Combinatorial alloying improves bismuth vanadate photoanodes via reduced monoclinic distortion

DOE PAGES

Newhouse, P. F.; Guevarra, D.; Umehara, M.; ...

2018-01-01

Energy technologies are enabled by materials innovations, requiring efficient methods to search high dimensional parameter spaces, such as multi-element alloying for enhancing solar fuels photoanodes.

Detection of Subtle Context-Dependent Model Inaccuracies in High-Dimensional Robot Domains.

PubMed

Mendoza, Juan Pablo; Simmons, Reid; Veloso, Manuela

2016-12-01

Autonomous robots often rely on models of their sensing and actions for intelligent decision making. However, when operating in unconstrained environments, the complexity of the world makes it infeasible to create models that are accurate in every situation. This article addresses the problem of using potentially large and high-dimensional sets of robot execution data to detect situations in which a robot model is inaccurate-that is, detecting context-dependent model inaccuracies in a high-dimensional context space. To find inaccuracies tractably, the robot conducts an informed search through low-dimensional projections of execution data to find parametric Regions of Inaccurate Modeling (RIMs). Empirical evidence from two robot domains shows that this approach significantly enhances the detection power of existing RIM-detection algorithms in high-dimensional spaces.

Signatures of a globally optimal searching strategy in the three-dimensional foraging flights of bumblebees

NASA Astrophysics Data System (ADS)

Lihoreau, Mathieu; Ings, Thomas C.; Chittka, Lars; Reynolds, Andy M.

2016-07-01

Simulated annealing is a powerful stochastic search algorithm for locating a global maximum that is hidden among many poorer local maxima in a search space. It is frequently implemented in computers working on complex optimization problems but until now has not been directly observed in nature as a searching strategy adopted by foraging animals. We analysed high-speed video recordings of the three-dimensional searching flights of bumblebees (Bombus terrestris) made in the presence of large or small artificial flowers within a 0.5 m3 enclosed arena. Analyses of the three-dimensional flight patterns in both conditions reveal signatures of simulated annealing searches. After leaving a flower, bees tend to scan back-and forth past that flower before making prospecting flights (loops), whose length increases over time. The search pattern becomes gradually more expansive and culminates when another rewarding flower is found. Bees then scan back and forth in the vicinity of the newly discovered flower and the process repeats. This looping search pattern, in which flight step lengths are typically power-law distributed, provides a relatively simple yet highly efficient strategy for pollinators such as bees to find best quality resources in complex environments made of multiple ephemeral feeding sites with nutritionally variable rewards.

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

PubMed

Wang, Xueyi

2012-02-08

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

Weather prediction using a genetic memory

NASA Technical Reports Server (NTRS)

Rogers, David

1990-01-01

Kanaerva's sparse distributed memory (SDM) is an associative memory model based on the mathematical properties of high dimensional binary address spaces. Holland's genetic algorithms are a search technique for high dimensional spaces inspired by evolutional processes of DNA. Genetic Memory is a hybrid of the above two systems, in which the memory uses a genetic algorithm to dynamically reconfigure its physical storage locations to reflect correlations between the stored addresses and data. This architecture is designed to maximize the ability of the system to scale-up to handle real world problems.

A Belief-Space Approach to Integrated Intelligence - Research Area 10.3: Intelligent Networks

DTIC Science & Technology

2017-12-05

A Belief-Space Approach to Integrated Intelligence- Research Area 10.3: Intelligent Networks The views , opinions and/or findings contained in this...high dimensionality and multi -modality of their hybrid configuration spaces. Planners that perform a purely geometric search are prohibitively slow...Hamburg, January Paper Title: Hierarchical planning for multi -contact non-prehensile manipulation Publication Type: Conference Paper or Presentation

Putting Man in the Machine: Exploiting Expertise to Enhance Multiobjective Design of Water Supply Monitoring Network

NASA Astrophysics Data System (ADS)

Bode, F.; Nowak, W.; Reed, P. M.; Reuschen, S.

2016-12-01

Drinking-water well catchments need effective early-warning monitoring networks. Groundwater water supply wells in complex urban environments are in close proximity to a myriad of potential industrial pollutant sources that could irreversibly damage their source aquifers. These urban environments pose fiscal and physical challenges to designing monitoring networks. Ideal early-warning monitoring networks would satisfy three objectives: to detect (1) all potential contaminations within the catchment (2) as early as possible before they reach the pumping wells, (3) while minimizing costs. Obviously, the ideal case is nonexistent, so we search for tradeoffs using multiobjective optimization. The challenge of this optimization problem is the high number of potential monitoring-well positions (the search space) and the non-linearity of the underlying groundwater flow-and-transport problem. This study evaluates (1) different ways to effectively restrict the search space in an efficient way, with and without expert knowledge, (2) different methods to represent the search space during the optimization and (3) the influence of incremental increases in uncertainty in the system. Conductivity, regional flow direction and potential source locations are explored as key uncertainties. We show the need and the benefit of our methods by comparing optimized monitoring networks for different uncertainty levels with networks that seek to effectively exploit expert knowledge. The study's main contributions are the different approaches restricting and representing the search space. The restriction algorithms are based on a point-wise comparison of decision elements of the search space. The representation of the search space can be either binary or continuous. For both cases, the search space must be adjusted properly. Our results show the benefits and drawbacks of binary versus continuous search space representations and the high potential of automated search space restriction algorithms for high-dimensional, highly non-linear optimization problems.

Expedite random structure searching using objects from Wyckoff positions

NASA Astrophysics Data System (ADS)

Wang, Shu-Wei; Hsing, Cheng-Rong; Wei, Ching-Ming

2018-02-01

Random structure searching has been proved to be a powerful approach to search and find the global minimum and the metastable structures. A true random sampling is in principle needed yet it would be highly time-consuming and/or practically impossible to find the global minimum for the complicated systems in their high-dimensional configuration space. Thus the implementations of reasonable constraints, such as adopting system symmetries to reduce the independent dimension in structural space and/or imposing chemical information to reach and relax into low-energy regions, are the most essential issues in the approach. In this paper, we propose the concept of "object" which is either an atom or composed of a set of atoms (such as molecules or carbonates) carrying a symmetry defined by one of the Wyckoff positions of space group and through this process it allows the searching of global minimum for a complicated system to be confined in a greatly reduced structural space and becomes accessible in practice. We examined several representative materials, including Cd3As2 crystal, solid methanol, high-pressure carbonates (FeCO3), and Si(111)-7 × 7 reconstructed surface, to demonstrate the power and the advantages of using "object" concept in random structure searching.

Global Interior Robot Localisation by a Colour Content Image Retrieval System

NASA Astrophysics Data System (ADS)

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

2007-12-01

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

Computer Series, 32.

ERIC Educational Resources Information Center

Moore, John W., Ed.

1982-01-01

Ten computer programs (available from authors) and a noncomputer calculation of the electron in one-dimensional, one-Bohr box are described, including programs for analytical chemistry, space group generation using Pascal, mass-spectral search system (Applesoft), microcomputer-simulated liquid chromatography, voltammetry/amperometric titrations,…

Adaptive Shape Kernel-Based Mean Shift Tracker in Robot Vision System

PubMed Central

2016-01-01

This paper proposes an adaptive shape kernel-based mean shift tracker using a single static camera for the robot vision system. The question that we address in this paper is how to construct such a kernel shape that is adaptive to the object shape. We perform nonlinear manifold learning technique to obtain the low-dimensional shape space which is trained by training data with the same view as the tracking video. The proposed kernel searches the shape in the low-dimensional shape space obtained by nonlinear manifold learning technique and constructs the adaptive kernel shape in the high-dimensional shape space. It can improve mean shift tracker performance to track object position and object contour and avoid the background clutter. In the experimental part, we take the walking human as example to validate that our method is accurate and robust to track human position and describe human contour. PMID:27379165

Euclidean sections of protein conformation space and their implications in dimensionality reduction

PubMed Central

Duan, Mojie; Li, Minghai; Han, Li; Huo, Shuanghong

2014-01-01

Dimensionality reduction is widely used in searching for the intrinsic reaction coordinates for protein conformational changes. We find the dimensionality–reduction methods using the pairwise root–mean–square deviation as the local distance metric face a challenge. We use Isomap as an example to illustrate the problem. We believe that there is an implied assumption for the dimensionality–reduction approaches that aim to preserve the geometric relations between the objects: both the original space and the reduced space have the same kind of geometry, such as Euclidean geometry vs. Euclidean geometry or spherical geometry vs. spherical geometry. When the protein free energy landscape is mapped onto a 2D plane or 3D space, the reduced space is Euclidean, thus the original space should also be Euclidean. For a protein with N atoms, its conformation space is a subset of the 3N-dimensional Euclidean space R3N. We formally define the protein conformation space as the quotient space of R3N by the equivalence relation of rigid motions. Whether the quotient space is Euclidean or not depends on how it is parameterized. When the pairwise root–mean–square deviation is employed as the local distance metric, implicit representations are used for the protein conformation space, leading to no direct correspondence to a Euclidean set. We have demonstrated that an explicit Euclidean-based representation of protein conformation space and the local distance metric associated to it improve the quality of dimensionality reduction in the tetra-peptide and β–hairpin systems. PMID:24913095

An iterative bidirectional heuristic placement algorithm for solving the two-dimensional knapsack packing problem

NASA Astrophysics Data System (ADS)

Shiangjen, Kanokwatt; Chaijaruwanich, Jeerayut; Srisujjalertwaja, Wijak; Unachak, Prakarn; Somhom, Samerkae

2018-02-01

This article presents an efficient heuristic placement algorithm, namely, a bidirectional heuristic placement, for solving the two-dimensional rectangular knapsack packing problem. The heuristic demonstrates ways to maximize space utilization by fitting the appropriate rectangle from both sides of the wall of the current residual space layer by layer. The iterative local search along with a shift strategy is developed and applied to the heuristic to balance the exploitation and exploration tasks in the solution space without the tuning of any parameters. The experimental results on many scales of packing problems show that this approach can produce high-quality solutions for most of the benchmark datasets, especially for large-scale problems, within a reasonable duration of computational time.

Two-dimensional fluid droplet arrays generated using a single nozzle

DOEpatents

Lee, Eric R.; Perl, Martin L.

1999-11-02

Amplitudes of drive pulses received by a horizontally-placed dropper determine the horizontal displacements of droplets relative to an ejection aperture of the dropper. The drive pulses are varied such that the dropper generates a two-dimensional array of vertically-falling droplets. Vertical and horizontal interdroplet spacings may be varied in real time. Applications include droplet analysis experiments such as Millikan fractional charge searches and aerosol characterization, as well as material deposition applications.

Truss Optimization for a Manned Nuclear Electric Space Vehicle using Genetic Algorithms

NASA Technical Reports Server (NTRS)

Benford, Andrew; Tinker, Michael L.

2004-01-01

The purpose of this paper is to utilize the genetic algorithm (GA) optimization method for structural design of a nuclear propulsion vehicle. Genetic algorithms provide a guided, random search technique that mirrors biological adaptation. To verify the GA capabilities, other traditional optimization methods were used to generate results for comparison to the GA results, first for simple two-dimensional structures, and then for full-scale three-dimensional truss designs.

The Hantzsche-Wendt manifold in cosmic topology

NASA Astrophysics Data System (ADS)

Aurich, R.; Lustig, S.

2014-08-01

The Hantzsche-Wendt space is one of the 17 multiply connected spaces of the three-dimensional Euclidean space {{{E}}^{3}}. It is a compact and orientable manifold which can serve as a model for a spatial finite universe. Since it possesses much fewer matched back-to-back circle pairs on the cosmic microwave background (CMB) sky than the other compact flat spaces, it can escape the detection by a search for matched circle pairs. The suppression of temperature correlations C(\\vartheta ) on large angular scales on the CMB sky is studied. It is shown that the large-scale correlations are of the same order as for the three-torus topology but express a much larger variability. The Hantzsche-Wendt manifold provides a topological possibility with reduced large-angle correlations that can hide from searches for matched back-to-back circle pairs.

GNSS Single Frequency, Single Epoch Reliable Attitude Determination Method with Baseline Vector Constraint.

PubMed

Gong, Ang; Zhao, Xiubin; Pang, Chunlei; Duan, Rong; Wang, Yong

2015-12-02

For Global Navigation Satellite System (GNSS) single frequency, single epoch attitude determination, this paper proposes a new reliable method with baseline vector constraint. First, prior knowledge of baseline length, heading, and pitch obtained from other navigation equipment or sensors are used to reconstruct objective function rigorously. Then, searching strategy is improved. It substitutes gradually Enlarged ellipsoidal search space for non-ellipsoidal search space to ensure correct ambiguity candidates are within it and make the searching process directly be carried out by least squares ambiguity decorrelation algorithm (LAMBDA) method. For all vector candidates, some ones are further eliminated by derived approximate inequality, which accelerates the searching process. Experimental results show that compared to traditional method with only baseline length constraint, this new method can utilize a priori baseline three-dimensional knowledge to fix ambiguity reliably and achieve a high success rate. Experimental tests also verify it is not very sensitive to baseline vector error and can perform robustly when angular error is not great.

Euclidean chemical spaces from molecular fingerprints: Hamming distance and Hempel's ravens.

PubMed

Martin, Eric; Cao, Eddie

2015-05-01

Molecules are often characterized by sparse binary fingerprints, where 1s represent the presence of substructures and 0s represent their absence. Fingerprints are especially useful for similarity calculations, such as database searching or clustering, generally measuring similarity as the Tanimoto coefficient. In other cases, such as visualization, design of experiments, or latent variable regression, a low-dimensional Euclidian "chemical space" is more useful, where proximity between points reflects chemical similarity. A temptation is to apply principal components analysis (PCA) directly to these fingerprints to obtain a low dimensional continuous chemical space. However, Gower has shown that distances from PCA on bit vectors are proportional to the square root of Hamming distance. Unlike Tanimoto similarity, Hamming similarity (HS) gives equal weight to shared 0s as to shared 1s, that is, HS gives as much weight to substructures that neither molecule contains, as to substructures which both molecules contain. Illustrative examples show that proximity in the corresponding chemical space reflects mainly similar size and complexity rather than shared chemical substructures. These spaces are ill-suited for visualizing and optimizing coverage of chemical space, or as latent variables for regression. A more suitable alternative is shown to be Multi-dimensional scaling on the Tanimoto distance matrix, which produces a space where proximity does reflect structural similarity.

Search Techniques for Self-Organizing Systems

DTIC Science & Technology

1975-07-01

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

Switching Reinforcement Learning for Continuous Action Space

NASA Astrophysics Data System (ADS)

Nagayoshi, Masato; Murao, Hajime; Tamaki, Hisashi

Reinforcement Learning (RL) attracts much attention as a technique of realizing computational intelligence such as adaptive and autonomous decentralized systems. In general, however, it is not easy to put RL into practical use. This difficulty includes a problem of designing a suitable action space of an agent, i.e., satisfying two requirements in trade-off: (i) to keep the characteristics (or structure) of an original search space as much as possible in order to seek strategies that lie close to the optimal, and (ii) to reduce the search space as much as possible in order to expedite the learning process. In order to design a suitable action space adaptively, we propose switching RL model to mimic a process of an infant's motor development in which gross motor skills develop before fine motor skills. Then, a method for switching controllers is constructed by introducing and referring to the “entropy”. Further, through computational experiments by using robot navigation problems with one and two-dimensional continuous action space, the validity of the proposed method has been confirmed.

The search for di-lepton signatures from squarks and gluinos in antiproton-proton collisions at 1.8 TeV

NASA Astrophysics Data System (ADS)

Genik, Richard Joyner, II

1998-12-01

A search for Supergravity squark and gluino decays into di-leptons is presented. A novel search strategy of optimizing kinematic thresholds at each point in the three dimensional space of m0- m1/2-tan β is employed. The model space is randomly scanned using a parameterized fast Monte Carlo. No events are observed above Standard Model background in 107.6 pb-1 of Tevatron data collected by the DØ detector between 1993-96. Exclusion contours are presented in the m0-m 1/2 plane. At the 95% confidence level, a lower limit is set on the mass of gluinos of 129 GeV/c2 and on the mass of squarks of 138 GeV/c2 for all tan β < 10.

Estimation of Magnetic Field Growth and Construction of Adaptive Mesh in Corner Domain for the Magnetostatic Problem in Three-Dimensional Space

NASA Astrophysics Data System (ADS)

Perepelkin, Eugene; Tarelkin, Aleksandr

2018-02-01

A magnetostatics problem arises when searching for the distribution of the magnetic field generated by magnet systems of many physics research facilities, e.g., accelerators. The domain in which the boundary-value problem is solved often has a piecewise smooth boundary. In this case, numerical calculations of the problem require consideration of the solution behavior in the corner domain. In this work we obtained an upper estimation of the magnetic field growth using integral formulation of the magnetostatic problem and propose a method for condensing the differential mesh near the corner domain of the vacuum in the three-dimensional space based on this estimation.

Quantum Search in Hilbert Space

NASA Technical Reports Server (NTRS)

Zak, Michail

2003-01-01

A proposed quantum-computing algorithm would perform a search for an item of information in a database stored in a Hilbert-space memory structure. The algorithm is intended to make it possible to search relatively quickly through a large database under conditions in which available computing resources would otherwise be considered inadequate to perform such a task. The algorithm would apply, more specifically, to a relational database in which information would be stored in a set of N complex orthonormal vectors, each of N dimensions (where N can be exponentially large). Each vector would constitute one row of a unitary matrix, from which one would derive the Hamiltonian operator (and hence the evolutionary operator) of a quantum system. In other words, all the stored information would be mapped onto a unitary operator acting on a quantum state that would represent the item of information to be retrieved. Then one could exploit quantum parallelism: one could pose all search queries simultaneously by performing a quantum measurement on the system. In so doing, one would effectively solve the search problem in one computational step. One could exploit the direct- and inner-product decomposability of the unitary matrix to make the dimensionality of the memory space exponentially large by use of only linear resources. However, inasmuch as the necessary preprocessing (the mapping of the stored information into a Hilbert space) could be exponentially expensive, the proposed algorithm would likely be most beneficial in applications in which the resources available for preprocessing were much greater than those available for searching.

VLBI-based Products - Naval Oceanography Portal

Science.gov Websites

section Advanced Search... Sections Home Time Earth Orientation Astronomy Meteorology Oceanography Ice You terrestrial reference frames and to predict the variable orientation of the Earth in three-dimensional space antennas that define a VLBI-based Terrestrial Reference Frame (TRF) and the Earth Orientation Parameters

Improving 3d Spatial Queries Search: Newfangled Technique of Space Filling Curves in 3d City Modeling

NASA Astrophysics Data System (ADS)

Uznir, U.; Anton, F.; Suhaibah, A.; Rahman, A. A.; Mioc, D.

2013-09-01

The advantages of three dimensional (3D) city models can be seen in various applications including photogrammetry, urban and regional planning, computer games, etc.. They expand the visualization and analysis capabilities of Geographic Information Systems on cities, and they can be developed using web standards. However, these 3D city models consume much more storage compared to two dimensional (2D) spatial data. They involve extra geometrical and topological information together with semantic data. Without a proper spatial data clustering method and its corresponding spatial data access method, retrieving portions of and especially searching these 3D city models, will not be done optimally. Even though current developments are based on an open data model allotted by the Open Geospatial Consortium (OGC) called CityGML, its XML-based structure makes it challenging to cluster the 3D urban objects. In this research, we propose an opponent data constellation technique of space-filling curves (3D Hilbert curves) for 3D city model data representation. Unlike previous methods, that try to project 3D or n-dimensional data down to 2D or 3D using Principal Component Analysis (PCA) or Hilbert mappings, in this research, we extend the Hilbert space-filling curve to one higher dimension for 3D city model data implementations. The query performance was tested using a CityGML dataset of 1,000 building blocks and the results are presented in this paper. The advantages of implementing space-filling curves in 3D city modeling will improve data retrieval time by means of optimized 3D adjacency, nearest neighbor information and 3D indexing. The Hilbert mapping, which maps a subinterval of the [0, 1] interval to the corresponding portion of the d-dimensional Hilbert's curve, preserves the Lebesgue measure and is Lipschitz continuous. Depending on the applications, several alternatives are possible in order to cluster spatial data together in the third dimension compared to its clustering in 2D.

An ultrasonic method for determination of elastic moduli, density, attenuation and thickness of a polymer coating on a stiff plate.

PubMed

Lavrentyev, A I; Rokhlin, S I

2001-04-01

An ultrasonic method proposed by us for determination of the complete set of acoustical and geometrical properties of a thin isotropic layer between semispaces (J. Acoust. Soc. Am. 102 (1997) 3467) is extended to determination of the properties of a coating on a thin plate. The method allows simultaneous determination of the coating thickness, density, elastic moduli and attenuation (longitudinal and shear) from normal and oblique incidence reflection (transmission) frequency spectra. Reflection (transmission) from the coated plate is represented as a function of six nondimensional parameters of the coating which are determined from two experimentally measured spectra: one at normal and one at oblique incidence. The introduction of the set of nondimensional parameters allows one to transform the reconstruction process from one search in a six-dimensional space to two searches in three-dimensional spaces (one search for normal incidence and one for oblique). Thickness, density, and longitudinal and shear elastic moduli of the coating are calculated from the nondimensional parameters determined. The sensitivity of the method to individual properties and its stability against experimental noise are studied and the inversion algorithm is accordingly optimized. An example of the method and experimental measurement for comparison is given for a polypropylene coating on a steel foil.

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

PubMed

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

2014-05-01

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

Cross Validation Through Two-Dimensional Solution Surface for Cost-Sensitive SVM.

PubMed

Gu, Bin; Sheng, Victor S; Tay, Keng Yeow; Romano, Walter; Li, Shuo

2017-06-01

Model selection plays an important role in cost-sensitive SVM (CS-SVM). It has been proven that the global minimum cross validation (CV) error can be efficiently computed based on the solution path for one parameter learning problems. However, it is a challenge to obtain the global minimum CV error for CS-SVM based on one-dimensional solution path and traditional grid search, because CS-SVM is with two regularization parameters. In this paper, we propose a solution and error surfaces based CV approach (CV-SES). More specifically, we first compute a two-dimensional solution surface for CS-SVM based on a bi-parameter space partition algorithm, which can fit solutions of CS-SVM for all values of both regularization parameters. Then, we compute a two-dimensional validation error surface for each CV fold, which can fit validation errors of CS-SVM for all values of both regularization parameters. Finally, we obtain the CV error surface by superposing K validation error surfaces, which can find the global minimum CV error of CS-SVM. Experiments are conducted on seven datasets for cost sensitive learning and on four datasets for imbalanced learning. Experimental results not only show that our proposed CV-SES has a better generalization ability than CS-SVM with various hybrids between grid search and solution path methods, and than recent proposed cost-sensitive hinge loss SVM with three-dimensional grid search, but also show that CV-SES uses less running time.

Spinoff from a Moon Boot (Dynacoil)

NASA Technical Reports Server (NTRS)

1986-01-01

Three dimensional "space" material used in the Apollo lunar suit has been encapsulated in a polyurethane foam carrier and forms the base of the Dynacoil Athletic shoe cushioning system. Kangaroos USA, Inc. developed the system after a search by ARAC. The shoes lose almost none of their shock absorbing capabilities and have superior stability and motion control.

Dimensionality of consumer search space drives trophic interaction strengths.

PubMed

Pawar, Samraat; Dell, Anthony I; Savage, Van M

2012-06-28

Trophic interactions govern biomass fluxes in ecosystems, and stability in food webs. Knowledge of how trophic interaction strengths are affected by differences among habitats is crucial for understanding variation in ecological systems. Here we show how substantial variation in consumption-rate data, and hence trophic interaction strengths, arises because consumers tend to encounter resources more frequently in three dimensions (3D) (for example, arboreal and pelagic zones) than two dimensions (2D) (for example, terrestrial and benthic zones). By combining new theory with extensive data (376 species, with body masses ranging from 5.24 × 10(-14) kg to 800 kg), we find that consumption rates scale sublinearly with consumer body mass (exponent of approximately 0.85) for 2D interactions, but superlinearly (exponent of approximately 1.06) for 3D interactions. These results contradict the currently widespread assumption of a single exponent (of approximately 0.75) in consumer-resource and food-web research. Further analysis of 2,929 consumer-resource interactions shows that dimensionality of consumer search space is probably a major driver of species coexistence, and the stability and abundance of populations.

ODF Maxima Extraction in Spherical Harmonic Representation via Analytical Search Space Reduction

PubMed Central

Aganj, Iman; Lenglet, Christophe; Sapiro, Guillermo

2015-01-01

By revealing complex fiber structure through the orientation distribution function (ODF), q-ball imaging has recently become a popular reconstruction technique in diffusion-weighted MRI. In this paper, we propose an analytical dimension reduction approach to ODF maxima extraction. We show that by expressing the ODF, or any antipodally symmetric spherical function, in the common fourth order real and symmetric spherical harmonic basis, the maxima of the two-dimensional ODF lie on an analytically derived one-dimensional space, from which we can detect the ODF maxima. This method reduces the computational complexity of the maxima detection, without compromising the accuracy. We demonstrate the performance of our technique on both artificial and human brain data. PMID:20879302

Walking the Filament of Feasibility: Global Optimization of Highly-Constrained, Multi-Modal Interplanetary Trajectories Using a Novel Stochastic Search Technique

NASA Technical Reports Server (NTRS)

Englander, Arnold C.; Englander, Jacob A.

2017-01-01

Interplanetary trajectory optimization problems are highly complex and are characterized by a large number of decision variables and equality and inequality constraints as well as many locally optimal solutions. Stochastic global search techniques, coupled with a large-scale NLP solver, have been shown to solve such problems but are inadequately robust when the problem constraints become very complex. In this work, we present a novel search algorithm that takes advantage of the fact that equality constraints effectively collapse the solution space to lower dimensionality. This new approach walks the filament'' of feasibility to efficiently find the global optimal solution.

Marginal space learning for efficient detection of 2D/3D anatomical structures in medical images.

PubMed

Zheng, Yefeng; Georgescu, Bogdan; Comaniciu, Dorin

2009-01-01

Recently, marginal space learning (MSL) was proposed as a generic approach for automatic detection of 3D anatomical structures in many medical imaging modalities [1]. To accurately localize a 3D object, we need to estimate nine pose parameters (three for position, three for orientation, and three for anisotropic scaling). Instead of exhaustively searching the original nine-dimensional pose parameter space, only low-dimensional marginal spaces are searched in MSL to improve the detection speed. In this paper, we apply MSL to 2D object detection and perform a thorough comparison between MSL and the alternative full space learning (FSL) approach. Experiments on left ventricle detection in 2D MRI images show MSL outperforms FSL in both speed and accuracy. In addition, we propose two novel techniques, constrained MSL and nonrigid MSL, to further improve the efficiency and accuracy. In many real applications, a strong correlation may exist among pose parameters in the same marginal spaces. For example, a large object may have large scaling values along all directions. Constrained MSL exploits this correlation for further speed-up. The original MSL only estimates the rigid transformation of an object in the image, therefore cannot accurately localize a nonrigid object under a large deformation. The proposed nonrigid MSL directly estimates the nonrigid deformation parameters to improve the localization accuracy. The comparison experiments on liver detection in 226 abdominal CT volumes demonstrate the effectiveness of the proposed methods. Our system takes less than a second to accurately detect the liver in a volume.

Optimization of High-Dimensional Functions through Hypercube Evaluation

PubMed Central

Abiyev, Rahib H.; Tunay, Mustafa

2015-01-01

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

Bayesian Analysis of High Dimensional Classification

NASA Astrophysics Data System (ADS)

Mukhopadhyay, Subhadeep; Liang, Faming

2009-12-01

Modern data mining and bioinformatics have presented an important playground for statistical learning techniques, where the number of input variables is possibly much larger than the sample size of the training data. In supervised learning, logistic regression or probit regression can be used to model a binary output and form perceptron classification rules based on Bayesian inference. In these cases , there is a lot of interest in searching for sparse model in High Dimensional regression(/classification) setup. we first discuss two common challenges for analyzing high dimensional data. The first one is the curse of dimensionality. The complexity of many existing algorithms scale exponentially with the dimensionality of the space and by virtue of that algorithms soon become computationally intractable and therefore inapplicable in many real applications. secondly, multicollinearities among the predictors which severely slowdown the algorithm. In order to make Bayesian analysis operational in high dimension we propose a novel 'Hierarchical stochastic approximation monte carlo algorithm' (HSAMC), which overcomes the curse of dimensionality, multicollinearity of predictors in high dimension and also it possesses the self-adjusting mechanism to avoid the local minima separated by high energy barriers. Models and methods are illustrated by simulation inspired from from the feild of genomics. Numerical results indicate that HSAMC can work as a general model selection sampler in high dimensional complex model space.

Dark matter interpretations of ATLAS searches for the electroweak production of supersymmetric particles in s = 8 $$ \\sqrt{s}=8 $$ TeV proton-proton collisions

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

Aaboud, M.; Aad, G.; Abbott, B.

2016-09-01

A selection of searches by the ATLAS experiment at the LHC for the electroweak production of SUSY particles are used to study their impact on the constraints on dark matter candidates. The searches use 20 fb-1 of proton-proton collision data at s√=8s=8 TeV. A likelihood-driven scan of a five-dimensional effective model focusing on the gaugino-higgsino and Higgs sector of the phenomenological minimal supersymmetric Standard Model is performed. This scan uses data from direct dark matter detection experiments, the relic dark matter density and precision flavour physics results. Further constraints from the ATLAS Higgs mass measurement and SUSY searches at LEPmore » are also applied. A subset of models selected from this scan are used to assess the impact of the selected ATLAS searches in this five-dimensional parameter space. These ATLAS searches substantially impact those models for which the mass m(χ~01)m(χ~10) of the lightest neutralino is less than 65 GeV, excluding 86% of such models. The searches have limited impact on models with larger m(χ~01)m(χ~10) due to either heavy electroweakinos or compressed mass spectra where the mass splittings between the produced particles and the lightest supersymmetric particle is small.« less

Dark matter interpretations of ATLAS searches for the electroweak production of supersymmetric particles in $$ \\sqrt{s}=8 $$ TeV proton-proton collisions

DOE PAGES

Aaboud, M.; Aad, G.; Abbott, B.; ...

2016-09-30

A selection of searches by the ATLAS experiment at the LHC for the electroweak production of SUSY particles are used to study their impact on the constraints on dark matter candidates. The searches use 20 fb -1 of proton-proton collision data at √s=8 TeV. A likelihood-driven scan of a five-dimensional effective model focusing on the gaugino-higgsino and Higgs sector of the phenomenological minimal supersymmetric Standard Model is performed. This scan uses data from direct dark matter detection experiments, the relic dark matter density and precision flavour physics results. Further constraints from the ATLAS Higgs mass measurement and SUSY searches at LEP are also applied. A subset of models selected from this scan are used to assess the impact of the selected ATLAS searches in this five-dimensional parameter space. These ATLAS searches substantially impact those models for which the mass m(more » $$\\tilde{χ}$$$0\\atop{1}$$) of the lightest neutralino is less than 65 GeV, excluding 86% of such models. The searches have limited impact on models with larger m($$\\tilde{χ}$$$0\\atop{1}$$) due to either heavy electroweakinos or compressed mass spectra where the mass splittings between the produced particles and the lightest supersymmetric particle is small.« less

Entropy-Based Search Algorithm for Experimental Design

NASA Astrophysics Data System (ADS)

Malakar, N. K.; Knuth, K. H.

2011-03-01

The scientific method relies on the iterated processes of inference and inquiry. The inference phase consists of selecting the most probable models based on the available data; whereas the inquiry phase consists of using what is known about the models to select the most relevant experiment. Optimizing inquiry involves searching the parameterized space of experiments to select the experiment that promises, on average, to be maximally informative. In the case where it is important to learn about each of the model parameters, the relevance of an experiment is quantified by Shannon entropy of the distribution of experimental outcomes predicted by a probable set of models. If the set of potential experiments is described by many parameters, we must search this high-dimensional entropy space. Brute force search methods will be slow and computationally expensive. We present an entropy-based search algorithm, called nested entropy sampling, to select the most informative experiment for efficient experimental design. This algorithm is inspired by Skilling's nested sampling algorithm used in inference and borrows the concept of a rising threshold while a set of experiment samples are maintained. We demonstrate that this algorithm not only selects highly relevant experiments, but also is more efficient than brute force search. Such entropic search techniques promise to greatly benefit autonomous experimental design.

Molecular docking.

PubMed

Morris, Garrett M; Lim-Wilby, Marguerita

2008-01-01

Molecular docking is a key tool in structural molecular biology and computer-assisted drug design. The goal of ligand-protein docking is to predict the predominant binding mode(s) of a ligand with a protein of known three-dimensional structure. Successful docking methods search high-dimensional spaces effectively and use a scoring function that correctly ranks candidate dockings. Docking can be used to perform virtual screening on large libraries of compounds, rank the results, and propose structural hypotheses of how the ligands inhibit the target, which is invaluable in lead optimization. The setting up of the input structures for the docking is just as important as the docking itself, and analyzing the results of stochastic search methods can sometimes be unclear. This chapter discusses the background and theory of molecular docking software, and covers the usage of some of the most-cited docking software.

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.

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.

Application of firefly algorithm to the dynamic model updating problem

NASA Astrophysics Data System (ADS)

Shabbir, Faisal; Omenzetter, Piotr

2015-04-01

Model updating can be considered as a branch of optimization problems in which calibration of the finite element (FE) model is undertaken by comparing the modal properties of the actual structure with these of the FE predictions. The attainment of a global solution in a multi dimensional search space is a challenging problem. The nature-inspired algorithms have gained increasing attention in the previous decade for solving such complex optimization problems. This study applies the novel Firefly Algorithm (FA), a global optimization search technique, to a dynamic model updating problem. This is to the authors' best knowledge the first time FA is applied to model updating. The working of FA is inspired by the flashing characteristics of fireflies. Each firefly represents a randomly generated solution which is assigned brightness according to the value of the objective function. The physical structure under consideration is a full scale cable stayed pedestrian bridge with composite bridge deck. Data from dynamic testing of the bridge was used to correlate and update the initial model by using FA. The algorithm aimed at minimizing the difference between the natural frequencies and mode shapes of the structure. The performance of the algorithm is analyzed in finding the optimal solution in a multi dimensional search space. The paper concludes with an investigation of the efficacy of the algorithm in obtaining a reference finite element model which correctly represents the as-built original structure.

Evolving discriminators for querying video sequences

NASA Astrophysics Data System (ADS)

Iyengar, Giridharan; Lippman, Andrew B.

1997-01-01

In this paper we present a framework for content based query and retrieval of information from large video databases. This framework enables content based retrieval of video sequences by characterizing the sequences using motion, texture and colorimetry cues. This characterization is biologically inspired and results in a compact parameter space where every segment of video is represented by an 8 dimensional vector. Searching and retrieval is done in real- time with accuracy in this parameter space. Using this characterization, we then evolve a set of discriminators using Genetic Programming Experiments indicate that these discriminators are capable of analyzing and characterizing video. The VideoBook is able to search and retrieve video sequences with 92% accuracy in real-time. Experiments thus demonstrate that the characterization is capable of extracting higher level structure from raw pixel values.

Application of Optimization Techniques to Design of Unconventional Rocket Nozzle Configurations

NASA Technical Reports Server (NTRS)

Follett, W.; Ketchum, A.; Darian, A.; Hsu, Y.

1996-01-01

Several current rocket engine concepts such as the bell-annular tri-propellant engine, and the linear aerospike being proposed for the X-33 require unconventional three dimensional rocket nozzles which must conform to rectangular or sector shaped envelopes to meet integration constraints. These types of nozzles exist outside the current experience database, therefore, the application of efficient design methods for these propulsion concepts is critical to the success of launch vehicle programs. The objective of this work is to optimize several different nozzle configurations, including two- and three-dimensional geometries. Methodology includes coupling computational fluid dynamic (CFD) analysis to genetic algorithms and Taguchi methods as well as implementation of a streamline tracing technique. Results of applications are shown for several geometeries including: three dimensional thruster nozzles with round or super elliptic throats and rectangualar exits, two- and three-dimensional thrusters installed within a bell nozzle, and three dimensional thrusters with round throats and sector shaped exits. Due to the novel designs considered for this study, there is little experience which can be used to guide the effort and limit the design space. With a nearly infinite parameter space to explore, simple parametric design studies cannot possibly search the entire design space within the time frame required to impact the design cycle. For this reason, robust and efficient optimization methods are required to explore and exploit the design space to achieve high performance engine designs. Five case studies which examine the application of various techniques in the engineering environment are presented in this paper.

Three-dimensional changes to the upper airway after maxillomandibular advancement with counterclockwise rotation: a systematic review and meta-analysis.

PubMed

Louro, R S; Calasans-Maia, J A; Mattos, C T; Masterson, D; Calasans-Maia, M D; Maia, L C

2018-05-01

The aim of this study was to evaluate the effect of counterclockwise (CCW) rotation and maxillomandibular advancement (MMA) on the upper airway space using three-dimensional images. An electronic search was performed in the PubMed, Cochrane Library, Scopus, Virtual Health Library, Web of Science, and OpenGrey databases (end date July 2016); a hand-search of primary study reference lists was also conducted. The inclusion criteria encompassed computed tomography evaluations of the upper airway spaces of adult patients undergoing orthognathic surgery with CCW rotation and MMA. The articles were evaluated for risk of bias with a tool for before-and-after studies. A meta-analysis was performed with the mean differences using a random-effects model. Heterogeneity was assessed with the Q-test and the I 2 index. The meta-analysis revealed significant (P<0.001) increases in both the total airway volume (effect size of 6832mm 3 and confidence interval of 5554-8109mm 3 ) and the minimum axial area (effect size of 92mm 2 and confidence interval of 70-113mm 2 ). The heterogeneity was low in both comparisons (I 2 =38% and 7%, respectively). The technique of mandibular advancement with CCW rotation produced significant increases in the volumes and areas of the upper airway spaces. Copyright © 2017 International Association of Oral and Maxillofacial Surgeons. Published by Elsevier Ltd. All rights reserved.

Four-Dimensional Golden Search

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

Fenimore, Edward E.

2015-02-25

The Golden search technique is a method to search a multiple-dimension space to find the minimum. It basically subdivides the possible ranges of parameters until it brackets, to within an arbitrarily small distance, the minimum. It has the advantages that (1) the function to be minimized can be non-linear, (2) it does not require derivatives of the function, (3) the convergence criterion does not depend on the magnitude of the function. Thus, if the function is a goodness of fit parameter such as chi-square, the convergence does not depend on the noise being correctly estimated or the function correctly followingmore » the chi-square statistic. And, (4) the convergence criterion does not depend on the shape of the function. Thus, long shallow surfaces can be searched without the problem of premature convergence. As with many methods, the Golden search technique can be confused by surfaces with multiple minima.« less

A geometrical upper bound on the inflaton range

NASA Astrophysics Data System (ADS)

Cicoli, Michele; Ciupke, David; Mayrhofer, Christoph; Shukla, Pramod

2018-05-01

We argue that in type IIB LVS string models, after including the leading order moduli stabilisation effects, the moduli space for the remaining flat directions is compact due the Calabi-Yau Kähler cone conditions. In cosmological applications, this gives an inflaton field range which is bounded from above, in analogy with recent results from the weak gravity and swampland conjectures. We support our claim by explicitly showing that it holds for all LVS vacua with h 1,1 = 3 obtained from 4-dimensional reflexive polytopes. In particular, we first search for all Calabi-Yau threefolds from the Kreuzer-Skarke list with h 1,1 = 2, 3 and 4 which allow for LVS vacua, finding several new LVS geometries which were so far unknown. We then focus on the h 1,1 = 3 cases and show that the Kähler cones of all toric hypersurface threefolds force the effective 1-dimensional LVS moduli space to be compact. We find that the moduli space size can generically be trans-Planckian only for K3 fibred examples.

Universal approximators for multi-objective direct policy search in water reservoir management problems: a comparative analysis

NASA Astrophysics Data System (ADS)

Giuliani, Matteo; Mason, Emanuele; Castelletti, Andrea; Pianosi, Francesca

2014-05-01

The optimal operation of water resources systems is a wide and challenging problem due to non-linearities in the model and the objectives, high dimensional state-control space, and strong uncertainties in the hydroclimatic regimes. The application of classical optimization techniques (e.g., SDP, Q-learning, gradient descent-based algorithms) is strongly limited by the dimensionality of the system and by the presence of multiple, conflicting objectives. This study presents a novel approach which combines Direct Policy Search (DPS) and Multi-Objective Evolutionary Algorithms (MOEAs) to solve high-dimensional state and control space problems involving multiple objectives. DPS, also known as parameterization-simulation-optimization in the water resources literature, is a simulation-based approach where the reservoir operating policy is first parameterized within a given family of functions and, then, the parameters optimized with respect to the objectives of the management problem. The selection of a suitable class of functions to which the operating policy belong to is a key step, as it might restrict the search for the optimal policy to a subspace of the decision space that does not include the optimal solution. In the water reservoir literature, a number of classes have been proposed. However, many of these rules are based largely on empirical or experimental successes and they were designed mostly via simulation and for single-purpose reservoirs. In a multi-objective context similar rules can not easily inferred from the experience and the use of universal function approximators is generally preferred. In this work, we comparatively analyze two among the most common universal approximators: artificial neural networks (ANN) and radial basis functions (RBF) under different problem settings to estimate their scalability and flexibility in dealing with more and more complex problems. The multi-purpose HoaBinh water reservoir in Vietnam, accounting for hydropower production and flood control, is used as a case study. Preliminary results show that the RBF policy parametrization is more effective than the ANN one. In particular, the approximated Pareto front obtained with RBF control policies successfully explores the full tradeoff space between the two conflicting objectives, while most of the ANN solutions results to be Pareto-dominated by the RBF ones.

Higher groupoid bundles, higher spaces, and self-dual tensor field equations

NASA Astrophysics Data System (ADS)

Jurčo, Branislav; Sämann, Christian; Wolf, Martin

2016-08-01

We develop a description of higher gauge theory with higher groupoids as gauge structure from first principles. This approach captures ordinary gauge theories and gauged sigma models as well as their categorifications on a very general class of (higher) spaces comprising presentable differentiable stacks, as e.g. orbifolds. We start off with a self-contained review on simplicial sets as models of $(\\infty,1)$-categories. We then discuss principal bundles in terms of simplicial maps and their homotopies. We explain in detail a differentiation procedure, suggested by Severa, that maps higher groupoids to $L_\\infty$-algebroids. Generalising this procedure, we define connections for higher groupoid bundles. As an application, we obtain six-dimensional superconformal field theories via a Penrose-Ward transform of higher groupoid bundles over a twistor space. This construction reduces the search for non-Abelian self-dual tensor field equations in six dimensions to a search for the appropriate (higher) gauge structure. The treatment aims to be accessible to theoretical physicists.

Cognitive Mechanisms of Insight: The Role of Heuristics and Representational Change in Solving the Eight-Coin Problem

ERIC Educational Resources Information Center

Öllinger, Michael; Jones, Gary; Faber, Amory H.; Knoblich, Günther

2013-01-01

The 8-coin insight problem requires the problem solver to move 2 coins so that each coin touches exactly 3 others. Ormerod, MacGregor, and Chronicle (2002) explained differences in task performance across different versions of the 8-coin problem using the availability of particular moves in a 2-dimensional search space. We explored 2 further…

Supersymmetry without prejudice at the LHC

NASA Astrophysics Data System (ADS)

Conley, John A.; Gainer, James S.; Hewett, JoAnne L.; Le, My Phuong; Rizzo, Thomas G.

2011-07-01

The discovery and exploration of Supersymmetry in a model-independent fashion will be a daunting task due to the large number of soft-breaking parameters in the MSSM. In this paper, we explore the capability of the ATLAS detector at the LHC (sqrt{s}=14 TeV, 1 fb-1) to find SUSY within the 19-dimensional pMSSM subspace of the MSSM using their standard transverse missing energy and long-lived particle searches that were essentially designed for mSUGRA. To this end, we employ a set of ˜71k previously generated model points in the 19-dimensional parameter space that satisfy all of the existing experimental and theoretical constraints. Employing ATLAS-generated SM backgrounds and following their approach in each of 11 missing energy analyses as closely as possible, we explore all of these 71k model points for a possible SUSY signal. To test our analysis procedure, we first verify that we faithfully reproduce the published ATLAS results for the signal distributions for their benchmark mSUGRA model points. We then show that, requiring all sparticle masses to lie below 1(3) TeV, almost all (two-thirds) of the pMSSM model points are discovered with a significance S>5 in at least one of these 11 analyses assuming a 50% systematic error on the SM background. If this systematic error can be reduced to only 20% then this parameter space coverage is increased. These results are indicative that the ATLAS SUSY search strategy is robust under a broad class of Supersymmetric models. We then explore in detail the properties of the kinematically accessible model points which remain unobservable by these search analyses in order to ascertain problematic cases which may arise in general SUSY searches.

Genetic Algorithm for Optimization: Preprocessing with n Dimensional Bisection and Error Estimation

NASA Technical Reports Server (NTRS)

Sen, S. K.; Shaykhian, Gholam Ali

2006-01-01

A knowledge of the appropriate values of the parameters of a genetic algorithm (GA) such as the population size, the shrunk search space containing the solution, crossover and mutation probabilities is not available a priori for a general optimization problem. Recommended here is a polynomial-time preprocessing scheme that includes an n-dimensional bisection and that determines the foregoing parameters before deciding upon an appropriate GA for all problems of similar nature and type. Such a preprocessing is not only fast but also enables us to get the global optimal solution and its reasonably narrow error bounds with a high degree of confidence.

Cognitive mechanisms of insight: the role of heuristics and representational change in solving the eight-coin problem.

PubMed

Öllinger, Michael; Jones, Gary; Faber, Amory H; Knoblich, Günther

2013-05-01

The 8-coin insight problem requires the problem solver to move 2 coins so that each coin touches exactly 3 others. Ormerod, MacGregor, and Chronicle (2002) explained differences in task performance across different versions of the 8-coin problem using the availability of particular moves in a 2-dimensional search space. We explored 2 further explanations by developing 6 new versions of the 8-coin problem in order to investigate the influence of grouping and self-imposed constraints on solutions. The results identified 2 sources of problem difficulty: first, the necessity to overcome the constraint that a solution can be found in 2-dimensional space and, second, the necessity to decompose perceptual groupings. A detailed move analysis suggested that the selection of moves was driven by the established representation rather than the application of the appropriate heuristics. Both results support the assumptions of representational change theory (Ohlsson, 1992).

Query-Based Outlier Detection in Heterogeneous Information Networks.

PubMed

Kuck, Jonathan; Zhuang, Honglei; Yan, Xifeng; Cam, Hasan; Han, Jiawei

2015-03-01

Outlier or anomaly detection in large data sets is a fundamental task in data science, with broad applications. However, in real data sets with high-dimensional space, most outliers are hidden in certain dimensional combinations and are relative to a user's search space and interest. It is often more effective to give power to users and allow them to specify outlier queries flexibly, and the system will then process such mining queries efficiently. In this study, we introduce the concept of query-based outlier in heterogeneous information networks, design a query language to facilitate users to specify such queries flexibly, define a good outlier measure in heterogeneous networks, and study how to process outlier queries efficiently in large data sets. Our experiments on real data sets show that following such a methodology, interesting outliers can be defined and uncovered flexibly and effectively in large heterogeneous networks.

Query-Based Outlier Detection in Heterogeneous Information Networks

PubMed Central

Kuck, Jonathan; Zhuang, Honglei; Yan, Xifeng; Cam, Hasan; Han, Jiawei

2015-01-01

Outlier or anomaly detection in large data sets is a fundamental task in data science, with broad applications. However, in real data sets with high-dimensional space, most outliers are hidden in certain dimensional combinations and are relative to a user’s search space and interest. It is often more effective to give power to users and allow them to specify outlier queries flexibly, and the system will then process such mining queries efficiently. In this study, we introduce the concept of query-based outlier in heterogeneous information networks, design a query language to facilitate users to specify such queries flexibly, define a good outlier measure in heterogeneous networks, and study how to process outlier queries efficiently in large data sets. Our experiments on real data sets show that following such a methodology, interesting outliers can be defined and uncovered flexibly and effectively in large heterogeneous networks. PMID:27064397

Large-scale hydropower system optimization using dynamic programming and object-oriented programming: the case of the Northeast China Power Grid.

PubMed

Li, Ji-Qing; Zhang, Yu-Shan; Ji, Chang-Ming; Wang, Ai-Jing; Lund, Jay R

2013-01-01

This paper examines long-term optimal operation using dynamic programming for a large hydropower system of 10 reservoirs in Northeast China. Besides considering flow and hydraulic head, the optimization explicitly includes time-varying electricity market prices to maximize benefit. Two techniques are used to reduce the 'curse of dimensionality' of dynamic programming with many reservoirs. Discrete differential dynamic programming (DDDP) reduces the search space and computer memory needed. Object-oriented programming (OOP) and the ability to dynamically allocate and release memory with the C++ language greatly reduces the cumulative effect of computer memory for solving multi-dimensional dynamic programming models. The case study shows that the model can reduce the 'curse of dimensionality' and achieve satisfactory results.

Simulating Nonequilibrium Radiation via Orthogonal Polynomial Refinement

DTIC Science & Technology

2015-01-07

measured by the preprocessing time, computer memory space, and average query time. In many search procedures for the number of points np of a data set, a...analytic expression for the radiative flux density is possible by the commonly accepted local thermal equilibrium ( LTE ) approximation. A semi...Vol. 227, pp. 9463-9476, 2008. 10. Galvez, M., Ray-Tracing model for radiation transport in three-dimensional LTE system, App. Physics, Vol. 38

A Memetic Algorithm for Global Optimization of Multimodal Nonseparable Problems.

PubMed

Zhang, Geng; Li, Yangmin

2016-06-01

It is a big challenging issue of avoiding falling into local optimum especially when facing high-dimensional nonseparable problems where the interdependencies among vector elements are unknown. In order to improve the performance of optimization algorithm, a novel memetic algorithm (MA) called cooperative particle swarm optimizer-modified harmony search (CPSO-MHS) is proposed in this paper, where the CPSO is used for local search and the MHS for global search. The CPSO, as a local search method, uses 1-D swarm to search each dimension separately and thus converges fast. Besides, it can obtain global optimum elements according to our experimental results and analyses. MHS implements the global search by recombining different vector elements and extracting global optimum elements. The interaction between local search and global search creates a set of local search zones, where global optimum elements reside within the search space. The CPSO-MHS algorithm is tested and compared with seven other optimization algorithms on a set of 28 standard benchmarks. Meanwhile, some MAs are also compared according to the results derived directly from their corresponding references. The experimental results demonstrate a good performance of the proposed CPSO-MHS algorithm in solving multimodal nonseparable problems.

3D/2D image registration using weighted histogram of gradient directions

NASA Astrophysics Data System (ADS)

Ghafurian, Soheil; Hacihaliloglu, Ilker; Metaxas, Dimitris N.; Tan, Virak; Li, Kang

2015-03-01

Three dimensional (3D) to two dimensional (2D) image registration is crucial in many medical applications such as image-guided evaluation of musculoskeletal disorders. One of the key problems is to estimate the 3D CT- reconstructed bone model positions (translation and rotation) which maximize the similarity between the digitally reconstructed radiographs (DRRs) and the 2D fluoroscopic images using a registration method. This problem is computational-intensive due to a large search space and the complicated DRR generation process. Also, finding a similarity measure which converges to the global optimum instead of local optima adds to the challenge. To circumvent these issues, most existing registration methods need a manual initialization, which requires user interaction and is prone to human error. In this paper, we introduce a novel feature-based registration method using the weighted histogram of gradient directions of images. This method simplifies the computation by searching the parameter space (rotation and translation) sequentially rather than simultaneously. In our numeric simulation experiments, the proposed registration algorithm was able to achieve sub-millimeter and sub-degree accuracies. Moreover, our method is robust to the initial guess. It can tolerate up to +/-90°rotation offset from the global optimal solution, which minimizes the need for human interaction to initialize the algorithm.

An improved stochastic fractal search algorithm for 3D protein structure prediction.

