábor; Cassak, Paul; Jia, Xianzhe; Gombosi, Tamas I.; Slavin, James A.; Markidis, Stefano; Peng, Ivy Bo; Jordanova, Vania K.; Henderson, Michael G.

2017-10-01

We perform a three-dimensional (3-D) global simulation of Earth's magnetosphere with kinetic reconnection physics to study the flux transfer events (FTEs) and dayside magnetic reconnection with the recently developed magnetohydrodynamics with embedded particle-in-cell model. During the 1 h long simulation, the FTEs are generated quasi-periodically near the subsolar point and move toward the poles. We find that the magnetic field signature of FTEs at their early formation stage is similar to a "crater FTE," which is characterized by a magnetic field strength dip at the FTE center. After the FTE core field grows to a significant value, it becomes an FTE with typical flux rope structure. When an FTE moves across the cusp, reconnection between the FTE field lines and the cusp field lines can dissipate the FTE. The kinetic features are also captured by our model. A crescent electron phase space distribution is found near the reconnection site. A similar distribution is found for ions at the location where the Larmor electric field appears. The lower hybrid drift instability (LHDI) along the current sheet direction also arises at the interface of magnetosheath and magnetosphere plasma. The LHDI electric field is about 8 mV/m, and its dominant wavelength relative to the electron gyroradius agrees reasonably with Magnetospheric Multiscale (MMS) observations.

Topology of quantum discord

NASA Astrophysics Data System (ADS)

Nguyen, Nga T. T.; Joynt, Robert

2017-04-01

Quantum discord is an important measure of quantum correlations that can serve as a resource for certain types of quantum information processing. Like entanglement, discord is subject to destruction by external noise. The routes by which this destruction can take place depends on the shape of the hypersurface of zero discord C in the space of generalized Bloch vectors. For 2 qubits, we show that with a few points subtracted, this hypersurface is a simply-connected 9-dimensional manifold embedded in a 15-dimensional background space. We do this by constructing an explicit homeomorphism from a known manifold to the subtracted version of C . We also construct a coordinate map on C that can be used for integration or other purposes. This topological characterization of C has important implications for the classification of the possible time evolutions of discord in physical models. The classification for discord contrasts sharply with the possible evolutions of entanglement. We classify the possible joint evolutions of entanglement and discord. There are 9 allowed categories: 6 categories for a Markovian process and 3 categories for a non-Markovian process, respectively. We illustrate these conclusions with an anisotropic XY spin model. All 9 categories can be obtained by adjusting parameters in this model.

On learning navigation behaviors for small mobile robots with reservoir computing architectures.

PubMed

Antonelo, Eric Aislan; Schrauwen, Benjamin

2015-04-01

This paper proposes a general reservoir computing (RC) learning framework that can be used to learn navigation behaviors for mobile robots in simple and complex unknown partially observable environments. RC provides an efficient way to train recurrent neural networks by letting the recurrent part of the network (called reservoir) be fixed while only a linear readout output layer is trained. The proposed RC framework builds upon the notion of navigation attractor or behavior that can be embedded in the high-dimensional space of the reservoir after learning. The learning of multiple behaviors is possible because the dynamic robot behavior, consisting of a sensory-motor sequence, can be linearly discriminated in the high-dimensional nonlinear space of the dynamic reservoir. Three learning approaches for navigation behaviors are shown in this paper. The first approach learns multiple behaviors based on the examples of navigation behaviors generated by a supervisor, while the second approach learns goal-directed navigation behaviors based only on rewards. The third approach learns complex goal-directed behaviors, in a supervised way, using a hierarchical architecture whose internal predictions of contextual switches guide the sequence of basic navigation behaviors toward the goal.

GPU surface extraction using the closest point embedding

NASA Astrophysics Data System (ADS)

Kim, Mark; Hansen, Charles

2015-01-01

Isosurface extraction is a fundamental technique used for both surface reconstruction and mesh generation. One method to extract well-formed isosurfaces is a particle system; unfortunately, particle systems can be slow. In this paper, we introduce an enhanced parallel particle system that uses the closest point embedding as the surface representation to speedup the particle system for isosurface extraction. The closest point embedding is used in the Closest Point Method (CPM), a technique that uses a standard three dimensional numerical PDE solver on two dimensional embedded surfaces. To fully take advantage of the closest point embedding, it is coupled with a Barnes-Hut tree code on the GPU. This new technique produces well-formed, conformal unstructured triangular and tetrahedral meshes from labeled multi-material volume datasets. Further, this new parallel implementation of the particle system is faster than any known methods for conformal multi-material mesh extraction. The resulting speed-ups gained in this implementation can reduce the time from labeled data to mesh from hours to minutes and benefits users, such as bioengineers, who employ triangular and tetrahedral meshes

De Sitter Space Without Dynamical Quantum Fluctuations

NASA Astrophysics Data System (ADS)

Boddy, Kimberly K.; Carroll, Sean M.; Pollack, Jason

2016-06-01

We argue that, under certain plausible assumptions, de Sitter space settles into a quiescent vacuum in which there are no dynamical quantum fluctuations. Such fluctuations require either an evolving microstate, or time-dependent histories of out-of-equilibrium recording devices, which we argue are absent in stationary states. For a massive scalar field in a fixed de Sitter background, the cosmic no-hair theorem implies that the state of the patch approaches the vacuum, where there are no fluctuations. We argue that an analogous conclusion holds whenever a patch of de Sitter is embedded in a larger theory with an infinite-dimensional Hilbert space, including semiclassical quantum gravity with false vacua or complementarity in theories with at least one Minkowski vacuum. This reasoning provides an escape from the Boltzmann brain problem in such theories. It also implies that vacuum states do not uptunnel to higher-energy vacua and that perturbations do not decohere while slow-roll inflation occurs, suggesting that eternal inflation is much less common than often supposed. On the other hand, if a de Sitter patch is a closed system with a finite-dimensional Hilbert space, there will be Poincaré recurrences and dynamical Boltzmann fluctuations into lower-entropy states. Our analysis does not alter the conventional understanding of the origin of density fluctuations from primordial inflation, since reheating naturally generates a high-entropy environment and leads to decoherence, nor does it affect the existence of non-dynamical vacuum fluctuations such as those that give rise to the Casimir effect.

Stable orthogonal local discriminant embedding for linear dimensionality reduction.

PubMed

Gao, Quanxue; Ma, Jingjie; Zhang, Hailin; Gao, Xinbo; Liu, Yamin

2013-07-01

Manifold learning is widely used in machine learning and pattern recognition. However, manifold learning only considers the similarity of samples belonging to the same class and ignores the within-class variation of data, which will impair the generalization and stableness of the algorithms. For this purpose, we construct an adjacency graph to model the intraclass variation that characterizes the most important properties, such as diversity of patterns, and then incorporate the diversity into the discriminant objective function for linear dimensionality reduction. Finally, we introduce the orthogonal constraint for the basis vectors and propose an orthogonal algorithm called stable orthogonal local discriminate embedding. Experimental results on several standard image databases demonstrate the effectiveness of the proposed dimensionality reduction approach.

Discriminative graph embedding for label propagation.

PubMed

Nguyen, Canh Hao; Mamitsuka, Hiroshi

2011-09-01

In many applications, the available information is encoded in graph structures. This is a common problem in biological networks, social networks, web communities and document citations. We investigate the problem of classifying nodes' labels on a similarity graph given only a graph structure on the nodes. Conventional machine learning methods usually require data to reside in some Euclidean spaces or to have a kernel representation. Applying these methods to nodes on graphs would require embedding the graphs into these spaces. By embedding and then learning the nodes on graphs, most methods are either flexible with different learning objectives or efficient enough for large scale applications. We propose a method to embed a graph into a feature space for a discriminative purpose. Our idea is to include label information into the embedding process, making the space representation tailored to the task. We design embedding objective functions that the following learning formulations become spectral transforms. We then reformulate these spectral transforms into multiple kernel learning problems. Our method, while being tailored to the discriminative tasks, is efficient and can scale to massive data sets. We show the need of discriminative embedding on some simulations. Applying to biological network problems, our method is shown to outperform baselines.

Supervised linear dimensionality reduction with robust margins for object recognition

NASA Astrophysics Data System (ADS)

Dornaika, F.; Assoum, A.

2013-01-01

Linear Dimensionality Reduction (LDR) techniques have been increasingly important in computer vision and pattern recognition since they permit a relatively simple mapping of data onto a lower dimensional subspace, leading to simple and computationally efficient classification strategies. Recently, many linear discriminant methods have been developed in order to reduce the dimensionality of visual data and to enhance the discrimination between different groups or classes. Many existing linear embedding techniques relied on the use of local margins in order to get a good discrimination performance. However, dealing with outliers and within-class diversity has not been addressed by margin-based embedding method. In this paper, we explored the use of different margin-based linear embedding methods. More precisely, we propose to use the concepts of Median miss and Median hit for building robust margin-based criteria. Based on such margins, we seek the projection directions (linear embedding) such that the sum of local margins is maximized. Our proposed approach has been applied to the problem of appearance-based face recognition. Experiments performed on four public face databases show that the proposed approach can give better generalization performance than the classic Average Neighborhood Margin Maximization (ANMM). Moreover, thanks to the use of robust margins, the proposed method down-grades gracefully when label outliers contaminate the training data set. In particular, we show that the concept of Median hit was crucial in order to get robust performance in the presence of outliers.

Nonlinear dimensionality reduction of data lying on the multicluster manifold.

PubMed

Meng, Deyu; Leung, Yee; Fung, Tung; Xu, Zongben

2008-08-01

A new method, which is called decomposition-composition (D-C) method, is proposed for the nonlinear dimensionality reduction (NLDR) of data lying on the multicluster manifold. The main idea is first to decompose a given data set into clusters and independently calculate the low-dimensional embeddings of each cluster by the decomposition procedure. Based on the intercluster connections, the embeddings of all clusters are then composed into their proper positions and orientations by the composition procedure. Different from other NLDR methods for multicluster data, which consider associatively the intracluster and intercluster information, the D-C method capitalizes on the separate employment of the intracluster neighborhood structures and the intercluster topologies for effective dimensionality reduction. This, on one hand, isometrically preserves the rigid-body shapes of the clusters in the embedding process and, on the other hand, guarantees the proper locations and orientations of all clusters. The theoretical arguments are supported by a series of experiments performed on the synthetic and real-life data sets. In addition, the computational complexity of the proposed method is analyzed, and its efficiency is theoretically analyzed and experimentally demonstrated. Related strategies for automatic parameter selection are also examined.

Classification of molecular structure images by using ANN, RF, LBP, HOG, and size reduction methods for early stomach cancer detection

NASA Astrophysics Data System (ADS)

Aytaç Korkmaz, Sevcan; Binol, Hamidullah

2018-03-01

Patients who die from stomach cancer are still present. Early diagnosis is crucial in reducing the mortality rate of cancer patients. Therefore, computer aided methods have been developed for early detection in this article. Stomach cancer images were obtained from Fırat University Medical Faculty Pathology Department. The Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG) features of these images are calculated. At the same time, Sammon mapping, Stochastic Neighbor Embedding (SNE), Isomap, Classical multidimensional scaling (MDS), Local Linear Embedding (LLE), Linear Discriminant Analysis (LDA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Laplacian Eigenmaps methods are used for dimensional the reduction of the features. The high dimension of these features has been reduced to lower dimensions using dimensional reduction methods. Artificial neural networks (ANN) and Random Forest (RF) classifiers were used to classify stomach cancer images with these new lower feature sizes. New medical systems have developed to measure the effects of these dimensions by obtaining features in different dimensional with dimensional reduction methods. When all the methods developed are compared, it has been found that the best accuracy results are obtained with LBP_MDS_ANN and LBP_LLE_ANN methods.

Stress-controlled Poisson ratio of a crystalline membrane: Application to graphene

NASA Astrophysics Data System (ADS)

Burmistrov, I. Â. S.; Gornyi, I. Â. V.; Kachorovskii, V. Â. Yu.; Katsnelson, M. Â. I.; Los, J. Â. H.; Mirlin, A. Â. D.

2018-03-01

We demonstrate that a key elastic parameter of a suspended crystalline membrane—the Poisson ratio (PR) ν —is a nontrivial function of the applied stress σ and of the system size L , i.e., ν =νL(σ ) . We consider a generic two-dimensional membrane embedded into space of dimensionality 2 +dc . (The physical situation corresponds to dc=1 .) A particularly important application of our results is to freestanding graphene. We find that at a very low stress, when the membrane exhibits linear response, the PR νL(0 ) decreases with increasing system size L and saturates for L →∞ at a value which depends on the boundary conditions and is essentially different from the value ν =-1 /3 previously predicted by the membrane theory within a self-consistent scaling analysis. By increasing σ , one drives a sufficiently large membrane (with the length L much larger than the Ginzburg length) into a nonlinear regime characterized by a universal value of PR that depends solely on dc, in close connection with the critical index η controlling the renormalization of bending rigidity. This universal nonlinear PR acquires its minimum value νmin=-1 in the limit dc→∞ , when η →0 . With the further increase of σ , the PR changes sign and finally saturates at a positive nonuniversal value prescribed by the conventional elasticity theory. We also show that one should distinguish between the absolute and differential PR (ν and νdiff, respectively). While coinciding in the limits of very low and very high stress, they differ in general: ν ≠νdiff . In particular, in the nonlinear universal regime, νdiff takes a universal value which, similarly to the absolute PR, is a function solely of dc (or, equivalently, of η ) but is different from the universal value of ν . In the limit of infinite dimensionality of the embedding space, dc→∞ (i.e., η →0 ), the universal value of νdiff tends to -1 /3 , at variance with the limiting value -1 of ν . Finally, we briefly discuss generalization of these results to a disordered membrane.

Zero-gravity growth of NaF-NaCl eutectics in the NASA Skylab program

NASA Technical Reports Server (NTRS)

Yue, A. S.; Allen, F. G.; Yu, J. G.

1976-01-01

Continuous and discontinuous NaF fibers, embedded in a NaCl matrix, were produced in space and on earth. The production of continuous fibers in a eutectic mixture is attributed to the absence of convection current in the liquid during solidification in space. Image transmission and optical transmittance measurements of transverse sections of the space-grown and earth-grown ingots were made with a light microscope and a spectrometer. It is shown that better optical properties were obtained from samples grown in space. This was attributed to a better alignment of NaF fibers along the ingot axis. A new concept is advanced to explain the phenomenon of transmittance versus far infrared wavelength of the directionally solidified NaCl-NaF eutectic in terms of the two-dimensional Bragg Scattering and the polarization effect of Rayleigh scattering. This concept can be applied to other eutectic systems as long as the index of refraction of the matrix over a range of wavelengths is known. Experimental data are in agreement with the theoretical prediction.

Exploring nonlinear feature space dimension reduction and data representation in breast CADx with Laplacian eigenmaps and t-SNE

PubMed Central

Jamieson, Andrew R.; Giger, Maryellen L.; Drukker, Karen; Li, Hui; Yuan, Yading; Bhooshan, Neha

2010-01-01

Purpose: In this preliminary study, recently developed unsupervised nonlinear dimension reduction (DR) and data representation techniques were applied to computer-extracted breast lesion feature spaces across three separate imaging modalities: Ultrasound (U.S.) with 1126 cases, dynamic contrast enhanced magnetic resonance imaging with 356 cases, and full-field digital mammography with 245 cases. Two methods for nonlinear DR were explored: Laplacian eigenmaps [M. Belkin and P. Niyogi, “Laplacian eigenmaps for dimensionality reduction and data representation,” Neural Comput. 15, 1373–1396 (2003)] and t-distributed stochastic neighbor embedding (t-SNE) [L. van der Maaten and G. Hinton, “Visualizing data using t-SNE,” J. Mach. Learn. Res. 9, 2579–2605 (2008)]. Methods: These methods attempt to map originally high dimensional feature spaces to more human interpretable lower dimensional spaces while preserving both local and global information. The properties of these methods as applied to breast computer-aided diagnosis (CADx) were evaluated in the context of malignancy classification performance as well as in the visual inspection of the sparseness within the two-dimensional and three-dimensional mappings. Classification performance was estimated by using the reduced dimension mapped feature output as input into both linear and nonlinear classifiers: Markov chain Monte Carlo based Bayesian artificial neural network (MCMC-BANN) and linear discriminant analysis. The new techniques were compared to previously developed breast CADx methodologies, including automatic relevance determination and linear stepwise (LSW) feature selection, as well as a linear DR method based on principal component analysis. Using ROC analysis and 0.632+bootstrap validation, 95% empirical confidence intervals were computed for the each classifier’s AUC performance. Results: In the large U.S. data set, sample high performance results include, AUC0.632+=0.88 with 95% empirical bootstrap interval [0.787;0.895] for 13 ARD selected features and AUC0.632+=0.87 with interval [0.817;0.906] for four LSW selected features compared to 4D t-SNE mapping (from the original 81D feature space) giving AUC0.632+=0.90 with interval [0.847;0.919], all using the MCMC-BANN. Conclusions: Preliminary results appear to indicate capability for the new methods to match or exceed classification performance of current advanced breast lesion CADx algorithms. While not appropriate as a complete replacement of feature selection in CADx problems, DR techniques offer a complementary approach, which can aid elucidation of additional properties associated with the data. Specifically, the new techniques were shown to possess the added benefit of delivering sparse lower dimensional representations for visual interpretation, revealing intricate data structure of the feature space. PMID:20175497

Transparency-enhancing technology allows three-dimensional assessment of gastrointestinal mucosa: A porcine model.

PubMed

Mizutani, Hiroya; Ono, Satoshi; Ushiku, Tetsuo; Kudo, Yotaro; Ikemura, Masako; Kageyama, Natsuko; Yamamichi, Nobutake; Fujishiro, Mitsuhiro; Someya, Takao; Fukayama, Masashi; Koike, Kazuhiko; Onodera, Hiroshi

2018-02-01

Although high-resolution three-dimensional imaging of endoscopically resected gastrointestinal specimens can help elucidating morphological features of gastrointestinal mucosa or tumor, there are no established methods to achieve this without breaking specimens apart. We evaluated the utility of transparency-enhancing technology for three-dimensional assessment of gastrointestinal mucosa in porcine models. Esophagus, stomach, and colon mucosa samples obtained from a sacrificed swine were formalin-fixed and paraffin-embedded, and subsequently deparaffinized for analysis. The samples were fluorescently stained, optically cleared using transparency-enhancing technology: ilLUmination of Cleared organs to IDentify target molecules method (LUCID), and visualized using laser scanning microscopy. After observation, all specimens were paraffin-embedded again and evaluated by conventional histopathological assessment to measure the impact of transparency-enhancing procedures. As a result, microscopic observation revealed horizontal section views of mucosa at deeper levels and enabled the three-dimensional image reconstruction of glandular and vascular structures. Besides, paraffin-embedded specimens after transparency-enhancing procedures were all assessed appropriately by conventional histopathological staining. These results suggest that transparency-enhancing technology may be feasible for clinical application and enable the three-dimensional structural analysis of endoscopic resected specimen non-destructively. Although there remain many limitations or problems to be solved, this promising technology might represent a novel histopathological method for evaluating gastrointestinal cancers. © 2018 Japanese Society of Pathology and John Wiley & Sons Australia, Ltd.

Fabrication of three-dimensional hybrid nanostructure-embedded ITO and its application as a transparent electrode for high-efficiency solution processable organic photovoltaic devices.

PubMed

Kim, Jeong Won; Jeon, Hwan-Jin; Lee, Chang-Lyoul; Ahn, Chi Won

2017-03-02

Well-aligned, high-resolution (10 nm), three-dimensional (3D) hybrid nanostructures consisting of patterned cylinders and Au islands were fabricated on ITO substrates using an ion bombardment process and a tilted deposition process. The fabricated 3D hybrid nanostructure-embedded ITO maintained its excellent electrical and optical properties after applying a surface-structuring process. The solution processable organic photovoltaic device (SP-OPV) employing a 3D hybrid nanostructure-embedded ITO as the anode displayed a 10% enhancement in the photovoltaic performance compared to the photovoltaic device prepared using a flat ITO electrode, due to the improved charge collection (extraction and transport) efficiency as well as light absorbance by the photo-active layer.

Concordance of Gleason grading with three-dimensional ultrasound systematic biopsy and biopsy core pre-embedding.

PubMed

van der Aa, Anouk A M A; Mannaerts, Christophe K; van der Linden, Hans; Gayet, Maudy; Schrier, Bart Ph; Mischi, Massimo; Beerlage, Harrie P; Wijkstra, Hessel

2018-02-01

To determine the value of a three-dimensional (3D) greyscale transrectal ultrasound (TRUS)-guided prostate biopsy system and biopsy core pre-embedding method on concordance between Gleason scores of needle biopsies and radical prostatectomy (RP) specimens. Retrospective analysis of prostate biopsies and subsequent RP for PCa in the Jeroen Bosch Hospital, the Netherlands, from 2007 to 2016. Two cohorts were analysed: conventional 2D TRUS-guided biopsies and RP (2007-2013, n = 266) versus 3D TRUS-guided biopsies with pre-embedding (2013-2016, n = 129). The impact of 3D TRUS-guidance with pre-embedding on Gleason score (GS) concordance between biopsy and RP was evaluated using the κ-coefficient. Predictors of biopsy GS 6 upgrading were assessed using logistic regression models. Gleason concordance was comparable between the two cohorts with a κ = 0.44 for the 3D cohort, compared to κ = 0.42 for the 2D cohort. 3D TRUS-guidance with pre-embedding, did not significantly affect the risk of biopsy GS 6 upgrading in univariate and multivariate analysis. 3D TRUS-guidance with biopsy core pre-embedding did not improve Gleason concordance. Improved detection techniques are needed for recognition of low-grade disease upgrading.

Fractal structures in centrifugal flywheel governor system

NASA Astrophysics Data System (ADS)

Rao, Xiao-Bo; Chu, Yan-Dong; Lu-Xu; Chang, Ying-Xiang; Zhang, Jian-Gang

2017-09-01

The global structure of nonlinear response of mechanical centrifugal governor, forming in two-dimensional parameter space, is studied in this paper. By using three kinds of phases, we describe how responses of periodicity, quasi-periodicity and chaos organize some self-similarity structures with parameters varying. For several parameter combinations, the regular vibration shows fractal characteristic, that is, the comb-shaped self-similarity structure is generated by alternating periodic response with intermittent chaos, and Arnold's tongues embedded in quasi-periodic response are organized according to Stern-Brocot tree. In particular, a new type of mixed-mode oscillations (MMOs) is found in the periodic response. These unique structures reveal the natural connection of various responses between part and part, part and the whole in parameter space based on self-similarity of fractal. Meanwhile, the remarkable and unexpected results are to contribute a valid dynamic reference for practical applications with respect to mechanical centrifugal governor.

Percolation of spatially constraint networks

NASA Astrophysics Data System (ADS)

Li, Daqing; Li, Guanliang; Kosmidis, Kosmas; Stanley, H. E.; Bunde, Armin; Havlin, Shlomo

2011-03-01

We study how spatial constraints are reflected in the percolation properties of networks embedded in one-dimensional chains and two-dimensional lattices. We assume long-range connections between sites on the lattice where two sites at distance r are chosen to be linked with probability p(r)~r-δ. Similar distributions have been found in spatially embedded real networks such as social and airline networks. We find that for networks embedded in two dimensions, with 2<δ<4, the percolation properties show new intermediate behavior different from mean field, with critical exponents that depend on δ. For δ<2, the percolation transition belongs to the universality class of percolation in Erdös-Rényi networks (mean field), while for δ>4 it belongs to the universality class of percolation in regular lattices. For networks embedded in one dimension, we find that, for δ<1, the percolation transition is mean field. For 1<δ<2, the critical exponents depend on δ, while for δ>2 there is no percolation transition as in regular linear chains.

Heterotic supergravity with internal almost-Kähler spaces; instantons for SO(32), or E 8 × E 8, gauge groups; and deformed black holes with soliton, quasiperiodic and/or pattern-forming structures

NASA Astrophysics Data System (ADS)

Bubuianu, Laurenţiu; Irwin, Klee; Vacaru, Sergiu I.

2017-04-01

Heterotic supergravity with (1  +  3)-dimensional domain wall configurations and (warped) internal, six dimensional, almost-Kähler manifolds {{}6}\\text{X} are studied. Considering ten dimensional spacetimes with nonholonomic distributions and conventional double fibrations, 2  +  2  +  ...  =  2  +  2  +  3  +  3, and associated SU(3) structures on internal space, we generalize for real, internal, almost symplectic gravitational structures the constructions with gravitational and gauge instantons of tanh-kink type [1, 2]. They include the first {α\\prime} corrections to the heterotic supergravity action, parameterized in a form to imply nonholonomic deformations of the Yang-Mills sector and corresponding Bianchi identities. We show how it is possible to construct a variety of solutions depending on the type of nonholonomic distributions and deformations of ‘prime’ instanton configurations characterized by two real supercharges. This corresponds to N=1/2 supersymmetric, nonholonomic manifolds from the four dimensional point of view. Our method provides a unified description of embedding nonholonomically deformed tanh-kink-type instantons into half-BPS solutions of heterotic supergravity. This allows us to elaborate new geometric methods of constructing exact solutions of motion equations, with first order {α\\prime} corrections to the heterotic supergravity. Such a formalism is applied for general and/or warped almost-Kähler configurations, which allows us to generate nontrivial (1  +  3)-d domain walls and black hole deformations determined by quasiperiodic internal space structures. This formalism is utilized in our associated publication [3] in order to construct and study generic off-diagonal nonholonomic deformations of the Kerr metric, encoding contributions from heterotic supergravity.

Embedding of the brane into six dimensions

NASA Astrophysics Data System (ADS)

Gogberashvili, Merab

2002-10-01

Embedding of the brane metric into Euclidean (2+4)-space is found. Brane geometry can be visualized as the surface of the hypersphere in six dimensions which ``radius'' is governed by the cosmological constant. Minkowski space in this picture is placed on the intersection of this surface with the plane formed by the extra space-like and time-like coordinates.

Computational study of 3-D hot-spot initiation in shocked insensitive high-explosive

NASA Astrophysics Data System (ADS)

Najjar, F. M.; Howard, W. M.; Fried, L. E.; Manaa, M. R.; Nichols, A., III; Levesque, G.

2012-03-01

High-explosive (HE) material consists of large-sized grains with micron-sized embedded impurities and pores. Under various mechanical/thermal insults, these pores collapse generating hightemperature regions leading to ignition. A hydrodynamic study has been performed to investigate the mechanisms of pore collapse and hot spot initiation in TATB crystals, employing a multiphysics code, ALE3D, coupled to the chemistry module, Cheetah. This computational study includes reactive dynamics. Two-dimensional high-resolution large-scale meso-scale simulations have been performed. The parameter space is systematically studied by considering various shock strengths, pore diameters and multiple pore configurations. Preliminary 3-D simulations are undertaken to quantify the 3-D dynamics.

Who Needs 3D When the Universe Is Flat?

ERIC Educational Resources Information Center

Eriksson, Urban; Linder, Cedric; Airey, John; Redfors, Andreas

2014-01-01

An overlooked feature in astronomy education is the need for students to learn to extrapolate three-dimensionality and the challenges that this may involve. Discerning critical features in the night sky that are embedded in dimensionality is a long-term learning process. Several articles have addressed the usefulness of three-dimensional (3D)…

Electromagnetic Scattering by Multiple Cavities Embedded in the Infinite 2D Ground Plane

DTIC Science & Technology

2014-07-01

Electromagnetic Scattering by Multiple Cavities Embedded in the Infinite 2D Ground Plane Peijun Li 1 and Aihua W. Wood 2 1 Department of...of the electromagnetic wave scattering by multiple open cavities, which are embedded in an infinite two-dimensional ground plane . By introducing a...equation, variational formulation. I. INTRODUCTION A cavity is referred to as a local perturbation of the infinite ground plane . Given the cavity

ULF foreshock under radial IMF: THEMIS observations and global kinetic simulation Vlasiator results compared

NASA Astrophysics Data System (ADS)

Palmroth, Minna; Rami, Vainio; Archer, Martin; Hietala, Heli; Afanasiev, Alexandr; Kempf, Yann; Hoilijoki, Sanni; von Alfthan, Sebastian

2015-04-01

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 with an average angle of 20 degrees with respect of 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 the six-dimensional phase space utilising the Vlasov equation for protons, while electrons are a charge-neutralising fluid. 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 characterised. Vlasiator produces the ion distribution functions and the related kinetic physics in unprecedented detail, in the global scale magnetospheric scale with a resolution of a couple of hundred kilometres in the ordinary space and 20 km/s in the velocity space. We run Vlasiator under a radial IMF in five dimensions consisting of the three-dimensional velocity space embedded in the ecliptic plane. We observe the generation of the 30-second ULF waves, and characterize their evolution and physical properties in time. We compare the results both to THEMIS observations and to the quasi-linear theory. We find that Vlasiator reproduces the foreshock ULF 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 discuss the issues related to the long-standing question of oblique propagation.

Multiplexed immunosensing and kinetics monitoring in nanofluidic devices with highly enhanced target capture efficiency

PubMed Central

Lin, Yii-Lih; Huang, Yen-Jun; Teerapanich, Pattamon; Leïchlé, Thierry

2016-01-01

Nanofluidic devices promise high reaction efficiency and fast kinetic responses due to the spatial constriction of transported biomolecules with confined molecular diffusion. However, parallel detection of multiple biomolecules, particularly proteins, in highly confined space remains challenging. This study integrates extended nanofluidics with embedded protein microarray to achieve multiplexed real-time biosensing and kinetics monitoring. Implementation of embedded standard-sized antibody microarray is attained by epoxy-silane surface modification and a room-temperature low-aspect-ratio bonding technique. An effective sample transport is achieved by electrokinetic pumping via electroosmotic flow. Through the nanoslit-based spatial confinement, the antigen-antibody binding reaction is enhanced with ∼100% efficiency and may be directly observed with fluorescence microscopy without the requirement of intermediate washing steps. The image-based data provide numerous spatially distributed reaction kinetic curves and are collectively modeled using a simple one-dimensional convection-reaction model. This study represents an integrated nanofluidic solution for real-time multiplexed immunosensing and kinetics monitoring, starting from device fabrication, protein immobilization, device bonding, sample transport, to data analysis at Péclet number less than 1. PMID:27375819

A Domain-Decomposed Multilevel Method for Adaptively Refined Cartesian Grids with Embedded Boundaries

NASA Technical Reports Server (NTRS)

Aftosmis, M. J.; Berger, M. J.; Adomavicius, G.

2000-01-01

Preliminary verification and validation of an efficient Euler solver for adaptively refined Cartesian meshes with embedded boundaries is presented. The parallel, multilevel method makes use of a new on-the-fly parallel domain decomposition strategy based upon the use of space-filling curves, and automatically generates a sequence of coarse meshes for processing by the multigrid smoother. The coarse mesh generation algorithm produces grids which completely cover the computational domain at every level in the mesh hierarchy. A series of examples on realistically complex three-dimensional configurations demonstrate that this new coarsening algorithm reliably achieves mesh coarsening ratios in excess of 7 on adaptively refined meshes. Numerical investigations of the scheme's local truncation error demonstrate an achieved order of accuracy between 1.82 and 1.88. Convergence results for the multigrid scheme are presented for both subsonic and transonic test cases and demonstrate W-cycle multigrid convergence rates between 0.84 and 0.94. Preliminary parallel scalability tests on both simple wing and complex complete aircraft geometries shows a computational speedup of 52 on 64 processors using the run-time mesh partitioner.

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

Potash, Peter J.; Bell, Eric B.; Harrison, Joshua J.

Predictive models for tweet deletion have been a relatively unexplored area of Twitter-related computational research. We first approach the deletion of tweets as a spam detection problem, applying a small set of handcrafted features to improve upon the current state-of-the- art in predicting deleted tweets. Next, we apply our approach to a dataset of deleted tweets that better reflects the current deletion rate. Since tweets are deleted for reasons beyond just the presence of spam, we apply topic modeling and text embeddings in order to capture the semantic content of tweets that can lead to tweet deletion. Our goal ismore » to create an effective model that has a low-dimensional feature space and is also language-independent. A lean model would be computationally advantageous processing high-volumes of Twitter data, which can reach 9,885 tweets per second. Our results show that a small set of spam-related features combined with word topics and character-level text embeddings provide the best f1 when trained with a random forest model. The highest precision of the deleted tweet class is achieved by a modification of paragraph2vec to capture author identity.« less

End-to-End ASR-Free Keyword Search From Speech

NASA Astrophysics Data System (ADS)

Audhkhasi, Kartik; Rosenberg, Andrew; Sethy, Abhinav; Ramabhadran, Bhuvana; Kingsbury, Brian

2017-12-01

End-to-end (E2E) systems have achieved competitive results compared to conventional hybrid hidden Markov model (HMM)-deep neural network based automatic speech recognition (ASR) systems. Such E2E systems are attractive due to the lack of dependence on alignments between input acoustic and output grapheme or HMM state sequence during training. This paper explores the design of an ASR-free end-to-end system for text query-based keyword search (KWS) from speech trained with minimal supervision. Our E2E KWS system consists of three sub-systems. The first sub-system is a recurrent neural network (RNN)-based acoustic auto-encoder trained to reconstruct the audio through a finite-dimensional representation. The second sub-system is a character-level RNN language model using embeddings learned from a convolutional neural network. Since the acoustic and text query embeddings occupy different representation spaces, they are input to a third feed-forward neural network that predicts whether the query occurs in the acoustic utterance or not. This E2E ASR-free KWS system performs respectably despite lacking a conventional ASR system and trains much faster.

Long-Range Repulsion Between Spatially Confined van der Waals Dimers

NASA Astrophysics Data System (ADS)

Sadhukhan, Mainak; Tkatchenko, Alexandre

2017-05-01

It is an undisputed textbook fact that nonretarded van der Waals (vdW) interactions between isotropic dimers are attractive, regardless of the polarizability of the interacting systems or spatial dimensionality. The universality of vdW attraction is attributed to the dipolar coupling between fluctuating electron charge densities. Here, we demonstrate that the long-range interaction between spatially confined vdW dimers becomes repulsive when accounting for the full Coulomb interaction between charge fluctuations. Our analytic results are obtained by using the Coulomb potential as a perturbation over dipole-correlated states for two quantum harmonic oscillators embedded in spaces with reduced dimensionality; however, the long-range repulsion is expected to be a general phenomenon for spatially confined quantum systems. We suggest optical experiments to test our predictions, analyze their relevance in the context of intermolecular interactions in nanoscale environments, and rationalize the recent observation of anomalously strong screening of the lateral vdW interactions between aromatic hydrocarbons adsorbed on metal surfaces.

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.

Topological Vortex and Knotted Dissipative Optical 3D Solitons Generated by 2D Vortex Solitons

NASA Astrophysics Data System (ADS)

Veretenov, N. A.; Fedorov, S. V.; Rosanov, N. N.

2017-12-01

We predict a new class of three-dimensional (3D) topological dissipative optical one-component solitons in homogeneous laser media with fast saturable absorption. Their skeletons formed by vortex lines where the field vanishes are tangles, i.e., Nc knotted or unknotted, linked or unlinked closed lines and M unclosed lines that thread all the closed lines and end at the infinitely far soliton periphery. They are generated by embedding two-dimensional laser solitons or their complexes in 3D space after their rotation around an unclosed, infinite vortex line with topological charge M0 (Nc , M , and M0 are integers). With such structure propagation, the "hula-hoop" solitons form; their stability is confirmed numerically. For the solitons found, all vortex lines have unit topological charge: the number of closed lines Nc=1 and 2 (unknots, trefoils, and Solomon knots links); unclosed vortex lines are unknotted and unlinked, their number M =1 , 2, and 3.

Topological Vortex and Knotted Dissipative Optical 3D Solitons Generated by 2D Vortex Solitons.

PubMed

Veretenov, N A; Fedorov, S V; Rosanov, N N

2017-12-29

We predict a new class of three-dimensional (3D) topological dissipative optical one-component solitons in homogeneous laser media with fast saturable absorption. Their skeletons formed by vortex lines where the field vanishes are tangles, i.e., N_{c} knotted or unknotted, linked or unlinked closed lines and M unclosed lines that thread all the closed lines and end at the infinitely far soliton periphery. They are generated by embedding two-dimensional laser solitons or their complexes in 3D space after their rotation around an unclosed, infinite vortex line with topological charge M_{0} (N_{c}, M, and M_{0} are integers). With such structure propagation, the "hula-hoop" solitons form; their stability is confirmed numerically. For the solitons found, all vortex lines have unit topological charge: the number of closed lines N_{c}=1 and 2 (unknots, trefoils, and Solomon knots links); unclosed vortex lines are unknotted and unlinked, their number M=1, 2, and 3.

Nonlinear vs. linear biasing in Trp-cage folding simulations

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

Spiwok, Vojtěch, E-mail: spiwokv@vscht.cz; Oborský, Pavel; Králová, Blanka

2015-03-21

Biased simulations have great potential for the study of slow processes, including protein folding. Atomic motions in molecules are nonlinear, which suggests that simulations with enhanced sampling of collective motions traced by nonlinear dimensionality reduction methods may perform better than linear ones. In this study, we compare an unbiased folding simulation of the Trp-cage miniprotein with metadynamics simulations using both linear (principle component analysis) and nonlinear (Isomap) low dimensional embeddings as collective variables. Folding of the mini-protein was successfully simulated in 200 ns simulation with linear biasing and non-linear motion biasing. The folded state was correctly predicted as the free energymore » minimum in both simulations. We found that the advantage of linear motion biasing is that it can sample a larger conformational space, whereas the advantage of nonlinear motion biasing lies in slightly better resolution of the resulting free energy surface. In terms of sampling efficiency, both methods are comparable.« less

Computation of multi-dimensional viscous supersonic flow

NASA Technical Reports Server (NTRS)

Buggeln, R. C.; Kim, Y. N.; Mcdonald, H.

1986-01-01

A method has been developed for two- and three-dimensional computations of viscous supersonic jet flows interacting with an external flow. The approach employs a reduced form of the Navier-Stokes equations which allows solution as an initial-boundary value problem in space, using an efficient noniterative forward marching algorithm. Numerical instability associated with forward marching algorithms for flows with embedded subsonic regions is avoided by approximation of the reduced form of the Navier-Stokes equations in the subsonic regions of the boundary layers. Supersonic and subsonic portions of the flow field are simultaneously calculated by a consistently split linearized block implicit computational algorithm. The results of computations for a series of test cases associated with supersonic jet flow is presented and compared with other calculations for axisymmetric cases. Demonstration calculations indicate that the computational technique has great promise as a tool for calculating a wide range of supersonic flow problems including jet flow. Finally, a User's Manual is presented for the computer code used to perform the calculations.

Global three-dimensional simulation of Earth's dayside reconnection using a two-way coupled magnetohydrodynamics with embedded particle-in-cell model: initial results: 3D MHD-EPIC simulation of magnetosphere

DOE PAGES

Chen, Yuxi; Tóth, Gábor; Cassak, Paul; ...

2017-09-18

Here, we perform a three-dimensional (3D) global simulation of Earth's magnetosphere with kinetic reconnection physics to study the flux transfer events (FTEs) and dayside magnetic reconnection with the recently developed magnetohydrodynamics with embedded particle-in-cell model (MHD-EPIC). During the one-hour long simulation, the FTEs are generated quasi-periodically near the subsolar point and move toward the poles. We also find the magnetic field signature of FTEs at their early formation stage is similar to a ‘crater FTE’, which is characterized by a magnetic field strength dip at the FTE center. After the FTE core field grows to a significant value, it becomesmore » an FTE with typical flux rope structure. When an FTE moves across the cusp, reconnection between the FTE field lines and the cusp field lines can dissipate the FTE. The kinetic features are also captured by our model. A crescent electron phase space distribution is found near the reconnection site. A similar distribution is found for ions at the location where the Larmor electric field appears. The lower hybrid drift instability (LHDI) along the current sheet direction also arises at the interface of magnetosheath and magnetosphere plasma. Finally, the LHDI electric field is about 8 mV/m and its dominant wavelength relative to the electron gyroradius agrees reasonably with MMS observations.« less

Global three-dimensional simulation of Earth's dayside reconnection using a two-way coupled magnetohydrodynamics with embedded particle-in-cell model: initial results: 3D MHD-EPIC simulation of magnetosphere

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

Chen, Yuxi; Tóth, Gábor; Cassak, Paul

Here, we perform a three-dimensional (3D) global simulation of Earth's magnetosphere with kinetic reconnection physics to study the flux transfer events (FTEs) and dayside magnetic reconnection with the recently developed magnetohydrodynamics with embedded particle-in-cell model (MHD-EPIC). During the one-hour long simulation, the FTEs are generated quasi-periodically near the subsolar point and move toward the poles. We also find the magnetic field signature of FTEs at their early formation stage is similar to a ‘crater FTE’, which is characterized by a magnetic field strength dip at the FTE center. After the FTE core field grows to a significant value, it becomesmore » an FTE with typical flux rope structure. When an FTE moves across the cusp, reconnection between the FTE field lines and the cusp field lines can dissipate the FTE. The kinetic features are also captured by our model. A crescent electron phase space distribution is found near the reconnection site. A similar distribution is found for ions at the location where the Larmor electric field appears. The lower hybrid drift instability (LHDI) along the current sheet direction also arises at the interface of magnetosheath and magnetosphere plasma. Finally, the LHDI electric field is about 8 mV/m and its dominant wavelength relative to the electron gyroradius agrees reasonably with MMS observations.« less

Optimal Embedding for Shape Indexing in Medical Image Databases

PubMed Central

Qian, Xiaoning; Tagare, Hemant D.; Fulbright, Robert K.; Long, Rodney; Antani, Sameer

2010-01-01

This paper addresses the problem of indexing shapes in medical image databases. Shapes of organs are often indicative of disease, making shape similarity queries important in medical image databases. Mathematically, shapes with landmarks belong to shape spaces which are curved manifolds with a well defined metric. The challenge in shape indexing is to index data in such curved spaces. One natural indexing scheme is to use metric trees, but metric trees are prone to inefficiency. This paper proposes a more efficient alternative. We show that it is possible to optimally embed finite sets of shapes in shape space into a Euclidean space. After embedding, classical coordinate-based trees can be used for efficient shape retrieval. The embedding proposed in the paper is optimal in the sense that it least distorts the partial Procrustes shape distance. The proposed indexing technique is used to retrieve images by vertebral shape from the NHANES II database of cervical and lumbar spine x-ray images maintained at the National Library of Medicine. Vertebral shape strongly correlates with the presence of osteophytes, and shape similarity retrieval is proposed as a tool for retrieval by osteophyte presence and severity. Experimental results included in the paper evaluate (1) the usefulness of shape-similarity as a proxy for osteophytes, (2) the computational and disk access efficiency of the new indexing scheme, (3) the relative performance of indexing with embedding to the performance of indexing without embedding, and (4) the computational cost of indexing using the proposed embedding versus the cost of an alternate embedding. The experimental results clearly show the relevance of shape indexing and the advantage of using the proposed embedding. PMID:20163981

Optimal embedding for shape indexing in medical image databases.

PubMed

Qian, Xiaoning; Tagare, Hemant D; Fulbright, Robert K; Long, Rodney; Antani, Sameer

2010-06-01

This paper addresses the problem of indexing shapes in medical image databases. Shapes of organs are often indicative of disease, making shape similarity queries important in medical image databases. Mathematically, shapes with landmarks belong to shape spaces which are curved manifolds with a well defined metric. The challenge in shape indexing is to index data in such curved spaces. One natural indexing scheme is to use metric trees, but metric trees are prone to inefficiency. This paper proposes a more efficient alternative. We show that it is possible to optimally embed finite sets of shapes in shape space into a Euclidean space. After embedding, classical coordinate-based trees can be used for efficient shape retrieval. The embedding proposed in the paper is optimal in the sense that it least distorts the partial Procrustes shape distance. The proposed indexing technique is used to retrieve images by vertebral shape from the NHANES II database of cervical and lumbar spine X-ray images maintained at the National Library of Medicine. Vertebral shape strongly correlates with the presence of osteophytes, and shape similarity retrieval is proposed as a tool for retrieval by osteophyte presence and severity. Experimental results included in the paper evaluate (1) the usefulness of shape similarity as a proxy for osteophytes, (2) the computational and disk access efficiency of the new indexing scheme, (3) the relative performance of indexing with embedding to the performance of indexing without embedding, and (4) the computational cost of indexing using the proposed embedding versus the cost of an alternate embedding. The experimental results clearly show the relevance of shape indexing and the advantage of using the proposed embedding. Copyright (c) 2010 Elsevier B.V. All rights reserved.

Three-Dimensional Multi-fluid Moment Simulation of Ganymede

NASA Astrophysics Data System (ADS)

Wang, L.; Germaschewski, K.; Hakim, A.; Bhattacharjee, A.; Dong, C.

2016-12-01

Plasmas in space environments, such as solar wind and Earth's magnetosphere, are often constituted of multiple species. Conventional MHD-based, single-fluid systems, have additional complications when multiple fluid species are introduced. We suggest space application of an alternative multi-fluid moment approach, treating each species on equal footing using exact evolution equations for moments of their distribution function, and electromagnetic fields through full Maxwell equations. Non-ideal effects like Hall effect, inertia, and even tensorial pressures, are self-consistently embedded without the need to explicitly solve a complicated Ohm's law. Previously, we have benchmarked this approach in classical test problems like the Orszag-Tang vortex and GEM reconnection challenge problem. Recently, we performed three-dimensional two-fluid simulation of the magnetosphere of Ganymede, using both five-moment (scalar pressures) and ten-moment (tensorial pressures) models. In both models, the formation of Alfven wing structure due to subsonic inflow is correctly captured, and the magnetic field data agree well with in-situ measurements from the Galileo flyby G8. The ten-moment simulation also showed the contribution of pressure tensor divergence to the reconnecting electric field. Initial results of coupling to state-of-art global simulation codes like OpenGGCM will also be shown, which will in the future provide a rigorous way for integration of ionospheric physics.

Transport, diffusion, and energy studies in the Arnold-Beltrami-Childress map

NASA Astrophysics Data System (ADS)

Das, Swetamber; Gupte, Neelima

2017-09-01

We study the transport and diffusion properties of passive inertial particles described by a six-dimensional dissipative bailout embedding map. The base map chosen for the study is the three-dimensional incompressible Arnold-Beltrami-Childress (ABC) map chosen as a representation of volume preserving flows. There are two distinct cases: the two-action and the one-action cases, depending on whether two or one of the parameters (A ,B ,C ) exceed 1. The embedded map dynamics is governed by two parameters (α ,γ ), which quantify the mass density ratio and dissipation, respectively. There are important differences between the aerosol (α <1 ) and the bubble (α >1 ) regimes. We have studied the diffusive behavior of the system and constructed the phase diagram in the parameter space by computing the diffusion exponents η . Three classes have been broadly classified—subdiffusive transport (η <1 ), normal diffusion (η ≈1 ), and superdiffusion (η >1 ) with η ≈2 referred to as the ballistic regime. Correlating the diffusive phase diagram with the phase diagram for dynamical regimes seen earlier, we find that the hyperchaotic bubble regime is largely correlated with normal and superdiffusive behavior. In contrast, in the aerosol regime, ballistic superdiffusion is seen in regions that largely show periodic dynamical behaviors, whereas subdiffusive behavior is seen in both periodic and chaotic regimes. The probability distributions of the diffusion exponents show power-law scaling for both aerosol and bubbles in the superdiffusive regimes. We further study the Poincáre recurrence times statistics of the system. Here, we find that recurrence time distributions show power law regimes due to the existence of partial barriers to transport in the phase space. Moreover, the plot of average particle kinetic energies versus the mass density ratio for the two-action case exhibits a devil's staircase-like structure for higher dissipation values. We explain these results and discuss their implications for realistic systems.

Information driven self-organization of complex robotic behaviors.

PubMed

Martius, Georg; Der, Ralf; Ay, Nihat

2013-01-01

Information theory is a powerful tool to express principles to drive autonomous systems because it is domain invariant and allows for an intuitive interpretation. This paper studies the use of the predictive information (PI), also called excess entropy or effective measure complexity, of the sensorimotor process as a driving force to generate behavior. We study nonlinear and nonstationary systems and introduce the time-local predicting information (TiPI) which allows us to derive exact results together with explicit update rules for the parameters of the controller in the dynamical systems framework. In this way the information principle, formulated at the level of behavior, is translated to the dynamics of the synapses. We underpin our results with a number of case studies with high-dimensional robotic systems. We show the spontaneous cooperativity in a complex physical system with decentralized control. Moreover, a jointly controlled humanoid robot develops a high behavioral variety depending on its physics and the environment it is dynamically embedded into. The behavior can be decomposed into a succession of low-dimensional modes that increasingly explore the behavior space. This is a promising way to avoid the curse of dimensionality which hinders learning systems to scale well.

Static three-dimensional topological solitons in fluid chiral ferromagnets and colloids

NASA Astrophysics Data System (ADS)

Ackerman, Paul J.; Smalyukh, Ivan I.

2017-04-01

Three-dimensional (3D) topological solitons are continuous but topologically nontrivial field configurations localized in 3D space and embedded in a uniform far-field background, that behave like particles and cannot be transformed to a uniform state through smooth deformations. Many topologically nontrivial 3D solitonic fields have been proposed. Yet, according to the Hobart-Derrick theorem, physical systems cannot host them, except for nonlinear theories with higher-order derivatives such as the Skyrme-Faddeev model. Experimental discovery of such solitons is hindered by the need for spatial imaging of the 3D fields, which is difficult in high-energy physics and cosmology. Here we experimentally realize and numerically model stationary topological solitons in a fluid chiral ferromagnet formed by colloidal dispersions of magnetic nanoplates. Such solitons have closed-loop preimages--3D regions with a single orientation of the magnetization field. We discuss localized structures with different linking of preimages quantified by topological Hopf invariants. The chirality is found to help in overcoming the constraints of the Hobart-Derrick theorem, like in two-dimensional ferromagnetic solitons, dubbed `baby skyrmions'. Our experimental platform may lead to solitonic condensed matter phases and technological applications.

A bulk localized state and new holographic renormalization group flow in 3D spin-3 gravity

NASA Astrophysics Data System (ADS)

Nakayama, Ryuichi; Suzuki, Tomotaka

2018-04-01

We construct a localized state of a scalar field in 3D spin-3 gravity. 3D spin-3 gravity is thought to be holographically dual to W3-extended CFT on a boundary at infinity. It is known that while W3 algebra is a nonlinear algebra, in the limit of large central charge c a linear finite-dimensional subalgebra generated by Wn (n = 0,±1,±2) and Ln (n = 0,±1) is singled out. The localized state is constructed in terms of these generators. To write down an equation of motion for a scalar field which is satisfied by this localized state, it is necessary to introduce new variables for an internal space α±, β±, γ, in addition to ordinary coordinates x± and y. The higher-dimensional space, which combines the bulk space-time with the “internal space,” which is an analog of superspace in supersymmetric theory, is introduced. The “physical bulk space-time” is a 3D hypersurface with constant α±, β± and γ embedded in this space. We will work in Poincaré coordinates of AdS space and consider W-quasi-primary operators Φh(x+) with a conformal weight h in the boundary and study two and three point functions of W-quasi-primary operators transformed as eix+L‑1heβ+W‑1hΦh(0)e‑β+W‑1he‑ix+L‑1h. Here, Lnh and Wnh are sl(3,R) generators in the hyperbolic basis for Poincaré coordinates. It is shown that in the β+ →∞ limit, the conformal weight changes to a new value h‧ = h/2. This may be regarded as a Renormalization Group (RG) flow. It is argued that this RG flow will be triggered by terms ΔS ∝ β+W ‑1h + β‑W¯ ‑1h added to the action.

A local crack-tracking strategy to model three-dimensional crack propagation with embedded methods

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

Annavarapu, Chandrasekhar; Settgast, Randolph R.; Vitali, Efrem

We develop a local, implicit crack tracking approach to propagate embedded failure surfaces in three-dimensions. We build on the global crack-tracking strategy of Oliver et al. (Int J. Numer. Anal. Meth. Geomech., 2004; 28:609–632) that tracks all potential failure surfaces in a problem at once by solving a Laplace equation with anisotropic conductivity. We discuss important modifications to this algorithm with a particular emphasis on the effect of the Dirichlet boundary conditions for the Laplace equation on the resultant crack path. Algorithmic and implementational details of the proposed method are provided. Finally, several three-dimensional benchmark problems are studied and resultsmore » are compared with available literature. Lastly, the results indicate that the proposed method addresses pathological cases, exhibits better behavior in the presence of closely interacting fractures, and provides a viable strategy to robustly evolve embedded failure surfaces in 3D.« less

Data-Driven Sampling Matrix Boolean Optimization for Energy-Efficient Biomedical Signal Acquisition by Compressive Sensing.

PubMed

Wang, Yuhao; Li, Xin; Xu, Kai; Ren, Fengbo; Yu, Hao

2017-04-01

Compressive sensing is widely used in biomedical applications, and the sampling matrix plays a critical role on both quality and power consumption of signal acquisition. It projects a high-dimensional vector of data into a low-dimensional subspace by matrix-vector multiplication. An optimal sampling matrix can ensure accurate data reconstruction and/or high compression ratio. Most existing optimization methods can only produce real-valued embedding matrices that result in large energy consumption during data acquisition. In this paper, we propose an efficient method that finds an optimal Boolean sampling matrix in order to reduce the energy consumption. Compared to random Boolean embedding, our data-driven Boolean sampling matrix can improve the image recovery quality by 9 dB. Moreover, in terms of sampling hardware complexity, it reduces the energy consumption by 4.6× and the silicon area by 1.9× over the data-driven real-valued embedding.

A local crack-tracking strategy to model three-dimensional crack propagation with embedded methods

DOE PAGES

Annavarapu, Chandrasekhar; Settgast, Randolph R.; Vitali, Efrem; ...

2016-09-29

We develop a local, implicit crack tracking approach to propagate embedded failure surfaces in three-dimensions. We build on the global crack-tracking strategy of Oliver et al. (Int J. Numer. Anal. Meth. Geomech., 2004; 28:609–632) that tracks all potential failure surfaces in a problem at once by solving a Laplace equation with anisotropic conductivity. We discuss important modifications to this algorithm with a particular emphasis on the effect of the Dirichlet boundary conditions for the Laplace equation on the resultant crack path. Algorithmic and implementational details of the proposed method are provided. Finally, several three-dimensional benchmark problems are studied and resultsmore » are compared with available literature. Lastly, the results indicate that the proposed method addresses pathological cases, exhibits better behavior in the presence of closely interacting fractures, and provides a viable strategy to robustly evolve embedded failure surfaces in 3D.« less

Three-dimensional printing of complex biological structures by freeform reversible embedding of suspended hydrogels

PubMed Central

Hinton, Thomas J.; Jallerat, Quentin; Palchesko, Rachelle N.; Park, Joon Hyung; Grodzicki, Martin S.; Shue, Hao-Jan; Ramadan, Mohamed H.; Hudson, Andrew R.; Feinberg, Adam W.

2015-01-01

We demonstrate the additive manufacturing of complex three-dimensional (3D) biological structures using soft protein and polysaccharide hydrogels that are challenging or impossible to create using traditional fabrication approaches. These structures are built by embedding the printed hydrogel within a secondary hydrogel that serves as a temporary, thermoreversible, and biocompatible support. This process, termed freeform reversible embedding of suspended hydrogels, enables 3D printing of hydrated materials with an elastic modulus <500 kPa including alginate, collagen, and fibrin. Computer-aided design models of 3D optical, computed tomography, and magnetic resonance imaging data were 3D printed at a resolution of ~200 μm and at low cost by leveraging open-source hardware and software tools. Proof-of-concept structures based on femurs, branched coronary arteries, trabeculated embryonic hearts, and human brains were mechanically robust and recreated complex 3D internal and external anatomical architectures. PMID:26601312

Unsupervised nonlinear dimensionality reduction machine learning methods applied to multiparametric MRI in cerebral ischemia: preliminary results

NASA Astrophysics Data System (ADS)

Parekh, Vishwa S.; Jacobs, Jeremy R.; Jacobs, Michael A.

2014-03-01

The evaluation and treatment of acute cerebral ischemia requires a technique that can determine the total area of tissue at risk for infarction using diagnostic magnetic resonance imaging (MRI) sequences. Typical MRI data sets consist of T1- and T2-weighted imaging (T1WI, T2WI) along with advanced MRI parameters of diffusion-weighted imaging (DWI) and perfusion weighted imaging (PWI) methods. Each of these parameters has distinct radiological-pathological meaning. For example, DWI interrogates the movement of water in the tissue and PWI gives an estimate of the blood flow, both are critical measures during the evolution of stroke. In order to integrate these data and give an estimate of the tissue at risk or damaged; we have developed advanced machine learning methods based on unsupervised non-linear dimensionality reduction (NLDR) techniques. NLDR methods are a class of algorithms that uses mathematically defined manifolds for statistical sampling of multidimensional classes to generate a discrimination rule of guaranteed statistical accuracy and they can generate a two- or three-dimensional map, which represents the prominent structures of the data and provides an embedded image of meaningful low-dimensional structures hidden in their high-dimensional observations. In this manuscript, we develop NLDR methods on high dimensional MRI data sets of preclinical animals and clinical patients with stroke. On analyzing the performance of these methods, we observed that there was a high of similarity between multiparametric embedded images from NLDR methods and the ADC map and perfusion map. It was also observed that embedded scattergram of abnormal (infarcted or at risk) tissue can be visualized and provides a mechanism for automatic methods to delineate potential stroke volumes and early tissue at risk.

Metal-superconductor transition in low-dimensional superconducting clusters embedded in two-dimensional electron systems

NASA Astrophysics Data System (ADS)

Bucheli, D.; Caprara, S.; Castellani, C.; Grilli, M.

2013-02-01

Motivated by recent experimental data on thin film superconductors and oxide interfaces, we propose a random-resistor network apt to describe the occurrence of a metal-superconductor transition in a two-dimensional electron system with disorder on the mesoscopic scale. We consider low-dimensional (e.g. filamentary) structures of a superconducting cluster embedded in the two-dimensional network and we explore the separate effects and the interplay of the superconducting structure and of the statistical distribution of local critical temperatures. The thermal evolution of the resistivity is determined by a numerical calculation of the random-resistor network and, for comparison, a mean-field approach called effective medium theory (EMT). Our calculations reveal the relevance of the distribution of critical temperatures for clusters with low connectivity. In addition, we show that the presence of spatial correlations requires a modification of standard EMT to give qualitative agreement with the numerical results. Applying the present approach to an LaTiO3/SrTiO3 oxide interface, we find that the measured resistivity curves are compatible with a network of spatially dense but loosely connected superconducting islands.

The XTT Cell Proliferation Assay Applied to Cell Layers Embedded in Three-Dimensional Matrix

PubMed Central

Huyck, Lynn; Ampe, Christophe

2012-01-01

Abstract Cell proliferation, a main target in cancer therapy, is influenced by the surrounding three-dimensional (3D) extracellular matrix (ECM). In vitro drug screening is, thus, optimally performed under conditions in which cells are grown (embedded or trapped) in dense 3D matrices, as these most closely mimic the adhesive and mechanical properties of natural ECM. Measuring cell proliferation under these conditions is, however, technically more challenging compared with two-dimensional (2D) culture and other “3D culture conditions,” such as growth on top of a matrix (pseudo-3D) or in spongy scaffolds with large pore sizes. Consequently, such measurements are only slowly applied on a wider scale. To advance this, we report on the equal quality (dynamic range, background, linearity) of measuring the proliferation of cell layers embedded in dense 3D matrices (collagen, Matrigel) compared with cells in 2D culture using the easy (one-step) and in 2D well-validated, 2,3-bis-(2-methoxy-4-nitro-5-sulfophenyl)-2H-tetrazolium-5-carboxanilide (XTT)-assay. The comparison stresses the differences in proliferation kinetics and drug sensitivity of matrix-embedded cells versus 2D culture. Using the specific cell-layer-embedded 3D matrix setup, quantitative measurements of cell proliferation and cell invasion are shown to be possible in similar assay conditions, and cytostatic, cytotoxic, and anti-invasive drug effects can thus be reliably determined and compared in physiologically relevant settings. This approach in the 3D matrix holds promise for improving early-stage, high-throughput drug screening, targeting either highly invasive or highly proliferative subpopulations of cancers or both. PMID:22574651

Interesting viewpoints to those who will put Ada into practice

NASA Technical Reports Server (NTRS)

Carlsson, Arne

1986-01-01

Ada will most probably be used as the programming language for computers in the NASA Space Station. It is reasonable to suppose that Ada will be used for at least embedded computers, because the high software costs for these embedded computers were the reason why Ada activities were initiated about ten years ago. The on-board computers are designed for use in space applications, where maintenance by man is impossible. All manipulation of such computers has to be performed in an autonomous way or remote with commands from the ground. In a manned Space Station some maintenance work can be performed by service people on board, but there are still a lot of applications, which require autonomous computers, for example, vital Space Station functions and unmanned orbital transfer vehicles. Those aspect which have come out of the analysis of Ada characteristics together with the experience of requirements for embedded on-board computers in space applications are examined.

Dynamics of influence and social balance in spatially-embedded regular and random networks

NASA Astrophysics Data System (ADS)

Singh, P.; Sreenivasan, S.; Szymanski, B.; Korniss, G.

2015-03-01

Structural balance - the tendency of social relationship triads to prefer specific states of polarity - can be a fundamental driver of beliefs, behavior, and attitudes on social networks. Here we study how structural balance affects deradicalization in an otherwise polarized population of leftists and rightists constituting the nodes of a low-dimensional social network. Specifically, assuming an externally moderating influence that converts leftists or rightists to centrists with probability p, we study the critical value p =pc , below which the presence of metastable mixed population states exponentially delay the achievement of centrist consensus. Above the critical value, centrist consensus is the only fixed point. Complementing our previously shown results for complete graphs, we present results for the process on low-dimensional networks, and show that the low-dimensional embedding of the underlying network significantly affects the critical value of probability p. Intriguingly, on low-dimensional networks, the critical value pc can show non-monotonicity as the dimensionality of the network is varied. We conclude by analyzing the scaling behavior of temporal variation of unbalanced triad density in the network for different low-dimensional network topologies. Supported in part by ARL NS-CTA, ONR, and ARO.

Three-dimensional boundary layers approaching separation

NASA Technical Reports Server (NTRS)

Williams, J. C., III

1976-01-01

The theory of semi-similar solutions of the laminar boundary layer equations is applied to several flows in which the boundary layer approaches a three-dimensional separation line. The solutions obtained are used to deduce the nature of three-dimensional separation. It is shown that in these cases separation is of the "ordinary" type. A solution is also presented for a case in which a vortex is embedded within the three-dimensional boundary layer.

Two-dimensional mesh embedding for Galerkin B-spline methods

NASA Technical Reports Server (NTRS)

Shariff, Karim; Moser, Robert D.

1995-01-01

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

Multispectral embedding-based deep neural network for three-dimensional human pose recovery

NASA Astrophysics Data System (ADS)

Yu, Jialin; Sun, Jifeng

2018-01-01

Monocular image-based three-dimensional (3-D) human pose recovery aims to retrieve 3-D poses using the corresponding two-dimensional image features. Therefore, the pose recovery performance highly depends on the image representations. We propose a multispectral embedding-based deep neural network (MSEDNN) to automatically obtain the most discriminative features from multiple deep convolutional neural networks and then embed their penultimate fully connected layers into a low-dimensional manifold. This compact manifold can explore not only the optimum output from multiple deep networks but also the complementary properties of them. Furthermore, the distribution of each hierarchy discriminative manifold is sufficiently smooth so that the training process of our MSEDNN can be effectively implemented only using few labeled data. Our proposed network contains a body joint detector and a human pose regressor that are jointly trained. Extensive experiments conducted on four databases show that our proposed MSEDNN can achieve the best recovery performance compared with the state-of-the-art methods.

Spectral singularity in composite systems and simulation of a resonant lasing cavity

NASA Astrophysics Data System (ADS)

Zhang, X. Z.; Li, G. R.; Song, Z.

2017-10-01

We investigate herein the existence of spectral singularities (SSs) in composite systems that consist of two separate scattering centers A and B embedded in one-dimensional free space, with at least one scattering center being non-Hermitian. We show that such composite systems have an SS at kc if the reflection amplitudes rA≤ft(kc\\right) and rB≤ft(kc\\right) of the two scattering centers satisfy the condition rR A≤ft(kc\\right) rLB≤ft(kc\\right) ei2kc≤ft(xB-xA\\right) =1 . We also extend the condition to the system with multi-scattering centers. As an application, we construct a simple system to simulate a resonant lasing cavity.

Spin-gapless and half-metallic ferromagnetism in potassium and calcium δ-doped GaN digital magnetic heterostructures for possible spintronic applications: insights from first principles

NASA Astrophysics Data System (ADS)

Du, Jiangtao; Dong, Shengjie; Zhou, Baozeng; Zhao, Hui; Feng, Liefeng

2017-04-01

The reports previously issued predominantly paid attention to the d-block magnetic elements δ-doped digital magnetic materials. In this work, GaN δ-doped with non-magnetic main group s-block elements K and Ca as digital magnetic heterostructures were purposed and explored theoretically. We found that K- and Ca-embedded GaN digital alloys exhibit spin-gapless and half-metallic ferromagnetic characteristics, respectively. All compounds obey the Slater-Pauling rule with diverse electronic and magnetic properties. For these digital ferromagnetic heterostructures, spin polarization occurs in nitrogen within a confined space around the δ-doped layer, demonstrating a hole-mediated two-dimensional magnetic phenomenon.

Embedding methods for the steady Euler equations

NASA Technical Reports Server (NTRS)

Chang, S. H.; Johnson, G. M.

1983-01-01

An approach to the numerical solution of the steady Euler equations is to embed the first-order Euler system in a second-order system and then to recapture the original solution by imposing additional boundary conditions. Initial development of this approach and computational experimentation with it were previously based on heuristic physical reasoning. This has led to the construction of a relaxation procedure for the solution of two-dimensional steady flow problems. The theoretical justification for the embedding approach is addressed. It is proven that, with the appropriate choice of embedding operator and additional boundary conditions, the solution to the embedded system is exactly the one to the original Euler equations. Hence, solving the embedded version of the Euler equations will not produce extraneous solutions.

Embedding of multidimensional time-dependent observations.

PubMed

Barnard, J P; Aldrich, C; Gerber, M

2001-10-01

A method is proposed to reconstruct dynamic attractors by embedding of multivariate observations of dynamic nonlinear processes. The Takens embedding theory is combined with independent component analysis to transform the embedding into a vector space of linearly independent vectors (phase variables). The method is successfully tested against prediction of the unembedded state vector in two case studies of simulated chaotic processes.

Embedding of multidimensional time-dependent observations

NASA Astrophysics Data System (ADS)

Barnard, Jakobus P.; Aldrich, Chris; Gerber, Marius

2001-10-01

A method is proposed to reconstruct dynamic attractors by embedding of multivariate observations of dynamic nonlinear processes. The Takens embedding theory is combined with independent component analysis to transform the embedding into a vector space of linearly independent vectors (phase variables). The method is successfully tested against prediction of the unembedded state vector in two case studies of simulated chaotic processes.

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.

Emergence of energy dependence in the fragmentation of heterogeneous materials

NASA Astrophysics Data System (ADS)

Pál, Gergő; Varga, Imre; Kun, Ferenc

2014-12-01

The most important characteristics of the fragmentation of heterogeneous solids is that the mass (size) distribution of pieces is described by a power law functional form. The exponent of the distribution displays a high degree of universality depending mainly on the dimensionality and on the brittle-ductile mechanical response of the system. Recently, experiments and computer simulations have reported an energy dependence of the exponent increasing with the imparted energy. These novel findings question the phase transition picture of fragmentation phenomena, and have also practical importance for industrial applications. Based on large scale computer simulations here we uncover a robust mechanism which leads to the emergence of energy dependence in fragmentation processes resolving controversial issues on the problem: studying the impact induced breakup of platelike objects with varying thickness in three dimensions we show that energy dependence occurs when a lower dimensional fragmenting object is embedded into a higher dimensional space. The reason is an underlying transition between two distinct fragmentation mechanisms controlled by the impact velocity at low plate thicknesses, while it is hindered for three-dimensional bulk systems. The mass distributions of the subsets of fragments dominated by the two cracking mechanisms proved to have an astonishing robustness at all plate thicknesses, which implies that the nonuniversality of the complete mass distribution is the consequence of blending the contributions of universal partial processes.

Augmented design and analysis of computer experiments: a novel tolerance embedded global optimization approach applied to SWIR hyperspectral illumination design.

PubMed

Keresztes, Janos C; John Koshel, R; D'huys, Karlien; De Ketelaere, Bart; Audenaert, Jan; Goos, Peter; Saeys, Wouter

2016-12-26

A novel meta-heuristic approach for minimizing nonlinear constrained problems is proposed, which offers tolerance information during the search for the global optimum. The method is based on the concept of design and analysis of computer experiments combined with a novel two phase design augmentation (DACEDA), which models the entire merit space using a Gaussian process, with iteratively increased resolution around the optimum. The algorithm is introduced through a series of cases studies with increasing complexity for optimizing uniformity of a short-wave infrared (SWIR) hyperspectral imaging (HSI) illumination system (IS). The method is first demonstrated for a two-dimensional problem consisting of the positioning of analytical isotropic point sources. The method is further applied to two-dimensional (2D) and five-dimensional (5D) SWIR HSI IS versions using close- and far-field measured source models applied within the non-sequential ray-tracing software FRED, including inherent stochastic noise. The proposed method is compared to other heuristic approaches such as simplex and simulated annealing (SA). It is shown that DACEDA converges towards a minimum with 1 % improvement compared to simplex and SA, and more importantly requiring only half the number of simulations. Finally, a concurrent tolerance analysis is done within DACEDA for to the five-dimensional case such that further simulations are not required.

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.

Spatial network surrogates for disentangling complex system structure from spatial embedding of nodes

NASA Astrophysics Data System (ADS)

Wiedermann, Marc; Donges, Jonathan F.; Kurths, Jürgen; Donner, Reik V.

2016-04-01

Networks with nodes embedded in a metric space have gained increasing interest in recent years. The effects of spatial embedding on the networks' structural characteristics, however, are rarely taken into account when studying their macroscopic properties. Here, we propose a hierarchy of null models to generate random surrogates from a given spatially embedded network that can preserve certain global and local statistics associated with the nodes' embedding in a metric space. Comparing the original network's and the resulting surrogates' global characteristics allows one to quantify to what extent these characteristics are already predetermined by the spatial embedding of the nodes and links. We apply our framework to various real-world spatial networks and show that the proposed models capture macroscopic properties of the networks under study much better than standard random network models that do not account for the nodes' spatial embedding. Depending on the actual performance of the proposed null models, the networks are categorized into different classes. Since many real-world complex networks are in fact spatial networks, the proposed approach is relevant for disentangling the underlying complex system structure from spatial embedding of nodes in many fields, ranging from social systems over infrastructure and neurophysiology to climatology.

Visually Lossless Data Compression for Real-Time Frame/Pushbroom Space Science Imagers

NASA Technical Reports Server (NTRS)

Yeh, Pen-Shu; Venbrux, Jack; Bhatia, Prakash; Miller, Warner H.

2000-01-01

A visually lossless data compression technique is currently being developed for space science applications under the requirement of high-speed push-broom scanning. The technique is also applicable to frame based imaging and is error-resilient in that error propagation is contained within a few scan lines. The algorithm is based on a block transform of a hybrid of modulated lapped transform (MLT) and discrete cosine transform (DCT), or a 2-dimensional lapped transform, followed by bit-plane encoding; this combination results in an embedded bit string with exactly the desirable compression rate as desired by the user. The approach requires no unique table to maximize its performance. The compression scheme performs well on a suite of test images typical of images from spacecraft instruments. Flight qualified hardware implementations are in development; a functional chip set is expected by the end of 2001. The chip set is being designed to compress data in excess of 20 Msamples/sec and support quantizations from 2 to 16 bits.

Diverse power iteration embeddings: Theory and practice

DOE PAGES

Huang, Hao; Yoo, Shinjae; Yu, Dantong; ...

2015-11-09

Manifold learning, especially spectral embedding, is known as one of the most effective learning approaches on high dimensional data, but for real-world applications it raises a serious computational burden in constructing spectral embeddings for large datasets. To overcome this computational complexity, we propose a novel efficient embedding construction, Diverse Power Iteration Embedding (DPIE). DPIE shows almost the same effectiveness of spectral embeddings and yet is three order of magnitude faster than spectral embeddings computed from eigen-decomposition. Our DPIE is unique in that (1) it finds linearly independent embeddings and thus shows diverse aspects of dataset; (2) the proposed regularized DPIEmore » is effective if we need many embeddings; (3) we show how to efficiently orthogonalize DPIE if one needs; and (4) Diverse Power Iteration Value (DPIV) provides the importance of each DPIE like an eigen value. As a result, such various aspects of DPIE and DPIV ensure that our algorithm is easy to apply to various applications, and we also show the effectiveness and efficiency of DPIE on clustering, anomaly detection, and feature selection as our case studies.« less

Evidencing Learning Outcomes: A Multi-Level, Multi-Dimensional Course Alignment Model

ERIC Educational Resources Information Center

Sridharan, Bhavani; Leitch, Shona; Watty, Kim

2015-01-01

This conceptual framework proposes a multi-level, multi-dimensional course alignment model to implement a contextualised constructive alignment of rubric design that authentically evidences and assesses learning outcomes. By embedding quality control mechanisms at each level for each dimension, this model facilitates the development of an aligned…

Surface Control of Actuated Hybrid Space Mirrors

DTIC Science & Technology

2010-10-01

precision Nanolaminate foil facesheet and Silicon Carbide ( SiC ) substrate embedded with electroactive ceramic actuators. Wavefront sensors are used to...integrate precision Nanolaminate foil facesheet with Silicon Carbide ( SiC ) substrate equipped with embedded electroactive ceramic actuators...IAC-10.C2.5.8 SURFACE CONTROL OF ACTUATED HYBRID SPACE MIRRORS Brij. N. Agrawal Naval Postgraduate School, Monterey, CA, 93943, agrawal

Experience with 3-D composite grids

NASA Technical Reports Server (NTRS)

Benek, J. A.; Donegan, T. L.; Suhs, N. E.

1987-01-01

Experience with the three-dimensional (3-D), chimera grid embedding scheme is described. Applications of the inviscid version to a multiple-body configuration, a wind/body/tail configuration, and an estimate of wind tunnel wall interference are described. Applications to viscous flows include a 3-D cavity and another multi-body configuration. A variety of grid generators is used, and several embedding strategies are described.

Electronic structure and orientation relationship of Li nanoclusters embedded in MgO studied by depth-selective positron annihilation two-dimensional angular correlation

NASA Astrophysics Data System (ADS)

Falub, C. V.; Mijnarends, P. E.; Eijt, S. W.; van Huis, M. A.; van Veen, A.; Schut, H.

2002-08-01

Quantum-confined positrons are sensitive probes for determining the electronic structure of nanoclusters embedded in materials. In this work, a depth-selective positron annihilation 2D-ACAR (two-dimensional angular correlation of annihilation radiation) method is used to determine the electronic structure of Li nanoclusters formed by implantation of 1016-cm-2 30-keV 6Li ions in MgO (100) and (110) crystals and by subsequent annealing at 950 K. Owing to the difference between the positron affinities of lithium and MgO, the Li nanoclusters act as quantum dots for positrons. 2D-ACAR distributions for different projections reveal a semicoherent fitting of the embedded metallic Li nanoclusters to the host MgO lattice. Ab initio Korringa-Kohn-Rostoker calculations of the momentum density show that the anisotropies of the experimental distributions are consistent with an fcc crystal structure of the Li nanoclusters. The observed reduction of the width of the experimental 2D-ACAR distribution is attributed to positron trapping in vacancies associated with Li clusters. This work proposes a method for studying the electronic structure of metallic quantum dots embedded in an insulating material.

Internship Abstract and Final Reflection

NASA Technical Reports Server (NTRS)

Sandor, Edward

2016-01-01

The primary objective for this internship is the evaluation of an embedded natural language processor (NLP) as a way to introduce voice control into future space suits. An embedded natural language processor would provide an astronaut hands-free control for making adjustments to the environment of the space suit and checking status of consumables procedures and navigation. Additionally, the use of an embedded NLP could potentially reduce crew fatigue, increase the crewmember's situational awareness during extravehicular activity (EVA) and improve the ability to focus on mission critical details. The use of an embedded NLP may be valuable for other human spaceflight applications desiring hands-free control as well. An embedded NLP is unique because it is a small device that performs language tasks, including speech recognition, which normally require powerful processors. The dedicated device could perform speech recognition locally with a smaller form-factor and lower power consumption than traditional methods.