PubMed

Zhou, Changjun; Sun, Chuan; Wang, Bin; Wang, Xiaojun

2018-05-03

Protein structure prediction (PSP) is a significant area for biological information research, disease treatment, and drug development and so on. In this paper, three-dimensional structures of proteins are predicted based on the known amino acid sequences, and the structure prediction problem is transformed into a typical NP problem by an AB off-lattice model. This work applies a novel improved Stochastic Fractal Search algorithm (ISFS) to solve the problem. The Stochastic Fractal Search algorithm (SFS) is an effective evolutionary algorithm that performs well in exploring the search space but falls into local minimums sometimes. In order to avoid the weakness, Lvy flight and internal feedback information are introduced in ISFS. In the experimental process, simulations are conducted by ISFS algorithm on Fibonacci sequences and real peptide sequences. Experimental results prove that the ISFS performs more efficiently and robust in terms of finding the global minimum and avoiding getting stuck in local minimums.

SASS: A symmetry adapted stochastic search algorithm exploiting site symmetry

NASA Astrophysics Data System (ADS)

Wheeler, Steven E.; Schleyer, Paul v. R.; Schaefer, Henry F.

2007-03-01

A simple symmetry adapted search algorithm (SASS) exploiting point group symmetry increases the efficiency of systematic explorations of complex quantum mechanical potential energy surfaces. In contrast to previously described stochastic approaches, which do not employ symmetry, candidate structures are generated within simple point groups, such as C2, Cs, and C2v. This facilitates efficient sampling of the 3N-6 Pople's dimensional configuration space and increases the speed and effectiveness of quantum chemical geometry optimizations. Pople's concept of framework groups [J. Am. Chem. Soc. 102, 4615 (1980)] is used to partition the configuration space into structures spanning all possible distributions of sets of symmetry equivalent atoms. This provides an efficient means of computing all structures of a given symmetry with minimum redundancy. This approach also is advantageous for generating initial structures for global optimizations via genetic algorithm and other stochastic global search techniques. Application of the SASS method is illustrated by locating 14 low-lying stationary points on the cc-pwCVDZ ROCCSD(T) potential energy surface of Li5H2. The global minimum structure is identified, along with many unique, nonintuitive, energetically favorable isomers.

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

PubMed

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

2009-03-01

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

Java Radar Analysis Tool

NASA Technical Reports Server (NTRS)

Zaczek, Mariusz P.

2005-01-01

Java Radar Analysis Tool (JRAT) is a computer program for analyzing two-dimensional (2D) scatter plots derived from radar returns showing pieces of the disintegrating Space Shuttle Columbia. JRAT can also be applied to similar plots representing radar returns showing aviation accidents, and to scatter plots in general. The 2D scatter plots include overhead map views and side altitude views. The superposition of points in these views makes searching difficult. JRAT enables three-dimensional (3D) viewing: by use of a mouse and keyboard, the user can rotate to any desired viewing angle. The 3D view can include overlaid trajectories and search footprints to enhance situational awareness in searching for pieces. JRAT also enables playback: time-tagged radar-return data can be displayed in time order and an animated 3D model can be moved through the scene to show the locations of the Columbia (or other vehicle) at the times of the corresponding radar events. The combination of overlays and playback enables the user to correlate a radar return with a position of the vehicle to determine whether the return is valid. JRAT can optionally filter single radar returns, enabling the user to selectively hide or highlight a desired radar return.

Initiating heavy-atom-based phasing by multi-dimensional molecular replacement.

PubMed

Pedersen, Bjørn Panyella; Gourdon, Pontus; Liu, Xiangyu; Karlsen, Jesper Lykkegaard; Nissen, Poul

2016-03-01

To obtain an electron-density map from a macromolecular crystal the phase problem needs to be solved, which often involves the use of heavy-atom derivative crystals and concomitant heavy-atom substructure determination. This is typically performed by dual-space methods, direct methods or Patterson-based approaches, which however may fail when only poorly diffracting derivative crystals are available. This is often the case for, for example, membrane proteins. Here, an approach for heavy-atom site identification based on a molecular-replacement parameter matrix (MRPM) is presented. It involves an n-dimensional search to test a wide spectrum of molecular-replacement parameters, such as different data sets and search models with different conformations. Results are scored by the ability to identify heavy-atom positions from anomalous difference Fourier maps. The strategy was successfully applied in the determination of a membrane-protein structure, the copper-transporting P-type ATPase CopA, when other methods had failed to determine the heavy-atom substructure. MRPM is well suited to proteins undergoing large conformational changes where multiple search models should be considered, and it enables the identification of weak but correct molecular-replacement solutions with maximum contrast to prime experimental phasing efforts.

Initiating heavy-atom-based phasing by multi-dimensional molecular replacement

PubMed Central

Pedersen, Bjørn Panyella; Gourdon, Pontus; Liu, Xiangyu; Karlsen, Jesper Lykkegaard; Nissen, Poul

2016-01-01

To obtain an electron-density map from a macromolecular crystal the phase problem needs to be solved, which often involves the use of heavy-atom derivative crystals and concomitant heavy-atom substructure determination. This is typically performed by dual-space methods, direct methods or Patterson-based approaches, which however may fail when only poorly diffracting derivative crystals are available. This is often the case for, for example, membrane proteins. Here, an approach for heavy-atom site identification based on a molecular-replacement parameter matrix (MRPM) is presented. It involves an n-dimensional search to test a wide spectrum of molecular-replacement parameters, such as different data sets and search models with different conformations. Results are scored by the ability to identify heavy-atom positions from anomalous difference Fourier maps. The strategy was successfully applied in the determination of a membrane-protein structure, the copper-transporting P-type ATPase CopA, when other methods had failed to determine the heavy-atom substructure. MRPM is well suited to proteins undergoing large conformational changes where multiple search models should be considered, and it enables the identification of weak but correct molecular-replacement solutions with maximum contrast to prime experimental phasing efforts. PMID:26960131

Contextual Cueing Effect in Spatial Layout Defined by Binocular Disparity

PubMed Central

Zhao, Guang; Zhuang, Qian; Ma, Jie; Tu, Shen; Liu, Qiang; Sun, Hong-jin

2017-01-01

Repeated visual context induces higher search efficiency, revealing a contextual cueing effect, which depends on the association between the target and its visual context. In this study, participants performed a visual search task where search items were presented with depth information defined by binocular disparity. When the 3-dimensional (3D) configurations were repeated over blocks, the contextual cueing effect was obtained (Experiment 1). When depth information was in chaos over repeated configurations, visual search was not facilitated and the contextual cueing effect largely crippled (Experiment 2). However, when we made the search items within a tiny random displacement in the 2-dimentional (2D) plane but maintained the depth information constant, the contextual cueing was preserved (Experiment 3). We concluded that the contextual cueing effect was robust in the context provided by 3D space with stereoscopic information, and more importantly, the visual system prioritized stereoscopic information in learning of spatial information when depth information was available. PMID:28912739

Contextual Cueing Effect in Spatial Layout Defined by Binocular Disparity.

PubMed

Zhao, Guang; Zhuang, Qian; Ma, Jie; Tu, Shen; Liu, Qiang; Sun, Hong-Jin

2017-01-01

Repeated visual context induces higher search efficiency, revealing a contextual cueing effect, which depends on the association between the target and its visual context. In this study, participants performed a visual search task where search items were presented with depth information defined by binocular disparity. When the 3-dimensional (3D) configurations were repeated over blocks, the contextual cueing effect was obtained (Experiment 1). When depth information was in chaos over repeated configurations, visual search was not facilitated and the contextual cueing effect largely crippled (Experiment 2). However, when we made the search items within a tiny random displacement in the 2-dimentional (2D) plane but maintained the depth information constant, the contextual cueing was preserved (Experiment 3). We concluded that the contextual cueing effect was robust in the context provided by 3D space with stereoscopic information, and more importantly, the visual system prioritized stereoscopic information in learning of spatial information when depth information was available.

The metric on field space, functional renormalization, and metric–torsion quantum gravity

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

Reuter, Martin, E-mail: reuter@thep.physik.uni-mainz.de; Schollmeyer, Gregor M., E-mail: schollmeyer@thep.physik.uni-mainz.de

Searching for new non-perturbatively renormalizable quantum gravity theories, functional renormalization group (RG) flows are studied on a theory space of action functionals depending on the metric and the torsion tensor, the latter parameterized by three irreducible component fields. A detailed comparison with Quantum Einstein–Cartan Gravity (QECG), Quantum Einstein Gravity (QEG), and “tetrad-only” gravity, all based on different theory spaces, is performed. It is demonstrated that, over a generic theory space, the construction of a functional RG equation (FRGE) for the effective average action requires the specification of a metric on the infinite-dimensional field manifold as an additional input. A modifiedmore » FRGE is obtained if this metric is scale-dependent, as it happens in the metric–torsion system considered.« less

Efficient and effective pruning strategies for health data de-identification.

PubMed

Prasser, Fabian; Kohlmayer, Florian; Kuhn, Klaus A

2016-04-30

Privacy must be protected when sensitive biomedical data is shared, e.g. for research purposes. Data de-identification is an important safeguard, where datasets are transformed to meet two conflicting objectives: minimizing re-identification risks while maximizing data quality. Typically, de-identification methods search a solution space of possible data transformations to find a good solution to a given de-identification problem. In this process, parts of the search space must be excluded to maintain scalability. The set of transformations which are solution candidates is typically narrowed down by storing the results obtained during the search process and then using them to predict properties of the output of other transformations in terms of privacy (first objective) and data quality (second objective). However, due to the exponential growth of the size of the search space, previous implementations of this method are not well-suited when datasets contain many attributes which need to be protected. As this is often the case with biomedical research data, e.g. as a result of longitudinal collection, we have developed a novel method. Our approach combines the mathematical concept of antichains with a data structure inspired by prefix trees to represent properties of a large number of data transformations while requiring only a minimal amount of information to be stored. To analyze the improvements which can be achieved by adopting our method, we have integrated it into an existing algorithm and we have also implemented a simple best-first branch and bound search (BFS) algorithm as a first step towards methods which fully exploit our approach. We have evaluated these implementations with several real-world datasets and the k-anonymity privacy model. When integrated into existing de-identification algorithms for low-dimensional data, our approach reduced memory requirements by up to one order of magnitude and execution times by up to 25 %. This allowed us to increase the size of solution spaces which could be processed by almost a factor of 10. When using the simple BFS method, we were able to further increase the size of the solution space by a factor of three. When used as a heuristic strategy for high-dimensional data, the BFS approach outperformed a state-of-the-art algorithm by up to 12 % in terms of the quality of output data. This work shows that implementing methods of data de-identification for real-world applications is a challenging task. Our approach solves a problem often faced by data custodians: a lack of scalability of de-identification software when used with datasets having realistic schemas and volumes. The method described in this article has been implemented into ARX, an open source de-identification software for biomedical data.

Software for project-based learning of robot motion planning

NASA Astrophysics Data System (ADS)

Moll, Mark; Bordeaux, Janice; Kavraki, Lydia E.

2013-12-01

Motion planning is a core problem in robotics concerned with finding feasible paths for a given robot. Motion planning algorithms perform a search in the high-dimensional continuous space of robot configurations and exemplify many of the core algorithmic concepts of search algorithms and associated data structures. Motion planning algorithms can be explained in a simplified two-dimensional setting, but this masks many of the subtleties and complexities of the underlying problem. We have developed software for project-based learning of motion planning that enables deep learning. The projects that we have developed allow advanced undergraduate students and graduate students to reflect on the performance of existing textbook algorithms and their own variations on such algorithms. Formative assessment has been conducted at three institutions. The core of the software used for this teaching module is also used within the Robot Operating System, a widely adopted platform by the robotics research community. This allows for transfer of knowledge and skills to robotics research projects involving a large variety robot hardware platforms.

A random forest learning assisted "divide and conquer" approach for peptide conformation search.

PubMed

Chen, Xin; Yang, Bing; Lin, Zijing

2018-06-11

Computational determination of peptide conformations is challenging as it is a problem of finding minima in a high-dimensional space. The "divide and conquer" approach is promising for reliably reducing the search space size. A random forest learning model is proposed here to expand the scope of applicability of the "divide and conquer" approach. A random forest classification algorithm is used to characterize the distributions of the backbone φ-ψ units ("words"). A random forest supervised learning model is developed to analyze the combinations of the φ-ψ units ("grammar"). It is found that amino acid residues may be grouped as equivalent "words", while the φ-ψ combinations in low-energy peptide conformations follow a distinct "grammar". The finding of equivalent words empowers the "divide and conquer" method with the flexibility of fragment substitution. The learnt grammar is used to improve the efficiency of the "divide and conquer" method by removing unfavorable φ-ψ combinations without the need of dedicated human effort. The machine learning assisted search method is illustrated by efficiently searching the conformations of GGG/AAA/GGGG/AAAA/GGGGG through assembling the structures of GFG/GFGG. Moreover, the computational cost of the new method is shown to increase rather slowly with the peptide length.

Applications of numerical optimization methods to helicopter design problems: A survey

NASA Technical Reports Server (NTRS)

Miura, H.

1984-01-01

A survey of applications of mathematical programming methods is used to improve the design of helicopters and their components. Applications of multivariable search techniques in the finite dimensional space are considered. Five categories of helicopter design problems are considered: (1) conceptual and preliminary design, (2) rotor-system design, (3) airframe structures design, (4) control system design, and (5) flight trajectory planning. Key technical progress in numerical optimization methods relevant to rotorcraft applications are summarized.

Applications of numerical optimization methods to helicopter design problems - A survey

NASA Technical Reports Server (NTRS)

Miura, H.

1985-01-01

A survey of applications of mathematical programming methods is used to improve the design of helicopters and their components. Applications of multivariable search techniques in the finite dimensional space are considered. Five categories of helicopter design problems are considered: (1) conceptual and preliminary design, (2) rotor-system design, (3) airframe structures design, (4) control system design, and (5) flight trajectory planning. Key technical progress in numerical optimization methods relevant to rotorcraft applications are summarized.

Applications of numerical optimization methods to helicopter design problems - A survey

NASA Technical Reports Server (NTRS)

Miura, H.

1984-01-01

A survey of applications of mathematical programming methods is used to improve the design of helicopters and their components. Applications of multivariable search techniques in the finite dimensional space are considered. Five categories of helicopter design problems are considered: (1) conceptual and preliminary design, (2) rotor-system design, (3) airframe structures design, (4) control system design, and (5) flight trajectory planning. Key technical progress in numerical optimization methods relevant to rotorcraft applications are summarized.

N-Dimensional LLL Reduction Algorithm with Pivoted Reflection

PubMed Central

Deng, Zhongliang; Zhu, Di

2018-01-01

The Lenstra-Lenstra-Lovász (LLL) lattice reduction algorithm and many of its variants have been widely used by cryptography, multiple-input-multiple-output (MIMO) communication systems and carrier phase positioning in global navigation satellite system (GNSS) to solve the integer least squares (ILS) problem. In this paper, we propose an n-dimensional LLL reduction algorithm (n-LLL), expanding the Lovász condition in LLL algorithm to n-dimensional space in order to obtain a further reduced basis. We also introduce pivoted Householder reflection into the algorithm to optimize the reduction time. For an m-order positive definite matrix, analysis shows that the n-LLL reduction algorithm will converge within finite steps and always produce better results than the original LLL reduction algorithm with n > 2. The simulations clearly prove that n-LLL is better than the original LLL in reducing the condition number of an ill-conditioned input matrix with 39% improvement on average for typical cases, which can significantly reduce the searching space for solving ILS problem. The simulation results also show that the pivoted reflection has significantly declined the number of swaps in the algorithm by 57%, making n-LLL a more practical reduction algorithm. PMID:29351224

Inference-Based Similarity Search in Randomized Montgomery Domains for Privacy-Preserving Biometric Identification.

PubMed

Wang, Yi; Wan, Jianwu; Guo, Jun; Cheung, Yiu-Ming; Yuen, Pong C; Yi Wang; Jianwu Wan; Jun Guo; Yiu-Ming Cheung; Yuen, Pong C; Cheung, Yiu-Ming; Guo, Jun; Yuen, Pong C; Wan, Jianwu; Wang, Yi

2018-07-01

Similarity search is essential to many important applications and often involves searching at scale on high-dimensional data based on their similarity to a query. In biometric applications, recent vulnerability studies have shown that adversarial machine learning can compromise biometric recognition systems by exploiting the biometric similarity information. Existing methods for biometric privacy protection are in general based on pairwise matching of secured biometric templates and have inherent limitations in search efficiency and scalability. In this paper, we propose an inference-based framework for privacy-preserving similarity search in Hamming space. Our approach builds on an obfuscated distance measure that can conceal Hamming distance in a dynamic interval. Such a mechanism enables us to systematically design statistically reliable methods for retrieving most likely candidates without knowing the exact distance values. We further propose to apply Montgomery multiplication for generating search indexes that can withstand adversarial similarity analysis, and show that information leakage in randomized Montgomery domains can be made negligibly small. Our experiments on public biometric datasets demonstrate that the inference-based approach can achieve a search accuracy close to the best performance possible with secure computation methods, but the associated cost is reduced by orders of magnitude compared to cryptographic primitives.

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

PubMed

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

2009-01-01

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

Particle swarm optimization-based automatic parameter selection for deep neural networks and its applications in large-scale and high-dimensional data

PubMed Central

2017-01-01

In this paper, we propose a new automatic hyperparameter selection approach for determining the optimal network configuration (network structure and hyperparameters) for deep neural networks using particle swarm optimization (PSO) in combination with a steepest gradient descent algorithm. In the proposed approach, network configurations were coded as a set of real-number m-dimensional vectors as the individuals of the PSO algorithm in the search procedure. During the search procedure, the PSO algorithm is employed to search for optimal network configurations via the particles moving in a finite search space, and the steepest gradient descent algorithm is used to train the DNN classifier with a few training epochs (to find a local optimal solution) during the population evaluation of PSO. After the optimization scheme, the steepest gradient descent algorithm is performed with more epochs and the final solutions (pbest and gbest) of the PSO algorithm to train a final ensemble model and individual DNN classifiers, respectively. The local search ability of the steepest gradient descent algorithm and the global search capabilities of the PSO algorithm are exploited to determine an optimal solution that is close to the global optimum. We constructed several experiments on hand-written characters and biological activity prediction datasets to show that the DNN classifiers trained by the network configurations expressed by the final solutions of the PSO algorithm, employed to construct an ensemble model and individual classifier, outperform the random approach in terms of the generalization performance. Therefore, the proposed approach can be regarded an alternative tool for automatic network structure and parameter selection for deep neural networks. PMID:29236718

Development of flood routing simulation system of digital Qingjiang based on integrated spatial information technology

NASA Astrophysics Data System (ADS)

Yuan, Yanbin; Zhou, You; Zhu, Yaqiong; Yuan, Xiaohui; Sælthun, N. R.

2007-11-01

Based on digital technology, flood routing simulation system development is an important component of "digital catchment". Taking QingJiang catchment as a pilot case, in-depth analysis on informatization of Qingjiang catchment management being the basis, aiming at catchment data's multi-source, - dimension, -element, -subject, -layer and -class feature, the study brings the design thought and method of "subject-point-source database" (SPSD) to design system structure in order to realize the unified management of catchments data in great quantity. Using the thought of integrated spatial information technology for reference, integrating hierarchical structure development model of digital catchment is established. The model is general framework of the flood routing simulation system analysis, design and realization. In order to satisfy the demands of flood routing three-dimensional simulation system, the object-oriented spatial data model are designed. We can analyze space-time self-adapting relation between flood routing and catchments topography, express grid data of terrain by using non-directed graph, apply breadth first search arithmetic, set up search method for the purpose of dynamically searching stream channel on the basis of simulated three-dimensional terrain. The system prototype is therefore realized. Simulation results have demonstrated that the proposed approach is feasible and effective in the application.

Optimization in optical systems revisited: Beyond genetic algorithms

NASA Astrophysics Data System (ADS)

Gagnon, Denis; Dumont, Joey; Dubé, Louis

2013-05-01

Designing integrated photonic devices such as waveguides, beam-splitters and beam-shapers often requires optimization of a cost function over a large solution space. Metaheuristics - algorithms based on empirical rules for exploring the solution space - are specifically tailored to those problems. One of the most widely used metaheuristics is the standard genetic algorithm (SGA), based on the evolution of a population of candidate solutions. However, the stochastic nature of the SGA sometimes prevents access to the optimal solution. Our goal is to show that a parallel tabu search (PTS) algorithm is more suited to optimization problems in general, and to photonics in particular. PTS is based on several search processes using a pool of diversified initial solutions. To assess the performance of both algorithms (SGA and PTS), we consider an integrated photonics design problem, the generation of arbitrary beam profiles using a two-dimensional waveguide-based dielectric structure. The authors acknowledge financial support from the Natural Sciences and Engineering Research Council of Canada (NSERC).

Experimental design for estimating unknown groundwater pumping using genetic algorithm and reduced order model

NASA Astrophysics Data System (ADS)

Ushijima, Timothy T.; Yeh, William W.-G.

2013-10-01

An optimal experimental design algorithm is developed to select locations for a network of observation wells that provide maximum information about unknown groundwater pumping in a confined, anisotropic aquifer. The design uses a maximal information criterion that chooses, among competing designs, the design that maximizes the sum of squared sensitivities while conforming to specified design constraints. The formulated optimization problem is non-convex and contains integer variables necessitating a combinatorial search. Given a realistic large-scale model, the size of the combinatorial search required can make the problem difficult, if not impossible, to solve using traditional mathematical programming techniques. Genetic algorithms (GAs) can be used to perform the global search; however, because a GA requires a large number of calls to a groundwater model, the formulated optimization problem still may be infeasible to solve. As a result, proper orthogonal decomposition (POD) is applied to the groundwater model to reduce its dimensionality. Then, the information matrix in the full model space can be searched without solving the full model. Results from a small-scale test case show identical optimal solutions among the GA, integer programming, and exhaustive search methods. This demonstrates the GA's ability to determine the optimal solution. In addition, the results show that a GA with POD model reduction is several orders of magnitude faster in finding the optimal solution than a GA using the full model. The proposed experimental design algorithm is applied to a realistic, two-dimensional, large-scale groundwater problem. The GA converged to a solution for this large-scale problem.

Structural optimization via a design space hierarchy

NASA Technical Reports Server (NTRS)

Vanderplaats, G. N.

1976-01-01

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

Geometric analysis characterizes molecular rigidity in generic and non-generic protein configurations

PubMed Central

Budday, Dominik; Leyendecker, Sigrid; van den Bedem, Henry

2015-01-01

Proteins operate and interact with partners by dynamically exchanging between functional substates of a conformational ensemble on a rugged free energy landscape. Understanding how these substates are linked by coordinated, collective motions requires exploring a high-dimensional space, which remains a tremendous challenge. While molecular dynamics simulations can provide atomically detailed insight into the dynamics, computational demands to adequately sample conformational ensembles of large biomolecules and their complexes often require tremendous resources. Kinematic models can provide high-level insights into conformational ensembles and molecular rigidity beyond the reach of molecular dynamics by reducing the dimensionality of the search space. Here, we model a protein as a kinematic linkage and present a new geometric method to characterize molecular rigidity from the constraint manifold Q and its tangent space Q at the current configuration q. In contrast to methods based on combinatorial constraint counting, our method is valid for both generic and non-generic, e.g., singular configurations. Importantly, our geometric approach provides an explicit basis for collective motions along floppy modes, resulting in an efficient procedure to probe conformational space. An atomically detailed structural characterization of coordinated, collective motions would allow us to engineer or allosterically modulate biomolecules by selectively stabilizing conformations that enhance or inhibit function with broad implications for human health. PMID:26213417

Geometric analysis characterizes molecular rigidity in generic and non-generic protein configurations

NASA Astrophysics Data System (ADS)

Budday, Dominik; Leyendecker, Sigrid; van den Bedem, Henry

2015-10-01

Proteins operate and interact with partners by dynamically exchanging between functional substates of a conformational ensemble on a rugged free energy landscape. Understanding how these substates are linked by coordinated, collective motions requires exploring a high-dimensional space, which remains a tremendous challenge. While molecular dynamics simulations can provide atomically detailed insight into the dynamics, computational demands to adequately sample conformational ensembles of large biomolecules and their complexes often require tremendous resources. Kinematic models can provide high-level insights into conformational ensembles and molecular rigidity beyond the reach of molecular dynamics by reducing the dimensionality of the search space. Here, we model a protein as a kinematic linkage and present a new geometric method to characterize molecular rigidity from the constraint manifold Q and its tangent space Tq Q at the current configuration q. In contrast to methods based on combinatorial constraint counting, our method is valid for both generic and non-generic, e.g., singular configurations. Importantly, our geometric approach provides an explicit basis for collective motions along floppy modes, resulting in an efficient procedure to probe conformational space. An atomically detailed structural characterization of coordinated, collective motions would allow us to engineer or allosterically modulate biomolecules by selectively stabilizing conformations that enhance or inhibit function with broad implications for human health.

Directional Bias and Pheromone for Discovery and Coverage on Networks

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

Fink, Glenn A.; Berenhaut, Kenneth S.; Oehmen, Christopher S.

2012-09-11

Natural multi-agent systems often rely on “correlated random walks” (random walks that are biased toward a current heading) to distribute their agents over a space (e.g., for foraging, search, etc.). Our contribution involves creation of a new movement and pheromone model that applies the concept of heading bias in random walks to a multi-agent, digital-ants system designed for cyber-security monitoring. We examine the relative performance effects of both pheromone and heading bias on speed of discovery of a target and search-area coverage in a two-dimensional network layout. We found that heading bias was unexpectedly helpful in reducing search time andmore » that it was more influential than pheromone for improving coverage. We conclude that while pheromone is very important for rapid discovery, heading bias can also greatly improve both performance metrics.« less

Search for Higgs bosons predicted in two-Higgs-doublet models via decays to tau lepton pairs in 1.96 TeV pp collisions.

PubMed

Aaltonen, T; Adelman, J; Akimoto, T; Alvarez González, B; Amerio, S; Amidei, D; Anastassov, A; Annovi, A; Antos, J; Apollinari, G; Apresyan, A; Arisawa, T; Artikov, A; Ashmanskas, W; Attal, A; Aurisano, A; Azfar, F; Badgett, W; Barbaro-Galtieri, A; Barnes, V E; Barnett, B A; Barria, P; Bartos, P; Bartsch, V; Bauer, G; Beauchemin, P-H; Bedeschi, F; Beecher, D; Behari, S; Bellettini, G; Bellinger, J; Benjamin, D; Beretvas, A; Beringer, J; Bhatti, A; Binkley, M; Bisello, D; Bizjak, I; Blair, R E; Blocker, C; Blumenfeld, B; Bocci, A; Bodek, A; Boisvert, V; Bolla, G; Bortoletto, D; Boudreau, J; Boveia, A; Brau, B; Bridgeman, A; Brigliadori, L; Bromberg, C; Brubaker, E; Budagov, J; Budd, H S; Budd, S; Burke, S; Burkett, K; Busetto, G; Bussey, P; Buzatu, A; Byrum, K L; Cabrera, S; Calancha, C; Campanelli, M; Campbell, M; Canelli, F; Canepa, A; Carls, B; Carlsmith, D; Carosi, R; Carrillo, S; Carron, S; Casal, B; Casarsa, M; Castro, A; Catastini, P; Cauz, D; Cavaliere, V; Cavalli-Sforza, M; Cerri, A; Cerrito, L; Chang, S H; Chen, Y C; Chertok, M; Chiarelli, G; Chlachidze, G; Chlebana, F; Cho, K; Chokheli, D; Chou, J P; Choudalakis, G; Chuang, S H; Chung, K; Chung, W H; Chung, Y S; Chwalek, T; Ciobanu, C I; Ciocci, M A; Clark, A; Clark, D; Compostella, G; Convery, M E; Conway, J; Cordelli, M; Cortiana, G; Cox, C A; Cox, D J; Crescioli, F; Cuenca Almenar, C; Cuevas, J; Culbertson, R; Cully, J C; Dagenhart, D; Datta, M; Davies, T; de Barbaro, P; De Cecco, S; Deisher, A; De Lorenzo, G; Dell'Orso, M; Deluca, C; Demortier, L; Deng, J; Deninno, M; Derwent, P F; Di Canto, A; di Giovanni, G P; Dionisi, C; Di Ruzza, B; Dittmann, J R; D'Onofrio, M; Donati, S; Dong, P; Donini, J; Dorigo, T; Dube, S; Efron, J; Elagin, A; Erbacher, R; Errede, D; Errede, S; Eusebi, R; Fang, H C; Farrington, S; Fedorko, W T; Feild, R G; Feindt, M; Fernandez, J P; Ferrazza, C; Field, R; Flanagan, G; Forrest, R; Frank, M J; Franklin, M; Freeman, J C; Furic, I; Gallinaro, M; Galyardt, J; Garberson, F; Garcia, J E; Garfinkel, A F; Garosi, P; Genser, K; Gerberich, H; Gerdes, D; Gessler, A; Giagu, S; Giakoumopoulou, V; Giannetti, P; Gibson, K; Gimmell, J L; Ginsburg, C M; Giokaris, N; Giordani, M; Giromini, P; Giunta, M; Giurgiu, G; Glagolev, V; Glenzinski, D; Gold, M; Goldschmidt, N; Golossanov, A; Gomez, G; Gomez-Ceballos, G; Goncharov, M; González, O; Gorelov, I; Goshaw, A T; Goulianos, K; Gresele, A; Grinstein, S; Grosso-Pilcher, C; Group, R C; Grundler, U; Guimaraes da Costa, J; Gunay-Unalan, Z; Haber, C; Hahn, K; Hahn, S R; Halkiadakis, E; Han, B-Y; Han, J Y; Happacher, F; Hara, K; Hare, D; Hare, M; Harper, S; Harr, R F; Harris, R M; Hartz, M; Hatakeyama, K; Hays, C; Heck, M; Heijboer, A; Heinrich, J; Henderson, C; Herndon, M; Heuser, J; Hewamanage, S; Hidas, D; Hill, C S; Hirschbuehl, D; Hocker, A; Hou, S; Houlden, M; Hsu, S-C; Huffman, B T; Hughes, R E; Husemann, U; Hussein, M; Huston, J; Incandela, J; Introzzi, G; Iori, M; Ivanov, A; James, E; Jang, D; Jayatilaka, B; Jeon, E J; Jha, M K; Jindariani, S; Johnson, W; Jones, M; Joo, K K; Jun, S Y; Jung, J E; Junk, T R; Kamon, T; Kar, D; Karchin, P E; Kato, Y; Kephart, R; Ketchum, W; Keung, J; Khotilovich, V; Kilminster, B; Kim, D H; Kim, H S; Kim, H W; Kim, J E; Kim, M J; Kim, S B; Kim, S H; Kim, Y K; Kimura, N; Kirsch, L; Klimenko, S; Knuteson, B; Ko, B R; Kondo, K; Kong, D J; Konigsberg, J; Korytov, A; Kotwal, A V; Kreps, M; Kroll, J; Krop, D; Krumnack, N; Kruse, M; Krutelyov, V; Kubo, T; Kuhr, T; Kulkarni, N P; Kurata, M; Kwang, S; Laasanen, A T; Lami, S; Lammel, S; Lancaster, M; Lander, R L; Lannon, K; Lath, A; Latino, G; Lazzizzera, I; LeCompte, T; Lee, E; Lee, H S; Lee, S W; Leone, S; Lewis, J D; Lin, C-S; Linacre, J; Lindgren, M; Lipeles, E; Lister, A; Litvintsev, D O; Liu, C; Liu, T; Lockyer, N S; Loginov, A; Loreti, M; Lovas, L; Lucchesi, D; Luci, C; Lueck, J; Lujan, P; Lukens, P; Lungu, G; Lyons, L; Lys, J; Lysak, R; MacQueen, D; Madrak, R; Maeshima, K; Makhoul, K; Maki, T; Maksimovic, P; Malde, S; Malik, S; Manca, G; Manousakis-Katsikakis, A; Margaroli, F; Marino, C; Marino, C P; Martin, A; Martin, V; Martínez, M; Martínez-Ballarín, R; Maruyama, T; Mastrandrea, P; Masubuchi, T; Mathis, M; Mattson, M E; Mazzanti, P; McFarland, K S; McIntyre, P; McNulty, R; Mehta, A; Mehtala, P; Menzione, A; Merkel, P; Mesropian, C; Miao, T; Miladinovic, N; Miller, R; Mills, C; Milnik, M; Mitra, A; Mitselmakher, G; Miyake, H; Moggi, N; Mondragon, M N; Moon, C S; Moore, R; Morello, M J; Morlock, J; Movilla Fernandez, P; Mülmenstädt, J; Mukherjee, A; Muller, Th; Mumford, R; Murat, P; Mussini, M; Nachtman, J; Nagai, Y; Nagano, A; Naganoma, J; Nakamura, K; Nakano, I; Napier, A; Necula, V; Nett, J; Neu, C; Neubauer, M S; Neubauer, S; Nielsen, J; Nodulman, L; Norman, M; Norniella, O; Nurse, E; Oakes, L; Oh, S H; Oh, Y D; Oksuzian, I; Okusawa, T; Orava, R; Osterberg, K; Pagan Griso, S; Pagliarone, C; Palencia, E; Papadimitriou, V; Papaikonomou, A; Paramonov, A A; Parks, B; Pashapour, S; Patrick, J; Pauletta, G; Paulini, M; Paus, C; Peiffer, T; Pellett, D E; Penzo, A; Phillips, T J; Piacentino, G; Pianori, E; Pinera, L; Pitts, K; Plager, C; Pondrom, L; Poukhov, O; Pounder, N; Prakoshyn, F; Pronko, A; Proudfoot, J; Ptohos, F; Pueschel, E; Punzi, G; Pursley, J; Rademacker, J; Rahaman, A; Ramakrishnan, V; Ranjan, N; Redondo, I; Renton, P; Renz, M; Rescigno, M; Richter, S; Rimondi, F; Ristori, L; Robson, A; Rodrigo, T; Rodriguez, T; Rogers, E; Rolli, S; Roser, R; Rossi, M; Rossin, R; Roy, P; Ruiz, A; Russ, J; Rusu, V; Rutherford, B; Saarikko, H; Safonov, A; Sakumoto, W K; Saltó, O; Santi, L; Sarkar, S; Sartori, L; Sato, K; Savoy-Navarro, A; Schlabach, P; Schmidt, A; Schmidt, E E; Schmidt, M A; Schmidt, M P; Schmitt, M; Schwarz, T; Scodellaro, L; Scribano, A; Scuri, F; Sedov, A; Seidel, S; Seiya, Y; Semenov, A; Sexton-Kennedy, L; Sforza, F; Sfyrla, A; Shalhout, S Z; Shears, T; Shepard, P F; Shimojima, M; Shiraishi, S; Shochet, M; Shon, Y; Shreyber, I; Sinervo, P; Sisakyan, A; Slaughter, A J; Slaunwhite, J; Sliwa, K; Smith, J R; Snider, F D; Snihur, R; Soha, A; Somalwar, S; Sorin, V; Spreitzer, T; Squillacioti, P; Stanitzki, M; St Denis, R; Stelzer, B; Stelzer-Chilton, O; Stentz, D; Strologas, J; Strycker, G L; Suh, J S; Sukhanov, A; Suslov, I; Suzuki, T; Taffard, A; Takashima, R; Takeuchi, Y; Tanaka, R; Tecchio, M; Teng, P K; Terashi, K; Thom, J; Thompson, A S; Thompson, G A; Thomson, E; Tipton, P; Ttito-Guzmán, P; Tkaczyk, S; Toback, D; Tokar, S; Tollefson, K; Tomura, T; Tonelli, D; Torre, S; Torretta, D; Totaro, P; Tourneur, S; Trovato, M; Tsai, S-Y; Tu, Y; Turini, N; Ukegawa, F; Vallecorsa, S; van Remortel, N; Varganov, A; Vataga, E; Vázquez, F; Velev, G; Vellidis, C; Vidal, M; Vidal, R; Vila, I; Vilar, R; Vine, T; Vogel, M; Volobouev, I; Volpi, G; Wagner, P; Wagner, R G; Wagner, R L; Wagner, W; Wagner-Kuhr, J; Wakisaka, T; Wallny, R; Wang, S M; Warburton, A; Waters, D; Weinberger, M; Weinelt, J; Wester, W C; Whitehouse, B; Whiteson, D; Wicklund, A B; Wicklund, E; Wilbur, S; Williams, G; Williams, H H; Wilson, P; Winer, B L; Wittich, P; Wolbers, S; Wolfe, C; Wright, T; Wu, X; Würthwein, F; Xie, S; Yagil, A; Yamamoto, K; Yamaoka, J; Yang, U K; Yang, Y C; Yao, W M; Yeh, G P; Yi, K; Yoh, J; Yorita, K; Yoshida, T; Yu, G B; Yu, I; Yu, S S; Yun, J C; Zanello, L; Zanetti, A; Zhang, X; Zheng, Y; Zucchelli, S

2009-11-13

We present the results of a search for Higgs bosons predicted in two-Higgs-doublet models, in the case where the Higgs bosons decay to tau lepton pairs, using 1.8 fb(-1) of integrated luminosity of pp collisions recorded by the CDF II experiment at the Fermilab Tevatron. Studying the mass distribution in events where one or both tau leptons decay leptonically, no evidence for a Higgs boson signal is observed. The result is used to infer exclusion limits in the two-dimensional space of tanbeta versus m(A) (the ratio of the vacuum expectation values of the two Higgs doublets and the mass of the pseudoscalar boson, respectively).

Asteroid families - An initial search

NASA Technical Reports Server (NTRS)

Williams, James G.

1992-01-01

A stereo examination was conducted for clusters in three-dimensional proper element space within a sample of both numbered and faint Palomar-Leiden Survey (PLS) asteroids. The clusters were then objectively filtered for small Poisson probability of chance occurrence; 104 were accepted as families with 4- to 12-member populations, and are interpreted as impact-generated. Structure is common in the well-populated families: the better-sampled families are accordingly discussed in terms of their geometry and taxonomy. Some families are very rich in faint PLS members.

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

A systematic construction of microstate geometries with low angular momentum

NASA Astrophysics Data System (ADS)

Bena, Iosif; Heidmann, Pierre; Ramírez, Pedro F.

2017-10-01

We outline a systematic procedure to obtain horizonless microstate geometries that have the same charges as three-charge five-dimensional black holes with a macroscopically-large horizon area and an arbitrarily-small angular momentum. There are two routes through which such solutions can be constructed: using multi-center Gibbons-Hawking (GH) spaces or using superstratum technology. So far the only solutions corre-sponding to microstate geometries for black holes with no angular momentum have been obtained via superstrata [1], and multi-center Gibbons-Hawking spaces have been believed to give rise only to microstate geometries of BMPV black holes with a large angular mo-mentum [2]. We perform a thorough search throughout the parameter space of smooth horizonless solutions with four GH centers and find that these have an angular momentum that is generally larger than 80% of the cosmic censorship bound. However, we find that solutions with three GH centers and one supertube (which are smooth in six-dimensional supergravity) can have an arbitrarily-low angular momentum. Our construction thus gives a recipe to build large classes of microstate geometries for zero-angular-momentum black holes without resorting to superstratum technology.

Chemical Space: Big Data Challenge for Molecular Diversity.

PubMed

Awale, Mahendra; Visini, Ricardo; Probst, Daniel; Arús-Pous, Josep; Reymond, Jean-Louis

2017-10-25

Chemical space describes all possible molecules as well as multi-dimensional conceptual spaces representing the structural diversity of these molecules. Part of this chemical space is available in public databases ranging from thousands to billions of compounds. Exploiting these databases for drug discovery represents a typical big data problem limited by computational power, data storage and data access capacity. Here we review recent developments of our laboratory, including progress in the chemical universe databases (GDB) and the fragment subset FDB-17, tools for ligand-based virtual screening by nearest neighbor searches, such as our multi-fingerprint browser for the ZINC database to select purchasable screening compounds, and their application to discover potent and selective inhibitors for calcium channel TRPV6 and Aurora A kinase, the polypharmacology browser (PPB) for predicting off-target effects, and finally interactive 3D-chemical space visualization using our online tools WebDrugCS and WebMolCS. All resources described in this paper are available for public use at www.gdb.unibe.ch.

Clustering methods for the optimization of atomic cluster structure

NASA Astrophysics Data System (ADS)

Bagattini, Francesco; Schoen, Fabio; Tigli, Luca

2018-04-01

In this paper, we propose a revised global optimization method and apply it to large scale cluster conformation problems. In the 1990s, the so-called clustering methods were considered among the most efficient general purpose global optimization techniques; however, their usage has quickly declined in recent years, mainly due to the inherent difficulties of clustering approaches in large dimensional spaces. Inspired from the machine learning literature, we redesigned clustering methods in order to deal with molecular structures in a reduced feature space. Our aim is to show that by suitably choosing a good set of geometrical features coupled with a very efficient descent method, an effective optimization tool is obtained which is capable of finding, with a very high success rate, all known putative optima for medium size clusters without any prior information, both for Lennard-Jones and Morse potentials. The main result is that, beyond being a reliable approach, the proposed method, based on the idea of starting a computationally expensive deep local search only when it seems worth doing so, is capable of saving a huge amount of searches with respect to an analogous algorithm which does not employ a clustering phase. In this paper, we are not claiming the superiority of the proposed method compared to specific, refined, state-of-the-art procedures, but rather indicating a quite straightforward way to save local searches by means of a clustering scheme working in a reduced variable space, which might prove useful when included in many modern methods.

Understanding Innovation Engines: Automated Creativity and Improved Stochastic Optimization via Deep Learning.

PubMed

Nguyen, A; Yosinski, J; Clune, J

2016-01-01

The Achilles Heel of stochastic optimization algorithms is getting trapped on local optima. Novelty Search mitigates this problem by encouraging exploration in all interesting directions by replacing the performance objective with a reward for novel behaviors. This reward for novel behaviors has traditionally required a human-crafted, behavioral distance function. While Novelty Search is a major conceptual breakthrough and outperforms traditional stochastic optimization on certain problems, it is not clear how to apply it to challenging, high-dimensional problems where specifying a useful behavioral distance function is difficult. For example, in the space of images, how do you encourage novelty to produce hawks and heroes instead of endless pixel static? Here we propose a new algorithm, the Innovation Engine, that builds on Novelty Search by replacing the human-crafted behavioral distance with a Deep Neural Network (DNN) that can recognize interesting differences between phenotypes. The key insight is that DNNs can recognize similarities and differences between phenotypes at an abstract level, wherein novelty means interesting novelty. For example, a DNN-based novelty search in the image space does not explore in the low-level pixel space, but instead creates a pressure to create new types of images (e.g., churches, mosques, obelisks, etc.). Here, we describe the long-term vision for the Innovation Engine algorithm, which involves many technical challenges that remain to be solved. We then implement a simplified version of the algorithm that enables us to explore some of the algorithm's key motivations. Our initial results, in the domain of images, suggest that Innovation Engines could ultimately automate the production of endless streams of interesting solutions in any domain: for example, producing intelligent software, robot controllers, optimized physical components, and art.

Critical string from non-Abelian vortex in four dimensions

DOE PAGES

Shifman, M.; Yung, A.

2015-09-25

In a class of non-Abelian solitonic vortex strings supported in certain N = 2 super-Yang–Mills theories we search for the vortex which can behave as a critical fundamental string. We use the Polchinski–Strominger criterion of the ultraviolet completeness. We identify an appropriate four-dimensional bulk theory: it has the U(2) gauge group, the Fayet–Iliopoulos term and four flavor hypermultiplets. It supports semilocal vortices with the world-sheet theory for orientational (size) moduli described by the weighted CP(2,2) model. The latter is superconformal. Its target space is six-dimensional. The overall Virasoro central charge is critical. Lastly, we show that the world-sheet theory onmore » the vortex supported in this bulk model is the bona fide critical string.« less

DISCO: Distance and Spectrum Correlation Optimization Alignment for Two Dimensional Gas Chromatography Time-of-Flight Mass Spectrometry-based Metabolomics

PubMed Central

Wang, Bing; Fang, Aiqin; Heim, John; Bogdanov, Bogdan; Pugh, Scott; Libardoni, Mark; Zhang, Xiang

2010-01-01

A novel peak alignment algorithm using a distance and spectrum correlation optimization (DISCO) method has been developed for two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC/TOF-MS) based metabolomics. This algorithm uses the output of the instrument control software, ChromaTOF, as its input data. It detects and merges multiple peak entries of the same metabolite into one peak entry in each input peak list. After a z-score transformation of metabolite retention times, DISCO selects landmark peaks from all samples based on both two-dimensional retention times and mass spectrum similarity of fragment ions measured by Pearson’s correlation coefficient. A local linear fitting method is employed in the original two-dimensional retention time space to correct retention time shifts. A progressive retention time map searching method is used to align metabolite peaks in all samples together based on optimization of the Euclidean distance and mass spectrum similarity. The effectiveness of the DISCO algorithm is demonstrated using data sets acquired under different experiment conditions and a spiked-in experiment. PMID:20476746

Tuning Monotonic Basin Hopping: Improving the Efficiency of Stochastic Search as Applied to Low-Thrust Trajectory Optimization

NASA Technical Reports Server (NTRS)

Englander, Jacob; Englander, Arnold

2014-01-01

Trajectory optimization methods using MBH have become well developed during the past decade. An essential component of MBH is a controlled random search through the multi-dimensional space of possible solutions. Historically, the randomness has been generated by drawing RVs from a uniform probability distribution. Here, we investigate the generating the randomness by drawing the RVs from Cauchy and Pareto distributions, chosen because of their characteristic long tails. We demonstrate that using Cauchy distributions (as first suggested by Englander significantly improves MBH performance, and that Pareto distributions provide even greater improvements. Improved performance is defined in terms of efficiency and robustness, where efficiency is finding better solutions in less time, and robustness is efficiency that is undiminished by (a) the boundary conditions and internal constraints of the optimization problem being solved, and (b) by variations in the parameters of the probability distribution. Robustness is important for achieving performance improvements that are not problem specific. In this work we show that the performance improvements are the result of how these long-tailed distributions enable MBH to search the solution space faster and more thoroughly. In developing this explanation, we use the concepts of sub-diffusive, normally-diffusive, and super-diffusive RWs originally developed in the field of statistical physics.

Search for a Heavy Neutrino and Right-Handed W of the Left-Right Symmetric Model with Cms Detector

NASA Astrophysics Data System (ADS)

Tlisov, Danila

2013-11-01

This work describes the first search for signals from the production of right-handed WR bosons and heavy neutrinos Nℓ (ℓ = e, μ), that arise naturally in the left-right symmetric extension to the Standard Model, with the CMS Experiment at the LHC using the 7 TeV pp collision data collected in 2010 and 2011 corresponding to an integrated luminosity of 240 pb-1. No excess over expectations from Standard Model processes is observed. For models with exact left-right symmetry (the same coupling in the left and right sectors) we exclude the region in the two-dimensional parameter space that extends to (MWR, MNℓ) = (1700 GeV, 600 GeV).

Method for using global optimization to the estimation of surface-consistent residual statics

DOEpatents

Reister, David B.; Barhen, Jacob; Oblow, Edward M.

2001-01-01

An efficient method for generating residual statics corrections to compensate for surface-consistent static time shifts in stacked seismic traces. The method includes a step of framing the residual static corrections as a global optimization problem in a parameter space. The method also includes decoupling the global optimization problem involving all seismic traces into several one-dimensional problems. The method further utilizes a Stochastic Pijavskij Tunneling search to eliminate regions in the parameter space where a global minimum is unlikely to exist so that the global minimum may be quickly discovered. The method finds the residual statics corrections by maximizing the total stack power. The stack power is a measure of seismic energy transferred from energy sources to receivers.

Current algebra, statistical mechanics and quantum models

NASA Astrophysics Data System (ADS)

Vilela Mendes, R.

2017-11-01

Results obtained in the past for free boson systems at zero and nonzero temperatures are revisited to clarify the physical meaning of current algebra reducible functionals which are associated to systems with density fluctuations, leading to observable effects on phase transitions. To use current algebra as a tool for the formulation of quantum statistical mechanics amounts to the construction of unitary representations of diffeomorphism groups. Two mathematical equivalent procedures exist for this purpose. One searches for quasi-invariant measures on configuration spaces, the other for a cyclic vector in Hilbert space. Here, one argues that the second approach is closer to the physical intuition when modelling complex systems. An example of application of the current algebra methodology to the pairing phenomenon in two-dimensional fermion systems is discussed.

A new effective operator for the hybrid algorithm for solving global optimisation problems

NASA Astrophysics Data System (ADS)

Duc, Le Anh; Li, Kenli; Nguyen, Tien Trong; Yen, Vu Minh; Truong, Tung Khac

2018-04-01

Hybrid algorithms have been recently used to solve complex single-objective optimisation problems. The ultimate goal is to find an optimised global solution by using these algorithms. Based on the existing algorithms (HP_CRO, PSO, RCCRO), this study proposes a new hybrid algorithm called MPC (Mean-PSO-CRO), which utilises a new Mean-Search Operator. By employing this new operator, the proposed algorithm improves the search ability on areas of the solution space that the other operators of previous algorithms do not explore. Specifically, the Mean-Search Operator helps find the better solutions in comparison with other algorithms. Moreover, the authors have proposed two parameters for balancing local and global search and between various types of local search, as well. In addition, three versions of this operator, which use different constraints, are introduced. The experimental results on 23 benchmark functions, which are used in previous works, show that our framework can find better optimal or close-to-optimal solutions with faster convergence speed for most of the benchmark functions, especially the high-dimensional functions. Thus, the proposed algorithm is more effective in solving single-objective optimisation problems than the other existing algorithms.

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.

A six degree of freedom, plume-fuel optimal trajectory planner for spacecraft proximity operations using an A* node search. M.S. Thesis - MIT

NASA Technical Reports Server (NTRS)

Jackson, Mark Charles

1994-01-01

Spacecraft proximity operations are complicated by the fact that exhaust plume impingement from the reaction control jets of space vehicles can cause structural damage, contamination of sensitive arrays and instruments, or attitude misalignment during docking. The occurrence and effect of jet plume impingement can be reduced by planning approach trajectories with plume effects considered. An A* node search is used to find plume-fuel optimal trajectories through a discretized six dimensional attitude-translation space. A plume cost function which approximates jet plume isopressure envelopes is presented. The function is then applied to find relative costs for predictable 'trajectory altering' firings and unpredictable 'deadbanding' firings. Trajectory altering firings are calculated by running the spacecraft jet selection algorithm and summing the cost contribution from each jet fired. A 'deadbanding effects' function is defined and integrated to determine the potential for deadbanding impingement along candidate trajectories. Plume costs are weighed against fuel costs in finding the optimal solution. A* convergence speed is improved by solving approach trajectory problems in reverse time. Results are obtained on a high fidelity space shuttle/space station simulation. Trajectory following is accomplished by a six degree of freedom autopilot. Trajectories planned with, and without, plume costs are compared in terms of force applied to the target structure.

PDB-Explorer: a web-based interactive map of the protein data bank in shape space.

PubMed

Jin, Xian; Awale, Mahendra; Zasso, Michaël; Kostro, Daniel; Patiny, Luc; Reymond, Jean-Louis

2015-10-23

The RCSB Protein Data Bank (PDB) provides public access to experimentally determined 3D-structures of biological macromolecules (proteins, peptides and nucleic acids). While various tools are available to explore the PDB, options to access the global structural diversity of the entire PDB and to perceive relationships between PDB structures remain very limited. A 136-dimensional atom pair 3D-fingerprint for proteins (3DP) counting categorized atom pairs at increasing through-space distances was designed to represent the molecular shape of PDB-entries. Nearest neighbor searches examples were reported exemplifying the ability of 3DP-similarity to identify closely related biomolecules from small peptides to enzyme and large multiprotein complexes such as virus particles. The principle component analysis was used to obtain the visualization of PDB in 3DP-space. The 3DP property space groups proteins and protein assemblies according to their 3D-shape similarity, yet shows exquisite ability to distinguish between closely related structures. An interactive website called PDB-Explorer is presented featuring a color-coded interactive map of PDB in 3DP-space. Each pixel of the map contains one or more PDB-entries which are directly visualized as ribbon diagrams when the pixel is selected. The PDB-Explorer website allows performing 3DP-nearest neighbor searches of any PDB-entry or of any structure uploaded as protein-type PDB file. All functionalities on the website are implemented in JavaScript in a platform-independent manner and draw data from a server that is updated daily with the latest PDB additions, ensuring complete and up-to-date coverage. The essentially instantaneous 3DP-similarity search with the PDB-Explorer provides results comparable to those of much slower 3D-alignment algorithms, and automatically clusters proteins from the same superfamilies in tight groups. A chemical space classification of PDB based on molecular shape was obtained using a new atom-pair 3D-fingerprint for proteins and implemented in a web-based database exploration tool comprising an interactive color-coded map of the PDB chemical space and a nearest neighbor search tool. The PDB-Explorer website is freely available at www.cheminfo.org/pdbexplorer and represents an unprecedented opportunity to interactively visualize and explore the structural diversity of the PDB. ᅟ

Aggregated Indexing of Biomedical Time Series Data

PubMed Central

Woodbridge, Jonathan; Mortazavi, Bobak; Sarrafzadeh, Majid; Bui, Alex A.T.

2016-01-01

Remote and wearable medical sensing has the potential to create very large and high dimensional datasets. Medical time series databases must be able to efficiently store, index, and mine these datasets to enable medical professionals to effectively analyze data collected from their patients. Conventional high dimensional indexing methods are a two stage process. First, a superset of the true matches is efficiently extracted from the database. Second, supersets are pruned by comparing each of their objects to the query object and rejecting any objects falling outside a predetermined radius. This pruning stage heavily dominates the computational complexity of most conventional search algorithms. Therefore, indexing algorithms can be significantly improved by reducing the amount of pruning. This paper presents an online algorithm to aggregate biomedical times series data to significantly reduce the search space (index size) without compromising the quality of search results. This algorithm is built on the observation that biomedical time series signals are composed of cyclical and often similar patterns. This algorithm takes in a stream of segments and groups them to highly concentrated collections. Locality Sensitive Hashing (LSH) is used to reduce the overall complexity of the algorithm, allowing it to run online. The output of this aggregation is used to populate an index. The proposed algorithm yields logarithmic growth of the index (with respect to the total number of objects) while keeping sensitivity and specificity simultaneously above 98%. Both memory and runtime complexities of time series search are improved when using aggregated indexes. In addition, data mining tasks, such as clustering, exhibit runtimes that are orders of magnitudes faster when run on aggregated indexes. PMID:27617298

Limitation of predictive 2-D liquid chromatography in reducing the database search space in shotgun proteomics: in silico studies.

PubMed

Moskovets, Eugene; Goloborodko, Anton A; Gorshkov, Alexander V; Gorshkov, Mikhail V

2012-07-01

A two-dimensional (2-D) liquid chromatography (LC) separation of complex peptide mixtures that combines a normal phase utilizing hydrophilic interactions and a reversed phase offers reportedly the highest level of 2-D LC orthogonality by providing an even spread of peptides across multiple LC fractions. Matching experimental peptide retention times to those predicted by empirical models describing chromatographic separation in each LC dimension leads to a significant reduction in a database search space. In this work, we calculated the retention times of tryptic peptides separated in the C18 reversed phase at different separation conditions (pH 2 and pH 10) and in TSK gel Amide-80 normal phase. We show that retention times calculated for different 2-D LC separation schemes utilizing these phases start to correlate once the mass range of peptides under analysis becomes progressively narrow. This effect is explained by high degree of correlation between retention coefficients in the considered phases. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Extracting galactic structure parameters from multivariated density estimation

NASA Technical Reports Server (NTRS)

Chen, B.; Creze, M.; Robin, A.; Bienayme, O.

1992-01-01

Multivariate statistical analysis, including includes cluster analysis (unsupervised classification), discriminant analysis (supervised classification) and principle component analysis (dimensionlity reduction method), and nonparameter density estimation have been successfully used to search for meaningful associations in the 5-dimensional space of observables between observed points and the sets of simulated points generated from a synthetic approach of galaxy modelling. These methodologies can be applied as the new tools to obtain information about hidden structure otherwise unrecognizable, and place important constraints on the space distribution of various stellar populations in the Milky Way. In this paper, we concentrate on illustrating how to use nonparameter density estimation to substitute for the true densities in both of the simulating sample and real sample in the five-dimensional space. In order to fit model predicted densities to reality, we derive a set of equations which include n lines (where n is the total number of observed points) and m (where m: the numbers of predefined groups) unknown parameters. A least-square estimation will allow us to determine the density law of different groups and components in the Galaxy. The output from our software, which can be used in many research fields, will also give out the systematic error between the model and the observation by a Bayes rule.

Parietal substrates for dimensional effects in visual search: evidence from lesion-symptom mapping

PubMed Central

Humphreys, Glyn W.; Chechlacz, Magdalena

2013-01-01

In visual search, the detection of pop-out targets is facilitated when the target-defining dimension remains the same compared with when it changes across trials. We tested the brain regions necessary for these dimensional carry-over effects using a voxel-based morphometry study with brain-lesioned patients. Participants had to search for targets defined by either their colour (red or blue) or orientation (right- or left-tilted), and the target dimension either stayed the same or changed on consecutive trials. Twenty-five patients were categorized according to whether they showed an effect of dimensional change on search or not. The two groups did not differ with regard to their performance on several working memory tasks, and the dimensional carry-over effects were not correlated with working memory performance. With spatial, sustained attention and working memory deficits as well as lesion volume controlled, damage within the right inferior parietal lobule (the angular and supramarginal gyri) extending into the intraparietal sulcus was associated with an absence of dimensional carry-over (P < 0.001, cluster-level corrected for multiple comparisons). The data suggest that these regions of parietal cortex are necessary to implement attention shifting in the context of visual dimensional change. PMID:23404335

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.

Exploring the SDSS Dataset with Linked Scatter Plots: I. EMP, CEMP, and CV Stars

NASA Technical Reports Server (NTRS)

Carbon, Duane F.; Henze, Christopher; Nelson, Bron C.

2017-01-01

We present the results of a search for EMP, CEMP, and cataclysmic variable stars using a new exploration tool based on linked scatter plots (LSPs). Our approach is especially designed to work with very large spectrum data sets such as the SDSS, LAMOST, RAVE, and Gaia data sets and can be applied to stellar, galaxy, and quasar spectra. As a demonstration, we conduct a search for EMP, CEMP, and cataclysmic variable stars in the SDSS DR10 data set. We first created a 3326-dimensional phase space containing nearly 2 billion measures of the strengths of over 1600 spectral features in 569,738 SDSS stars. These measures capture essentially all the stellar atomic and molecular species visible at the resolution of SDSS spectra. We show how LSPs can be used to quickly isolate and examine interesting portions of this phase space. To illustrate, we use LSPs coupled with cuts in selected portions of phase space to extract EMP stars, C-rich EMP stars, and CV stars. We present identifications for 59 previously unrecognized candidate EMP stars and 11 previously unrecognized candidate CEMP stars. We also call attention to 2 candidate He II emission CV stars found by the LSP approach that have not yet been discussed in the literature.

Exploring the SDSS Dataset with Linked Scatter Plots: I. EMP, CEMP, and CV Stars.

PubMed

Carbon, Duane F; Henze, Christopher; Nelson, Bron C

2017-02-01

We present the results of a search for EMP, CEMP, and cataclysmic variable stars using a new exploration tool based on linked scatter plots (LSPs). Our approach is especially designed to work with very large spectrum data sets such as the SDSS, LAMOST, RAVE, and Gaia data sets and can be applied to stellar, galaxy, and quasar spectra. As a demonstration, we conduct a search for EMP, CEMP, and cataclysmic variable stars in the SDSS DR10 data set. We first created a 3326-dimensional phase space containing nearly 2 billion measures of the strengths of over 1600 spectral features in 569,738 SDSS stars. These measures capture essentially all the stellar atomic and molecular species visible at the resolution of SDSS spectra. We show how LSPs can be used to quickly isolate and examine interesting portions of this phase space. To illustrate, we use LSPs coupled with cuts in selected portions of phase space to extract EMP stars, C-rich EMP stars, and CV stars. We present identifications for 59 previously unrecognized candidate EMP stars and 11 previously unrecognized candidate CEMP stars. We also call attention to 2 candidate He II emission CV stars found by the LSP approach that have not yet been discussed in the literature.

Exploring the SDSS Data Set with Linked Scatter Plots. I. EMP, CEMP, and CV Stars

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

Carbon, Duane F.; Henze, Christopher; Nelson, Bron C., E-mail: Duane.F.Carbon@nasa.gov

We present the results of a search for extremely metal-poor (EMP), carbon-enhanced metal-poor (CEMP), and cataclysmic variable (CV) stars using a new exploration tool based on linked scatter plots (LSPs). Our approach is especially designed to work with very large spectrum data sets such as the SDSS, LAMOST, RAVE, and Gaia data sets, and it can be applied to stellar, galaxy, and quasar spectra. As a demonstration, we conduct our search using the SDSS DR10 data set. We first created a 3326-dimensional phase space containing nearly 2 billion measures of the strengths of over 1600 spectral features in 569,738 SDSSmore » stars. These measures capture essentially all the stellar atomic and molecular species visible at the resolution of SDSS spectra. We show how LSPs can be used to quickly isolate and examine interesting portions of this phase space. To illustrate, we use LSPs coupled with cuts in selected portions of phase space to extract EMP stars, CEMP stars, and CV stars. We present identifications for 59 previously unrecognized candidate EMP stars and 11 previously unrecognized candidate CEMP stars. We also call attention to 2 candidate He ii emission CV stars found by the LSP approach that have not yet been discussed in the literature.« less

VizieR Online Data Catalog: Exploring the SDSS data set. I. EMP & CV stars (Carbon+, 2017)

NASA Astrophysics Data System (ADS)

Carbon, D. F.; Henze, C.; Nelson, B. C.

2017-08-01

We present the results of a search for extremely metal-poor (EMP), carbon-enhanced metal-poor (CEMP), and cataclysmic variable (CV) stars using a new exploration tool based on linked scatter plots (LSPs). Our approach is especially designed to work with very large spectrum data sets such as the SDSS, LAMOST, RAVE, and Gaia data sets, and it can be applied to stellar, galaxy, and quasar spectra. As a demonstration, we conduct our search using the SDSS DR10 data set. We first created a 3326-dimensional phase space containing nearly 2 billion measures of the strengths of over 1600 spectral features in 569738 SDSS stars. These measures capture essentially all the stellar atomic and molecular species visible at the resolution of SDSS spectra. We show how LSPs can be used to quickly isolate and examine interesting portions of this phase space. To illustrate, we use LSPs coupled with cuts in selected portions of phase space to extract EMP stars, CEMP stars, and CV stars. We present identifications for 59 previously unrecognized candidate EMP stars and 11 previously unrecognized candidate CEMP stars. We also call attention to 2 candidate He II emission CV stars found by the LSP approach that have not yet been discussed in the literature. (3 data files).

Exploring the SDSS Dataset with Linked Scatter Plots: I. EMP, CEMP, and CV Stars

PubMed Central

Carbon, Duane F.; Henze, Christopher; Nelson, Bron C.

2017-01-01

We present the results of a search for EMP, CEMP, and cataclysmic variable stars using a new exploration tool based on linked scatter plots (LSPs). Our approach is especially designed to work with very large spectrum data sets such as the SDSS, LAMOST, RAVE, and Gaia data sets and can be applied to stellar, galaxy, and quasar spectra. As a demonstration, we conduct a search for EMP, CEMP, and cataclysmic variable stars in the SDSS DR10 data set. We first created a 3326-dimensional phase space containing nearly 2 billion measures of the strengths of over 1600 spectral features in 569,738 SDSS stars. These measures capture essentially all the stellar atomic and molecular species visible at the resolution of SDSS spectra. We show how LSPs can be used to quickly isolate and examine interesting portions of this phase space. To illustrate, we use LSPs coupled with cuts in selected portions of phase space to extract EMP stars, C-rich EMP stars, and CV stars. We present identifications for 59 previously unrecognized candidate EMP stars and 11 previously unrecognized candidate CEMP stars. We also call attention to 2 candidate He II emission CV stars found by the LSP approach that have not yet been discussed in the literature. PMID:28684884

Exploring the SDSS Data Set with Linked Scatter Plots. I. EMP, CEMP, and CV Stars

NASA Astrophysics Data System (ADS)

Carbon, Duane F.; Henze, Christopher; Nelson, Bron C.

2017-02-01

We present the results of a search for extremely metal-poor (EMP), carbon-enhanced metal-poor (CEMP), and cataclysmic variable (CV) stars using a new exploration tool based on linked scatter plots (LSPs). Our approach is especially designed to work with very large spectrum data sets such as the SDSS, LAMOST, RAVE, and Gaia data sets, and it can be applied to stellar, galaxy, and quasar spectra. As a demonstration, we conduct our search using the SDSS DR10 data set. We first created a 3326-dimensional phase space containing nearly 2 billion measures of the strengths of over 1600 spectral features in 569,738 SDSS stars. These measures capture essentially all the stellar atomic and molecular species visible at the resolution of SDSS spectra. We show how LSPs can be used to quickly isolate and examine interesting portions of this phase space. To illustrate, we use LSPs coupled with cuts in selected portions of phase space to extract EMP stars, CEMP stars, and CV stars. We present identifications for 59 previously unrecognized candidate EMP stars and 11 previously unrecognized candidate CEMP stars. We also call attention to 2 candidate He II emission CV stars found by the LSP approach that have not yet been discussed in the literature.

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.

In Search of the Ultimate Building Blocks

NASA Astrophysics Data System (ADS)

't Hooft, Gerard

1996-12-01

An apology; 1. The beginning of the journey to the small: cutting paper; 2. To molecules and atoms; 3. The magic mystery of the quanta; 4. Dazzling velocities; 5. The elementary particle zoo before 1970; 6. Life and death; 7. The crazy kaons; 8. The invisible quarks; 9. Fields or bootstraps?; 10. The Yang-Mills bonanza; 11. Superconducting empty space: the Higgs-Kibble machine; 12. Models; 13. Colouring in the strong forces; 14. The magnetic monopole; 15. Gypsy; 16. The brilliance of the standard model; 17. Anomalies; 18. Deceptive perfection; 19. Weighing neutrinos; 20. The great desert; 21. Technicolor; 22. Grand unification; 23. Supergravity; 24. Eleven dimensional space-time; 25. Attaching the super string; 26. Into the black hole; 27. Theories that do not yet exist … ; 28. Dominance of the rule of the smallest.

Reasoning from non-stationarity

NASA Astrophysics Data System (ADS)

Struzik, Zbigniew R.; van Wijngaarden, Willem J.; Castelo, Robert

2002-11-01

Complex real-world (biological) systems often exhibit intrinsically non-stationary behaviour of their temporal characteristics. We discuss local measures of scaling which can capture and reveal changes in a system's behaviour. Such measures offer increased insight into a system's behaviour and are superior to global, spectral characteristics like the multifractal spectrum. They are, however, often inadequate for fully understanding and modelling the phenomenon. We illustrate an attempt to capture complex model characteristics by analysing (multiple order) correlations in a high dimensional space of parameters of the (biological) system being studied. Both temporal information, among others local scaling information, and external descriptors/parameters, possibly influencing the system's state, are used to span the search space investigated for the presence of a (sub-)optimal model. As an example, we use fetal heartbeat monitored during labour.

An Integrated Framework for Parameter-based Optimization of Scientific Workflows.

PubMed

Kumar, Vijay S; Sadayappan, P; Mehta, Gaurang; Vahi, Karan; Deelman, Ewa; Ratnakar, Varun; Kim, Jihie; Gil, Yolanda; Hall, Mary; Kurc, Tahsin; Saltz, Joel

2009-01-01

Data analysis processes in scientific applications can be expressed as coarse-grain workflows of complex data processing operations with data flow dependencies between them. Performance optimization of these workflows can be viewed as a search for a set of optimal values in a multi-dimensional parameter space. While some performance parameters such as grouping of workflow components and their mapping to machines do not a ect the accuracy of the output, others may dictate trading the output quality of individual components (and of the whole workflow) for performance. This paper describes an integrated framework which is capable of supporting performance optimizations along multiple dimensions of the parameter space. Using two real-world applications in the spatial data analysis domain, we present an experimental evaluation of the proposed framework.

FastBit Reference Manual

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

Wu, Kesheng

2007-08-02

An index in a database system is a data structure that utilizes redundant information about the base data to speed up common searching and retrieval operations. Most commonly used indexes are variants of B-trees, such as B+-tree and B*-tree. FastBit implements a set of alternative indexes call compressed bitmap indexes. Compared with B-tree variants, these indexes provide very efficient searching and retrieval operations by sacrificing the efficiency of updating the indexes after the modification of an individual record. In addition to the well-known strengths of bitmap indexes, FastBit has a special strength stemming from the bitmap compression scheme used. Themore » compression method is called the Word-Aligned Hybrid (WAH) code. It reduces the bitmap indexes to reasonable sizes and at the same time allows very efficient bitwise logical operations directly on the compressed bitmaps. Compared with the well-known compression methods such as LZ77 and Byte-aligned Bitmap code (BBC), WAH sacrifices some space efficiency for a significant improvement in operational efficiency. Since the bitwise logical operations are the most important operations needed to answer queries, using WAH compression has been shown to answer queries significantly faster than using other compression schemes. Theoretical analyses showed that WAH compressed bitmap indexes are optimal for one-dimensional range queries. Only the most efficient indexing schemes such as B+-tree and B*-tree have this optimality property. However, bitmap indexes are superior because they can efficiently answer multi-dimensional range queries by combining the answers to one-dimensional queries.« less

Stabilization of a locally minimal forest

NASA Astrophysics Data System (ADS)

Ivanov, A. O.; Mel'nikova, A. E.; Tuzhilin, A. A.

2014-03-01

The method of partial stabilization of locally minimal networks, which was invented by Ivanov and Tuzhilin to construct examples of shortest trees with given topology, is developed. According to this method, boundary vertices of degree 2 are not added to all edges of the original locally minimal tree, but only to some of them. The problem of partial stabilization of locally minimal trees in a finite-dimensional Euclidean space is solved completely in the paper, that is, without any restrictions imposed on the number of edges remaining free of subdivision. A criterion for the realizability of such stabilization is established. In addition, the general problem of searching for the shortest forest connecting a finite family of boundary compact sets in an arbitrary metric space is formalized; it is shown that such forests exist for any family of compact sets if and only if for any finite subset of the ambient space there exists a shortest tree connecting it. The theory developed here allows us to establish further generalizations of the stabilization theorem both for arbitrary metric spaces and for metric spaces with some special properties. Bibliography: 10 titles.

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

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

PubMed Central

Luo, Liyan; Xu, Luping; Zhang, Hua

2015-01-01

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

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

PubMed

Luo, Liyan; Xu, Luping; Zhang, Hua

2015-07-07

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

Improving the Fitness of High-Dimensional Biomechanical Models via Data-Driven Stochastic Exploration

PubMed Central

Bustamante, Carlos D.; Valero-Cuevas, Francisco J.

2010-01-01

The field of complex biomechanical modeling has begun to rely on Monte Carlo techniques to investigate the effects of parameter variability and measurement uncertainty on model outputs, search for optimal parameter combinations, and define model limitations. However, advanced stochastic methods to perform data-driven explorations, such as Markov chain Monte Carlo (MCMC), become necessary as the number of model parameters increases. Here, we demonstrate the feasibility and, what to our knowledge is, the first use of an MCMC approach to improve the fitness of realistically large biomechanical models. We used a Metropolis–Hastings algorithm to search increasingly complex parameter landscapes (3, 8, 24, and 36 dimensions) to uncover underlying distributions of anatomical parameters of a “truth model” of the human thumb on the basis of simulated kinematic data (thumbnail location, orientation, and linear and angular velocities) polluted by zero-mean, uncorrelated multivariate Gaussian “measurement noise.” Driven by these data, ten Markov chains searched each model parameter space for the subspace that best fit the data (posterior distribution). As expected, the convergence time increased, more local minima were found, and marginal distributions broadened as the parameter space complexity increased. In the 36-D scenario, some chains found local minima but the majority of chains converged to the true posterior distribution (confirmed using a cross-validation dataset), thus demonstrating the feasibility and utility of these methods for realistically large biomechanical problems. PMID:19272906

Tuning Monotonic Basin Hopping: Improving the Efficiency of Stochastic Search as Applied to Low-Thrust Trajectory Optimization

NASA Technical Reports Server (NTRS)

Englander, Jacob A.; Englander, Arnold C.

2014-01-01

Trajectory optimization methods using monotonic basin hopping (MBH) have become well developed during the past decade [1, 2, 3, 4, 5, 6]. An essential component of MBH is a controlled random search through the multi-dimensional space of possible solutions. Historically, the randomness has been generated by drawing random variable (RV)s from a uniform probability distribution. Here, we investigate the generating the randomness by drawing the RVs from Cauchy and Pareto distributions, chosen because of their characteristic long tails. We demonstrate that using Cauchy distributions (as first suggested by J. Englander [3, 6]) significantly improves monotonic basin hopping (MBH) performance, and that Pareto distributions provide even greater improvements. Improved performance is defined in terms of efficiency and robustness. Efficiency is finding better solutions in less time. Robustness is efficiency that is undiminished by (a) the boundary conditions and internal constraints of the optimization problem being solved, and (b) by variations in the parameters of the probability distribution. Robustness is important for achieving performance improvements that are not problem specific. In this work we show that the performance improvements are the result of how these long-tailed distributions enable MBH to search the solution space faster and more thoroughly. In developing this explanation, we use the concepts of sub-diffusive, normally-diffusive, and super-diffusive random walks (RWs) originally developed in the field of statistical physics.

Rapid and Robust Cross-Correlation-Based Seismic Phase Identification Using an Approximate Nearest Neighbor Method

NASA Astrophysics Data System (ADS)

Tibi, R.; Young, C. J.; Gonzales, A.; Ballard, S.; Encarnacao, A. V.

2016-12-01

The matched filtering technique involving the cross-correlation of a waveform of interest with archived signals from a template library has proven to be a powerful tool for detecting events in regions with repeating seismicity. However, waveform correlation is computationally expensive, and therefore impractical for large template sets unless dedicated distributed computing hardware and software are used. In this study, we introduce an Approximate Nearest Neighbor (ANN) approach that enables the use of very large template libraries for waveform correlation without requiring a complex distributed computing system. Our method begins with a projection into a reduced dimensionality space based on correlation with a randomized subset of the full template archive. Searching for a specified number of nearest neighbors is accomplished by using randomized K-dimensional trees. We used the approach to search for matches to each of 2700 analyst-reviewed signal detections reported for May 2010 for the IMS station MKAR. The template library in this case consists of a dataset of more than 200,000 analyst-reviewed signal detections for the same station from 2002-2014 (excluding May 2010). Of these signal detections, 60% are teleseismic first P, and 15% regional phases (Pn, Pg, Sn, and Lg). The analyses performed on a standard desktop computer shows that the proposed approach performs the search of the large template libraries about 20 times faster than the standard full linear search, while achieving recall rates greater than 80%, with the recall rate increasing for higher correlation values. To decide whether to confirm a match, we use a hybrid method involving a cluster approach for queries with two or more matches, and correlation score for single matches. Of the signal detections that passed our confirmation process, 52% were teleseismic first P, and 30% were regional phases.

Forms of null Lagrangians in field theories of continuum mechanics

NASA Astrophysics Data System (ADS)

Kovalev, V. A.; Radaev, Yu. N.

2012-02-01

The divergence representation of a null Lagrangian that is regular in a star-shaped domain is used to obtain its general expression containing field gradients of order ≤ 1 in the case of spacetime of arbitrary dimension. It is shown that for a static three-component field in the three-dimensional space, a null Lagrangian can contain up to 15 independent elements in total. The general form of a null Lagrangian in the four-dimensional Minkowski spacetime is obtained (the number of physical field variables is assumed arbitrary). A complete theory of the null Lagrangian for the n-dimensional spacetime manifold (including the four-dimensional Minkowski spacetime as a special case) is given. Null Lagrangians are then used as a basis for solving an important variational problem of an integrating factor. This problem involves searching for factors that depend on the spacetime variables, field variables, and their gradients and, for a given system of partial differential equations, ensure the equality between the scalar product of a vector multiplier by the system vector and some divergence expression for arbitrary field variables and, hence, allow one to formulate a divergence conservation law on solutions to the system.

A Self-Organizing State-Space-Model Approach for Parameter Estimation in Hodgkin-Huxley-Type Models of Single Neurons

PubMed Central

Vavoulis, Dimitrios V.; Straub, Volko A.; Aston, John A. D.; Feng, Jianfeng

2012-01-01

Traditional approaches to the problem of parameter estimation in biophysical models of neurons and neural networks usually adopt a global search algorithm (for example, an evolutionary algorithm), often in combination with a local search method (such as gradient descent) in order to minimize the value of a cost function, which measures the discrepancy between various features of the available experimental data and model output. In this study, we approach the problem of parameter estimation in conductance-based models of single neurons from a different perspective. By adopting a hidden-dynamical-systems formalism, we expressed parameter estimation as an inference problem in these systems, which can then be tackled using a range of well-established statistical inference methods. The particular method we used was Kitagawa's self-organizing state-space model, which was applied on a number of Hodgkin-Huxley-type models using simulated or actual electrophysiological data. We showed that the algorithm can be used to estimate a large number of parameters, including maximal conductances, reversal potentials, kinetics of ionic currents, measurement and intrinsic noise, based on low-dimensional experimental data and sufficiently informative priors in the form of pre-defined constraints imposed on model parameters. The algorithm remained operational even when very noisy experimental data were used. Importantly, by combining the self-organizing state-space model with an adaptive sampling algorithm akin to the Covariance Matrix Adaptation Evolution Strategy, we achieved a significant reduction in the variance of parameter estimates. The algorithm did not require the explicit formulation of a cost function and it was straightforward to apply on compartmental models and multiple data sets. Overall, the proposed methodology is particularly suitable for resolving high-dimensional inference problems based on noisy electrophysiological data and, therefore, a potentially useful tool in the construction of biophysical neuron models. PMID:22396632

Elementary particles, dark matter candidate and new extended standard model

NASA Astrophysics Data System (ADS)

Hwang, Jaekwang

2017-01-01

Elementary particle decays and reactions are discussed in terms of the three-dimensional quantized space model beyond the standard model. Three generations of the leptons and quarks correspond to the lepton charges. Three heavy leptons and three heavy quarks are introduced. And the bastons (new particles) are proposed as the possible candidate of the dark matters. Dark matter force, weak force and strong force are explained consistently. Possible rest masses of the new particles are, tentatively, proposed for the experimental searches. For more details, see the conference paper at https://www.researchgate.net/publication/308723916.

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.

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

PubMed Central

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

2008-01-01

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

3D statistical shape models incorporating 3D random forest regression voting for robust CT liver segmentation

NASA Astrophysics Data System (ADS)

Norajitra, Tobias; Meinzer, Hans-Peter; Maier-Hein, Klaus H.

2015-03-01

During image segmentation, 3D Statistical Shape Models (SSM) usually conduct a limited search for target landmarks within one-dimensional search profiles perpendicular to the model surface. In addition, landmark appearance is modeled only locally based on linear profiles and weak learners, altogether leading to segmentation errors from landmark ambiguities and limited search coverage. We present a new method for 3D SSM segmentation based on 3D Random Forest Regression Voting. For each surface landmark, a Random Regression Forest is trained that learns a 3D spatial displacement function between the according reference landmark and a set of surrounding sample points, based on an infinite set of non-local randomized 3D Haar-like features. Landmark search is then conducted omni-directionally within 3D search spaces, where voxelwise forest predictions on landmark position contribute to a common voting map which reflects the overall position estimate. Segmentation experiments were conducted on a set of 45 CT volumes of the human liver, of which 40 images were randomly chosen for training and 5 for testing. Without parameter optimization, using a simple candidate selection and a single resolution approach, excellent results were achieved, while faster convergence and better concavity segmentation were observed, altogether underlining the potential of our approach in terms of increased robustness from distinct landmark detection and from better search coverage.

SA-Search: a web tool for protein structure mining based on a Structural Alphabet

PubMed Central

Guyon, Frédéric; Camproux, Anne-Claude; Hochez, Joëlle; Tufféry, Pierre

2004-01-01

SA-Search is a web tool that can be used to mine for protein structures and extract structural similarities. It is based on a hidden Markov model derived Structural Alphabet (SA) that allows the compression of three-dimensional (3D) protein conformations into a one-dimensional (1D) representation using a limited number of prototype conformations. Using such a representation, classical methods developed for amino acid sequences can be employed. Currently, SA-Search permits the performance of fast 3D similarity searches such as the extraction of exact words using a suffix tree approach, and the search for fuzzy words viewed as a simple 1D sequence alignment problem. SA-Search is available at http://bioserv.rpbs.jussieu.fr/cgi-bin/SA-Search. PMID:15215446

SA-Search: a web tool for protein structure mining based on a Structural Alphabet.

PubMed

Guyon, Frédéric; Camproux, Anne-Claude; Hochez, Joëlle; Tufféry, Pierre

2004-07-01

SA-Search is a web tool that can be used to mine for protein structures and extract structural similarities. It is based on a hidden Markov model derived Structural Alphabet (SA) that allows the compression of three-dimensional (3D) protein conformations into a one-dimensional (1D) representation using a limited number of prototype conformations. Using such a representation, classical methods developed for amino acid sequences can be employed. Currently, SA-Search permits the performance of fast 3D similarity searches such as the extraction of exact words using a suffix tree approach, and the search for fuzzy words viewed as a simple 1D sequence alignment problem. SA-Search is available at http://bioserv.rpbs.jussieu.fr/cgi-bin/SA-Search.

A two-dimensional air-to-air combat game - Toward an air-combat advisory system

NASA Technical Reports Server (NTRS)

Neuman, Frank

1987-01-01

Air-to-air combat is modeled as a discrete differential game, and by constraining the game to searching for the best guidance laws from the sets of those considered for each opponent, feedback and outcome charts are obtained which can be used to turn one of the automatic opponents into an intelligent opponent against a human pilot. A one-on-one two-dimensional fully automatic, or manned versus automatic, air-to-air combat game has been designed which includes both attack and evasion alternatives for both aircraft. Guidance law selection occurs by flooding the initial-condition space with four simulated fights for each initial condition, depicting the various attack/evasion strategies for the two opponents, and recording the outcomes. For each initial condition, the minimax method from differential games is employed to determine the best choice from the available strategies.

Visibiome: an efficient microbiome search engine based on a scalable, distributed architecture.

PubMed

Azman, Syafiq Kamarul; Anwar, Muhammad Zohaib; Henschel, Andreas

2017-07-24

Given the current influx of 16S rRNA profiles of microbiota samples, it is conceivable that large amounts of them eventually are available for search, comparison and contextualization with respect to novel samples. This process facilitates the identification of similar compositional features in microbiota elsewhere and therefore can help to understand driving factors for microbial community assembly. We present Visibiome, a microbiome search engine that can perform exhaustive, phylogeny based similarity search and contextualization of user-provided samples against a comprehensive dataset of 16S rRNA profiles environments, while tackling several computational challenges. In order to scale to high demands, we developed a distributed system that combines web framework technology, task queueing and scheduling, cloud computing and a dedicated database server. To further ensure speed and efficiency, we have deployed Nearest Neighbor search algorithms, capable of sublinear searches in high-dimensional metric spaces in combination with an optimized Earth Mover Distance based implementation of weighted UniFrac. The search also incorporates pairwise (adaptive) rarefaction and optionally, 16S rRNA copy number correction. The result of a query microbiome sample is the contextualization against a comprehensive database of microbiome samples from a diverse range of environments, visualized through a rich set of interactive figures and diagrams, including barchart-based compositional comparisons and ranking of the closest matches in the database. Visibiome is a convenient, scalable and efficient framework to search microbiomes against a comprehensive database of environmental samples. The search engine leverages a popular but computationally expensive, phylogeny based distance metric, while providing numerous advantages over the current state of the art tool.

Topology-Scaling Identification of Layered Solids and Stable Exfoliated 2D Materials.

PubMed

Ashton, Michael; Paul, Joshua; Sinnott, Susan B; Hennig, Richard G

2017-03-10

The Materials Project crystal structure database has been searched for materials possessing layered motifs in their crystal structures using a topology-scaling algorithm. The algorithm identifies and measures the sizes of bonded atomic clusters in a structure's unit cell, and determines their scaling with cell size. The search yielded 826 stable layered materials that are considered as candidates for the formation of two-dimensional monolayers via exfoliation. Density-functional theory was used to calculate the exfoliation energy of each material and 680 monolayers emerge with exfoliation energies below those of already-existent two-dimensional materials. The crystal structures of these two-dimensional materials provide templates for future theoretical searches of stable two-dimensional materials. The optimized structures and other calculated data for all 826 monolayers are provided at our database (https://materialsweb.org).

Optimizing energy growth as a tool for finding exact coherent structures

NASA Astrophysics Data System (ADS)

Olvera, D.; Kerswell, R. R.

2017-08-01

We discuss how searching for finite-amplitude disturbances of a given energy that maximize their subsequent energy growth after a certain later time T can be used to probe the phase space around a reference state and ultimately to find other nearby solutions. The procedure relies on the fact that of all the initial disturbances on a constant-energy hypersphere, the optimization procedure will naturally select the one that lies closest to the stable manifold of a nearby solution in phase space if T is large enough. Then, when in its subsequent evolution the optimal disturbance transiently approaches the new solution, a flow state at this point can be used as an initial guess to converge the solution to machine precision. We illustrate this approach in plane Couette flow by rediscovering the spanwise-localized "snake" solutions of Schneider et al. [Phys. Rev. Lett. 104, 104501 (2010), 10.1103/PhysRevLett.104.104501], probing phase space at very low Reynolds numbers (less than 127.7 ) where the constant-shear solution is believed to be the global attractor and examining how the edge between laminar and turbulent flow evolves when stable stratification eliminates the turbulence. We also show that the steady snake solution smoothly delocalizes as unstable stratification is gradually turned on until it connects (via an intermediary global three-dimensional solution) to two-dimensional Rayleigh-Bénard roll solutions.

A predictive machine learning approach for microstructure optimization and materials design

NASA Astrophysics Data System (ADS)

Liu, Ruoqian; Kumar, Abhishek; Chen, Zhengzhang; Agrawal, Ankit; Sundararaghavan, Veera; Choudhary, Alok

2015-06-01

This paper addresses an important materials engineering question: How can one identify the complete space (or as much of it as possible) of microstructures that are theoretically predicted to yield the desired combination of properties demanded by a selected application? We present a problem involving design of magnetoelastic Fe-Ga alloy microstructure for enhanced elastic, plastic and magnetostrictive properties. While theoretical models for computing properties given the microstructure are known for this alloy, inversion of these relationships to obtain microstructures that lead to desired properties is challenging, primarily due to the high dimensionality of microstructure space, multi-objective design requirement and non-uniqueness of solutions. These challenges render traditional search-based optimization methods incompetent in terms of both searching efficiency and result optimality. In this paper, a route to address these challenges using a machine learning methodology is proposed. A systematic framework consisting of random data generation, feature selection and classification algorithms is developed. Experiments with five design problems that involve identification of microstructures that satisfy both linear and nonlinear property constraints show that our framework outperforms traditional optimization methods with the average running time reduced by as much as 80% and with optimality that would not be achieved otherwise.

Anatomy of the inert two-Higgs-doublet model in the light of the LHC and non-LHC dark matter searches

NASA Astrophysics Data System (ADS)

Belyaev, Alexander; Cacciapaglia, Giacomo; Ivanov, Igor P.; Rojas-Abatte, Felipe; Thomas, Marc

2018-02-01

The inert two-Higgs-doublet model (i2HDM) is a theoretically well-motivated example of a minimal consistent dark matter (DM) model which provides monojet, mono-Z , mono-Higgs, and vector-boson-fusion +ETmiss signatures at the LHC, complemented by signals in direct and indirect DM search experiments. In this paper we have performed a detailed analysis of the constraints in the full five-dimensional parameter space of the i2HDM, coming from perturbativity, unitarity, electroweak precision data, Higgs data from the LHC, DM relic density, direct/indirect DM detection, and LHC monojet analysis, as well as implications of experimental LHC studies on disappearing charged tracks relevant to a high DM mass region. We demonstrate the complementarity of the above constraints and present projections for future LHC data and direct DM detection experiments to probe further i2HDM parameter space. The model is implemented into the CalcHEP and micrOMEGAs packages, which are publicly available at the HEPMDB database, and it is ready for a further exploration in the context of the LHC, relic density, and DM direct detection.

Spatial search on a two-dimensional lattice with long-range interactions

NASA Astrophysics Data System (ADS)

Osada, Tomo; Sanaka, Kaoru; Munro, William J.; Nemoto, Kae

2018-06-01

Quantum-walk-based algorithms that search a marked location among N locations on a d -dimensional lattice succeeds in time O (√{N }) for d >2 , while this is not found to be possible when d =2 . In this paper, we consider a spatial search algorithm using continuous-time quantum walk on a two-dimensional square lattice with the existence of additional long-range edges. We examined such a search on a probabilistic graph model where an edge connecting non-nearest-neighbor lattice points i and j apart by a distance |i -j | is added by probability pi j=|i-j | -α(α ≥0 ) . Through numerical analysis, we found that the search succeeds in time O (√{N }) when α ≤αc=2.4 ±0.1 . For α >2 , the expectation value of the additional long-range edges on each node scales as a constant when N →∞ , which means that search time of O (√{N }) is achieved on a graph with average degree scaling as a constant.

A Model-Based Approach for the Measurement of Eye Movements Using Image Processing

NASA Technical Reports Server (NTRS)

Sung, Kwangjae; Reschke, Millard F.

1997-01-01

This paper describes a video eye-tracking algorithm which searches for the best fit of the pupil modeled as a circular disk. The algorithm is robust to common image artifacts such as the droopy eyelids and light reflections while maintaining the measurement resolution available by the centroid algorithm. The presented algorithm is used to derive the pupil size and center coordinates, and can be combined with iris-tracking techniques to measure ocular torsion. A comparison search method of pupil candidates using pixel coordinate reference lookup tables optimizes the processing requirements for a least square fit of the circular disk model. This paper includes quantitative analyses and simulation results for the resolution and the robustness of the algorithm. The algorithm presented in this paper provides a platform for a noninvasive, multidimensional eye measurement system which can be used for clinical and research applications requiring the precise recording of eye movements in three-dimensional space.

Asteroseismic Constraints on the Models of Hot B Subdwarfs: Convective Helium-Burning Cores

NASA Astrophysics Data System (ADS)

Schindler, Jan-Torge; Green, Elizabeth M.; Arnett, W. David

2017-10-01

Asteroseismology of non-radial pulsations in Hot B Subdwarfs (sdB stars) offers a unique view into the interior of core-helium-burning stars. Ground-based and space-borne high precision light curves allow for the analysis of pressure and gravity mode pulsations to probe the structure of sdB stars deep into the convective core. As such asteroseismological analysis provides an excellent opportunity to test our understanding of stellar evolution. In light of the newest constraints from asteroseismology of sdB and red clump stars, standard approaches of convective mixing in 1D stellar evolution models are called into question. The problem lies in the current treatment of overshooting and the entrainment at the convective boundary. Unfortunately no consistent algorithm of convective mixing exists to solve the problem, introducing uncertainties to the estimates of stellar ages. Three dimensional simulations of stellar convection show the natural development of an overshooting region and a boundary layer. In search for a consistent prescription of convection in one dimensional stellar evolution models, guidance from three dimensional simulations and asteroseismological results is indispensable.

Indoor high precision three-dimensional positioning system based on visible light communication using modified genetic algorithm

NASA Astrophysics Data System (ADS)

Chen, Hao; Guan, Weipeng; Li, Simin; Wu, Yuxiang

2018-04-01

To improve the precision of indoor positioning and actualize three-dimensional positioning, a reversed indoor positioning system based on visible light communication (VLC) using genetic algorithm (GA) is proposed. In order to solve the problem of interference between signal sources, CDMA modulation is used. Each light-emitting diode (LED) in the system broadcasts a unique identity (ID) code using CDMA modulation. Receiver receives mixed signal from every LED reference point, by the orthogonality of spreading code in CDMA modulation, ID information and intensity attenuation information from every LED can be obtained. According to positioning principle of received signal strength (RSS), the coordinate of the receiver can be determined. Due to system noise and imperfection of device utilized in the system, distance between receiver and transmitters will deviate from the real value resulting in positioning error. By introducing error correction factors to global parallel search of genetic algorithm, coordinates of the receiver in three-dimensional space can be determined precisely. Both simulation results and experimental results show that in practical application scenarios, the proposed positioning system can realize high precision positioning service.

Application of separable parameter space techniques to multi-tracer PET compartment modeling.

PubMed

Zhang, Jeff L; Michael Morey, A; Kadrmas, Dan J

2016-02-07

Multi-tracer positron emission tomography (PET) can image two or more tracers in a single scan, characterizing multiple aspects of biological functions to provide new insights into many diseases. The technique uses dynamic imaging, resulting in time-activity curves that contain contributions from each tracer present. The process of separating and recovering separate images and/or imaging measures for each tracer requires the application of kinetic constraints, which are most commonly applied by fitting parallel compartment models for all tracers. Such multi-tracer compartment modeling presents challenging nonlinear fits in multiple dimensions. This work extends separable parameter space kinetic modeling techniques, previously developed for fitting single-tracer compartment models, to fitting multi-tracer compartment models. The multi-tracer compartment model solution equations were reformulated to maximally separate the linear and nonlinear aspects of the fitting problem, and separable least-squares techniques were applied to effectively reduce the dimensionality of the nonlinear fit. The benefits of the approach are then explored through a number of illustrative examples, including characterization of separable parameter space multi-tracer objective functions and demonstration of exhaustive search fits which guarantee the true global minimum to within arbitrary search precision. Iterative gradient-descent algorithms using Levenberg-Marquardt were also tested, demonstrating improved fitting speed and robustness as compared to corresponding fits using conventional model formulations. The proposed technique overcomes many of the challenges in fitting simultaneous multi-tracer PET compartment models.

Hyperspace geography: visualizing fitness landscapes beyond 4D.

PubMed

Wiles, Janet; Tonkes, Bradley

2006-01-01

Human perception is finely tuned to extract structure about the 4D world of time and space as well as properties such as color and texture. Developing intuitions about spatial structure beyond 4D requires exploiting other perceptual and cognitive abilities. One of the most natural ways to explore complex spaces is for a user to actively navigate through them, using local explorations and global summaries to develop intuitions about structure, and then testing the developing ideas by further exploration. This article provides a brief overview of a technique for visualizing surfaces defined over moderate-dimensional binary spaces, by recursively unfolding them onto a 2D hypergraph. We briefly summarize the uses of a freely available Web-based visualization tool, Hyperspace Graph Paper (HSGP), for exploring fitness landscapes and search algorithms in evolutionary computation. HSGP provides a way for a user to actively explore a landscape, from simple tasks such as mapping the neighborhood structure of different points, to seeing global properties such as the size and distribution of basins of attraction or how different search algorithms interact with landscape structure. It has been most useful for exploring recursive and repetitive landscapes, and its strength is that it allows intuitions to be developed through active navigation by the user, and exploits the visual system's ability to detect pattern and texture. The technique is most effective when applied to continuous functions over Boolean variables using 4 to 16 dimensions.

Maxillary incisors changes during space closure with conventional and skeletal anchorage methods: a systematic review.

PubMed

Jayaratne, Yasas Shri Nalaka; Uribe, Flavio; Janakiraman, Nandakumar

2017-01-01

The objective of this systematic review was to compare the antero-posterior, vertical and angular changes of maxillary incisors with conventional anchorage control techniques and mini-implant based space closure methods. The electronic databases Pubmed, Scopus, ISI Web of knowledge, Cochrane Library and Open Grey were searched for potentially eligible studies using a set of predetermined keywords. Full texts meeting the inclusion criteria as well as their references were manually searched. The primary outcome data (linear, angular, and vertical maxillary incisor changes) and secondary outcome data (overbite changes, soft tissue changes, biomechanical factors, root resorption and treatment duration) were extracted from the selected articles and entered into spreadsheets based on the type of anchorage used. The methodological quality of each study was assessed. Six studies met the inclusion criteria. The amount of incisor retraction was greater with buccally placed mini-implants than conventional anchorage techniques. The incisor retraction with indirect anchorage from palatal mini-implants was less when compared with buccally placed mini-implants. Incisor intrusion occurred with buccal mini-implants, whereas extrusion was seen with conventional anchorage. Limited data on the biomechanical variables or adverse effects such as root resorption were reported in these studies. More RCT's that take in to account relevant biomechanical variables and employ three-dimensional quantification of tooth movements are required to provide information on incisor changes during space closure.

Application of separable parameter space techniques to multi-tracer PET compartment modeling

NASA Astrophysics Data System (ADS)

Zhang, Jeff L.; Morey, A. Michael; Kadrmas, Dan J.

2016-02-01

Multi-tracer positron emission tomography (PET) can image two or more tracers in a single scan, characterizing multiple aspects of biological functions to provide new insights into many diseases. The technique uses dynamic imaging, resulting in time-activity curves that contain contributions from each tracer present. The process of separating and recovering separate images and/or imaging measures for each tracer requires the application of kinetic constraints, which are most commonly applied by fitting parallel compartment models for all tracers. Such multi-tracer compartment modeling presents challenging nonlinear fits in multiple dimensions. This work extends separable parameter space kinetic modeling techniques, previously developed for fitting single-tracer compartment models, to fitting multi-tracer compartment models. The multi-tracer compartment model solution equations were reformulated to maximally separate the linear and nonlinear aspects of the fitting problem, and separable least-squares techniques were applied to effectively reduce the dimensionality of the nonlinear fit. The benefits of the approach are then explored through a number of illustrative examples, including characterization of separable parameter space multi-tracer objective functions and demonstration of exhaustive search fits which guarantee the true global minimum to within arbitrary search precision. Iterative gradient-descent algorithms using Levenberg-Marquardt were also tested, demonstrating improved fitting speed and robustness as compared to corresponding fits using conventional model formulations. The proposed technique overcomes many of the challenges in fitting simultaneous multi-tracer PET compartment models.

(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.

Bayesian screening for active compounds in high-dimensional chemical spaces combining property descriptors and molecular fingerprints.

PubMed

Vogt, Martin; Bajorath, Jürgen

2008-01-01

Bayesian classifiers are increasingly being used to distinguish active from inactive compounds and search large databases for novel active molecules. We introduce an approach to directly combine the contributions of property descriptors and molecular fingerprints in the search for active compounds that is based on a Bayesian framework. Conventionally, property descriptors and fingerprints are used as alternative features for virtual screening methods. Following the approach introduced here, probability distributions of descriptor values and fingerprint bit settings are calculated for active and database molecules and the divergence between the resulting combined distributions is determined as a measure of biological activity. In test calculations on a large number of compound activity classes, this methodology was found to consistently perform better than similarity searching using fingerprints and multiple reference compounds or Bayesian screening calculations using probability distributions calculated only from property descriptors. These findings demonstrate that there is considerable synergy between different types of property descriptors and fingerprints in recognizing diverse structure-activity relationships, at least in the context of Bayesian modeling.

Feature Integration Theory Revisited: Dissociating Feature Detection and Attentional Guidance in Visual Search

ERIC Educational Resources Information Center

Chan, Louis K. H.; Hayward, William G.

2009-01-01

In feature integration theory (FIT; A. Treisman & S. Sato, 1990), feature detection is driven by independent dimensional modules, and other searches are driven by a master map of locations that integrates dimensional information into salience signals. Although recent theoretical models have largely abandoned this distinction, some observed…

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

A fast image matching algorithm based on key points

NASA Astrophysics Data System (ADS)

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

2014-05-01

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

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

Exhaustive Versus Randomized Searchers for Nonlinear Optimization in 21st Century Computing: Solar Application

NASA Technical Reports Server (NTRS)

Sen, Syamal K.; AliShaykhian, Gholam

2010-01-01

We present a simple multi-dimensional exhaustive search method to obtain, in a reasonable time, the optimal solution of a nonlinear programming problem. It is more relevant in the present day non-mainframe computing scenario where an estimated 95% computing resources remains unutilized and computing speed touches petaflops. While the processor speed is doubling every 18 months, the band width is doubling every 12 months, and the hard disk space is doubling every 9 months. A randomized search algorithm or, equivalently, an evolutionary search method is often used instead of an exhaustive search algorithm. The reason is that a randomized approach is usually polynomial-time, i.e., fast while an exhaustive search method is exponential-time i.e., slow. We discuss the increasing importance of exhaustive search in optimization with the steady increase of computing power for solving many real-world problems of reasonable size. We also discuss the computational error and complexity of the search algorithm focusing on the fact that no measuring device can usually measure a quantity with an accuracy greater than 0.005%. We stress the fact that the quality of solution of the exhaustive search - a deterministic method - is better than that of randomized search. In 21 st century computing environment, exhaustive search cannot be left aside as an untouchable and it is not always exponential. We also describe a possible application of these algorithms in improving the efficiency of solar cells - a real hot topic - in the current energy crisis. These algorithms could be excellent tools in the hands of experimentalists and could save not only large amount of time needed for experiments but also could validate the theory against experimental results fast.

Insight and search in Katona's five-square problem.

PubMed

Ollinger, Michael; Jones, Gary; Knoblich, Günther

2014-01-01

Insights are often productive outcomes of human thinking. We provide a cognitive model that explains insight problem solving by the interplay of problem space search and representational change, whereby the problem space is constrained or relaxed based on the problem representation. By introducing different experimental conditions that either constrained the initial search space or helped solvers to initiate a representational change, we investigated the interplay of problem space search and representational change in Katona's five-square problem. Testing 168 participants, we demonstrated that independent hints relating to the initial search space and to representational change had little effect on solution rates. However, providing both hints caused a significant increase in solution rates. Our results show the interplay between problem space search and representational change in insight problem solving: The initial problem space can be so large that people fail to encounter impasse, but even when representational change is achieved the resulting problem space can still provide a major obstacle to finding the solution.

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.

Search for unconventional superconductors among the YTE 2Si2 compounds (TE  =  Cr, Co, Ni, Rh, Pd, Pt)

NASA Astrophysics Data System (ADS)

Pikul, A. P.; Samsel–Czekała, M.; Chajewski, G.; Romanova, T.; Hackemer, A.; Gorzelniak, R.; Wiśniewski, P.; Kaczorowski, D.

2017-05-01

Motivated by the recent discovery of exotic superconductivity in YFe2Ge2 we undertook reinvestigation of formation and physical properties of yttrium-based 1:2:2 silicides. Here we report on syntheses and crystal structures of the YTE 2Si2 compounds with TE  =  Cr, Co, Ni, Rh, Pd and Pt, and their low-temperature physical properties measurements, supplemented by results of fully relativistic full-potential local-orbital minimum basis band structure calculations. We confirm that most of the members of that family crystallize in a tetragonal ThCr2Si2-type structure (space group I4/mmm) and have three-dimensional Fermi surface, while only one of them (YPt2Si2) forms with a closely-related primitive CaBe2Ge2-type unit cell (space group P4/nmm) and possess quasi-two-dimensional Fermi surface sheets. Physical measurements indicated that BCS-like superconductivity is observed only in YPt2Si2 (T c  =  1.54 K) and YPd2Si2 (T c  =  0.43 K), while no superconducting phase transition was found in other systems at least down to 0.35 K. Thermal analysis showed no polymorphism in both superconducting phases. No clear relation between the superconductivity and the crystal structure (and dimensionality of the Fermi surface) was observed.

Electronic structures and superconductivity in LuTE2Si2 phases (TE = d-electron transition metal)

NASA Astrophysics Data System (ADS)

Samsel-Czekała, M.; Chajewski, G.; Wiśniewski, P.; Romanova, T.; Hackemer, A.; Gorzelniak, R.; Pikul, A. P.; Kaczorowski, D.

2018-05-01

In the course of our search for unconventional superconductors amidst the 1:2:2 phases, we have re-investigated the LuTE2Si2 compounds with TE = Fe, Co, Ni, Ru, Pd and Pt. In this paper, we present the results of our fully relativistic ab initio calculations of the band structures, performed using the full-potential local-orbital code. The theoretical data are supplemented by the results of low-temperature electrical transport and specific heat measurements performed down to 0.35 K. All the materials studied but LuPt2Si2 crystallize with the body-centered tetragonal ThCr2Si2-type structure (space group I4/mmm). Their Fermi surfaces exhibit a three-dimensional multi-band character. In turn, the Pt-bearing compound adopts the primitive tetragonal CaBe2Ge2-type structure (space group P4/nmm), and its Fermi surface consists of predominantly quasi-two-dimensional sheets. Bulk superconductivity was found only in LuPd2Si2 and LuPt2Si2 (independent of the structure type and dimensionality of the Fermi surface). The key superconducting characteristics indicate a fully-gapped BCS type character. Though the electronic structure of LuFe2Si2 closely resembles that of the unconventional superconductor YFe2Ge2, this Lu-based silicide exhibits neither superconductivity nor spin fluctuations at least down to 0.35 K.