Three-dimensional localization and optical imaging of objects in turbid media with independent component analysis.

PubMed

Xu, M; Alrubaiee, M; Gayen, S K; Alfano, R R

2005-04-01

A new approach for optical imaging and localization of objects in turbid media that makes use of the independent component analysis (ICA) from information theory is demonstrated. Experimental arrangement realizes a multisource illumination of a turbid medium with embedded objects and a multidetector acquisition of transmitted light on the medium boundary. The resulting spatial diversity and multiple angular observations provide robust data for three-dimensional localization and characterization of absorbing and scattering inhomogeneities embedded in a turbid medium. ICA of the perturbations in the spatial intensity distribution on the medium boundary sorts out the embedded objects, and their locations are obtained from Green's function analysis based on any appropriate light propagation model. Imaging experiments were carried out on two highly scattering samples of thickness approximately 50 times the transport mean-free path of the respective medium. One turbid medium had two embedded absorptive objects, and the other had four scattering objects. An independent component separation of the signal, in conjunction with diffusive photon migration theory, was used to locate the embedded inhomogeneities. In both cases, improved lateral and axial localizations of the objects over the result obtained by use of common photon migration reconstruction algorithms were achieved. The approach is applicable to different medium geometries, can be used with any suitable photon propagation model, and is amenable to near-real-time imaging applications.

Spectral embedding finds meaningful (relevant) structure in image and microarray data

PubMed Central

Higgs, Brandon W; Weller, Jennifer; Solka, Jeffrey L

2006-01-01

Background Accurate methods for extraction of meaningful patterns in high dimensional data have become increasingly important with the recent generation of data types containing measurements across thousands of variables. Principal components analysis (PCA) is a linear dimensionality reduction (DR) method that is unsupervised in that it relies only on the data; projections are calculated in Euclidean or a similar linear space and do not use tuning parameters for optimizing the fit to the data. However, relationships within sets of nonlinear data types, such as biological networks or images, are frequently mis-rendered into a low dimensional space by linear methods. Nonlinear methods, in contrast, attempt to model important aspects of the underlying data structure, often requiring parameter(s) fitting to the data type of interest. In many cases, the optimal parameter values vary when different classification algorithms are applied on the same rendered subspace, making the results of such methods highly dependent upon the type of classifier implemented. Results We present the results of applying the spectral method of Lafon, a nonlinear DR method based on the weighted graph Laplacian, that minimizes the requirements for such parameter optimization for two biological data types. We demonstrate that it is successful in determining implicit ordering of brain slice image data and in classifying separate species in microarray data, as compared to two conventional linear methods and three nonlinear methods (one of which is an alternative spectral method). This spectral implementation is shown to provide more meaningful information, by preserving important relationships, than the methods of DR presented for comparison. Tuning parameter fitting is simple and is a general, rather than data type or experiment specific approach, for the two datasets analyzed here. Tuning parameter optimization is minimized in the DR step to each subsequent classification method, enabling the possibility of valid cross-experiment comparisons. Conclusion Results from the spectral method presented here exhibit the desirable properties of preserving meaningful nonlinear relationships in lower dimensional space and requiring minimal parameter fitting, providing a useful algorithm for purposes of visualization and classification across diverse datasets, a common challenge in systems biology. PMID:16483359

Polyhedra and packings from hyperbolic honeycombs.

PubMed

Pedersen, Martin Cramer; Hyde, Stephen T

2018-06-20

We derive more than 80 embeddings of 2D hyperbolic honeycombs in Euclidean 3 space, forming 3-periodic infinite polyhedra with cubic symmetry. All embeddings are "minimally frustrated," formed by removing just enough isometries of the (regular, but unphysical) 2D hyperbolic honeycombs [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] to allow embeddings in Euclidean 3 space. Nearly all of these triangulated "simplicial polyhedra" have symmetrically identical vertices, and most are chiral. The most symmetric examples include 10 infinite "deltahedra," with equilateral triangular faces, 6 of which were previously unknown and some of which can be described as packings of Platonic deltahedra. We describe also related cubic crystalline packings of equal hyperbolic discs in 3 space that are frustrated analogues of optimally dense hyperbolic disc packings. The 10-coordinated packings are the least "loosened" Euclidean embeddings, although frustration swells all of the hyperbolic disc packings to give less dense arrays than the flat penny-packing even though their unfrustrated analogues in [Formula: see text] are denser.

The embedding problem in topological dynamics and Takens’ theorem

NASA Astrophysics Data System (ADS)

Gutman, Yonatan; Qiao, Yixiao; Szabó, Gábor

2018-02-01

We prove that every {Z}k -action (X, {Z}k, T) of mean dimension less than D/2 admitting a factor (Y, {Z}k, S) of Rokhlin dimension not greater than L embeds in (([0, 1](L+1)D){\\hspace{0pt}}{Zk}× Y, σ× S) , where D\\in{N} , L\\in{N}\\cup\\{0\\} and σ is the shift on the Hilbert cube ([0, 1](L+1)D){\\hspace{0pt}}{Zk} ; in particular, when (Y, {Z}k, S) is an irrational {Z}k -rotation on the k-torus, (X, {Z}k, T) embeds in (([0, 1]2^kD+1){\\hspace{0pt}}{Z^k}, σ) , which is compared to a previous result in Gutman, Lindenstrauss and Tsukamoto (2016 Geom. Funct. Anal. 3 778-817). Moreover, we give a complete and detailed proof of Takens’ embedding theorem with a continuous observable for {Z} -actions and deduce the analogous result for {Z}k -actions. Lastly, we show that the Lindenstrauss-Tsukamoto conjecture for {Z} -actions holds generically, discuss an analogous conjecture for {Z}k -actions in Gutman, Qiao and Tsukamoto (2017 arXiv:1709.00125) and verify it for {Z}k -actions on finite dimensional spaces.

New potentials for conformal mechanics

NASA Astrophysics Data System (ADS)

Papadopoulos, G.

2013-04-01

We find under some mild assumptions that the most general potential of one-dimensional conformal systems with time-independent couplings is expressed as V = V0 + V1, where V0 is a homogeneous function with respect to a homothetic motion in configuration space and V1 is determined from an equation with source a homothetic potential. Such systems admit at most an SL(2,{R}) conformal symmetry which, depending on the couplings, is embedded in {Diff}({R}) in three different ways. In one case, SL(2,{R}) is also embedded in Diff(S1). Examples of such models include those with potential V = αx2 + βx-2 for arbitrary couplings α and β, the Calogero models with harmonic oscillator couplings and nonlinear models with suitable metrics and potentials. In addition, we give the conditions on the couplings for a class of gauge theories to admit a SL(2,{R}) conformal symmetry. We present examples of such systems with general gauge groups and global symmetries that include the isometries of AdS2 × S3 and AdS2 × S3 × S3 which arise as backgrounds in AdS2/CFT1.

Three-dimensional tracking of Cuvier's beaked whales' echolocation sounds using nested hydrophone arrays.

PubMed

Gassmann, Martin; Wiggins, Sean M; Hildebrand, John A

2015-10-01

Cuvier's beaked whales (Ziphius cavirostris) were tracked using two volumetric small-aperture (∼1 m element spacing) hydrophone arrays, embedded into a large-aperture (∼1 km element spacing) seafloor hydrophone array of five nodes. This array design can reduce the minimum number of nodes that are needed to record the arrival of a strongly directional echolocation sound from 5 to 2, while providing enough time-differences of arrivals for a three-dimensional localization without depending on any additional information such as multipath arrivals. To illustrate the capabilities of this technique, six encounters of up to three Cuvier's beaked whales were tracked over a two-month recording period within an area of 20 km(2) in the Southern California Bight. Encounter periods ranged from 11 min to 33 min. Cuvier's beaked whales were found to reduce the time interval between echolocation clicks while alternating between two inter-click-interval regimes during their descent towards the seafloor. Maximum peak-to-peak source levels of 179 and 224 dB re 1 μPa @ 1 m were estimated for buzz sounds and on-axis echolocation clicks (directivity index = 30 dB), respectively. Source energy spectra of the on-axis clicks show significant frequency components between 70 and 90 kHz, in addition to their typically noted FM upsweep at 40-60 kHz.

Neural Representation of Spatial Topology in the Rodent Hippocampus

PubMed Central

Chen, Zhe; Gomperts, Stephen N.; Yamamoto, Jun; Wilson, Matthew A.

2014-01-01

Pyramidal cells in the rodent hippocampus often exhibit clear spatial tuning in navigation. Although it has been long suggested that pyramidal cell activity may underlie a topological code rather than a topographic code, it remains unclear whether an abstract spatial topology can be encoded in the ensemble spiking activity of hippocampal place cells. Using a statistical approach developed previously, we investigate this question and related issues in greater details. We recorded ensembles of hippocampal neurons as rodents freely foraged in one and two-dimensional spatial environments, and we used a “decode-to-uncover” strategy to examine the temporally structured patterns embedded in the ensemble spiking activity in the absence of observed spatial correlates during periods of rodent navigation or awake immobility. Specifically, the spatial environment was represented by a finite discrete state space. Trajectories across spatial locations (“states”) were associated with consistent hippocampal ensemble spiking patterns, which were characterized by a state transition matrix. From this state transition matrix, we inferred a topology graph that defined the connectivity in the state space. In both one and two-dimensional environments, the extracted behavior patterns from the rodent hippocampal population codes were compared against randomly shuffled spike data. In contrast to a topographic code, our results support the efficiency of topological coding in the presence of sparse sample size and fuzzy space mapping. This computational approach allows us to quantify the variability of ensemble spiking activity, to examine hippocampal population codes during off-line states, and to quantify the topological complexity of the environment. PMID:24102128

Homo-endotaxial one-dimensional Si nanostructures

DOE PAGES

Song, Jiaming; Hudak, Bethany M.; Sims, Hunter; ...

2017-11-29

One-dimensional (1D) nanostructures are highly sought after, both for their novel electronic properties as well as for their improved functionality. However, due to their nanoscale dimensions, these properties are significantly affected by the environment in which they are embedded. Here in this paper, we report on the creation of 1D homo-endotaxial Si nanostructures, i.e. 1D Si nanostructures with a lattice structure that is uniquely different from the Si diamond lattice in which they are embedded. We use scanning tunneling microscopy and spectroscopy, scanning transmission electron microscopy, density functional theory, and conductive atomic force microscopy to elucidate their formation and properties.more » Depending on kinetic constraints during growth, they can be prepared as endotaxial 1D Si nanostructures completely embedded in crystalline Si, or underneath a stripe of amorphous Si containing a large concentration of Bi atoms. Lastly, these homo-endotaxial 1D Si nanostructures have the potential to be useful components in nanoelectronic devices based on the technologically mature Si platform.« less

Holographic renormalization group and cosmology in theories with quasilocalized gravity

NASA Astrophysics Data System (ADS)

Csáki, Csaba; Erlich, Joshua; Hollowood, Timothy J.; Terning, John

2001-03-01

We study the long distance behavior of brane theories with quasilocalized gravity. The five-dimensional (5D) effective theory at large scales follows from a holographic renormalization group flow. As intuitively expected, the graviton is effectively four dimensional at intermediate scales and becomes five dimensional at large scales. However, in the holographic effective theory the essentially 4D radion dominates at long distances and gives rise to scalar antigravity. The holographic description shows that at large distances the Gregory-Rubakov-Sibiryakov (GRS) model is equivalent to the model recently proposed by Dvali, Gabadadze, and Porrati (DGP), where a tensionless brane is embedded into 5D Minkowski space, with an additional induced 4D Einstein-Hilbert term on the brane. In the holographic description the radion of the GRS model is automatically localized on the tensionless brane, and provides the ghostlike field necessary to cancel the extra graviton polarization of the DGP model. Thus, there is a holographic duality between these theories. This analysis provides physical insight into how the GRS model works at intermediate scales; in particular it sheds light on the size of the width of the graviton resonance, and also demonstrates how the holographic renormalization group can be used as a practical tool for calculations.

Holographic renormalization group and cosmology in theories with quasilocalized gravity

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

Csaki, Csaba; Erlich, Joshua; Hollowood, Timothy J.

2001-03-15

We study the long distance behavior of brane theories with quasilocalized gravity. The five-dimensional (5D) effective theory at large scales follows from a holographic renormalization group flow. As intuitively expected, the graviton is effectively four dimensional at intermediate scales and becomes five dimensional at large scales. However, in the holographic effective theory the essentially 4D radion dominates at long distances and gives rise to scalar antigravity. The holographic description shows that at large distances the Gregory-Rubakov-Sibiryakov (GRS) model is equivalent to the model recently proposed by Dvali, Gabadadze, and Porrati (DGP), where a tensionless brane is embedded into 5D Minkowskimore » space, with an additional induced 4D Einstein-Hilbert term on the brane. In the holographic description the radion of the GRS model is automatically localized on the tensionless brane, and provides the ghostlike field necessary to cancel the extra graviton polarization of the DGP model. Thus, there is a holographic duality between these theories. This analysis provides physical insight into how the GRS model works at intermediate scales; in particular it sheds light on the size of the width of the graviton resonance, and also demonstrates how the holographic renormalization group can be used as a practical tool for calculations.« less

Density-based clustering: A 'landscape view' of multi-channel neural data for inference and dynamic complexity analysis.

PubMed

Baglietto, Gabriel; Gigante, Guido; Del Giudice, Paolo

2017-01-01

Two, partially interwoven, hot topics in the analysis and statistical modeling of neural data, are the development of efficient and informative representations of the time series derived from multiple neural recordings, and the extraction of information about the connectivity structure of the underlying neural network from the recorded neural activities. In the present paper we show that state-space clustering can provide an easy and effective option for reducing the dimensionality of multiple neural time series, that it can improve inference of synaptic couplings from neural activities, and that it can also allow the construction of a compact representation of the multi-dimensional dynamics, that easily lends itself to complexity measures. We apply a variant of the 'mean-shift' algorithm to perform state-space clustering, and validate it on an Hopfield network in the glassy phase, in which metastable states are largely uncorrelated from memories embedded in the synaptic matrix. In this context, we show that the neural states identified as clusters' centroids offer a parsimonious parametrization of the synaptic matrix, which allows a significant improvement in inferring the synaptic couplings from the neural activities. Moving to the more realistic case of a multi-modular spiking network, with spike-frequency adaptation inducing history-dependent effects, we propose a procedure inspired by Boltzmann learning, but extending its domain of application, to learn inter-module synaptic couplings so that the spiking network reproduces a prescribed pattern of spatial correlations; we then illustrate, in the spiking network, how clustering is effective in extracting relevant features of the network's state-space landscape. Finally, we show that the knowledge of the cluster structure allows casting the multi-dimensional neural dynamics in the form of a symbolic dynamics of transitions between clusters; as an illustration of the potential of such reduction, we define and analyze a measure of complexity of the neural time series.

Transformation diffusion reconstruction of three-dimensional histology volumes from two-dimensional image stacks.

PubMed

Casero, Ramón; Siedlecka, Urszula; Jones, Elizabeth S; Gruscheski, Lena; Gibb, Matthew; Schneider, Jürgen E; Kohl, Peter; Grau, Vicente

2017-05-01

Traditional histology is the gold standard for tissue studies, but it is intrinsically reliant on two-dimensional (2D) images. Study of volumetric tissue samples such as whole hearts produces a stack of misaligned and distorted 2D images that need to be reconstructed to recover a congruent volume with the original sample's shape. In this paper, we develop a mathematical framework called Transformation Diffusion (TD) for stack alignment refinement as a solution to the heat diffusion equation. This general framework does not require contour segmentation, is independent of the registration method used, and is trivially parallelizable. After the first stack sweep, we also replace registration operations by operations in the space of transformations, several orders of magnitude faster and less memory-consuming. Implementing TD with operations in the space of transformations produces our Transformation Diffusion Reconstruction (TDR) algorithm, applicable to general transformations that are closed under inversion and composition. In particular, we provide formulas for translation and affine transformations. We also propose an Approximated TDR (ATDR) algorithm that extends the same principles to tensor-product B-spline transformations. Using TDR and ATDR, we reconstruct a full mouse heart at pixel size 0.92µm×0.92µm, cut 10µm thick, spaced 20µm (84G). Our algorithms employ only local information from transformations between neighboring slices, but the TD framework allows theoretical analysis of the refinement as applying a global Gaussian low-pass filter to the unknown stack misalignments. We also show that reconstruction without an external reference produces large shape artifacts in a cardiac specimen while still optimizing slice-to-slice alignment. To overcome this problem, we use a pre-cutting blockface imaging process previously developed by our group that takes advantage of Brewster's angle and a polarizer to capture the outline of only the topmost layer of wax in the block containing embedded tissue for histological sectioning. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

(2+1)-dimensional stars

NASA Astrophysics Data System (ADS)

Lubo, M.; Rooman, M.; Spindel, Ph.

1999-02-01

We investigate, in the framework of (2+1)-dimensional gravity, stationary rotationally symmetric gravitational sources of the perfect fluid type, embedded in a space of an arbitrary cosmological constant. We show that the matching conditions between the interior and exterior geometries imply restrictions on the physical parameters of the solutions. In particular, imposing finite sources and the absence of closed timelike curves privileges negative values of the cosmological constant, yielding exterior vacuum geometries of rotating black hole type. In the special case of static sources, we prove the complete integrability of the field equations and show that the sources' masses are bounded from above and, for a vanishing cosmological constant, generally equal to 1. We also discuss and illustrate the stationary configurations by explicitly solving the field equations for constant mass-energy densities. If the pressure vanishes, we recover as interior geometries Gödel-like metrics defined on causally well behaved domains, but with unphysical values of the mass to angular momentum ratio. The introduction of pressure in the sources cures the latter problem and leads to physically more relevant models.

Space-time evolution of cataclasis in carbonate fault zones

NASA Astrophysics Data System (ADS)

Ferraro, Francesco; Grieco, Donato Stefano; Agosta, Fabrizio; Prosser, Giacomo

2018-05-01

The present contribution focuses on the micro-mechanisms associated to cataclasis of both calcite- and dolomite-rich fault rocks. This work combines field and laboratory data of carbonate fault cores currently exposed in central and southern Italy. By first deciphering the main fault rock textures, their spatial distribution, crosscutting relationships and multi-scale dimensional properties, the relative timing of Intragranular Extensional Fracturing (IEF), chipping, and localized shear is inferred. IEF was predominant within already fractured carbonates, forming coarse and angular rock fragments, and likely lasted for a longer period within the dolomitic fault rocks. Chipping occurred in both lithologies, and was activated by grain rolling forming minute, sub-rounded survivor grains embedded in a powder-like carbonate matrix. The largest fault zones, which crosscut either limestones or dolostones, were subjected to localized shear and, eventually, to flash temperature increase which caused thermal decomposition of calcite within narrow (cm-thick) slip zones. Results are organized in a synoptic panel including the main dimensional properties of survivor grains. Finally, a conceptual model of the time-dependent evolution of cataclastic deformation in carbonate rocks is proposed.

Dispersion and decay rate of exciton-polaritons and radiative modes in transition metal dichalcogenide monolayers

NASA Astrophysics Data System (ADS)

Alpeggiani, Filippo; Gong, Su-Hyun; Kuipers, L.

2018-05-01

The two-dimensional excitons of transition metal dichalcogenide (TMDC) monolayers make these materials extremely promising for optical and optoelectronic applications. When the excitons interact with the electromagnetic field, they will give rise to exciton-polaritons, i.e., modes that propagate in the material plane while being confined in the out-of-plane direction. In this work, we derive the characteristic equations that determine both radiative and polaritonic modes in TMDC monolayers and we analyze the dispersion and decay rate of the modes. The condition for the existence of exciton-polaritons can be described in terms of a strong-coupling regime for the interaction between the exciton and the three-dimensional continuum of free-space electromagnetic modes. We show that the threshold for the strong-coupling regime critically depends on the interplay between nonradiative losses and the dielectric function imbalance at the two sides of the monolayer. Our results illustrate that a fine control of the dielectric function of the embedding media is essential for realizing exciton-polaritons in the strong-coupling regime.

Taub-NUT Spacetime in the (A)dS/CFT and M-Theory [electronic resource

NASA Astrophysics Data System (ADS)

Clarkson, Richard

In the following thesis, I will conduct a thermodynamic analysis of the Taub-NUT spacetime in various dimensions, as well as show uses for Taub-NUT and other Hyper-Kahler spacetimes. Thermodynamic analysis (by which I mean the calculation of the entropy and other thermodynamic quantities, and the analysis of these quantities) has in the past been done by use of background subtraction. The recent derivation of the (A)dS/CFT correspondences from String theory has allowed for easier and quicker analysis. I will use Taub-NUT space as a template to test these correspondences against the standard thermodynamic calculations (via the N?ether method), with (in the Taub-NUT-dS case especially) some very interesting results. There is also interest in obtaining metrics in eleven dimensions that can be reduced down to ten dimensional string theory metrics. Taub-NUT and other Hyper-Kahler metrics already possess the form to easily facilitate the Kaluza-Klein reduction, and embedding such metricsinto eleven dimensional metrics containing M2 or M5 branes produces metrics with interesting Dp-brane results.

Intangible pointlike tracers for liquid-crystal-based microsensors

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

Brasselet, Etienne; Juodkazis, Saulius

2010-12-15

We propose an optical detection technique for liquid-crystal-based sensors that is based on polarization-resolved tracking of optical singularities and does not rely on standard observation of light-intensity changes caused by modifications of the liquid crystal orientational ordering. It uses a natural two-dimensional network of polarization singularities embedded in the transverse cross section of a probe beam that passes through a liquid crystal sample, in our case, a nematic droplet held in laser tweezers. The identification and spatial evolution of such a topological fingerprint is retrieved from subwavelength polarization-resolved imaging, and the mechanical constraint exerted on the molecular ordering by themore » trapping beam itself is chosen as the control parameter. By restricting our analysis to one type of point singularity, C points, which correspond to location in space where the polarization azimuth is undefined, we show that polarization singularities appear as intangible pointlike tracers for liquid-crystal-based three-dimensional microsensors. The method has a superresolution potential and can be used to visualize changes at the nanoscale.« less

REVIEWS OF TOPICAL PROBLEMS: Cosmological branes and macroscopic extra dimensions

NASA Astrophysics Data System (ADS)

Barvinsky, Andrei O.

2005-06-01

The idea of adding extra dimensions to the physical world — thus making the observable universe a timelike surface (or brane) embedded in a higher-dimensional space-time — is briefly reviewed, which is believed to hold serious promise for solving fundamental problems concerning the hierarchy of physical interactions and the cosmological constant. Brane localization of massless gravitons is discussed as a mechanism leading to the effective four-dimensional Einstein gravity theory on the brane in the low-energy limit. It is shown that this mechanism is a corollary of the AdS/CFT correspondence principle well-known from string theory. Inflation and other cosmological evolution scenarios induced by the local and nonlocal structures of the effective action of the gravitational brane are considered, as are the effects that enable the developing gravitational-wave astronomy to be used in the search for extra dimensions. Finally, a new approach to the cosmological constant and cosmological acceleration problems is discussed, which involves variable local and nonlocal gravitational 'constants' arising in the infrared modifications of the Einstein theory that incorporate brane-induced gravity models and models of massive gravitons.

Loop Quantization and Symmetry: Configuration Spaces

NASA Astrophysics Data System (ADS)

Fleischhack, Christian

2018-06-01

Given two sets S 1, S 2 and unital C *-algebras A_1, A_2 of functions thereon, we show that a map {σ : {S}_1 \\longrightarrow {S}_2} can be lifted to a continuous map \\barσ : spec A_1 \\longrightarrow spec A_2 iff σ^\\ast A_2 := σ^\\ast f | f \\in A_2 \\subseteq A_1. Moreover, \\bar σ is unique if existing, and injective iff σ^\\ast A_2 is dense. Then, we apply these results to loop quantum gravity and loop quantum cosmology. For all usual technical conventions, we decide whether the cosmological quantum configuration space is embedded into the gravitational one; indeed, both are spectra of some C *-algebras, say, A_cosm and A_grav, respectively. Typically, there is no embedding, but one can always get an embedding by the defining A_cosm := C^\\ast(σ^\\ast A_grav), where {σ} denotes the embedding between the classical configuration spaces. Finally, we explicitly determine {C^\\ast(σ^\\ast A_grav) in the homogeneous isotropic case for A_grav generated by the matrix functions of parallel transports along analytic paths. The cosmological quantum configuration space so equals the disjoint union of R and the Bohr compactification of R, appropriately glued together.

Loop Quantization and Symmetry: Configuration Spaces

NASA Astrophysics Data System (ADS)

Fleischhack, Christian

2018-04-01

Given two sets S 1, S 2 and unital C *-algebras A_1, A_2 of functions thereon, we show that a map σ : S_1 \\longrightarrow S_2 can be lifted to a continuous map \\barσ : spec A_1 \\longrightarrow spec A_2 iff σ^\\ast A_2 := σ^\\ast f | f \\in A_2 \\subseteq A_1. Moreover, \\bar σ is unique if existing, and injective iff {σ^\\ast A_2 is dense. Then, we apply these results to loop quantum gravity and loop quantum cosmology. For all usual technical conventions, we decide whether the cosmological quantum configuration space is embedded into the gravitational one; indeed, both are spectra of some C *-algebras, say, A_cosm and A_grav, respectively. Typically, there is no embedding, but one can always get an embedding by the defining A_cosm := C^\\ast(σ^\\ast A_grav), where σ denotes the embedding between the classical configuration spaces. Finally, we explicitly determine C^\\ast(σ^\\ast A_grav) in the homogeneous isotropic case for A_grav generated by the matrix functions of parallel transports along analytic paths. The cosmological quantum configuration space so equals the disjoint union of R and the Bohr compactification of R , appropriately glued together.

Multi-photon microfabrication of three-dimensional capillary-scale vascular networks

NASA Astrophysics Data System (ADS)

Skylar-Scott, Mark A.; Liu, Man-Chi; Wu, Yuelong; Yanik, Mehmet Fatih

2017-02-01

Biomimetic models of microvasculature could enable assays of complex cellular behavior at the capillary-level, and enable efficient nutrient perfusion for the maintenance of tissues. However, existing three-dimensional printing methods for generating perfusable microvasculature with have insufficient resolution to recapitulate the microscale geometry of capillaries. Here, we present a collection of multiphoton microfabrication methods that enable the production of precise, three-dimensional, branched microvascular networks in collagen. When endothelial cells are added to the channels, they form perfusable lumens with diameters as small as 10 μm. Using a similar photochemistry, we also demonstrate the micropatterning of proteins embedded in microfabricated collagen scaffolds, producing hybrid scaffolds with both defined microarchitecture with integrated gradients of chemical cues. We provide examples for how these hybrid microfabricated scaffolds could be used in angiogenesis and cell homing assays. Finally, we describe a new method for increasing the micropatterning speed by synchronous laser and stage scanning. Using these technologies, we are working towards large-scale (>1 cm), high resolution ( 1 μm) scaffolds with both microarchitecture and embedded protein cues, with applications in three-dimensional assays of cellular behavior.

Network embedding-based representation learning for single cell RNA-seq data.

PubMed

Li, Xiangyu; Chen, Weizheng; Chen, Yang; Zhang, Xuegong; Gu, Jin; Zhang, Michael Q

2017-11-02

Single cell RNA-seq (scRNA-seq) techniques can reveal valuable insights of cell-to-cell heterogeneities. Projection of high-dimensional data into a low-dimensional subspace is a powerful strategy in general for mining such big data. However, scRNA-seq suffers from higher noise and lower coverage than traditional bulk RNA-seq, hence bringing in new computational difficulties. One major challenge is how to deal with the frequent drop-out events. The events, usually caused by the stochastic burst effect in gene transcription and the technical failure of RNA transcript capture, often render traditional dimension reduction methods work inefficiently. To overcome this problem, we have developed a novel Single Cell Representation Learning (SCRL) method based on network embedding. This method can efficiently implement data-driven non-linear projection and incorporate prior biological knowledge (such as pathway information) to learn more meaningful low-dimensional representations for both cells and genes. Benchmark results show that SCRL outperforms other dimensional reduction methods on several recent scRNA-seq datasets. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Terahertz computed tomography of NASA thermal protection system materials

NASA Astrophysics Data System (ADS)

Roth, D. J.; Reyes-Rodriguez, S.; Zimdars, D. A.; Rauser, R. W.; Ussery, W. W.

2012-05-01

A terahertz (THz) axial computed tomography system has been developed that uses time domain measurements in order to form cross-sectional image slices and three dimensional volume renderings of terahertz-transparent materials. The system can inspect samples as large as 0.0283 m3 (1 ft3) with no safety concerns as for x-ray computed tomography. In this study, the THz-CT system was evaluated for its ability to detect and characterize 1) an embedded void in Space Shuttle external fuel tank thermal protection system (TPS) foam material and 2) impact damage in a TPS configuration under consideration for use in NASA's multi-purpose Orion crew module (CM). Micro-focus X-ray CT is utilized to characterize the flaws and provide a baseline for which to compare the THz CT results.

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.

A globally well-posed finite element algorithm for aerodynamics applications

NASA Technical Reports Server (NTRS)

Iannelli, G. S.; Baker, A. J.

1991-01-01

A finite element CFD algorithm is developed for Euler and Navier-Stokes aerodynamic applications. For the linear basis, the resultant approximation is at least second-order-accurate in time and space for synergistic use of three procedures: (1) a Taylor weak statement, which provides for derivation of companion conservation law systems with embedded dispersion-error control mechanisms; (2) a stiffly stable second-order-accurate implicit Rosenbrock-Runge-Kutta temporal algorithm; and (3) a matrix tensor product factorization that permits efficient numerical linear algebra handling of the terminal large-matrix statement. Thorough analyses are presented regarding well-posed boundary conditions for inviscid and viscous flow specifications. Numerical solutions are generated and compared for critical evaluation of quasi-one- and two-dimensional Euler and Navier-Stokes benchmark test problems.

Mode locking and quasiperiodicity in a discrete-time Chialvo neuron model

NASA Astrophysics Data System (ADS)

Wang, Fengjuan; Cao, Hongjun

2018-03-01

The two-dimensional parameter spaces of a discrete-time Chialvo neuron model are investigated. Our studies demonstrate that for all our choice of two parameters (i) the fixed point is destabilized via Neimark-Sacker bifurcation; (ii) there exist mode locking structures like Arnold tongues and shrimps, with periods organized in a Farey tree sequence, embedded in quasiperiodic/chaotic region. We determine analytically the location of the parameter sets where Neimark-Sacker bifurcation occurs, and the location on this curve where Arnold tongues of arbitrary period are born. Properties of the transition that follows the so-called two-torus from quasiperiodicity to chaos are presented clearly and proved strictly by using numerical simulations such as bifurcation diagrams, the largest Lyapunov exponent diagram on MATLAB and C++.

Multi-dimensional effects in radiation pressure acceleration of ions

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

Tripathi, V. K., E-mail: tripathivipin@yahoo.co.in

A laser carries momentum. On reflection from an ultra-thin overdense plasma foil, it deposits recoil momentum on the foil, i.e. exerts radiation pressure on the foil electrons and pushes them to the rear. The space charge field thus created takes the ions along, accelerating the electron-ion double layer as a single unit. When the foil has surface ripple, of wavelength comparable to laser wavelength, the radiation pressure acts non-uniformly on the foil and the perturbation grows as Reyleigh-Taylor (RT) instability as the foil moves. The finite spot size of the laser causes foil to bend. These effects limit the quasi-monomore » energy acceleration of ions. Multi-ion foils, e.g., diamond like carbon foil embedded with protons offer the possibility of suppressing RT instability.« less

Amperometric Biosensors Based on 3-Dimensional Hydrogel-Forming Epoxy Networks

DTIC Science & Technology

1993-05-24

are epoxy- embedded and contained in a 0.3mm diameter biocompatible polyimide tubing. The ensemble of epoxy-embedded fiber tips is coated with the...electrode is then overcoated with a biocompatible film. The electrode’s sensitivity is 2.5xI0 2 A cm’ 2 M 1. It can be stored at 40 C for 4 months with no

On-chip photonic-phononic emitter-receiver apparatus

DOEpatents

Cox, Jonathan Albert; Jarecki, Jr., Robert L.; Rakich, Peter Thomas; Wang, Zheng; Shin, Heedeuk; Siddiqui, Aleem; Starbuck, Andrew Lea

2017-07-04

A radio-frequency photonic devices employs photon-phonon coupling for information transfer. The device includes a membrane in which a two-dimensionally periodic phononic crystal (PnC) structure is patterned. The device also includes at least a first optical waveguide embedded in the membrane. At least a first line-defect region interrupts the PnC structure. The first optical waveguide is embedded within the line-defect region.

Characterization and application of shape-changing panels with embedded rubber muscle actuators

NASA Astrophysics Data System (ADS)

Peel, Larry D.; Molina, Enrique, Jr.; Baur, Jeffery W.; Justice, Ryan S.

2013-09-01

Cylindrical soft actuators efficiently convert fluid pressure into mechanical energy and thus offer excellent force-to-weight ratios while behaving similar to biological muscle. McKibben-like rubber muscle actuators (RMAs) were embedded into neat elastomer and act as shape-changing panels. The effect of actuator spacing and modeling methods on the performance of these panels was investigated. Simulations from nonlinear finite element models were compared with results from test panels containing four RMAs that were spaced 0, 1/2, 1, and 1.3 RMA diameters apart. Nonlinear ‘laminated plate’ and ‘rod & plate’ finite element (FE) models of individual (non-embedded) RMAs and panels with embedded RMAs were developed. Due to model complexity and resource limitations, several simplified 2D and 3D FE model types, including a 3D ‘Unit Cell’ were created. After subtracting the ‘activation pressure’ needed to initiate contraction, all the models for the individual actuators produced forces consistent with experimental values, but only the more resource-intensive rod & plate models replicated fiber/braid re-orientation and produced more realistic values for actuator contraction. For panel models, the Full 3D rod & plate model appeared to be the most accurate for panel contraction and force, but was not completed for all configurations due to resource limitations. Most embedded panel FE models produced maximum panel actuator force and maximum contraction when the embedded actuators are spaced between 1/2 and 1 diameter apart. Seven panels with embedded RMAs were experimentally fabricated and tested. Panel tests confirmed that maximum or optimal performance occurs when the RMAs are spaced between 1/2 and 1 diameter apart. The tested actuator force was fairly constant in this range, suggesting that minor design or manufacturing differences may not significantly affect panel performance. However, the amount of axial force and contraction decreases significantly at greater than optimal spacing. This multi-faceted work provides useful design, simulation fabrication, and test characteristics for shape-adaptive panels. Bending panels were demonstrated but not modeled. Developers of future shape-adaptive air vehicles have been provided with additional simulation and design tools.

Embedded Data Representations.

PubMed

Willett, Wesley; Jansen, Yvonne; Dragicevic, Pierre

2017-01-01

We introduce embedded data representations, the use of visual and physical representations of data that are deeply integrated with the physical spaces, objects, and entities to which the data refers. Technologies like lightweight wireless displays, mixed reality hardware, and autonomous vehicles are making it increasingly easier to display data in-context. While researchers and artists have already begun to create embedded data representations, the benefits, trade-offs, and even the language necessary to describe and compare these approaches remain unexplored. In this paper, we formalize the notion of physical data referents - the real-world entities and spaces to which data corresponds - and examine the relationship between referents and the visual and physical representations of their data. We differentiate situated representations, which display data in proximity to data referents, and embedded representations, which display data so that it spatially coincides with data referents. Drawing on examples from visualization, ubiquitous computing, and art, we explore the role of spatial indirection, scale, and interaction for embedded representations. We also examine the tradeoffs between non-situated, situated, and embedded data displays, including both visualizations and physicalizations. Based on our observations, we identify a variety of design challenges for embedded data representation, and suggest opportunities for future research and applications.

One-dimensional, two-dimensional, and three-dimensional photonic crystals fabricated with interferometric techniques on ultrafine-grain silver halide emulsions

NASA Astrophysics Data System (ADS)

Ulibarrena, Manuel; Carretero, Luis; Acebal, Pablo; Madrigal, Roque; Blaya, Salvador; Fimia, Antonio

2004-09-01

Holographic techniques have been used for manufacturing multiple band one-dimensional, two-dimensional, and three-dimensional photonic crystals with different configurations, by multiplexing reflection and transmission setups on a single layer of holographic material. The recording material used for storage is an ultra fine grain silver halide emulsion, with an average grain size around 20 nm. The results are a set of photonic crystals with the one-dimensional, two-dimensional, and three-dimensional index modulation structure consisting of silver halide particles embedded in the gelatin layer of the emulsion. The characterisation of the fabricated photonic crystals by measuring their transmission band structures has been done and compared with theoretical calculations.

WebCSD: the online portal to the Cambridge Structural Database

PubMed Central

Thomas, Ian R.; Bruno, Ian J.; Cole, Jason C.; Macrae, Clare F.; Pidcock, Elna; Wood, Peter A.

2010-01-01

WebCSD, a new web-based application developed by the Cambridge Crystallographic Data Centre, offers fast searching of the Cambridge Structural Database using only a standard internet browser. Search facilities include two-dimensional substructure, molecular similarity, text/numeric and reduced cell searching. Text, chemical diagrams and three-dimensional structural information can all be studied in the results browser using the efficient entry summaries and embedded three-dimensional viewer. PMID:22477776

Analytical model for vibration prediction of two parallel tunnels in a full-space

NASA Astrophysics Data System (ADS)

He, Chao; Zhou, Shunhua; Guo, Peijun; Di, Honggui; Zhang, Xiaohui

2018-06-01

This paper presents a three-dimensional analytical model for the prediction of ground vibrations from two parallel tunnels embedded in a full-space. The two tunnels are modelled as cylindrical shells of infinite length, and the surrounding soil is modelled as a full-space with two cylindrical cavities. A virtual interface is introduced to divide the soil into the right layer and the left layer. By transforming the cylindrical waves into the plane waves, the solution of wave propagation in the full-space with two cylindrical cavities is obtained. The transformations from the plane waves to cylindrical waves are then used to satisfy the boundary conditions on the tunnel-soil interfaces. The proposed model provides a highly efficient tool to predict the ground vibration induced by the underground railway, which accounts for the dynamic interaction between neighbouring tunnels. Analysis of the vibration fields produced over a range of frequencies and soil properties is conducted. When the distance between the two tunnels is smaller than three times the tunnel diameter, the interaction between neighbouring tunnels is highly significant, at times in the order of 20 dB. It is necessary to consider the interaction between neighbouring tunnels for the prediction of ground vibrations induced underground railways.