Experimental two-dimensional quantum walk on a photonic chip

PubMed Central

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

2018-01-01

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

Experimental two-dimensional quantum walk on a photonic chip.

PubMed

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

2018-05-01

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

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

PubMed

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

2014-09-01

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

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

Scene analysis for effective visual search in rough three-dimensional-modeling scenes

NASA Astrophysics Data System (ADS)

Wang, Qi; Hu, Xiaopeng

2016-11-01

Visual search is a fundamental technology in the computer vision community. It is difficult to find an object in complex scenes when there exist similar distracters in the background. We propose a target search method in rough three-dimensional-modeling scenes based on a vision salience theory and camera imaging model. We give the definition of salience of objects (or features) and explain the way that salience measurements of objects are calculated. Also, we present one type of search path that guides to the target through salience objects. Along the search path, when the previous objects are localized, the search region of each subsequent object decreases, which is calculated through imaging model and an optimization method. The experimental results indicate that the proposed method is capable of resolving the ambiguities resulting from distracters containing similar visual features with the target, leading to an improvement of search speed by over 50%.

Parallel algorithm for solving Kepler’s equation on Graphics Processing Units: Application to analysis of Doppler exoplanet searches

NASA Astrophysics Data System (ADS)

Ford, Eric B.

2009-05-01

We present the results of a highly parallel Kepler equation solver using the Graphics Processing Unit (GPU) on a commercial nVidia GeForce 280GTX and the "Compute Unified Device Architecture" (CUDA) programming environment. We apply this to evaluate a goodness-of-fit statistic (e.g., χ2) for Doppler observations of stars potentially harboring multiple planetary companions (assuming negligible planet-planet interactions). Given the high-dimensionality of the model parameter space (at least five dimensions per planet), a global search is extremely computationally demanding. We expect that the underlying Kepler solver and model evaluator will be combined with a wide variety of more sophisticated algorithms to provide efficient global search, parameter estimation, model comparison, and adaptive experimental design for radial velocity and/or astrometric planet searches. We tested multiple implementations using single precision, double precision, pairs of single precision, and mixed precision arithmetic. We find that the vast majority of computations can be performed using single precision arithmetic, with selective use of compensated summation for increased precision. However, standard single precision is not adequate for calculating the mean anomaly from the time of observation and orbital period when evaluating the goodness-of-fit for real planetary systems and observational data sets. Using all double precision, our GPU code outperforms a similar code using a modern CPU by a factor of over 60. Using mixed precision, our GPU code provides a speed-up factor of over 600, when evaluating nsys > 1024 models planetary systems each containing npl = 4 planets and assuming nobs = 256 observations of each system. We conclude that modern GPUs also offer a powerful tool for repeatedly evaluating Kepler's equation and a goodness-of-fit statistic for orbital models when presented with a large parameter space.

Prospects of detection of the first sources with SKA using matched filters

NASA Astrophysics Data System (ADS)

Ghara, Raghunath; Choudhury, T. Roy; Datta, Kanan K.; Mellema, Garrelt; Choudhuri, Samir; Majumdar, Suman; Giri, Sambit K.

2018-05-01

The matched filtering technique is an efficient method to detect H ii bubbles and absorption regions in radio interferometric observations of the redshifted 21-cm signal from the epoch of reionization and the Cosmic Dawn. Here, we present an implementation of this technique to the upcoming observations such as the SKA1-low for a blind search of absorption regions at the Cosmic Dawn. The pipeline explores four dimensional parameter space on the simulated mock visibilities using a MCMC algorithm. The framework is able to efficiently determine the positions and sizes of the absorption/H ii regions in the field of view.

Consistent searches for SMEFT effects in non-resonant dijet events

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

Alte, Stefan; Konig, Matthias; Shepherd, William

Here, we investigate the bounds which can be placed on generic new-physics contributions to dijet production at the LHC using the framework of the Standard Model Effective Field Theory, deriving the first consistently-treated EFT bounds from non-resonant high-energy data. We recast an analysis searching for quark compositeness, equivalent to treating the SM with one higher-dimensional operator as a complete UV model. In order to reach consistent, model-independent EFT conclusions, it is necessary to truncate the EFT effects consistently at ordermore » $$1/\\Lambda^2$$ and to include the possibility of multiple operators simultaneously contributing to the observables, neither of which has been done in previous searches of this nature. Furthermore, it is important to give consistent error estimates for the theoretical predictions of the signal model, particularly in the region of phase space where the probed energy is approaching the cutoff scale of the EFT. There are two linear combinations of operators which contribute to dijet production in the SMEFT with distinct angular behavior; we identify those linear combinations and determine the ability of LHC searches to constrain them simultaneously. Consistently treating the EFT generically leads to weakened bounds on new-physics parameters. These constraints will be a useful input to future global analyses in the SMEFT framework, and the techniques used here to consistently search for EFT effects are directly applicable to other off-resonance signals.« less

Consistent searches for SMEFT effects in non-resonant dijet events

DOE PAGES

Alte, Stefan; Konig, Matthias; Shepherd, William

2018-01-19

Here, we investigate the bounds which can be placed on generic new-physics contributions to dijet production at the LHC using the framework of the Standard Model Effective Field Theory, deriving the first consistently-treated EFT bounds from non-resonant high-energy data. We recast an analysis searching for quark compositeness, equivalent to treating the SM with one higher-dimensional operator as a complete UV model. In order to reach consistent, model-independent EFT conclusions, it is necessary to truncate the EFT effects consistently at ordermore » $$1/\\Lambda^2$$ and to include the possibility of multiple operators simultaneously contributing to the observables, neither of which has been done in previous searches of this nature. Furthermore, it is important to give consistent error estimates for the theoretical predictions of the signal model, particularly in the region of phase space where the probed energy is approaching the cutoff scale of the EFT. There are two linear combinations of operators which contribute to dijet production in the SMEFT with distinct angular behavior; we identify those linear combinations and determine the ability of LHC searches to constrain them simultaneously. Consistently treating the EFT generically leads to weakened bounds on new-physics parameters. These constraints will be a useful input to future global analyses in the SMEFT framework, and the techniques used here to consistently search for EFT effects are directly applicable to other off-resonance signals.« less

Reliability based design including future tests and multiagent approaches

NASA Astrophysics Data System (ADS)

Villanueva, Diane

The initial stages of reliability-based design optimization involve the formulation of objective functions and constraints, and building a model to estimate the reliability of the design with quantified uncertainties. However, even experienced hands often overlook important objective functions and constraints that affect the design. In addition, uncertainty reduction measures, such as tests and redesign, are often not considered in reliability calculations during the initial stages. This research considers two areas that concern the design of engineering systems: 1) the trade-off of the effect of a test and post-test redesign on reliability and cost and 2) the search for multiple candidate designs as insurance against unforeseen faults in some designs. In this research, a methodology was developed to estimate the effect of a single future test and post-test redesign on reliability and cost. The methodology uses assumed distributions of computational and experimental errors with re-design rules to simulate alternative future test and redesign outcomes to form a probabilistic estimate of the reliability and cost for a given design. Further, it was explored how modeling a future test and redesign provides a company an opportunity to balance development costs versus performance by simultaneously designing the design and the post-test redesign rules during the initial design stage. The second area of this research considers the use of dynamic local surrogates, or surrogate-based agents, to locate multiple candidate designs. Surrogate-based global optimization algorithms often require search in multiple candidate regions of design space, expending most of the computation needed to define multiple alternate designs. Thus, focusing on solely locating the best design may be wasteful. We extended adaptive sampling surrogate techniques to locate multiple optima by building local surrogates in sub-regions of the design space to identify optima. The efficiency of this method was studied, and the method was compared to other surrogate-based optimization methods that aim to locate the global optimum using two two-dimensional test functions, a six-dimensional test function, and a five-dimensional engineering example.

Gradient gravitational search: An efficient metaheuristic algorithm for global optimization.

PubMed

Dash, Tirtharaj; Sahu, Prabhat K

2015-05-30

The adaptation of novel techniques developed in the field of computational chemistry to solve the concerned problems for large and flexible molecules is taking the center stage with regard to efficient algorithm, computational cost and accuracy. In this article, the gradient-based gravitational search (GGS) algorithm, using analytical gradients for a fast minimization to the next local minimum has been reported. Its efficiency as metaheuristic approach has also been compared with Gradient Tabu Search and others like: Gravitational Search, Cuckoo Search, and Back Tracking Search algorithms for global optimization. Moreover, the GGS approach has also been applied to computational chemistry problems for finding the minimal value potential energy of two-dimensional and three-dimensional off-lattice protein models. The simulation results reveal the relative stability and physical accuracy of protein models with efficient computational cost. © 2015 Wiley Periodicals, Inc.

Memory and visual search in naturalistic 2D and 3D environments

PubMed Central

Li, Chia-Ling; Aivar, M. Pilar; Kit, Dmitry M.; Tong, Matthew H.; Hayhoe, Mary M.

2016-01-01

The role of memory in guiding attention allocation in daily behaviors is not well understood. In experiments with two-dimensional (2D) images, there is mixed evidence about the importance of memory. Because the stimulus context in laboratory experiments and daily behaviors differs extensively, we investigated the role of memory in visual search, in both two-dimensional (2D) and three-dimensional (3D) environments. A 3D immersive virtual apartment composed of two rooms was created, and a parallel 2D visual search experiment composed of snapshots from the 3D environment was developed. Eye movements were tracked in both experiments. Repeated searches for geometric objects were performed to assess the role of spatial memory. Subsequently, subjects searched for realistic context objects to test for incidental learning. Our results show that subjects learned the room-target associations in 3D but less so in 2D. Gaze was increasingly restricted to relevant regions of the room with experience in both settings. Search for local contextual objects, however, was not facilitated by early experience. Incidental fixations to context objects do not necessarily benefit search performance. Together, these results demonstrate that memory for global aspects of the environment guides search by restricting allocation of attention to likely regions, whereas task relevance determines what is learned from the active search experience. Behaviors in 2D and 3D environments are comparable, although there is greater use of memory in 3D. PMID:27299769

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.

Evolutionary tabu search strategies for the simultaneous registration of multiple atomic structures in cryo-EM reconstructions.

PubMed

Rusu, Mirabela; Birmanns, Stefan

2010-04-01

A structural characterization of multi-component cellular assemblies is essential to explain the mechanisms governing biological function. Macromolecular architectures may be revealed by integrating information collected from various biophysical sources - for instance, by interpreting low-resolution electron cryomicroscopy reconstructions in relation to the crystal structures of the constituent fragments. A simultaneous registration of multiple components is beneficial when building atomic models as it introduces additional spatial constraints to facilitate the native placement inside the map. The high-dimensional nature of such a search problem prevents the exhaustive exploration of all possible solutions. Here we introduce a novel method based on genetic algorithms, for the efficient exploration of the multi-body registration search space. The classic scheme of a genetic algorithm was enhanced with new genetic operations, tabu search and parallel computing strategies and validated on a benchmark of synthetic and experimental cryo-EM datasets. Even at a low level of detail, for example 35-40 A, the technique successfully registered multiple component biomolecules, measuring accuracies within one order of magnitude of the nominal resolutions of the maps. The algorithm was implemented using the Sculptor molecular modeling framework, which also provides a user-friendly graphical interface and enables an instantaneous, visual exploration of intermediate solutions. (c) 2009 Elsevier Inc. All rights reserved.

A search for resonances decaying into a Higgs boson and a new particle X in the XH → qqbb final state with the ATLAS detector

NASA Astrophysics Data System (ADS)

Aaboud, M.; Aad, G.; Abbott, B.; Abdinov, O.; Abeloos, B.; Abidi, S. H.; Abouzeid, O. S.; Abraham, N. L.; Abramowicz, H.; Abreu, H.; Abreu, R.; Abulaiti, Y.; Acharya, B. S.; Adachi, S.; Adamczyk, L.; Adelman, J.; Adersberger, M.; Adye, T.; Affolder, A. A.; Afik, Y.; Agheorghiesei, C.; Aguilar-Saavedra, J. A.; Ahlen, S. P.; Ahmadov, F.; Aielli, G.; Akatsuka, S.; Akerstedt, H.; Åkesson, T. P. A.; Akilli, E.; Akimov, A. V.; Alberghi, G. L.; Albert, J.; Albicocco, P.; Alconada Verzini, M. J.; Alderweireldt, S. C.; Aleksa, M.; Aleksandrov, I. N.; Alexa, C.; Alexander, G.; Alexopoulos, T.; Alhroob, M.; Ali, B.; Aliev, M.; Alimonti, G.; Alison, J.; Alkire, S. P.; Allbrooke, B. M. M.; Allen, B. W.; Allport, P. P.; Aloisio, A.; Alonso, A.; Alonso, F.; Alpigiani, C.; Alshehri, A. A.; Alstaty, M. I.; Alvarez Gonzalez, B.; Álvarez Piqueras, D.; Alviggi, M. G.; Amadio, B. T.; Amaral Coutinho, Y.; Amelung, C.; Amidei, D.; Amor Dos Santos, S. P.; Amoroso, S.; Anastopoulos, C.; Ancu, L. S.; Andari, N.; Andeen, T.; Anders, C. F.; Anders, J. K.; Anderson, K. J.; Andreazza, A.; Andrei, V.; Angelidakis, S.; Angelozzi, I.; Angerami, A.; Anisenkov, A. V.; Anjos, N.; Annovi, A.; Antel, C.; Antonelli, M.; Antonov, A.; Antrim, D. J.; Anulli, F.; Aoki, M.; Aperio Bella, L.; Arabidze, G.; Arai, Y.; Araque, J. P.; Araujo Ferraz, V.; Arce, A. T. H.; Ardell, R. E.; Arduh, F. A.; Arguin, J.-F.; Argyropoulos, S.; Arik, M.; Armbruster, A. J.; Armitage, L. J.; Arnaez, O.; Arnold, H.; Arratia, M.; Arslan, O.; Artamonov, A.; Artoni, G.; Artz, S.; Asai, S.; Asbah, N.; Ashkenazi, A.; Asquith, L.; Assamagan, K.; Astalos, R.; Atkinson, M.; Atlay, N. B.; Augsten, K.; Avolio, G.; Axen, B.; Ayoub, M. K.; Azuelos, G.; Baas, A. E.; Baca, M. J.; Bachacou, H.; Bachas, K.; Backes, M.; Bagnaia, P.; Bahmani, M.; Bahrasemani, H.; Baines, J. T.; Bajic, M.; Baker, O. K.; Bakker, P. J.; Baldin, E. M.; Balek, P.; Balli, F.; Balunas, W. K.; Banas, E.; Bandyopadhyay, A.; Banerjee, Sw.; Bannoura, A. A. E.; Barak, L.; Barberio, E. L.; Barberis, D.; Barbero, M.; Barillari, T.; Barisits, M.-S.; Barkeloo, J. T.; Barklow, T.; Barlow, N.; Barnes, S. L.; Barnett, B. M.; Barnett, R. M.; Barnovska-Blenessy, Z.; Baroncelli, A.; Barone, G.; Barr, A. J.; Barranco Navarro, L.; Barreiro, F.; Barreiro Guimarães da Costa, J.; Bartoldus, R.; Barton, A. E.; Bartos, P.; Basalaev, A.; Bassalat, A.; Bates, R. L.; Batista, S. J.; Batley, J. R.; Battaglia, M.; Bauce, M.; Bauer, F.; Bauer, K. T.; Bawa, H. S.; Beacham, J. B.; Beattie, M. D.; Beau, T.; Beauchemin, P. H.; Bechtle, P.; Beck, H. P.; Beck, H. C.; Becker, K.; Becker, M.; Becot, C.; Beddall, A. J.; Beddall, A.; Bednyakov, V. A.; Bedognetti, M.; Bee, C. P.; Beermann, T. A.; Begalli, M.; Begel, M.; Behr, J. K.; Bell, A. S.; Bella, G.; Bellagamba, L.; Bellerive, A.; Bellomo, M.; Belotskiy, K.; Beltramello, O.; Belyaev, N. L.; Benary, O.; Benchekroun, D.; Bender, M.; Benekos, N.; Benhammou, Y.; Benhar Noccioli, E.; Benitez, J.; Benjamin, D. P.; Benoit, M.; Bensinger, J. R.; Bentvelsen, S.; Beresford, L.; Beretta, M.; Berge, D.; Bergeaas Kuutmann, E.; Berger, N.; Bergsten, L. J.; Beringer, J.; Berlendis, S.; Bernard, N. R.; Bernardi, G.; Bernius, C.; Bernlochner, F. U.; Berry, T.; Berta, P.; Bertella, C.; Bertoli, G.; Bertram, I. A.; Bertsche, C.; Besjes, G. J.; Bessidskaia Bylund, O.; Bessner, M.; Besson, N.; Bethani, A.; Bethke, S.; Betti, A.; Bevan, A. J.; Beyer, J.; Bianchi, R. M.; Biebel, O.; Biedermann, D.; Bielski, R.; Bierwagen, K.; Biesuz, N. V.; Biglietti, M.; Billoud, T. R. V.; Bilokon, H.; Bindi, M.; Bingul, A.; Bini, C.; Biondi, S.; Bisanz, T.; Bittrich, C.; Bjergaard, D. M.; Black, J. E.; Black, K. M.; Blair, R. E.; Blazek, T.; Bloch, I.; Blocker, C.; Blue, A.; Blumenschein, U.; Blunier, S.; Bobbink, G. J.; Bobrovnikov, V. S.; Bocchetta, S. S.; Bocci, A.; Bock, C.; Boehler, M.; Boerner, D.; Bogavac, D.; Bogdanchikov, A. G.; Bohm, C.; Boisvert, V.; Bokan, P.; Bold, T.; Boldyrev, A. S.; Bolz, A. E.; Bomben, M.; Bona, M.; Boonekamp, M.; Borisov, A.; Borissov, G.; Bortfeldt, J.; Bortoletto, D.; Bortolotto, V.; Boscherini, D.; Bosman, M.; Bossio Sola, J. D.; Boudreau, J.; Bouhova-Thacker, E. V.; Boumediene, D.; Bourdarios, C.; Boutle, S. K.; Boveia, A.; Boyd, J.; Boyko, I. R.; Bozson, A. J.; Bracinik, J.; Brandt, A.; Brandt, G.; Brandt, O.; Braren, F.; Bratzler, U.; Brau, B.; Brau, J. E.; Breaden Madden, W. D.; Brendlinger, K.; Brennan, A. J.; Brenner, L.; Brenner, R.; Bressler, S.; Briglin, D. L.; Bristow, T. M.; Britton, D.; Britzger, D.; Brochu, F. M.; Brock, I.; Brock, R.; Brooijmans, G.; Brooks, T.; Brooks, W. K.; Brost, E.; Broughton, J. H.; Bruckman de Renstrom, P. A.; Bruncko, D.; Bruni, A.; Bruni, G.; Bruni, L. S.; Bruno, S.; Brunt, Bh; Bruschi, M.; Bruscino, N.; Bryant, P.; Bryngemark, L.; Buanes, T.; Buat, Q.; Buchholz, P.; Buckley, A. G.; Budagov, I. A.; Buehrer, F.; Bugge, M. K.; Bulekov, O.; Bullock, D.; Burch, T. J.; Burdin, S.; Burgard, C. D.; Burger, A. 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G.; Govender, N.; Gozani, E.; Grabowska-Bold, I.; Gradin, P. O. J.; Graham, E. C.; Gramling, J.; Gramstad, E.; Grancagnolo, S.; Gratchev, V.; Gravila, P. M.; Gray, C.; Gray, H. M.; Greenwood, Z. D.; Grefe, C.; Gregersen, K.; Gregor, I. M.; Grenier, P.; Grevtsov, K.; Griffiths, J.; Grillo, A. A.; Grimm, K.; Grinstein, S.; Gris, Ph.; Grivaz, J.-F.; Groh, S.; Gross, E.; Grosse-Knetter, J.; Grossi, G. C.; Grout, Z. J.; Grummer, A.; Guan, L.; Guan, W.; Guenther, J.; Guescini, F.; Guest, D.; Gueta, O.; Gui, B.; Guido, E.; Guillemin, T.; Guindon, S.; Gul, U.; Gumpert, C.; Guo, J.; Guo, W.; Guo, Y.; Gupta, R.; Gurbuz, S.; Gustavino, G.; Gutelman, B. J.; Gutierrez, P.; Gutierrez Ortiz, N. G.; Gutschow, C.; Guyot, C.; Guzik, M. P.; Gwenlan, C.; Gwilliam, C. B.; Haas, A.; Haber, C.; Hadavand, H. K.; Haddad, N.; Hadef, A.; Hageböck, S.; Hagihara, M.; Hakobyan, H.; Haleem, M.; Haley, J.; Halladjian, G.; Hallewell, G. D.; Hamacher, K.; Hamal, P.; Hamano, K.; Hamilton, A.; Hamity, G. N.; Hamnett, P. 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H.; Huo, P.; Huseynov, N.; Huston, J.; Huth, J.; Hyneman, R.; Iacobucci, G.; Iakovidis, G.; Ibragimov, I.; Iconomidou-Fayard, L.; Idrissi, Z.; Iengo, P.; Igonkina, O.; Iizawa, T.; Ikegami, Y.; Ikeno, M.; Ilchenko, Y.; Iliadis, D.; Ilic, N.; Iltzsche, F.; Introzzi, G.; Ioannou, P.; Iodice, M.; Iordanidou, K.; Ippolito, V.; Isacson, M. F.; Ishijima, N.; Ishino, M.; Ishitsuka, M.; Issever, C.; Istin, S.; Ito, F.; Iturbe Ponce, J. M.; Iuppa, R.; Iwasaki, H.; Izen, J. M.; Izzo, V.; Jabbar, S.; Jackson, P.; Jacobs, R. M.; Jain, V.; Jakobi, K. B.; Jakobs, K.; Jakobsen, S.; Jakoubek, T.; Jamin, D. O.; Jana, D. K.; Jansky, R.; Janssen, J.; Janus, M.; Janus, P. A.; Jarlskog, G.; Javadov, N.; Javůrek, T.; Javurkova, M.; Jeanneau, F.; Jeanty, L.; Jejelava, J.; Jelinskas, A.; Jenni, P.; Jeske, C.; Jézéquel, S.; Ji, H.; Jia, J.; Jiang, H.; Jiang, Y.; Jiang, Z.; Jiggins, S.; Jimenez Pena, J.; Jin, S.; Jinaru, A.; Jinnouchi, O.; Jivan, H.; Johansson, P.; Johns, K. A.; Johnson, C. A.; Johnson, W. J.; Jon-And, K.; Jones, R. W. L.; Jones, S. D.; Jones, S.; Jones, T. J.; Jongmanns, J.; Jorge, P. M.; Jovicevic, J.; Ju, X.; Juste Rozas, A.; Köhler, M. K.; Kaczmarska, A.; Kado, M.; Kagan, H.; Kagan, M.; Kahn, S. J.; Kaji, T.; Kajomovitz, E.; Kalderon, C. W.; Kaluza, A.; Kama, S.; Kamenshchikov, A.; Kanaya, N.; Kanjir, L.; Kantserov, V. A.; Kanzaki, J.; Kaplan, B.; Kaplan, L. S.; Kar, D.; Karakostas, K.; Karastathis, N.; Kareem, M. J.; Karentzos, E.; Karpov, S. N.; Karpova, Z. M.; Kartvelishvili, V.; Karyukhin, A. N.; Kasahara, K.; Kashif, L.; Kass, R. D.; Kastanas, A.; Kataoka, Y.; Kato, C.; Katre, A.; Katzy, J.; Kawade, K.; Kawagoe, K.; Kawamoto, T.; Kawamura, G.; Kay, E. F.; Kazanin, V. F.; Keeler, R.; Kehoe, R.; Keller, J. S.; Kellermann, E.; Kempster, J. J.; Kendrick, J.; Keoshkerian, H.; Kepka, O.; Kerševan, B. P.; Kersten, S.; Keyes, R. A.; Khader, M.; Khalil-Zada, F.; Khanov, A.; Kharlamov, A. G.; Kharlamova, T.; Khodinov, A.; Khoo, T. J.; Khovanskiy, V.; Khramov, E.; Khubua, J.; Kido, S.; Kilby, C. R.; Kim, H. Y.; Kim, S. H.; Kim, Y. K.; Kimura, N.; Kind, O. M.; King, B. T.; Kirchmeier, D.; Kirk, J.; Kiryunin, A. E.; Kishimoto, T.; Kisielewska, D.; Kitali, V.; Kivernyk, O.; Kladiva, E.; Klapdor-Kleingrothaus, T.; Klein, M. H.; Klein, M.; Klein, U.; Kleinknecht, K.; Klimek, P.; Klimentov, A.; Klingenberg, R.; Klingl, T.; Klioutchnikova, T.; Klitzner, F. F.; Kluge, E.-E.; Kluit, P.; Kluth, S.; Kneringer, E.; Knoops, E. B. F. G.; Knue, A.; Kobayashi, A.; Kobayashi, D.; Kobayashi, T.; Kobel, M.; Kocian, M.; Kodys, P.; Koffas, T.; Koffeman, E.; Köhler, N. M.; Koi, T.; Kolb, M.; Koletsou, I.; Kondo, T.; Kondrashova, N.; Köneke, K.; König, A. C.; Kono, T.; Konoplich, R.; Konstantinidis, N.; Konya, B.; Kopeliansky, R.; Koperny, S.; Korcyl, K.; Kordas, K.; Korn, A.; Korolkov, I.; Korolkova, E. V.; Kortner, O.; Kortner, S.; Kosek, T.; Kostyukhin, V. 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J.; Tepel, F.; Terada, S.; Terashi, K.; Terron, J.; Terzo, S.; Testa, M.; Teuscher, R. J.; Thais, S. J.; Theveneaux-Pelzer, T.; Thiele, F.; Thomas, J. P.; Thomas-Wilsker, J.; Thompson, P. D.; Thompson, A. S.; Thomsen, L. A.; Thomson, E.; Tian, Y.; Tibbetts, M. J.; Ticse Torres, R. E.; Tikhomirov, V. O.; Tikhonov, Yu. A.; Timoshenko, S.; Tipton, P.; Tisserant, S.; Todome, K.; Todorova-Nova, S.; Todt, S.; Tojo, J.; Tokár, S.; Tokushuku, K.; Tolley, E.; Tomlinson, L.; Tomoto, M.; Tompkins, L.; Toms, K.; Tong, B.; Tornambe, P.; Torrence, E.; Torres, H.; Torró Pastor, E.; Toth, J.; Touchard, F.; Tovey, D. R.; Treado, C. J.; Trefzger, T.; Tresoldi, F.; Tricoli, A.; Trigger, I. M.; Trincaz-Duvoid, S.; Tripiana, M. F.; Trischuk, W.; Trocmé, B.; Trofymov, A.; Troncon, C.; Trovatelli, M.; Truong, L.; Trzebinski, M.; Trzupek, A.; Tsang, K. W.; Tseng, J. C.-L.; Tsiareshka, P. V.; Tsirintanis, N.; Tsiskaridze, S.; Tsiskaridze, V.; Tskhadadze, E. G.; Tsukerman, I. I.; Tsulaia, V.; Tsuno, S.; Tsybychev, D.; Tu, Y.; Tudorache, A.; Tudorache, V.; Tulbure, T. T.; Tuna, A. N.; Turchikhin, S.; Turgeman, D.; Turk Cakir, I.; Turra, R.; Tuts, P. M.; Ucchielli, G.; Ueda, I.; Ughetto, M.; Ukegawa, F.; Unal, G.; Undrus, A.; Unel, G.; Ungaro, F. C.; Unno, Y.; Uno, K.; Urban, J.; Urquijo, P.; Urrejola, P.; Usai, G.; Usui, J.; Vacavant, L.; Vacek, V.; Vachon, B.; Vadla, K. O. H.; Vaidya, A.; Valderanis, C.; Valdes Santurio, E.; Valente, M.; Valentinetti, S.; Valero, A.; Valéry, L.; Vallier, A.; Valls Ferrer, J. A.; van den Wollenberg, W.; van der Graaf, H.; van Gemmeren, P.; van Nieuwkoop, J.; van Vulpen, I.; van Woerden, M. C.; Vanadia, M.; Vandelli, W.; Vaniachine, A.; Vankov, P.; Vardanyan, G.; Vari, R.; Varnes, E. W.; Varni, C.; Varol, T.; Varouchas, D.; Vartapetian, A.; Varvell, K. E.; Vasquez, J. G.; Vasquez, G. A.; Vazeille, F.; Vazquez Furelos, D.; Vazquez Schroeder, T.; Veatch, J.; Veeraraghavan, V.; Veloce, L. M.; Veloso, F.; Veneziano, S.; Ventura, A.; Venturi, M.; Venturi, N.; Vercesi, V.; Verducci, M.; Verkerke, W.; Vermeulen, A. T.; Vermeulen, J. C.; Vetterli, M. C.; Viaux Maira, N.; Viazlo, O.; Vichou, I.; Vickey, T.; Vickey Boeriu, O. E.; Viehhauser, G. H. A.; Viel, S.; Vigani, L.; Villa, M.; Villaplana Perez, M.; Vilucchi, E.; Vincter, M. G.; Vinogradov, V. B.; Vishwakarma, A.; Vittori, C.; Vivarelli, I.; Vlachos, S.; Vogel, M.; Vokac, P.; Volpi, G.; von der Schmitt, H.; von Toerne, E.; Vorobel, V.; Vorobev, K.; Vos, M.; Voss, R.; Vossebeld, J. H.; Vranjes, N.; Vranjes Milosavljevic, M.; Vrba, V.; Vreeswijk, M.; Vuillermet, R.; Vukotic, I.; Wagner, P.; Wagner, W.; Wagner-Kuhr, J.; Wahlberg, H.; Wahrmund, S.; Wakamiya, K.; Walder, J.; Walker, R.; Walkowiak, W.; Wallangen, V.; Wang, C.; Wang, C.; Wang, F.; Wang, H.; Wang, H.; Wang, J.; Wang, J.; Wang, Q.; Wang, R.-J.; Wang, R.; Wang, S. M.; Wang, T.; Wang, W.; Wang, W.; Wang, Z.; Wanotayaroj, C.; Warburton, A.; Ward, C. P.; Wardrope, D. R.; Washbrook, A.; Watkins, P. M.; Watson, A. T.; Watson, M. F.; Watts, G.; Watts, S.; Waugh, B. M.; Webb, A. F.; Webb, S.; Weber, M. S.; Weber, S. M.; Weber, S. W.; Weber, S. A.; Webster, J. S.; Weidberg, A. R.; Weinert, B.; Weingarten, J.; Weirich, M.; Weiser, C.; Wells, P. S.; Wenaus, T.; Wengler, T.; Wenig, S.; Wermes, N.; Werner, M. D.; Werner, P.; Wessels, M.; Weston, T. D.; Whalen, K.; Whallon, N. L.; Wharton, A. M.; White, A. S.; White, A.; White, M. J.; White, R.; Whiteson, D.; Whitmore, B. W.; Wickens, F. J.; Wiedenmann, W.; Wielers, M.; Wiglesworth, C.; Wiik-Fuchs, L. A. M.; Wildauer, A.; Wilk, F.; Wilkens, H. G.; Williams, H. H.; Williams, S.; Willis, C.; Willocq, S.; Wilson, J. A.; Wingerter-Seez, I.; Winkels, E.; Winklmeier, F.; Winston, O. J.; Winter, B. T.; Wittgen, M.; Wobisch, M.; Wolf, A.; Wolf, T. M. H.; Wolff, R.; Wolter, M. W.; Wolters, H.; Wong, V. W. S.; Woods, N. L.; Worm, S. D.; Wosiek, B. K.; Wotschack, J.; Wozniak, K. W.; Wu, M.; Wu, S. L.; Wu, X.; Wu, Y.; Wyatt, T. R.; Wynne, B. M.; Xella, S.; Xi, Z.; Xia, L.; Xu, D.; Xu, L.; Xu, T.; Xu, W.; Yabsley, B.; Yacoob, S.; Yamaguchi, D.; Yamaguchi, Y.; Yamamoto, A.; Yamamoto, S.; Yamanaka, T.; Yamane, F.; Yamatani, M.; Yamazaki, T.; Yamazaki, Y.; Yan, Z.; Yang, H.; Yang, H.; Yang, Y.; Yang, Z.; Yao, W.-M.; Yap, Y. C.; Yasu, Y.; Yatsenko, E.; Yau Wong, K. H.; Ye, J.; Ye, S.; Yeletskikh, I.; Yigitbasi, E.; Yildirim, E.; Yorita, K.; Yoshihara, K.; Young, C.; Young, C. J. S.; Yu, J.; Yu, J.; Yuen, S. P. Y.; Yusuff, I.; Zabinski, B.; Zacharis, G.; Zaidan, R.; Zaitsev, A. M.; Zakharchuk, N.; Zalieckas, J.; Zaman, A.; Zambito, S.; Zanzi, D.; Zeitnitz, C.; Zemaityte, G.; Zemla, A.; Zeng, J. C.; Zeng, Q.; Zenin, O.; Ženiš, T.; Zerwas, D.; Zhang, D.; Zhang, D.; Zhang, F.; Zhang, G.; Zhang, H.; Zhang, J.; Zhang, L.; Zhang, L.; Zhang, M.; Zhang, P.; Zhang, R.; Zhang, R.; Zhang, X.; Zhang, Y.; Zhang, Z.; Zhao, X.; Zhao, Y.; Zhao, Z.; Zhemchugov, A.; Zhou, B.; Zhou, C.; Zhou, L.; Zhou, M.; Zhou, M.; Zhou, N.; Zhou, Y.; Zhu, C. G.; Zhu, H.; Zhu, J.; Zhu, Y.; Zhuang, X.; Zhukov, K.; Zibell, A.; Zieminska, D.; Zimine, N. I.; Zimmermann, C.; Zimmermann, S.; Zinonos, Z.; Zinser, M.; Ziolkowski, M.; Živković, L.; Zobernig, G.; Zoccoli, A.; Zou, R.; Zur Nedden, M.; Zwalinski, L.; Atlas Collaboration

2018-04-01

A search for heavy resonances decaying into a Higgs boson (H) and a new particle (X) is reported, utilizing 36.1 fb-1 of proton-proton collision data at √{ s } = 13TeV collected during 2015 and 2016 with the ATLAS detector at the CERN Large Hadron Collider. The particle X is assumed to decay to a pair of light quarks, and the fully hadronic final state XH → qqbar‧ b b bar is analysed. The search considers the regime of high XH resonance masses, where the X and H bosons are both highly Lorentz-boosted and are each reconstructed using a single jet with large radius parameter. A two-dimensional phase space of XH mass versus X mass is scanned for evidence of a signal, over a range of XH resonance mass values between 1 TeV and 4 TeV, and for X particles with masses from 50 GeV to 1000 GeV. All search results are consistent with the expectations for the background due to Standard Model processes, and 95% CL upper limits are set, as a function of XH and X masses, on the production cross-section of the XH → qqbar‧ b b bar resonance.

Protein 3D Structure and Electron Microscopy Map Retrieval Using 3D-SURFER2.0 and EM-SURFER.

PubMed

Han, Xusi; Wei, Qing; Kihara, Daisuke

2017-12-08

With the rapid growth in the number of solved protein structures stored in the Protein Data Bank (PDB) and the Electron Microscopy Data Bank (EMDB), it is essential to develop tools to perform real-time structure similarity searches against the entire structure database. Since conventional structure alignment methods need to sample different orientations of proteins in the three-dimensional space, they are time consuming and unsuitable for rapid, real-time database searches. To this end, we have developed 3D-SURFER and EM-SURFER, which utilize 3D Zernike descriptors (3DZD) to conduct high-throughput protein structure comparison, visualization, and analysis. Taking an atomic structure or an electron microscopy map of a protein or a protein complex as input, the 3DZD of a query protein is computed and compared with the 3DZD of all other proteins in PDB or EMDB. In addition, local geometrical characteristics of a query protein can be analyzed using VisGrid and LIGSITE CSC in 3D-SURFER. This article describes how to use 3D-SURFER and EM-SURFER to carry out protein surface shape similarity searches, local geometric feature analysis, and interpretation of the search results. © 2017 by John Wiley & Sons, Inc. Copyright © 2017 John Wiley & Sons, Inc.

A lock-free priority queue design based on multi-dimensional linked lists

DOE PAGES

Dechev, Damian; Zhang, Deli

2015-04-03

The throughput of concurrent priority queues is pivotal to multiprocessor applications such as discrete event simulation, best-first search and task scheduling. Existing lock-free priority queues are mostly based on skiplists, which probabilistically create shortcuts in an ordered list for fast insertion of elements. The use of skiplists eliminates the need of global rebalancing in balanced search trees and ensures logarithmic sequential search time on average, but the worst-case performance is linear with respect to the input size. In this paper, we propose a quiescently consistent lock-free priority queue based on a multi-dimensional list that guarantees worst-case search time of O(logN)more » for key universe of size N. The novel multi-dimensional list (MDList) is composed of nodes that contain multiple links to child nodes arranged by their dimensionality. The insertion operation works by first injectively mapping the scalar key to a high-dimensional vector, then uniquely locating the target position by using the vector as coordinates. Nodes in MDList are ordered by their coordinate prefixes and the ordering property of the data structure is readily maintained during insertion without rebalancing nor randomization. Furthermore, in our experimental evaluation using a micro-benchmark, our priority queue achieves an average of 50% speedup over the state of the art approaches under high concurrency.« less

A lock-free priority queue design based on multi-dimensional linked lists

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

Dechev, Damian; Zhang, Deli

The throughput of concurrent priority queues is pivotal to multiprocessor applications such as discrete event simulation, best-first search and task scheduling. Existing lock-free priority queues are mostly based on skiplists, which probabilistically create shortcuts in an ordered list for fast insertion of elements. The use of skiplists eliminates the need of global rebalancing in balanced search trees and ensures logarithmic sequential search time on average, but the worst-case performance is linear with respect to the input size. In this paper, we propose a quiescently consistent lock-free priority queue based on a multi-dimensional list that guarantees worst-case search time of O(logN)more » for key universe of size N. The novel multi-dimensional list (MDList) is composed of nodes that contain multiple links to child nodes arranged by their dimensionality. The insertion operation works by first injectively mapping the scalar key to a high-dimensional vector, then uniquely locating the target position by using the vector as coordinates. Nodes in MDList are ordered by their coordinate prefixes and the ordering property of the data structure is readily maintained during insertion without rebalancing nor randomization. Furthermore, in our experimental evaluation using a micro-benchmark, our priority queue achieves an average of 50% speedup over the state of the art approaches under high concurrency.« less

Teaching Particle Dynamics by Fulldome Animations

NASA Astrophysics Data System (ADS)

Bartolone, L. M.; Reiff, P.; Sumners, C.

2008-12-01

Plasma particle dynamics can be difficult to understand, even for a professional. Creating animations that can be understood and appreciated by the general public are even more challenging. Missions that have "pretty pictures" are much easier to show to the public, whereas creating animations that show how charged particles behave in certain situations is difficult at best. Intrinsically three-dimensional processes such as particle drifts, rotating tilted dipole magnetic fields, charge exchange, and magnetic reconnection can be put into a domed space to put the viewer "into the scene". This allows the viewer to experience, not just see, the process. Examples of animations from our fulldome planetarium shows "IBEX: Search for the Educage of the Solar System", "Force 5" and "Saturn the Ring World 2" will be shown. If space permits, we will bring a portable dome and show these animations in a fulldome theater format.

Photogrammetry and ballistic analysis of a high-flying projectile in the STS-124 space shuttle launch

NASA Astrophysics Data System (ADS)

Metzger, Philip T.; Lane, John E.; Carilli, Robert A.; Long, Jason M.; Shawn, Kathy L.

2010-07-01

A method combining photogrammetry with ballistic analysis is demonstrated to identify flying debris in a rocket launch environment. Debris traveling near the STS-124 Space Shuttle was captured on cameras viewing the launch pad within the first few seconds after launch. One particular piece of debris caught the attention of investigators studying the release of flame trench fire bricks because its high trajectory could indicate a flight risk to the Space Shuttle. Digitized images from two pad perimeter high-speed 16-mm film cameras were processed using photogrammetry software based on a multi-parameter optimization technique. Reference points in the image were found from 3D CAD models of the launch pad and from surveyed points on the pad. The three-dimensional reference points were matched to the equivalent two-dimensional camera projections by optimizing the camera model parameters using a gradient search optimization technique. Using this method of solving the triangulation problem, the xyz position of the object's path relative to the reference point coordinate system was found for every set of synchronized images. This trajectory was then compared to a predicted trajectory while performing regression analysis on the ballistic coefficient and other parameters. This identified, with a high degree of confidence, the object's material density and thus its probable origin within the launch pad environment. Future extensions of this methodology may make it possible to diagnose the underlying causes of debris-releasing events in near-real time, thus improving flight safety.

Environmental Visualization and Horizontal Fusion

DTIC Science & Technology

2005-10-01

the section on EVIS Rules. Federated Search – Discovering Content Another method of discovering services and their content has been implemented...in HF through a next-generation knowledge discovery framework called Federated Search . A virtual information space, called Collateral Space was...environmental mission effects products, is presented later in the paper. Federated Search allows users to search through Collateral Space data that is

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.

Application of separable parameter space techniques to multi-tracer PET compartment modeling

PubMed Central

Zhang, Jeff L; Morey, A Michael; Kadrmas, Dan J

2016-01-01

Multi-tracer positron emission tomography (PET) can image two or more tracers in a single scan, characterizing multiple aspects of biological functions to provide new insights into many diseases. The technique uses dynamic imaging, resulting in time-activity curves that contain contributions from each tracer present. The process of separating and recovering separate images and/or imaging measures for each tracer requires the application of kinetic constraints, which are most commonly applied by fitting parallel compartment models for all tracers. Such multi-tracer compartment modeling presents challenging nonlinear fits in multiple dimensions. This work extends separable parameter space kinetic modeling techniques, previously developed for fitting single-tracer compartment models, to fitting multi-tracer compartment models. The multi-tracer compartment model solution equations were reformulated to maximally separate the linear and nonlinear aspects of the fitting problem, and separable least-squares techniques were applied to effectively reduce the dimensionality of the nonlinear fit. The benefits of the approach are then explored through a number of illustrative examples, including characterization of separable parameter space multi-tracer objective functions and demonstration of exhaustive search fits which guarantee the true global minimum to within arbitrary search precision. Iterative gradient-descent algorithms using Levenberg–Marquardt were also tested, demonstrating improved fitting speed and robustness as compared to corresponding fits using conventional model formulations. The proposed technique overcomes many of the challenges in fitting simultaneous multi-tracer PET compartment models. PMID:26788888

Generalized Likelihood Uncertainty Estimation (GLUE) Using Multi-Optimization Algorithm as Sampling Method

NASA Astrophysics Data System (ADS)

Wang, Z.

2015-12-01

For decades, distributed and lumped hydrological models have furthered our understanding of hydrological system. The development of hydrological simulation in large scale and high precision elaborated the spatial descriptions and hydrological behaviors. Meanwhile, the new trend is also followed by the increment of model complexity and number of parameters, which brings new challenges of uncertainty quantification. Generalized Likelihood Uncertainty Estimation (GLUE) has been widely used in uncertainty analysis for hydrological models referring to Monte Carlo method coupled with Bayesian estimation. However, the stochastic sampling method of prior parameters adopted by GLUE appears inefficient, especially in high dimensional parameter space. The heuristic optimization algorithms utilizing iterative evolution show better convergence speed and optimality-searching performance. In light of the features of heuristic optimization algorithms, this study adopted genetic algorithm, differential evolution, shuffled complex evolving algorithm to search the parameter space and obtain the parameter sets of large likelihoods. Based on the multi-algorithm sampling, hydrological model uncertainty analysis is conducted by the typical GLUE framework. To demonstrate the superiority of the new method, two hydrological models of different complexity are examined. The results shows the adaptive method tends to be efficient in sampling and effective in uncertainty analysis, providing an alternative path for uncertainty quantilization.

A predictive machine learning approach for microstructure optimization and materials design

DOE PAGES

Liu, Ruoqian; Kumar, Abhishek; Chen, Zhengzhang; ...

2015-06-23

This paper addresses an important materials engineering question: How can one identify the complete space (or as much of it as possible) of microstructures that are theoretically predicted to yield the desired combination of properties demanded by a selected application? We present a problem involving design of magnetoelastic Fe-Ga alloy microstructure for enhanced elastic, plastic and magnetostrictive properties. While theoretical models for computing properties given the microstructure are known for this alloy, inversion of these relationships to obtain microstructures that lead to desired properties is challenging, primarily due to the high dimensionality of microstructure space, multi-objective design requirement and non-uniquenessmore » of solutions. These challenges render traditional search-based optimization methods incompetent in terms of both searching efficiency and result optimality. In this paper, a route to address these challenges using a machine learning methodology is proposed. A systematic framework consisting of random data generation, feature selection and classification algorithms is developed. In conclusion, experiments with five design problems that involve identification of microstructures that satisfy both linear and nonlinear property constraints show that our framework outperforms traditional optimization methods with the average running time reduced by as much as 80% and with optimality that would not be achieved otherwise.« less

Eye movements, visual search and scene memory, in an immersive virtual environment.

PubMed

Kit, Dmitry; Katz, Leor; Sullivan, Brian; Snyder, Kat; Ballard, Dana; Hayhoe, Mary

2014-01-01

Visual memory has been demonstrated to play a role in both visual search and attentional prioritization in natural scenes. However, it has been studied predominantly in experimental paradigms using multiple two-dimensional images. Natural experience, however, entails prolonged immersion in a limited number of three-dimensional environments. The goal of the present experiment was to recreate circumstances comparable to natural visual experience in order to evaluate the role of scene memory in guiding eye movements in a natural environment. Subjects performed a continuous visual-search task within an immersive virtual-reality environment over three days. We found that, similar to two-dimensional contexts, viewers rapidly learn the location of objects in the environment over time, and use spatial memory to guide search. Incidental fixations did not provide obvious benefit to subsequent search, suggesting that semantic contextual cues may often be just as efficient, or that many incidentally fixated items are not held in memory in the absence of a specific task. On the third day of the experience in the environment, previous search items changed in color. These items were fixated upon with increased probability relative to control objects, suggesting that memory-guided prioritization (or Surprise) may be a robust mechanisms for attracting gaze to novel features of natural environments, in addition to task factors and simple spatial saliency.

Quality metrics in high-dimensional data visualization: an overview and systematization.

PubMed

Bertini, Enrico; Tatu, Andrada; Keim, Daniel

2011-12-01

In this paper, we present a systematization of techniques that use quality metrics to help in the visual exploration of meaningful patterns in high-dimensional data. In a number of recent papers, different quality metrics are proposed to automate the demanding search through large spaces of alternative visualizations (e.g., alternative projections or ordering), allowing the user to concentrate on the most promising visualizations suggested by the quality metrics. Over the last decade, this approach has witnessed a remarkable development but few reflections exist on how these methods are related to each other and how the approach can be developed further. For this purpose, we provide an overview of approaches that use quality metrics in high-dimensional data visualization and propose a systematization based on a thorough literature review. We carefully analyze the papers and derive a set of factors for discriminating the quality metrics, visualization techniques, and the process itself. The process is described through a reworked version of the well-known information visualization pipeline. We demonstrate the usefulness of our model by applying it to several existing approaches that use quality metrics, and we provide reflections on implications of our model for future research. © 2010 IEEE

Two-dimensional topological photonic systems

NASA Astrophysics Data System (ADS)

Sun, Xiao-Chen; He, Cheng; Liu, Xiao-Ping; Lu, Ming-Hui; Zhu, Shi-Ning; Chen, Yan-Feng

2017-09-01

The topological phase of matter, originally proposed and first demonstrated in fermionic electronic systems, has drawn considerable research attention in the past decades due to its robust transport of edge states and its potential with respect to future quantum information, communication, and computation. Recently, searching for such a unique material phase in bosonic systems has become a hot research topic worldwide. So far, many bosonic topological models and methods for realizing them have been discovered in photonic systems, acoustic systems, mechanical systems, etc. These discoveries have certainly yielded vast opportunities in designing material phases and related properties in the topological domain. In this review, we first focus on some of the representative photonic topological models and employ the underlying Dirac model to analyze the edge states and geometric phase. On the basis of these models, three common types of two-dimensional topological photonic systems are discussed: 1) photonic quantum Hall effect with broken time-reversal symmetry; 2) photonic topological insulator and the associated pseudo-time-reversal symmetry-protected mechanism; 3) time/space periodically modulated photonic Floquet topological insulator. Finally, we provide a summary and extension of this emerging field, including a brief introduction to the Weyl point in three-dimensional systems.

Predicted Realization of Cubic Dirac Fermion in Quasi-One-Dimensional Transition-Metal Monochalcogenides

NASA Astrophysics Data System (ADS)

Liu, Qihang; Zunger, Alex

2017-04-01

We show that the previously predicted "cubic Dirac fermion," composed of six conventional Weyl fermions including three with left-handed and three with right-handed chirality, is realized in a specific, stable solid state system that has been made years ago, but was not appreciated as a "cubically dispersed Dirac semimetal" (CDSM). We identify the crystal symmetry constraints and find the space group P 63/m as one of the two that can support a CDSM, of which the characteristic band crossing has linear dispersion along the principle axis but cubic dispersion in the plane perpendicular to it. We then conduct a material search using density functional theory, identifying a group of quasi-one-dimensional molybdenum monochalcogenide compounds AI(MoXVI)3 (AI=Na , K, Rb, In, Tl; XVI=S , Se, Te) as ideal CDSM candidates. Studying the stability of the A (MoX) 3 family reveals a few candidates such as Rb (MoTe) 3 and Tl (MoTe) 3 that are predicted to be resilient to Peierls distortion, thus retaining the metallic character. Furthermore, the combination of one dimensionality and metallic nature in this family provides a platform for unusual optical signature—polarization-dependent metallic vs insulating response.

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.

Enhanced Conformational Sampling in Molecular Dynamics Simulations of Solvated Peptides: Fragment-Based Local Elevation Umbrella Sampling.

PubMed

Hansen, Halvor S; Daura, Xavier; Hünenberger, Philippe H

2010-09-14

A new method, fragment-based local elevation umbrella sampling (FB-LEUS), is proposed to enhance the conformational sampling in explicit-solvent molecular dynamics (MD) simulations of solvated polymers. The method is derived from the local elevation umbrella sampling (LEUS) method [ Hansen and Hünenberger , J. Comput. Chem. 2010 , 31 , 1 - 23 ], which combines the local elevation (LE) conformational searching and the umbrella sampling (US) conformational sampling approaches into a single scheme. In LEUS, an initial (relatively short) LE build-up (searching) phase is used to construct an optimized (grid-based) biasing potential within a subspace of conformationally relevant degrees of freedom, which is then frozen and used in a (comparatively longer) US sampling phase. This combination dramatically enhances the sampling power of MD simulations but, due to computational and memory costs, is only applicable to relevant subspaces of low dimensionalities. As an attempt to expand the scope of the LEUS approach to solvated polymers with more than a few relevant degrees of freedom, the FB-LEUS scheme involves an US sampling phase that relies on a superposition of low-dimensionality biasing potentials optimized using LEUS at the fragment level. The feasibility of this approach is tested using polyalanine (poly-Ala) and polyvaline (poly-Val) oligopeptides. Two-dimensional biasing potentials are preoptimized at the monopeptide level, and subsequently applied to all dihedral-angle pairs within oligopeptides of 4,  6,  8, or 10 residues. Two types of fragment-based biasing potentials are distinguished: (i) the basin-filling (BF) potentials act so as to "fill" free-energy basins up to a prescribed free-energy level above the global minimum; (ii) the valley-digging (VD) potentials act so as to "dig" valleys between the (four) free-energy minima of the two-dimensional maps, preserving barriers (relative to linearly interpolated free-energy changes) of a prescribed magnitude. The application of these biasing potentials may lead to an impressive enhancement of the searching power (volume of conformational space visited in a given amount of simulation time). However, this increase is largely offset by a deterioration of the statistical efficiency (representativeness of the biased ensemble in terms of the conformational distribution appropriate for the physical ensemble). As a result, it appears difficult to engineer FB-LEUS schemes representing a significant improvement over plain MD, at least for the systems considered here.