Learning Embedded Software Design in an Open 3A Multiuser Laboratory

ERIC Educational Resources Information Center

Shih, Chien-Chou; Hwang, Lain-Jinn

2011-01-01

The need for professional programmers in embedded applications has become critical for industry growth. This need has increased the popularity of embedded software design courses, which are resource-intensive and space-limited in traditional real lab-based instruction. To overcome geographic and time barriers in enhancing practical skills that…

Branes and the Kraft-Procesi transition: classical case

NASA Astrophysics Data System (ADS)

Cabrera, Santiago; Hanany, Amihay

2018-04-01

Moduli spaces of a large set of 3 d N=4 effective gauge theories are known to be closures of nilpotent orbits. This set of theories has recently acquired a special status, due to Namikawa's theorem. As a consequence of this theorem, closures of nilpotent orbits are the simplest non-trivial moduli spaces that can be found in three dimensional theories with eight supercharges. In the early 80's mathematicians Hanspeter Kraft and Claudio Procesi characterized an inclusion relation between nilpotent orbit closures of the same classical Lie algebra. We recently [1] showed a physical realization of their work in terms of the motion of D3-branes on the Type IIB superstring embedding of the effective gauge theories. This analysis is restricted to A-type Lie algebras. The present note expands our previous discussion to the remaining classical cases: orthogonal and symplectic algebras. In order to do so we introduce O3-planes in the superstring description. We also find a brane realization for the mathematical map between two partitions of the same integer number known as collapse. Another result is that basic Kraft-Procesi transitions turn out to be described by the moduli space of orthosymplectic quivers with varying boundary conditions.

Impact of embedded voids on thin-films with high thermal expansion coefficients mismatch

NASA Astrophysics Data System (ADS)

Khafagy, Khaled H.; Hatem, Tarek M.; Bedair, Salah M.

2018-01-01

Using technology to reduce defects at heterogeneous interfaces of thin-films is at a high-priority for modern semiconductors. The current work utilizes a three-dimensional multiple-slip crystal-plasticity model and specialized finite-element formulations to study the impact of the embedded void approach (EVA) to reduce defects in thin-films deposited on a substrate with a highly mismatched thermal expansion coefficient, in particular, the growth of an InGaN thin-film on a Si substrate, where EVA has shown a remarkable reduction in stresses on the side of the embedded voids.

NASA Tech Briefs, January 2010

NASA Technical Reports Server (NTRS)

2010-01-01

Topics covered include: Cryogenic Flow Sensor; Multi-Sensor Mud Detection; Gas Flow Detection System; Mapping Capacitive Coupling Among Pixels in a Sensor Array; Fiber-Based Laser Transmitter for Oxygen A-Band Spectroscopy and Remote Sensing; Low-Profile, Dual-Wavelength, Dual-Polarized Antenna; Time-Separating Heating and Sensor Functions of Thermistors in Precision Thermal Control Applications; Cellular Reflectarray Antenna; A One-Dimensional Synthetic-Aperture Microwave Radiometer; Electrical Switching of Perovskite Thin-Film Resistors; Two-Dimensional Synthetic-Aperture Radiometer; Ethernet-Enabled Power and Communication Module for Embedded Processors; Electrically Variable Resistive Memory Devices; Improved Attachment in a Hybrid Inflatable Pressure Vessel; Electrostatic Separator for Beneficiation of Lunar Soil; Amorphous Rover; Space-Frame Antenna; Gear-Driven Turnbuckle Actuator; In-Situ Focusing Inside a Thermal Vacuum Chamber; Space-Frame Lunar Lander; Wider-Opening Dewar Flasks for Cryogenic Storage; Silicon Oxycarbide Aerogels for High-Temperature Thermal Insulation; Supercapacitor Electrolyte Solvents with Liquid Range Below -80 C; Designs and Materials for Better Coronagraph Occulting Masks; Fuel-Cell-Powered Vehicle with Hybrid Power Management; Fine-Water-Mist Multiple-Orientation-Discharge Fire Extinguisher; Fuel-Cell Water Separator; Turbulence and the Stabilization Principle; Improved Cloud Condensation Nucleus Spectrometer; Better Modeling of Electrostatic Discharge in an Insulator; Sub-Aperture Interferometers; Terahertz Mapping of Microstructure and Thickness Variations; Multiparallel Three-Dimensional Optical Microscopy; Stabilization of Phase of a Sinusoidal Signal Transmitted Over Optical Fiber; Vacuum-Compatible Wideband White Light and Laser Combiner Source System; Optical Tapers as White-Light WGM Resonators; EPR Imaging at a Few Megahertz Using SQUID Detectors; Reducing Field Distortion in Magnetic Resonance Imaging; Fluorogenic Cell-Based Biosensors for Monitoring Microbes; A Constant-Force Resistive Exercise Unit; GUI to Facilitate Research on Biological Damage from Radiation; On-Demand Urine Analyzer; More-Realistic Digital Modeling of a Human Body; and Advanced Liquid-Cooling Garment Using Highly Thermally Conductive Sheets.

Riemannian Metric Optimization on Surfaces (RMOS) for Intrinsic Brain Mapping in the Laplace-Beltrami Embedding Space

PubMed Central

Gahm, Jin Kyu; Shi, Yonggang

2018-01-01

Surface mapping methods play an important role in various brain imaging studies from tracking the maturation of adolescent brains to mapping gray matter atrophy patterns in Alzheimer’s disease. Popular surface mapping approaches based on spherical registration, however, have inherent numerical limitations when severe metric distortions are present during the spherical parameterization step. In this paper, we propose a novel computational framework for intrinsic surface mapping in the Laplace-Beltrami (LB) embedding space based on Riemannian metric optimization on surfaces (RMOS). Given a diffeomorphism between two surfaces, an isometry can be defined using the pullback metric, which in turn results in identical LB embeddings from the two surfaces. The proposed RMOS approach builds upon this mathematical foundation and achieves general feature-driven surface mapping in the LB embedding space by iteratively optimizing the Riemannian metric defined on the edges of triangular meshes. At the core of our framework is an optimization engine that converts an energy function for surface mapping into a distance measure in the LB embedding space, which can be effectively optimized using gradients of the LB eigen-system with respect to the Riemannian metrics. In the experimental results, we compare the RMOS algorithm with spherical registration using large-scale brain imaging data, and show that RMOS achieves superior performance in the prediction of hippocampal subfields and cortical gyral labels, and the holistic mapping of striatal surfaces for the construction of a striatal connectivity atlas from substantia nigra. PMID:29574399

A Soft Sensor-Based Three-Dimensional (3-D) Finger Motion Measurement System

PubMed Central

Park, Wookeun; Ro, Kyongkwan; Kim, Suin; Bae, Joonbum

2017-01-01

In this study, a soft sensor-based three-dimensional (3-D) finger motion measurement system is proposed. The sensors, made of the soft material Ecoflex, comprise embedded microchannels filled with a conductive liquid metal (EGaln). The superior elasticity, light weight, and sensitivity of soft sensors allows them to be embedded in environments in which conventional sensors cannot. Complicated finger joints, such as the carpometacarpal (CMC) joint of the thumb are modeled to specify the location of the sensors. Algorithms to decouple the signals from soft sensors are proposed to extract the pure flexion, extension, abduction, and adduction joint angles. The performance of the proposed system and algorithms are verified by comparison with a camera-based motion capture system. PMID:28241414

Prediction of enhancer-promoter interactions via natural language processing.

PubMed

Zeng, Wanwen; Wu, Mengmeng; Jiang, Rui

2018-05-09

Precise identification of three-dimensional genome organization, especially enhancer-promoter interactions (EPIs), is important to deciphering gene regulation, cell differentiation and disease mechanisms. Currently, it is a challenging task to distinguish true interactions from other nearby non-interacting ones since the power of traditional experimental methods is limited due to low resolution or low throughput. We propose a novel computational framework EP2vec to assay three-dimensional genomic interactions. We first extract sequence embedding features, defined as fixed-length vector representations learned from variable-length sequences using an unsupervised deep learning method in natural language processing. Then, we train a classifier to predict EPIs using the learned representations in supervised way. Experimental results demonstrate that EP2vec obtains F1 scores ranging from 0.841~ 0.933 on different datasets, which outperforms existing methods. We prove the robustness of sequence embedding features by carrying out sensitivity analysis. Besides, we identify motifs that represent cell line-specific information through analysis of the learned sequence embedding features by adopting attention mechanism. Last, we show that even superior performance with F1 scores 0.889~ 0.940 can be achieved by combining sequence embedding features and experimental features. EP2vec sheds light on feature extraction for DNA sequences of arbitrary lengths and provides a powerful approach for EPIs identification.

Securely Partitioning Spacecraft Computing Resources: Validation of a Separation Kernel

NASA Astrophysics Data System (ADS)

Bremer, Leon; Schreutelkamp, Erwin

2011-08-01

The F-35 Lightning II, also known as the Joint Strike Fighter, will be the first operational fighter aircraft equipped with an operational MultiShip Embedded Training capability. This onboard training system allows teams of fighter pilots to jointly operate their F-35 in flight against virtual threats, avoiding the need for real adversary air threats and surface threat systems in their training. The European Real-time Operations Simulator (EuroSim) framework is well known in the space domain, particularly in support of engineering and test phases of space system development. In the MultiShip Embedded Training project, EuroSim is not only the essential tool for development and verification throughout the project but is also the engine of the final embedded simulator on board of the F-35 aircraft. The novel ways in which EuroSim is applied in the project in relation to distributed simulation problems, team collaboration, tool chains and embedded systems can benefit many projects and applications. The paper describes the application of EuroSim as the simulation engine of the F-35 Embedded Training solution, the extensions to the EuroSim product that enable this application, and its usage in development and verification of the whole project as carried out at the sites of Dutch Space and the National Aerospace Laboratory (NLR).

An embedding for the big bang

NASA Technical Reports Server (NTRS)

Wesson, Paul S.

1994-01-01

A cosmological model is given that has good physical properties for the early and late universe but is a hypersurface in a flat five-dimensional manifold. The big bang can therefore be regarded as an effect of a choice of coordinates in a truncated higher-dimensional geometry. Thus the big bang is in some sense a geometrical illusion.

Baryon spectrum from superconformal quantum mechanics and its light-front holographic embedding

DOE PAGES

de Teramond, Guy F.; Dosch, Hans Gunter; Brodsky, Stanley J.

2015-02-27

We describe the observed light-baryon spectrum by extending superconformal quantum mechanics to the light front and its embedding in AdS space. This procedure uniquely determines the confinement potential for arbitrary half-integer spin. To this end, we show that fermionic wave equations in AdS space are dual to light-front supersymmetric quantum-mechanical bound-state equations in physical space-time. The specific breaking of conformal invariance explains hadronic properties common to light mesons and baryons, such as the observed mass pattern in the radial and orbital excitations, from the spectrum generating algebra. Lastly, the holographic embedding in AdS also explains distinctive and systematic features, suchmore » as the spin-J degeneracy for states with the same orbital angular momentum, observed in the light-baryon spectrum.« less

Image Capture and Display Based on Embedded Linux

NASA Astrophysics Data System (ADS)

Weigong, Zhang; Suran, Di; Yongxiang, Zhang; Liming, Li

For the requirement of building a highly reliable communication system, SpaceWire was selected in the integrated electronic system. There was a need to test the performance of SpaceWire. As part of the testing work, the goal of this paper is to transmit image data from CMOS camera through SpaceWire and display real-time images on the graphical user interface with Qt in the embedded development platform of Linux & ARM. A point-to-point mode of transmission was chosen; the running result showed the two communication ends basically reach a consensus picture in succession. It suggests that the SpaceWire can transmit the data reliably.

Nonlinear Prediction As A Tool For Determining Parameters For Phase Space Reconstruction In Meteorology

NASA Astrophysics Data System (ADS)

Miksovsky, J.; Raidl, A.

Time delays phase space reconstruction represents one of useful tools of nonlinear time series analysis, enabling number of applications. Its utilization requires the value of time delay to be known, as well as the value of embedding dimension. There are sev- eral methods how to estimate both these parameters. Typically, time delay is computed first, followed by embedding dimension. Our presented approach is slightly different - we reconstructed phase space for various combinations of mentioned parameters and used it for prediction by means of the nearest neighbours in the phase space. Then some measure of prediction's success was computed (correlation or RMSE, e.g.). The position of its global maximum (minimum) should indicate the suitable combination of time delay and embedding dimension. Several meteorological (particularly clima- tological) time series were used for the computations. We have also created a MS- Windows based program in order to implement this approach - its basic features will be presented as well.

Visual analysis of mass cytometry data by hierarchical stochastic neighbour embedding reveals rare cell types.

PubMed

van Unen, Vincent; Höllt, Thomas; Pezzotti, Nicola; Li, Na; Reinders, Marcel J T; Eisemann, Elmar; Koning, Frits; Vilanova, Anna; Lelieveldt, Boudewijn P F

2017-11-23

Mass cytometry allows high-resolution dissection of the cellular composition of the immune system. However, the high-dimensionality, large size, and non-linear structure of the data poses considerable challenges for the data analysis. In particular, dimensionality reduction-based techniques like t-SNE offer single-cell resolution but are limited in the number of cells that can be analyzed. Here we introduce Hierarchical Stochastic Neighbor Embedding (HSNE) for the analysis of mass cytometry data sets. HSNE constructs a hierarchy of non-linear similarities that can be interactively explored with a stepwise increase in detail up to the single-cell level. We apply HSNE to a study on gastrointestinal disorders and three other available mass cytometry data sets. We find that HSNE efficiently replicates previous observations and identifies rare cell populations that were previously missed due to downsampling. Thus, HSNE removes the scalability limit of conventional t-SNE analysis, a feature that makes it highly suitable for the analysis of massive high-dimensional data sets.

Terahertz Imaging of Three-Dimensional Dehydrated Breast Cancer Tumors

NASA Astrophysics Data System (ADS)

Bowman, Tyler; Wu, Yuhao; Gauch, John; Campbell, Lucas K.; El-Shenawee, Magda

2017-06-01

This work presents the application of terahertz imaging to three-dimensional formalin-fixed, paraffin-embedded human breast cancer tumors. The results demonstrate the capability of terahertz for in-depth scanning to produce cross section images without the need to slice the tumor. Samples of tumors excised from women diagnosed with infiltrating ductal carcinoma and lobular carcinoma are investigated using a pulsed terahertz time domain imaging system. A time of flight estimation is used to obtain vertical and horizontal cross section images of tumor tissues embedded in paraffin block. Strong agreement is shown comparing the terahertz images obtained by electronically scanning the tumor in-depth in comparison with histopathology images. The detection of cancer tissue inside the block is found to be accurate to depths over 1 mm. Image processing techniques are applied to provide improved contrast and automation of the obtained terahertz images. In particular, unsharp masking and edge detection methods are found to be most effective for three-dimensional block imaging.

Spectral Regression Discriminant Analysis for Hyperspectral Image Classification

NASA Astrophysics Data System (ADS)

Pan, Y.; Wu, J.; Huang, H.; Liu, J.

2012-08-01

Dimensionality reduction algorithms, which aim to select a small set of efficient and discriminant features, have attracted great attention for Hyperspectral Image Classification. The manifold learning methods are popular for dimensionality reduction, such as Locally Linear Embedding, Isomap, and Laplacian Eigenmap. However, a disadvantage of many manifold learning methods is that their computations usually involve eigen-decomposition of dense matrices which is expensive in both time and memory. In this paper, we introduce a new dimensionality reduction method, called Spectral Regression Discriminant Analysis (SRDA). SRDA casts the problem of learning an embedding function into a regression framework, which avoids eigen-decomposition of dense matrices. Also, with the regression based framework, different kinds of regularizes can be naturally incorporated into our algorithm which makes it more flexible. It can make efficient use of data points to discover the intrinsic discriminant structure in the data. Experimental results on Washington DC Mall and AVIRIS Indian Pines hyperspectral data sets demonstrate the effectiveness of the proposed method.

Quantitative analysis of eyes and other optical systems in linear optics.

PubMed

Harris, William F; Evans, Tanya; van Gool, Radboud D

2017-05-01

To show that 14-dimensional spaces of augmented point P and angle Q characteristics, matrices obtained from the ray transference, are suitable for quantitative analysis although only the latter define an inner-product space and only on it can one define distances and angles. The paper examines the nature of the spaces and their relationships to other spaces including symmetric dioptric power space. The paper makes use of linear optics, a three-dimensional generalization of Gaussian optics. Symmetric 2 × 2 dioptric power matrices F define a three-dimensional inner-product space which provides a sound basis for quantitative analysis (calculation of changes, arithmetic means, etc.) of refractive errors and thin systems. For general systems the optical character is defined by the dimensionally-heterogeneous 4 × 4 symplectic matrix S, the transference, or if explicit allowance is made for heterocentricity, the 5 × 5 augmented symplectic matrix T. Ordinary quantitative analysis cannot be performed on them because matrices of neither of these types constitute vector spaces. Suitable transformations have been proposed but because the transforms are dimensionally heterogeneous the spaces are not naturally inner-product spaces. The paper obtains 14-dimensional spaces of augmented point P and angle Q characteristics. The 14-dimensional space defined by the augmented angle characteristics Q is dimensionally homogenous and an inner-product space. A 10-dimensional subspace of the space of augmented point characteristics P is also an inner-product space. The spaces are suitable for quantitative analysis of the optical character of eyes and many other systems. Distances and angles can be defined in the inner-product spaces. The optical systems may have multiple separated astigmatic and decentred refracting elements. © 2017 The Authors Ophthalmic & Physiological Optics © 2017 The College of Optometrists.

Anisotropic fractal media by vector calculus in non-integer dimensional space

NASA Astrophysics Data System (ADS)

Tarasov, Vasily E.

2014-08-01

A review of different approaches to describe anisotropic fractal media is proposed. In this paper, differentiation and integration non-integer dimensional and multi-fractional spaces are considered as tools to describe anisotropic fractal materials and media. We suggest a generalization of vector calculus for non-integer dimensional space by using a product measure method. The product of fractional and non-integer dimensional spaces allows us to take into account the anisotropy of the fractal media in the framework of continuum models. The integration over non-integer-dimensional spaces is considered. In this paper differential operators of first and second orders for fractional space and non-integer dimensional space are suggested. The differential operators are defined as inverse operations to integration in spaces with non-integer dimensions. Non-integer dimensional space that is product of spaces with different dimensions allows us to give continuum models for anisotropic type of the media. The Poisson's equation for fractal medium, the Euler-Bernoulli fractal beam, and the Timoshenko beam equations for fractal material are considered as examples of application of suggested generalization of vector calculus for anisotropic fractal materials and media.

Two dimensional exciton polaritons in microcavities with embedded quantum wires

NASA Astrophysics Data System (ADS)

Kavokin, A. V.; Ivchenko, E. L.; Vladimirova, M. R.; Kaliteevski, M. A.; Goupalov, S. V.

1998-02-01

Optical anisotropy of the periodical array of quantum wires embedded in a semiconductor microcavity is shown to result in polarization-dependent vacuum-field Rabi-splitting and a triple-anticrossing shape of the exciton-polariton dispersion curves. Both effects originate from the resonant diffraction of light at the grating of quantum wires. The calculation has been done within the nonlocal dielectric response theory and using the 4 × 4 transfer matrix technique.

Isolation of Thermal and Strain Responses in Composites Using Embedded Fiber Bragg Grating Temperature Sensors

DTIC Science & Technology

2013-05-10

13. SUPPLEMENTARY NOTES 14. ABSTRACT In this research, fiber Bragg grating ( FBG ) optical temperature sensors are used for structural health...surface of a composite structure. FBG sensors also respond to axial strain in the optical fiber, thus any structural strain experienced by the composite...features. First, a three-dimensional array of FBG temperature sensors has been embedded in a carbon/epoxy composite structure, consisting of both in

Stabilizing embedology: Geometry-preserving delay-coordinate maps

NASA Astrophysics Data System (ADS)

Eftekhari, Armin; Yap, Han Lun; Wakin, Michael B.; Rozell, Christopher J.

2018-02-01

Delay-coordinate mapping is an effective and widely used technique for reconstructing and analyzing the dynamics of a nonlinear system based on time-series outputs. The efficacy of delay-coordinate mapping has long been supported by Takens' embedding theorem, which guarantees that delay-coordinate maps use the time-series output to provide a reconstruction of the hidden state space that is a one-to-one embedding of the system's attractor. While this topological guarantee ensures that distinct points in the reconstruction correspond to distinct points in the original state space, it does not characterize the quality of this embedding or illuminate how the specific parameters affect the reconstruction. In this paper, we extend Takens' result by establishing conditions under which delay-coordinate mapping is guaranteed to provide a stable embedding of a system's attractor. Beyond only preserving the attractor topology, a stable embedding preserves the attractor geometry by ensuring that distances between points in the state space are approximately preserved. In particular, we find that delay-coordinate mapping stably embeds an attractor of a dynamical system if the stable rank of the system is large enough to be proportional to the dimension of the attractor. The stable rank reflects the relation between the sampling interval and the number of delays in delay-coordinate mapping. Our theoretical findings give guidance to choosing system parameters, echoing the tradeoff between irrelevancy and redundancy that has been heuristically investigated in the literature. Our initial result is stated for attractors that are smooth submanifolds of Euclidean space, with extensions provided for the case of strange attractors.

Riemannian metric optimization on surfaces (RMOS) for intrinsic brain mapping in the Laplace-Beltrami embedding space.

PubMed

Gahm, Jin Kyu; Shi, Yonggang

2018-05-01

Surface mapping methods play an important role in various brain imaging studies from tracking the maturation of adolescent brains to mapping gray matter atrophy patterns in Alzheimer's disease. Popular surface mapping approaches based on spherical registration, however, have inherent numerical limitations when severe metric distortions are present during the spherical parameterization step. In this paper, we propose a novel computational framework for intrinsic surface mapping in the Laplace-Beltrami (LB) embedding space based on Riemannian metric optimization on surfaces (RMOS). Given a diffeomorphism between two surfaces, an isometry can be defined using the pullback metric, which in turn results in identical LB embeddings from the two surfaces. The proposed RMOS approach builds upon this mathematical foundation and achieves general feature-driven surface mapping in the LB embedding space by iteratively optimizing the Riemannian metric defined on the edges of triangular meshes. At the core of our framework is an optimization engine that converts an energy function for surface mapping into a distance measure in the LB embedding space, which can be effectively optimized using gradients of the LB eigen-system with respect to the Riemannian metrics. In the experimental results, we compare the RMOS algorithm with spherical registration using large-scale brain imaging data, and show that RMOS achieves superior performance in the prediction of hippocampal subfields and cortical gyral labels, and the holistic mapping of striatal surfaces for the construction of a striatal connectivity atlas from substantia nigra. Copyright © 2018 Elsevier B.V. All rights reserved.

Stabilizing embedology: Geometry-preserving delay-coordinate maps.

PubMed

Eftekhari, Armin; Yap, Han Lun; Wakin, Michael B; Rozell, Christopher J

2018-02-01

Delay-coordinate mapping is an effective and widely used technique for reconstructing and analyzing the dynamics of a nonlinear system based on time-series outputs. The efficacy of delay-coordinate mapping has long been supported by Takens' embedding theorem, which guarantees that delay-coordinate maps use the time-series output to provide a reconstruction of the hidden state space that is a one-to-one embedding of the system's attractor. While this topological guarantee ensures that distinct points in the reconstruction correspond to distinct points in the original state space, it does not characterize the quality of this embedding or illuminate how the specific parameters affect the reconstruction. In this paper, we extend Takens' result by establishing conditions under which delay-coordinate mapping is guaranteed to provide a stable embedding of a system's attractor. Beyond only preserving the attractor topology, a stable embedding preserves the attractor geometry by ensuring that distances between points in the state space are approximately preserved. In particular, we find that delay-coordinate mapping stably embeds an attractor of a dynamical system if the stable rank of the system is large enough to be proportional to the dimension of the attractor. The stable rank reflects the relation between the sampling interval and the number of delays in delay-coordinate mapping. Our theoretical findings give guidance to choosing system parameters, echoing the tradeoff between irrelevancy and redundancy that has been heuristically investigated in the literature. Our initial result is stated for attractors that are smooth submanifolds of Euclidean space, with extensions provided for the case of strange attractors.

Disease Prediction based on Functional Connectomes using a Scalable and Spatially-Informed Support Vector Machine

PubMed Central

Watanabe, Takanori; Kessler, Daniel; Scott, Clayton; Angstadt, Michael; Sripada, Chandra

2014-01-01

Substantial evidence indicates that major psychiatric disorders are associated with distributed neural dysconnectivity, leading to strong interest in using neuroimaging methods to accurately predict disorder status. In this work, we are specifically interested in a multivariate approach that uses features derived from whole-brain resting state functional connectomes. However, functional connectomes reside in a high dimensional space, which complicates model interpretation and introduces numerous statistical and computational challenges. Traditional feature selection techniques are used to reduce data dimensionality, but are blind to the spatial structure of the connectomes. We propose a regularization framework where the 6-D structure of the functional connectome (defined by pairs of points in 3-D space) is explicitly taken into account via the fused Lasso or the GraphNet regularizer. Our method only restricts the loss function to be convex and margin-based, allowing non-differentiable loss functions such as the hinge-loss to be used. Using the fused Lasso or GraphNet regularizer with the hinge-loss leads to a structured sparse support vector machine (SVM) with embedded feature selection. We introduce a novel efficient optimization algorithm based on the augmented Lagrangian and the classical alternating direction method, which can solve both fused Lasso and GraphNet regularized SVM with very little modification. We also demonstrate that the inner subproblems of the algorithm can be solved efficiently in analytic form by coupling the variable splitting strategy with a data augmentation scheme. Experiments on simulated data and resting state scans from a large schizophrenia dataset show that our proposed approach can identify predictive regions that are spatially contiguous in the 6-D “connectome space,” offering an additional layer of interpretability that could provide new insights about various disease processes. PMID:24704268

Influence of the composite material thermal expansion on embedded highly birefringent polymer microstructured optical fibers

NASA Astrophysics Data System (ADS)

SzelÄ g, M.; Lesiak, P.; Kuczkowski, M.; Domański, A. W.; Woliński, T. R.

2013-05-01

Results of our research on embedded highly birefringent polymer microstructured fibers are presented. A composite material sample with fibers embedded between two layers of a multi-layer composite structure is fabricated and characterized. Temperature sensitivities of the polymer fibers are measured in a free space and compared with the fibers embedded in the composite material. It appeared that highly birefringent polymer microstructured fibers exhibit a strong increase in temperature sensitivity when embedded in the composite material, which is due to the stress-induced changes in birefringence created by thermally-induced strain.

Embedded spacecraft thermal control using ultrasonic consolidation

NASA Astrophysics Data System (ADS)

Clements, Jared W.

Research has been completed in order to rapidly manufacture spacecraft thermal control technologies embedded in spacecraft structural panels using ultrasonic consolidation. This rapid manufacturing process enables custom thermal control designs in the time frame necessary for responsive space. Successfully embedded components include temperature sensors, heaters, wire harnessing, pre-manufactured heat pipes, and custom integral heat pipes. High conductivity inserts and custom integral pulsating heat pipes were unsuccessfully attempted. This research shows the viability of rapid manufacturing of spacecraft structures with embedded thermal control using ultrasonic consolidation.

X-ray structural analysis of two-dimensional assembling lead sulfide nanocrystals of different sizes

NASA Astrophysics Data System (ADS)

Ushakova, Elena V.; Golubkov, Valery V.; Litvin, Aleksandr P.; Parfenov, Peter S.; Cherevkov, Sergei A.; Fedorov, Anatoly V.; Baranov, Alexander V.

2016-08-01

We report on the structural investigation of self-organized assemblies of PbS nanocrystals (NCs) of different sizes, which were deposited on a glass substrate or embedded in a porous matrix. Regardless of the NC size and the type of the substrate and matrix, the assemblies were ordered in two-dimensional superlattices with densely packed NCs.

Radiation Shielding Systems Using Nanotechnology

NASA Technical Reports Server (NTRS)

Chen, Bin (Inventor); McKay, Christoper P. (Inventor)

2011-01-01

A system for shielding personnel and/or equipment from radiation particles. In one embodiment, a first substrate is connected to a first array or perpendicularly oriented metal-like fingers, and a second, electrically conducting substrate has an array of carbon nanostructure (CNS) fingers, coated with an electro-active polymer extending toward, but spaced apart from, the first substrate fingers. An electric current and electric charge discharge and dissipation system, connected to the second substrate, receives a current and/or voltage pulse initially generated when the first substrate receives incident radiation. In another embodiment, an array of CNSs is immersed in a first layer of hydrogen-rich polymers and in a second layer of metal-like material. In another embodiment, a one- or two-dimensional assembly of fibers containing CNSs embedded in a metal-like matrix serves as a radiation-protective fabric or body covering.

Supersymmetry across the light and heavy-light hadronic spectrum. II.

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

Dosch, Hans Gunter; de Téramond, Guy F.; Brodsky, Stanley J.

We extend our analysis of the implications of hadronic supersymmetry for heavy-light hadrons in light-front holographic QCD. Although conformal symmetry is strongly broken by the heavy quark mass, supersymmetry and the holographic embedding of semiclassical light-front dynamics derived from five-dimensional anti-de Sitter space nevertheless determine the form of the confining potential in the light-front Hamiltonian to be harmonic. The resulting light-front bound-state equations lead to a heavy-light Regge-like spectrum for both mesons and baryons. The confinement hadron mass scale and their Regge slopes depend, however, on the mass of the heavy quark in the meson or baryon as expected frommore » heavy quark effective theory. Furthermore, this procedure reproduces the observed spectra of heavy-light hadrons with good precision and makes predictions for yet unobserved states.« less

Hawking temperature of constant curvature black holes

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

Cai Ronggen; Myung, Yun Soo; Institute of Basic Science and School of Computer Aided Science, Inje University, Gimhae 621-749

2011-05-15

The constant curvature (CC) black holes are higher dimensional generalizations of Banados-Teitelboim-Zanelli black holes. It is known that these black holes have the unusual topology of M{sub D-1}xS{sup 1}, where D is the spacetime dimension and M{sub D-1} stands for a conformal Minkowski spacetime in D-1 dimensions. The unusual topology and time-dependence for the exterior of these black holes cause some difficulties to derive their thermodynamic quantities. In this work, by using a globally embedding approach, we obtain the Hawking temperature of the CC black holes. We find that the Hawking temperature takes the same form when using both themore » static and global coordinates. Also, it is identical to the Gibbons-Hawking temperature of the boundary de Sitter spaces of these CC black holes.« less

Supersymmetry across the light and heavy-light hadronic spectrum. II.

DOE PAGES

Dosch, Hans Gunter; de Téramond, Guy F.; Brodsky, Stanley J.

2017-02-15

We extend our analysis of the implications of hadronic supersymmetry for heavy-light hadrons in light-front holographic QCD. Although conformal symmetry is strongly broken by the heavy quark mass, supersymmetry and the holographic embedding of semiclassical light-front dynamics derived from five-dimensional anti-de Sitter space nevertheless determine the form of the confining potential in the light-front Hamiltonian to be harmonic. The resulting light-front bound-state equations lead to a heavy-light Regge-like spectrum for both mesons and baryons. The confinement hadron mass scale and their Regge slopes depend, however, on the mass of the heavy quark in the meson or baryon as expected frommore » heavy quark effective theory. Furthermore, this procedure reproduces the observed spectra of heavy-light hadrons with good precision and makes predictions for yet unobserved states.« less

Characterization of the Tumor Secretome from Tumor Interstitial Fluid (TIF).

PubMed

Gromov, Pavel; Gromova, Irina

2016-01-01

Tumor interstitial fluid (TIF) surrounds and perfuses bodily tumorigenic tissues and cells, and can accumulate by-products of tumors and stromal cells in a relatively local space. Interstitial fluid offers several important advantages for biomarker and therapeutic target discovery, especially for cancer. Here, we describe the most currently accepted method for recovering TIF from tumor and nonmalignant tissues that was initially performed using breast cancer tissue. TIF recovery is achieved by passive extraction of fluid from small, surgically dissected tissue specimens in phosphate-buffered saline. We also present protocols for hematoxylin and eosin (H&E) staining of snap-frozen and formalin-fixed, paraffin-embedded (FFPE) tumor sections and for proteomic profiling of TIF and matched tumor samples by high-resolution two-dimensional gel electrophoresis (2D-PAGE) to enable comparative analysis of tumor secretome and paired tumor tissue.

MUSIC electromagnetic imaging with enhanced resolution for small inclusions

NASA Astrophysics Data System (ADS)

Chen, Xudong; Zhong, Yu

2009-01-01

This paper investigates the influence of the test dipole on the resolution of the multiple signal classification (MUSIC) imaging method applied to the electromagnetic inverse scattering problem of determining the locations of a collection of small objects embedded in a known background medium. Based on the analysis of the induced electric dipoles in eigenstates, an algorithm is proposed to determine the test dipole that generates a pseudo-spectrum with enhanced resolution. The amplitudes in three directions of the optimal test dipole are not necessarily in phase, i.e., the optimal test dipole may not correspond to a physical direction in the real three-dimensional space. In addition, the proposed test-dipole-searching algorithm is able to deal with some special scenarios, due to the shapes and materials of objects, to which the standard MUSIC does not apply.

A new MUSIC electromagnetic imaging method with enhanced resolution for small inclusions

NASA Astrophysics Data System (ADS)

Zhong, Yu; Chen, Xudong

2008-11-01

This paper investigates the influence of test dipole on the resolution of the multiple signal classification (MUSIC) imaging method applied to the electromagnetic inverse scattering problem of determining the locations of a collection of small objects embedded in a known background medium. Based on the analysis of the induced electric dipoles in eigenstates, an algorithm is proposed to determine the test dipole that generates a pseudo-spectrum with enhanced resolution. The amplitudes in three directions of the optimal test dipole are not necessarily in phase, i.e., the optimal test dipole may not correspond to a physical direction in the real three-dimensional space. In addition, the proposed test-dipole-searching algorithm is able to deal with some special scenarios, due to the shapes and materials of objects, to which the standard MUSIC doesn't apply.

Undecalcified temporal bone morphology: a methodology useful for gross to fine observation and three-dimensional reconstruction.

PubMed

Fujiyoshi, T; Mogi, G; Watanabe, T; Matsushita, F

1992-01-01

Using a novel method of cutting undecalcified temporal bone specimens, quantitative structural analysis in the human and the Japanese monkey was undertaken. One millimeter thick serial slices made from unembedded temporal bones retained fine structure. Therefore, gross to fine observation could be performed systematically at the macroscopic, light, scanning, and transmission electron microscopic levels. The entire temporal bone three-dimensional reconstruction was completed from embedded sections; consequently, the volume of the tubotympanum and air cell system could be calculated. Available methods by embedding, tungsten carbide sectioning, grinding, and microwave irradiation for decalcification were also examined. These morphologic studies suggest that these novel methods offer timesaving advantages over any presently available techniques, and allow for elucidation of temporal bone morphology with only a few specimens.

Monte Carlo study of one-dimensional confined fluids with Gay-Berne intermolecular potential

NASA Astrophysics Data System (ADS)

Moradi, M.; Hashemi, S.

2011-11-01

The thermodynamic quantities of a one dimensional system of particles with Gay-Berne model potential confined between walls have been obtained by means of Monte Carlo computer simulations. For a number of temperatures, the systems were considered and their density profiles, order parameter, pressure, configurational temperature and average potential energy per particle are reported. The results show that by decreasing the temperature, the soft particles become more ordered and they align to the walls and also they don't show any tendency to be near the walls at very low temperatures. We have also changed the structure of the walls by embedding soft ellipses in them, this change increases the total density near the wall whereas, increasing or decreasing the order parameter depend on the angle of embedded ellipses.

Generalizations of the Toda molecule

NASA Astrophysics Data System (ADS)

Van Velthoven, W. P. G.; Bais, F. A.

1986-12-01

Finite-energy monopole solutions are constructed for the self-dual equations with spherical symmetry in an arbitrary integer graded Lie algebra. The constraint of spherical symmetry in a complex noncoordinate basis leads to a dimensional reduction. The resulting two-dimensional ( r, t) equations are of second order and furnish new generalizations of the Toda molecule equations. These are then solved by a technique which is due to Leznov and Saveliev. For time-independent solutions a further reduction is made, leading to an ansatz for all SU(2) embeddings of the Lie algebra. The regularity condition at the origin for the solutions, needed to ensure finite energy, is also solved for a special class of nonmaximal embeddings. Explicit solutions are given for the groups SU(2), SO(4), Sp(4) and SU(4).

Legendre submanifolds in contact manifolds as attractors and geometric nonequilibrium thermodynamics

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

Goto, Shin-itiro, E-mail: sgoto@ims.ac.jp

It has been proposed that equilibrium thermodynamics is described on Legendre submanifolds in contact geometry. It is shown in this paper that Legendre submanifolds embedded in a contact manifold can be expressed as attractors in phase space for a certain class of contact Hamiltonian vector fields. By giving a physical interpretation that points outside the Legendre submanifold can represent nonequilibrium states of thermodynamic variables, in addition to that points of a given Legendre submanifold can represent equilibrium states of the variables, this class of contact Hamiltonian vector fields is physically interpreted as a class of relaxation processes, in which thermodynamicmore » variables achieve an equilibrium state from a nonequilibrium state through a time evolution, a typical nonequilibrium phenomenon. Geometric properties of such vector fields on contact manifolds are characterized after introducing a metric tensor field on a contact manifold. It is also shown that a contact manifold and a strictly convex function induce a lower dimensional dually flat space used in information geometry where a geometrization of equilibrium statistical mechanics is constructed. Legendre duality on contact manifolds is explicitly stated throughout.« less

Nonequilibrium dynamics of probe filaments in actin-myosin networks

NASA Astrophysics Data System (ADS)

Gladrow, J.; Broedersz, C. P.; Schmidt, C. F.

2017-08-01

Active dynamic processes of cells are largely driven by the cytoskeleton, a complex and adaptable semiflexible polymer network, motorized by mechanoenzymes. Small dimensions, confined geometries, and hierarchical structures make it challenging to probe dynamics and mechanical response of such networks. Embedded semiflexible probe polymers can serve as nonperturbing multiscale probes to detect force distributions in active polymer networks. We show here that motor-induced forces transmitted to the probe polymers are reflected in nonequilibrium bending dynamics, which we analyze in terms of spatial eigenmodes of an elastic beam under steady-state conditions. We demonstrate how these active forces induce correlations among the mode amplitudes, which furthermore break time-reversal symmetry. This leads to a breaking of detailed balance in this mode space. We derive analytical predictions for the magnitude of resulting probability currents in mode space in the white-noise limit of motor activity. We relate the structure of these currents to the spatial profile of motor-induced forces along the probe polymers and provide a general relation for observable currents on two-dimensional hyperplanes.