The Paragon Algorithm, a next generation search engine that uses sequence temperature values and feature probabilities to identify peptides from tandem mass spectra.

PubMed

Shilov, Ignat V; Seymour, Sean L; Patel, Alpesh A; Loboda, Alex; Tang, Wilfred H; Keating, Sean P; Hunter, Christie L; Nuwaysir, Lydia M; Schaeffer, Daniel A

2007-09-01

The Paragon Algorithm, a novel database search engine for the identification of peptides from tandem mass spectrometry data, is presented. Sequence Temperature Values are computed using a sequence tag algorithm, allowing the degree of implication by an MS/MS spectrum of each region of a database to be determined on a continuum. Counter to conventional approaches, features such as modifications, substitutions, and cleavage events are modeled with probabilities rather than by discrete user-controlled settings to consider or not consider a feature. The use of feature probabilities in conjunction with Sequence Temperature Values allows for a very large increase in the effective search space with only a very small increase in the actual number of hypotheses that must be scored. The algorithm has a new kind of user interface that removes the user expertise requirement, presenting control settings in the language of the laboratory that are translated to optimal algorithmic settings. To validate this new algorithm, a comparison with Mascot is presented for a series of analogous searches to explore the relative impact of increasing search space probed with Mascot by relaxing the tryptic digestion conformance requirements from trypsin to semitrypsin to no enzyme and with the Paragon Algorithm using its Rapid mode and Thorough mode with and without tryptic specificity. Although they performed similarly for small search space, dramatic differences were observed in large search space. With the Paragon Algorithm, hundreds of biological and artifact modifications, all possible substitutions, and all levels of conformance to the expected digestion pattern can be searched in a single search step, yet the typical cost in search time is only 2-5 times that of conventional small search space. Despite this large increase in effective search space, there is no drastic loss of discrimination that typically accompanies the exploration of large search space.

PyDREAM: high-dimensional parameter inference for biological models in python.

PubMed

Shockley, Erin M; Vrugt, Jasper A; Lopez, Carlos F; Valencia, Alfonso

2018-02-15

Biological models contain many parameters whose values are difficult to measure directly via experimentation and therefore require calibration against experimental data. Markov chain Monte Carlo (MCMC) methods are suitable to estimate multivariate posterior model parameter distributions, but these methods may exhibit slow or premature convergence in high-dimensional search spaces. Here, we present PyDREAM, a Python implementation of the (Multiple-Try) Differential Evolution Adaptive Metropolis [DREAM(ZS)] algorithm developed by Vrugt and ter Braak (2008) and Laloy and Vrugt (2012). PyDREAM achieves excellent performance for complex, parameter-rich models and takes full advantage of distributed computing resources, facilitating parameter inference and uncertainty estimation of CPU-intensive biological models. PyDREAM is freely available under the GNU GPLv3 license from the Lopez lab GitHub repository at http://github.com/LoLab-VU/PyDREAM. c.lopez@vanderbilt.edu. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

Acoustic source localization in mixed field using spherical microphone arrays

NASA Astrophysics Data System (ADS)

Huang, Qinghua; Wang, Tong

2014-12-01

Spherical microphone arrays have been used for source localization in three-dimensional space recently. In this paper, a two-stage algorithm is developed to localize mixed far-field and near-field acoustic sources in free-field environment. In the first stage, an array signal model is constructed in the spherical harmonics domain. The recurrent relation of spherical harmonics is independent of far-field and near-field mode strengths. Therefore, it is used to develop spherical estimating signal parameter via rotational invariance technique (ESPRIT)-like approach to estimate directions of arrival (DOAs) for both far-field and near-field sources. In the second stage, based on the estimated DOAs, simple one-dimensional MUSIC spectrum is exploited to distinguish far-field and near-field sources and estimate the ranges of near-field sources. The proposed algorithm can avoid multidimensional search and parameter pairing. Simulation results demonstrate the good performance for localizing far-field sources, or near-field ones, or mixed field sources.

First-Principles Prediction of New Electrides with Nontrivial Band Topology Based on One-Dimensional Building Blocks

NASA Astrophysics Data System (ADS)

Park, Changwon; Kim, Sung Wng; Yoon, Mina

2018-01-01

We introduce a new class of electrides with nontrivial band topology by coupling materials database searches and first-principles-calculations-based analysis. Cs3O and Ba3N are for the first time identified as a new class of electrides, consisting of one-dimensional (1D) nanorod building blocks. Their crystal structures mimic β -TiCl3 with the position of anions and cations exchanged. Unlike the weakly coupled nanorods of β -TiCl3 , Cs3O and Ba3N retain 1D anionic electrons along the hollow interrod sites; additionally, a strong interrod interaction in C3O and Ba3N induces band inversion in a 2D superatomic triangular lattice, resulting in Dirac-node lines. The new class of electrides can serve as a prototype for new electrides with a large cavity space that can be utilized for various applications such as gas storage, ion transport, and metal intercalation.

Proteomic profiling of human pleural effusion using two-dimensional nano liquid chromatography tandem mass spectrometry.

PubMed

Tyan, Yu-Chang; Wu, Hsin-Yi; Lai, Wu-Wei; Su, Wu-Chou; Liao, Pao-Chi

2005-01-01

Pleural effusion, an accumulation of pleural fluid, contains proteins originated from plasma filtrate and, especially when tissues are damaged, parenchyma interstitial spaces of lungs and/or other organs. This study details protein profiles in human pleural effusion from 43 lung adenocarcinoma patients by a two-dimensional nano-high performance liquid chromatography electrospray ionization tandem mass spectrometry (2D nano-HPLC-ESI-MS/MS) system. The experimental results revealed the identification of 1415 unique proteins from human pleural effusion. Among these 124 proteins identified with higher confidence levels, some proteins have not been reported in plasma and may represent proteins specifically present in pleural effusion. These proteins are valuable for mass identification of differentially expressed proteins involved in proteomics database and screening biomarker to further study in human lung adenocarcinoma. The significance of the use of proteomics analysis of human pleural fluid for the search of new lung cancer marker proteins, and for their simultaneous display and analysis in patients suffering from lung disorders has been examined.

Systematic Dimensionality Reduction for Quantum Walks: Optimal Spatial Search and Transport on Non-Regular Graphs

PubMed Central

Novo, Leonardo; Chakraborty, Shantanav; Mohseni, Masoud; Neven, Hartmut; Omar, Yasser

2015-01-01

Continuous time quantum walks provide an important framework for designing new algorithms and modelling quantum transport and state transfer problems. Often, the graph representing the structure of a problem contains certain symmetries that confine the dynamics to a smaller subspace of the full Hilbert space. In this work, we use invariant subspace methods, that can be computed systematically using the Lanczos algorithm, to obtain the reduced set of states that encompass the dynamics of the problem at hand without the specific knowledge of underlying symmetries. First, we apply this method to obtain new instances of graphs where the spatial quantum search algorithm is optimal: complete graphs with broken links and complete bipartite graphs, in particular, the star graph. These examples show that regularity and high-connectivity are not needed to achieve optimal spatial search. We also show that this method considerably simplifies the calculation of quantum transport efficiencies. Furthermore, we observe improved efficiencies by removing a few links from highly symmetric graphs. Finally, we show that this reduction method also allows us to obtain an upper bound for the fidelity of a single qubit transfer on an XY spin network. PMID:26330082

Target-distractor similarity has a larger impact on visual search in school-age children than spacing.

PubMed

Huurneman, Bianca; Boonstra, F Nienke

2015-01-22

In typically developing children, crowding decreases with increasing age. The influence of target-distractor similarity with respect to orientation and element spacing on visual search performance was investigated in 29 school-age children with normal vision (4- to 6-year-olds [N = 16], 7- to 8-year-olds [N = 13]). Children were instructed to search for a target E among distractor Es (feature search: all flanking Es pointing right; conjunction search: flankers in three orientations). Orientation of the target was manipulated in four directions: right (target absent), left (inversed), up, and down (vertical). Spacing was varied in four steps: 0.04°, 0.5°, 1°, and 2°. During feature search, high target-distractor similarity had a stronger impact on performance than spacing: Orientation affected accuracy until spacing was 1°, and spacing only influenced accuracy for identifying inversed targets. Spatial analyses showed that orientation affected oculomotor strategy: Children made more fixations in the "inversed" target area (4.6) than the vertical target areas (1.8 and 1.9). Furthermore, age groups differed in fixation duration: 4- to 6-year-old children showed longer fixation durations than 7- to 8-year-olds at the two largest element spacings (p = 0.039 and p = 0.027). Conjunction search performance was unaffected by spacing. Four conclusions can be drawn from this study: (a) Target-distractor similarity governs visual search performance in school-age children, (b) children make more fixations in target areas when target-distractor similarity is high, (c) 4- to 6-year-olds show longer fixation durations than 7- to 8-year-olds at 1° and 2° element spacing, and (d) spacing affects feature but not conjunction search-a finding that might indicate top-down control ameliorates crowding in children. © 2015 ARVO.

Maximum-Likelihood Estimation With a Contracting-Grid Search Algorithm

PubMed Central

Hesterman, Jacob Y.; Caucci, Luca; Kupinski, Matthew A.; Barrett, Harrison H.; Furenlid, Lars R.

2010-01-01

A fast search algorithm capable of operating in multi-dimensional spaces is introduced. As a sample application, we demonstrate its utility in the 2D and 3D maximum-likelihood position-estimation problem that arises in the processing of PMT signals to derive interaction locations in compact gamma cameras. We demonstrate that the algorithm can be parallelized in pipelines, and thereby efficiently implemented in specialized hardware, such as field-programmable gate arrays (FPGAs). A 2D implementation of the algorithm is achieved in Cell/BE processors, resulting in processing speeds above one million events per second, which is a 20× increase in speed over a conventional desktop machine. Graphics processing units (GPUs) are used for a 3D application of the algorithm, resulting in processing speeds of nearly 250,000 events per second which is a 250× increase in speed over a conventional desktop machine. These implementations indicate the viability of the algorithm for use in real-time imaging applications. PMID:20824155

Multiobjective immune algorithm with nondominated neighbor-based selection.

PubMed

Gong, Maoguo; Jiao, Licheng; Du, Haifeng; Bo, Liefeng

2008-01-01

Abstract Nondominated Neighbor Immune Algorithm (NNIA) is proposed for multiobjective optimization by using a novel nondominated neighbor-based selection technique, an immune inspired operator, two heuristic search operators, and elitism. The unique selection technique of NNIA only selects minority isolated nondominated individuals in the population. The selected individuals are then cloned proportionally to their crowding-distance values before heuristic search. By using the nondominated neighbor-based selection and proportional cloning, NNIA pays more attention to the less-crowded regions of the current trade-off front. We compare NNIA with NSGA-II, SPEA2, PESA-II, and MISA in solving five DTLZ problems, five ZDT problems, and three low-dimensional problems. The statistical analysis based on three performance metrics including the coverage of two sets, the convergence metric, and the spacing, show that the unique selection method is effective, and NNIA is an effective algorithm for solving multiobjective optimization problems. The empirical study on NNIA's scalability with respect to the number of objectives shows that the new algorithm scales well along the number of objectives.

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.

Supersymmetric Dark Matter after LHC Run 1

DOE PAGES

Bagnaschi, E. A.; Buchmueller, O.; Cavanaugh, R.; ...

2015-10-23

Different mechanisms operate in various regions of the MSSM parameter space to bring the relic density of the lightest neutralino, χ ~0 1, assumed here to be the lightest SUSY particle (LSP) and thus the dark matter (DM) particle, into the range allowed by astrophysics and cosmology. These mechanisms include coannihilation with some nearly degenerate next-to-lightest supersymmetric particle such as the lighter stau τ ~ 1, stop t ~ 1 or chargino χ ~± 1, resonant annihilation via direct-channel heavy Higgs bosons H / A, the light Higgs boson h or the Z boson, and enhanced annihilation via a largermore » Higgsino component of the LSP in the focus-point region. These mechanisms typically select lower-dimensional subspaces in MSSM scenarios such as the CMSSM, NUHM1, NUHM2, and pMSSM10. We analyze how future LHC and direct DM searches can complement each other in the exploration of the different DM mechanisms within these scenarios. We find that the τ~1 coannihilation regions of the CMSSM, NUHM1, NUHM2 can largely be explored at the LHC via searches for /E T events and long-lived charged particles, whereas theirH / A funnel, focus-point and χ ~± 1 coannihilation regions can largely be explored by the LZ and Darwin DM direct detection experiments. Furthermore, we find that the dominant DM mechanism in our pMSSM10 analysis is χ ~ ±1 coannihilation: parts of its parameter space can be explored by the LHC, and a larger portion by future direct DM searches.« less

Supersymmetric dark matter after LHC run 1

NASA Astrophysics Data System (ADS)

Bagnaschi, E. A.; Buchmueller, O.; Cavanaugh, R.; Citron, M.; De Roeck, A.; Dolan, M. J.; Ellis, J. R.; Flächer, H.; Heinemeyer, S.; Isidori, G.; Malik, S.; Martínez Santos, D.; Olive, K. A.; Sakurai, K.; de Vries, K. J.; Weiglein, G.

2015-10-01

Different mechanisms operate in various regions of the MSSM parameter space to bring the relic density of the lightest neutralino, tilde{χ }^01, assumed here to be the lightest SUSY particle (LSP) and thus the dark matter (DM) particle, into the range allowed by astrophysics and cosmology. These mechanisms include coannihilation with some nearly degenerate next-to-lightest supersymmetric particle such as the lighter stau tilde{τ }1, stop tilde{t}1 or chargino tilde{χ }^± 1, resonant annihilation via direct-channel heavy Higgs bosons H / A, the light Higgs boson h or the Z boson, and enhanced annihilation via a larger Higgsino component of the LSP in the focus-point region. These mechanisms typically select lower-dimensional subspaces in MSSM scenarios such as the CMSSM, NUHM1, NUHM2, and pMSSM10. We analyze how future LHC and direct DM searches can complement each other in the exploration of the different DM mechanisms within these scenarios. We find that the {tilde{τ }_1} coannihilation regions of the CMSSM, NUHM1, NUHM2 can largely be explored at the LHC via searches for / E_T events and long-lived charged particles, whereas their H / A funnel, focus-point and tilde{χ }^± 1 coannihilation regions can largely be explored by the LZ and Darwin DM direct detection experiments. We find that the dominant DM mechanism in our pMSSM10 analysis is tilde{χ }^± 1 coannihilation: parts of its parameter space can be explored by the LHC, and a larger portion by future direct DM searches.

Surface displacements and energy release rates for constant stress drop slip zones in joined elastic quarter spaces

NASA Astrophysics Data System (ADS)

Rodgers, Michael J.; Wen, Shengmin; Keer, Leon M.

2000-08-01

A three-dimensional quasi-static model of faulting in an elastic half-space with a horizontal change of material properties (i.e., joined elastic quarter spaces) is considered. A boundary element method is used with a stress drop slip zone approach so that the fault surface relative displacements as well as the free surface displacements are approximated in elements over their respective domains. Stress intensity factors and free surface displacements are calculated for a variety of cases to show the phenomenological behavior of faulting in such a medium. These calculations showed that the behavior could be distinguished from a uniform half-space. Slip in a stiffer material increases, while slip in a softer material decreases the energy release rate and the free surface displacements. Also, the 1989 Kalapana earthquake was located on the basis of a series of forward searches using this method and leveling data. The located depth is 8 km, which is the closer to the seismically inferred depth than that determined from other models. Finally, the energy release rate, which can be used as a fracture criterion for fracture at this depth, is calculated to be 11.1×106 J m-2.

Turtle 24.0 diffusion depletion code

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

Altomare, S.; Barry, R.F.

1971-09-01

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

WFIRST: Microlensing Analysis Data Challenge

NASA Astrophysics Data System (ADS)

Street, Rachel; WFIRST Microlensing Science Investigation Team

2018-01-01

WFIRST will produce thousands of high cadence, high photometric precision lightcurves of microlensing events, from which a wealth of planetary and stellar systems will be discovered. However, the analysis of such lightcurves has historically been very time consuming and expensive in both labor and computing facilities. This poses a potential bottleneck to deriving the full science potential of the WFIRST mission. To address this problem, the WFIRST Microlensing Science Investigation Team designing a series of data challenges to stimulate research to address outstanding problems of microlensing analysis. These range from the classification and modeling of triple lens events to methods to efficiently yet thoroughly search a high-dimensional parameter space for the best fitting models.

Applications of genetic programming in cancer research.

PubMed

Worzel, William P; Yu, Jianjun; Almal, Arpit A; Chinnaiyan, Arul M

2009-02-01

The theory of Darwinian evolution is the fundamental keystones of modern biology. Late in the last century, computer scientists began adapting its principles, in particular natural selection, to complex computational challenges, leading to the emergence of evolutionary algorithms. The conceptual model of selective pressure and recombination in evolutionary algorithms allow scientists to efficiently search high dimensional space for solutions to complex problems. In the last decade, genetic programming has been developed and extensively applied for analysis of molecular data to classify cancer subtypes and characterize the mechanisms of cancer pathogenesis and development. This article reviews current successes using genetic programming and discusses its potential impact in cancer research and treatment in the near future.

PLQP & Company: Decidable Logics for Quantum Algorithms

NASA Astrophysics Data System (ADS)

Baltag, Alexandru; Bergfeld, Jort; Kishida, Kohei; Sack, Joshua; Smets, Sonja; Zhong, Shengyang

2014-10-01

We introduce a probabilistic modal (dynamic-epistemic) quantum logic PLQP for reasoning about quantum algorithms. We illustrate its expressivity by using it to encode the correctness of the well-known quantum search algorithm, as well as of a quantum protocol known to solve one of the paradigmatic tasks from classical distributed computing (the leader election problem). We also provide a general method (extending an idea employed in the decidability proof in Dunn et al. (J. Symb. Log. 70:353-359, 2005)) for proving the decidability of a range of quantum logics, interpreted on finite-dimensional Hilbert spaces. We give general conditions for the applicability of this method, and in particular we apply it to prove the decidability of PLQP.

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.

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.

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.

Robust hashing with local models for approximate similarity search.

PubMed

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

2014-07-01

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

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.

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.

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.

DOA estimation of noncircular signals for coprime linear array via locally reduced-dimensional Capon

NASA Astrophysics Data System (ADS)

Zhai, Hui; Zhang, Xiaofei; Zheng, Wang

2018-05-01

We investigate the issue of direction of arrival (DOA) estimation of noncircular signals for coprime linear array (CLA). The noncircular property enhances the degree of freedom and improves angle estimation performance, but it leads to a more complex angle ambiguity problem. To eliminate ambiguity, we theoretically prove that the actual DOAs of noncircular signals can be uniquely estimated by finding the coincide results from the two decomposed subarrays based on the coprimeness. We propose a locally reduced-dimensional (RD) Capon algorithm for DOA estimation of noncircular signals for CLA. The RD processing is used in the proposed algorithm to avoid two dimensional (2D) spectral peak search, and coprimeness is employed to avoid the global spectral peak search. The proposed algorithm requires one-dimensional locally spectral peak search, and it has very low computational complexity. Furthermore, the proposed algorithm needs no prior knowledge of the number of sources. We also derive the Crámer-Rao bound of DOA estimation of noncircular signals in CLA. Numerical simulation results demonstrate the effectiveness and superiority of the algorithm.

GPU-powered Shotgun Stochastic Search for Dirichlet process mixtures of Gaussian Graphical Models

PubMed Central

Mukherjee, Chiranjit; Rodriguez, Abel

2016-01-01

Gaussian graphical models are popular for modeling high-dimensional multivariate data with sparse conditional dependencies. A mixture of Gaussian graphical models extends this model to the more realistic scenario where observations come from a heterogenous population composed of a small number of homogeneous sub-groups. In this paper we present a novel stochastic search algorithm for finding the posterior mode of high-dimensional Dirichlet process mixtures of decomposable Gaussian graphical models. Further, we investigate how to harness the massive thread-parallelization capabilities of graphical processing units to accelerate computation. The computational advantages of our algorithms are demonstrated with various simulated data examples in which we compare our stochastic search with a Markov chain Monte Carlo algorithm in moderate dimensional data examples. These experiments show that our stochastic search largely outperforms the Markov chain Monte Carlo algorithm in terms of computing-times and in terms of the quality of the posterior mode discovered. Finally, we analyze a gene expression dataset in which Markov chain Monte Carlo algorithms are too slow to be practically useful. PMID:28626348

GPU-powered Shotgun Stochastic Search for Dirichlet process mixtures of Gaussian Graphical Models.

PubMed

Mukherjee, Chiranjit; Rodriguez, Abel

2016-01-01

Gaussian graphical models are popular for modeling high-dimensional multivariate data with sparse conditional dependencies. A mixture of Gaussian graphical models extends this model to the more realistic scenario where observations come from a heterogenous population composed of a small number of homogeneous sub-groups. In this paper we present a novel stochastic search algorithm for finding the posterior mode of high-dimensional Dirichlet process mixtures of decomposable Gaussian graphical models. Further, we investigate how to harness the massive thread-parallelization capabilities of graphical processing units to accelerate computation. The computational advantages of our algorithms are demonstrated with various simulated data examples in which we compare our stochastic search with a Markov chain Monte Carlo algorithm in moderate dimensional data examples. These experiments show that our stochastic search largely outperforms the Markov chain Monte Carlo algorithm in terms of computing-times and in terms of the quality of the posterior mode discovered. Finally, we analyze a gene expression dataset in which Markov chain Monte Carlo algorithms are too slow to be practically useful.

Eye Movements, Visual Search and Scene Memory, in an Immersive Virtual Environment

PubMed Central

Sullivan, Brian; Snyder, Kat; Ballard, Dana; Hayhoe, Mary

2014-01-01

Visual memory has been demonstrated to play a role in both visual search and attentional prioritization in natural scenes. However, it has been studied predominantly in experimental paradigms using multiple two-dimensional images. Natural experience, however, entails prolonged immersion in a limited number of three-dimensional environments. The goal of the present experiment was to recreate circumstances comparable to natural visual experience in order to evaluate the role of scene memory in guiding eye movements in a natural environment. Subjects performed a continuous visual-search task within an immersive virtual-reality environment over three days. We found that, similar to two-dimensional contexts, viewers rapidly learn the location of objects in the environment over time, and use spatial memory to guide search. Incidental fixations did not provide obvious benefit to subsequent search, suggesting that semantic contextual cues may often be just as efficient, or that many incidentally fixated items are not held in memory in the absence of a specific task. On the third day of the experience in the environment, previous search items changed in color. These items were fixated upon with increased probability relative to control objects, suggesting that memory-guided prioritization (or Surprise) may be a robust mechanisms for attracting gaze to novel features of natural environments, in addition to task factors and simple spatial saliency. PMID:24759905

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.

Search space mapping: getting a picture of coherent laser control.

PubMed

Shane, Janelle C; Lozovoy, Vadim V; Dantus, Marcos

2006-10-12

Search space mapping is a method for quickly visualizing the experimental parameters that can affect the outcome of a coherent control experiment. We demonstrate experimental search space mapping for the selective fragmentation and ionization of para-nitrotoluene and show how this method allows us to gather information about the dominant trends behind our achieved control.

Inversion of Surface Wave Phase Velocities for Radial Anisotropy to an Depth of 1200 km

NASA Astrophysics Data System (ADS)

Xing, Z.; Beghein, C.; Yuan, K.

2012-12-01

This study aims to evaluate three dimensional radial anisotropy to an depth of 1200 km. Radial anisotropy describes the difference in velocity between horizontally polarized Rayleigh waves and vertically polarized Love waves. Its presence in the uppermost 200 km mantle has well been documented by different groups, and has been regarded as an indicator of mantle convection which aligns the intrinsically anisotropic minerals, largely olivine, to form large scale anisotropy. However, there is no global agreement on whether anisotropy exists in the region below 200 km. Recent models also associate a fast vertically polarized shear wave with vertical upwelling mantle flow. The data used in this study is the globally isotropic phase velocity models of fundamental and higher mode Love and Rayleigh waves (Visser, 2008). The inclusion of higher mode surface wave phase velocity provides sensitivities to structure at depth that extends to below the transition zone. While the data is the same as used by Visser (2008), a quite different parameterization is applied. All the six parameters - five elastic parameters A, C, F, L, N and density - are now regarded as independent, which rules out possible biased conclusions induced by scaling relation method used in several previous studies to reduce the number of parameters partly due to limited computing resources. The data need to be modified by crustal corrections (Crust2.0) as we want to look at the mantle structure only. We do this by eliminating the perturbation in surface wave phase velocity caused by the difference in crustal structure with respect to the referent model PREM. Sambridge's Neighborhood Algorithm is used to search the parameter space. The introduction of such a direct search technique pales the traditional inversion method, which requires regularization or some unnecessary priori restriction on the model space. On the contrary, the new method will search the full model space, providing probability density function of each anisotropic parameter and the corresponding resolution.

A search for resonances decaying into a Higgs boson and a new particle X in the XH → qqbb final state with the ATLAS detector

DOE PAGES

Aaboud, M.; Aad, G.; Abbott, B.; ...

2018-04-10

In this paper, a search for heavy resonances decaying into a Higgs boson (H) and a new particle (X) is reported, utilizing 36.1 fb -1 of proton–proton collision data atmore » $$\\sqrt{s}$$ = 13 TeV collected during 2015 and 2016 with the ATLAS detector at the CERN Large Hadron Collider. The particle X is assumed to decay to a pair of light quarks, and the fully hadronic final state XH → $$q\\bar{q}$$'$$b\\bar{b}$$ is analysed. The search considers the regime of high XH resonance masses, where the X and H bosons are both highly Lorentz-boosted and are each reconstructed using a single jet with large radius parameter. A two-dimensional phase space of XH mass versus X mass is scanned for evidence of a signal, over a range of XH resonance mass values between 1 TeV and 4 TeV, and for X particles with masses from 50 GeV to 1000 GeV. All search results are consistent with the expectations for the background due to Standard Model processes, and 95% CL upper limits are set, as a function of XH and X masses, on the production cross-section of the XH → $$q\\bar{q}$$'$$b\\bar{b}$$ resonance.« less

A search for resonances decaying into a Higgs boson and a new particle X in the XH → qqbb final state with the ATLAS detector

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

Aaboud, M.; Aad, G.; Abbott, B.

In this paper, a search for heavy resonances decaying into a Higgs boson (H) and a new particle (X) is reported, utilizing 36.1 fb -1 of proton–proton collision data atmore » $$\\sqrt{s}$$ = 13 TeV collected during 2015 and 2016 with the ATLAS detector at the CERN Large Hadron Collider. The particle X is assumed to decay to a pair of light quarks, and the fully hadronic final state XH → $$q\\bar{q}$$'$$b\\bar{b}$$ is analysed. The search considers the regime of high XH resonance masses, where the X and H bosons are both highly Lorentz-boosted and are each reconstructed using a single jet with large radius parameter. A two-dimensional phase space of XH mass versus X mass is scanned for evidence of a signal, over a range of XH resonance mass values between 1 TeV and 4 TeV, and for X particles with masses from 50 GeV to 1000 GeV. All search results are consistent with the expectations for the background due to Standard Model processes, and 95% CL upper limits are set, as a function of XH and X masses, on the production cross-section of the XH → $$q\\bar{q}$$'$$b\\bar{b}$$ resonance.« less

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

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

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.

Joint search and sensor management for geosynchronous satellites

NASA Astrophysics Data System (ADS)

Zatezalo, A.; El-Fallah, A.; Mahler, R.; Mehra, R. K.; Pham, K.

2008-04-01

Joint search and sensor management for space situational awareness presents daunting scientific and practical challenges as it requires a simultaneous search for new, and the catalog update of the current space objects. We demonstrate a new approach to joint search and sensor management by utilizing the Posterior Expected Number of Targets (PENT) as the objective function, an observation model for a space-based EO/IR sensor, and a Probability Hypothesis Density Particle Filter (PHD-PF) tracker. Simulation and results using actual Geosynchronous Satellites are presented.

Dimensional oscillation. A fast variation of energy embedding gives good results with the AMBER potential energy function.

PubMed

Snow, M E; Crippen, G M

1991-08-01

The structure of the AMBER potential energy surface of the cyclic tetrapeptide cyclotetrasarcosyl is analyzed as a function of the dimensionality of coordinate space. It is found that the number of local energy minima decreases as the dimensionality of the space increases until some limit at which point equipotential subspaces appear. The applicability of energy embedding methods to finding global energy minima in this type of energy-conformation space is explored. Dimensional oscillation, a computationally fast variant of energy embedding is introduced and found to sample conformation space widely and to do a good job of finding global and near-global energy minima.

Optimal search strategies of space-time coupled random walkers with finite lifetimes

NASA Astrophysics Data System (ADS)

Campos, D.; Abad, E.; Méndez, V.; Yuste, S. B.; Lindenberg, K.

2015-05-01

We present a simple paradigm for detection of an immobile target by a space-time coupled random walker with a finite lifetime. The motion of the walker is characterized by linear displacements at a fixed speed and exponentially distributed duration, interrupted by random changes in the direction of motion and resumption of motion in the new direction with the same speed. We call these walkers "mortal creepers." A mortal creeper may die at any time during its motion according to an exponential decay law characterized by a finite mean death rate ωm. While still alive, the creeper has a finite mean frequency ω of change of the direction of motion. In particular, we consider the efficiency of the target search process, characterized by the probability that the creeper will eventually detect the target. Analytic results confirmed by numerical results show that there is an ωm-dependent optimal frequency ω =ωopt that maximizes the probability of eventual target detection. We work primarily in one-dimensional (d =1 ) domains and examine the role of initial conditions and of finite domain sizes. Numerical results in d =2 domains confirm the existence of an optimal frequency of change of direction, thereby suggesting that the observed effects are robust to changes in dimensionality. In the d =1 case, explicit expressions for the probability of target detection in the long time limit are given. In the case of an infinite domain, we compute the detection probability for arbitrary times and study its early- and late-time behavior. We further consider the survival probability of the target in the presence of many independent creepers beginning their motion at the same location and at the same time. We also consider a version of the standard "target problem" in which many creepers start at random locations at the same time.

Three-dimensional tracking and imaging laser scanner for space operations

NASA Astrophysics Data System (ADS)

Laurin, Denis G.; Beraldin, J. A.; Blais, Francois; Rioux, Marc; Cournoyer, Luc

1999-05-01

This paper presents the development of a laser range scanner (LARS) as a three-dimensional sensor for space applications. The scanner is a versatile system capable of doing surface imaging, target ranging and tracking. It is capable of short range (0.5 m to 20 m) and long range (20 m to 10 km) sensing using triangulation and time-of-flight (TOF) methods respectively. At short range (1 m), the resolution is sub-millimeter and drops gradually with distance (2 cm at 10 m). For long range, the TOF provides a constant resolution of plus or minus 3 cm, independent of range. The LARS could complement the existing Canadian Space Vision System (CSVS) for robotic manipulation. As an active vision system, the LARS is immune to sunlight and adverse lighting; this is a major advantage over the CSVS, as outlined in this paper. The LARS could also replace existing radar systems used for rendezvous and docking. There are clear advantages of an optical system over a microwave radar in terms of size, mass, power and precision. Equipped with two high-speed galvanometers, the laser can be steered to address any point in a 30 degree X 30 degree field of view. The scanning can be continuous (raster scan, Lissajous) or direct (random). This gives the scanner the ability to register high-resolution 3D images of range and intensity (up to 4000 X 4000 pixels) and to perform point target tracking as well as object recognition and geometrical tracking. The imaging capability of the scanner using an eye-safe laser is demonstrated. An efficient fiber laser delivers 60 mW of CW or 3 (mu) J pulses at 20 kHz for TOF operation. Implementation of search and track of multiple targets is also demonstrated. For a single target, refresh rates up to 137 Hz is possible. Considerations for space qualification of the scanner are discussed. Typical space operations, such as docking, object attitude tracking, and inspections are described.

Use of an informed search space maximizes confidence of site-specific assignment of glycoprotein glycosylation.

PubMed

Khatri, Kshitij; Klein, Joshua A; Zaia, Joseph

2017-01-01

In order to interpret glycopeptide tandem mass spectra, it is necessary to estimate the theoretical glycan compositions and peptide sequences, known as the search space. The simplest way to do this is to build a naïve search space from sets of glycan compositions from public databases and to assume that the target glycoprotein is pure. Often, however, purified glycoproteins contain co-purified glycoprotein contaminants that have the potential to confound assignment of tandem mass spectra based on naïve assumptions. In addition, there is increasing need to characterize glycopeptides from complex biological mixtures. Fortunately, liquid chromatography-mass spectrometry (LC-MS) methods for glycomics and proteomics are now mature and accessible. We demonstrate the value of using an informed search space built from measured glycomes and proteomes to define the search space for interpretation of glycoproteomics data. We show this using α-1-acid glycoprotein (AGP) mixed into a set of increasingly complex matrices. As the mixture complexity increases, the naïve search space balloons and the ability to assign glycopeptides with acceptable confidence diminishes. In addition, it is not possible to identify glycopeptides not foreseen as part of the naïve search space. A search space built from released glycan glycomics and proteomics data is smaller than its naïve counterpart while including the full range of proteins detected in the mixture. This maximizes the ability to assign glycopeptide tandem mass spectra with confidence. As the mixture complexity increases, the number of tandem mass spectra per glycopeptide precursor ion decreases, resulting in lower overall scores and reduced depth of coverage for the target glycoprotein. We suggest use of α-1-acid glycoprotein as a standard to gauge effectiveness of analytical methods and bioinformatics search parameters for glycoproteomics studies. Graphical Abstract Assignment of site specific glycosylation from LC-tandemMS data.

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

NASA Astrophysics Data System (ADS)

Bohn, Bastian; Garcke, Jochen; Griebel, Michael

2016-03-01

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

Path Complexity in Virtual Water Maze Navigation: Differential Associations with Age, Sex, and Regional Brain Volume.

PubMed

Daugherty, Ana M; Yuan, Peng; Dahle, Cheryl L; Bender, Andrew R; Yang, Yiqin; Raz, Naftali

2015-09-01

Studies of human navigation in virtual maze environments have consistently linked advanced age with greater distance traveled between the start and the goal and longer duration of the search. Observations of search path geometry suggest that routes taken by older adults may be unnecessarily complex and that excessive path complexity may be an indicator of cognitive difficulties experienced by older navigators. In a sample of healthy adults, we quantify search path complexity in a virtual Morris water maze with a novel method based on fractal dimensionality. In a two-level hierarchical linear model, we estimated improvement in navigation performance across trials by a decline in route length, shortening of search time, and reduction in fractal dimensionality of the path. While replicating commonly reported age and sex differences in time and distance indices, a reduction in fractal dimension of the path accounted for improvement across trials, independent of age or sex. The volumes of brain regions associated with the establishment of cognitive maps (parahippocampal gyrus and hippocampus) were related to path dimensionality, but not to the total distance and time. Thus, fractal dimensionality of a navigational path may present a useful complementary method of quantifying performance in navigation. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

On the Ck-embedding of Lorentzian manifolds in Ricci-flat spaces

NASA Astrophysics Data System (ADS)

Avalos, R.; Dahia, F.; Romero, C.

2018-05-01

In this paper, we investigate the problem of non-analytic embeddings of Lorentzian manifolds in Ricci-flat semi-Riemannian spaces. In order to do this, we first review some relevant results in the area and then motivate both the mathematical and physical interests in this problem. We show that any n-dimensional compact Lorentzian manifold (Mn, g), with g in the Sobolev space Hs+3, s >n/2 , admits an isometric embedding in a (2n + 2)-dimensional Ricci-flat semi-Riemannian manifold. The sharpest result available for these types of embeddings, in the general setting, comes as a corollary of Greene's remarkable embedding theorems R. Greene [Mem. Am. Math. Soc. 97, 1 (1970)], which guarantee the embedding of a compact n-dimensional semi-Riemannian manifold into an n(n + 5)-dimensional semi-Euclidean space, thereby guaranteeing the embedding into a Ricci-flat space with the same dimension. The theorem presented here improves this corollary in n2 + 3n - 2 codimensions by replacing the Riemann-flat condition with the Ricci-flat one from the beginning. Finally, we will present a corollary of this theorem, which shows that a compact strip in an n-dimensional globally hyperbolic space-time can be embedded in a (2n + 2)-dimensional Ricci-flat semi-Riemannian manifold.

A sub-space greedy search method for efficient Bayesian Network inference.

PubMed

Zhang, Qing; Cao, Yong; Li, Yong; Zhu, Yanming; Sun, Samuel S M; Guo, Dianjing

2011-09-01

Bayesian network (BN) has been successfully used to infer the regulatory relationships of genes from microarray dataset. However, one major limitation of BN approach is the computational cost because the calculation time grows more than exponentially with the dimension of the dataset. In this paper, we propose a sub-space greedy search method for efficient Bayesian Network inference. Particularly, this method limits the greedy search space by only selecting gene pairs with higher partial correlation coefficients. Using both synthetic and real data, we demonstrate that the proposed method achieved comparable results with standard greedy search method yet saved ∼50% of the computational time. We believe that sub-space search method can be widely used for efficient BN inference in systems biology. Copyright © 2011 Elsevier Ltd. All rights reserved.

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

Maximum projection designs for computer experiments

DOE PAGES

Joseph, V. Roshan; Gul, Evren; Ba, Shan

2015-03-18

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

Finite element probabilistic risk assessment of transmission line insulation flashovers caused by lightning strokes

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

Bacvarov, D.C.

1981-01-01

A new method for probabilistic risk assessment of transmission line insulation flashovers caused by lightning strokes is presented. The utilized approach of applying the finite element method for probabilistic risk assessment is demonstrated to be very powerful. The reasons for this are two. First, the finite element method is inherently suitable for analysis of three dimensional spaces where the parameters, such as three variate probability densities of the lightning currents, are non-uniformly distributed. Second, the finite element method permits non-uniform discretization of the three dimensional probability spaces thus yielding high accuracy in critical regions, such as the area of themore » low probability events, while at the same time maintaining coarse discretization in the non-critical areas to keep the number of grid points and the size of the problem to a manageable low level. The finite element probabilistic risk assessment method presented here is based on a new multidimensional search algorithm. It utilizes an efficient iterative technique for finite element interpolation of the transmission line insulation flashover criteria computed with an electro-magnetic transients program. Compared to other available methods the new finite element probabilistic risk assessment method is significantly more accurate and approximately two orders of magnitude computationally more efficient. The method is especially suited for accurate assessment of rare, very low probability events.« less

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

PubMed

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

2015-04-01

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

Elasticity of fractal materials using the continuum model with non-integer dimensional space

NASA Astrophysics Data System (ADS)

Tarasov, Vasily E.

2015-01-01

Using a generalization of vector calculus for space with non-integer dimension, we consider elastic properties of fractal materials. Fractal materials are described by continuum models with non-integer dimensional space. A generalization of elasticity equations for non-integer dimensional space, and its solutions for the equilibrium case of fractal materials are suggested. Elasticity problems for fractal hollow ball and cylindrical fractal elastic pipe with inside and outside pressures, for rotating cylindrical fractal pipe, for gradient elasticity and thermoelasticity of fractal materials are solved.

Phases of five-dimensional theories, monopole walls, and melting crystals

NASA Astrophysics Data System (ADS)

Cherkis, Sergey A.

2014-06-01

Moduli spaces of doubly periodic monopoles, also called monopole walls or monowalls, are hyperkähler; thus, when four-dimensional, they are self-dual gravitational instantons. We find all monowalls with lowest number of moduli. Their moduli spaces can be identified, on the one hand, with Coulomb branches of five-dimensional supersymmetric quantum field theories on 3 × T 2 and, on the other hand, with moduli spaces of local Calabi-Yau metrics on the canonical bundle of a del Pezzo surface. We explore the asymptotic metric of these moduli spaces and compare our results with Seiberg's low energy description of the five-dimensional quantum theories. We also give a natural description of the phase structure of general monowall moduli spaces in terms of triangulations of Newton polygons, secondary polyhedra, and associahedral projections of secondary fans.

Properties of heuristic search strategies

NASA Technical Reports Server (NTRS)

Vanderbrug, G. J.

1973-01-01

A directed graph is used to model the search space of a state space representation with single input operators, an AND/OR is used for problem reduction representations, and a theorem proving graph is used for state space representations with multiple input operators. These three graph models and heuristic strategies for searching them are surveyed. The completeness, admissibility, and optimality properties of search strategies which use the evaluation function f = (1 - omega)g = omega(h) are presented and interpreted using a representation of the search process in the plane. The use of multiple output operators to imply dependent successors, and thus obtain a formalism which includes all three types of representations, is discussed.

Hiding and Searching Strategies of Adult Humans in a Virtual and a Real-Space Room

ERIC Educational Resources Information Center

Talbot, Katherine J.; Legge, Eric L. G.; Bulitko, Vadim; Spetch, Marcia L.

2009-01-01

Adults searched for or cached three objects in nine hiding locations in a virtual room or a real-space room. In both rooms, the locations selected by participants differed systematically between searching and hiding. Specifically, participants moved farther from origin and dispersed their choices more when hiding objects than when searching for…

A Tool for Parameter-space Explorations

NASA Astrophysics Data System (ADS)

Murase, Yohsuke; Uchitane, Takeshi; Ito, Nobuyasu

A software for managing simulation jobs and results, named "OACIS", is presented. It controls a large number of simulation jobs executed in various remote servers, keeps these results in an organized way, and manages the analyses on these results. The software has a web browser front end, and users can submit various jobs to appropriate remote hosts from a web browser easily. After these jobs are finished, all the result files are automatically downloaded from the computational hosts and stored in a traceable way together with the logs of the date, host, and elapsed time of the jobs. Some visualization functions are also provided so that users can easily grasp the overview of the results distributed in a high-dimensional parameter space. Thus, OACIS is especially beneficial for the complex simulation models having many parameters for which a lot of parameter searches are required. By using API of OACIS, it is easy to write a code that automates parameter selection depending on the previous simulation results. A few examples of the automated parameter selection are also demonstrated.

Z boson mediated dark matter beyond the effective theory

DOE PAGES

Kearney, John; Orlofsky, Nicholas; Pierce, Aaron

2017-02-17

Here, direct detection bounds are beginning to constrain a very simple model of weakly interacting dark matter—a Majorana fermion with a coupling to the Z boson. In a particularly straightforward gauge-invariant realization, this coupling is introduced via a higher-dimensional operator. While attractive in its simplicity, this model generically induces a large ρ parameter. An ultraviolet completion that avoids an overly large contribution to ρ is the singlet-doublet model. We revisit this model, focusing on the Higgs blind spot region of parameter space where spin-independent interactions are absent. This model successfully reproduces dark matter with direct detection mediated by the Zmore » boson but whose cosmology may depend on additional couplings and states. Future direct detection experiments should effectively probe a significant portion of this parameter space, aside from a small coannihilating region. As such, Z-mediated thermal dark matter as realized in the singlet-doublet model represents an interesting target for future searches.« less

Image Recommendation Algorithm Using Feature-Based Collaborative Filtering

NASA Astrophysics Data System (ADS)

Kim, Deok-Hwan

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

Predicted Realization of Cubic Dirac Fermion in Quasi-One-Dimensional Transition-Metal Monochalcogenides

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

Liu, Qihang; Zunger, Alex

We show that the previously predicted “cubic Dirac fermion,” composed of six conventional Weyl fermions including three with left-handed and three with right-handed chirality, is realized in a specific, stable solid state system that has been made years ago, but was not appreciated as a “cubically dispersed Dirac semimetal” (CDSM). We identify the crystal symmetry constraints and find the space group P6 3/m as one of the two that can support a CDSM, of which the characteristic band crossing has linear dispersion along the principle axis but cubic dispersion in the plane perpendicular to it. We then conduct a materialmore » search using density functional theory, identifying a group of quasi-one-dimensional molybdenum monochalcogenide compounds A I(MoX VI) 3 (AI = Na, K, Rb, In, Tl; X VI = S , Se, Te) as ideal CDSM candidates. Studying the stability of the A ( MoX ) 3 family reveals a few candidates such as Rb(MoTe) 3 and Tl(MoTe) 3 that are predicted to be resilient to Peierls distortion, thus retaining the metallic character. Furthermore, the combination of one dimensionality and metallic nature in this family provides a platform for unusual optical signature—polarization-dependent metallic vs insulating response.« less

Predicted Realization of Cubic Dirac Fermion in Quasi-One-Dimensional Transition-Metal Monochalcogenides

DOE PAGES

Liu, Qihang; Zunger, Alex

2017-05-09

We show that the previously predicted “cubic Dirac fermion,” composed of six conventional Weyl fermions including three with left-handed and three with right-handed chirality, is realized in a specific, stable solid state system that has been made years ago, but was not appreciated as a “cubically dispersed Dirac semimetal” (CDSM). We identify the crystal symmetry constraints and find the space group P6 3/m as one of the two that can support a CDSM, of which the characteristic band crossing has linear dispersion along the principle axis but cubic dispersion in the plane perpendicular to it. We then conduct a materialmore » search using density functional theory, identifying a group of quasi-one-dimensional molybdenum monochalcogenide compounds A I(MoX VI) 3 (AI = Na, K, Rb, In, Tl; X VI = S , Se, Te) as ideal CDSM candidates. Studying the stability of the A ( MoX ) 3 family reveals a few candidates such as Rb(MoTe) 3 and Tl(MoTe) 3 that are predicted to be resilient to Peierls distortion, thus retaining the metallic character. Furthermore, the combination of one dimensionality and metallic nature in this family provides a platform for unusual optical signature—polarization-dependent metallic vs insulating response.« less

Facilitated Diffusion of Transcription Factor Proteins with Anomalous Bulk Diffusion.

PubMed

Liu, Lin; Cherstvy, Andrey G; Metzler, Ralf

2017-02-16

What are the physical laws of the diffusive search of proteins for their specific binding sites on DNA in the presence of the macromolecular crowding in cells? We performed extensive computer simulations to elucidate the protein target search on DNA. The novel feature is the viscoelastic non-Brownian protein bulk diffusion recently observed experimentally. We examine the influence of the protein-DNA binding affinity and the anomalous diffusion exponent on the target search time. In all cases an optimal search time is found. The relative contribution of intermittent three-dimensional bulk diffusion and one-dimensional sliding of proteins along the DNA is quantified. Our results are discussed in the light of recent single molecule tracking experiments, aiming at a better understanding of the influence of anomalous kinetics of proteins on the facilitated diffusion mechanism.

Mixing times in quantum walks on two-dimensional grids

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

Marquezino, F. L.; Portugal, R.; Abal, G.

2010-10-15

Mixing properties of discrete-time quantum walks on two-dimensional grids with toruslike boundary conditions are analyzed, focusing on their connection to the complexity of the corresponding abstract search algorithm. In particular, an exact expression for the stationary distribution of the coherent walk over odd-sided lattices is obtained after solving the eigenproblem for the evolution operator for this particular graph. The limiting distribution and mixing time of a quantum walk with a coin operator modified as in the abstract search algorithm are obtained numerically. On the basis of these results, the relation between the mixing time of the modified walk and themore » running time of the corresponding abstract search algorithm is discussed.« less

Mixing times in quantum walks on two-dimensional grids

NASA Astrophysics Data System (ADS)

Marquezino, F. L.; Portugal, R.; Abal, G.

2010-10-01

Mixing properties of discrete-time quantum walks on two-dimensional grids with toruslike boundary conditions are analyzed, focusing on their connection to the complexity of the corresponding abstract search algorithm. In particular, an exact expression for the stationary distribution of the coherent walk over odd-sided lattices is obtained after solving the eigenproblem for the evolution operator for this particular graph. The limiting distribution and mixing time of a quantum walk with a coin operator modified as in the abstract search algorithm are obtained numerically. On the basis of these results, the relation between the mixing time of the modified walk and the running time of the corresponding abstract search algorithm is discussed.

A general-purpose optimization program for engineering design

NASA Technical Reports Server (NTRS)

Vanderplaats, G. N.; Sugimoto, H.

1986-01-01

A new general-purpose optimization program for engineering design is described. ADS (Automated Design Synthesis) is a FORTRAN program for nonlinear constrained (or unconstrained) function minimization. The optimization process is segmented into three levels: Strategy, Optimizer, and One-dimensional search. At each level, several options are available so that a total of nearly 100 possible combinations can be created. An example of available combinations is the Augmented Lagrange Multiplier method, using the BFGS variable metric unconstrained minimization together with polynomial interpolation for the one-dimensional search.

Failure tolerance strategy of space manipulator for large load carrying tasks

NASA Astrophysics Data System (ADS)

Chen, Gang; Yuan, Bonan; Jia, Qingxuan; Sun, Hanxu; Guo, Wen

2018-07-01

During the execution of large load carrying tasks in long term service, there is a notable risk of space manipulator suffering from locked-joint failure, thus space manipulator should be with enough failure tolerance performance. A research on evaluating failure tolerance performance and re-planning feasible task trajectory for space manipulator performing large load carrying tasks is conducted in this paper. The effects of locked-joint failure on critical performance(reachability and load carrying capacity) of space manipulator are analyzed at first. According to the requirements of load carrying tasks, we further propose a new concept of failure tolerance workspace with load carrying capacity(FTWLCC) to evaluate failure tolerance performance, and improve the classic A* algorithm to search the feasible task trajectory. Through the normalized FTWLCC and the improved A* algorithm, the reachability and load carrying capacity of the degraded space manipulator are evaluated, and the reachable and capable trajectory can be obtained. The establishment of FTWLCC provides a novel idea that combines mathematical statistics with failure tolerance performance to illustrate the distribution of load carrying capacity in three-dimensional space, so multiple performance indices can be analyzed simultaneously and visually. And the full consideration of all possible failure situations and motion states makes FTWLCC and improved A* algorithm be universal and effective enough to be appropriate for random joint failure and variety of requirement of large load carrying tasks, so they can be extended to other types of manipulators.

Generalized filtering of laser fields in optimal control theory: application to symmetry filtering of quantum gate operations

NASA Astrophysics Data System (ADS)

Schröder, Markus; Brown, Alex

2009-10-01

We present a modified version of a previously published algorithm (Gollub et al 2008 Phys. Rev. Lett.101 073002) for obtaining an optimized laser field with more general restrictions on the search space of the optimal field. The modification leads to enforcement of the constraints on the optimal field while maintaining good convergence behaviour in most cases. We demonstrate the general applicability of the algorithm by imposing constraints on the temporal symmetry of the optimal fields. The temporal symmetry is used to reduce the number of transitions that have to be optimized for quantum gate operations that involve inversion (NOT gate) or partial inversion (Hadamard gate) of the qubits in a three-dimensional model of ammonia.

The pseudo-Boolean optimization approach to form the N-version software structure

NASA Astrophysics Data System (ADS)

Kovalev, I. V.; Kovalev, D. I.; Zelenkov, P. V.; Voroshilova, A. A.

2015-10-01

The problem of developing an optimal structure of N-version software system presents a kind of very complex optimization problem. This causes the use of deterministic optimization methods inappropriate for solving the stated problem. In this view, exploiting heuristic strategies looks more rational. In the field of pseudo-Boolean optimization theory, the so called method of varied probabilities (MVP) has been developed to solve problems with a large dimensionality. Some additional modifications of MVP have been made to solve the problem of N-version systems design. Those algorithms take into account the discovered specific features of the objective function. The practical experiments have shown the advantage of using these algorithm modifications because of reducing a search space.

Development of Condensing Mesh Method for Corner Domain at Numerical Simulation Magnetic System

NASA Astrophysics Data System (ADS)

Perepelkin, E.; Tarelkin, A.; Polyakova, R.; Kovalenko, A.

2018-05-01

A magnetostatic problem arises in searching for the distribution of the magnetic field generated by magnet systems of many physics research facilities, e.g., accelerators. The domain in which the boundaryvalue problem is solved often has a piecewise smooth boundary. In this case, numerical calculations of the problem require the consideration of the solution behavior in the corner domain. In this work we obtained the upper estimation of the magnetic field growth and propose a method of condensing the differential grid near the corner domain of vacuum in case of 3-dimensional space based on this estimation. An example of calculating a real model problem for SDP NICA in the domain containing a corner point is given.

Dimensionality reduction of collective motion by principal manifolds

NASA Astrophysics Data System (ADS)

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

2015-01-01

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

Unexpected series of regular frequency spacing of δ Scuti stars in the non-asymptotic regime - I. The methodology

DOE PAGES

Paparo, M.; Benko, J. M.; Hareter, M.; ...

2016-05-11

In this study, a sequence search method was developed to search the regular frequency spacing in δ Scuti stars through visual inspection and an algorithmic search. We searched for sequences of quasi-equally spaced frequencies, containing at least four members per sequence, in 90 δ Scuti stars observed by CoRoT. We found an unexpectedly large number of independent series of regular frequency spacing in 77 δ Scuti stars (from one to eight sequences) in the non-asymptotic regime. We introduce the sequence search method presenting the sequences and echelle diagram of CoRoT 102675756 and the structure of the algorithmic search. Four sequencesmore » (echelle ridges) were found in the 5–21 d –1 region where the pairs of the sequences are shifted (between 0.5 and 0.59 d –1) by twice the value of the estimated rotational splitting frequency (0.269 d –1). The general conclusions for the whole sample are also presented in this paper. The statistics of the spacings derived by the sequence search method, by FT (Fourier transform of the frequencies), and the statistics of the shifts are also compared. In many stars more than one almost equally valid spacing appeared. The model frequencies of FG Vir and their rotationally split components were used to formulate the possible explanation that one spacing is the large separation while the other is the sum of the large separation and the rotational frequency. In CoRoT 102675756, the two spacings (2.249 and 1.977 d –1) are in better agreement with the sum of a possible 1.710 d –1 large separation and two or one times, respectively, the value of the rotational frequency.« less

Unexpected series of regular frequency spacing of δ Scuti stars in the non-asymptotic regime - I. The methodology

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

Paparo, M.; Benko, J. M.; Hareter, M.

In this study, a sequence search method was developed to search the regular frequency spacing in δ Scuti stars through visual inspection and an algorithmic search. We searched for sequences of quasi-equally spaced frequencies, containing at least four members per sequence, in 90 δ Scuti stars observed by CoRoT. We found an unexpectedly large number of independent series of regular frequency spacing in 77 δ Scuti stars (from one to eight sequences) in the non-asymptotic regime. We introduce the sequence search method presenting the sequences and echelle diagram of CoRoT 102675756 and the structure of the algorithmic search. Four sequencesmore » (echelle ridges) were found in the 5–21 d –1 region where the pairs of the sequences are shifted (between 0.5 and 0.59 d –1) by twice the value of the estimated rotational splitting frequency (0.269 d –1). The general conclusions for the whole sample are also presented in this paper. The statistics of the spacings derived by the sequence search method, by FT (Fourier transform of the frequencies), and the statistics of the shifts are also compared. In many stars more than one almost equally valid spacing appeared. The model frequencies of FG Vir and their rotationally split components were used to formulate the possible explanation that one spacing is the large separation while the other is the sum of the large separation and the rotational frequency. In CoRoT 102675756, the two spacings (2.249 and 1.977 d –1) are in better agreement with the sum of a possible 1.710 d –1 large separation and two or one times, respectively, the value of the rotational frequency.« less

Likelihood analysis of supersymmetric SU(5) GUTs

DOE PAGES

Bagnaschi, Emanuele; Costa, J. C.; Sakurai, K.; ...

2017-02-16

Here, we perform a likelihood analysis of the constraints from accelerator experiments and astrophysical observations on supersymmetric (SUSY) models with SU(5) boundary conditions on soft SUSY-breaking parameters at the GUT scale. The parameter space of the models studied has 7 parameters: a universal gaugino massmore » $$m_{1/2}$$, distinct masses for the scalar partners of matter fermions in five- and ten-dimensional representations of SU(5), $$m_5$$ and $$m_{10}$$, and for the $$\\mathbf{5}$$ and $$\\mathbf{\\bar 5}$$ Higgs representations $$m_{H_u}$$ and $$m_{H_d}$$, a universal trilinear soft SUSY-breaking parameter $$A_0$$, and the ratio of Higgs vevs $$\\tan \\beta$$. In addition to previous constraints from direct sparticle searches, low-energy and flavour observables, we incorporate constraints based on preliminary results from 13 TeV LHC searches for jets + MET events and long-lived particles, as well as the latest PandaX-II and LUX searches for direct Dark Matter detection. In addition to previously-identified mechanisms for bringing the supersymmetric relic density into the range allowed by cosmology, we identify a novel $${\\tilde u_R}/{\\tilde c_R} - \\tilde{\\chi}^0_1$$ coannihilation mechanism that appears in the supersymmetric SU(5) GUT model and discuss the role of $${\\tilde \

Hybridization of Strength Pareto Multiobjective Optimization with Modified Cuckoo Search Algorithm for Rectangular Array

NASA Astrophysics Data System (ADS)

Abdul Rani, Khairul Najmy; Abdulmalek, Mohamedfareq; A. Rahim, Hasliza; Siew Chin, Neoh; Abd Wahab, Alawiyah

2017-04-01

This research proposes the various versions of modified cuckoo search (MCS) metaheuristic algorithm deploying the strength Pareto evolutionary algorithm (SPEA) multiobjective (MO) optimization technique in rectangular array geometry synthesis. Precisely, the MCS algorithm is proposed by incorporating the Roulette wheel selection operator to choose the initial host nests (individuals) that give better results, adaptive inertia weight to control the positions exploration of the potential best host nests (solutions), and dynamic discovery rate to manage the fraction probability of finding the best host nests in 3-dimensional search space. In addition, the MCS algorithm is hybridized with the particle swarm optimization (PSO) and hill climbing (HC) stochastic techniques along with the standard strength Pareto evolutionary algorithm (SPEA) forming the MCSPSOSPEA and MCSHCSPEA, respectively. All the proposed MCS-based algorithms are examined to perform MO optimization on Zitzler-Deb-Thiele’s (ZDT’s) test functions. Pareto optimum trade-offs are done to generate a set of three non-dominated solutions, which are locations, excitation amplitudes, and excitation phases of array elements, respectively. Overall, simulations demonstrates that the proposed MCSPSOSPEA outperforms other compatible competitors, in gaining a high antenna directivity, small half-power beamwidth (HPBW), low average side lobe level (SLL) suppression, and/or significant predefined nulls mitigation, simultaneously.

Search for Heavy Right-Handed W Bosons and Heavy Right-Handed Neutrinos Produced by 7 TeV pp Collisions Inside the CMS Detector

NASA Astrophysics Data System (ADS)

Dudero, Phillip Russell

2011-12-01

This paper describes a search for signals from the production of right-handed WR bosons and heavy neutrinos Nℓ (ℓ = mu) that arise naturally in the SUC(3) ⊗ SUL(2) ⊗ SUR(2) ⊗ U(1) left-right symmetric model. The search was performed using data collected from proton-proton collisions produced by the Large Hadron Collider at a center-of-mass energy of √s = 7TeV, and occurring inside the Compact Muon Solenoid experiment. The data was collected during the 2010 running period and corresponds to a total integrated luminosity of L = 36 pb-1 . No excess over expectations from Standard Model processes is observed. For models with exact left-right symmetry (the same coupling in the right sector), an exclusion region is established in the two-dimensional parameter space (MWR, MN ℓ) that extends to MWR = 1360 GeV/c2, exceeding prior limits set by the Tevatron proton-antiproton collider. Prospects for discovery or exclusion of these particles in the year 2011 using a projected 1 fb-1 total integrated luminosity are presented. In addition, a method of precision timing of calorimetric deposits in the hadron calorimeter of CMS is discussed.

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

PubMed

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

2017-06-28

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

Surface shape affects the three-dimensional exploratory movements of nocturnal arboreal snakes.

PubMed

Jayne, Bruce C; Olberding, Jeffrey P; Athreya, Dilip; Riley, Michael A

2012-12-01

Movement and searching behaviors at diverse spatial scales are important for understanding how animals interact with their environment. Although the shapes of branches and the voids in arboreal habitats seem likely to affect searching behaviors, their influence is poorly understood. To gain insights into how both environmental structure and the attributes of an animal may affect movement and searching, we compared the three-dimensional exploratory movements of snakes in the dark on two simulated arboreal surfaces (disc and horizontal cylinder). Most of the exploratory movements of snakes in the dark were a small fraction of the distances they could reach while bridging gaps in the light. The snakes extended farther away from the edge of the supporting surface at the ends of the cylinder than from the sides of the cylinder or from any direction from the surface of the disc. The exploratory movements were not random, and the surface shape and three-dimensional directions had significant interactive effects on how the movements were structured in time. Thus, the physical capacity for reaching did not limit the area that was explored, but the shape of the supporting surface and the orientation relative to gravity did create biased searching patterns.

Supervised Classification Techniques for Hyperspectral Data

NASA Technical Reports Server (NTRS)

Jimenez, Luis O.

1997-01-01

The recent development of more sophisticated remote sensing systems enables the measurement of radiation in many mm-e spectral intervals than previous possible. An example of this technology is the AVIRIS system, which collects image data in 220 bands. The increased dimensionality of such hyperspectral data provides a challenge to the current techniques for analyzing such data. Human experience in three dimensional space tends to mislead one's intuition of geometrical and statistical properties in high dimensional space, properties which must guide our choices in the data analysis process. In this paper high dimensional space properties are mentioned with their implication for high dimensional data analysis in order to illuminate the next steps that need to be taken for the next generation of hyperspectral data classifiers.

Balancing Newtonian gravity and spin to create localized structures

NASA Astrophysics Data System (ADS)

Bush, Michael; Lindner, John

2015-03-01

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

An Energy Model of Place Cell Network in Three Dimensional Space.

PubMed

Wang, Yihong; Xu, Xuying; Wang, Rubin

2018-01-01

Place cells are important elements in the spatial representation system of the brain. A considerable amount of experimental data and classical models are achieved in this area. However, an important question has not been addressed, which is how the three dimensional space is represented by the place cells. This question is preliminarily surveyed by energy coding method in this research. Energy coding method argues that neural information can be expressed by neural energy and it is convenient to model and compute for neural systems due to the global and linearly addable properties of neural energy. Nevertheless, the models of functional neural networks based on energy coding method have not been established. In this work, we construct a place cell network model to represent three dimensional space on an energy level. Then we define the place field and place field center and test the locating performance in three dimensional space. The results imply that the model successfully simulates the basic properties of place cells. The individual place cell obtains unique spatial selectivity. The place fields in three dimensional space vary in size and energy consumption. Furthermore, the locating error is limited to a certain level and the simulated place field agrees to the experimental results. In conclusion, this is an effective model to represent three dimensional space by energy method. The research verifies the energy efficiency principle of the brain during the neural coding for three dimensional spatial information. It is the first step to complete the three dimensional spatial representing system of the brain, and helps us further understand how the energy efficiency principle directs the locating, navigating, and path planning function of the brain.

Optimal Fungal Space Searching Algorithms.

PubMed

Asenova, Elitsa; Lin, Hsin-Yu; Fu, Eileen; Nicolau, Dan V; Nicolau, Dan V

2016-10-01

Previous experiments have shown that fungi use an efficient natural algorithm for searching the space available for their growth in micro-confined networks, e.g., mazes. This natural "master" algorithm, which comprises two "slave" sub-algorithms, i.e., collision-induced branching and directional memory, has been shown to be more efficient than alternatives, with one, or the other, or both sub-algorithms turned off. In contrast, the present contribution compares the performance of the fungal natural algorithm against several standard artificial homologues. It was found that the space-searching fungal algorithm consistently outperforms uninformed algorithms, such as Depth-First-Search (DFS). Furthermore, while the natural algorithm is inferior to informed ones, such as A*, this under-performance does not importantly increase with the increase of the size of the maze. These findings suggest that a systematic effort of harvesting the natural space searching algorithms used by microorganisms is warranted and possibly overdue. These natural algorithms, if efficient, can be reverse-engineered for graph and tree search strategies.

Multidimensionally encoded magnetic resonance imaging.

PubMed

Lin, Fa-Hsuan

2013-07-01

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

Six-dimensional real and reciprocal space small-angle X-ray scattering tomography

NASA Astrophysics Data System (ADS)

Schaff, Florian; Bech, Martin; Zaslansky, Paul; Jud, Christoph; Liebi, Marianne; Guizar-Sicairos, Manuel; Pfeiffer, Franz

2015-11-01

When used in combination with raster scanning, small-angle X-ray scattering (SAXS) has proven to be a valuable imaging technique of the nanoscale, for example of bone, teeth and brain matter. Although two-dimensional projection imaging has been used to characterize various materials successfully, its three-dimensional extension, SAXS computed tomography, poses substantial challenges, which have yet to be overcome. Previous work using SAXS computed tomography was unable to preserve oriented SAXS signals during reconstruction. Here we present a solution to this problem and obtain a complete SAXS computed tomography, which preserves oriented scattering information. By introducing virtual tomography axes, we take advantage of the two-dimensional SAXS information recorded on an area detector and use it to reconstruct the full three-dimensional scattering distribution in reciprocal space for each voxel of the three-dimensional object in real space. The presented method could be of interest for a combined six-dimensional real and reciprocal space characterization of mesoscopic materials with hierarchically structured features with length scales ranging from a few nanometres to a few millimetres—for example, biomaterials such as bone or teeth, or functional materials such as fuel-cell or battery components.

Six-dimensional real and reciprocal space small-angle X-ray scattering tomography.

PubMed

Schaff, Florian; Bech, Martin; Zaslansky, Paul; Jud, Christoph; Liebi, Marianne; Guizar-Sicairos, Manuel; Pfeiffer, Franz

2015-11-19

When used in combination with raster scanning, small-angle X-ray scattering (SAXS) has proven to be a valuable imaging technique of the nanoscale, for example of bone, teeth and brain matter. Although two-dimensional projection imaging has been used to characterize various materials successfully, its three-dimensional extension, SAXS computed tomography, poses substantial challenges, which have yet to be overcome. Previous work using SAXS computed tomography was unable to preserve oriented SAXS signals during reconstruction. Here we present a solution to this problem and obtain a complete SAXS computed tomography, which preserves oriented scattering information. By introducing virtual tomography axes, we take advantage of the two-dimensional SAXS information recorded on an area detector and use it to reconstruct the full three-dimensional scattering distribution in reciprocal space for each voxel of the three-dimensional object in real space. The presented method could be of interest for a combined six-dimensional real and reciprocal space characterization of mesoscopic materials with hierarchically structured features with length scales ranging from a few nanometres to a few millimetres--for example, biomaterials such as bone or teeth, or functional materials such as fuel-cell or battery components.

Optimization of a Boiling Water Reactor Loading Pattern Using an Improved Genetic Algorithm

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

Kobayashi, Yoko; Aiyoshi, Eitaro

2003-08-15

A search method based on genetic algorithms (GA) using deterministic operators has been developed to generate optimized boiling water reactor (BWR) loading patterns (LPs). The search method uses an Improved GA operator, that is, crossover, mutation, and selection. The handling of the encoding technique and constraint conditions is designed so that the GA reflects the peculiar characteristics of the BWR. In addition, some strategies such as elitism and self-reproduction are effectively used to improve the search speed. LP evaluations were performed with a three-dimensional diffusion code that coupled neutronic and thermal-hydraulic models. Strong axial heterogeneities and three-dimensional-dependent constraints have alwaysmore » necessitated the use of three-dimensional core simulators for BWRs, so that an optimization method is required for computational efficiency. The proposed algorithm is demonstrated by successfully generating LPs for an actual BWR plant applying the Haling technique. In test calculations, candidates that shuffled fresh and burned fuel assemblies within a reasonable computation time were obtained.« less

Cosmological perturbations in the (1 + 3 + 6)-dimensional space-times

NASA Astrophysics Data System (ADS)

Tomita, K.

2014-12-01

Cosmological perturbations in the (1+3+6)-dimensional space-times including photon gas without viscous processes are studied on the basis of Abbott et al.'s formalism [R. B. Abbott, B. Bednarz, and S. D. Ellis, Phys. Rev. D 33, 2147 (1986)]. Space-times consist of outer space (the 3-dimensional expanding section) and inner space (the 6-dimensional section). The inner space expands initially and later contracts. Abbott et al. derived only power-type solutions, which appear at the final stage of the space-times, in the small wave-number limit. In this paper, we derive not only small wave-number solutions, but also large wave-number solutions. It is found that the latter solutions depend on the two wave-numbers k_r and k_R (which are defined in the outer and inner spaces, respectively), and that the k_r-dependent and k_R-dependent parts dominate the total perturbations when (k_r/r(t))/(k_R/R(t)) ≫ 1 or ≪ 1, respectively, where r(t) and R(t) are the scale-factors in the outer and inner spaces. By comparing the behaviors of these perturbations, moreover, changes in the spectrum of perturbations in the outer space with time are discussed.

Markerless human motion tracking using hierarchical multi-swarm cooperative particle swarm optimization.

PubMed

Saini, Sanjay; Zakaria, Nordin; Rambli, Dayang Rohaya Awang; Sulaiman, Suziah

2015-01-01

The high-dimensional search space involved in markerless full-body articulated human motion tracking from multiple-views video sequences has led to a number of solutions based on metaheuristics, the most recent form of which is Particle Swarm Optimization (PSO). However, the classical PSO suffers from premature convergence and it is trapped easily into local optima, significantly affecting the tracking accuracy. To overcome these drawbacks, we have developed a method for the problem based on Hierarchical Multi-Swarm Cooperative Particle Swarm Optimization (H-MCPSO). The tracking problem is formulated as a non-linear 34-dimensional function optimization problem where the fitness function quantifies the difference between the observed image and a projection of the model configuration. Both the silhouette and edge likelihoods are used in the fitness function. Experiments using Brown and HumanEva-II dataset demonstrated that H-MCPSO performance is better than two leading alternative approaches-Annealed Particle Filter (APF) and Hierarchical Particle Swarm Optimization (HPSO). Further, the proposed tracking method is capable of automatic initialization and self-recovery from temporary tracking failures. Comprehensive experimental results are presented to support the claims.

Coupling effect of topological states and Chern insulators in two-dimensional triangular lattices

NASA Astrophysics Data System (ADS)

Zhang, Jiayong; Zhao, Bao; Xue, Yang; Zhou, Tong; Yang, Zhongqin

2018-03-01

We investigate topological states of two-dimensional (2D) triangular lattices with multiorbitals. Tight-binding model calculations of a 2D triangular lattice based on px and py orbitals exhibit very interesting doubly degenerate energy points at different positions (Γ and K /K' ) in momentum space, with quadratic non-Dirac and linear Dirac band dispersions, respectively. Counterintuitively, the system shows a global topologically trivial rather than nontrivial state with consideration of spin-orbit coupling due to the "destructive interference effect" between the topological states at the Γ and K /K' points. The topologically nontrivial state can emerge by introducing another set of triangular lattices to the system (bitriangular lattices) due to the breakdown of the interference effect. With first-principles calculations, we predict an intrinsic Chern insulating behavior (quantum anomalous Hall effect) in a family of the 2D triangular lattice metal-organic framework of Co(C21N3H15) (TPyB-Co) from this scheme. Our results provide a different path and theoretical guidance for the search for and design of new 2D topological quantum materials.

Fermion dark matter in gauge-Higgs unification

DOE PAGES

Maru, Nobuhito; Miyaji, Takashi; Okada, Nobuchika; ...

2017-07-11

Here, we propose a Majorana fermion dark matter in the context of a s imple gauge-Higgs Unification (GHU) scenario based on the gauge group SU(3)×U(1)' in 5-dimensional Minkowski space with a compactification of the 5th dimension on S 1/Z 2 orbifold. The dark matter particle is identified with the lightest mode in SU(3) triplet fermions additionally introduced in the 5-dimensional bulk. We find an allowed parameter region for the dark matter mass around a half of the Standard Model Higgs boson mass, which is consistent with the observed dark matter density and the constraint from the LUX 2016 result formore » the direct dark matter search. The entire allowed region will be covered by, for example, the LUX-ZEPLIN dark matter experiment in the near future. We also show that in the presence of the bulk SU(3) triplet fermions the 125 GeV Higgs boson mas s is reproduced through the renormalization group evolution of Higgs quartic coupling with the compactification scale of around 10 8 GeV.« 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.

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

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

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

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

CellAtlasSearch: a scalable search engine for single cells.

PubMed

Srivastava, Divyanshu; Iyer, Arvind; Kumar, Vibhor; Sengupta, Debarka

2018-05-21

Owing to the advent of high throughput single cell transcriptomics, past few years have seen exponential growth in production of gene expression data. Recently efforts have been made by various research groups to homogenize and store single cell expression from a large number of studies. The true value of this ever increasing data deluge can be unlocked by making it searchable. To this end, we propose CellAtlasSearch, a novel search architecture for high dimensional expression data, which is massively parallel as well as light-weight, thus infinitely scalable. In CellAtlasSearch, we use a Graphical Processing Unit (GPU) friendly version of Locality Sensitive Hashing (LSH) for unmatched speedup in data processing and query. Currently, CellAtlasSearch features over 300 000 reference expression profiles including both bulk and single-cell data. It enables the user query individual single cell transcriptomes and finds matching samples from the database along with necessary meta information. CellAtlasSearch aims to assist researchers and clinicians in characterizing unannotated single cells. It also facilitates noise free, low dimensional representation of single-cell expression profiles by projecting them on a wide variety of reference samples. The web-server is accessible at: http://www.cellatlassearch.com.

Foraging in Semantic Fields: How We Search Through Memory.

PubMed

Hills, Thomas T; Todd, Peter M; Jones, Michael N

2015-07-01

When searching for concepts in memory--as in the verbal fluency task of naming all the animals one can think of--people appear to explore internal mental representations in much the same way that animals forage in physical space: searching locally within patches of information before transitioning globally between patches. However, the definition of the patches being searched in mental space is not well specified. Do we search by activating explicit predefined categories (e.g., pets) and recall items from within that category (categorical search), or do we activate and recall a connected sequence of individual items without using categorical information, with each item recalled leading to the retrieval of an associated item in a stream (associative search), or both? Using semantic representations in a search of associative memory framework and data from the animal fluency task, we tested competing hypotheses based on associative and categorical search models. Associative, but not categorical, patch transitions took longer to make than position-matched productions, suggesting that categorical transitions were not true transitions. There was also clear evidence of associative search even within categorical patch boundaries. Furthermore, most individuals' behavior was best explained by an associative search model without the addition of categorical information. Thus, our results support a search process that does not use categorical information, but for which patch boundaries shift with each recall and local search is well described by a random walk in semantic space, with switches to new regions of the semantic space when the current region is depleted. Copyright © 2015 Cognitive Science Society, Inc.

Stochastic optimization of broadband reflecting photonic structures.

PubMed

Estrada-Wiese, D; Del Río-Chanona, E A; Del Río, J A

2018-01-19

Photonic crystals (PCs) are built to control the propagation of light within their structure. These can be used for an assortment of applications where custom designed devices are of interest. Among them, one-dimensional PCs can be produced to achieve the reflection of specific and broad wavelength ranges. However, their design and fabrication are challenging due to the diversity of periodic arrangement and layer configuration that each different PC needs. In this study, we present a framework to design high reflecting PCs for any desired wavelength range. Our method combines three stochastic optimization algorithms (Random Search, Particle Swarm Optimization and Simulated Annealing) along with a reduced space-search methodology to obtain a custom and optimized PC configuration. The optimization procedure is evaluated through theoretical reflectance spectra calculated by using the Equispaced Thickness Method, which improves the simulations due to the consideration of incoherent light transmission. We prove the viability of our procedure by fabricating different reflecting PCs made of porous silicon and obtain good agreement between experiment and theory using a merit function. With this methodology, diverse reflecting PCs can be designed for any applications and fabricated with different materials.

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.

Quasi-One-Dimensional Ultracold Fermi Gases

NASA Astrophysics Data System (ADS)

Revelle, Melissa C.

Ultracold atoms have become an essential tool in studying condensed matter phenomena. The advantage of atomic physics experiments is that they provide an easily tunable system. This experiment uses the lowest two ground state hyperfine levels of fermionic lithium. Having two different states creates a pseudo-spin- 1/2 system and allows us to emulate electronic systems, such as superconductors and crystal lattices. In our experiment, we can control the ratio between these two states resulting in either a spin-balanced or a spin-imbalanced gas. Imposing an imbalance is analogous to applying a magnetic field to a superconductor which causes the electrons in the material to align to the field (thus breaking the electron pairs which cause superconductivity). This motivates us to understand the phases created when a spin-imbalance is created and the effect of changing the atomic interactions. In a 3D system, we find where superfluidity is suppressed throughout the BEC to BCS crossover. Using phase separation as a guide, we probe the dimensional crossover between 1D and 3D. The phase separation in 1D is inverted from that in 3D, which provides a unique characteristic to distinguish between the dimensions. By varying the tunneling between tubes and the atomic interactions in a 2D optical lattice, we control whether the system is 1D, 3D, or in between. Using the properties of a 3D gas as a guide, we directly observe when the gas has crossed over from being dominated by 1D-like behavior to 3D. In this way, we have found a universal value for the dimensional crossover. The 1D-3D crossover paves the way to search for the exotic FFLO (Fulde-Ferrell-Larkin-Ovchinnikov) superconductor. While most superconductors do not coexist with magnetism, the FFLO phase requires large magnetic fields to support its pairing mechanism. Additionally, this phase is more likely to be found in lower dimensional systems. However, at low dimensions, the effect of temperature fluctuations on the phase is destabilizing, but these temperature effects are reduced with higher dimensionality. Thus, the quasi-1D regime is the optimal region of parameter space to find this phase. The search for direct evidence of FFLO continues in this regime.

Low-Dimensional Statistics of Anatomical Variability via Compact Representation of Image Deformations.

PubMed

Zhang, Miaomiao; Wells, William M; Golland, Polina

2016-10-01

Using image-based descriptors to investigate clinical hypotheses and therapeutic implications is challenging due to the notorious "curse of dimensionality" coupled with a small sample size. In this paper, we present a low-dimensional analysis of anatomical shape variability in the space of diffeomorphisms and demonstrate its benefits for clinical studies. To combat the high dimensionality of the deformation descriptors, we develop a probabilistic model of principal geodesic analysis in a bandlimited low-dimensional space that still captures the underlying variability of image data. We demonstrate the performance of our model on a set of 3D brain MRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our model yields a more compact representation of group variation at substantially lower computational cost than models based on the high-dimensional state-of-the-art approaches such as tangent space PCA (TPCA) and probabilistic principal geodesic analysis (PPGA).

Modelling Parsing Constraints with High-Dimensional Context Space.

ERIC Educational Resources Information Center

Burgess, Curt; Lund, Kevin

1997-01-01

Presents a model of high-dimensional context space, the Hyperspace Analogue to Language (HAL), with a series of simulations modelling human empirical results. Proposes that HAL's context space can be used to provide a basic categorization of semantic and grammatical concepts; model certain aspects of morphological ambiguity in verbs; and provide…

Generalized Minimum-Time Follow-up Approaches Applied to Tasking Electro-Optical Sensor Tasking

NASA Astrophysics Data System (ADS)

Murphy, T. S.; Holzinger, M. J.

This work proposes a methodology for tasking of sensors to search an area of state space for a particular object, group of objects, or class of objects. This work creates a general unified mathematical framework for analyzing reacquisition, search, scheduling, and custody operations. In particular, this work looks at searching for unknown space object(s) with prior knowledge in the form of a set, which can be defined via an uncorrelated track, region of state space, or a variety of other methods. The follow-up tasking can occur from a variable location and time, which often requires searching a large region of the sky. This work analyzes the area of a search region over time to inform a time optimal search method. Simulation work looks at analyzing search regions relative to a particular sensor, and testing a tasking algorithm to search through the region. The tasking algorithm is also validated on a reacquisition problem with a telescope system at Georgia Tech.

Faceted Visualization of Three Dimensional Neuroanatomy By Combining Ontology with Faceted Search

PubMed Central

Veeraraghavan, Harini; Miller, James V.

2013-01-01

In this work, we present a faceted-search based approach for visualization of anatomy by combining a three dimensional digital atlas with an anatomy ontology. Specifically, our approach provides a drill-down search interface that exposes the relevant pieces of information (obtained by searching the ontology) for a user query. Hence, the user can produce visualizations starting with minimally specified queries. Furthermore, by automatically translating the user queries into the controlled terminology our approach eliminates the need for the user to use controlled terminology. We demonstrate the scalability of our approach using an abdominal atlas and the same ontology. We implemented our visualization tool on the opensource 3D Slicer software. We present results of our visualization approach by combining a modified Foundational Model of Anatomy (FMA) ontology with the Surgical Planning Laboratory (SPL) Brain 3D digital atlas, and geometric models specific to patients computed using the SPL brain tumor dataset. PMID:24006207

Faceted visualization of three dimensional neuroanatomy by combining ontology with faceted search.

PubMed

Veeraraghavan, Harini; Miller, James V

2014-04-01

In this work, we present a faceted-search based approach for visualization of anatomy by combining a three dimensional digital atlas with an anatomy ontology. Specifically, our approach provides a drill-down search interface that exposes the relevant pieces of information (obtained by searching the ontology) for a user query. Hence, the user can produce visualizations starting with minimally specified queries. Furthermore, by automatically translating the user queries into the controlled terminology our approach eliminates the need for the user to use controlled terminology. We demonstrate the scalability of our approach using an abdominal atlas and the same ontology. We implemented our visualization tool on the opensource 3D Slicer software. We present results of our visualization approach by combining a modified Foundational Model of Anatomy (FMA) ontology with the Surgical Planning Laboratory (SPL) Brain 3D digital atlas, and geometric models specific to patients computed using the SPL brain tumor dataset.

Gravitational Wave Detection of Compact Binaries Through Multivariate Analysis

NASA Astrophysics Data System (ADS)

Atallah, Dany Victor; Dorrington, Iain; Sutton, Patrick

2017-01-01

The first detection of gravitational waves (GW), GW150914, as produced by a binary black hole merger, has ushered in the era of GW astronomy. The detection technique used to find GW150914 considered only a fraction of the information available describing the candidate event: mainly the detector signal to noise ratios and chi-squared values. In hopes of greatly increasing detection rates, we want to take advantage of all the information available about candidate events. We employ a technique called Multivariate Analysis (MVA) to improve LIGO sensitivity to GW signals. MVA techniques are efficient ways to scan high dimensional data spaces for signal/noise classification. Our goal is to use MVA to classify compact-object binary coalescence (CBC) events composed of any combination of black holes and neutron stars. CBC waveforms are modeled through numerical relativity. Templates of the modeled waveforms are used to search for CBCs and quantify candidate events. Different MVA pipelines are under investigation to look for CBC signals and un-modelled signals, with promising results. One such MVA pipeline used for the un-modelled search can theoretically analyze far more data than the MVA pipelines currently explored for CBCs, potentially making a more powerful classifier. In principle, this extra information could improve the sensitivity to GW signals. We will present the results from our efforts to adapt an MVA pipeline used in the un-modelled search to classify candidate events from the CBC search.

Optimization and universality of Brownian search in a basic model of quenched heterogeneous media

NASA Astrophysics Data System (ADS)

Godec, Aljaž; Metzler, Ralf

2015-05-01

The kinetics of a variety of transport-controlled processes can be reduced to the problem of determining the mean time needed to arrive at a given location for the first time, the so-called mean first-passage time (MFPT) problem. The occurrence of occasional large jumps or intermittent patterns combining various types of motion are known to outperform the standard random walk with respect to the MFPT, by reducing oversampling of space. Here we show that a regular but spatially heterogeneous random walk can significantly and universally enhance the search in any spatial dimension. In a generic minimal model we consider a spherically symmetric system comprising two concentric regions with piecewise constant diffusivity. The MFPT is analyzed under the constraint of conserved average dynamics, that is, the spatially averaged diffusivity is kept constant. Our analytical calculations and extensive numerical simulations demonstrate the existence of an optimal heterogeneity minimizing the MFPT to the target. We prove that the MFPT for a random walk is completely dominated by what we term direct trajectories towards the target and reveal a remarkable universality of the spatially heterogeneous search with respect to target size and system dimensionality. In contrast to intermittent strategies, which are most profitable in low spatial dimensions, the spatially inhomogeneous search performs best in higher dimensions. Discussing our results alongside recent experiments on single-particle tracking in living cells, we argue that the observed spatial heterogeneity may be beneficial for cellular signaling processes.

Fully Three-Dimensional Virtual-Reality System

NASA Technical Reports Server (NTRS)

Beckman, Brian C.

1994-01-01

Proposed virtual-reality system presents visual displays to simulate free flight in three-dimensional space. System, virtual space pod, is testbed for control and navigation schemes. Unlike most virtual-reality systems, virtual space pod would not depend for orientation on ground plane, which hinders free flight in three dimensions. Space pod provides comfortable seating, convenient controls, and dynamic virtual-space images for virtual traveler. Controls include buttons plus joysticks with six degrees of freedom.

Diffusion with resetting inside a circle

NASA Astrophysics Data System (ADS)

Chatterjee, Abhinava; Christou, Christos; Schadschneider, Andreas

2018-06-01

We study the Brownian motion of a particle in a bounded circular two-dimensional domain in search for a stationary target on the boundary of the domain. The process switches between two modes: one where it performs a two-dimensional diffusion inside the circle and one where it diffuses along the one-dimensional boundary. During the process, the Brownian particle resets to its initial position with a constant rate r . The Fokker-Planck formalism allows us to calculate the mean time to absorption (MTA) as well as the optimal resetting rate for which the MTA is minimized. From the derived analytical results the parameter regions where resetting reduces the search time can be specified. We also provide a numerical method for the verification of our results.

Kinematic state estimation and motion planning for stochastic nonholonomic systems using the exponential map.

PubMed

Park, Wooram; Liu, Yan; Zhou, Yu; Moses, Matthew; Chirikjian, Gregory S

2008-04-11

A nonholonomic system subjected to external noise from the environment, or internal noise in its own actuators, will evolve in a stochastic manner described by an ensemble of trajectories. This ensemble of trajectories is equivalent to the solution of a Fokker-Planck equation that typically evolves on a Lie group. If the most likely state of such a system is to be estimated, and plans for subsequent motions from the current state are to be made so as to move the system to a desired state with high probability, then modeling how the probability density of the system evolves is critical. Methods for solving Fokker-Planck equations that evolve on Lie groups then become important. Such equations can be solved using the operational properties of group Fourier transforms in which irreducible unitary representation (IUR) matrices play a critical role. Therefore, we develop a simple approach for the numerical approximation of all the IUR matrices for two of the groups of most interest in robotics: the rotation group in three-dimensional space, SO(3), and the Euclidean motion group of the plane, SE(2). This approach uses the exponential mapping from the Lie algebras of these groups, and takes advantage of the sparse nature of the Lie algebra representation matrices. Other techniques for density estimation on groups are also explored. The computed densities are applied in the context of probabilistic path planning for kinematic cart in the plane and flexible needle steering in three-dimensional space. In these examples the injection of artificial noise into the computational models (rather than noise in the actual physical systems) serves as a tool to search the configuration spaces and plan paths. Finally, we illustrate how density estimation problems arise in the characterization of physical noise in orientational sensors such as gyroscopes.

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

NASA Astrophysics Data System (ADS)

Platonov, S. S.

2018-02-01

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

Simulating Scenes In Outer Space

NASA Technical Reports Server (NTRS)

Callahan, John D.

1989-01-01

Multimission Interactive Picture Planner, MIP, computer program for scientifically accurate and fast, three-dimensional animation of scenes in deep space. Versatile, reasonably comprehensive, and portable, and runs on microcomputers. New techniques developed to perform rapidly calculations and transformations necessary to animate scenes in scientifically accurate three-dimensional space. Written in FORTRAN 77 code. Primarily designed to handle Voyager, Galileo, and Space Telescope. Adapted to handle other missions.

Antenna concepts for interstellar search systems

NASA Technical Reports Server (NTRS)

Basler, R. P.; Johnson, G. L.; Vondrak, R. R.

1977-01-01

An evaluation is made of microwave receiving systems designed to search for signals from extraterrestrial intelligence. Specific design concepts are analyzed parametrically to determine whether the optimum antenna system location is on earth, in space, or on the moon. Parameters considered include the hypothesized number of transmitting civilizations, the number of stars that must be searched to give any desired probability of receiving a signal, the antenna collecting area, the search time, the search range, and the cost. This analysis suggests that (1) search systems based on the moon are not cost-competitive, (2) if the search is extended only a few hundred light years from the earth, a Cyclops-type array on earth may be the most cost-effective system, (3) for a search extending to 500 light years or more, a substantial cost and search-time advantage can be achieved with a large spherical reflector in space with multiple feeds, (4) radio frequency interference shields can be provided for space systems, and (5) cost can range from a few hundred million to tens of billions of dollars, depending on the parameter values assumed.

The NUHM2 after LHC Run 1

DOE PAGES

Buchmueller, O.; Cavanaugh, R.; Citron, M.; ...

2014-12-17

We make a frequentist analysis of the parameter space of the NUHM2, in which the soft supersymmetry (SUSY)-breaking contributions to the masses of the two Higgs multiplets, m 2 Hu,d, vary independently from the universal soft SUSY-breaking contributions m 2 0 to the masses of squarks and sleptons. Our analysis uses the MultiNest sampling algorithm with over 4 × 10⁸ points to sample the NUHM2 parameter space. It includes the ATLAS and CMS Higgs mass measurements as well as the ATLAS search for supersymmetric jets + /E T signals using the full LHC Run 1 data, the measurements of BR(Bmore » s→μ⁺μ⁻) by LHCb and CMS together with other B-physics observables, electroweak precision observables and the XENON100 and LUX searches for spin-independent dark-matter scattering. We find that the preferred regions of the NUHM2 parameter space have negative SUSY-breaking scalar masses squared at the GUT scale for squarks and sleptons, m 2 0 < 0, as well as m 2 Hu < m 2 Hd < 0. The tension present in the CMSSM and NUHM1 between the supersymmetric interpretation of (g – 2)μ and the absence to date of SUSY at the LHC is not significantly alleviated in the NUHM2. We find that the minimum χ 2 = 32.5 with 21 degrees of freedom (dof) in the NUHM2, to be compared with χ 2/dof = 35.0/23 in the CMSSM, and χ 2/dof = 32.7/22 in the NUHM1. We find that the one-dimensional likelihood functions for sparticle masses and other observables are similar to those found previously in the CMSSM and NUHM1.« less

A Hybrid Color Space for Skin Detection Using Genetic Algorithm Heuristic Search and Principal Component Analysis Technique

PubMed Central

2015-01-01

Color is one of the most prominent features of an image and used in many skin and face detection applications. Color space transformation is widely used by researchers to improve face and skin detection performance. Despite the substantial research efforts in this area, choosing a proper color space in terms of skin and face classification performance which can address issues like illumination variations, various camera characteristics and diversity in skin color tones has remained an open issue. This research proposes a new three-dimensional hybrid color space termed SKN by employing the Genetic Algorithm heuristic and Principal Component Analysis to find the optimal representation of human skin color in over seventeen existing color spaces. Genetic Algorithm heuristic is used to find the optimal color component combination setup in terms of skin detection accuracy while the Principal Component Analysis projects the optimal Genetic Algorithm solution to a less complex dimension. Pixel wise skin detection was used to evaluate the performance of the proposed color space. We have employed four classifiers including Random Forest, Naïve Bayes, Support Vector Machine and Multilayer Perceptron in order to generate the human skin color predictive model. The proposed color space was compared to some existing color spaces and shows superior results in terms of pixel-wise skin detection accuracy. Experimental results show that by using Random Forest classifier, the proposed SKN color space obtained an average F-score and True Positive Rate of 0.953 and False Positive Rate of 0.0482 which outperformed the existing color spaces in terms of pixel wise skin detection accuracy. The results also indicate that among the classifiers used in this study, Random Forest is the most suitable classifier for pixel wise skin detection applications. PMID:26267377

Parameter-space metric of semicoherent searches for continuous gravitational waves

NASA Astrophysics Data System (ADS)

Pletsch, Holger J.

2010-08-01

Continuous gravitational-wave (CW) signals such as emitted by spinning neutron stars are an important target class for current detectors. However, the enormous computational demand prohibits fully coherent broadband all-sky searches for prior unknown CW sources over wide ranges of parameter space and for yearlong observation times. More efficient hierarchical “semicoherent” search strategies divide the data into segments much shorter than one year, which are analyzed coherently; then detection statistics from different segments are combined incoherently. To optimally perform the incoherent combination, understanding of the underlying parameter-space structure is requisite. This problem is addressed here by using new coordinates on the parameter space, which yield the first analytical parameter-space metric for the incoherent combination step. This semicoherent metric applies to broadband all-sky surveys (also embedding directed searches at fixed sky position) for isolated CW sources. Furthermore, the additional metric resolution attained through the combination of segments is studied. From the search parameters (sky position, frequency, and frequency derivatives), solely the metric resolution in the frequency derivatives is found to significantly increase with the number of segments.

A Bayesian trans-dimensional approach for the fusion of multiple geophysical datasets

NASA Astrophysics Data System (ADS)

JafarGandomi, Arash; Binley, Andrew

2013-09-01

We propose a Bayesian fusion approach to integrate multiple geophysical datasets with different coverage and sensitivity. The fusion strategy is based on the capability of various geophysical methods to provide enough resolution to identify either subsurface material parameters or subsurface structure, or both. We focus on electrical resistivity as the target material parameter and electrical resistivity tomography (ERT), electromagnetic induction (EMI), and ground penetrating radar (GPR) as the set of geophysical methods. However, extending the approach to different sets of geophysical parameters and methods is straightforward. Different geophysical datasets are entered into a trans-dimensional Markov chain Monte Carlo (McMC) search-based joint inversion algorithm. The trans-dimensional property of the McMC algorithm allows dynamic parameterisation of the model space, which in turn helps to avoid bias of the post-inversion results towards a particular model. Given that we are attempting to develop an approach that has practical potential, we discretize the subsurface into an array of one-dimensional earth-models. Accordingly, the ERT data that are collected by using two-dimensional acquisition geometry are re-casted to a set of equivalent vertical electric soundings. Different data are inverted either individually or jointly to estimate one-dimensional subsurface models at discrete locations. We use Shannon's information measure to quantify the information obtained from the inversion of different combinations of geophysical datasets. Information from multiple methods is brought together via introducing joint likelihood function and/or constraining the prior information. A Bayesian maximum entropy approach is used for spatial fusion of spatially dispersed estimated one-dimensional models and mapping of the target parameter. We illustrate the approach with a synthetic dataset and then apply it to a field dataset. We show that the proposed fusion strategy is successful not only in enhancing the subsurface information but also as a survey design tool to identify the appropriate combination of the geophysical tools and show whether application of an individual method for further investigation of a specific site is beneficial.

RNA FRABASE 2.0: an advanced web-accessible database with the capacity to search the three-dimensional fragments within RNA structures

PubMed Central

2010-01-01

Background Recent discoveries concerning novel functions of RNA, such as RNA interference, have contributed towards the growing importance of the field. In this respect, a deeper knowledge of complex three-dimensional RNA structures is essential to understand their new biological functions. A number of bioinformatic tools have been proposed to explore two major structural databases (PDB, NDB) in order to analyze various aspects of RNA tertiary structures. One of these tools is RNA FRABASE 1.0, the first web-accessible database with an engine for automatic search of 3D fragments within PDB-derived RNA structures. This search is based upon the user-defined RNA secondary structure pattern. In this paper, we present and discuss RNA FRABASE 2.0. This second version of the system represents a major extension of this tool in terms of providing new data and a wide spectrum of novel functionalities. An intuitionally operated web server platform enables very fast user-tailored search of three-dimensional RNA fragments, their multi-parameter conformational analysis and visualization. Description RNA FRABASE 2.0 has stored information on 1565 PDB-deposited RNA structures, including all NMR models. The RNA FRABASE 2.0 search engine algorithms operate on the database of the RNA sequences and the new library of RNA secondary structures, coded in the dot-bracket format extended to hold multi-stranded structures and to cover residues whose coordinates are missing in the PDB files. The library of RNA secondary structures (and their graphics) is made available. A high level of efficiency of the 3D search has been achieved by introducing novel tools to formulate advanced searching patterns and to screen highly populated tertiary structure elements. RNA FRABASE 2.0 also stores data and conformational parameters in order to provide "on the spot" structural filters to explore the three-dimensional RNA structures. An instant visualization of the 3D RNA structures is provided. RNA FRABASE 2.0 is freely available at http://rnafrabase.cs.put.poznan.pl. Conclusions RNA FRABASE 2.0 provides a novel database and powerful search engine which is equipped with new data and functionalities that are unavailable elsewhere. Our intention is that this advanced version of the RNA FRABASE will be of interest to all researchers working in the RNA field. PMID:20459631

RNA FRABASE 2.0: an advanced web-accessible database with the capacity to search the three-dimensional fragments within RNA structures.

PubMed

Popenda, Mariusz; Szachniuk, Marta; Blazewicz, Marek; Wasik, Szymon; Burke, Edmund K; Blazewicz, Jacek; Adamiak, Ryszard W

2010-05-06

Recent discoveries concerning novel functions of RNA, such as RNA interference, have contributed towards the growing importance of the field. In this respect, a deeper knowledge of complex three-dimensional RNA structures is essential to understand their new biological functions. A number of bioinformatic tools have been proposed to explore two major structural databases (PDB, NDB) in order to analyze various aspects of RNA tertiary structures. One of these tools is RNA FRABASE 1.0, the first web-accessible database with an engine for automatic search of 3D fragments within PDB-derived RNA structures. This search is based upon the user-defined RNA secondary structure pattern. In this paper, we present and discuss RNA FRABASE 2.0. This second version of the system represents a major extension of this tool in terms of providing new data and a wide spectrum of novel functionalities. An intuitionally operated web server platform enables very fast user-tailored search of three-dimensional RNA fragments, their multi-parameter conformational analysis and visualization. RNA FRABASE 2.0 has stored information on 1565 PDB-deposited RNA structures, including all NMR models. The RNA FRABASE 2.0 search engine algorithms operate on the database of the RNA sequences and the new library of RNA secondary structures, coded in the dot-bracket format extended to hold multi-stranded structures and to cover residues whose coordinates are missing in the PDB files. The library of RNA secondary structures (and their graphics) is made available. A high level of efficiency of the 3D search has been achieved by introducing novel tools to formulate advanced searching patterns and to screen highly populated tertiary structure elements. RNA FRABASE 2.0 also stores data and conformational parameters in order to provide "on the spot" structural filters to explore the three-dimensional RNA structures. An instant visualization of the 3D RNA structures is provided. RNA FRABASE 2.0 is freely available at http://rnafrabase.cs.put.poznan.pl. RNA FRABASE 2.0 provides a novel database and powerful search engine which is equipped with new data and functionalities that are unavailable elsewhere. Our intention is that this advanced version of the RNA FRABASE will be of interest to all researchers working in the RNA field.

Basis adaptation in homogeneous chaos spaces

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

Tipireddy, Ramakrishna; Ghanem, Roger

2014-02-01

We present a new meth for the characterization of subspaces associated with low-dimensional quantities of interet (QoI). The probability density function of these QoI is found to be concentrated around one-dimensional subspaces for which we develop projection operators. Our approach builds on the properties of Gaussian Hilbert spaces and associated tensor product spaces.

Robust video copy detection approach based on local tangent space alignment

NASA Astrophysics Data System (ADS)

Nie, Xiushan; Qiao, Qianping

2012-04-01

We propose a robust content-based video copy detection approach based on local tangent space alignment (LTSA), which is an efficient dimensionality reduction algorithm. The idea is motivated by the fact that the content of video becomes richer and the dimension of content becomes higher. It does not give natural tools for video analysis and understanding because of the high dimensionality. The proposed approach reduces the dimensionality of video content using LTSA, and then generates video fingerprints in low dimensional space for video copy detection. Furthermore, a dynamic sliding window is applied to fingerprint matching. Experimental results show that the video copy detection approach has good robustness and discrimination.

Paradigm shift regarding the transversalis fascia, preperitoneal space, and Retzius' space.

PubMed

Asakage, N

2018-06-01

There has been confusion in the anatomical recognition when performing inguinal hernia operations in Japan. From now on, a paradigm shift from the concept of two-dimensional layer structure to the three-dimensional space recognition is necessary to promote an understanding of anatomy. Along with the formation of the abdominal wall, the extraperitoneal space is formed by the transversalis fascia and preperitoneal space. The transversalis fascia is a somatic vascular fascia originating from an arteriovenous fascia. It is a dense areolar tissue layer at the outermost of the extraperitoneal space that runs under the diaphragm and widely lines the body wall muscle. The umbilical funiculus is taken into the abdominal wall and transformed into the preperitoneal space that is a local three-dimensional cavity enveloping preperitoneal fasciae composed of the renal fascia, vesicohypogastric fascia, and testiculoeferential fascia. The Retzius' space is an artificial cavity formed at the boundary between the transversalis fascia and preperitoneal space. In the underlay mesh repair, the mesh expands in the range spanning across the Retzius' space and preperitoneal space.

Hörmander multipliers on two-dimensional dyadic Hardy spaces

NASA Astrophysics Data System (ADS)

Daly, J.; Fridli, S.

2008-12-01

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

Creating Body Shapes From Verbal Descriptions by Linking Similarity Spaces.

PubMed

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

2016-11-01

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

A search for experiments to exploit the space shuttle environment, volume 2

NASA Technical Reports Server (NTRS)

Fenn, J. B.

1979-01-01

Institutions and laboratories in India, Japan, and Western Europe which were visited during a search for experiments to exploit the space shuttle environment are described. The facilities and current research interests of the various centers are discussed with particular emphasis given to the Indian Space Research Organization.

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

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

Tomizawa, Shinya; Nozawa, Masato

2006-06-15

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

AGT/ℤ2

NASA Astrophysics Data System (ADS)

Le Floch, Bruno; Turiaci, Gustavo J.

2017-12-01

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

UNEXPECTED SERIES OF REGULAR FREQUENCY SPACING OF δ SCUTI STARS IN THE NON-ASYMPTOTIC REGIME. I. THE METHODOLOGY

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

Paparó, M.; Benkő, J. M.; Hareter, M.

A sequence search method was developed to search the regular frequency spacing in δ Scuti stars through visual inspection and an algorithmic search. We searched for sequences of quasi-equally spaced frequencies, containing at least four members per sequence, in 90 δ Scuti stars observed by CoRoT . We found an unexpectedly large number of independent series of regular frequency spacing in 77 δ Scuti stars (from one to eight sequences) in the non-asymptotic regime. We introduce the sequence search method presenting the sequences and echelle diagram of CoRoT 102675756 and the structure of the algorithmic search. Four sequences (echelle ridges)more » were found in the 5–21 d{sup −1} region where the pairs of the sequences are shifted (between 0.5 and 0.59 d{sup −1}) by twice the value of the estimated rotational splitting frequency (0.269 d{sup −1}). The general conclusions for the whole sample are also presented in this paper. The statistics of the spacings derived by the sequence search method, by FT (Fourier transform of the frequencies), and the statistics of the shifts are also compared. In many stars more than one almost equally valid spacing appeared. The model frequencies of FG Vir and their rotationally split components were used to formulate the possible explanation that one spacing is the large separation while the other is the sum of the large separation and the rotational frequency. In CoRoT 102675756, the two spacings (2.249 and 1.977 d{sup −1}) are in better agreement with the sum of a possible 1.710 d{sup −1} large separation and two or one times, respectively, the value of the rotational frequency.« less

Direct solution of the H(1s)-H + long-range interaction problem in momentum space

NASA Astrophysics Data System (ADS)

Koga, Toshikatsu

1985-02-01

Perturbation equations for the H(1s)-H+ long-range interaction are solved directly in momentum space up to the fourth order with respect to the reciprocal of the internuclear distance. As in the hydrogen atom problem, the Fock transformation is used which projects the momentum vector of an electron from the three-dimensional hyperplane onto the four-dimensional hypersphere. Solutions are given as linear combinations of several four-dimensional spherical harmonics. The present results add an example to the momentum-space solution of the nonspherical potential problem.