Ideas of Flat and Curved Space in History of Physics

NASA Astrophysics Data System (ADS)

Berezin, Alexander A.

2006-04-01

Since ``everything which is not prohibited is compulsory'' (assigned to Gell-Mann) we can postulate infinite flat Cartesian N-dimensional (N: any integer) space-time (ST) as embedding for any curved ST. Ergodicity raises quest of whether total number of inflationary and/or Everett bubbles (mini-verses) is finite, countably infinite (aleph-zero) or uncountably infinite (aleph-one). Are these bubbles form Gaussian distribution or form some non-random subsetting? Perhaps, communication between mini-verses (idea of D.Deutsch) can be facilitated by a kind of minimax non-local dynamics akin to Fermat principle? (Minimax Principle in Bubble Cosmology). Even such classical effects as magnetism and polarization have some non-local features. Can we go below the Planck length to perhaps Compton wavelength of our ``Hubble's bubble'' (h/Mc = 10 to minus 95 m, if M = 10 to 54 kg)? When talking about time loops and ergodicity (eternal return paradigm) is there some hysterisis in the way quantum states are accessed in ``forward'' or ``reverse'' direction? (reverse direction implies backward causality of J.Wheeler and/or Aristotelian final causation).

Statistical and dynamical properties of a dissipative kicked rotator

NASA Astrophysics Data System (ADS)

Oliveira, Diego F. M.; Leonel, Edson D.

2014-11-01

Some dynamical and statistical properties for a conservative as well as the dissipative problem of relativistic particles in a waveguide are considered. For the first time, two different types of dissipation namely: (i) due to viscosity and; (ii) due to inelastic collision (upon the kick) are considered individually and acting together. For the first case, and contrary to what is expected for the original Zaslavsky’s relativistic model, we show there is a critical parameter where a transition from local to global chaos occurs. On the other hand, after considering the introduction of dissipation also on the kick, the structure of the phase space changes in the sense that chaotic and periodic attractors appear. We study also the chaotic sea by using scaling arguments and we proposed an analytical argument to reinforce the validity of the scaling exponents obtained numerically. In principle such an approach can be extended to any two-dimensional map. Finally, based on the Lyapunov exponent, we show that the parameter space exhibits infinite families of self-similar shrimp-shape structures, corresponding to periodic attractors, embedded in a large region corresponding to chaotic attractors.

Embedded Thermal Control for Spacecraft Subsystems Miniaturization

NASA Technical Reports Server (NTRS)

Didion, Jeffrey R.

2014-01-01

Optimization of spacecraft size, weight and power (SWaP) resources is an explicit technical priority at Goddard Space Flight Center. Embedded Thermal Control Subsystems are a promising technology with many cross cutting NSAA, DoD and commercial applications: 1.) CubeSatSmallSat spacecraft architecture, 2.) high performance computing, 3.) On-board spacecraft electronics, 4.) Power electronics and RF arrays. The Embedded Thermal Control Subsystem technology development efforts focus on component, board and enclosure level devices that will ultimately include intelligent capabilities. The presentation will discuss electric, capillary and hybrid based hardware research and development efforts at Goddard Space Flight Center. The Embedded Thermal Control Subsystem development program consists of interrelated sub-initiatives, e.g., chip component level thermal control devices, self-sensing thermal management, advanced manufactured structures. This presentation includes technical status and progress on each of these investigations. Future sub-initiatives, technical milestones and program goals will be presented.

Advanced DC/DC Converters Towards Higher Volumetric Efficiencies For Space Applications

NASA Technical Reports Server (NTRS)

Shaw, Harry; Shue, Jack; Liu, David; Wang, Bright; Plante, Jeanette

2005-01-01

A new emphasis on planetary exploration by NASA drives the need for small, high power DC/DC converters which are functionally modular. NASA GSFC and other government space organizations are supporting technology development in the DC/DC converter area to both meet new needs and to promote more sources of supply. New technologies which enable miniaturization such as embedded passive technologies and thermal management using high thermal conductivity materials are features of the new designs. Construction of some simple DC/DC converter core circuits using embedded components was found to be successful for increasing volumetric efficiency to 37 W/inch. The embedded passives were also able to perform satisfactorily in this application in cryogenic temperatures.

Joint spatial-spectral hyperspectral image clustering using block-diagonal amplified affinity matrix

NASA Astrophysics Data System (ADS)

Fan, Lei; Messinger, David W.

2018-03-01

The large number of spectral channels in a hyperspectral image (HSI) produces a fine spectral resolution to differentiate between materials in a scene. However, difficult classes that have similar spectral signatures are often confused while merely exploiting information in the spectral domain. Therefore, in addition to spectral characteristics, the spatial relationships inherent in HSIs should also be considered for incorporation into classifiers. The growing availability of high spectral and spatial resolution of remote sensors provides rich information for image clustering. Besides the discriminating power in the rich spectrum, contextual information can be extracted from the spatial domain, such as the size and the shape of the structure to which one pixel belongs. In recent years, spectral clustering has gained popularity compared to other clustering methods due to the difficulty of accurate statistical modeling of data in high dimensional space. The joint spatial-spectral information could be effectively incorporated into the proximity graph for spectral clustering approach, which provides a better data representation by discovering the inherent lower dimensionality from the input space. We embedded both spectral and spatial information into our proposed local density adaptive affinity matrix, which is able to handle multiscale data by automatically selecting the scale of analysis for every pixel according to its neighborhood of the correlated pixels. Furthermore, we explored the "conductivity method," which aims at amplifying the block diagonal structure of the affinity matrix to further improve the performance of spectral clustering on HSI datasets.

Learning linear transformations between counting-based and prediction-based word embeddings

PubMed Central

Hayashi, Kohei; Kawarabayashi, Ken-ichi

2017-01-01

Despite the growing interest in prediction-based word embedding learning methods, it remains unclear as to how the vector spaces learnt by the prediction-based methods differ from that of the counting-based methods, or whether one can be transformed into the other. To study the relationship between counting-based and prediction-based embeddings, we propose a method for learning a linear transformation between two given sets of word embeddings. Our proposal contributes to the word embedding learning research in three ways: (a) we propose an efficient method to learn a linear transformation between two sets of word embeddings, (b) using the transformation learnt in (a), we empirically show that it is possible to predict distributed word embeddings for novel unseen words, and (c) empirically it is possible to linearly transform counting-based embeddings to prediction-based embeddings, for frequent words, different POS categories, and varying degrees of ambiguities. PMID:28926629

One-dimensional sections of exotic spacetimes with superconducting circuits

NASA Astrophysics Data System (ADS)

Sabín, Carlos

2018-05-01

We introduce analogue quantum simulations of 1 + 1 dimensional sections of exotic 3 + 1 dimensional spacetimes, such as Alcubierre warp-drive spacetime, Gödel rotating universe and Kerr highly-rotating black hole metric. Suitable magnetic flux profiles along a SQUID array embedded in a superconducting transmission line allow to generate an effective spatiotemporal dependence in the speed of light, which is able to mimic the corresponding light propagation in a dimensionally-reduced exotic spacetime. In each case, we discuss the technical constraints and the links with possible chronology protection mechanisms and we find the optimal region of parameters for the experimental implementation.

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

Characteristics of strain-sensitive photonic crystal cavities in a flexible substrate.

PubMed

No, You-Shin; Choi, Jae-Hyuck; Kim, Kyoung-Ho; Park, Hong-Gyu

2016-11-14

High-index semiconductor photonic crystal (PhC) cavities in a flexible substrate support strong and tunable optical resonances that can be used for highly sensitive and spatially localized detection of mechanical deformations in physical systems. Here, we report theoretical studies and fundamental understandings of resonant behavior of an optical mode excited in strain-sensitive rod-type PhC cavities consisting of high-index dielectric nanorods embedded in a low-index flexible polymer substrate. Using the three-dimensional finite-difference time-domain simulation method, we calculated two-dimensional transverse-electric-like photonic band diagrams and the three-dimensional dispersion surfaces near the first Γ-point band edge of unidirectionally strained PhCs. A broken rotational symmetry in the PhCs modifies the photonic band structures and results in the asymmetric distributions and different levels of changes in normalized frequencies near the first Γ-point band edge in the reciprocal space, which consequently reveals strain-dependent directional optical losses and selected emission patterns. The calculated electric fields, resonant wavelengths, and quality factors of the band-edge modes in the strained PhCs show an excellent agreement with the results of qualitative analysis of modified dispersion surfaces. Furthermore, polarization-resolved time-averaged Poynting vectors exhibit characteristic dipole-like emission patterns with preferentially selected linear polarizations, originating from the asymmetric band structures in the strained PhCs.

Data series embedding and scale invariant statistics.

PubMed

Michieli, I; Medved, B; Ristov, S

2010-06-01

Data sequences acquired from bio-systems such as human gait data, heart rate interbeat data, or DNA sequences exhibit complex dynamics that is frequently described by a long-memory or power-law decay of autocorrelation function. One way of characterizing that dynamics is through scale invariant statistics or "fractal-like" behavior. For quantifying scale invariant parameters of physiological signals several methods have been proposed. Among them the most common are detrended fluctuation analysis, sample mean variance analyses, power spectral density analysis, R/S analysis, and recently in the realm of the multifractal approach, wavelet analysis. In this paper it is demonstrated that embedding the time series data in the high-dimensional pseudo-phase space reveals scale invariant statistics in the simple fashion. The procedure is applied on different stride interval data sets from human gait measurements time series (Physio-Bank data library). Results show that introduced mapping adequately separates long-memory from random behavior. Smaller gait data sets were analyzed and scale-free trends for limited scale intervals were successfully detected. The method was verified on artificially produced time series with known scaling behavior and with the varying content of noise. The possibility for the method to falsely detect long-range dependence in the artificially generated short range dependence series was investigated. (c) 2009 Elsevier B.V. All rights reserved.

A tool for multi-scale modelling of the renal nephron

PubMed Central

Nickerson, David P.; Terkildsen, Jonna R.; Hamilton, Kirk L.; Hunter, Peter J.

2011-01-01

We present the development of a tool, which provides users with the ability to visualize and interact with a comprehensive description of a multi-scale model of the renal nephron. A one-dimensional anatomical model of the nephron has been created and is used for visualization and modelling of tubule transport in various nephron anatomical segments. Mathematical models of nephron segments are embedded in the one-dimensional model. At the cellular level, these segment models use models encoded in CellML to describe cellular and subcellular transport kinetics. A web-based presentation environment has been developed that allows the user to visualize and navigate through the multi-scale nephron model, including simulation results, at the different spatial scales encompassed by the model description. The Zinc extension to Firefox is used to provide an interactive three-dimensional view of the tubule model and the native Firefox rendering of scalable vector graphics is used to present schematic diagrams for cellular and subcellular scale models. The model viewer is embedded in a web page that dynamically presents content based on user input. For example, when viewing the whole nephron model, the user might be presented with information on the various embedded segment models as they select them in the three-dimensional model view. Alternatively, the user chooses to focus the model viewer on a cellular model located in a particular nephron segment in order to view the various membrane transport proteins. Selecting a specific protein may then present the user with a description of the mathematical model governing the behaviour of that protein—including the mathematical model itself and various simulation experiments used to validate the model against the literature. PMID:22670210

Open/closed string duality and relativistic fluids

NASA Astrophysics Data System (ADS)

Niarchos, Vasilis

2016-07-01

We propose an open/closed string duality in general backgrounds extending previous ideas about open string completeness by Ashoke Sen. Our proposal sets up a general version of holography that works in gravity as a tomographic principle. We argue, in particular, that previous expectations of a supergravity/Dirac-Born-Infeld (DBI) correspondence are naturally embedded in this conjecture and can be tested in a well-defined manner. As an example, we consider the correspondence between open string field theories on extremal D-brane setups in flat space in the large-N , large 't Hooft limit, and asymptotically flat solutions in ten-dimensional type II supergravity. We focus on a convenient long-wavelength regime, where specific effects of higher-spin open string modes can be traced explicitly in the dual supergravity computation. For instance, in this regime we show how the full Abelian DBI action arises from supergravity as a straightforward reformulation of relativistic hydrodynamics. In the example of a (2 +1 )-dimensional open string theory this reformulation involves an Abelian Hodge duality. We also point out how different deformations of the DBI action, related to higher-derivative corrections and non-Abelian effects, can arise in this context as deformations in corresponding relativistic hydrodynamics.

Chirped or time modulated excitation compared to short pulses for photoacoustic imaging in acoustic attenuating media

NASA Astrophysics Data System (ADS)

Burgholzer, P.; Motz, C.; Lang, O.; Berer, T.; Huemer, M.

2018-02-01

In photoacoustic imaging, optically generated acoustic waves transport the information about embedded structures to the sample surface. Usually, short laser pulses are used for the acoustic excitation. Acoustic attenuation increases for higher frequencies, which reduces the bandwidth and limits the spatial resolution. One could think of more efficient waveforms than single short pulses, such as pseudo noise codes, chirped, or harmonic excitation, which could enable a higher information-transfer from the samples interior to its surface by acoustic waves. We used a linear state space model to discretize the wave equation, such as the Stoke's equation, but this method could be used for any other linear wave equation. Linear estimators and a non-linear function inversion were applied to the measured surface data, for onedimensional image reconstruction. The proposed estimation method allows optimizing the temporal modulation of the excitation laser such that the accuracy and spatial resolution of the reconstructed image is maximized. We have restricted ourselves to one-dimensional models, as for higher dimensions the one-dimensional reconstruction, which corresponds to the acoustic wave without attenuation, can be used as input for any ultrasound imaging method, such as back-projection or time-reversal method.

Conservative supra-characteristics method for splitting the hyperbolic systems of gasdynamics for real and perfect gases

NASA Technical Reports Server (NTRS)

Lombard, C. K.

1982-01-01

A conservative flux difference splitting is presented for the hyperbolic systems of gasdynamics. The stable robust method is suitable for wide application in a variety of schemes, explicit or implicit, iterative or direct, for marching in either time or space. The splitting is modeled on the local quasi one dimensional characteristics system for multi-dimensional flow similar to Chakravarthy's nonconservative split coefficient matrix method; but, as the result of maintaining global conservation, the method is able to capture sharp shocks correctly. The embedded characteristics formulation is cast in a primitive variable the volumetric internal energy (rather than the pressure) that is effective for treating real as well as perfect gases. Finally the relationship of the splitting to characteristics boundary conditions is discussed and the associated conservative matrix formulation for a computed blown wall boundary condition is developed as an example. The theoretical development employs and extends the notion of Roe of constructing stable upwind difference formulae by sending split simple one sided flux difference pieces to appropriate mesh sites. The developments are also believed to have the potential for aiding in the analysis of both existing and new conservative difference schemes.

Two-Dimensional DOA and Polarization Estimation for a Mixture of Uncorrelated and Coherent Sources with Sparsely-Distributed Vector Sensor Array

PubMed Central

Si, Weijian; Zhao, Pinjiao; Qu, Zhiyu

2016-01-01

This paper presents an L-shaped sparsely-distributed vector sensor (SD-VS) array with four different antenna compositions. With the proposed SD-VS array, a novel two-dimensional (2-D) direction of arrival (DOA) and polarization estimation method is proposed to handle the scenario where uncorrelated and coherent sources coexist. The uncorrelated and coherent sources are separated based on the moduli of the eigenvalues. For the uncorrelated sources, coarse estimates are acquired by extracting the DOA information embedded in the steering vectors from estimated array response matrix of the uncorrelated sources, and they serve as coarse references to disambiguate fine estimates with cyclical ambiguity obtained from the spatial phase factors. For the coherent sources, four Hankel matrices are constructed, with which the coherent sources are resolved in a similar way as for the uncorrelated sources. The proposed SD-VS array requires only two collocated antennas for each vector sensor, thus the mutual coupling effects across the collocated antennas are reduced greatly. Moreover, the inter-sensor spacings are allowed beyond a half-wavelength, which results in an extended array aperture. Simulation results demonstrate the effectiveness and favorable performance of the proposed method. PMID:27258271

Mapping Chemical Selection Pathways for Designing Multicomponent Alloys: an informatics framework for materials design.

PubMed

Srinivasan, Srikant; Broderick, Scott R; Zhang, Ruifeng; Mishra, Amrita; Sinnott, Susan B; Saxena, Surendra K; LeBeau, James M; Rajan, Krishna

2015-12-18

A data driven methodology is developed for tracking the collective influence of the multiple attributes of alloying elements on both thermodynamic and mechanical properties of metal alloys. Cobalt-based superalloys are used as a template to demonstrate the approach. By mapping the high dimensional nature of the systematics of elemental data embedded in the periodic table into the form of a network graph, one can guide targeted first principles calculations that identify the influence of specific elements on phase stability, crystal structure and elastic properties. This provides a fundamentally new means to rapidly identify new stable alloy chemistries with enhanced high temperature properties. The resulting visualization scheme exhibits the grouping and proximity of elements based on their impact on the properties of intermetallic alloys. Unlike the periodic table however, the distance between neighboring elements uncovers relationships in a complex high dimensional information space that would not have been easily seen otherwise. The predictions of the methodology are found to be consistent with reported experimental and theoretical studies. The informatics based methodology presented in this study can be generalized to a framework for data analysis and knowledge discovery that can be applied to many material systems and recreated for different design objectives.

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.

Fractional-dimensional Child-Langmuir law for a rough cathode

NASA Astrophysics Data System (ADS)

Zubair, M.; Ang, L. K.

2016-07-01

This work presents a self-consistent model of space charge limited current transport in a gap combined of free-space and fractional-dimensional space (Fα), where α is the fractional dimension in the range 0 < α ≤ 1. In this approach, a closed-form fractional-dimensional generalization of Child-Langmuir (CL) law is derived in classical regime which is then used to model the effect of cathode surface roughness in a vacuum diode by replacing the rough cathode with a smooth cathode placed in a layer of effective fractional-dimensional space. Smooth transition of CL law from the fractional-dimensional to integer-dimensional space is also demonstrated. The model has been validated by comparing results with an experiment.

High-dimensional vector semantics

NASA Astrophysics Data System (ADS)

Andrecut, M.

In this paper we explore the “vector semantics” problem from the perspective of “almost orthogonal” property of high-dimensional random vectors. We show that this intriguing property can be used to “memorize” random vectors by simply adding them, and we provide an efficient probabilistic solution to the set membership problem. Also, we discuss several applications to word context vector embeddings, document sentences similarity, and spam filtering.

A new method of preparing embeddment-free sections for transmission electron microscopy: applications to the cytoskeletal framework and other three-dimensional networks.

PubMed

Capco, D G; Krochmalnic, G; Penman, S

1984-05-01

Diethylene glycol distearate is used as a removable embedding medium to produce embeddment -free sections for transmission electron microscopy. The easily cut sections of this material float and form ribbons in a water-filled knife trough and exhibit interference colors that aid in the selection of sections of equal thickness. The images obtained with embeddment -free sections are compared with those from the more conventional epoxy-embedded sections, and illustrate that embedding medium can obscure important biological structures, especially protein filament networks. The embeddment -free section methodology is well suited for morphological studies of cytoskeletal preparations obtained by extraction of cells with nonionic detergent in cytoskeletal stabilizing medium. The embeddment -free section also serves to bridge the very different images afforded by embedded sections and unembedded whole mounts.

Embedded Hyperchaotic Generators: A Comparative Analysis

NASA Astrophysics Data System (ADS)

Sadoudi, Said; Tanougast, Camel; Azzaz, Mohamad Salah; Dandache, Abbas

In this paper, we present a comparative analysis of FPGA implementation performances, in terms of throughput and resources cost, of five well known autonomous continuous hyperchaotic systems. The goal of this analysis is to identify the embedded hyperchaotic generator which leads to designs with small logic area cost, satisfactory throughput rates, low power consumption and low latency required for embedded applications such as secure digital communications between embedded systems. To implement the four-dimensional (4D) chaotic systems, we use a new structural hardware architecture based on direct VHDL description of the forth order Runge-Kutta method (RK-4). The comparative analysis shows that the hyperchaotic Lorenz generator provides attractive performances compared to that of others. In fact, its hardware implementation requires only 2067 CLB-slices, 36 multipliers and no block RAMs, and achieves a throughput rate of 101.6 Mbps, at the output of the FPGA circuit, at a clock frequency of 25.315 MHz with a low latency time of 316 ns. Consequently, these good implementation performances offer to the embedded hyperchaotic Lorenz generator the advantage of being the best candidate for embedded communications applications.

Bubbling and on-off intermittency in bailout embeddings.

PubMed

Cartwright, Julyan H E; Magnasco, Marcelo O; Piro, Oreste; Tuval, Idan

2003-07-01

We establish and investigate the conceptual connection between the dynamics of the bailout embedding of a Hamiltonian system and the dynamical regimes associated with the occurrence of bubbling and blowout bifurcations. The roles of the invariant manifold and the dynamics restricted to it, required in bubbling and blowout bifurcating systems, are played in the bailout embedding by the embedded Hamiltonian dynamical system. The Hamiltonian nature of the dynamics is precisely the distinctive feature of this instance of a bubbling or blowout bifurcation. The detachment of the embedding trajectories from the original ones can thus be thought of as transient on-off intermittency, and noise-induced avoidance of some regions of the embedded phase space can be recognized as Hamiltonian bubbling.

A biomedical sensor system for real-time monitoring of astronauts' physiological parameters during extra-vehicular activities.

PubMed

Fei, Ding-Yu; Zhao, Xiaoming; Boanca, Cosmin; Hughes, Esther; Bai, Ou; Merrell, Ronald; Rafiq, Azhar

2010-07-01

To design and test an embedded biomedical sensor system that can monitor astronauts' comprehensive physiological parameters, and provide real-time data display during extra-vehicle activities (EVA) in the space exploration. An embedded system was developed with an array of biomedical sensors that can be integrated into the spacesuit. Wired communications were tested for physiological data acquisition and data transmission to a computer mounted on the spacesuit during task performances simulating EVA sessions. The sensor integration, data collection and communication, and the real-time data monitoring were successfully validated in the NASA field tests. The developed system may work as an embedded system for monitoring health status during long-term space mission. Copyright 2010 Elsevier Ltd. All rights reserved.

Representing high-dimensional data to intelligent prostheses and other wearable assistive robots: A first comparison of tile coding and selective Kanerva coding.

PubMed

Travnik, Jaden B; Pilarski, Patrick M

2017-07-01

Prosthetic devices have advanced in their capabilities and in the number and type of sensors included in their design. As the space of sensorimotor data available to a conventional or machine learning prosthetic control system increases in dimensionality and complexity, it becomes increasingly important that this data be represented in a useful and computationally efficient way. Well structured sensory data allows prosthetic control systems to make informed, appropriate control decisions. In this study, we explore the impact that increased sensorimotor information has on current machine learning prosthetic control approaches. Specifically, we examine the effect that high-dimensional sensory data has on the computation time and prediction performance of a true-online temporal-difference learning prediction method as embedded within a resource-limited upper-limb prosthesis control system. We present results comparing tile coding, the dominant linear representation for real-time prosthetic machine learning, with a newly proposed modification to Kanerva coding that we call selective Kanerva coding. In addition to showing promising results for selective Kanerva coding, our results confirm potential limitations to tile coding as the number of sensory input dimensions increases. To our knowledge, this study is the first to explicitly examine representations for realtime machine learning prosthetic devices in general terms. This work therefore provides an important step towards forming an efficient prosthesis-eye view of the world, wherein prompt and accurate representations of high-dimensional data may be provided to machine learning control systems within artificial limbs and other assistive rehabilitation technologies.

A numerical study of the 2- and 3-dimensional unsteady Navier-Stokes equations in velocity-vorticity variables using compact difference schemes

NASA Technical Reports Server (NTRS)

Gatski, T. B.; Grosch, C. E.

1984-01-01

A compact finite-difference approximation to the unsteady Navier-Stokes equations in velocity-vorticity variables is used to numerically simulate a number of flows. These include two-dimensional laminar flow of a vortex evolving over a flat plate with an embedded cavity, the unsteady flow over an elliptic cylinder, and aspects of the transient dynamics of the flow over a rearward facing step. The methodology required to extend the two-dimensional formulation to three-dimensions is presented.

Two-dimensional electron gas in monolayer InN quantum wells

DOE PAGES

Pan, Wei; Dimakis, Emmanouil; Wang, George T.; ...

2014-11-24

We report in this letter experimental results that confirm the two-dimensional nature of the electron systems in monolayer InN quantum wells embedded in GaN barriers. The electron density and mobility of the two-dimensional electron system (2DES) in these InN quantum wells are 5×10 15 cm -2 and 420 cm 2 /Vs, respectively. Moreover, the diagonal resistance of the 2DES shows virtually no temperature dependence in a wide temperature range, indicating the topological nature of the 2DES.

Preservation of three-dimensional spatial structure in the gut microbiome.

PubMed

Hasegawa, Yuko; Mark Welch, Jessica L; Rossetti, Blair J; Borisy, Gary G

2017-01-01

Preservation of three-dimensional structure in the gut is necessary in order to analyze the spatial organization of the gut microbiota and gut luminal contents. In this study, we evaluated preparation methods for mouse gut with the goal of preserving micron-scale spatial structure while performing fluorescence imaging assays. Our evaluation of embedding methods showed that commonly used media such as Tissue-Tek Optimal Cutting Temperature (OCT) compound, paraffin, and polyester waxes resulted in redistribution of luminal contents. By contrast, a hydrophilic methacrylate resin, Technovit H8100, preserved three-dimensional organization. Our mouse intestinal preparation protocol optimized using the Technovit H8100 embedding method was compatible with microbial fluorescence in situ hybridization (FISH) and other labeling techniques, including immunostaining and staining with both wheat germ agglutinin (WGA) and 4', 6-diamidino-2-phenylindole (DAPI). Mucus could be visualized whether the sample was fixed with paraformaldehyde (PFA) or with Carnoy's fixative. The protocol optimized in this study enabled simultaneous visualization of micron-scale spatial patterns formed by microbial cells in the mouse intestines along with biogeographical landmarks such as host-derived mucus and food particles.

Reconstructing latent dynamical noise for better forecasting observables

NASA Astrophysics Data System (ADS)

Hirata, Yoshito

2018-03-01

I propose a method for reconstructing multi-dimensional dynamical noise inspired by the embedding theorem of Muldoon et al. [Dyn. Stab. Syst. 13, 175 (1998)] by regarding multiple predictions as different observables. Then, applying the embedding theorem by Stark et al. [J. Nonlinear Sci. 13, 519 (2003)] for a forced system, I produce time series forecast by supplying the reconstructed past dynamical noise as auxiliary information. I demonstrate the proposed method on toy models driven by auto-regressive models or independent Gaussian noise.

(3 + 1)-dimensional topological phases and self-dual quantum geometries encoded on Heegaard surfaces

NASA Astrophysics Data System (ADS)

Dittrich, Bianca

2017-05-01

We apply the recently suggested strategy to lift state spaces and operators for (2 + 1)-dimensional topological quantum field theories to state spaces and operators for a (3 + 1)-dimensional TQFT with defects. We start from the (2 + 1)-dimensional TuraevViro theory and obtain a state space, consistent with the state space expected from the Crane-Yetter model with line defects.

Parametric binary dissection

NASA Technical Reports Server (NTRS)

Bokhari, Shahid H.; Crockett, Thomas W.; Nicol, David M.

1993-01-01

Binary dissection is widely used to partition non-uniform domains over parallel computers. This algorithm does not consider the perimeter, surface area, or aspect ratio of the regions being generated and can yield decompositions that have poor communication to computation ratio. Parametric Binary Dissection (PBD) is a new algorithm in which each cut is chosen to minimize load + lambda x(shape). In a 2 (or 3) dimensional problem, load is the amount of computation to be performed in a subregion and shape could refer to the perimeter (respectively surface) of that subregion. Shape is a measure of communication overhead and the parameter permits us to trade off load imbalance against communication overhead. When A is zero, the algorithm reduces to plain binary dissection. This algorithm can be used to partition graphs embedded in 2 or 3-d. Load is the number of nodes in a subregion, shape the number of edges that leave that subregion, and lambda the ratio of time to communicate over an edge to the time to compute at a node. An algorithm is presented that finds the depth d parametric dissection of an embedded graph with n vertices and e edges in O(max(n log n, de)) time, which is an improvement over the O(dn log n) time of plain binary dissection. Parallel versions of this algorithm are also presented; the best of these requires O((n/p) log(sup 3)p) time on a p processor hypercube, assuming graphs of bounded degree. How PBD is applied to 3-d unstructured meshes and yields partitions that are better than those obtained by plain dissection is described. Its application to the color image quantization problem is also discussed, in which samples in a high-resolution color space are mapped onto a lower resolution space in a way that minimizes the color error.

Detection of a Fermi-level crossing in Si(557)-Au with inverse photoemission

NASA Astrophysics Data System (ADS)

Lipton-Duffin, J. A.; MacLeod, J. M.; McLean, A. B.

2006-06-01

The unoccupied energy bands of the quasi-one-dimensional (1D) Si(557)-Au system have been studied with momentum-resolved inverse photoemission. A band is found that lies (0.4±0.4)eV above the Fermi level at the center of the surface Brillouin zone (Γ¯) . It disperses to higher binding energy, along the Γ Kmacr direction, and crosses the Fermi level at k‖=0.5±0.1Å-1 . The corresponding direction in real space is parallel to both the rows of silicon adatoms and the rows of embedded gold atoms that are distinctive features of this surface reconstruction. The location of the crossing is in good agreement with previously published photoemission data [Altmann , Phys. Rev. B 64, 035406 (2001); Ahn , Phys. Rev. Lett. 91, 196403 (2003)], where two closely spaced bands were found to disperse from the Kmacr zone boundary to lower binding energy and then cross the Fermi level. In addition to the band mentioned above, a band was found that has parabolic dispersion along Γ Kmacr , the direction that is parallel to the rows of embedded gold atoms. The band minimum for the parabolic band lies (0.8±0.4)eV below the vacuum level and it has an effective mass m*=(1.0±0.1)me , where me is the free electron mass. Perpendicular to the rows of gold atoms, as expected for a state with quasi-1D symmetry, it has flat dispersion. This band may be an image state resonance, overlapping the silicon conduction band continuum, and it is spatially localized to the edge of the silicon terraces.

Romans supergravity from five-dimensional holograms

NASA Astrophysics Data System (ADS)

Chang, Chi-Ming; Fluder, Martin; Lin, Ying-Hsuan; Wang, Yifan

2018-05-01

We study five-dimensional superconformal field theories and their holographic dual, matter-coupled Romans supergravity. On the one hand, some recently derived formulae allow us to extract the central charges from deformations of the supersymmetric five-sphere partition function, whose large N expansion can be computed using matrix model techniques. On the other hand, the conformal and flavor central charges can be extracted from the six-dimensional supergravity action, by carefully analyzing its embedding into type I' string theory. The results match on the two sides of the holographic duality. Our results also provide analytic evidence for the symmetry enhancement in five-dimensional superconformal field theories.

Management of asymptomatic intracardiac missiles using echocardiography.

PubMed

Robison, R J; Brown, J W; Caldwell, R; Stone, K S; King, H

1988-09-01

A child sustained a low-velocity airgun pellet injury to the left ventricle. No cardiovascular compromise was produced. The foreign body was localized by two-dimensional echocardiography to the left ventricular chamber near the mitral valve, and subsequently removed through a left atriotomy incision. In asymptomatic patients, missiles clearly embedded within a chamber wall may be observed; all others should be removed. Two-dimensional echocardiography is recommended for localization.

7A projection map of the S-layer protein sbpA obtained with trehalose-embedded monolayer crystals.

PubMed

Norville, Julie E; Kelly, Deborah F; Knight, Thomas F; Belcher, Angela M; Walz, Thomas

2007-12-01

Two-dimensional crystallization on lipid monolayers is a versatile tool to obtain structural information of proteins by electron microscopy. An inherent problem with this approach is to prepare samples in a way that preserves the crystalline order of the protein array and produces specimens that are sufficiently flat for high-resolution data collection at high tilt angles. As a test specimen to optimize the preparation of lipid monolayer crystals for electron microscopy imaging, we used the S-layer protein sbpA, a protein with potential for designing arrays of both biological and inorganic materials with engineered properties for a variety of nanotechnology applications. Sugar embedding is currently considered the best method to prepare two-dimensional crystals of membrane proteins reconstituted into lipid bilayers. We found that using a loop to transfer lipid monolayer crystals to an electron microscopy grid followed by embedding in trehalose and quick-freezing in liquid ethane also yielded the highest resolution images for sbpA lipid monolayer crystals. Using images of specimens prepared in this way we could calculate a projection map of sbpA at 7A resolution, one of the highest resolution projection structures obtained with lipid monolayer crystals to date.

Dark-field X-ray microscopy for multiscale structural characterization

NASA Astrophysics Data System (ADS)

Simons, H.; King, A.; Ludwig, W.; Detlefs, C.; Pantleon, W.; Schmidt, S.; Snigireva, I.; Snigirev, A.; Poulsen, H. F.

2015-01-01

Many physical and mechanical properties of crystalline materials depend strongly on their internal structure, which is typically organized into grains and domains on several length scales. Here we present dark-field X-ray microscopy; a non-destructive microscopy technique for the three-dimensional mapping of orientations and stresses on lengths scales from 100 nm to 1 mm within embedded sampling volumes. The technique, which allows ‘zooming’ in and out in both direct and angular space, is demonstrated by an annealing study of plastically deformed aluminium. Facilitating the direct study of the interactions between crystalline elements is a key step towards the formulation and validation of multiscale models that account for the entire heterogeneity of a material. Furthermore, dark-field X-ray microscopy is well suited to applied topics, where the structural evolution of internal nanoscale elements (for example, positioned at interfaces) is crucial to the performance and lifetime of macro-scale devices and components thereof.

Locality preserving non-negative basis learning with graph embedding.

PubMed

Ghanbari, Yasser; Herrington, John; Gur, Ruben C; Schultz, Robert T; Verma, Ragini

2013-01-01

The high dimensionality of connectivity networks necessitates the development of methods identifying the connectivity building blocks that not only characterize the patterns of brain pathology but also reveal representative population patterns. In this paper, we present a non-negative component analysis framework for learning localized and sparse sub-network patterns of connectivity matrices by decomposing them into two sets of discriminative and reconstructive bases. In order to obtain components that are designed towards extracting population differences, we exploit the geometry of the population by using a graphtheoretical scheme that imposes locality-preserving properties as well as maintaining the underlying distance between distant nodes in the original and the projected space. The effectiveness of the proposed framework is demonstrated by applying it to two clinical studies using connectivity matrices derived from DTI to study a population of subjects with ASD, as well as a developmental study of structural brain connectivity that extracts gender differences.

Semi-automated segmentation of the prostate gland boundary in ultrasound images using a machine learning approach

NASA Astrophysics Data System (ADS)

Diaz, Kristians; Castaneda, Benjamin

2008-03-01

This paper presents a semi-automated algorithm for prostate boundary segmentation from three-dimensional (3D) ultrasound (US) images. The US volume is sampled into 72 slices which go through the center of the prostate gland and are separated at a uniform angular spacing of 2.5 degrees. The approach requires the user to select four points from slices (at 0, 45, 90 and 135 degrees) which are used to initialize a discrete dynamic contour (DDC) algorithm. 4 Support Vector Machines (SVMs) are trained over the output of the DDC and classify the rest of the slices. The output of the SVMs is refined using binary morphological operations and DDC to produce the final result. The algorithm was tested on seven ex vivo 3D US images of prostate glands embedded in an agar mold. Results show good agreement with manual segmentation.

A three-dimensional actuated origami-inspired transformable metamaterial with multiple degrees of freedom

NASA Astrophysics Data System (ADS)

Overvelde, Johannes T. B.; de Jong, Twan A.; Shevchenko, Yanina; Becerra, Sergio A.; Whitesides, George M.; Weaver, James C.; Hoberman, Chuck; Bertoldi, Katia

2016-03-01

Reconfigurable devices, whose shape can be drastically altered, are central to expandable shelters, deployable space structures, reversible encapsulation systems and medical tools and robots. All these applications require structures whose shape can be actively controlled, both for deployment and to conform to the surrounding environment. While most current reconfigurable designs are application specific, here we present a mechanical metamaterial with tunable shape, volume and stiffness. Our approach exploits a simple modular origami-like design consisting of rigid faces and hinges, which are connected to form a periodic structure consisting of extruded cubes. We show both analytically and experimentally that the transformable metamaterial has three degrees of freedom, which can be actively deformed into numerous specific shapes through embedded actuation. The proposed metamaterial can be used to realize transformable structures with arbitrary architectures, highlighting a robust strategy for the design of reconfigurable devices over a wide range of length scales.

Nuclear-effects model embedded stochastically in simulation (NEMESIS) summary report. Technical paper

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

Youngren, M.A.

1989-11-01

An analytic probability model of tactical nuclear warfare in the theater is presented in this paper. The model addresses major problems associated with representing nuclear warfare in the theater. Current theater representations of a potential nuclear battlefield are developed in context of low-resolution, theater-level models or scenarios. These models or scenarios provide insufficient resolution in time and space for modeling a nuclear exchange. The model presented in this paper handles the spatial uncertainty in potentially targeted unit locations by proposing two-dimensional multivariate probability models for the actual and perceived locations of units subordinate to the major (division-level) units represented inmore » theater scenarios. The temporal uncertainty in the activities of interest represented in our theater-level Force Evaluation Model (FORCEM) is handled through probability models of the acquisition and movement of potential nuclear target units.« less

Intrinsic cavity QED and emergent quasinormal modes for a single photon

NASA Astrophysics Data System (ADS)

Dong, H.; Gong, Z. R.; Ian, H.; Zhou, Lan; Sun, C. P.

2009-06-01

We propose a special cavity design that is constructed by terminating a one-dimensional waveguide with a perfect mirror at one end and doping a two-level atom at the other. We show that this atom plays the intrinsic role of a semitransparent mirror for single-photon transports such that quasinormal modes emerge spontaneously in the cavity system. This atomic mirror has its reflection coefficient tunable through its level spacing and its coupling to the cavity field, for which the cavity system can be regarded as a two-end resonator with a continuously tunable leakage. The overall investigation predicts the existence of quasibound states in the waveguide continuum. Solid-state implementations based on a dc-superconducting quantum interference device circuit and a defected line resonator embedded in a photonic crystal are illustrated to show the experimental accessibility of the generic model.

CKM pattern from localized generations in extra dimension

NASA Astrophysics Data System (ADS)

Matti, C.