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

NASA Astrophysics Data System (ADS)

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

2013-11-01

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

On the frames of spaces of finite-dimensional Lie algebras of dimension at most 6

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

Gorbatsevich, V V

2014-05-31

In this paper, the frames of spaces of complex n-dimensional Lie algebras (that is, the intersections of all irreducible components of these spaces) are studied. A complete description of the frames and their projectivizations for n ≤ 6 is given. It is also proved that for n ≤ 6 the projectivizations of these spaces are simply connected. Bibliography: 7 titles.

Learning Parsimonious Classification Rules from Gene Expression Data Using Bayesian Networks with Local Structure.

PubMed

Lustgarten, Jonathan Lyle; Balasubramanian, Jeya Balaji; Visweswaran, Shyam; Gopalakrishnan, Vanathi

2017-03-01

The comprehensibility of good predictive models learned from high-dimensional gene expression data is attractive because it can lead to biomarker discovery. Several good classifiers provide comparable predictive performance but differ in their abilities to summarize the observed data. We extend a Bayesian Rule Learning (BRL-GSS) algorithm, previously shown to be a significantly better predictor than other classical approaches in this domain. It searches a space of Bayesian networks using a decision tree representation of its parameters with global constraints, and infers a set of IF-THEN rules. The number of parameters and therefore the number of rules are combinatorial to the number of predictor variables in the model. We relax these global constraints to a more generalizable local structure (BRL-LSS). BRL-LSS entails more parsimonious set of rules because it does not have to generate all combinatorial rules. The search space of local structures is much richer than the space of global structures. We design the BRL-LSS with the same worst-case time-complexity as BRL-GSS while exploring a richer and more complex model space. We measure predictive performance using Area Under the ROC curve (AUC) and Accuracy. We measure model parsimony performance by noting the average number of rules and variables needed to describe the observed data. We evaluate the predictive and parsimony performance of BRL-GSS, BRL-LSS and the state-of-the-art C4.5 decision tree algorithm, across 10-fold cross-validation using ten microarray gene-expression diagnostic datasets. In these experiments, we observe that BRL-LSS is similar to BRL-GSS in terms of predictive performance, while generating a much more parsimonious set of rules to explain the same observed data. BRL-LSS also needs fewer variables than C4.5 to explain the data with similar predictive performance. We also conduct a feasibility study to demonstrate the general applicability of our BRL methods on the newer RNA sequencing gene-expression data.

Cubic Dirac fermions in quasi-one-dimensional transition-metal chalcogenide semimetals immune to Peierls distortion

NASA Astrophysics Data System (ADS)

Liu, Qihang; Zunger, Alex

A Cubic Dirac Fermion in condensed-matter physics refers to a band crossing in periodic solids that has 4-fold degeneracy with cubic dispersions in certain directions. Such a crystalline symmetry induced fermion is composed of 6 Weyl fermions where 3 have left-handed and 3 have right-handed chirality, and constitutes one of the ``new fermions'' that have no counterpart in high-energy physics. However, no prediction has yet pointed to a plausible example of a material candidate hosting such a cubically-dispersed Dirac semimetal (CDSM). Here we establish the design principles for CDSM finding that only 2 out of 230 space groups possess the required symmetry elements. Adding the required band occupancy criteria, we conduct a material search using density functional band theory identifying a group of quasi-one-dimensional molybdenum chalcogenide compounds A(MoX)3 (A = Na, K, Rb, In, Tl; X = S, Se, Te) with space group P63/m as ideal CDSM candidates. Studying the stability of the A(MoX)3 family towards a Peierls distortion reveals a few candidates such as Rb(MoTe)3 and Tl(MoTe)3 that are resilliant to Peierls distortion, thus retaining the metallic character. This work was supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division under Grant No. DE-FG02-13ER46959 to University of Colorado, Boulder.

EM in high-dimensional spaces.

PubMed

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

2005-06-01

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

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

PubMed Central

Tian, Xin

2014-01-01

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

LEAP: biomarker inference through learning and evaluating association patterns.

PubMed

Jiang, Xia; Neapolitan, Richard E

2015-03-01

Single nucleotide polymorphism (SNP) high-dimensional datasets are available from Genome Wide Association Studies (GWAS). Such data provide researchers opportunities to investigate the complex genetic basis of diseases. Much of genetic risk might be due to undiscovered epistatic interactions, which are interactions in which combination of several genes affect disease. Research aimed at discovering interacting SNPs from GWAS datasets proceeded in two directions. First, tools were developed to evaluate candidate interactions. Second, algorithms were developed to search over the space of candidate interactions. Another problem when learning interacting SNPs, which has not received much attention, is evaluating how likely it is that the learned SNPs are associated with the disease. A complete system should provide this information as well. We develop such a system. Our system, called LEAP, includes a new heuristic search algorithm for learning interacting SNPs, and a Bayesian network based algorithm for computing the probability of their association. We evaluated the performance of LEAP using 100 1,000-SNP simulated datasets, each of which contains 15 SNPs involved in interactions. When learning interacting SNPs from these datasets, LEAP outperformed seven others methods. Furthermore, only SNPs involved in interactions were found to be probable. We also used LEAP to analyze real Alzheimer's disease and breast cancer GWAS datasets. We obtained interesting and new results from the Alzheimer's dataset, but limited results from the breast cancer dataset. We conclude that our results support that LEAP is a useful tool for extracting candidate interacting SNPs from high-dimensional datasets and determining their probability. © 2015 The Authors. *Genetic Epidemiology published by Wiley Periodicals, Inc.

Covalently Interlocked Cyclohexa-m-phenylenes and Their Assembly: En Route to Supramolecular 3D Carbon Nanostructures.

PubMed

Dumslaff, Bastian; Reuss, Anna N; Wagner, Manfred; Feng, Xinliang; Narita, Akimitsu; Fytas, George; Müllen, Klaus

2017-08-21

In our search to cluster as many phenylene units as possible in a given space, we have proceeded to the three-dimensional world of benzene-based molecules by employing covalently interlocked cyclohexa-m-phenylenes, as present in the unique paddlewheel-shaped polyphenylene 10. A precursor was conceived, in which freely rotating m-chlorophenylene units provide sufficient solubility along with the necessary proximity for the final ring closure to give 10. Monitoring the assembly of solubilized tert-butyl derivatives of 10 into supramolecular carbon nanostructures by dynamic light scattering (DLS) and Brillouin light scattering (BLS) revealed the dimensions of the initially formed aggregates as well as the amorphous character of the solid state. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

A comparative study of the A* heuristic search algorithm used to solve efficiently a puzzle game

NASA Astrophysics Data System (ADS)

Iordan, A. E.

2018-01-01

The puzzle game presented in this paper consists in polyhedra (prisms, pyramids or pyramidal frustums) which can be moved using the free available spaces. The problem requires to be found the minimum number of movements in order the game reaches to a goal configuration starting from an initial configuration. Because the problem is enough complex, the principal difficulty in solving it is given by dimension of search space, that leads to necessity of a heuristic search. The improving of the search method consists into determination of a strong estimation by the heuristic function which will guide the search process to the most promising side of the search tree. The comparative study is realized among Manhattan heuristic and the Hamming heuristic using A* search algorithm implemented in Java. This paper also presents the necessary stages in object oriented development of a software used to solve efficiently this puzzle game. The modelling of the software is achieved through specific UML diagrams representing the phases of analysis, design and implementation, the system thus being described in a clear and practical manner. With the purpose to confirm the theoretical results which demonstrates that Manhattan heuristic is more efficient was used space complexity criterion. The space complexity was measured by the number of generated nodes from the search tree, by the number of the expanded nodes and by the effective branching factor. From the experimental results obtained by using the Manhattan heuristic, improvements were observed regarding space complexity of A* algorithm versus Hamming heuristic.

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.

Small-angle X-ray scattering tensor tomography: model of the three-dimensional reciprocal-space map, reconstruction algorithm and angular sampling requirements.

PubMed

Liebi, Marianne; Georgiadis, Marios; Kohlbrecher, Joachim; Holler, Mirko; Raabe, Jörg; Usov, Ivan; Menzel, Andreas; Schneider, Philipp; Bunk, Oliver; Guizar-Sicairos, Manuel

2018-01-01

Small-angle X-ray scattering tensor tomography, which allows reconstruction of the local three-dimensional reciprocal-space map within a three-dimensional sample as introduced by Liebi et al. [Nature (2015), 527, 349-352], is described in more detail with regard to the mathematical framework and the optimization algorithm. For the case of trabecular bone samples from vertebrae it is shown that the model of the three-dimensional reciprocal-space map using spherical harmonics can adequately describe the measured data. The method enables the determination of nanostructure orientation and degree of orientation as demonstrated previously in a single momentum transfer q range. This article presents a reconstruction of the complete reciprocal-space map for the case of bone over extended ranges of q. In addition, it is shown that uniform angular sampling and advanced regularization strategies help to reduce the amount of data required.

Toward two-dimensional search engines

NASA Astrophysics Data System (ADS)

Ermann, L.; Chepelianskii, A. D.; Shepelyansky, D. L.

2012-07-01

We study the statistical properties of various directed networks using ranking of their nodes based on the dominant vectors of the Google matrix known as PageRank and CheiRank. On average PageRank orders nodes proportionally to a number of ingoing links, while CheiRank orders nodes proportionally to a number of outgoing links. In this way, the ranking of nodes becomes two dimensional which paves the way for the development of two-dimensional search engines of a new type. Statistical properties of information flow on the PageRank-CheiRank plane are analyzed for networks of British, French and Italian universities, Wikipedia, Linux Kernel, gene regulation and other networks. A special emphasis is done for British universities networks using the large database publicly available in the UK. Methods of spam links control are also analyzed.

Charged black lens in de Sitter space

NASA Astrophysics Data System (ADS)

Tomizawa, Shinya

2018-02-01

We obtain a charged black lens solution in the five-dimensional Einstein-Maxwell-Chern-Simons theory with a positive cosmological constant. It is shown that the solution obtained here describes the formation of a black hole with the spatial cross section of a sphere from that of the lens space of L (n ,1 ) in five-dimensional de Sitter space.

Three-Dimensional Localized-Delocalized Anderson Transition in the Time Domain

NASA Astrophysics Data System (ADS)

Delande, Dominique; Morales-Molina, Luis; Sacha, Krzysztof

2017-12-01

Systems which can spontaneously reveal periodic evolution are dubbed time crystals. This is in analogy with space crystals that display periodic behavior in configuration space. While space crystals are modeled with the help of space periodic potentials, crystalline phenomena in time can be modeled by periodically driven systems. Disorder in the periodic driving can lead to Anderson localization in time: the probability for detecting a system at a fixed point of configuration space becomes exponentially localized around a certain moment in time. We here show that a three-dimensional system exposed to a properly disordered pseudoperiodic driving may display a localized-delocalized Anderson transition in the time domain, in strong analogy with the usual three-dimensional Anderson transition in disordered systems. Such a transition could be experimentally observed with ultracold atomic gases.

Learning Problem-Solving Rules as Search through a Hypothesis Space

ERIC Educational Resources Information Center

Lee, Hee Seung; Betts, Shawn; Anderson, John R.

2016-01-01

Learning to solve a class of problems can be characterized as a search through a space of hypotheses about the rules for solving these problems. A series of four experiments studied how different learning conditions affected the search among hypotheses about the solution rule for a simple computational problem. Experiment 1 showed that a problem…

PF2fit: Polar Fast Fourier Matched Alignment of Atomistic Structures with 3D Electron Microscopy Maps.

PubMed

Bettadapura, Radhakrishna; Rasheed, Muhibur; Vollrath, Antje; Bajaj, Chandrajit

2015-10-01

There continue to be increasing occurrences of both atomistic structure models in the PDB (possibly reconstructed from X-ray diffraction or NMR data), and 3D reconstructed cryo-electron microscopy (3D EM) maps (albeit at coarser resolution) of the same or homologous molecule or molecular assembly, deposited in the EMDB. To obtain the best possible structural model of the molecule at the best achievable resolution, and without any missing gaps, one typically aligns (match and fits) the atomistic structure model with the 3D EM map. We discuss a new algorithm and generalized framework, named PF(2) fit (Polar Fast Fourier Fitting) for the best possible structural alignment of atomistic structures with 3D EM. While PF(2) fit enables only a rigid, six dimensional (6D) alignment method, it augments prior work on 6D X-ray structure and 3D EM alignment in multiple ways: Scoring. PF(2) fit includes a new scoring scheme that, in addition to rewarding overlaps between the volumes occupied by the atomistic structure and 3D EM map, rewards overlaps between the volumes complementary to them. We quantitatively demonstrate how this new complementary scoring scheme improves upon existing approaches. PF(2) fit also includes two scoring functions, the non-uniform exterior penalty and the skeleton-secondary structure score, and implements the scattering potential score as an alternative to traditional Gaussian blurring. Search. PF(2) fit utilizes a fast polar Fourier search scheme, whose main advantage is the ability to search over uniformly and adaptively sampled subsets of the space of rigid-body motions. PF(2) fit also implements a new reranking search and scoring methodology that considerably improves alignment metrics in results obtained from the initial search.

PF2 fit: Polar Fast Fourier Matched Alignment of Atomistic Structures with 3D Electron Microscopy Maps

PubMed Central

Bettadapura, Radhakrishna; Rasheed, Muhibur; Vollrath, Antje; Bajaj, Chandrajit

2015-01-01

There continue to be increasing occurrences of both atomistic structure models in the PDB (possibly reconstructed from X-ray diffraction or NMR data), and 3D reconstructed cryo-electron microscopy (3D EM) maps (albeit at coarser resolution) of the same or homologous molecule or molecular assembly, deposited in the EMDB. To obtain the best possible structural model of the molecule at the best achievable resolution, and without any missing gaps, one typically aligns (match and fits) the atomistic structure model with the 3D EM map. We discuss a new algorithm and generalized framework, named PF2 fit (Polar Fast Fourier Fitting) for the best possible structural alignment of atomistic structures with 3D EM. While PF2 fit enables only a rigid, six dimensional (6D) alignment method, it augments prior work on 6D X-ray structure and 3D EM alignment in multiple ways: Scoring. PF2 fit includes a new scoring scheme that, in addition to rewarding overlaps between the volumes occupied by the atomistic structure and 3D EM map, rewards overlaps between the volumes complementary to them. We quantitatively demonstrate how this new complementary scoring scheme improves upon existing approaches. PF2 fit also includes two scoring functions, the non-uniform exterior penalty and the skeleton-secondary structure score, and implements the scattering potential score as an alternative to traditional Gaussian blurring. Search. PF2 fit utilizes a fast polar Fourier search scheme, whose main advantage is the ability to search over uniformly and adaptively sampled subsets of the space of rigid-body motions. PF2 fit also implements a new reranking search and scoring methodology that considerably improves alignment metrics in results obtained from the initial search. PMID:26469938

Feature integration theory revisited: dissociating feature detection and attentional guidance in visual search.

PubMed

Chan, Louis K H; Hayward, William G

2009-02-01

In feature integration theory (FIT; A. Treisman & S. Sato, 1990), feature detection is driven by independent dimensional modules, and other searches are driven by a master map of locations that integrates dimensional information into salience signals. Although recent theoretical models have largely abandoned this distinction, some observed results are difficult to explain in its absence. The present study measured dimension-specific performance during detection and localization, tasks that require operation of dimensional modules and the master map, respectively. Results showed a dissociation between tasks in terms of both dimension-switching costs and cross-dimension attentional capture, reflecting a dimension-specific nature for detection tasks and a dimension-general nature for localization tasks. In a feature-discrimination task, results precluded an explanation based on response mode. These results are interpreted to support FIT's postulation that different mechanisms are involved in parallel and focal attention searches. This indicates that the FIT architecture should be adopted to explain the current results and that a variety of visual attention findings can be addressed within this framework. Copyright 2009 APA, all rights reserved.

Active Solution Space and Search on Job-shop Scheduling Problem

NASA Astrophysics Data System (ADS)

Watanabe, Masato; Ida, Kenichi; Gen, Mitsuo

In this paper we propose a new searching method of Genetic Algorithm for Job-shop scheduling problem (JSP). The coding method that represent job number in order to decide a priority to arrange a job to Gannt Chart (called the ordinal representation with a priority) in JSP, an active schedule is created by using left shift. We define an active solution at first. It is solution which can create an active schedule without using left shift, and set of its defined an active solution space. Next, we propose an algorithm named Genetic Algorithm with active solution space search (GA-asol) which can create an active solution while solution is evaluated, in order to search the active solution space effectively. We applied it for some benchmark problems to compare with other method. The experimental results show good performance.

Quantum computation in the analysis of hyperspectral data

NASA Astrophysics Data System (ADS)

Gomez, Richard B.; Ghoshal, Debabrata; Jayanna, Anil

2004-08-01

Recent research on the topic of quantum computation provides us with some quantum algorithms with higher efficiency and speedup compared to their classical counterparts. In this paper, it is our intent to provide the results of our investigation of several applications of such quantum algorithms - especially the Grover's Search algorithm - in the analysis of Hyperspectral Data. We found many parallels with Grover's method in existing data processing work that make use of classical spectral matching algorithms. Our efforts also included the study of several methods dealing with hyperspectral image analysis work where classical computation methods involving large data sets could be replaced with quantum computation methods. The crux of the problem in computation involving a hyperspectral image data cube is to convert the large amount of data in high dimensional space to real information. Currently, using the classical model, different time consuming methods and steps are necessary to analyze these data including: Animation, Minimum Noise Fraction Transform, Pixel Purity Index algorithm, N-dimensional scatter plot, Identification of Endmember spectra - are such steps. If a quantum model of computation involving hyperspectral image data can be developed and formalized - it is highly likely that information retrieval from hyperspectral image data cubes would be a much easier process and the final information content would be much more meaningful and timely. In this case, dimensionality would not be a curse, but a blessing.

Extraction of process zones and low-dimensional attractive subspaces in stochastic fracture mechanics

PubMed Central

Kerfriden, P.; Schmidt, K.M.; Rabczuk, T.; Bordas, S.P.A.

2013-01-01

We propose to identify process zones in heterogeneous materials by tailored statistical tools. The process zone is redefined as the part of the structure where the random process cannot be correctly approximated in a low-dimensional deterministic space. Such a low-dimensional space is obtained by a spectral analysis performed on pre-computed solution samples. A greedy algorithm is proposed to identify both process zone and low-dimensional representative subspace for the solution in the complementary region. In addition to the novelty of the tools proposed in this paper for the analysis of localised phenomena, we show that the reduced space generated by the method is a valid basis for the construction of a reduced order model. PMID:27069423

G-Strands on symmetric spaces

PubMed Central

2017-01-01

We study the G-strand equations that are extensions of the classical chiral model of particle physics in the particular setting of broken symmetries described by symmetric spaces. These equations are simple field theory models whose configuration space is a Lie group, or in this case a symmetric space. In this class of systems, we derive several models that are completely integrable on finite dimensional Lie group G, and we treat in more detail examples with symmetric space SU(2)/S1 and SO(4)/SO(3). The latter model simplifies to an apparently new integrable nine-dimensional system. We also study the G-strands on the infinite dimensional group of diffeomorphisms, which gives, together with the Sobolev norm, systems of 1+2 Camassa–Holm equations. The solutions of these equations on the complementary space related to the Witt algebra decomposition are the odd function solutions. PMID:28413343

Hypercyclic subspaces for Frechet space operators

NASA Astrophysics Data System (ADS)

Petersson, Henrik

2006-07-01

A continuous linear operator is hypercyclic if there is an such that the orbit {Tnx} is dense, and such a vector x is said to be hypercyclic for T. Recent progress show that it is possible to characterize Banach space operators that have a hypercyclic subspace, i.e., an infinite dimensional closed subspace of, except for zero, hypercyclic vectors. The following is known to hold: A Banach space operator T has a hypercyclic subspace if there is a sequence (ni) and an infinite dimensional closed subspace such that T is hereditarily hypercyclic for (ni) and Tni->0 pointwise on E. In this note we extend this result to the setting of Frechet spaces that admit a continuous norm, and study some applications for important function spaces. As an application we also prove that any infinite dimensional separable Frechet space with a continuous norm admits an operator with a hypercyclic subspace.

On the n-symplectic structure of faithful irreducible representations

NASA Astrophysics Data System (ADS)

Norris, L. K.

2017-04-01

Each faithful irreducible representation of an N-dimensional vector space V1 on an n-dimensional vector space V2 is shown to define a unique irreducible n-symplectic structure on the product manifold V1×V2 . The basic details of the associated Poisson algebra are developed for the special case N = n2, and 2n-dimensional symplectic submanifolds are shown to exist.

[Development of domain specific search engines].

PubMed

Takai, T; Tokunaga, M; Maeda, K; Kaminuma, T

2000-01-01

As cyber space exploding in a pace that nobody has ever imagined, it becomes very important to search cyber space efficiently and effectively. One solution to this problem is search engines. Already a lot of commercial search engines have been put on the market. However these search engines respond with such cumbersome results that domain specific experts can not tolerate. Using a dedicate hardware and a commercial software called OpenText, we have tried to develop several domain specific search engines. These engines are for our institute's Web contents, drugs, chemical safety, endocrine disruptors, and emergent response for chemical hazard. These engines have been on our Web site for testing.

Three-Dimensional Lissajous Figures.

ERIC Educational Resources Information Center

D'Mura, John M.

1989-01-01

Described is a mechanically driven device for generating three-dimensional harmonic space figures with different frequencies and phase angles on the X, Y, and Z axes. Discussed are apparatus, viewing stereo pairs, equations of motion, and using space figures in classroom. (YP)

Three dimensional δf simulations of beams in the SSC

NASA Astrophysics Data System (ADS)

Koga, J.; Tajima, T.; Machida, S.

1993-12-01

A three dimensional δf strong-strong algorithm has been developed to apply to the study of such effects as space charge and beam-beam interaction phenomena in the Superconducting Super Collider (SSC). The algorithm is obtained from the merging of the particle tracking code Simpsons used for 3 dimensional space charge effects and a δf code. The δf method is used to follow the evolution of the non-gaussian part of the beam distribution. The advantages of this method are twofold. First, the Simpsons code utilizes a realistic accelerator model including synchrotron oscillations and energy ramping in 6 dimensional phase space with electromagnetic fields of the beams calculated using a realistic 3 dimensional field solver. Second, the beams are evolving in the fully self-consistent strong-strong sense with finite particle fluctuation noise is greatly reduced as opposed to the weak-strong models where one beam is fixed.

Self-dual Skyrmions on the spheres S2 N +1

NASA Astrophysics Data System (ADS)

Amari, Y.; Ferreira, L. A.

2018-04-01

We construct self-dual sectors for scalar field theories on a (2 N +2 )-dimensional Minkowski space-time with the target space being the 2 N +1 -dimensional sphere S2 N +1. The construction of such self-dual sectors is made possible by the introduction of an extra functional in the action that renders the static energy and the self-duality equations conformally invariant on the (2 N +1 )-dimensional spatial submanifold. The conformal and target-space symmetries are used to build an ansatz that leads to an infinite number of exact self-dual solutions with arbitrary values of the topological charge. The five-dimensional case is discussed in detail, where it is shown that two types of theories admit self-dual sectors. Our work generalizes the known results in the three-dimensional case that lead to an infinite set of self-dual Skyrmion solutions.

Using Genetic Programming with Prior Formula Knowledge to Solve Symbolic Regression Problem.

PubMed

Lu, Qiang; Ren, Jun; Wang, Zhiguang

2016-01-01

A researcher can infer mathematical expressions of functions quickly by using his professional knowledge (called Prior Knowledge). But the results he finds may be biased and restricted to his research field due to limitation of his knowledge. In contrast, Genetic Programming method can discover fitted mathematical expressions from the huge search space through running evolutionary algorithms. And its results can be generalized to accommodate different fields of knowledge. However, since GP has to search a huge space, its speed of finding the results is rather slow. Therefore, in this paper, a framework of connection between Prior Formula Knowledge and GP (PFK-GP) is proposed to reduce the space of GP searching. The PFK is built based on the Deep Belief Network (DBN) which can identify candidate formulas that are consistent with the features of experimental data. By using these candidate formulas as the seed of a randomly generated population, PFK-GP finds the right formulas quickly by exploring the search space of data features. We have compared PFK-GP with Pareto GP on regression of eight benchmark problems. The experimental results confirm that the PFK-GP can reduce the search space and obtain the significant improvement in the quality of SR.

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

PubMed

Pacharawongsakda, Eakasit; Theeramunkong, Thanaruk

2013-12-01

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

Federated Space-Time Query for Earth Science Data Using OpenSearch Conventions

NASA Astrophysics Data System (ADS)

Lynnes, C.; Beaumont, B.; Duerr, R. E.; Hua, H.

2009-12-01

The past decade has seen a burgeoning of remote sensing and Earth science data providers, as evidenced in the growth of the Earth Science Information Partner (ESIP) federation. At the same time, the need to combine diverse data sets to enable understanding of the Earth as a system has also grown. While the expansion of data providers is in general a boon to such studies, the diversity presents a challenge to finding useful data for a given study. Locating all the data files with aerosol information for a particular volcanic eruption, for example, may involve learning and using several different search tools to execute the requisite space-time queries. To address this issue, the ESIP federation is developing a federated space-time query framework, based on the OpenSearch convention (www.opensearch.org), with Geo and Time extensions. In this framework, data providers publish OpenSearch Description Documents that describe in a machine-readable form how to execute queries against the provider. The novelty of OpenSearch is that the space-time query interface becomes both machine callable and easy enough to integrate into the web browser's search box. This flexibility, together with a simple REST (HTTP-get) interface, should allow a variety of data providers to participate in the federated search framework, from large institutional data centers to individual scientists. The simple interface enables trivial querying of multiple data sources and participation in recursive-like federated searches--all using the same common OpenSearch interface. This simplicity also makes the construction of clients easy, as does existing OpenSearch client libraries in a variety of languages. Moreover, a number of clients and aggregation services already exist and OpenSearch is already supported by a number of web browsers such as Firefox and Internet Explorer.

Search for new physics in trilepton events and limits on the associated chargino-neutralino production at CDF

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

Aaltonen, T.; Amerio, S.; Amidei, D.

2014-07-23

We perform a search for new physics using final states consisting of three leptons and a large imbalance in transverse momentum resulting from proton-antiproton collisions at 1.96 TeV center-of-mass energy. We use data corresponding to 5.8 fb -1 of integrated luminosity recorded by the CDF II detector at the Tevatron collider. Our main objective is to investigate possible new low-momentum (down to 5 GeV/c) multi-leptonic final states not investigated by LHC experiments. Relative to previous CDF analyses, we expand the geometric and kinematic coverage of electrons and muons and utilize tau leptons that decay hadronically. Inclusion of tau leptons ismore » particularly important for supersymmetry (SUSY) searches. The results are consistent with standard-model predictions. By optimizing our event selection to increase sensitivity to the minimal supergravity (mSUGRA) SUSY model, we set limits on the associated production of chargino and neutralino, the SUSY partners of the electroweak gauge bosons. We exclude cross sections up to 0.1 pb and chargino masses up to 168 GeV/c 2 at 95% CL, for a suited set of mSUGRA parameters. We also exclude a region of the two-dimensional space of the masses of the neutralino and the supersymmetric partner of the tau lepton, not previously excluded at the Tevatron.« less

KENNEDY SPACE CENTER, FLA. - An area of the Vehicle Assembly Building is being prepared to store the debris collected from Space Shuttle Columbia. About 83,000 pieces were shipped to KSC during search and recovery efforts in East Texas.

NASA Image and Video Library

2003-09-02

KENNEDY SPACE CENTER, FLA. - An area of the Vehicle Assembly Building is being prepared to store the debris collected from Space Shuttle Columbia. About 83,000 pieces were shipped to KSC during search and recovery efforts in East Texas.

Optimization and uncertainty assessment of strongly nonlinear groundwater models with high parameter dimensionality

NASA Astrophysics Data System (ADS)

Keating, Elizabeth H.; Doherty, John; Vrugt, Jasper A.; Kang, Qinjun

2010-10-01

Highly parameterized and CPU-intensive groundwater models are increasingly being used to understand and predict flow and transport through aquifers. Despite their frequent use, these models pose significant challenges for parameter estimation and predictive uncertainty analysis algorithms, particularly global methods which usually require very large numbers of forward runs. Here we present a general methodology for parameter estimation and uncertainty analysis that can be utilized in these situations. Our proposed method includes extraction of a surrogate model that mimics key characteristics of a full process model, followed by testing and implementation of a pragmatic uncertainty analysis technique, called null-space Monte Carlo (NSMC), that merges the strengths of gradient-based search and parameter dimensionality reduction. As part of the surrogate model analysis, the results of NSMC are compared with a formal Bayesian approach using the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm. Such a comparison has never been accomplished before, especially in the context of high parameter dimensionality. Despite the highly nonlinear nature of the inverse problem, the existence of multiple local minima, and the relatively large parameter dimensionality, both methods performed well and results compare favorably with each other. Experiences gained from the surrogate model analysis are then transferred to calibrate the full highly parameterized and CPU intensive groundwater model and to explore predictive uncertainty of predictions made by that model. The methodology presented here is generally applicable to any highly parameterized and CPU-intensive environmental model, where efficient methods such as NSMC provide the only practical means for conducting predictive uncertainty analysis.

Echocardiography Comparison Between Two and Three Dimensional Echocardiograms

NASA Technical Reports Server (NTRS)

2003-01-01

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

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

PubMed

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

2015-04-01

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

Nebula: reconstruction and visualization of scattering data in reciprocal space

PubMed Central

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

2015-01-01

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

Stochastic solution to quantum dynamics

NASA Technical Reports Server (NTRS)

John, Sarah; Wilson, John W.

1994-01-01

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

Mirador: A Simple, Fast Search Interface for Remote Sensing Data

NASA Technical Reports Server (NTRS)

Lynnes, Christopher; Strub, Richard; Seiler, Edward; Joshi, Talak; MacHarrie, Peter

2008-01-01

A major challenge for remote sensing science researchers is searching and acquiring relevant data files for their research projects based on content, space and time constraints. Several structured query (SQ) and hierarchical navigation (HN) search interfaces have been develop ed to satisfy this requirement, yet the dominant search engines in th e general domain are based on free-text search. The Goddard Earth Sci ences Data and Information Services Center has developed a free-text search interface named Mirador that supports space-time queries, inc luding a gazetteer and geophysical event gazetteer. In order to compe nsate for a slightly reduced search precision relative to SQ and HN t echniques, Mirador uses several search optimizations to return result s quickly. The quick response enables a more iterative search strateg y than is available with many SQ and HN techniques.

Protein structural similarity search by Ramachandran codes

PubMed Central

Lo, Wei-Cheng; Huang, Po-Jung; Chang, Chih-Hung; Lyu, Ping-Chiang

2007-01-01

Background Protein structural data has increased exponentially, such that fast and accurate tools are necessary to access structure similarity search. To improve the search speed, several methods have been designed to reduce three-dimensional protein structures to one-dimensional text strings that are then analyzed by traditional sequence alignment methods; however, the accuracy is usually sacrificed and the speed is still unable to match sequence similarity search tools. Here, we aimed to improve the linear encoding methodology and develop efficient search tools that can rapidly retrieve structural homologs from large protein databases. Results We propose a new linear encoding method, SARST (Structural similarity search Aided by Ramachandran Sequential Transformation). SARST transforms protein structures into text strings through a Ramachandran map organized by nearest-neighbor clustering and uses a regenerative approach to produce substitution matrices. Then, classical sequence similarity search methods can be applied to the structural similarity search. Its accuracy is similar to Combinatorial Extension (CE) and works over 243,000 times faster, searching 34,000 proteins in 0.34 sec with a 3.2-GHz CPU. SARST provides statistically meaningful expectation values to assess the retrieved information. It has been implemented into a web service and a stand-alone Java program that is able to run on many different platforms. Conclusion As a database search method, SARST can rapidly distinguish high from low similarities and efficiently retrieve homologous structures. It demonstrates that the easily accessible linear encoding methodology has the potential to serve as a foundation for efficient protein structural similarity search tools. These search tools are supposed applicable to automated and high-throughput functional annotations or predictions for the ever increasing number of published protein structures in this post-genomic era. PMID:17716377

Results from the Joint US/Russian Sensory-Motor Investigations

NASA Technical Reports Server (NTRS)

1997-01-01

In this session, Session FA3, the discussion focuses on the following topics: The Effect of Long Duration Space Flight on the Acquisition of Predictable Targets in Three Dimensional Space; Effects of Microgravity on Spinal Reflex Mechanisms; Three Dimensional Head Movement Control During Locomotion After Long-Duration Space Flight; Human Body Shock Wave Transmission Properties After Long Duration Space Flight; Adaptation of Neuromuscular Activation Patterns During Locomotion After Long Duration Space Flight; Balance Control Deficits Following Long-Duration Space Flight; Influence of Weightlessness on Postural Muscular Activity Coordination; and The Use of Inflight Foot Pressure as a Countermeasure to Neuromuscular Degradation.

The effects of mental representation on performance in a navigation task

NASA Technical Reports Server (NTRS)

Barshi, Immanuel; Healy, Alice F.

2002-01-01

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

Adiabatic Invariant Approach to Transverse Instability: Landau Dynamics of Soliton Filaments.

PubMed

Kevrekidis, P G; Wang, Wenlong; Carretero-González, R; Frantzeskakis, D J

2017-06-16

Consider a lower-dimensional solitonic structure embedded in a higher-dimensional space, e.g., a 1D dark soliton embedded in 2D space, a ring dark soliton in 2D space, a spherical shell soliton in 3D space, etc. By extending the Landau dynamics approach [Phys. Rev. Lett. 93, 240403 (2004)PRLTAO0031-900710.1103/PhysRevLett.93.240403], we show that it is possible to capture the transverse dynamical modes (the "Kelvin modes") of the undulation of this "soliton filament" within the higher-dimensional space. These are the transverse stability or instability modes and are the ones potentially responsible for the breakup of the soliton into structures such as vortices, vortex rings, etc. We present the theory and case examples in 2D and 3D, corroborating the results by numerical stability and dynamical computations.

From Glass Formation to Icosahedral Ordering by Curving Three-Dimensional Space.

PubMed

Turci, Francesco; Tarjus, Gilles; Royall, C Patrick

2017-05-26

Geometric frustration describes the inability of a local molecular arrangement, such as icosahedra found in metallic glasses and in model atomic glass formers, to tile space. Local icosahedral order, however, is strongly frustrated in Euclidean space, which obscures any causal relationship with the observed dynamical slowdown. Here we relieve frustration in a model glass-forming liquid by curving three-dimensional space onto the surface of a 4-dimensional hypersphere. For sufficient curvature, frustration vanishes and the liquid "freezes" in a fully icosahedral structure via a sharp "transition." Frustration increases upon reducing the curvature, and the transition to the icosahedral state smoothens while glassy dynamics emerge. Decreasing the curvature leads to decoupling between dynamical and structural length scales and the decrease of kinetic fragility. This sheds light on the observed glass-forming behavior in Euclidean space.

Biosignatures from Earth-like planets around M dwarfs.

PubMed

Segura, Antígona; Kasting, James F; Meadows, Victoria; Cohen, Martin; Scalo, John; Crisp, David; Butler, Rebecca A H; Tinetti, Giovanna

2005-12-01

Coupled one-dimensional photochemical-climate calculations have been performed for hypothetical Earth-like planets around M dwarfs. Visible/near-infrared and thermal-infrared synthetic spectra of these planets were generated to determine which biosignature gases might be observed by a future, space-based telescope. Our star sample included two observed active M dwarfs-AD Leo and GJ 643-and three quiescent model stars. The spectral distribution of these stars in the ultraviolet generates a different photochemistry on these planets. As a result, the biogenic gases CH4, N2O, and CH3Cl have substantially longer lifetimes and higher mixing ratios than on Earth, making them potentially observable by space-based telescopes. On the active M-star planets, an ozone layer similar to Earth's was developed that resulted in a spectroscopic signature comparable to the terrestrial one. The simultaneous detection of O2 (or O3) and a reduced gas in a planet's atmosphere has been suggested as strong evidence for life. Planets circling M stars may be good locations to search for such evidence.

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

NASA Technical Reports Server (NTRS)

Redekopp, L. G.

1980-01-01

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

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

DOE PAGES

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

2017-10-10

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

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

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

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

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

A novel visualization model for web search results.

PubMed

Nguyen, Tien N; Zhang, Jin

2006-01-01

This paper presents an interactive visualization system, named WebSearchViz, for visualizing the Web search results and acilitating users' navigation and exploration. The metaphor in our model is the solar system with its planets and asteroids revolving around the sun. Location, color, movement, and spatial distance of objects in the visual space are used to represent the semantic relationships between a query and relevant Web pages. Especially, the movement of objects and their speeds add a new dimension to the visual space, illustrating the degree of relevance among a query and Web search results in the context of users' subjects of interest. By interacting with the visual space, users are able to observe the semantic relevance between a query and a resulting Web page with respect to their subjects of interest, context information, or concern. Users' subjects of interest can be dynamically changed, redefined, added, or deleted from the visual space.

Rapid and Robust Cross-Correlation-Based Seismic Signal Identification Using an Approximate Nearest Neighbor Method

DOE PAGES

Tibi, Rigobert; Young, Christopher; Gonzales, Antonio; ...

2017-07-04

The matched filtering technique that uses the cross correlation of a waveform of interest with archived signals from a template library has proven to be a powerful tool for detecting events in regions with repeating seismicity. However, waveform correlation is computationally expensive and therefore impractical for large template sets unless dedicated distributed computing hardware and software are used. In this paper, we introduce an approximate nearest neighbor (ANN) approach that enables the use of very large template libraries for waveform correlation. Our method begins with a projection into a reduced dimensionality space, based on correlation with a randomized subset ofmore » the full template archive. Searching for a specified number of nearest neighbors for a query waveform is accomplished by iteratively comparing it with the neighbors of its immediate neighbors. We used the approach to search for matches to each of ~2300 analyst-reviewed signal detections reported in May 2010 for the International Monitoring System station MKAR. The template library in this case consists of a data set of more than 200,000 analyst-reviewed signal detections for the same station from February 2002 to July 2016 (excluding May 2010). Of these signal detections, 73% are teleseismic first P and 17% regional phases (Pn, Pg, Sn, and Lg). Finally, the analyses performed on a standard desktop computer show that the proposed ANN approach performs a search of the large template libraries about 25 times faster than the standard full linear search and achieves recall rates greater than 80%, with the recall rate increasing for higher correlation thresholds.« less

Using a Genetic Algorithm to Model Broadband Regional Waveforms for Crustal Structure in the Western United States

NASA Technical Reports Server (NTRS)

Bhattacharyya, Joydeep; Sheehan, Anne F.; Tiampo, Kristy; Rundle, John

1999-01-01

In this study, we analyze regional seismograms to obtain the crustal structure in the eastern Great Basin and western Colorado plateau. Adopting a for- ward-modeling approach, we develop a genetic algorithm (GA) based parameter search technique to constrain the one-dimensional crustal structure in these regions. The data are broadband three-component seismograms recorded at the 1994-95 IRIS PASSCAL Colorado Plateau to Great Basin experiment (CPGB) stations and supplemented by data from U.S. National Seismic Network (USNSN) stations in Utah and Nevada. We use the southwestern Wyoming mine collapse event (M(sub b) = 5.2) that occurred on 3 February 1995 as the seismic source. We model the regional seismograms using a four-layer crustal model with constant layer parameters. Timing of teleseismic receiver functions at CPGB stations are added as an additional constraint in the modeling. GA allows us to efficiently search the model space. A carefully chosen fitness function and a windowing scheme are added to the algorithm to prevent search stagnation. The technique is tested with synthetic data, both with and without random Gaussian noise added to it. Several separate model searches are carried out to estimate the variability of the model parameters. The average Colorado plateau crustal structure is characterized by a 40-km-thick crust with velocity increases at depths of about 10 and 25 km and a fast lower crust while the Great Basin has approximately 35- km-thick crust and a 2.9-km-thick sedimentary layer.

Rapid and Robust Cross-Correlation-Based Seismic Signal Identification Using an Approximate Nearest Neighbor Method

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

Tibi, Rigobert; Young, Christopher; Gonzales, Antonio

The matched filtering technique that uses the cross correlation of a waveform of interest with archived signals from a template library has proven to be a powerful tool for detecting events in regions with repeating seismicity. However, waveform correlation is computationally expensive and therefore impractical for large template sets unless dedicated distributed computing hardware and software are used. In this paper, we introduce an approximate nearest neighbor (ANN) approach that enables the use of very large template libraries for waveform correlation. Our method begins with a projection into a reduced dimensionality space, based on correlation with a randomized subset ofmore » the full template archive. Searching for a specified number of nearest neighbors for a query waveform is accomplished by iteratively comparing it with the neighbors of its immediate neighbors. We used the approach to search for matches to each of ~2300 analyst-reviewed signal detections reported in May 2010 for the International Monitoring System station MKAR. The template library in this case consists of a data set of more than 200,000 analyst-reviewed signal detections for the same station from February 2002 to July 2016 (excluding May 2010). Of these signal detections, 73% are teleseismic first P and 17% regional phases (Pn, Pg, Sn, and Lg). Finally, the analyses performed on a standard desktop computer show that the proposed ANN approach performs a search of the large template libraries about 25 times faster than the standard full linear search and achieves recall rates greater than 80%, with the recall rate increasing for higher correlation thresholds.« less

Novel angle estimation for bistatic MIMO radar using an improved MUSIC

NASA Astrophysics Data System (ADS)

Li, Jianfeng; Zhang, Xiaofei; Chen, Han

2014-09-01

In this article, we study the problem of angle estimation for bistatic multiple-input multiple-output (MIMO) radar and propose an improved multiple signal classification (MUSIC) algorithm for joint direction of departure (DOD) and direction of arrival (DOA) estimation. The proposed algorithm obtains initial estimations of angles obtained from the signal subspace and uses the local one-dimensional peak searches to achieve the joint estimations of DOD and DOA. The angle estimation performance of the proposed algorithm is better than that of estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm, and is almost the same as that of two-dimensional MUSIC. Furthermore, the proposed algorithm can be suitable for irregular array geometry, obtain automatically paired DOD and DOA estimations, and avoid two-dimensional peak searching. The simulation results verify the effectiveness and improvement of the algorithm.

Automated Processing of Two-Dimensional Correlation Spectra

PubMed

Sengstschmid; Sterk; Freeman

1998-04-01

An automated scheme is described which locates the centers of cross peaks in two-dimensional correlation spectra, even under conditions of severe overlap. Double-quantum-filtered correlation (DQ-COSY) spectra have been investigated, but the method is also applicable to TOCSY and NOESY spectra. The search criterion is the intrinsic symmetry (or antisymmetry) of cross-peak multiplets. An initial global search provides the preliminary information to build up a two-dimensional "chemical shift grid." All genuine cross peaks must be centered at intersections of this grid, a fact that reduces the extent of the subsequent search program enormously. The program recognizes cross peaks by examining the symmetry of signals in a test zone centered at a grid intersection. This "symmetry filter" employs a "lowest value algorithm" to discriminate against overlapping responses from adjacent multiplets. A progressive multiplet subtraction scheme provides further suppression of overlap effects. The processed two-dimensional correlation spectrum represents cross peaks as points at the chemical shift coordinates, with some indication of their relative intensities. Alternatively, the information is presented in the form of a correlation table. The authenticity of a given cross peak is judged by a set of "confidence criteria" expressed as numerical parameters. Experimental results are presented for the 400-MHz double-quantum-filtered COSY spectrum of 4-androsten-3,17-dione, a case where there is severe overlap. Copyright 1998 Academic Press.

Modelling and Simulation of Search Engine

NASA Astrophysics Data System (ADS)

Nasution, Mahyuddin K. M.

2017-01-01

The best tool currently used to access information is a search engine. Meanwhile, the information space has its own behaviour. Systematically, an information space needs to be familiarized with mathematics so easily we identify the characteristics associated with it. This paper reveal some characteristics of search engine based on a model of document collection, which are then estimated the impact on the feasibility of information. We reveal some of characteristics of search engine on the lemma and theorem about singleton and doubleton, then computes statistically characteristic as simulating the possibility of using search engine. In this case, Google and Yahoo. There are differences in the behaviour of both search engines, although in theory based on the concept of documents collection.

Self-dual phase space for (3 +1 )-dimensional lattice Yang-Mills theory

NASA Astrophysics Data System (ADS)

Riello, Aldo

2018-01-01

I propose a self-dual deformation of the classical phase space of lattice Yang-Mills theory, in which both the electric and magnetic fluxes take value in the compact gauge Lie group. A local construction of the deformed phase space requires the machinery of "quasi-Hamiltonian spaces" by Alekseev et al., which is reviewed here. The results is a full-fledged finite-dimensional and gauge-invariant phase space, the self-duality properties of which are largely enhanced in (3 +1 ) spacetime dimensions. This enhancement is due to a correspondence with the moduli space of an auxiliary noncommutative flat connection living on a Riemann surface defined from the lattice itself, which in turn equips the duality between electric and magnetic fluxes with a neat geometrical interpretation in terms of a Heegaard splitting of the space manifold. Finally, I discuss the consequences of the proposed deformation on the quantization of the phase space, its quantum gravitational interpretation, as well as its relevance for the construction of (3 +1 )-dimensional topological field theories with defects.

Supporting Dynamic Quantization for High-Dimensional Data Analytics.

PubMed

Guzun, Gheorghi; Canahuate, Guadalupe

2017-05-01

Similarity searches are at the heart of exploratory data analysis tasks. Distance metrics are typically used to characterize the similarity between data objects represented as feature vectors. However, when the dimensionality of the data increases and the number of features is large, traditional distance metrics fail to distinguish between the closest and furthest data points. Localized distance functions have been proposed as an alternative to traditional distance metrics. These functions only consider dimensions close to query to compute the distance/similarity. Furthermore, in order to enable interactive explorations of high-dimensional data, indexing support for ad-hoc queries is needed. In this work we set up to investigate whether bit-sliced indices can be used for exploratory analytics such as similarity searches and data clustering for high-dimensional big-data. We also propose a novel dynamic quantization called Query dependent Equi-Depth (QED) quantization and show its effectiveness on characterizing high-dimensional similarity. When applying QED we observe improvements in kNN classification accuracy over traditional distance functions. Gheorghi Guzun and Guadalupe Canahuate. 2017. Supporting Dynamic Quantization for High-Dimensional Data Analytics. In Proceedings of Ex-ploreDB'17, Chicago, IL, USA, May 14-19, 2017, 6 pages. https://doi.org/http://dx.doi.org/10.1145/3077331.3077336.

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

NASA Astrophysics Data System (ADS)

Cui, Tiangang; Marzouk, Youssef; Willcox, Karen

2016-06-01

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

Higher dimensional Taub-NUT spaces and applications

NASA Astrophysics Data System (ADS)

Stelea, Cristian Ionut

In the first part of this thesis we discuss classes of new exact NUT-charged solutions in four dimensions and higher, while in the remainder of the thesis we make a study of their properties and their possible applications. Specifically, in four dimensions we construct new families of axisymmetric vacuum solutions using a solution-generating technique based on the hidden SL(2,R) symmetry of the effective action. In particular, using the Schwarzschild solution as a seed we obtain the Zipoy-Voorhees generalisation of the Taub-NUT solution and of the Eguchi-Hanson soliton. Using the C-metric as a seed, we obtain and study the accelerating versions of all the above solutions. In higher dimensions we present new classes of NUT-charged spaces, generalising the previously known even-dimensional solutions to odd and even dimensions, as well as to spaces with multiple NUT-parameters. We also find the most general form of the odd-dimensional Eguchi-Hanson solitons. We use such solutions to investigate the thermodynamic properties of NUT-charged spaces in (A)dS backgrounds. These have been shown to yield counter-examples to some of the conjectures advanced in the still elusive dS/CFT paradigm (such as the maximal mass conjecture and Bousso's entropic N-bound). One important application of NUT-charged spaces is to construct higher dimensional generalisations of Kaluza-Klein magnetic monopoles, generalising the known 5-dimensional Kaluza-Klein soliton. Another interesting application involves a study of time-dependent higher-dimensional bubbles-of-nothing generated from NUT-charged solutions. We use them to test the AdS/CFT conjecture as well as to generate, by using stringy Hopf-dualities, new interesting time-dependent solutions in string theory. Finally, we construct and study new NUT-charged solutions in higher-dimensional Einstein-Maxwell theories, generalising the known Reissner-Nordstrom solutions.

A probabilistic and continuous model of protein conformational space for template-free modeling.

PubMed

Zhao, Feng; Peng, Jian; Debartolo, Joe; Freed, Karl F; Sosnick, Tobin R; Xu, Jinbo

2010-06-01

One of the major challenges with protein template-free modeling is an efficient sampling algorithm that can explore a huge conformation space quickly. The popular fragment assembly method constructs a conformation by stringing together short fragments extracted from the Protein Data Base (PDB). The discrete nature of this method may limit generated conformations to a subspace in which the native fold does not belong. Another worry is that a protein with really new fold may contain some fragments not in the PDB. This article presents a probabilistic model of protein conformational space to overcome the above two limitations. This probabilistic model employs directional statistics to model the distribution of backbone angles and 2(nd)-order Conditional Random Fields (CRFs) to describe sequence-angle relationship. Using this probabilistic model, we can sample protein conformations in a continuous space, as opposed to the widely used fragment assembly and lattice model methods that work in a discrete space. We show that when coupled with a simple energy function, this probabilistic method compares favorably with the fragment assembly method in the blind CASP8 evaluation, especially on alpha or small beta proteins. To our knowledge, this is the first probabilistic method that can search conformations in a continuous space and achieves favorable performance. Our method also generated three-dimensional (3D) models better than template-based methods for a couple of CASP8 hard targets. The method described in this article can also be applied to protein loop modeling, model refinement, and even RNA tertiary structure prediction.

Ceos Wgiss Common Framework for Wgiss Connected Data Assets

NASA Astrophysics Data System (ADS)

Enloe, Y.; Mitchell, A. E.; Albani, M.; Yapur, M.

2016-12-01

The Committee on Earth Observation Satellites (CEOS), established in 1984 to coordinate civil space-borne observations of the Earth, has been building through its Working Group on Information Systems and Services (WGISS), a common data framework to identify and connect data assets at member agencies. Some of these data assets are federated systems such as the CEOS WGISS Integrated Catalog (CWIC), the European Space Agency's FedEO (Federated Earth Observations Missions Access) system, and the International Directory Network (IDN) which is an international effort developed by NASA to assist researchers in locating information on available data sets. A system level team provides coordination and oversight to make this loosely coupled federated system function and evolve. WGISS has identified 2 search standards, the Open Geospatial Consortium (OGC) Catalog Services for the Web (CSW) and the CEOS OpenSearch Best Practices (which references the OGC OpenSearch Geo and Time Extensions and OGC OpenSearch Extension for Earth Observation) as well as an interoperable metadata standard (ISO 19115) for use within the WGISS Connected Assets. Data partners must register their data collections in the IDN using the Global Change Master Directory (GCMD) Keywords. Data partners need to support one of the 2 search standards and be able to map their internal metadata to the ISO 19115 metadata elements. All searchable data must have a data access path. Clients can offer search and access to all or a subset of the satellite data available through the WGISS Connected Data Assets. Clients can offer support for a 2-step search: (1) Discovery through collection search using platform, instrument, science keywords, etc. at the IDN and (2) Search granule metadata at data partners through CWIC or FedEO. There are more than a dozen international agencies that offer their data through the WGISS Federation or working on developing their connections. This list includes European Space Agency, NASA, NOAA, USGS, National Institute for Space Research (Brazil), Canadian Center for Mapping and Earth Observations (CCMEO), the Academy for Opto-Electronics (China), the Indian Space Research Organization (ISRO), EUMETSAT, Russian Federal Space Agency (ROSCOSMOS) and several agencies within Australia.

Comparison of tibiofemoral joint space width measurements from standing CT and fixed flexion radiography.