2006-10-01

We revisit the issue of the quark masses and mixing angles in the framework of large extra dimension. We consider three identical standard model families resulting from higher-dimensional fields localized on different branes embedded in a large extra dimension. Furthermore we use a decaying profile in the bulk different form previous works. With the Higgs field also localized on a different brane, the hierarchy of masses between the families results from their different positions in the extra space. When the left-handed doublet and the right-handed singlets are localized with different couplings on the branes, we found a set of brane locations in one extra dimension which leads to the correct quark masses and mixing angles with the sufficient strength of CP-violation. We see that the decaying profile of the Higgs field plays a crucial role for producing the hierarchies in a rather natural way.

Theory of Space Charge Limited Current in Fractional Dimensional Space

NASA Astrophysics Data System (ADS)

Zubair, Muhammad; Ang, L. K.

The concept of fractional dimensional space has been effectively applied in many areas of physics to describe the fractional effects on the physical systems. We will present some recent developments of space charge limited (SCL) current in free space and solid in the framework of fractional dimensional space which may account for the effect of imperfectness or roughness of the electrode surface. For SCL current in free space, the governing law is known as the Child-Langmuir (CL) law. Its analogy in a trap-free solid (or dielectric) is known as Mott-Gurney (MG) law. This work extends the one-dimensional CL Law and MG Law for the case of a D-dimensional fractional space with 0 < D <= 1 where parameter D defines the degree of roughness of the electrode surface. Such a fractional dimensional space generalization of SCL current theory can be used to characterize the charge injection by the imperfectness or roughness of the surface in applications related to high current cathode (CL law), and organic electronics (MG law). In terms of operating regime, the model has included the quantum effects when the spacing between the electrodes is small.

Anisotropic fractal media by vector calculus in non-integer dimensional space

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

Tarasov, Vasily E., E-mail: tarasov@theory.sinp.msu.ru

2014-08-15

A review of different approaches to describe anisotropic fractal media is proposed. In this paper, differentiation and integration non-integer dimensional and multi-fractional spaces are considered as tools to describe anisotropic fractal materials and media. We suggest a generalization of vector calculus for non-integer dimensional space by using a product measure method. The product of fractional and non-integer dimensional spaces allows us to take into account the anisotropy of the fractal media in the framework of continuum models. The integration over non-integer-dimensional spaces is considered. In this paper differential operators of first and second orders for fractional space and non-integer dimensionalmore » space are suggested. The differential operators are defined as inverse operations to integration in spaces with non-integer dimensions. Non-integer dimensional space that is product of spaces with different dimensions allows us to give continuum models for anisotropic type of the media. The Poisson's equation for fractal medium, the Euler-Bernoulli fractal beam, and the Timoshenko beam equations for fractal material are considered as examples of application of suggested generalization of vector calculus for anisotropic fractal materials and media.« less

Efficient embedding of complex networks to hyperbolic space via their Laplacian

PubMed Central

Alanis-Lobato, Gregorio; Mier, Pablo; Andrade-Navarro, Miguel A.

2016-01-01

The different factors involved in the growth process of complex networks imprint valuable information in their observable topologies. How to exploit this information to accurately predict structural network changes is the subject of active research. A recent model of network growth sustains that the emergence of properties common to most complex systems is the result of certain trade-offs between node birth-time and similarity. This model has a geometric interpretation in hyperbolic space, where distances between nodes abstract this optimisation process. Current methods for network hyperbolic embedding search for node coordinates that maximise the likelihood that the network was produced by the afore-mentioned model. Here, a different strategy is followed in the form of the Laplacian-based Network Embedding, a simple yet accurate, efficient and data driven manifold learning approach, which allows for the quick geometric analysis of big networks. Comparisons against existing embedding and prediction techniques highlight its applicability to network evolution and link prediction. PMID:27445157

Efficient embedding of complex networks to hyperbolic space via their Laplacian

NASA Astrophysics Data System (ADS)

Alanis-Lobato, Gregorio; Mier, Pablo; Andrade-Navarro, Miguel A.

2016-07-01

The different factors involved in the growth process of complex networks imprint valuable information in their observable topologies. How to exploit this information to accurately predict structural network changes is the subject of active research. A recent model of network growth sustains that the emergence of properties common to most complex systems is the result of certain trade-offs between node birth-time and similarity. This model has a geometric interpretation in hyperbolic space, where distances between nodes abstract this optimisation process. Current methods for network hyperbolic embedding search for node coordinates that maximise the likelihood that the network was produced by the afore-mentioned model. Here, a different strategy is followed in the form of the Laplacian-based Network Embedding, a simple yet accurate, efficient and data driven manifold learning approach, which allows for the quick geometric analysis of big networks. Comparisons against existing embedding and prediction techniques highlight its applicability to network evolution and link prediction.

Three-dimensional plotting of a cell-laden alginate/methylcellulose blend: towards biofabrication of tissue engineering constructs with clinically relevant dimensions.

PubMed

Schütz, Kathleen; Placht, Anna-Maria; Paul, Birgit; Brüggemeier, Sophie; Gelinsky, Michael; Lode, Anja

2017-05-01

Biofabrication of tissue engineering constructs with tailored architecture and organized cell placement using rapid prototyping technologies is a major research focus in the field of regenerative therapies. This study describes a novel alginate-based material suitable for both cell embedding and fabrication of three-dimensional (3D) structures with predefined geometry by 3D plotting. The favourable printing properties of the material were achieved by using a simple strategy: addition of methylcellulose (MC) to a 3% alginate solution resulted in a strongly enhanced viscosity, which enabled accurate and easy deposition without high technical efforts. After scaffold plotting, the alginate chains were crosslinked with Ca 2+ ; MC did not contribute to the gelation and was released from the scaffolds during the following cultivation. The resulting constructs are characterized by high elasticity and stability, as well as an enhanced microporosity caused by the transient presence of MC. The suitability of the alginate/MC blend for cell embedding was evaluated by direct incorporation of mesenchymal stem cells during scaffold fabrication. The embedded cells showed high viability after 3 weeks of cultivation, which was similar to those of cells within pure alginate scaffolds which served as control. Maintenance of the differentiation potential of embedded cells, as an important requirement for the generation of functional tissue engineering constructs, was proven for adipogenic differentiation as a model for soft tissue formation. In conclusion, the temporary integration of MC into a low-concentrated alginate solution allowed the generation of scaffolds with dimensions in the range of centimetres without loss of the positive properties of low-concentrated alginate hydrogels with regard to cell embedding. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Fractional-dimensional Child-Langmuir law for a rough cathode

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

Zubair, M., E-mail: muhammad-zubair@sutd.edu.sg; Ang, L. K., E-mail: ricky-ang@sutd.edu.sg

This work presents a self-consistent model of space charge limited current transport in a gap combined of free-space and fractional-dimensional space (F{sup α}), where α is the fractional dimension in the range 0 < α ≤ 1. In this approach, a closed-form fractional-dimensional generalization of Child-Langmuir (CL) law is derived in classical regime which is then used to model the effect of cathode surface roughness in a vacuum diode by replacing the rough cathode with a smooth cathode placed in a layer of effective fractional-dimensional space. Smooth transition of CL law from the fractional-dimensional to integer-dimensional space is also demonstrated. The model has beenmore » validated by comparing results with an experiment.« less

A new method of preparing embeddment-free sections for transmission electron microscopy: applications to the cytoskeletal framework and other three-dimensional networks

PubMed Central

1984-01-01

Diethylene glycol distearate is used as a removable embedding medium to produce embeddment -free sections for transmission electron microscopy. The easily cut sections of this material float and form ribbons in a water-filled knife trough and exhibit interference colors that aid in the selection of sections of equal thickness. The images obtained with embeddment -free sections are compared with those from the more conventional epoxy-embedded sections, and illustrate that embedding medium can obscure important biological structures, especially protein filament networks. The embeddment -free section methodology is well suited for morphological studies of cytoskeletal preparations obtained by extraction of cells with nonionic detergent in cytoskeletal stabilizing medium. The embeddment -free section also serves to bridge the very different images afforded by embedded sections and unembedded whole mounts. PMID:6539336

Closed-form solution of temperature and heat flux in embedded cooling channels

NASA Astrophysics Data System (ADS)

Griggs, Steven Craig

1997-11-01

An analytical method is discussed for predicting temperature in a layered composite material with embedded cooling channels. The cooling channels are embedded in the material to maintain its temperature at acceptable levels. Problems of this type are encountered in the aerospace industry and include high-temperature or high-heat-flux protection for advanced composite-material skins of high-speed air vehicles; thermal boundary-layer flow control on supersonic transports; or infrared signature suppression on military vehicles. A Green's function solution of the diffusion equation is used to simultaneously predict the global and localized effects of temperature in the material and in the embedded cooling channels. The integral method is used to solve the energy equation with fluid flow to find the solution of temperature and heat flux in the cooling fluid and material simultaneously. This method of calculation preserves the three-dimensional nature of this problem.

Embedded Managers in Informal Learning Spaces

ERIC Educational Resources Information Center

Raisin, Victoria; Fennewald, Joseph

2016-01-01

Many universities have decided to invest in updating their informal learning spaces. One decision to be made in planning the space is how to staff it. The researchers carried out a qualitative case study to better understand the perspective of learning space managers who work in offices within their assigned space. The research generated six…

Oriented matroids—combinatorial structures underlying loop quantum gravity

NASA Astrophysics Data System (ADS)

Brunnemann, Johannes; Rideout, David

2010-10-01

We analyze combinatorial structures which play a central role in determining spectral properties of the volume operator (Ashtekar A and Lewandowski J 1998 Adv. Theor. Math. Phys. 1 388) in loop quantum gravity (LQG). These structures encode geometrical information of the embedding of arbitrary valence vertices of a graph in three-dimensional Riemannian space and can be represented by sign strings containing relative orientations of embedded edges. We demonstrate that these signature factors are a special representation of the general mathematical concept of an oriented matroid (Ziegler G M 1998 Electron. J. Comb.; Björner A et al 1999 Oriented Matroids (Cambridge: Cambridge University Press)). Moreover, we show that oriented matroids can also be used to describe the topology (connectedness) of directed graphs. Hence, the mathematical methods developed for oriented matroids can be applied to the difficult combinatorics of embedded graphs underlying the construction of LQG. As a first application we revisit the analysis of Brunnemann and Rideout (2008 Class. Quantum Grav. 25 065001 and 065002), and find that enumeration of all possible sign configurations used there is equivalent to enumerating all realizable oriented matroids of rank 3 (Ziegler G M 1998 Electron. J. Comb.; Björner A et al 1999 Oriented Matroids (Cambridge: Cambridge University Press)), and thus can be greatly simplified. We find that for 7-valent vertices having no coplanar triples of edge tangents, the smallest non-zero eigenvalue of the volume spectrum does not grow as one increases the maximum spin jmax at the vertex, for any orientation of the edge tangents. This indicates that, in contrast to the area operator, considering large jmax does not necessarily imply large volume eigenvalues. In addition we give an outlook to possible starting points for rewriting the combinatorics of LQG in terms of oriented matroids.

Fractal electrodynamics via non-integer dimensional space approach

NASA Astrophysics Data System (ADS)

Tarasov, Vasily E.

2015-09-01

Using the recently suggested vector calculus for non-integer dimensional space, we consider electrodynamics problems in isotropic case. This calculus allows us to describe fractal media in the framework of continuum models with non-integer dimensional space. We consider electric and magnetic fields of fractal media with charges and currents in the framework of continuum models with non-integer dimensional spaces. An application of the fractal Gauss's law, the fractal Ampere's circuital law, the fractal Poisson equation for electric potential, and equation for fractal stream of charges are suggested. Lorentz invariance and speed of light in fractal electrodynamics are discussed. An expression for effective refractive index of non-integer dimensional space is suggested.

The structure and development of streamwise vortex arrays embedded in a turbulent boundary layer. Ph.D. Thesis - Case Western Reserve Univ.

NASA Technical Reports Server (NTRS)

Wendt, Bruce J.; Greber, Isaac; Hingst, Warren R.

1991-01-01

An investigation of the structure and development of streamwise vortices embedded in a turbulent boundary layer was conducted. The vortices were generated by a single spanwise row of rectangular vortex generator blades. A single embedded vortex was examined, as well as arrays of embedded counter rotating vortices produced by equally spaced vortex generators. Measurements of the secondary velocity field in the crossplane provided the basis for characterization of vortex structure. Vortex structure was characterized by four descriptors. The center of each vortex core was located at the spanwise and normal position of peak streamwise vorticity. Vortex concentration was characterized by the magnitude of the peak streamwise vorticity, and the vortex strength by its circulation. Measurements of the secondary velocity field were conducted at two crossplane locations to examine the streamwise development of the vortex arrays. Large initial spacings of the vortex generators produced pairs of strong vortices which tended to move away from the wall region while smaller spacings produced tight arrays of weak vortices close to the wall. A model of vortex interaction and development is constructed using the experimental results. The model is based on the structure of the Oseen Vortex. Vortex trajectories are modelled by including the convective effects of neighbors.

A non-linear dimension reduction methodology for generating data-driven stochastic input models

NASA Astrophysics Data System (ADS)

Ganapathysubramanian, Baskar; Zabaras, Nicholas

2008-06-01

Stochastic analysis of random heterogeneous media (polycrystalline materials, porous media, functionally graded materials) provides information of significance only if realistic input models of the topology and property variations are used. This paper proposes a framework to construct such input stochastic models for the topology and thermal diffusivity variations in heterogeneous media using a data-driven strategy. Given a set of microstructure realizations (input samples) generated from given statistical information about the medium topology, the framework constructs a reduced-order stochastic representation of the thermal diffusivity. This problem of constructing a low-dimensional stochastic representation of property variations is analogous to the problem of manifold learning and parametric fitting of hyper-surfaces encountered in image processing and psychology. Denote by M the set of microstructures that satisfy the given experimental statistics. A non-linear dimension reduction strategy is utilized to map M to a low-dimensional region, A. We first show that M is a compact manifold embedded in a high-dimensional input space Rn. An isometric mapping F from M to a low-dimensional, compact, connected set A⊂Rd(d≪n) is constructed. Given only a finite set of samples of the data, the methodology uses arguments from graph theory and differential geometry to construct the isometric transformation F:M→A. Asymptotic convergence of the representation of M by A is shown. This mapping F serves as an accurate, low-dimensional, data-driven representation of the property variations. The reduced-order model of the material topology and thermal diffusivity variations is subsequently used as an input in the solution of stochastic partial differential equations that describe the evolution of dependant variables. A sparse grid collocation strategy (Smolyak algorithm) is utilized to solve these stochastic equations efficiently. We showcase the methodology by constructing low-dimensional input stochastic models to represent thermal diffusivity in two-phase microstructures. This model is used in analyzing the effect of topological variations of two-phase microstructures on the evolution of temperature in heat conduction processes.

Phylogenetic trees and Euclidean embeddings.

PubMed

Layer, Mark; Rhodes, John A

2017-01-01

It was recently observed by de Vienne et al. (Syst Biol 60(6):826-832, 2011) that a simple square root transformation of distances between taxa on a phylogenetic tree allowed for an embedding of the taxa into Euclidean space. While the justification for this was based on a diffusion model of continuous character evolution along the tree, here we give a direct and elementary explanation for it that provides substantial additional insight. We use this embedding to reinterpret the differences between the NJ and BIONJ tree building algorithms, providing one illustration of how this embedding reflects tree structures in data.

Histologic Changes as Indicators of Carcinogenicity of Tungsten Alloy in Rodents

DTIC Science & Technology

2008-01-01

into the the cavity - endocarditis pulmonary artery from where they can be removed Intracavitary or partially embedded in the mayocardium that are found...protruding into the cavity – endocarditis Fragments in the myocardium, pericardium & pericardial space - leave in place Not completely embedded in

SuperComputers for Space Applications

DTIC Science & Technology

2005-07-13

also ADM001791, Potentially Disruptive Technologies and Their Impact in Space Programs Held in Marseille, France on 4-6 July 2005. , The original...Performance Embedded Computing will allow Ambitious Space Science Investigation", Proc. First Symp. on Potentially Disruptive Technologies and Their Impact in Space Programs, 2005. ➦ SOMMAIRE/SUMMARY ➦ Data Processing

Global Three-dimensional Simulation of the Solar Wind-Magnetosphere Interaction Using a Two-way Coupled Magnetohydrodynamics with Embedded Particle-in-Cell Model

NASA Astrophysics Data System (ADS)

Chen, Y.; Toth, G.; Cassak, P.; Jia, X.; Gombosi, T. I.; Slavin, J. A.; Welling, D. T.; Markidis, S.; Peng, I. B.; Jordanova, V. K.; Henderson, M. G.

2017-12-01

We perform a three-dimensional (3D) global simulation of Earth's magnetosphere with kinetic reconnection physics to study the interaction between the solar wind and Earth's magnetosphere. In this global simulation with magnetohydrodynamics with embedded particle-in-cell model (MHD-EPIC), both the dayside magnetopause reconnection region and the magnetotail reconnection region are covered with a kinetic particle-in-cell code iPIC3D, which is two-way coupled with the global MHD model BATS-R-US. We will describe the dayside reconnection related phenomena, such as the lower hybrid drift instability (LHDI) and the evolution of the flux transfer events (FTEs) along the magnetopause, and compare the simulation results with observations. We will also discuss the response of the magnetotail to the southward IMF. The onset of the tail reconnection and the properties of the magnetotail flux ropes will be discussed.

Dimensionless embedding for nonlinear time series analysis

NASA Astrophysics Data System (ADS)

Hirata, Yoshito; Aihara, Kazuyuki

2017-09-01

Recently, infinite-dimensional delay coordinates (InDDeCs) have been proposed for predicting high-dimensional dynamics instead of conventional delay coordinates. Although InDDeCs can realize faster computation and more accurate short-term prediction, it is still not well-known whether InDDeCs can be used in other applications of nonlinear time series analysis in which reconstruction is needed for the underlying dynamics from a scalar time series generated from a dynamical system. Here, we give theoretical support for justifying the use of InDDeCs and provide numerical examples to show that InDDeCs can be used for various applications for obtaining the recurrence plots, correlation dimensions, and maximal Lyapunov exponents, as well as testing directional couplings and extracting slow-driving forces. We demonstrate performance of the InDDeCs using the weather data. Thus, InDDeCs can eventually realize "dimensionless embedding" while we enjoy faster and more reliable computations.

Three-dimensional imaging of crystalline inclusions embedded in intact maize stalks.

PubMed

Badger, John; Lal, Jyotsana; Harder, Ross; Inouye, Hideyo; Gleber, S Charlotte; Vogt, Stefan; Robinson, Ian; Makowski, Lee

2013-10-03

Mineral inclusions in biomass are attracting increased scrutiny due to their potential impact on processing methods designed to provide renewable feedstocks for the production of chemicals and fuels. These inclusions are often sculpted by the plant into shapes required to support functional roles that include the storage of specific elements, strengthening of the plant structure, and providing a defense against pathogens and herbivores. In situ characterization of these inclusions faces substantial challenges since they are embedded in an opaque, complex polymeric matrix. Here we describe the use of Bragg coherent diffraction imaging (BCDI) to study mineral inclusions within intact maize stalks. Three-dimensional BCDI data sets were collected and used to reconstruct images of mineral inclusions at 50-100 nm resolution. Asymmetries in the intensity distributions around the Bragg peaks provided detailed information about the deformation fields within these crystal particles revealing lattice defects that result in distinct internal crystal domains.

Estimation of Surface Temperature and Heat Flux by Inverse Heat Transfer Methods Using Internal Temperatures Measured While Radiantly Heating a Carbon/Carbon Specimen up to 1920 F

NASA Technical Reports Server (NTRS)

Pizzo, Michelle; Daryabeigi, Kamran; Glass, David

2015-01-01

The ability to solve the heat conduction equation is needed when designing materials to be used on vehicles exposed to extremely high temperatures; e.g. vehicles used for atmospheric entry or hypersonic flight. When using test and flight data, computational methods such as finite difference schemes may be used to solve for both the direct heat conduction problem, i.e., solving between internal temperature measurements, and the inverse heat conduction problem, i.e., using the direct solution to march forward in space to the surface of the material to estimate both surface temperature and heat flux. The completed research first discusses the methods used in developing a computational code to solve both the direct and inverse heat transfer problems using one dimensional, centered, implicit finite volume schemes and one dimensional, centered, explicit space marching techniques. The developed code assumed the boundary conditions to be specified time varying temperatures and also considered temperature dependent thermal properties. The completed research then discusses the results of analyzing temperature data measured while radiantly heating a carbon/carbon specimen up to 1920 F. The temperature was measured using thermocouple (TC) plugs (small carbon/carbon material specimens) with four embedded TC plugs inserted into the larger carbon/carbon specimen. The purpose of analyzing the test data was to estimate the surface heat flux and temperature values from the internal temperature measurements using direct and inverse heat transfer methods, thus aiding in the thermal and structural design and analysis of high temperature vehicles.

Locally linear embedding: dimension reduction of massive protostellar spectra

NASA Astrophysics Data System (ADS)

Ward, J. L.; Lumsden, S. L.

2016-09-01

We present the results of the application of locally linear embedding (LLE) to reduce the dimensionality of dereddened and continuum subtracted near-infrared spectra using a combination of models and real spectra of massive protostars selected from the Red MSX Source survey data base. A brief comparison is also made with two other dimension reduction techniques; principal component analysis (PCA) and Isomap using the same set of spectra as well as a more advanced form of LLE, Hessian locally linear embedding. We find that whilst LLE certainly has its limitations, it significantly outperforms both PCA and Isomap in classification of spectra based on the presence/absence of emission lines and provides a valuable tool for classification and analysis of large spectral data sets.

Improving Mixed Variable Optimization of Computational and Model Parameters Using Multiple Surrogate Functions

DTIC Science & Technology

2008-03-01

multiplicative corrections as well as space mapping transformations for models defined over a lower dimensional space. A corrected surrogate model for the...correction functions used in [72]. If the low fidelity model g(x̃) is defined over a lower dimensional space then a space mapping transformation is...required. As defined in [21, 72], space mapping is a method of mapping between models of different dimensionality or fidelity. Let P denote the space

Networks In Real Space: Characteristics and Analysis for Biology and Mechanics

NASA Astrophysics Data System (ADS)

Modes, Carl; Magnasco, Marcelo; Katifori, Eleni

Functional networks embedded in physical space play a crucial role in countless biological and physical systems, from the efficient dissemination of oxygen, blood sugars, and hormonal signals in vascular systems to the complex relaying of informational signals in the brain to the distribution of stress and strain in architecture or static sand piles. Unlike their more-studied abstract cousins, such as the hyperlinked internet, social networks, or economic and financial connections, these networks are both constrained by and intimately connected to the physicality of their real, embedding space. We report on the results of new computational and analytic approaches tailored to these physical networks with particular implications and insights for mammalian organ vasculature.

Modeling Musical Context With Word2Vec

NASA Astrophysics Data System (ADS)

Herremans, Dorien; Chuan, Ching-Hua

2017-05-01

We present a semantic vector space model for capturing complex polyphonic musical context. A word2vec model based on a skip-gram representation with negative sampling was used to model slices of music from a dataset of Beethoven's piano sonatas. A visualization of the reduced vector space using t-distributed stochastic neighbor embedding shows that the resulting embedded vector space captures tonal relationships, even without any explicit information about the musical contents of the slices. Secondly, an excerpt of the Moonlight Sonata from Beethoven was altered by replacing slices based on context similarity. The resulting music shows that the selected slice based on similar word2vec context also has a relatively short tonal distance from the original slice.

A dimensional comparison between embedded 3D-printed and silicon microchannels

NASA Astrophysics Data System (ADS)

O'Connor, J.; Punch, J.; Jeffers, N.; Stafford, J.

2014-07-01

The subject of this paper is the dimensional characterization of embedded microchannel arrays created using contemporary 3D-printing fabrication techniques. Conventional microchannel arrays, fabricated using deep reactive ion etching techniques (DRIE) and wet-etching (KOH), are used as a benchmark for comparison. Rectangular and trapezoidal cross-sectional shapes were investigated. The channel arrays were 3D-printed in vertical and horizontal directions, to examine the influence of print orientation on channel characteristics. The 3D-printed channels were benchmarked against Silicon channels in terms of the following dimensional characteristics: cross-sectional area (CSA), perimeter, and surface profiles. The 3D-printed microchannel arrays demonstrated variances in CSA of 6.6-20% with the vertical printing approach yielding greater dimensional conformity than the horizontal approach. The measured CSA and perimeter of the vertical channels were smaller than the nominal dimensions, while the horizontal channels were larger in both CSA and perimeter due to additional side-wall roughness present throughout the channel length. This side-wall roughness caused significant shape distortion. Surface profile measurements revealed that the base wall roughness was approximately the resolution of current 3D-printers. A spatial periodicity was found along the channel length which appeared at different frequencies for each channel array. This paper concludes that vertical 3D-printing is superior to the horizontal printing approach, in terms of both dimensional fidelity and shape conformity and can be applied in microfluidic device applications.

Exploratory Visual Analytics of a Dynamically Built Network of Nodes in a WebGL-Enabled Browser

DTIC Science & Technology

2014-01-01

dimensionality reduction, feature extraction, high-dimensional data, t-distributed stochastic neighbor embedding, neighbor retrieval visualizer, visual...WebGL-enabled rendering is supported natively by browsers such as the latest Mozilla Firefox , Google Chrome, and Microsoft Internet Explorer 11. At the...appropriate names. The resultant 26-node network is displayed in a Mozilla Firefox browser in figure 2 (also see appendix B). 3 Figure 1. The

Design and Implementation of Embedded Computer Vision Systems Based on Particle Filters

DTIC Science & Technology

2010-01-01

for hardware/software implementa- tion of multi-dimensional particle filter application and we explore this in the third application which is a 3D...methodology for hardware/software implementation of multi-dimensional particle filter application and we explore this in the third application which is a...and hence multiprocessor implementation of parti- cle filters is an important option to examine. A significant body of work exists on optimizing generic

From LPF to eLISA: new approach in payload software

NASA Astrophysics Data System (ADS)

Gesa, Ll.; Martin, V.; Conchillo, A.; Ortega, J. A.; Mateos, I.; Torrents, A.; Lopez-Zaragoza, J. P.; Rivas, F.; Lloro, I.; Nofrarias, M.; Sopuerta, CF.

2017-05-01

eLISA will be the first observatory in space to explore the Gravitational Universe. It will gather revolutionary information about the dark universe. This implies a robust and reliable embedded control software and hardware working together. With the lessons learnt with the LISA Pathfinder payload software as baseline, we will introduce in this short article the key concepts and new approaches that our group is working on in terms of software: multiprocessor, self-modifying-code strategies, 100% hardware and software monitoring, embedded scripting, Time and Space Partition among others.

Nonlinear research of an image motion stabilization system embedded in a space land-survey telescope

NASA Astrophysics Data System (ADS)

Somov, Yevgeny; Butyrin, Sergey; Siguerdidjane, Houria

2017-01-01

We consider an image motion stabilization system embedded into a space telescope for a scanning optoelectronic observation of terrestrial targets. Developed model of this system is presented taking into account physical hysteresis of piezo-ceramic driver and a time delay at a forming of digital control. We have presented elaborated algorithms for discrete filtering and digital control, obtained results on analysis of the image motion velocity oscillations in the telescope focal plane, and also methods for terrestrial and in-flight verification of the system.

Embedded solution for a microwave moisture meter

USDA-ARS?s Scientific Manuscript database

In this paper, the conversion of a PC or laptop-controlled microwave moisture meter to a stand-alone meter hosting its own embedded system is discussed. The moisture meter is based on the free-space transmission measurement technique and uses low-intensity microwaves to measure the attenuation and p...

Data management in the mission data system

NASA Technical Reports Server (NTRS)

Wagner, David A.

2005-01-01

As spacecraft evolve from simple embedded devices to become more sophisticated computing platforms with complex behaviors it is increasingly necessary to model and manage the flow of data, and to provide uniform models for managing data that promote adaptability, yet pay heed to the physical limitations of the embedded and space environments.

Bayesian uncertainty analysis for complex systems biology models: emulation, global parameter searches and evaluation of gene functions.

PubMed

Vernon, Ian; Liu, Junli; Goldstein, Michael; Rowe, James; Topping, Jen; Lindsey, Keith

2018-01-02

Many mathematical models have now been employed across every area of systems biology. These models increasingly involve large numbers of unknown parameters, have complex structure which can result in substantial evaluation time relative to the needs of the analysis, and need to be compared to observed data of various forms. The correct analysis of such models usually requires a global parameter search, over a high dimensional parameter space, that incorporates and respects the most important sources of uncertainty. This can be an extremely difficult task, but it is essential for any meaningful inference or prediction to be made about any biological system. It hence represents a fundamental challenge for the whole of systems biology. Bayesian statistical methodology for the uncertainty analysis of complex models is introduced, which is designed to address the high dimensional global parameter search problem. Bayesian emulators that mimic the systems biology model but which are extremely fast to evaluate are embeded within an iterative history match: an efficient method to search high dimensional spaces within a more formal statistical setting, while incorporating major sources of uncertainty. The approach is demonstrated via application to a model of hormonal crosstalk in Arabidopsis root development, which has 32 rate parameters, for which we identify the sets of rate parameter values that lead to acceptable matches between model output and observed trend data. The multiple insights into the model's structure that this analysis provides are discussed. The methodology is applied to a second related model, and the biological consequences of the resulting comparison, including the evaluation of gene functions, are described. Bayesian uncertainty analysis for complex models using both emulators and history matching is shown to be a powerful technique that can greatly aid the study of a large class of systems biology models. It both provides insight into model behaviour and identifies the sets of rate parameters of interest.

Brane-world extra dimensions in light of GW170817

NASA Astrophysics Data System (ADS)

Visinelli, Luca; Bolis, Nadia; Vagnozzi, Sunny

2018-03-01

The search for extra dimensions is a challenging endeavor to probe physics beyond the Standard Model. The joint detection of gravitational waves (GW) and electromagnetic (EM) signals from the merging of a binary system of compact objects like neutron stars can help constrain the geometry of extra dimensions beyond our 3 +1 spacetime ones. A theoretically well-motivated possibility is that our observable Universe is a 3 +1 -dimensional hypersurface, or brane, embedded in a higher 4 +1 -dimensional anti-de Sitter (AdS5 ) spacetime, in which gravity is the only force which propagates through the infinite bulk space, while other forces are confined to the brane. In these types of brane-world models, GW and EM signals between two points on the brane would, in general, travel different paths. This would result in a time lag between the detection of GW and EM signals emitted simultaneously from the same source. We consider the recent near-simultaneous detection of the GW event GW170817 from the LIGO/Virgo collaboration, and its EM counterpart, the short gamma-ray burst GRB170817A detected by the Fermi Gamma-ray Burst Monitor and the International Gamma-Ray Astrophysics Laboratory Anti-Coincidence Shield spectrometer. Assuming the standard Λ -cold dark matter scenario and performing a likelihood analysis which takes into account astrophysical uncertainties associated to the measured time lag, we set an upper limit of ℓ≲0.535 Mpc at 68% confidence level on the AdS5 radius of curvature ℓ. Although the bound is not competitive with current Solar System constraints, it is the first time that data from a multimessenger GW-EM measurement is used to constrain extra-dimensional models. Thus, our work provides a proof of principle for the possibility of using multimessenger astronomy for probing the geometry of our space-time.

Three-dimensional lattice matching of epitaxially embedded nanoparticles

NASA Astrophysics Data System (ADS)

May, Brelon J.; Anderson, Peter M.; Myers, Roberto C.

2017-02-01

For a given degree of in-plane lattice mismatch between a two-dimensional (2D) epitaxial layer and a substrate (ɛIP*), there is a critical thickness above which interfacial defects form to relax the elastic strain energy. Here, we extend the 2D lattice-matching conditions to three-dimensions in order to predict the critical size beyond which epitaxially encased nanoparticles, characterized by both ɛIP* and out-of-plane lattice mismatch (ɛOP*), relax by dislocation formation. The critical particle length (Lc) at which defect formation proceeds is determined by balancing the reduction in elastic energy associated with dislocation introduction with the corresponding increase in defect energy. Our results, which use a modified Eshelby inclusion technique for an embedded, arbitrarily-faceted nanoparticle, provide new insight to the nanoepitaxy of low dimensional structures, especially quantum dots and nanoprecipitates. By engineering ɛIP* and ɛOP* , the predicted Lc for nanoparticles can be increased to well beyond the case of encapsulation in a homogenous matrix. For the case of truncated pyramidal shaped InAs, Lc 10.8 nm when fully embedded in GaAs (ɛIP* = ɛOP* = - 0.072); 16.4 nm when the particle is grown on GaAs, but capped with InSb (ɛIP* = - 0.072 and ɛOP* =+0.065); and a maximum of 18.4 nm if capped with an alloy corresponding to ɛOP* =+0.037. The effect, which we term "3D Poisson-stabilization" provides a means to increase the epitaxial strain tolerance in epitaxial heterostructures by tailoring ɛOP*.

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.

Bacteria-Affinity 3D Macroporous Graphene/MWCNTs/Fe3O4 Foams for High-Performance Microbial Fuel Cells.

PubMed

Song, Rong-Bin; Zhao, Cui-E; Jiang, Li-Ping; Abdel-Halim, Essam Sayed; Zhang, Jian-Rong; Zhu, Jun-Jie

2016-06-29

Promoting the performance of microbial fuel cells (MFCs) relies heavily on the structure design and composition tailoring of electrode materials. In this work, three-dimensional (3D) macroporous graphene foams incorporated with intercalated spacer of multiwalled carbon nanotubes (MWCNTs) and bacterial anchor of Fe3O4 nanospheres (named as G/MWCNTs/Fe3O4 foams) were first synthesized and used as anodes for Shewanella-inoculated microbial fuel cells (MFCs). Thanks to the macroporous structure of 3D graphene foams, the expanded electrode surface by MWCNTs spacing, as well as the high affinity of Fe3O4 nanospheres toward Shewanella oneidensis MR-1, the anode exhibited high bacterial loading capability. In addition to spacing graphene nanosheets for accommodating bacterial cells, MWCNTs paved a smoother way for electron transport in the electrode substrate of MFCs. Meanwhile, the embedded bioaffinity Fe3O4 nanospheres capable of preserving the bacterial metabolic activity provided guarantee for the long-term durability of the MFCs. With these merits, the constructed MFC possessed significantly higher power output and stronger stability than that with conventional graphite rod anode.

Self-consistent analysis of radiation and relativistic electron beam dynamics in a helical wiggler using Lienard-Wiechert fields

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

Tecimer, M.; Elias, L.R.

1995-12-31

Lienard-Wiechert (LW) fields, which are exact solutions of the Wave Equation for a point charge in free space, are employed to formulate a self-consistent treatment of the electron beam dynamics and the evolution of the generated radiation in long undulators. In a relativistic electron beam the internal forces leading to the interaction of the electrons with each other can be computed by means of retarded LW fields. The resulting electron beam dynamics enables us to obtain three dimensional radiation fields starting from an initial incoherent spontaneous emission, without introducing a seed wave at start-up. Based on the formalism employed here,more » both the evolution of the multi-bucket electron phase space dynamics in the beam body as well as edges and the relative slippage of the radiation with respect to the electrons in the considered short bunch are naturally embedded into the simulation model. In this paper, we present electromagnetic radiation studies, including multi-bucket electron phase dynamics and angular distribution of radiation in the time and frequency domain produced by a relativistic short electron beam bunch interacting with a circularly polarized magnetic undulator.« less

Detection of sinkholes or anomalies using full seismic wave fields.

DOT National Transportation Integrated Search

2013-04-01

This research presents an application of two-dimensional (2-D) time-domain waveform tomography for detection of embedded sinkholes and anomalies. The measured seismic surface wave fields were inverted using a full waveform inversion (FWI) technique, ...

A 3-D chimera grid embedding technique

NASA Technical Reports Server (NTRS)

Benek, J. A.; Buning, P. G.; Steger, J. L.

1985-01-01

A three-dimensional (3-D) chimera grid-embedding technique is described. The technique simplifies the construction of computational grids about complex geometries. The method subdivides the physical domain into regions which can accommodate easily generated grids. Communication among the grids is accomplished by interpolation of the dependent variables at grid boundaries. The procedures for constructing the composite mesh and the associated data structures are described. The method is demonstrated by solution of the Euler equations for the transonic flow about a wing/body, wing/body/tail, and a configuration of three ellipsoidal bodies.

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

Phillips, Carolyn L.; Guo, Hanqi; Peterka, Tom

In type-II superconductors, the dynamics of magnetic flux vortices determine their transport properties. In the Ginzburg-Landau theory, vortices correspond to topological defects in the complex order parameter field. Earlier, in Phillips et al. [Phys. Rev. E 91, 023311 (2015)], we introduced a method for extracting vortices from the discretized complex order parameter field generated by a large-scale simulation of vortex matter. With this method, at a fixed time step, each vortex [simplistically, a one-dimensional (1D) curve in 3D space] can be represented as a connected graph extracted from the discretized field. Here we extend this method as a function ofmore » time as well. A vortex now corresponds to a 2D space-time sheet embedded in 4D space time that can be represented as a connected graph extracted from the discretized field over both space and time. Vortices that interact by merging or splitting correspond to disappearance and appearance of holes in the connected graph in the time direction. This method of tracking vortices, which makes no assumptions about the scale or behavior of the vortices, can track the vortices with a resolution as good as the discretization of the temporally evolving complex scalar field. Additionally, even details of the trajectory between time steps can be reconstructed from the connected graph. With this form of vortex tracking, the details of vortex dynamics in a model of a superconducting materials can be understood in greater detail than previously possible.« less

Tracking vortices in superconductors: Extracting singularities from a discretized complex scalar field evolving in time

DOE PAGES

Phillips, Carolyn L.; Guo, Hanqi; Peterka, Tom; ...

2016-02-19

In type-II superconductors, the dynamics of magnetic flux vortices determine their transport properties. In the Ginzburg-Landau theory, vortices correspond to topological defects in the complex order parameter field. Earlier, we introduced a method for extracting vortices from the discretized complex order parameter field generated by a large-scale simulation of vortex matter. With this method, at a fixed time step, each vortex [simplistically, a one-dimensional (1D) curve in 3D space] can be represented as a connected graph extracted from the discretized field. Here we extend this method as a function of time as well. A vortex now corresponds to a 2Dmore » space-time sheet embedded in 4D space time that can be represented as a connected graph extracted from the discretized field over both space and time. Vortices that interact by merging or splitting correspond to disappearance and appearance of holes in the connected graph in the time direction. This method of tracking vortices, which makes no assumptions about the scale or behavior of the vortices, can track the vortices with a resolution as good as the discretization of the temporally evolving complex scalar field. In addition, even details of the trajectory between time steps can be reconstructed from the connected graph. With this form of vortex tracking, the details of vortex dynamics in a model of a superconducting materials can be understood in greater detail than previously possible.« less

A density functional approach to ferrogels

NASA Astrophysics Data System (ADS)

Cremer, P.; Heinen, M.; Menzel, A. M.; Löwen, H.

2017-07-01

Ferrogels consist of magnetic colloidal particles embedded in an elastic polymer matrix. As a consequence, their structural and rheological properties are governed by a competition between magnetic particle-particle interactions and mechanical matrix elasticity. Typically, the particles are permanently fixed within the matrix, which makes them distinguishable by their positions. Over time, particle neighbors do not change due to the fixation by the matrix. Here we present a classical density functional approach for such ferrogels. We map the elastic matrix-induced interactions between neighboring colloidal particles distinguishable by their positions onto effective pairwise interactions between indistinguishable particles similar to a ‘pairwise pseudopotential’. Using Monte-Carlo computer simulations, we demonstrate for one-dimensional dipole-spring models of ferrogels that this mapping is justified. We then use the pseudopotential as an input into classical density functional theory of inhomogeneous fluids and predict the bulk elastic modulus of the ferrogel under various conditions. In addition, we propose the use of an ‘external pseudopotential’ when one switches from the viewpoint of a one-dimensional dipole-spring object to a one-dimensional chain embedded in an infinitely extended bulk matrix. Our mapping approach paves the way to describe various inhomogeneous situations of ferrogels using classical density functional concepts of inhomogeneous fluids.

The fictitious force method for efficient calculation of vibration from a tunnel embedded in a multi-layered half-space

NASA Astrophysics Data System (ADS)

Hussein, M. F. M.; François, S.; Schevenels, M.; Hunt, H. E. M.; Talbot, J. P.; Degrande, G.

2014-12-01

This paper presents an extension of the Pipe-in-Pipe (PiP) model for calculating vibrations from underground railways that allows for the incorporation of a multi-layered half-space geometry. The model is based on the assumption that the tunnel displacement is not influenced by the existence of a free surface or ground layers. The displacement at the tunnel-soil interface is calculated using a model of a tunnel embedded in a full space with soil properties corresponding to the soil in contact with the tunnel. Next, a full space model is used to determine the equivalent loads that produce the same displacements at the tunnel-soil interface. The soil displacements are calculated by multiplying these equivalent loads by Green's functions for a layered half-space. The results and the computation time of the proposed model are compared with those of an alternative coupled finite element-boundary element model that accounts for a tunnel embedded in a multi-layered half-space. While the overall response of the multi-layered half-space is well predicted, spatial shifts in the interference patterns are observed that result from the superposition of direct waves and waves reflected on the free surface and layer interfaces. The proposed model is much faster and can be run on a personal computer with much less use of memory. Therefore, it is a promising design tool to predict vibration from underground tunnels and to assess the performance of vibration countermeasures in an early design stage.

Physical Simulation for Probabilistic Motion Tracking

DTIC Science & Technology

2008-01-01

learn a low- dimensional embedding of the high-dimensional kinematic data and then attempt to solve the problem in this more man- ageable low...rotations and foot skate ). Such artifacts can be attributed to the general lack of physically plausible priors [2] (that can account for static and/or...temporal priors of the form p(xf+1|xf ) = N (xf + γf ,Σ) (where γf is scaled velocity learned or inferred), have also been proposed [13] and shown to

Self-assembly of carbon black into nanowires that form a conductive three dimensional micronetwork

NASA Astrophysics Data System (ADS)

Levine, L. E.; Long, G. G.; Ilavsky, J.; Gerhardt, R. A.; Ou, R.; Parker, C. A.

2007-01-01

The authors have used mechanical self-assembly of carbon-black nanoparticles to fabricate a three dimensional, electrically connected micronetwork of nanowires embedded within an insulating, supporting matrix of poly(methyl methacrylate). The electrical connectivity, mean wire diameter, and morphological transitions were characterized as a function of the carbon-black mass fraction. Conductive wires were produced with mean diameters as low as 24nm with lengths up to 100μm.

On the geometry of the space-time and motion of the spinning bodies

NASA Astrophysics Data System (ADS)

Trenčevski, Kostadin

2013-03-01

In this paper an alternative theory about space-time is given. First some preliminaries about 3-dimensional time and the reasons for its introduction are presented. Alongside the 3-dimensional space (S) the 3-dimensional space of spatial rotations (SR) is considered independently from the 3-dimensional space. Then it is given a model of the universe, based on the Lie groups of real and complex orthogonal 3 × 3 matrices in this 3+3+3-dimensional space. Special attention is dedicated for introduction and study of the space S × SR, which appears to be isomorphic to SO(3,ℝ) × SO(3,ℝ) or S 3 × S 3. The influence of the gravitational acceleration to the spinning bodies is considered. Some important applications of these results about spinning bodies are given, which naturally lead to violation of Newton's third law in its classical formulation. The precession of the spinning axis is also considered.

Pre-Service Teachers' Pedagogical Relationships and Experiences of Embedding Indigenous Australian Knowledge in Teaching Practicum

ERIC Educational Resources Information Center

Hart, Victor; Whatman, Susan; McLaughlin, Juliana; Sharma-Brymer, Vinathe

2012-01-01

This paper argues from the standpoint that embedding Indigenous knowledge and perspectives in Australian curricula occurs within a space of tension, "the cultural interface", in negotiation and contestation with other dominant knowledge systems. In this interface, Indigenous knowledge is in a state of constancy and flux, invisible and…

View from second level looking down on embedded weld strips ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

View from second level looking down on embedded weld strips and plugged, threaded anchors in the foundation slab. These were put in place to assist in adapting to future configurations of the test stand. - Marshall Space Flight Center, Saturn V Dynamic Test Facility, East Test Area, Huntsville, Madison County, AL

Identifying early-warning signals of critical transitions with strong noise by dynamical network markers

PubMed Central

Liu, Rui; Chen, Pei; Aihara, Kazuyuki; Chen, Luonan

2015-01-01

Identifying early-warning signals of a critical transition for a complex system is difficult, especially when the target system is constantly perturbed by big noise, which makes the traditional methods fail due to the strong fluctuations of the observed data. In this work, we show that the critical transition is not traditional state-transition but probability distribution-transition when the noise is not sufficiently small, which, however, is a ubiquitous case in real systems. We present a model-free computational method to detect the warning signals before such transitions. The key idea behind is a strategy: “making big noise smaller” by a distribution-embedding scheme, which transforms the data from the observed state-variables with big noise to their distribution-variables with small noise, and thus makes the traditional criteria effective because of the significantly reduced fluctuations. Specifically, increasing the dimension of the observed data by moment expansion that changes the system from state-dynamics to probability distribution-dynamics, we derive new data in a higher-dimensional space but with much smaller noise. Then, we develop a criterion based on the dynamical network marker (DNM) to signal the impending critical transition using the transformed higher-dimensional data. We also demonstrate the effectiveness of our method in biological, ecological and financial systems. PMID:26647650

A microfluidically perfused three dimensional human liver model.

PubMed

Rennert, Knut; Steinborn, Sandra; Gröger, Marko; Ungerböck, Birgit; Jank, Anne-Marie; Ehgartner, Josef; Nietzsche, Sandor; Dinger, Julia; Kiehntopf, Michael; Funke, Harald; Peters, Frank T; Lupp, Amelie; Gärtner, Claudia; Mayr, Torsten; Bauer, Michael; Huber, Otmar; Mosig, Alexander S

2015-12-01

Within the liver, non-parenchymal cells (NPCs) are critically involved in the regulation of hepatocyte polarization and maintenance of metabolic function. We here report the establishment of a liver organoid that integrates NPCs in a vascular layer composed of endothelial cells and tissue macrophages and a hepatic layer comprising stellate cells co-cultured with hepatocytes. The three-dimensional liver organoid is embedded in a microfluidically perfused biochip that enables sufficient nutrition supply and resembles morphological aspects of the human liver sinusoid. It utilizes a suspended membrane as a cell substrate mimicking the space of Disse. Luminescence-based sensor spots were integrated into the chip to allow online measurement of cellular oxygen consumption. Application of microfluidic flow induces defined expression of ZO-1, transferrin, ASGPR-1 along with an increased expression of MRP-2 transporter protein within the liver organoids. Moreover, perfusion was accompanied by an increased hepatobiliary secretion of 5(6)-carboxy-2',7'-dichlorofluorescein and an enhanced formation of hepatocyte microvilli. From this we conclude that the perfused liver organoid shares relevant morphological and functional characteristics with the human liver and represents a new in vitro research tool to study human hepatocellular physiology at the cellular level under conditions close to the physiological situation. Copyright © 2015 Elsevier Ltd. All rights reserved.

A Framework for Evaluating Regional-Scale Numerical Photochemical Modeling Systems

EPA Science Inventory

This paper discusses the need for critically evaluating regional-scale (~ 200-2000 km) three dimensional numerical photochemical air quality modeling systems to establish a model's credibility in simulating the spatio-temporal features embedded in the observations. Because of li...

Three-Dimensional Printing of Complex Structures by Freeform Reversible Embedding of Suspended Hydrogels (FRESH)

NASA Astrophysics Data System (ADS)

Feinberg, Adam

We demonstrate the additive manufacturing of complex three-dimensional (3D) structures using soft protein and polysaccharide hydrogels that are challenging or impossible to create using traditional fabrication approaches. These structures are built by embedding the printed hydrogel within a secondary hydrogel that serves as a temporary, thermoreversible, and biocompatible support. This process, termed freeform reversible embedding of suspended hydrogels (FRESH), enables 3D printing of hydrated materials with an elastic modulus less than 500 kPa including alginate, collagen, hyaluronic acid and fibrin. A range of crosslinking mechanisms can be used depending on the polymer being printed, including ionic, enzymatic, pH, thermal and light based approaches. CAD models of 3D optical, computed tomography, and magnetic resonance imaging data can be 3D printed at a resolution of 100 μm and at low cost by leveraging open-source hardware and software tools. Proof-of-concept structures based on femurs, branched coronary arteries, trabeculated embryonic hearts, and human brains are mechanically robust and recreate complex 3D internal and external anatomical architectures. Recent advances have improved the resolution and broadened the range of materials that can be FRESH 3D printed. This work was supported in part by the NIH Director's New Innovator Award (DP2HL117750) and the NSF CAREER Award (1454248).

Convergence of moment expansions for expectation values with embedded random matrix ensembles and quantum chaos

NASA Astrophysics Data System (ADS)

Kota, V. K. B.

2003-07-01

Smoothed forms for expectation values < K> E of positive definite operators K follow from the K-density moments either directly or in many other ways each giving a series expansion (involving polynomials in E). In large spectroscopic spaces one has to partition the many particle spaces into subspaces. Partitioning leads to new expansions for expectation values. It is shown that all the expansions converge to compact forms depending on the nature of the operator K and the operation of embedded random matrix ensembles and quantum chaos in many particle spaces. Explicit results are given for occupancies < ni> E, spin-cutoff factors < JZ2> E and strength sums < O†O> E, where O is a one-body transition operator.

Asymmetric distances for binary embeddings.

PubMed

Gordo, Albert; Perronnin, Florent; Gong, Yunchao; Lazebnik, Svetlana

2014-01-01

In large-scale query-by-example retrieval, embedding image signatures in a binary space offers two benefits: data compression and search efficiency. While most embedding algorithms binarize both query and database signatures, it has been noted that this is not strictly a requirement. Indeed, asymmetric schemes that binarize the database signatures but not the query still enjoy the same two benefits but may provide superior accuracy. In this work, we propose two general asymmetric distances that are applicable to a wide variety of embedding techniques including locality sensitive hashing (LSH), locality sensitive binary codes (LSBC), spectral hashing (SH), PCA embedding (PCAE), PCAE with random rotations (PCAE-RR), and PCAE with iterative quantization (PCAE-ITQ). We experiment on four public benchmarks containing up to 1M images and show that the proposed asymmetric distances consistently lead to large improvements over the symmetric Hamming distance for all binary embedding techniques.

Liquid on Paper: Rapid Prototyping of Soft Functional Components for Paper Electronics.

PubMed

Han, Yu Long; Liu, Hao; Ouyang, Cheng; Lu, Tian Jian; Xu, Feng

2015-07-01

This paper describes a novel approach to fabricate paper-based electric circuits consisting of a paper matrix embedded with three-dimensional (3D) microchannels and liquid metal. Leveraging the high electric conductivity and good flowability of liquid metal, and metallophobic property of paper, it is possible to keep electric and mechanical functionality of the electric circuit even after a thousand cycles of deformation. Embedding liquid metal into paper matrix is a promising method to rapidly fabricate low-cost, disposable, and soft electric circuits for electronics. As a demonstration, we designed a programmable displacement transducer and applied it as variable resistors and pressure sensors. The unique metallophobic property, combined with softness, low cost and light weight, makes paper an attractive alternative to other materials in which liquid metal are currently embedded.

Hamiltonian vs Lagrangian Embedding of a Massive Spin-One Theory Involving Two-Form Field

NASA Astrophysics Data System (ADS)

Harikumar, E.; Sivakumar, M.

We consider the Hamiltonian and Lagrangian embedding of a first-order, massive spin-one, gauge noninvariant theory involving antisymmetric tensor field. We apply the BFV-BRST generalized canonical approach to convert the model to a first class system and construct nilpotent BFV-BRST charge and a unitarizing Hamiltonian. The canonical analysis of the Stückelberg formulation of this model is presented. We bring out the contrasting feature in the constraint structure, specifically with respect to the reducibility aspect, of the Hamiltonian and the Lagrangian embedded model. We show that to obtain manifestly covariant Stückelberg Lagrangian from the BFV embedded Hamiltonian, phase space has to be further enlarged and show how the reducible gauge structure emerges in the embedded model.

A self-consistent density based embedding scheme applied to the adsorption of CO on Pd(111)

NASA Astrophysics Data System (ADS)

Lahav, D.; Klüner, T.

2007-06-01

We derive a variant of a density based embedded cluster approach as an improvement to a recently proposed embedding theory for metallic substrates (Govind et al 1999 J. Chem. Phys. 110 7677; Klüner et al 2001 Phys. Rev. Lett. 86 5954). In this scheme, a local region in space is represented by a small cluster which is treated by accurate quantum chemical methodology. The interaction of the cluster with the infinite solid is taken into account by an effective one-electron embedding operator representing the surrounding region. We propose a self-consistent embedding scheme which resolves intrinsic problems of the former theory, in particular a violation of strict density conservation. The proposed scheme is applied to the well-known benchmark system CO/Pd(111).

Vector calculus in non-integer dimensional space and its applications to fractal media

NASA Astrophysics Data System (ADS)

Tarasov, Vasily E.

2015-02-01

We suggest a generalization of vector calculus for the case of non-integer dimensional space. The first and second orders operations such as gradient, divergence, the scalar and vector Laplace operators for non-integer dimensional space are defined. For simplification we consider scalar and vector fields that are independent of angles. We formulate a generalization of vector calculus for rotationally covariant scalar and vector functions. This generalization allows us to describe fractal media and materials in the framework of continuum models with non-integer dimensional space. As examples of application of the suggested calculus, we consider elasticity of fractal materials (fractal hollow ball and fractal cylindrical pipe with pressure inside and outside), steady distribution of heat in fractal media, electric field of fractal charged cylinder. We solve the correspondent equations for non-integer dimensional space models.

Micro-precise spatiotemporal delivery system embedded in 3D printing for complex tissue regeneration.

PubMed

Tarafder, Solaiman; Koch, Alia; Jun, Yena; Chou, Conrad; Awadallah, Mary R; Lee, Chang H

2016-04-25

Three dimensional (3D) printing has emerged as an efficient tool for tissue engineering and regenerative medicine, given its advantages for constructing custom-designed scaffolds with tunable microstructure/physical properties. Here we developed a micro-precise spatiotemporal delivery system embedded in 3D printed scaffolds. PLGA microspheres (μS) were encapsulated with growth factors (GFs) and then embedded inside PCL microfibers that constitute custom-designed 3D scaffolds. Given the substantial difference in the melting points between PLGA and PCL and their low heat conductivity, μS were able to maintain its original structure while protecting GF's bioactivities. Micro-precise spatial control of multiple GFs was achieved by interchanging dispensing cartridges during a single printing process. Spatially controlled delivery of GFs, with a prolonged release, guided formation of multi-tissue interfaces from bone marrow derived mesenchymal stem/progenitor cells (MSCs). To investigate efficacy of the micro-precise delivery system embedded in 3D printed scaffold, temporomandibular joint (TMJ) disc scaffolds were fabricated with micro-precise spatiotemporal delivery of CTGF and TGFβ3, mimicking native-like multiphase fibrocartilage. In vitro, TMJ disc scaffolds spatially embedded with CTGF/TGFβ3-μS resulted in formation of multiphase fibrocartilaginous tissues from MSCs. In vivo, TMJ disc perforation was performed in rabbits, followed by implantation of CTGF/TGFβ3-μS-embedded scaffolds. After 4 wks, CTGF/TGFβ3-μS embedded scaffolds significantly improved healing of the perforated TMJ disc as compared to the degenerated TMJ disc in the control group with scaffold embedded with empty μS. In addition, CTGF/TGFβ3-μS embedded scaffolds significantly prevented arthritic changes on TMJ condyles. In conclusion, our micro-precise spatiotemporal delivery system embedded in 3D printing may serve as an efficient tool to regenerate complex and inhomogeneous tissues.

a Web Service Approach for Linking Sensors and Cellular Spaces

NASA Astrophysics Data System (ADS)

Isikdag, U.

2013-09-01

More and more devices are starting to be connected to the Internet. In the future the Internet will not only be a communication medium for people, it will in fact be a communication environment for devices. The connected devices which are also referred as Things will have an ability to interact with other devices over the Internet, i.) provide information in interoperable form and ii.) consume /utilize such information with the help of sensors embedded in them. This overall concept is known as Internet-of- Things (IoT). This requires new approaches to be investigated for system architectures to establish relations between spaces and sensors. The research presented in this paper elaborates on an architecture developed with this aim, i.e. linking spaces and sensors using a RESTful approach. The objective is making spaces aware of (sensor-embedded) devices, and making devices aware of spaces in a loosely coupled way (i.e. a state/usage/function change in the spaces would not have effect on sensors, similarly a location/state/usage/function change in sensors would not have any effect on spaces). The proposed architecture also enables the automatic assignment of sensors to spaces depending on space geometry and sensor location.

De-embedding technique for accurate modeling of compact 3D MMIC CPW transmission lines

NASA Astrophysics Data System (ADS)

Pohan, U. H.; KKyabaggu, P. B.; Sinulingga, E. P.

2018-02-01

Requirement for high-density and high-functionality microwave and millimeter-wave circuits have led to the innovative circuit architectures such as three-dimensional multilayer MMICs. The major advantage of the multilayer techniques is that one can employ passive and active components based on CPW technology. In this work, MMIC Coplanar Waveguide(CPW)components such as Transmission Line (TL) are modeled in their 3D layouts. Main characteristics of CPWTL suffered from the probe pads’ parasitic and resonant frequency effects have been studied. By understanding the parasitic effects, then the novel de-embedding technique are developed accurately in order to predict high frequency characteristics of the designed MMICs. The novel de-embedding technique has shown to be critical in reducing the probe pad parasitic significantly from the model. As results, high frequency characteristics of the designed MMICs have been presented with minimumparasitic effects of the probe pads. The de-embedding process optimises the determination of main characteristics of Compact 3D MMIC CPW transmission lines.

Sensitivity vector fields in time-delay coordinate embeddings: theory and experiment.

PubMed

Sloboda, A R; Epureanu, B I

2013-02-01

Identifying changes in the parameters of a dynamical system can be vital in many diagnostic and sensing applications. Sensitivity vector fields (SVFs) are one way of identifying such parametric variations by quantifying their effects on the morphology of a dynamical system's attractor. In many cases, SVFs are a more effective means of identification than commonly employed modal methods. Previously, it has only been possible to construct SVFs for a given dynamical system when a full set of state variables is available. This severely restricts SVF applicability because it may be cost prohibitive, or even impossible, to measure the entire state in high-dimensional systems. Thus, the focus of this paper is constructing SVFs with only partial knowledge of the state by using time-delay coordinate embeddings. Local models are employed in which the embedded states of a neighborhood are weighted in a way referred to as embedded point cloud averaging. Application of the presented methodology to both simulated and experimental time series demonstrates its utility and reliability.

Absolute judgment for one- and two-dimensional stimuli embedded in Gaussian noise

NASA Technical Reports Server (NTRS)

Kvalseth, T. O.

1977-01-01

This study examines the effect on human performance of adding Gaussian noise or disturbance to the stimuli in absolute judgment tasks involving both one- and two-dimensional stimuli. For each selected stimulus value (both an X-value and a Y-value were generated in the two-dimensional case), 10 values (or 10 pairs of values in the two-dimensional case) were generated from a zero-mean Gaussian variate, added to the selected stimulus value and then served as the coordinate values for the 10 points that were displayed sequentially on a CRT. The results show that human performance, in terms of the information transmitted and rms error as functions of stimulus uncertainty, was significantly reduced as the noise variance increased.

Planar reorientation of a free-free beam in space using embedded electromechanical actuators

NASA Technical Reports Server (NTRS)

Kolmanovsky, Ilya V.; Mcclamroch, N. Harris

1993-01-01

It is demonstrated that the planar reorientation of a free-free beam in zero gravity space can be accomplished by periodically changing the shape of the beam using embedded electromechanical actuators. The dynamics which determine the shape of the free-free beam is assumed to be characterized by the Euler-Bernoulli equation, including material damping, with appropriate boundary conditions. The coupling between the rigid body motion and the flexible motion is explained using the angular momentum expression which includes rotatory inertia and kinematically exact effects. A control scheme is proposed where the embedded actuators excite the flexible motion of the beam so that it rotates in the desired sense with respect to a fixed inertial reference. Relations are derived which relate the average rotation rate to the amplitudes and the frequencies of the periodic actuation signal and the properties of the beam. These reorientation maneuvers can be implemented by using feedback control.

Non-standard analysis and embedded software

NASA Technical Reports Server (NTRS)

Platek, Richard

1995-01-01

One model for computing in the future is ubiquitous, embedded computational devices analogous to embedded electrical motors. Many of these computers will control physical objects and processes. Such hidden computerized environments introduce new safety and correctness concerns whose treatment go beyond present Formal Methods. In particular, one has to begin to speak about Real Space software in analogy with Real Time software. By this we mean, computerized systems which have to meet requirements expressed in the real geometry of space. How to translate such requirements into ordinary software specifications and how to carry out proofs is a major challenge. In this talk we propose a research program based on the use of no-standard analysis. Much detail remains to be carried out. The purpose of the talk is to inform the Formal Methods community that Non-Standard Analysis provides a possible avenue to attack which we believe will be fruitful.

Recurrence plot statistics and the effect of embedding

NASA Astrophysics Data System (ADS)

March, T. K.; Chapman, S. C.; Dendy, R. O.

2005-01-01

Recurrence plots provide a graphical representation of the recurrent patterns in a timeseries, the quantification of which is a relatively new field. Here we derive analytical expressions which relate the values of key statistics, notably determinism and entropy of line length distribution, to the correlation sum as a function of embedding dimension. These expressions are obtained by deriving the transformation which generates an embedded recurrence plot from an unembedded plot. A single unembedded recurrence plot thus provides the statistics of all possible embedded recurrence plots. If the correlation sum scales exponentially with embedding dimension, we show that these statistics are determined entirely by the exponent of the exponential. This explains the results of Iwanski and Bradley [J.S. Iwanski, E. Bradley, Recurrence plots of experimental data: to embed or not to embed? Chaos 8 (1998) 861-871] who found that certain recurrence plot statistics are apparently invariant to embedding dimension for certain low-dimensional systems. We also examine the relationship between the mutual information content of two timeseries and the common recurrent structure seen in their recurrence plots. This allows time-localized contributions to mutual information to be visualized. This technique is demonstrated using geomagnetic index data; we show that the AU and AL geomagnetic indices share half their information, and find the timescale on which mutual features appear.

Measurement of longitudinal strain and estimation of peel stress in adhesive-bonded single-lap joint of CFRP adherend using embedded FBG sensor

NASA Astrophysics Data System (ADS)

Ning, X.; Murayama, H.; Kageyama, K.; Uzawa, K.; Wada, D.

2012-04-01

In this research, longitudinal strain and peel stress in adhesive-bonded single-lap joint of carbon fiber reinforced plastics (CFRP) were measured and estimated by embedded fiber Bragg grating (FBG) sensor. Two unidirectional CFRP substrates were bonded by epoxy to form a single-lap configuration. The distributed strain measurement system is used. It is based on optical frequency domain reflectometry (OFDR), which can provide measurement at an arbitrary position along FBG sensors with the high spatial resolution. The longitudinal strain was measured based on Bragg grating effect and the peel stress was estimated based on birefringence effect. Special manufacturing procedure was developed to ensure the embedded location of FBG sensor. A portion of the FBG sensor was embedded into one of CFRP adherends along fiber direction and another portion was kept free for temperature compensation. Photomicrograph of cross-section of specimen was taken to verify the sensor was embedded into proper location after adherend curing. The residual strain was monitored during specimen curing and adhesive joint bonding process. Tensile tests were carried out and longitudinal strain and peel stress of the bondline are measured and estimated by the embedded FBG sensor. A two-dimensional geometrically nonlinear finite element analysis was performed by ANSYS to evaluate the measurement precision.

Wave-front singularities for two-dimensional anisotropic elastic waves.

NASA Technical Reports Server (NTRS)

Payton, R. G.

1972-01-01

Wavefront singularities for the displacement functions, associated with the radiation of linear elastic waves from a point source embedded in a finitely strained two-dimensional elastic solid, are examined in detail. It is found that generally the singularities are of order d to the -1/2 power, where d measures distance away from the front. However, in certain exceptional cases singularities of order d to the -n power, where n = 1/4, 2/3, 3/4, may be encountered.

Estimating contrast transfer function and associated parameters by constrained non-linear optimization.

PubMed

Yang, C; Jiang, W; Chen, D-H; Adiga, U; Ng, E G; Chiu, W

2009-03-01

The three-dimensional reconstruction of macromolecules from two-dimensional single-particle electron images requires determination and correction of the contrast transfer function (CTF) and envelope function. A computational algorithm based on constrained non-linear optimization is developed to estimate the essential parameters in the CTF and envelope function model simultaneously and automatically. The application of this estimation method is demonstrated with focal series images of amorphous carbon film as well as images of ice-embedded icosahedral virus particles suspended across holes.

Endwall flows and blading design for axial flow compressors

NASA Astrophysics Data System (ADS)

Robinson, Christopher J.

Literature relevant to blading design in the endwall region is reviewed, and important three dimensional flow phenomena occurring in embedded stages of axial compressors are described. A low speed axial flow four stage compressor rig is described and bladings studied are detailed: two conventional and two with end bends. The application of a three dimensional Navier-Stokes solver to the bladings' stators, to assess the effectiveness of the code, is reported. Calculation results of exit whirl angles, losses, and surface static pressures are compared with experiment.

Method of Forming Three-Dimensional Semiconductors Structures

NASA Technical Reports Server (NTRS)

Fathauer, Robert W. (Inventor)

2002-01-01

Silicon and metal are coevaporated onto a silicon substrate in a molecular beam epitaxy system with a larger than stoichiometric amount of silicon so as to epitaxially grow columns of metal silicide embedded in a matrix of single crystal, epitaxially grown silicon. Higher substrate temperatures and lower deposition rates yield larger columns that are farther apart while more silicon produces smaller columns. Column shapes and locations are selected by seeding the substrate with metal silicide starting regions. A variety of 3-dimensional, exemplary electronic devices are disclosed.

Semantic Access to Embedded Words? Electrophysiological and Behavioral Evidence from Spanish and English

ERIC Educational Resources Information Center

Macizo, Pedro; Van Petten, Cyma; O'Rourke, Polly L.

2012-01-01

Many multisyllabic words contain shorter words that are not semantic units, like the CAP in HANDICAP and the DURA ("hard") in VERDURA ("vegetable"). The spaces between printed words identify word boundaries, but spurious identification of these embedded words is a potentially greater challenge for spoken language comprehension, a challenge that is…

Embedded Thermal Control for Subsystems for Next Generation Spacecraft Applications

NASA Technical Reports Server (NTRS)

Didion, Jeffrey R.

2015-01-01

Thermal Fluids and Analysis Workshop, Silver Spring MD NCTS 21070-15. NASA, the Defense Department and commercial interests are actively engaged in developing miniaturized spacecraft systems and scientific instruments to leverage smaller cheaper spacecraft form factors such as CubeSats. This paper outlines research and development efforts among Goddard Space Flight Center personnel and its several partners to develop innovative embedded thermal control subsystems. Embedded thermal control subsystems is a cross cutting enabling technology integrating advanced manufacturing techniques to develop multifunctional intelligent structures to reduce Size, Weight and Power (SWaP) consumption of both the thermal control subsystem and overall spacecraft. Embedded thermal control subsystems permit heat acquisition and rejection at higher temperatures than state of the art systems by employing both advanced heat transfer equipment (integrated heat exchangers) and high heat transfer phenomena. The Goddard Space Flight Center Thermal Engineering Branch has active investigations seeking to characterize advanced thermal control systems for near term spacecraft missions. The embedded thermal control subsystem development effort consists of fundamental research as well as development of breadboard and prototype hardware and spaceflight validation efforts. This paper will outline relevant fundamental investigations of micro-scale heat transfer and electrically driven liquid film boiling. The hardware development efforts focus upon silicon based high heat flux applications (electronic chips, power electronics etc.) and multifunctional structures. Flight validation efforts include variable gravity campaigns and a proposed CubeSat based flight demonstration of a breadboard embedded thermal control system. The CubeSat investigation is technology demonstration will characterize in long-term low earth orbit a breadboard embedded thermal subsystem and its individual components to develop optimized operational schema.

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.

Pole pulling apparatus and method

DOEpatents

McIntire, Gary L.

1989-01-01

An apparatus for removal of embedded utility-type poles which removes the poles quickly and efficiently from their embedded position without damage to the pole or surrounding structures. The apparatus includes at least 2 piston/cylinder members equally spaced about the pole, and a head member affixed to the top of each piston. Elongation of the piston induces rotation of the head into the pole to increase the gripping action and reduce slippage. Repeated actuation and retraction of the piston and head member will "jack" the pole from its embedded position.

Embedded expert system for space shuttle main engine maintenance

NASA Technical Reports Server (NTRS)

Pooley, J.; Thompson, W.; Homsley, T.; Teoh, W.; Jones, J.; Lewallen, P.

1987-01-01

The SPARTA Embedded Expert System (SEES) is an intelligent health monitoring system that directs analysis by placing confidence factors on possible engine status and then recommends a course of action to an engineer or engine controller. The technique can prevent catastropic failures or costly rocket engine down time because of false alarms. Further, the SEES has potential as an on-board flight monitor for reusable rocket engine systems. The SEES methodology synergistically integrates vibration analysis, pattern recognition and communications theory techniques with an artificial intelligence technique - the Embedded Expert System (EES).

Heat transfer in porous medium embedded with vertical plate: Non-equilibrium approach - Part A

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

Badruddin, Irfan Anjum; Quadir, G. A.

2016-06-08

Heat transfer in a porous medium embedded with vertical flat plate is investigated by using thermal non-equilibrium model. Darcy model is employed to simulate the flow inside porous medium. It is assumed that the heat transfer takes place by natural convection and radiation. The vertical plate is maintained at isothermal temperature. The governing partial differential equations are converted into non-dimensional form and solved numerically using finite element method. Results are presented in terms of isotherms and streamlines for various parameters such as heat transfer coefficient parameter, thermal conductivity ratio, and radiation parameter.

Finite element formulation with embedded weak discontinuities for strain localization under dynamic conditions

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

Jin, Tao; Mourad, Hashem M.; Bronkhorst, Curt A.

Here, we present an explicit finite element formulation designed for the treatment of strain localization under highly dynamic conditions. We also used a material stability analysis to detect the onset of localization behavior. Finite elements with embedded weak discontinuities are employed with the aim of representing subsequent localized deformation accurately. The formulation and its algorithmic implementation are described in detail. Numerical results are presented to illustrate the usefulness of this computational framework in the treatment of strain localization under highly dynamic conditions, and to examine its performance characteristics in the context of two-dimensional plane-strain problems.

Method for ion implantation induced embedded particle formation via reduction

DOEpatents

Hampikian, Janet M; Hunt, Eden M

2001-01-01

A method for ion implantation induced embedded particle formation via reduction with the steps of ion implantation with an ion/element that will chemically reduce the chosen substrate material, implantation of the ion/element to a sufficient concentration and at a sufficient energy for particle formation, and control of the temperature of the substrate during implantation. A preferred embodiment includes the formation of particles which are nano-dimensional (<100 m-n in size). The phase of the particles may be affected by control of the substrate temperature during and/or after the ion implantation process.

Finite element formulation with embedded weak discontinuities for strain localization under dynamic conditions

DOE PAGES

Jin, Tao; Mourad, Hashem M.; Bronkhorst, Curt A.; ...

2017-09-13

Here, we present an explicit finite element formulation designed for the treatment of strain localization under highly dynamic conditions. We also used a material stability analysis to detect the onset of localization behavior. Finite elements with embedded weak discontinuities are employed with the aim of representing subsequent localized deformation accurately. The formulation and its algorithmic implementation are described in detail. Numerical results are presented to illustrate the usefulness of this computational framework in the treatment of strain localization under highly dynamic conditions, and to examine its performance characteristics in the context of two-dimensional plane-strain problems.

Stability of Internal Space in Kaluza-Klein Theory

NASA Astrophysics Data System (ADS)

Maeda, K.; Soda, J.

1998-12-01

We extend a model studied by Li and Gott III to investigate a stability of internal space in Kaluza-Klein theory. Our model is a four-dimensional de-Sitter space plus a n-dimensional compactified internal space. We introduce a solution of the semi-classical Einstein equation which shows us the fact that a n-dimensional compactified internal space can be stable by the Casimir effect. The self-consistency of this solution is checked. One may apply this solution to study the issue of the Black Hole singularity.

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.

Bio-ISRU Concepts using microorganisms to release O2 and H2 on Moon and Mars

NASA Astrophysics Data System (ADS)

Slenzka, Klaus; Kempf, Juergen

Since space exploration missions begun, numerous spacecrafts were sent to space for examina-tion of other planets. One limiting factor of the endurance of such missions is the unlasting energy supply to run devices and motors of the space crafts as well as for locally habitats. The high weight and volume of fuels makes embedding of local resources necessary to allow ex-tension to long term missions. Nature demonstrates how to survive in extreme environments. Some more adapted microorganisms like Chlamydomonas reinhardii even release elementary hydrogen from water under special nutrition which might be used to run fuel cells and provide electric energy. The same organism release oxygen by photosysthesis under standard nutrition, the counterpart of hydrogen to operate fuel cells. Planets of interest are covered by potential toxic soil called "Regolith". Lunar regolith is known to be extremely aggressive and inhibit cells grows not only due to its sharp edges. First studies on lunar soil simulant tolerance of Chl.reinhardii have shown promising results. The single cells surround the substrate without any negative influence. A 3-dimensional tissue like matrix was build by the proliferating now adhering micro algae cells and the substrate. The photosynthesis rate was not negatively in-fluenced by the soil. This enables Chl.reinhardii to become a first settler organism of the lunar surface. Maybe a first step of terraforming to allow the growth of higher organisms. Lunar soil regolith consists of several components. Especially in minerals bound oxygen plays an out-standing role for industrial use. Some microorganisms of the proteobacteria type are reducing ferroxides to gain oxygen under anaerobic conditions while they produce electric energy simul-taneously. For a faster electron transfer the Shewanella bacteria built filamentous nanowire-like structures to connect one cell to the other. A bioreactor hosting specific microorganism might be run to provide oxygen to the life support system embedded in a permanent Moon or Mars base. This method demonstrates a low energetic oxygen release, a serious alternative to high the energetic oxygen separation of the ilmenite process, fluorination process, melting hydrol-ysis, vacuum distillation or photo dissociation, respectively. Not only oxygen production of the biological processes should be in focus of space application. Also the metal oxide reducing component of the process might run batteries to provide energy to devices of a Moon or Mars base.

Stable dissipative optical vortex clusters by inhomogeneous effective diffusion.

PubMed

Li, Huishan; Lai, Shiquan; Qui, Yunli; Zhu, Xing; Xie, Jianing; Mihalache, Dumitru; He, Yingji

2017-10-30

We numerically show the generation of robust vortex clusters embedded in a two-dimensional beam propagating in a dissipative medium described by the generic cubic-quintic complex Ginzburg-Landau equation with an inhomogeneous effective diffusion term, which is asymmetrical in the two transverse directions and periodically modulated in the longitudinal direction. We show the generation of stable optical vortex clusters for different values of the winding number (topological charge) of the input optical beam. We have found that the number of individual vortex solitons that form the robust vortex cluster is equal to the winding number of the input beam. We have obtained the relationships between the amplitudes and oscillation periods of the inhomogeneous effective diffusion and the cubic gain and diffusion (viscosity) parameters, which depict the regions of existence and stability of vortex clusters. The obtained results offer a method to form robust vortex clusters embedded in two-dimensional optical beams, and we envisage potential applications in the area of structured light.

Three dimensional quantitative characterization of magnetite nanoparticles embedded in mesoporous silicon: local curvature, demagnetizing factors and magnetic Monte Carlo simulations.

PubMed

Uusimäki, Toni; Margaris, Georgios; Trohidou, Kalliopi; Granitzer, Petra; Rumpf, Klemens; Sezen, Meltem; Kothleitner, Gerald

2013-12-07

Magnetite nanoparticles embedded within the pores of a mesoporous silicon template have been characterized using electron tomography. Linear least squares optimization was used to fit an arbitrary ellipsoid to each segmented particle from the three dimensional reconstruction. It was then possible to calculate the demagnetizing factors and the direction of the shape anisotropy easy axis for every particle. The demagnetizing factors, along with the knowledge of spatial and volume distribution of the superparamagnetic nanoparticles, were used as a model for magnetic Monte Carlo simulations, yielding zero field cooling/field cooling and magnetic hysteresis curves, which were compared to the measured ones. Additionally, the local curvature of the magnetite particles' docking site within the mesoporous silicon's surface was obtained in two different ways and a comparison will be given. A new iterative semi-automatic image alignment program was written and the importance of image segmentation for a truly objective analysis is also addressed.

Higher-dimensional Bianchi type-VIh cosmologies

NASA Astrophysics Data System (ADS)

Lorenz-Petzold, D.

1985-09-01

The higher-dimensional perfect fluid equations of a generalization of the (1 + 3)-dimensional Bianchi type-VIh space-time are discussed. Bianchi type-V and Bianchi type-III space-times are also included as special cases. It is shown that the Chodos-Detweiler (1980) mechanism of cosmological dimensional-reduction is possible in these cases.

High-Dimensional Function Approximation With Neural Networks for Large Volumes of Data.

PubMed

Andras, Peter

2018-02-01

Approximation of high-dimensional functions is a challenge for neural networks due to the curse of dimensionality. Often the data for which the approximated function is defined resides on a low-dimensional manifold and in principle the approximation of the function over this manifold should improve the approximation performance. It has been show that projecting the data manifold into a lower dimensional space, followed by the neural network approximation of the function over this space, provides a more precise approximation of the function than the approximation of the function with neural networks in the original data space. However, if the data volume is very large, the projection into the low-dimensional space has to be based on a limited sample of the data. Here, we investigate the nature of the approximation error of neural networks trained over the projection space. We show that such neural networks should have better approximation performance than neural networks trained on high-dimensional data even if the projection is based on a relatively sparse sample of the data manifold. We also find that it is preferable to use a uniformly distributed sparse sample of the data for the purpose of the generation of the low-dimensional projection. We illustrate these results considering the practical neural network approximation of a set of functions defined on high-dimensional data including real world data as well.

Exploring packaging strategies of nano-embedded thermoelectric generators

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

Singha, Aniket; Muralidharan, Bhaskaran, E-mail: bm@ee.iitb.ac.in; Mahanti, Subhendra D.

2015-10-15

Embedding nanostructures within a bulk matrix is an important practical approach towards the electronic engineering of high performance thermoelectric systems. For power generation applications, it ideally combines the efficiency benefit offered by low dimensional systems along with the high power output advantage offered by bulk systems. In this work, we uncover a few crucial details about how to embed nanowires and nanoflakes in a bulk matrix so that an overall advantage over pure bulk may be achieved. First and foremost, we point out that a performance degradation with respect to bulk is inevitable as the nanostructure transitions to a multimore » moded one. It is then shown that a nano embedded system of suitable cross-section offers a power density advantage over a wide range of efficiencies at higher packing fractions, and this range gradually narrows down to the high efficiency regime, as the packing fraction is reduced. Finally, we introduce a metric - the advantage factor, to elucidate quantitatively, the enhancement in the power density offered via nano-embedding at a given efficiency. In the end, we explore the maximum effective width of nano-embedding which serves as a reference in designing generators in the efficiency range of interest.« less

THE GENERALIZATION OF SIERPINSKI CARPET AND MENGER SPONGE IN n-DIMENSIONAL SPACE

NASA Astrophysics Data System (ADS)

Yang, Yun; Feng, Yuting; Yu, Yanhua

In this paper, we generalize Sierpinski carpet and Menger sponge in n-dimensional space, by using the generations and characterizations of affinely-equivalent Sierpinski carpet and Menger sponge. Exactly, Menger sponge in 4-dimensional space could be drawn out clearly under an affine transformation. Furthermore, the method could be used to a much broader class in fractals.

Optimal Detection Range of RFID Tag for RFID-based Positioning System Using the k-NN Algorithm.

PubMed

Han, Soohee; Kim, Junghwan; Park, Choung-Hwan; Yoon, Hee-Cheon; Heo, Joon

2009-01-01

Positioning technology to track a moving object is an important and essential component of ubiquitous computing environments and applications. An RFID-based positioning system using the k-nearest neighbor (k-NN) algorithm can determine the position of a moving reader from observed reference data. In this study, the optimal detection range of an RFID-based positioning system was determined on the principle that tag spacing can be derived from the detection range. It was assumed that reference tags without signal strength information are regularly distributed in 1-, 2- and 3-dimensional spaces. The optimal detection range was determined, through analytical and numerical approaches, to be 125% of the tag-spacing distance in 1-dimensional space. Through numerical approaches, the range was 134% in 2-dimensional space, 143% in 3-dimensional space.

Liquid on Paper: Rapid Prototyping of Soft Functional Components for Paper Electronics

PubMed Central

Long Han, Yu; Liu, Hao; Ouyang, Cheng; Jian Lu, Tian; Xu, Feng

2015-01-01

This paper describes a novel approach to fabricate paper-based electric circuits consisting of a paper matrix embedded with three-dimensional (3D) microchannels and liquid metal. Leveraging the high electric conductivity and good flowability of liquid metal, and metallophobic property of paper, it is possible to keep electric and mechanical functionality of the electric circuit even after a thousand cycles of deformation. Embedding liquid metal into paper matrix is a promising method to rapidly fabricate low-cost, disposable, and soft electric circuits for electronics. As a demonstration, we designed a programmable displacement transducer and applied it as variable resistors and pressure sensors. The unique metallophobic property, combined with softness, low cost and light weight, makes paper an attractive alternative to other materials in which liquid metal are currently embedded. PMID:26129723

Connes' embedding problem and winning strategies for quantum XOR games

NASA Astrophysics Data System (ADS)

Harris, Samuel J.

2017-12-01

We consider quantum XOR games, defined in the work of Regev and Vidick [ACM Trans. Comput. Theory 7, 43 (2015)], from the perspective of unitary correlations defined in the work of Harris and Paulsen [Integr. Equations Oper. Theory 89, 125 (2017)]. We show that the winning bias of a quantum XOR game in the tensor product model (respectively, the commuting model) is equal to the norm of its associated linear functional on the unitary correlation set from the appropriate model. We show that Connes' embedding problem has a positive answer if and only if every quantum XOR game has entanglement bias equal to the commuting bias. In particular, the embedding problem is equivalent to determining whether every quantum XOR game G with a winning strategy in the commuting model also has a winning strategy in the approximate finite-dimensional model.

Privacy-Preserving Predictive Modeling: Harmonization of Contextual Embeddings From Different Sources.

PubMed

Huang, Yingxiang; Lee, Junghye; Wang, Shuang; Sun, Jimeng; Liu, Hongfang; Jiang, Xiaoqian

2018-05-16

Data sharing has been a big challenge in biomedical informatics because of privacy concerns. Contextual embedding models have demonstrated a very strong representative capability to describe medical concepts (and their context), and they have shown promise as an alternative way to support deep-learning applications without the need to disclose original data. However, contextual embedding models acquired from individual hospitals cannot be directly combined because their embedding spaces are different, and naive pooling renders combined embeddings useless. The aim of this study was to present a novel approach to address these issues and to promote sharing representation without sharing data. Without sacrificing privacy, we also aimed to build a global model from representations learned from local private data and synchronize information from multiple sources. We propose a methodology that harmonizes different local contextual embeddings into a global model. We used Word2Vec to generate contextual embeddings from each source and Procrustes to fuse different vector models into one common space by using a list of corresponding pairs as anchor points. We performed prediction analysis with harmonized embeddings. We used sequential medical events extracted from the Medical Information Mart for Intensive Care III database to evaluate the proposed methodology in predicting the next likely diagnosis of a new patient using either structured data or unstructured data. Under different experimental scenarios, we confirmed that the global model built from harmonized local models achieves a more accurate prediction than local models and global models built from naive pooling. Such aggregation of local models using our unique harmonization can serve as the proxy for a global model, combining information from a wide range of institutions and information sources. It allows information unique to a certain hospital to become available to other sites, increasing the fluidity of information flow in health care. ©Yingxiang Huang, Junghye Lee, Shuang Wang, Jimeng Sun, Hongfang Liu, Xiaoqian Jiang. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 16.05.2018.

Embeddings of the "New Massive Gravity"

NASA Astrophysics Data System (ADS)

Dalmazi, D.; Mendonça, E. L.

2016-07-01

Here we apply different types of embeddings of the equations of motion of the linearized "New Massive Gravity" in order to generate alternative and even higher-order (in derivatives) massive gravity theories in D=2+1. In the first part of the work we use the Weyl symmetry as a guiding principle for the embeddings. First we show that a Noether gauge embedding of the Weyl symmetry leads to a sixth-order model in derivatives with either a massive or a massless ghost, according to the chosen overall sign of the theory. On the other hand, if the Weyl symmetry is implemented by means of a Stueckelberg field we obtain a new scalar-tensor model for massive gravitons. It is ghost-free and Weyl invariant at the linearized level around Minkowski space. The model can be nonlinearly completed into a scalar field coupled to the NMG theory. The elimination of the scalar field leads to a nonlocal modification of the NMG. In the second part of the work we prove to all orders in derivatives that there is no local, ghost-free embedding of the linearized NMG equations of motion around Minkowski space when written in terms of one symmetric tensor. Regarding that point, NMG differs from the Fierz-Pauli theory, since in the latter case we can replace the Einstein-Hilbert action by specific f(R,Box R) generalizations and still keep the theory ghost-free at the linearized level.

Low-Rank Discriminant Embedding for Multiview Learning.

PubMed

Li, Jingjing; Wu, Yue; Zhao, Jidong; Lu, Ke

2017-11-01

This paper focuses on the specific problem of multiview learning where samples have the same feature set but different probability distributions, e.g., different viewpoints or different modalities. Since samples lying in different distributions cannot be compared directly, this paper aims to learn a latent subspace shared by multiple views assuming that the input views are generated from this latent subspace. Previous approaches usually learn the common subspace by either maximizing the empirical likelihood, or preserving the geometric structure. However, considering the complementarity between the two objectives, this paper proposes a novel approach, named low-rank discriminant embedding (LRDE), for multiview learning by taking full advantage of both sides. By further considering the duality between data points and features of multiview scene, i.e., data points can be grouped based on their distribution on features, while features can be grouped based on their distribution on the data points, LRDE not only deploys low-rank constraints on both sample level and feature level to dig out the shared factors across different views, but also preserves geometric information in both the ambient sample space and the embedding feature space by designing a novel graph structure under the framework of graph embedding. Finally, LRDE jointly optimizes low-rank representation and graph embedding in a unified framework. Comprehensive experiments in both multiview manner and pairwise manner demonstrate that LRDE performs much better than previous approaches proposed in recent literatures.

Sequence2Vec: a novel embedding approach for modeling transcription factor binding affinity landscape.

PubMed

Dai, Hanjun; Umarov, Ramzan; Kuwahara, Hiroyuki; Li, Yu; Song, Le; Gao, Xin

2017-11-15

An accurate characterization of transcription factor (TF)-DNA affinity landscape is crucial to a quantitative understanding of the molecular mechanisms underpinning endogenous gene regulation. While recent advances in biotechnology have brought the opportunity for building binding affinity prediction methods, the accurate characterization of TF-DNA binding affinity landscape still remains a challenging problem. Here we propose a novel sequence embedding approach for modeling the transcription factor binding affinity landscape. Our method represents DNA binding sequences as a hidden Markov model which captures both position specific information and long-range dependency in the sequence. A cornerstone of our method is a novel message passing-like embedding algorithm, called Sequence2Vec, which maps these hidden Markov models into a common nonlinear feature space and uses these embedded features to build a predictive model. Our method is a novel combination of the strength of probabilistic graphical models, feature space embedding and deep learning. We conducted comprehensive experiments on over 90 large-scale TF-DNA datasets which were measured by different high-throughput experimental technologies. Sequence2Vec outperforms alternative machine learning methods as well as the state-of-the-art binding affinity prediction methods. Our program is freely available at https://github.com/ramzan1990/sequence2vec. xin.gao@kaust.edu.sa or lsong@cc.gatech.edu. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

Propagation of gaseous detonation waves in a spatially inhomogeneous reactive medium

NASA Astrophysics Data System (ADS)

Mi, XiaoCheng; Higgins, Andrew J.; Ng, Hoi Dick; Kiyanda, Charles B.; Nikiforakis, Nikolaos

2017-05-01

Detonation propagation in a compressible medium wherein the energy release has been made spatially inhomogeneous is examined via numerical simulation. The inhomogeneity is introduced via step functions in the reaction progress variable, with the local value of energy release correspondingly increased so as to maintain the same average energy density in the medium and thus a constant Chapman-Jouguet (CJ) detonation velocity. A one-step Arrhenius rate governs the rate of energy release in the reactive zones. The resulting dynamics of a detonation propagating in such systems with one-dimensional layers and two-dimensional squares are simulated using a Godunov-type finite-volume scheme. The resulting wave dynamics are analyzed by computing the average wave velocity and one-dimensional averaged wave structure. In the case of sufficiently inhomogeneous media wherein the spacing between reactive zones is greater than the inherent reaction zone length, average wave speeds significantly greater than the corresponding CJ speed of the homogenized medium are obtained. If the shock transit time between reactive zones is less than the reaction time scale, then the classical CJ detonation velocity is recovered. The spatiotemporal averaged structure of the waves in these systems is analyzed via a Favre-averaging technique, with terms associated with the thermal and mechanical fluctuations being explicitly computed. The analysis of the averaged wave structure identifies the super-CJ detonations as weak detonations owing to the existence of mechanical nonequilibrium at the effective sonic point embedded within the wave structure. The correspondence of the super-CJ behavior identified in this study with real detonation phenomena that may be observed in experiments is discussed.

Data-Driven Modeling of Complex Systems by means of a Dynamical ANN

NASA Astrophysics Data System (ADS)

Seleznev, A.; Mukhin, D.; Gavrilov, A.; Loskutov, E.; Feigin, A.

2017-12-01

The data-driven methods for modeling and prognosis of complex dynamical systems become more and more popular in various fields due to growth of high-resolution data. We distinguish the two basic steps in such an approach: (i) determining the phase subspace of the system, or embedding, from available time series and (ii) constructing an evolution operator acting in this reduced subspace. In this work we suggest a novel approach combining these two steps by means of construction of an artificial neural network (ANN) with special topology. The proposed ANN-based model, on the one hand, projects the data onto a low-dimensional manifold, and, on the other hand, models a dynamical system on this manifold. Actually, this is a recurrent multilayer ANN which has internal dynamics and capable of generating time series. Very important point of the proposed methodology is the optimization of the model allowing us to avoid overfitting: we use Bayesian criterion to optimize the ANN structure and estimate both the degree of evolution operator nonlinearity and the complexity of nonlinear manifold which the data are projected on. The proposed modeling technique will be applied to the analysis of high-dimensional dynamical systems: Lorenz'96 model of atmospheric turbulence, producing high-dimensional space-time chaos, and quasi-geostrophic three-layer model of the Earth's atmosphere with the natural orography, describing the dynamics of synoptical vortexes as well as mesoscale blocking systems. The possibility of application of the proposed methodology to analyze real measured data is also discussed. The study was supported by the Russian Science Foundation (grant #16-12-10198).

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.

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.

Curvature and temperature of complex networks.

PubMed

Krioukov, Dmitri; Papadopoulos, Fragkiskos; Vahdat, Amin; Boguñá, Marián

2009-09-01

We show that heterogeneous degree distributions in observed scale-free topologies of complex networks can emerge as a consequence of the exponential expansion of hidden hyperbolic space. Fermi-Dirac statistics provides a physical interpretation of hyperbolic distances as energies of links. The hidden space curvature affects the heterogeneity of the degree distribution, while clustering is a function of temperature. We embed the internet into the hyperbolic plane and find a remarkable congruency between the embedding and our hyperbolic model. Besides proving our model realistic, this embedding may be used for routing with only local information, which holds significant promise for improving the performance of internet routing.

Shock-Induced Separated Structures in Symmetric Corner Flows

NASA Technical Reports Server (NTRS)

DAmbrosio, Domenic; Marsilio, Roberto

1995-01-01

Three-dimensional supersonic viscous laminar flows over symmetric corners are considered in this paper. The characteristic features of such configurations are discussed and an historical survey on the past research work is presented. A new contribution based on a numerical technique that solves the parabolized form of the Navier-Stokes equations is presented. Such a method makes it possible to obtain very detailed descriptions of the flowfield with relatively modest CPU time and memory storage requirements. The numerical approach is based on a space-marching technique, uses a finite volume discretization and an upwind flux-difference splitting scheme (developed for the steady flow equations) for the evaluation of the inviscid fluxes. Second order accuracy is reached following the guidelines of the ENO schemes. Different free-stream conditions and geometrical configurations are considered. Primary and secondary streamwise vortical structures embedded in the boundary layer and originated by the interaction of the latter with shock waves are detected and studied. Computed results are compared with experimental data taken from literature.

Segmentation of nanotomographic cortical bone images for quantitative characterization of the osteoctyte lacuno-canalicular network

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

Ciani, A.; Kewish, C. M.; Guizar-Sicairos, M.

A newly developed data processing method able to characterize the osteocytes lacuno-canalicular network (LCN) is presented. Osteocytes are the most abundant cells in the bone, living in spaces called lacunae embedded inside the bone matrix and connected to each other with an extensive network of canals that allows for the exchange of nutrients and for mechanotransduction functions. The geometrical three-dimensional (3D) architecture is increasingly thought to be related to the macroscopic strength or failure of the bone and it is becoming the focus for investigating widely spread diseases such as osteoporosis. To obtain 3D LCN images non-destructively has been outmore » of reach until recently, since tens-of-nanometers scale resolution is required. Ptychographic tomography was validated for bone imaging in [1], showing clearly the LCN. The method presented here was applied to 3D ptychographic tomographic images in order to extract morphological and geometrical parameters of the lacuno-canalicular structures.« less

Description and detection of burst events in turbulent flows

NASA Astrophysics Data System (ADS)

Schmid, P. J.; García-Gutierrez, A.; Jiménez, J.

2018-04-01

A mathematical and computational framework is developed for the detection and identification of coherent structures in turbulent wall-bounded shear flows. In a first step, this data-based technique will use an embedding methodology to formulate the fluid motion as a phase-space trajectory, from which state-transition probabilities can be computed. Within this formalism, a second step then applies repeated clustering and graph-community techniques to determine a hierarchy of coherent structures ranked by their persistencies. This latter information will be used to detect highly transitory states that act as precursors to violent and intermittent events in turbulent fluid motion (e.g., bursts). Used as an analysis tool, this technique allows the objective identification of intermittent (but important) events in turbulent fluid motion; however, it also lays the foundation for advanced control strategies for their manipulation. The techniques are applied to low-dimensional model equations for turbulent transport, such as the self-sustaining process (SSP), for varying levels of complexity.

Finite-size correction scheme for supercell calculations in Dirac-point two-dimensional materials.

PubMed

Rocha, C G; Rocha, A R; Venezuela, P; Garcia, J H; Ferreira, M S

2018-06-19

Modern electronic structure calculations are predominantly implemented within the super cell representation in which unit cells are periodically arranged in space. Even in the case of non-crystalline materials, defect-embedded unit cells are commonly used to describe doped structures. However, this type of computation becomes prohibitively demanding when convergence rates are sufficiently slow and may require calculations with very large unit cells. Here we show that a hitherto unexplored feature displayed by several 2D materials may be used to achieve convergence in formation- and adsorption-energy calculations with relatively small unit-cell sizes. The generality of our method is illustrated with Density Functional Theory calculations for different 2D hosts doped with different impurities, all of which providing accuracy levels that would otherwise require enormously large unit cells. This approach provides an efficient route to calculating the physical properties of 2D systems in general but is particularly suitable for Dirac-point materials doped with impurities that break their sublattice symmetry.

The topology of large Open Connectome networks for the human brain.

PubMed

Gastner, Michael T; Ódor, Géza

2016-06-07

The structural human connectome (i.e. the network of fiber connections in the brain) can be analyzed at ever finer spatial resolution thanks to advances in neuroimaging. Here we analyze several large data sets for the human brain network made available by the Open Connectome Project. We apply statistical model selection to characterize the degree distributions of graphs containing up to nodes and edges. A three-parameter generalized Weibull (also known as a stretched exponential) distribution is a good fit to most of the observed degree distributions. For almost all networks, simple power laws cannot fit the data, but in some cases there is statistical support for power laws with an exponential cutoff. We also calculate the topological (graph) dimension D and the small-world coefficient σ of these networks. While σ suggests a small-world topology, we found that D < 4 showing that long-distance connections provide only a small correction to the topology of the embedding three-dimensional space.

The topology of large Open Connectome networks for the human brain

NASA Astrophysics Data System (ADS)

Gastner, Michael T.; Ódor, Géza

2016-06-01

The structural human connectome (i.e. the network of fiber connections in the brain) can be analyzed at ever finer spatial resolution thanks to advances in neuroimaging. Here we analyze several large data sets for the human brain network made available by the Open Connectome Project. We apply statistical model selection to characterize the degree distributions of graphs containing up to nodes and edges. A three-parameter generalized Weibull (also known as a stretched exponential) distribution is a good fit to most of the observed degree distributions. For almost all networks, simple power laws cannot fit the data, but in some cases there is statistical support for power laws with an exponential cutoff. We also calculate the topological (graph) dimension D and the small-world coefficient σ of these networks. While σ suggests a small-world topology, we found that D < 4 showing that long-distance connections provide only a small correction to the topology of the embedding three-dimensional space.

Segmentation of nanotomographic cortical bone images for quantitative characterization of the osteoctyte lacuno-canalicular network

NASA Astrophysics Data System (ADS)

Ciani, A.; Guizar-Sicairos, M.; Diaz, A.; Holler, M.; Pallu, S.; Achiou, Z.; Jennane, R.; Toumi, H.; Lespessailles, E.; Kewish, C. M.

2016-01-01

A newly developed data processing method able to characterize the osteocytes lacuno-canalicular network (LCN) is presented. Osteocytes are the most abundant cells in the bone, living in spaces called lacunae embedded inside the bone matrix and connected to each other with an extensive network of canals that allows for the exchange of nutrients and for mechanotransduction functions. The geometrical three-dimensional (3D) architecture is increasingly thought to be related to the macroscopic strength or failure of the bone and it is becoming the focus for investigating widely spread diseases such as osteoporosis. To obtain 3D LCN images non-destructively has been out of reach until recently, since tens-of-nanometers scale resolution is required. Ptychographic tomography was validated for bone imaging in [1], showing clearly the LCN. The method presented here was applied to 3D ptychographic tomographic images in order to extract morphological and geometrical parameters of the lacuno-canalicular structures.

Characterization of chaotic dynamics in the human menstrual cycle

NASA Astrophysics Data System (ADS)

Derry, Gregory; Derry, Paula

2010-03-01

The human menstrual cycle exhibits much unexplained variability, which is typically dismissed as random variation. Given the many delayed nonlinear feedbacks in the reproductive endocrine system, however, the menstrual cycle might well be a nonlinear dynamical system in a chaotic trajectory, and that this instead accounts for the observed variability. Here, we test this hypothesis by performing a time series analysis on data for 7438 menstrual cycles from 38 women in the 20-40 year age range, using the database maintained by the Tremin Research Program on Women's Health. Using phase space reconstruction techniques with a maximum embedding dimension of 6, we find appropriate scaling behavior in the correlation sums for this data, indicating low dimensional deterministic dynamics. A correlation dimension of 2.6 is measured in this scaling regime, and this result is confirmed by recalculation using the Takens estimator. These results may be interpreted as offering an approximation to the fractal dimension of a strange attractor governing the chaotic dynamics of the menstrual cycle.

Manifold decoding for neural representations of face viewpoint and gaze direction using magnetoencephalographic data.

PubMed

Kuo, Po-Chih; Chen, Yong-Sheng; Chen, Li-Fen

2018-05-01

The main challenge in decoding neural representations lies in linking neural activity to representational content or abstract concepts. The transformation from a neural-based to a low-dimensional representation may hold the key to encoding perceptual processes in the human brain. In this study, we developed a novel model by which to represent two changeable features of faces: face viewpoint and gaze direction. These features are embedded in spatiotemporal brain activity derived from magnetoencephalographic data. Our decoding results demonstrate that face viewpoint and gaze direction can be represented by manifold structures constructed from brain responses in the bilateral occipital face area and right superior temporal sulcus, respectively. Our results also show that the superposition of brain activity in the manifold space reveals the viewpoints of faces as well as directions of gazes as perceived by the subject. The proposed manifold representation model provides a novel opportunity to gain further insight into the processing of information in the human brain. © 2018 Wiley Periodicals, Inc.

Subsurface Void Characterization with 3-D Time Domain Full Waveform Tomography.

NASA Astrophysics Data System (ADS)

Nguyen, T. D.

2017-12-01

A new three dimensional full waveform inversion (3-D FWI) method is presented for subsurface site characterization at engineering scales (less than 30 m in depth). The method is based on a solution of 3-D elastic wave equations for forward modeling, and a cross-adjoint gradient approach for model updating. The staggered-grid finite-difference technique is used to solve the wave equations, together with implementation of the perfectly matched layer condition for boundary truncation. The gradient is calculated from the forward and backward wavefields. Reversed-in-time displacement residuals are induced as multiple sources at all receiver locations for the backward wavefield. The capability of the presented FWI method is tested on both synthetic and field experimental datasets. The test configuration uses 96 receivers and 117 shots at equal spacing (Fig 1). The inversion results from synthetic data show the ability of characterizing variable low- and high-velocity layers with embedded void (Figs 2-3). The synthetic study shows good potential for detection of voids and abnormalities in the field.

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

Application of Hyperspectal Techniques to Monitoring & Management of Invasive Plant Species Infestation

DTIC Science & Technology

2008-01-09

The image data as acquired from the sensor is a data cloud in multi- dimensional space with each band generating an axis of dimension. When the data... The color of a material is defined by the direction of its unit vector in n- dimensional spectral space . The length of the vector relates only to how...to n- dimensional space . SAM determines the similarity

Answers in search of a question: 'proofs' of the tri-dimensionality of space

NASA Astrophysics Data System (ADS)

Callender, Craig

From Kant's first published work to recent articles in the physics literature, philosophers and physicists have long sought an answer to the question: Why does space have three dimensions? In this paper, I will flesh out Kant's claim with a brief detour through Gauss' law. I then describe Büchel's version of the common argument that stable orbits are possible only if space is three dimensional. After examining objections by Russell and van Fraassen, I develop three original criticisms of my own. These criticisms are relevant to both historical and contemporary proofs of the dimensionality of space (in particular, a recent one by Burgbacher, Lämmerzahl, and Macias). In general, I argue that modern "proofs" of the dimensionality of space have gone off track.

Brane surgery: energy conditions, traversable wormholes, and voids

NASA Astrophysics Data System (ADS)

Barceló1, C.; Visser, M.

2000-09-01

Branes are ubiquitous elements of any low-energy limit of string theory. We point out that negative tension branes violate all the standard energy conditions of the higher-dimensional spacetime they are embedded in; this opens the door to very peculiar solutions of the higher-dimensional Einstein equations. Building upon the (/3+1)-dimensional implementation of fundamental string theory, we illustrate the possibilities by considering a toy model consisting of a (/2+1)-dimensional brane propagating through our observable (/3+1)-dimensional universe. Developing a notion of ``brane surgery'', based on the Israel-Lanczos-Sen ``thin shell'' formalism of general relativity, we analyze the dynamics and find traversable wormholes, closed baby universes, voids (holes in the spacetime manifold), and an evasion (not a violation) of both the singularity theorems and the positive mass theorem. These features appear generic to any brane model that permits negative tension branes: This includes the Randall-Sundrum models and their variants.

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.

A non-linear dimension reduction methodology for generating data-driven stochastic input models

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

Ganapathysubramanian, Baskar; Zabaras, Nicholas

Stochastic analysis of random heterogeneous media (polycrystalline materials, porous media, functionally graded materials) provides information of significance only if realistic input models of the topology and property variations are used. This paper proposes a framework to construct such input stochastic models for the topology and thermal diffusivity variations in heterogeneous media using a data-driven strategy. Given a set of microstructure realizations (input samples) generated from given statistical information about the medium topology, the framework constructs a reduced-order stochastic representation of the thermal diffusivity. This problem of constructing a low-dimensional stochastic representation of property variations is analogous to the problem ofmore » manifold learning and parametric fitting of hyper-surfaces encountered in image processing and psychology. Denote by M the set of microstructures that satisfy the given experimental statistics. A non-linear dimension reduction strategy is utilized to map M to a low-dimensional region, A. We first show that M is a compact manifold embedded in a high-dimensional input space R{sup n}. An isometric mapping F from M to a low-dimensional, compact, connected set A is contained in R{sup d}(d<

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.

Refining the boundaries of the classical de Sitter landscape

NASA Astrophysics Data System (ADS)

Andriot, David; Blåbäck, Johan

2017-03-01

We derive highly constraining no-go theorems for classical de Sitter backgrounds of string theory, with parallel sources; this should impact the embedding of cosmological models. We study ten-dimensional vacua of type II supergravities with parallel and backreacted orientifold O p -planes and D p -branes, on four-dimensional de Sitter spacetime times a compact manifold. Vacua for p = 3, 7 or 8 are completely excluded, and we obtain tight constraints for p = 4, 5, 6. This is achieved through the derivation of an enlightening expression for the four-dimensional Ricci scalar. Further interesting expressions and no-go theorems are obtained. The paper is self-contained so technical aspects, including conventions, might be of more general interest.

A parallel finite-difference method for computational aerodynamics

NASA Technical Reports Server (NTRS)

Swisshelm, Julie M.

1989-01-01

A finite-difference scheme for solving complex three-dimensional aerodynamic flow on parallel-processing supercomputers is presented. The method consists of a basic flow solver with multigrid convergence acceleration, embedded grid refinements, and a zonal equation scheme. Multitasking and vectorization have been incorporated into the algorithm. Results obtained include multiprocessed flow simulations from the Cray X-MP and Cray-2. Speedups as high as 3.3 for the two-dimensional case and 3.5 for segments of the three-dimensional case have been achieved on the Cray-2. The entire solver attained a factor of 2.7 improvement over its unitasked version on the Cray-2. The performance of the parallel algorithm on each machine is analyzed.

Bi-directional, buried-wire skin-friction gage

NASA Technical Reports Server (NTRS)

Higuchi, H.; Peake, D. J.

1978-01-01

A compact, nonobtrusive, bi-directional, skin-friction gage was developed to measure the mean shear stress beneath a three-dimensional boundary layer. The gage works by measuring the heat flux from two orthogonal wires embedded in the surface. Such a gage was constructed and its characteristics were determined for different angles of yaw in a calibration experiment in subsonic flow with a Preston tube used as a standard. Sample gages were then used in a fully three-dimensional turbulent boundary layer on a circular cone at high relative incidence, where there were regimes of favorable and adverse pressure gradients and three-dimensional separation. Both the direction and magnitude of skin friction were then obtained on the cone surface.

Mass-deformed ABJM and black holes in AdS4

NASA Astrophysics Data System (ADS)

Bobev, Nikolay; Min, Vincent S.; Pilch, Krzysztof

2018-03-01

We find a class of new supersymmetric dyonic black holes in four-dimensional maximal gauged supergravity which are asymptotic to the SU(3) × U(1) invariant AdS4 Warner vacuum. These black holes can be embedded in eleven-dimensional supergravity where they describe the backreaction of M2-branes wrapped on a Riemann surface. The holographic dual description of these supergravity backgrounds is given by a partial topological twist on a Riemann surface of a three-dimensional N=2 SCFT that is obtained by a mass-deformation of the ABJM theory. We compute explicitly the topologically twisted index of this SCFT and show that it accounts for the entropy of the black holes.

Face recognition based on two-dimensional discriminant sparse preserving projection

NASA Astrophysics Data System (ADS)

Zhang, Dawei; Zhu, Shanan

2018-04-01

In this paper, a supervised dimensionality reduction algorithm named two-dimensional discriminant sparse preserving projection (2DDSPP) is proposed for face recognition. In order to accurately model manifold structure of data, 2DDSPP constructs within-class affinity graph and between-class affinity graph by the constrained least squares (LS) and l1 norm minimization problem, respectively. Based on directly operating on image matrix, 2DDSPP integrates graph embedding (GE) with Fisher criterion. The obtained projection subspace preserves within-class neighborhood geometry structure of samples, while keeping away samples from different classes. The experimental results on the PIE and AR face databases show that 2DDSPP can achieve better recognition performance.

Comprehensive investigation of core-shell dimer nanoparticles size, distance and thicknesses on performance of a hybrid organic-inorganic halide perovskite solar cell

NASA Astrophysics Data System (ADS)

Heidarzadeh, Hamid

2018-03-01

Significant performance enhancement in an ultrathin perovskite (CH3NH3PbI3) solar cell is done using plasmonic embedded core–shell dimer nanoparticles. Three-dimensional finite difference time-domain (FDTD) method is used. A perovskite absorber with a volume of 400 × 400 × 200 nm3 is considered. At first, a cell with one embedded nanoparticle is simulated. Absorptance of CH3NH3PbI3 absorber and gold nanoparticle are obtained. An optimization is done. Then a cell with embedded dimer nanoparticles is evaluated. The results show higher photocurrent enhancement for that in compared to a cell with one embedded nanoparticle. To further photocurrent enhancement, gold-SiO2 core–shell nanoparticles are used. Photocurrents of 23.37 mA cm‑2, 23.3 mA cm‑2, 22.5 mA cm‑2 and 21.47 mA cm‑2 are obtained for a cell with two embedded core–shell nanoparticles with core radius of 60 nm and shell thickness of 2 nm, 5 nm, 10 nm and 20 nm, respectively. It is important to mention that the photocurrent is 17.9 mA cm‑2 for reference cell and 19.8 mA cm‑2 for a cell with one embedded nanoparticle. Higher photocurrent is due to the near-field plasmonic effect.

Embedded Data Processor and Portable Computer Technology testbeds

NASA Technical Reports Server (NTRS)

Alena, Richard; Liu, Yuan-Kwei; Goforth, Andre; Fernquist, Alan R.

1993-01-01

Attention is given to current activities in the Embedded Data Processor and Portable Computer Technology testbed configurations that are part of the Advanced Data Systems Architectures Testbed at the Information Sciences Division at NASA Ames Research Center. The Embedded Data Processor Testbed evaluates advanced microprocessors for potential use in mission and payload applications within the Space Station Freedom Program. The Portable Computer Technology (PCT) Testbed integrates and demonstrates advanced portable computing devices and data system architectures. The PCT Testbed uses both commercial and custom-developed devices to demonstrate the feasibility of functional expansion and networking for portable computers in flight missions.

General point dipole theory for periodic metasurfaces: magnetoelectric scattering lattices coupled to planar photonic structures.

PubMed

Chen, Yuntian; Zhang, Yan; Femius Koenderink, A

2017-09-04

We study semi-analytically the light emission and absorption properties of arbitrary stratified photonic structures with embedded two-dimensional magnetoelectric point scattering lattices, as used in recent plasmon-enhanced LEDs and solar cells. By employing dyadic Green's function for the layered structure in combination with the Ewald lattice summation to deal with the particle lattice, we develop an efficient method to study the coupling between planar 2D scattering lattices of plasmonic, or metamaterial point particles, coupled to layered structures. Using the 'array scanning method' we deal with localized sources. Firstly, we apply our method to light emission enhancement of dipole emitters in slab waveguides, mediated by plasmonic lattices. We benchmark the array scanning method against a reciprocity-based approach to find that the calculated radiative rate enhancement in k-space below the light cone shows excellent agreement. Secondly, we apply our method to study absorption-enhancement in thin-film solar cells mediated by periodic Ag nanoparticle arrays. Lastly, we study the emission distribution in k-space of a coupled waveguide-lattice system. In particular, we explore the dark mode excitation on the plasmonic lattice using the so-called array scanning method. Our method could be useful for simulating a broad range of complex nanophotonic structures, i.e., metasurfaces, plasmon-enhanced light emitting systems and photovoltaics.

Phase-Space Detection of Cyber Events

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

Hernandez Jimenez, Jarilyn M; Ferber, Aaron E; Prowell, Stacy J

Energy Delivery Systems (EDS) are a network of processes that produce, transfer and distribute energy. EDS are increasingly dependent on networked computing assets, as are many Industrial Control Systems. Consequently, cyber-attacks pose a real and pertinent threat, as evidenced by Stuxnet, Shamoon and Dragonfly. Hence, there is a critical need for novel methods to detect, prevent, and mitigate effects of such attacks. To detect cyber-attacks in EDS, we developed a framework for gathering and analyzing timing data that involves establishing a baseline execution profile and then capturing the effect of perturbations in the state from injecting various malware. The datamore » analysis was based on nonlinear dynamics and graph theory to improve detection of anomalous events in cyber applications. The goal was the extraction of changing dynamics or anomalous activity in the underlying computer system. Takens' theorem in nonlinear dynamics allows reconstruction of topologically invariant, time-delay-embedding states from the computer data in a sufficiently high-dimensional space. The resultant dynamical states were nodes, and the state-to-state transitions were links in a mathematical graph. Alternatively, sequential tabulation of executing instructions provides the nodes with corresponding instruction-to-instruction links. Graph theorems guarantee graph-invariant measures to quantify the dynamical changes in the running applications. Results showed a successful detection of cyber events.« less

Rasch Analysis for Binary Data with Nonignorable Nonresponses

ERIC Educational Resources Information Center

Bertoli-Barsotti, Lucio; Punzo, Antonio

2013-01-01

This paper introduces a two-dimensional Item Response Theory (IRT) model to deal with nonignorable nonresponses in tests with dichotomous items. One dimension provides information about the omitting behavior, while the other dimension is related to the person's "ability". The idea of embedding an IRT model for missingness into the measurement…

Supersymmetry in the Jaynes-Cummings model

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

Castanos, Octavio

2013-06-12

A review is presented of the Darboux method and its relation to the supersymmetric quantum mechanics, together with the embedding of a n-dimensional scalar Hamiltonian into a supersymmetric matrix. It is also shown that the Jaynes-Cummings model, with or without rotating wave approximation, admit a supersymmetric quantum mechanics description.

Maximum projection designs for computer experiments

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

Joseph, V. Roshan; Gul, Evren; Ba, Shan

Space-filling properties are important in designing computer experiments. The traditional maximin and minimax distance designs only consider space-filling in the full dimensional space. This can result in poor projections onto lower dimensional spaces, which is undesirable when only a few factors are active. Restricting maximin distance design to the class of Latin hypercubes can improve one-dimensional projections, but cannot guarantee good space-filling properties in larger subspaces. We propose designs that maximize space-filling properties on projections to all subsets of factors. We call our designs maximum projection designs. As a result, our design criterion can be computed at a cost nomore » more than a design criterion that ignores projection properties.« less