PubMed

Segal, Neil A; Frick, Eric; Duryea, Jeffrey; Nevitt, Michael C; Niu, Jingbo; Torner, James C; Felson, David T; Anderson, Donald D

2017-07-01

The objective of this project was to determine the relationship between medial tibiofemoral joint space width measured on fixed-flexion radiographs and the three-dimensional joint space width distribution on low-dose, standing CT (SCT) imaging. At the 84-month visit of the Multicenter Osteoarthritis Study, 20 participants were recruited. A commercial SCT scanner for the foot and ankle was modified to image knees while standing. Medial tibiofemoral joint space width was assessed on radiographs at fixed locations from 15% to 30% of compartment width using validated software and on SCT by mapping the distances between three-dimensional subchondral bone surfaces. Individual joint space width values from radiographs were compared with three-dimensional joint space width values from corresponding sagittal plane locations using paired t-tests and correlation coefficients. For the four medial-most tibiofemoral locations, radiographic joint space width values exceeded the minimal joint space width on SCT by a mean of 2.0 mm and were approximately equal to the 61st percentile value of the joint space width distribution at each respective sagittal-plane location. Correlation coefficients at these locations were 0.91-0.97 and the offsets between joint space width values from radiographs and SCT measurements were consistent. There were greater offsets and variability in the offsets between modalities closer to the tibial spine. Joint space width measurements on fixed-flexion radiographs are highly correlated with three-dimensional joint space width from SCT. In addition to avoiding bony overlap obscuring the joint, a limitation of radiographs, the current study supports a role for SCT in the evaluation of tibiofemoral OA. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 35:1388-1395, 2017. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc.

Restricted random search method based on taboo search in the multiple minima problem

NASA Astrophysics Data System (ADS)

Hong, Seung Do; Jhon, Mu Shik

1997-03-01

The restricted random search method is proposed as a simple Monte Carlo sampling method to search minima fast in the multiple minima problem. This method is based on taboo search applied recently to continuous test functions. The concept of the taboo region instead of the taboo list is used and therefore the sampling of a region near an old configuration is restricted in this method. This method is applied to 2-dimensional test functions and the argon clusters. This method is found to be a practical and efficient method to search near-global configurations of test functions and the argon clusters.

ColDICE: A parallel Vlasov–Poisson solver using moving adaptive simplicial tessellation

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

Sousbie, Thierry, E-mail: tsousbie@gmail.com; Department of Physics, The University of Tokyo, Tokyo 113-0033; Research Center for the Early Universe, School of Science, The University of Tokyo, Tokyo 113-0033

2016-09-15

Resolving numerically Vlasov–Poisson equations for initially cold systems can be reduced to following the evolution of a three-dimensional sheet evolving in six-dimensional phase-space. We describe a public parallel numerical algorithm consisting in representing the phase-space sheet with a conforming, self-adaptive simplicial tessellation of which the vertices follow the Lagrangian equations of motion. The algorithm is implemented both in six- and four-dimensional phase-space. Refinement of the tessellation mesh is performed using the bisection method and a local representation of the phase-space sheet at second order relying on additional tracers created when needed at runtime. In order to preserve in the bestmore » way the Hamiltonian nature of the system, refinement is anisotropic and constrained by measurements of local Poincaré invariants. Resolution of Poisson equation is performed using the fast Fourier method on a regular rectangular grid, similarly to particle in cells codes. To compute the density projected onto this grid, the intersection of the tessellation and the grid is calculated using the method of Franklin and Kankanhalli [65–67] generalised to linear order. As preliminary tests of the code, we study in four dimensional phase-space the evolution of an initially small patch in a chaotic potential and the cosmological collapse of a fluctuation composed of two sinusoidal waves. We also perform a “warm” dark matter simulation in six-dimensional phase-space that we use to check the parallel scaling of the code.« less

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

NASA Astrophysics Data System (ADS)

Jia, Bing

2014-03-01

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

On infinite-dimensional state spaces

NASA Astrophysics Data System (ADS)

Fritz, Tobias

2013-05-01

It is well known that the canonical commutation relation [x, p] = i can be realized only on an infinite-dimensional Hilbert space. While any finite set of experimental data can also be explained in terms of a finite-dimensional Hilbert space by approximating the commutation relation, Occam's razor prefers the infinite-dimensional model in which [x, p] = i holds on the nose. This reasoning one will necessarily have to make in any approach which tries to detect the infinite-dimensionality. One drawback of using the canonical commutation relation for this purpose is that it has unclear operational meaning. Here, we identify an operationally well-defined context from which an analogous conclusion can be drawn: if two unitary transformations U, V on a quantum system satisfy the relation V-1U2V = U3, then finite-dimensionality entails the relation UV-1UV = V-1UVU; this implication strongly fails in some infinite-dimensional realizations. This is a result from combinatorial group theory for which we give a new proof. This proof adapts to the consideration of cases where the assumed relation V-1U2V = U3 holds only up to ɛ and then yields a lower bound on the dimension.

First-principle study of pressure-induced phase transitions and electronic properties of electride Y2C

NASA Astrophysics Data System (ADS)

Feng, Caihui; Shan, Jingfeng; Xu, Aoshu; Xu, Yang; Zhang, Meiguang; Lin, Tingting

2017-10-01

Trigonal yttrium hypocarbide (Y2C), crystallizing in a layered hR3 structure, is an intriguing quasi-two-dimensional electride metal with potential application for the next generation of electronics. By using an efficient structure search method in combination with first-principles calculations, we have extensively explored the phase transitions and electronic properties of Y2C in a wide pressure range of 0-200 GPa. Three structural transformations were predicted, as hR3 → oP12 → tI12 → mC12. Calculated pressures of phase transition are 20, 118, and 126 GPa, respectively. The high-pressure oP12 phase exhibits a three-dimensional extended C-Y network built up from face- and edge-sharing CY8 hendecahedrons, whereas both the tI12 and mC12 phases are featured by the presence of C2 units. No anionic electrons confined to interstitial spaces have been found in the three predicted high-pressure phases, indicating that they are not electrides. Moreover, Y2C is dynamically stable and also energetically stable relative to the decomposition into its elemental solids.

High-temperature supersolid of He 4 in a one-dimensional periodic potential

DOE PAGES

Olsen, Raina J.

2015-03-02

The search for robust experimental proof of supersolidity has encountered many complicating factors, such as temperature dependent changes in the mechanical properties of solid 4He which mimic the signature of superfluid flow. As a result, the physical existence and true nature of this unique state of matter are still under debate. Here we consider 4He stabilized by a one-dimensional periodic potential whose lattice spacing is similar to the length scale of the 4He- 4He interaction. We use the Bogoliubov transformation to calculate the excitation spectrum, finding that when interactions between nearest or next-nearest neighbors are attractive, there is a finitemore » positive gap in energy between the delocalized ground state and the lowest energy excitations which, under certain conditions, is significantly larger than both the melting temperature and the lambda temperature. This means that it should be possible to observe a supersolid at a high enough temperature that superfluidity in bulk liquid 4He or changes in the mechanical properties of bulk solid 4He do not obscure it. Lastly, we also discuss the properties of experimentally achievable materials which could support this type of supersolid.« less

Application of a fast skyline computation algorithm for serendipitous searching problems

NASA Astrophysics Data System (ADS)

Koizumi, Kenichi; Hiraki, Kei; Inaba, Mary

2018-02-01

Skyline computation is a method of extracting interesting entries from a large population with multiple attributes. These entries, called skyline or Pareto optimal entries, are known to have extreme characteristics that cannot be found by outlier detection methods. Skyline computation is an important task for characterizing large amounts of data and selecting interesting entries with extreme features. When the population changes dynamically, the task of calculating a sequence of skyline sets is called continuous skyline computation. This task is known to be difficult to perform for the following reasons: (1) information of non-skyline entries must be stored since they may join the skyline in the future; (2) the appearance or disappearance of even a single entry can change the skyline drastically; (3) it is difficult to adopt a geometric acceleration algorithm for skyline computation tasks with high-dimensional datasets. Our new algorithm called jointed rooted-tree (JR-tree) manages entries using a rooted tree structure. JR-tree delays extend the tree to deep levels to accelerate tree construction and traversal. In this study, we presented the difficulties in extracting entries tagged with a rare label in high-dimensional space and the potential of fast skyline computation in low-latency cell identification technology.

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

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

PubMed

Sakhr, Jamal; Nieminen, John M

2006-04-01

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

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

NASA Astrophysics Data System (ADS)

Balog, János

2014-11-01

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

Three-dimensional high-precision indoor positioning strategy using Tabu search based on visible light communication

NASA Astrophysics Data System (ADS)

Peng, Qi; Guan, Weipeng; Wu, Yuxiang; Cai, Ye; Xie, Canyu; Wang, Pengfei

2018-01-01

This paper proposes a three-dimensional (3-D) high-precision indoor positioning strategy using Tabu search based on visible light communication. Tabu search is a powerful global optimization algorithm, and the 3-D indoor positioning can be transformed into an optimal solution problem. Therefore, in the 3-D indoor positioning, the optimal receiver coordinate can be obtained by the Tabu search algorithm. For all we know, this is the first time the Tabu search algorithm is applied to visible light positioning. Each light-emitting diode (LED) in the system broadcasts a unique identity (ID) and transmits the ID information. When the receiver detects optical signals with ID information from different LEDs, using the global optimization of the Tabu search algorithm, the 3-D high-precision indoor positioning can be realized when the fitness value meets certain conditions. Simulation results show that the average positioning error is 0.79 cm, and the maximum error is 5.88 cm. The extended experiment of trajectory tracking also shows that 95.05% positioning errors are below 1.428 cm. It can be concluded from the data that the 3-D indoor positioning based on the Tabu search algorithm achieves the requirements of centimeter level indoor positioning. The algorithm used in indoor positioning is very effective and practical and is superior to other existing methods for visible light indoor positioning.

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

NASA Astrophysics Data System (ADS)

Wang, Yan; Zhang, Yufeng; Zhang, Xiangzhi

2016-09-01

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

Probabilistic modeling of anatomical variability using a low dimensional parameterization of diffeomorphisms.

PubMed

Zhang, Miaomiao; Wells, William M; Golland, Polina

2017-10-01

We present an efficient probabilistic model of anatomical variability in a linear space of initial velocities of diffeomorphic transformations and demonstrate its benefits in clinical studies of brain anatomy. To overcome the computational challenges of the high dimensional deformation-based descriptors, we develop a latent variable model for principal geodesic analysis (PGA) based on a low dimensional shape descriptor that effectively captures the intrinsic variability in a population. We define a novel shape prior that explicitly represents principal modes as a multivariate complex Gaussian distribution on the initial velocities in a bandlimited space. We demonstrate the performance of our model on a set of 3D brain MRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our model yields a more compact representation of group variation at substantially lower computational cost than the state-of-the-art method such as tangent space PCA (TPCA) and probabilistic principal geodesic analysis (PPGA) that operate in the high dimensional image space. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

Three-dimensional motor schema based navigation

NASA Technical Reports Server (NTRS)

Arkin, Ronald C.

1989-01-01

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

Searching Fragment Spaces with feature trees.

PubMed

Lessel, Uta; Wellenzohn, Bernd; Lilienthal, Markus; Claussen, Holger

2009-02-01

Virtual combinatorial chemistry easily produces billions of compounds, for which conventional virtual screening cannot be performed even with the fastest methods available. An efficient solution for such a scenario is the generation of Fragment Spaces, which encode huge numbers of virtual compounds by their fragments/reagents and rules of how to combine them. Similarity-based searches can be performed in such spaces without ever fully enumerating all virtual products. Here we describe the generation of a huge Fragment Space encoding about 5 * 10(11) compounds based on established in-house synthesis protocols for combinatorial libraries, i.e., we encode practically evaluated combinatorial chemistry protocols in a machine readable form, rendering them accessible to in silico search methods. We show how such searches in this Fragment Space can be integrated as a first step in an overall workflow. It reduces the extremely huge number of virtual products by several orders of magnitude so that the resulting list of molecules becomes more manageable for further more elaborated and time-consuming analysis steps. Results of a case study are presented and discussed, which lead to some general conclusions for an efficient expansion of the chemical space to be screened in pharmaceutical companies.

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

PubMed

Kurt, Melike; Moored, Keith

2018-04-19

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

Numerical relativity for D dimensional axially symmetric space-times: Formalism and code tests

NASA Astrophysics Data System (ADS)

Zilhão, Miguel; Witek, Helvi; Sperhake, Ulrich; Cardoso, Vitor; Gualtieri, Leonardo; Herdeiro, Carlos; Nerozzi, Andrea

2010-04-01

The numerical evolution of Einstein’s field equations in a generic background has the potential to answer a variety of important questions in physics: from applications to the gauge-gravity duality, to modeling black hole production in TeV gravity scenarios, to analysis of the stability of exact solutions, and to tests of cosmic censorship. In order to investigate these questions, we extend numerical relativity to more general space-times than those investigated hitherto, by developing a framework to study the numerical evolution of D dimensional vacuum space-times with an SO(D-2) isometry group for D≥5, or SO(D-3) for D≥6. Performing a dimensional reduction on a (D-4) sphere, the D dimensional vacuum Einstein equations are rewritten as a 3+1 dimensional system with source terms, and presented in the Baumgarte, Shapiro, Shibata, and Nakamura formulation. This allows the use of existing 3+1 dimensional numerical codes with small adaptations. Brill-Lindquist initial data are constructed in D dimensions and a procedure to match them to our 3+1 dimensional evolution equations is given. We have implemented our framework by adapting the Lean code and perform a variety of simulations of nonspinning black hole space-times. Specifically, we present a modified moving puncture gauge, which facilitates long-term stable simulations in D=5. We further demonstrate the internal consistency of the code by studying convergence and comparing numerical versus analytic results in the case of geodesic slicing for D=5, 6.

Decrease of Fisher information and the information geometry of evolution equations for quantum mechanical probability amplitudes.

PubMed

Cafaro, Carlo; Alsing, Paul M

2018-04-01

The relevance of the concept of Fisher information is increasing in both statistical physics and quantum computing. From a statistical mechanical standpoint, the application of Fisher information in the kinetic theory of gases is characterized by its decrease along the solutions of the Boltzmann equation for Maxwellian molecules in the two-dimensional case. From a quantum mechanical standpoint, the output state in Grover's quantum search algorithm follows a geodesic path obtained from the Fubini-Study metric on the manifold of Hilbert-space rays. Additionally, Grover's algorithm is specified by constant Fisher information. In this paper, we present an information geometric characterization of the oscillatory or monotonic behavior of statistically parametrized squared probability amplitudes originating from special functional forms of the Fisher information function: constant, exponential decay, and power-law decay. Furthermore, for each case, we compute both the computational speed and the availability loss of the corresponding physical processes by exploiting a convenient Riemannian geometrization of useful thermodynamical concepts. Finally, we briefly comment on the possibility of using the proposed methods of information geometry to help identify a suitable trade-off between speed and thermodynamic efficiency in quantum search algorithms.

Decrease of Fisher information and the information geometry of evolution equations for quantum mechanical probability amplitudes

NASA Astrophysics Data System (ADS)

Cafaro, Carlo; Alsing, Paul M.

2018-04-01

The relevance of the concept of Fisher information is increasing in both statistical physics and quantum computing. From a statistical mechanical standpoint, the application of Fisher information in the kinetic theory of gases is characterized by its decrease along the solutions of the Boltzmann equation for Maxwellian molecules in the two-dimensional case. From a quantum mechanical standpoint, the output state in Grover's quantum search algorithm follows a geodesic path obtained from the Fubini-Study metric on the manifold of Hilbert-space rays. Additionally, Grover's algorithm is specified by constant Fisher information. In this paper, we present an information geometric characterization of the oscillatory or monotonic behavior of statistically parametrized squared probability amplitudes originating from special functional forms of the Fisher information function: constant, exponential decay, and power-law decay. Furthermore, for each case, we compute both the computational speed and the availability loss of the corresponding physical processes by exploiting a convenient Riemannian geometrization of useful thermodynamical concepts. Finally, we briefly comment on the possibility of using the proposed methods of information geometry to help identify a suitable trade-off between speed and thermodynamic efficiency in quantum search algorithms.

A Comparison of Risk Sensitive Path Planning Methods for Aircraft Emergency Landing

NASA Technical Reports Server (NTRS)

Meuleau, Nicolas; Plaunt, Christian; Smith, David E.; Smith, Tristan

2009-01-01

Determining the best site to land a damaged aircraft presents some interesting challenges for standard path planning techniques. There are multiple possible locations to consider, the space is 3-dimensional with dynamics, the criteria for a good path is determined by overall risk rather than distance or time, and optimization really matters, since an improved path corresponds to greater expected survival rate. We have investigated a number of different path planning methods for solving this problem, including cell decomposition, visibility graphs, probabilistic road maps (PRMs), and local search techniques. In their pure form, none of these techniques have proven to be entirely satisfactory - some are too slow or unpredictable, some produce highly non-optimal paths or do not find certain types of paths, and some do not cope well with the dynamic constraints when controllability is limited. In the end, we are converging towards a hybrid technique that involves seeding a roadmap with a layered visibility graph, using PRM to extend that roadmap, and using local search to further optimize the resulting paths. We describe the techniques we have investigated, report on our experiments with these techniques, and discuss when and why various techniques were unsatisfactory.

Computational genetic neuroanatomy of the developing mouse brain: dimensionality reduction, visualization, and clustering.

PubMed

Ji, Shuiwang

2013-07-11

The structured organization of cells in the brain plays a key role in its functional efficiency. This delicate organization is the consequence of unique molecular identity of each cell gradually established by precise spatiotemporal gene expression control during development. Currently, studies on the molecular-structural association are beginning to reveal how the spatiotemporal gene expression patterns are related to cellular differentiation and structural development. In this article, we aim at a global, data-driven study of the relationship between gene expressions and neuroanatomy in the developing mouse brain. To enable visual explorations of the high-dimensional data, we map the in situ hybridization gene expression data to a two-dimensional space by preserving both the global and the local structures. Our results show that the developing brain anatomy is largely preserved in the reduced gene expression space. To provide a quantitative analysis, we cluster the reduced data into groups and measure the consistency with neuroanatomy at multiple levels. Our results show that the clusters in the low-dimensional space are more consistent with neuroanatomy than those in the original space. Gene expression patterns and developing brain anatomy are closely related. Dimensionality reduction and visual exploration facilitate the study of this relationship.

Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding

PubMed Central

Wang, Xiang; Zheng, Yuan; Zhao, Zhenzhou; Wang, Jinping

2015-01-01

Fault diagnosis is essentially a kind of pattern recognition. The measured signal samples usually distribute on nonlinear low-dimensional manifolds embedded in the high-dimensional signal space, so how to implement feature extraction, dimensionality reduction and improve recognition performance is a crucial task. In this paper a novel machinery fault diagnosis approach based on a statistical locally linear embedding (S-LLE) algorithm which is an extension of LLE by exploiting the fault class label information is proposed. The fault diagnosis approach first extracts the intrinsic manifold features from the high-dimensional feature vectors which are obtained from vibration signals that feature extraction by time-domain, frequency-domain and empirical mode decomposition (EMD), and then translates the complex mode space into a salient low-dimensional feature space by the manifold learning algorithm S-LLE, which outperforms other feature reduction methods such as PCA, LDA and LLE. Finally in the feature reduction space pattern classification and fault diagnosis by classifier are carried out easily and rapidly. Rolling bearing fault signals are used to validate the proposed fault diagnosis approach. The results indicate that the proposed approach obviously improves the classification performance of fault pattern recognition and outperforms the other traditional approaches. PMID:26153771

An overview of expert systems. [artificial intelligence

NASA Technical Reports Server (NTRS)

Gevarter, W. B.

1982-01-01

An expert system is defined and its basic structure is discussed. The knowledge base, the inference engine, and uses of expert systems are discussed. Architecture is considered, including choice of solution direction, reasoning in the presence of uncertainty, searching small and large search spaces, handling large search spaces by transforming them and by developing alternative or additional spaces, and dealing with time. Existing expert systems are reviewed. Tools for building such systems, construction, and knowledge acquisition and learning are discussed. Centers of research and funding sources are listed. The state-of-the-art, current problems, required research, and future trends are summarized.

An efficient and practical approach to obtain a better optimum solution for structural optimization

NASA Astrophysics Data System (ADS)

Chen, Ting-Yu; Huang, Jyun-Hao

2013-08-01

For many structural optimization problems, it is hard or even impossible to find the global optimum solution owing to unaffordable computational cost. An alternative and practical way of thinking is thus proposed in this research to obtain an optimum design which may not be global but is better than most local optimum solutions that can be found by gradient-based search methods. The way to reach this goal is to find a smaller search space for gradient-based search methods. It is found in this research that data mining can accomplish this goal easily. The activities of classification, association and clustering in data mining are employed to reduce the original design space. For unconstrained optimization problems, the data mining activities are used to find a smaller search region which contains the global or better local solutions. For constrained optimization problems, it is used to find the feasible region or the feasible region with better objective values. Numerical examples show that the optimum solutions found in the reduced design space by sequential quadratic programming (SQP) are indeed much better than those found by SQP in the original design space. The optimum solutions found in a reduced space by SQP sometimes are even better than the solution found using a hybrid global search method with approximate structural analyses.

Discovering More Chemical Concepts from 3D Chemical Information Searches of Crystal Structure Databases

ERIC Educational Resources Information Center

Rzepa, Henry S.

2016-01-01

Three new examples are presented illustrating three-dimensional chemical information searches of the Cambridge structure database (CSD) from which basic core concepts in organic and inorganic chemistry emerge. These include connecting the regiochemistry of aromatic electrophilic substitution with the geometrical properties of hydrogen bonding…

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.

Supersymmetry and the rotation group

NASA Astrophysics Data System (ADS)

McKeon, D. G. C.

2018-04-01

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

Asymptotical AdS space from nonlinear gravitational models with stabilized extra dimensions

NASA Astrophysics Data System (ADS)

Günther, U.; Moniz, P.; Zhuk, A.

2002-08-01

We consider nonlinear gravitational models with a multidimensional warped product geometry. Particular attention is payed to models with quadratic scalar curvature terms. It is shown that for certain parameter ranges, the extra dimensions are stabilized if the internal spaces have a negative constant curvature. In this case, the four-dimensional effective cosmological constant as well as the bulk cosmological constant become negative. As a consequence, the homogeneous and isotropic external space is asymptotically AdS4. The connection between the D-dimensional and the four-dimensional fundamental mass scales sets a restriction on the parameters of the considered nonlinear models.

New View of Relativity Theory

NASA Astrophysics Data System (ADS)

Martini, Luiz Cesar

2014-04-01

This article results from Introducing the Dimensional Continuous Space-Time Theory that was published in reference 1. The Dimensional Continuous Space-Time Theory shows a series of facts relative to matter, energy, space and concludes that empty space is inelastic, absolutely stationary, motionless, perpetual, without possibility of deformation neither can it be destroyed or created. A elementary cell of empty space or a certain amount of empty space can be occupied by any quantity of energy or matter without any alteration or deformation. As a consequence of these properties and being a integral part of the theory, the principles of Relativity Theory must be changed to become simple and intuitive.

Multi-view non-negative tensor factorization as relation learning in healthcare data.

PubMed

Hang Wu; Wang, May D

2016-08-01

Discovering patterns in co-occurrences data between objects and groups of concepts is a useful task in many domains, such as healthcare data analysis, information retrieval, and recommender systems. These relational representations come from objects' behaviors in different views, posing a challenging task of integrating information from these views to uncover the shared latent structures. The problem is further complicated by the high dimension of data and the large ratio of missing data. We propose a new paradigm of learning semantic relations using tensor factorization, by jointly factorizing multi-view tensors and searching for a consistent underlying semantic space across each views. We formulate the idea as an optimization problem and propose efficient optimization algorithms, with a special treatment of missing data as well as high-dimensional data. Experiments results show the potential and effectiveness of our algorithms.

Methods of Optimizing X-Ray Optical Prescriptions for Wide-Field Applications

NASA Technical Reports Server (NTRS)

Elsner, R. F.; O'Dell, S. L.; Ramsey, B. D.; Weisskopf, M. C.

2010-01-01

We are working on the development of a method for optimizing wide-field x-ray telescope mirror prescriptions, including polynomial coefficients, mirror shell relative displacements, and (assuming 4 focal plane detectors) detector placement and tilt that does not require a search through the multi-dimensional parameter space. Under the assumption that the parameters are small enough that second order expansions are valid, we show that the performance at the detector surface can be expressed as a quadratic function of the parameters with numerical coefficients derived from a ray trace through the underlying Wolter I optic. The best values for the parameters are found by solving the linear system of equations creating by setting derivatives of this function with respect to each parameter to zero. We describe the present status of this development effort.

Structure, Elastic Constants and XRD Spectra of Extended Solids under High Pressure

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

Batyrev, I. G.; Coleman, S. P.; Ciezak-Jenkins, J. A.

We present results of evolutionary simulations based on density functional calculations of a potentially new type of energetic materials called extended solids: P-N and N-H. High-density structures with covalent bonds generated using variable and fixed concentration methods were analysed in terms of thermo-dynamical stability and agreement with experimental X-ray diffraction (XRD) spectra. X-ray diffraction spectra were calculated using a virtual diffraction algorithm that computes kinematic diffraction intensity in three-dimensional reciprocal space before being reduced to a two-theta line profile. Calculated XRD patterns were used to search for the structure of extended solids present at experimental pressures by optimizing data accordingmore » to experimental XRD peak position, peak intensity and theoretically calculated enthalpy. Elastic constants has been calculated for thermodynamically stable structures of P-N system.« less

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.

Universal fluid droplet ejector

DOEpatents

Lee, Eric R.; Perl, Martin L.

1999-08-24

A droplet generator comprises a fluid reservoir having a side wall made of glass or quartz, and an end cap made from a silicon plate. The end cap contains a micromachined aperture through which the fluid is ejected. The side wall is thermally fused to the end cap, and no adhesive is necessary. This means that the fluid only comes into contact with the side wall and the end cap, both of which are chemically inert. Amplitudes of drive pulses received by reservoir determine the horizontal displacements of droplets relative to the ejection aperture. The drive pulses are varied such that the dropper generates a two-dimensional array of vertically-falling droplets. Vertical and horizontal interdroplet spacings may be varied in real time. Applications include droplet analysis experiments such as Millikan fractional charge searches and aerosol characterization, as well as material deposition applications.

Universal fluid droplet ejector

DOEpatents

Lee, E.R.; Perl, M.L.

1999-08-24

A droplet generator comprises a fluid reservoir having a side wall made of glass or quartz, and an end cap made from a silicon plate. The end cap contains a micromachined aperture through which the fluid is ejected. The side wall is thermally fused to the end cap, and no adhesive is necessary. This means that the fluid only comes into contact with the side wall and the end cap, both of which are chemically inert. Amplitudes of drive pulses received by reservoir determine the horizontal displacements of droplets relative to the ejection aperture. The drive pulses are varied such that the dropper generates a two-dimensional array of vertically-falling droplets. Vertical and horizontal inter-droplet spacings may be varied in real time. Applications include droplet analysis experiments such as Millikan fractional charge searches and aerosol characterization, as well as material deposition applications. 8 figs.

The Structure Lacuna

PubMed Central

Boeyens, Jan C.A.; Levendis, Demetrius C.

2012-01-01

Molecular symmetry is intimately connected with the classical concept of three-dimensional molecular structure. In a non-classical theory of wave-like interaction in four-dimensional space-time, both of these concepts and traditional quantum mechanics lose their operational meaning, unless suitably modified. A required reformulation should emphasize the importance of four-dimensional effects like spin and the symmetry effects of space-time curvature that could lead to a fundamentally different understanding of molecular symmetry and structure in terms of elementary number theory. Isolated single molecules have no characteristic shape and macro-biomolecules only develop robust three-dimensional structure in hydrophobic response to aqueous cellular media. PMID:22942753

Handy elementary algebraic properties of the geometry of entanglement

NASA Astrophysics Data System (ADS)

Blair, Howard A.; Alsing, Paul M.

2013-05-01

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

Landscape Analysis and Algorithm Development for Plateau Plagued Search Spaces

DTIC Science & Technology

2011-02-28

Final Report for AFOSR #FA9550-08-1-0422 Landscape Analysis and Algorithm Development for Plateau Plagued Search Spaces August 1, 2008 to November 30...focused on developing high level general purpose algorithms , such as Tabu Search and Genetic Algorithms . However, understanding of when and why these... algorithms perform well still lags. Our project extended the theory of certain combi- natorial optimization problems to develop analytical

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

PubMed

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

2008-07-01

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

Similarity-dissimilarity plot for visualization of high dimensional data in biomedical pattern classification.

PubMed

Arif, Muhammad

2012-06-01

In pattern classification problems, feature extraction is an important step. Quality of features in discriminating different classes plays an important role in pattern classification problems. In real life, pattern classification may require high dimensional feature space and it is impossible to visualize the feature space if the dimension of feature space is greater than four. In this paper, we have proposed a Similarity-Dissimilarity plot which can project high dimensional space to a two dimensional space while retaining important characteristics required to assess the discrimination quality of the features. Similarity-dissimilarity plot can reveal information about the amount of overlap of features of different classes. Separable data points of different classes will also be visible on the plot which can be classified correctly using appropriate classifier. Hence, approximate classification accuracy can be predicted. Moreover, it is possible to know about whom class the misclassified data points will be confused by the classifier. Outlier data points can also be located on the similarity-dissimilarity plot. Various examples of synthetic data are used to highlight important characteristics of the proposed plot. Some real life examples from biomedical data are also used for the analysis. The proposed plot is independent of number of dimensions of the feature space.

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.

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

DOE PAGES

Qiang, Ji

2017-01-23

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

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

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

Qiang, Ji

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

Towards novel organic high-Tc superconductors: Data mining using density of states similarity search

NASA Astrophysics Data System (ADS)

Geilhufe, R. Matthias; Borysov, Stanislav S.; Kalpakchi, Dmytro; Balatsky, Alexander V.

2018-02-01

Identifying novel functional materials with desired key properties is an important part of bridging the gap between fundamental research and technological advancement. In this context, high-throughput calculations combined with data-mining techniques highly accelerated this process in different areas of research during the past years. The strength of a data-driven approach for materials prediction lies in narrowing down the search space of thousands of materials to a subset of prospective candidates. Recently, the open-access organic materials database OMDB was released providing electronic structure data for thousands of previously synthesized three-dimensional organic crystals. Based on the OMDB, we report about the implementation of a novel density of states similarity search tool which is capable of retrieving materials with similar density of states to a reference material. The tool is based on the approximate nearest neighbor algorithm as implemented in the ANNOY library and can be applied via the OMDB web interface. The approach presented here is wide ranging and can be applied to various problems where the density of states is responsible for certain key properties of a material. As the first application, we report about materials exhibiting electronic structure similarities to the aromatic hydrocarbon p-terphenyl which was recently discussed as a potential organic high-temperature superconductor exhibiting a transition temperature in the order of 120 K under strong potassium doping. Although the mechanism driving the remarkable transition temperature remains under debate, we argue that the density of states, reflecting the electronic structure of a material, might serve as a crucial ingredient for the observed high Tc. To provide candidates which might exhibit comparable properties, we present 15 purely organic materials with similar features to p-terphenyl within the electronic structure, which also tend to have structural similarities with p-terphenyl such as space group symmetries, chemical composition, and molecular structure. The experimental verification of these candidates might lead to a better understanding of the underlying mechanism in case similar superconducting properties are revealed.

Search for general relativistic effects in table-top displacement metrology

NASA Technical Reports Server (NTRS)

Halverson, Peter G.; Macdonald, Daniel R.; Diaz, Rosemary T.

2004-01-01

As displacement metrology accuracy improves, general relativistic effects will become noticeable. Metrology gauges developed for the Space Interferometry Mission were used to search for locally anisotropic space-time, with a null result at the 10 to the negative tenth power level.

Using sketch-map coordinates to analyze and bias molecular dynamics simulations

PubMed Central

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

2012-01-01

When examining complex problems, such as the folding of proteins, coarse grained descriptions of the system drive our investigation and help us to rationalize the results. Oftentimes collective variables (CVs), derived through some chemical intuition about the process of interest, serve this purpose. Because finding these CVs is the most difficult part of any investigation, we recently developed a dimensionality reduction algorithm, sketch-map, that can be used to build a low-dimensional map of a phase space of high-dimensionality. In this paper we discuss how these machine-generated CVs can be used to accelerate the exploration of phase space and to reconstruct free-energy landscapes. To do so, we develop a formalism in which high-dimensional configurations are no longer represented by low-dimensional position vectors. Instead, for each configuration we calculate a probability distribution, which has a domain that encompasses the entirety of the low-dimensional space. To construct a biasing potential, we exploit an analogy with metadynamics and use the trajectory to adaptively construct a repulsive, history-dependent bias from the distributions that correspond to the previously visited configurations. This potential forces the system to explore more of phase space by making it desirable to adopt configurations whose distributions do not overlap with the bias. We apply this algorithm to a small model protein and succeed in reproducing the free-energy surface that we obtain from a parallel tempering calculation. PMID:22427357

Development of an Evolutionary Algorithm for the ab Initio Discovery of Two-Dimensional Materials

NASA Astrophysics Data System (ADS)

Revard, Benjamin Charles

Crystal structure prediction is an important first step on the path toward computational materials design. Increasingly robust methods have become available in recent years for computing many materials properties, but because properties are largely a function of crystal structure, the structure must be known before these methods can be brought to bear. In addition, structure prediction is particularly useful for identifying low-energy structures of subperiodic materials, such as two-dimensional (2D) materials, which may adopt unexpected structures that differ from those of the corresponding bulk phases. Evolutionary algorithms, which are heuristics for global optimization inspired by biological evolution, have proven to be a fruitful approach for tackling the problem of crystal structure prediction. This thesis describes the development of an improved evolutionary algorithm for structure prediction and several applications of the algorithm to predict the structures of novel low-energy 2D materials. The first part of this thesis contains an overview of evolutionary algorithms for crystal structure prediction and presents our implementation, including details of extending the algorithm to search for clusters, wires, and 2D materials, improvements to efficiency when running in parallel, improved composition space sampling, and the ability to search for partial phase diagrams. We then present several applications of the evolutionary algorithm to 2D systems, including InP, the C-Si and Sn-S phase diagrams, and several group-IV dioxides. This thesis makes use of the Cornell graduate school's "papers" option. Chapters 1 and 3 correspond to the first-author publications of Refs. [131] and [132], respectively, and chapter 2 will soon be submitted as a first-author publication. The material in chapter 4 is taken from Ref. [144], in which I share joint first-authorship. In this case I have included only my own contributions.

PolySac3DB: an annotated data base of 3 dimensional structures of polysaccharides.

PubMed

Sarkar, Anita; Pérez, Serge

2012-11-14

Polysaccharides are ubiquitously present in the living world. Their structural versatility makes them important and interesting components in numerous biological and technological processes ranging from structural stabilization to a variety of immunologically important molecular recognition events. The knowledge of polysaccharide three-dimensional (3D) structure is important in studying carbohydrate-mediated host-pathogen interactions, interactions with other bio-macromolecules, drug design and vaccine development as well as material science applications or production of bio-ethanol. PolySac3DB is an annotated database that contains the 3D structural information of 157 polysaccharide entries that have been collected from an extensive screening of scientific literature. They have been systematically organized using standard names in the field of carbohydrate research into 18 categories representing polysaccharide families. Structure-related information includes the saccharides making up the repeat unit(s) and their glycosidic linkages, the expanded 3D representation of the repeat unit, unit cell dimensions and space group, helix type, diffraction diagram(s) (when applicable), experimental and/or simulation methods used for structure description, link to the abstract of the publication, reference and the atomic coordinate files for visualization and download. The database is accompanied by a user-friendly graphical user interface (GUI). It features interactive displays of polysaccharide structures and customized search options for beginners and experts, respectively. The site also serves as an information portal for polysaccharide structure determination techniques. The web-interface also references external links where other carbohydrate-related resources are available. PolySac3DB is established to maintain information on the detailed 3D structures of polysaccharides. All the data and features are available via the web-interface utilizing the search engine and can be accessed at http://polysac3db.cermav.cnrs.fr.

ON THE GEOMETRY OF MEASURABLE SETS IN N-DIMENSIONAL SPACE ON WHICH GENERALIZED LOCALIZATION HOLDS FOR MULTIPLE FOURIER SERIES OF FUNCTIONS IN L_p, p>1

NASA Astrophysics Data System (ADS)

Bloshanskiĭ, I. L.

1984-02-01

The precise geometry is found of measurable sets in N-dimensional Euclidean space on which generalized localization almost everywhere holds for multiple Fourier series which are rectangularly summable.Bibliography: 14 titles.

Analysis of spectral operators in one-dimensional domains

NASA Technical Reports Server (NTRS)

Maday, Y.

1985-01-01

Results are proven concerning certain projection operators on the space of all polynomials of degree less than or equal to N with respect to a class of one-dimensional weighted Sobolev spaces. The results are useful in the theory of the approximation of partial differential equations with spectral methods.

A real negative selection algorithm with evolutionary preference for anomaly detection

NASA Astrophysics Data System (ADS)

Yang, Tao; Chen, Wen; Li, Tao

2017-04-01

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

Speeding up biomolecular interactions by molecular sledding

DOE PAGES

Turkin, Alexander; Zhang, Lei; Marcozzi, Alessio; ...

2015-10-07

In numerous biological processes associations involve a protein with its binding partner, an event that is preceded by a diffusion-mediated search bringing the two partners together. Often hindered by crowding in biologically relevant environments, three-dimensional diffusion can be slow and result in long bimolecular association times. Moreover, the initial association step between two binding partners often represents a rate-limiting step in biotechnologically relevant reactions. We also demonstrate the practical use of an 11-a.a. DNA-interacting peptide derived from adenovirus to reduce the dimensionality of diffusional search processes and speed up associations between biological macromolecules. We functionalize binding partners with the peptidemore » and demonstrate that the ability of the peptide to one-dimensionally diffuse along DNA results in a 20-fold reduction in reaction time. We also show that modifying PCR primers with the peptide sled enables significant acceleration of standard PCR reactions.« less

Cooperative Search with Autonomous Vehicles in a 3D Aquatic Testbed

DTIC Science & Technology

2012-01-01

Cooperative Search with Autonomous Vehicles in a 3D Aquatic Testbed Matthew Keeter1, Daniel Moore2,3, Ryan Muller2,3, Eric Nieters1, Jennifer...Many applications for autonomous vehicles involve three-dimensional domains, notably aerial and aquatic environments. Such applications include mon...TYPE 3. DATES COVERED 00-00-2012 to 00-00-2012 4. TITLE AND SUBTITLE Cooperative Search With Autonomous Vehicles In A 3D Aquatic Testbed 5a

Target intersection probabilities for parallel-line and continuous-grid types of search

USGS Publications Warehouse

McCammon, R.B.

1977-01-01

The expressions for calculating the probability of intersection of hidden targets of different sizes and shapes for parallel-line and continuous-grid types of search can be formulated by vsing the concept of conditional probability. When the prior probability of the orientation of a widden target is represented by a uniform distribution, the calculated posterior probabilities are identical with the results obtained by the classic methods of probability. For hidden targets of different sizes and shapes, the following generalizations about the probability of intersection can be made: (1) to a first approximation, the probability of intersection of a hidden target is proportional to the ratio of the greatest dimension of the target (viewed in plane projection) to the minimum line spacing of the search pattern; (2) the shape of the hidden target does not greatly affect the probability of the intersection when the largest dimension of the target is small relative to the minimum spacing of the search pattern, (3) the probability of intersecting a target twice for a particular type of search can be used as a lower bound if there is an element of uncertainty of detection for a particular type of tool; (4) the geometry of the search pattern becomes more critical when the largest dimension of the target equals or exceeds the minimum spacing of the search pattern; (5) for elongate targets, the probability of intersection is greater for parallel-line search than for an equivalent continuous square-grid search when the largest dimension of the target is less than the minimum spacing of the search pattern, whereas the opposite is true when the largest dimension exceeds the minimum spacing; (6) the probability of intersection for nonorthogonal continuous-grid search patterns is not greatly different from the probability of intersection for the equivalent orthogonal continuous-grid pattern when the orientation of the target is unknown. The probability of intersection for an elliptically shaped target can be approximated by treating the ellipse as intermediate between a circle and a line. A search conducted along a continuous rectangular grid can be represented as intermediate between a search along parallel lines and along a continuous square grid. On this basis, an upper and lower bound for the probability of intersection of an elliptically shaped target for a continuous rectangular grid can be calculated. Charts have been constructed that permit the values for these probabilities to be obtained graphically. The use of conditional probability allows the explorationist greater flexibility in considering alternate search strategies for locating hidden targets. ?? 1977 Plenum Publishing Corp.

The Search for Life Beyond Earth

NASA Image and Video Library

2014-07-14

Matt Mountain, Director of the Space Telescope Science Institute and telescope scientist for the James Webb Space Telescope, speaks during a panel discussion on the search for life beyond Earth in the James E. Webb Auditorium at NASA Headquarters on Monday, July 14, 2014 in Washington, DC. The panel discussed how NASA's space-based observatories are making new discoveries and how the agency's new telescope, the James Webb Space Telescope, will continue this path of discovery after its schedule launch in 2018. Photo Credit: (NASA/Joel Kowsky)

The Search for Life Beyond Earth

NASA Image and Video Library

2014-07-14

John Mather, Nobel Laureate and Project Scientist for the James Webb Space Telescope at NASA's Goddard Space Flight Center, speaks during a panel discussion on the search for life beyond Earth in the James E. Webb Auditorium at NASA Headquarters on Monday, July 14, 2014 in Washington, DC. The panel discussed how NASA's space-based observatories are making new discoveries and how the agency's new telescope, the James Webb Space Telescope, will continue this path of discovery after its schedule launch in 2018. Photo Credit: (NASA/Joel Kowsky)

Influence of geomagnetic activity and earth weather changes on heart rate and blood pressure in young and healthy population

NASA Astrophysics Data System (ADS)

Ozheredov, V. A.; Chibisov, S. M.; Blagonravov, M. L.; Khodorovich, N. A.; Demurov, E. A.; Goryachev, V. A.; Kharlitskaya, E. V.; Eremina, I. S.; Meladze, Z. A.

2017-05-01

There are many references in the literature related to connection between the space weather and the state of human organism. The search of external factors influence on humans is a multi-factor problem and it is well known that humans have a meteo-sensitivity. A direct problem of finding the earth weather conditions, under which the space weather manifests itself most strongly, is discussed in the present work for the first time in the helio-biology. From a formal point of view, this problem requires identification of subset (magnetobiotropic region) in three-dimensional earth's weather parameters such as pressure, temperature, and humidity, corresponding to the days when the human body is the most sensitive to changes in the geomagnetic field variations and when it reacts by statistically significant increase (or decrease) of a particular physiological parameter. This formulation defines the optimization of the problem, and the solution of the latter is not possible without the involvement of powerful metaheuristic methods of searching. Using the algorithm of differential evolution, we prove the existence of magnetobiotropic regions in the earth's weather parameters, which exhibit magneto-sensitivity of systolic, diastolic blood pressure, and heart rate of healthy young subjects for three weather areas (combinations of atmospheric temperature, pressure, and humidity). The maximum value of the correlation confidence for the measurements attributable to the days of the weather conditions that fall into each of three magnetobiotropic areas is an order of 0.006, that is almost 10 times less than the confidence, equal to 0.05, accepted in many helio-biological researches.

Fault model of the 2014 Cephalonia seismic sequence - Evidence of spatiotemporal fault segmentation along the NW edge of Aegean Arc

NASA Astrophysics Data System (ADS)

Saltogianni, Vasso; Moschas, Fanis; Stiros, Stathis

2017-04-01

Finite fault models (FFM) are presented for the two main shocks of the 2014 Cephalonia (Ionian Sea, Greece) seismic sequence (M 6.0) which produced extreme peak ground accelerations ( 0.7g) in the west edge of the Aegean Arc, an area in which the poor coverage by seismological and GPS/INSAR data makes FFM a real challenge. Modeling was based on co-seismic GPS data and on the recently introduced TOPological INVersion algorithm. The latter is a novel uniform grid search-based technique in n-dimensional spaces, is based on the concept of stochastic variables and which can identify multiple unconstrained ("free") solutions in a specified search space. Derived FFMs for the 2014 earthquakes correspond to an essentially strike slip fault and of part of a shallow thrust, the surface projection of both of which run roughly along the west coast of Cephalonia. Both faults correlate with pre-existing faults. The 2014 faults, in combination with the faults of the 2003 and 2015 Leucas earthquakes farther NE, form a string of oblique slip, partly overlapping fault segments with variable geometric and kinematic characteristics along the NW edge of the Aegean Arc. This composite fault, usually regarded as the Cephalonia Transform Fault, accommodates shear along this part of the Arc. Because of the highly fragmented crust, dominated by major thrusts in this area, fault activity is associated with 20km long segments and magnitude 6.0-6.5 earthquakes recurring in intervals of a few seconds to 10 years.

Influence of geomagnetic activity and earth weather changes on heart rate and blood pressure in young and healthy population.

PubMed

Ozheredov, V A; Chibisov, S M; Blagonravov, M L; Khodorovich, N A; Demurov, E A; Goryachev, V A; Kharlitskaya, E V; Eremina, I S; Meladze, Z A

2017-05-01

There are many references in the literature related to connection between the space weather and the state of human organism. The search of external factors influence on humans is a multi-factor problem and it is well known that humans have a meteo-sensitivity. A direct problem of finding the earth weather conditions, under which the space weather manifests itself most strongly, is discussed in the present work for the first time in the helio-biology. From a formal point of view, this problem requires identification of subset (magnetobiotropic region) in three-dimensional earth's weather parameters such as pressure, temperature, and humidity, corresponding to the days when the human body is the most sensitive to changes in the geomagnetic field variations and when it reacts by statistically significant increase (or decrease) of a particular physiological parameter. This formulation defines the optimization of the problem, and the solution of the latter is not possible without the involvement of powerful metaheuristic methods of searching. Using the algorithm of differential evolution, we prove the existence of magnetobiotropic regions in the earth's weather parameters, which exhibit magneto-sensitivity of systolic, diastolic blood pressure, and heart rate of healthy young subjects for three weather areas (combinations of atmospheric temperature, pressure, and humidity). The maximum value of the correlation confidence for the measurements attributable to the days of the weather conditions that fall into each of three magnetobiotropic areas is an order of 0.006, that is almost 10 times less than the confidence, equal to 0.05, accepted in many helio-biological researches.

The Extraction of One-Dimensional Flow Properties from Multi-Dimensional Data Sets

NASA Technical Reports Server (NTRS)

Baurle, Robert A.; Gaffney, Richard L., Jr.

2007-01-01

The engineering design and analysis of air-breathing propulsion systems relies heavily on zero- or one-dimensional properties (e.g. thrust, total pressure recovery, mixing and combustion efficiency, etc.) for figures of merit. The extraction of these parameters from experimental data sets and/or multi-dimensional computational data sets is therefore an important aspect of the design process. A variety of methods exist for extracting performance measures from multi-dimensional data sets. Some of the information contained in the multi-dimensional flow is inevitably lost when any one-dimensionalization technique is applied. Hence, the unique assumptions associated with a given approach may result in one-dimensional properties that are significantly different than those extracted using alternative approaches. The purpose of this effort is to examine some of the more popular methods used for the extraction of performance measures from multi-dimensional data sets, reveal the strengths and weaknesses of each approach, and highlight various numerical issues that result when mapping data from a multi-dimensional space to a space of one dimension.

The Art of Extracting One-Dimensional Flow Properties from Multi-Dimensional Data Sets

NASA Technical Reports Server (NTRS)

Baurle, R. A.; Gaffney, R. L.

2007-01-01

The engineering design and analysis of air-breathing propulsion systems relies heavily on zero- or one-dimensional properties (e:g: thrust, total pressure recovery, mixing and combustion efficiency, etc.) for figures of merit. The extraction of these parameters from experimental data sets and/or multi-dimensional computational data sets is therefore an important aspect of the design process. A variety of methods exist for extracting performance measures from multi-dimensional data sets. Some of the information contained in the multi-dimensional flow is inevitably lost when any one-dimensionalization technique is applied. Hence, the unique assumptions associated with a given approach may result in one-dimensional properties that are significantly different than those extracted using alternative approaches. The purpose of this effort is to examine some of the more popular methods used for the extraction of performance measures from multi-dimensional data sets, reveal the strengths and weaknesses of each approach, and highlight various numerical issues that result when mapping data from a multi-dimensional space to a space of one dimension.

Search for general relativistic effects in table-top displacement metrology

NASA Technical Reports Server (NTRS)

Halverson, Peter G.; Diaz, Rosemary T.; Macdonald, Daniel R.

2004-01-01

As displacement metrology accuracy improves, general relativistic effects will become noticeable. Metrology gauges developed for the Space Interferometry Mission, were used to search for locally anisotropic space-time, with a null result at the 10 to the negative 10th power level.

TripAdvisor^{N-D}: A Tourism-Inspired High-Dimensional Space Exploration Framework with Overview and Detail.

PubMed

Nam, Julia EunJu; Mueller, Klaus

2013-02-01

Gaining a true appreciation of high-dimensional space remains difficult since all of the existing high-dimensional space exploration techniques serialize the space travel in some way. This is not so foreign to us since we, when traveling, also experience the world in a serial fashion. But we typically have access to a map to help with positioning, orientation, navigation, and trip planning. Here, we propose a multivariate data exploration tool that compares high-dimensional space navigation with a sightseeing trip. It decomposes this activity into five major tasks: 1) Identify the sights: use a map to identify the sights of interest and their location; 2) Plan the trip: connect the sights of interest along a specifyable path; 3) Go on the trip: travel along the route; 4) Hop off the bus: experience the location, look around, zoom into detail; and 5) Orient and localize: regain bearings in the map. We describe intuitive and interactive tools for all of these tasks, both global navigation within the map and local exploration of the data distributions. For the latter, we describe a polygonal touchpad interface which enables users to smoothly tilt the projection plane in high-dimensional space to produce multivariate scatterplots that best convey the data relationships under investigation. Motion parallax and illustrative motion trails aid in the perception of these transient patterns. We describe the use of our system within two applications: 1) the exploratory discovery of data configurations that best fit a personal preference in the presence of tradeoffs and 2) interactive cluster analysis via cluster sculpting in N-D.

Graphical Representations of Electronic Search Patterns.

ERIC Educational Resources Information Center

Lin, Xia; And Others

1991-01-01

Discussion of search behavior in electronic environments focuses on the development of GRIP (Graphic Representor of Interaction Patterns), a graphing tool based on HyperCard that produces graphic representations of search patterns. Search state spaces are explained, and forms of data available from electronic searches are described. (34…

Reorienting in Images of a Three-Dimensional Environment

ERIC Educational Resources Information Center

Kelly, Debbie M.; Bischof, Walter F.

2005-01-01

Adult humans searched for a hidden goal in images depicting 3-dimensional rooms. Images contained either featural cues, geometric cues, or both, which could be used to determine the correct location of the goal. In Experiment 1, participants learned to use featural and geometric information equally well. However, men and women showed significant…

3-Dimensional stereo implementation of photoacoustic imaging based on a new image reconstruction algorithm without using discrete Fourier transform

NASA Astrophysics Data System (ADS)

Ham, Woonchul; Song, Chulgyu

2017-05-01

In this paper, we propose a new three-dimensional stereo image reconstruction algorithm for a photoacoustic medical imaging system. We also introduce and discuss a new theoretical algorithm by using the physical concept of Radon transform. The main key concept of proposed theoretical algorithm is to evaluate the existence possibility of the acoustic source within a searching region by using the geometric distance between each sensor element of acoustic detector and the corresponding searching region denoted by grid. We derive the mathematical equation for the magnitude of the existence possibility which can be used for implementing a new proposed algorithm. We handle and derive mathematical equations of proposed algorithm for the one-dimensional sensing array case as well as two dimensional sensing array case too. A mathematical k-wave simulation data are used for comparing the image quality of the proposed algorithm with that of general conventional algorithm in which the FFT should be necessarily used. From the k-wave Matlab simulation results, we can prove the effectiveness of the proposed reconstruction algorithm.

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

NASA Astrophysics Data System (ADS)

Czajka, P.; Jezierski, J.

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

Wigner functions from the two-dimensional wavelet group.

PubMed

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

2000-12-01

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

On the existence of global solutions of the one-dimensional cubic NLS for initial data in the modulation space Mp,q (R)

NASA Astrophysics Data System (ADS)

Chaichenets, Leonid; Hundertmark, Dirk; Kunstmann, Peer; Pattakos, Nikolaos

2017-10-01

We prove global existence for the one-dimensional cubic nonlinear Schrödinger equation in modulation spaces Mp,p‧ for p sufficiently close to 2. In contrast to known results, [9] and [14], our result requires no smallness condition on initial data. The proof adapts a splitting method inspired by work of Vargas-Vega, Hyakuna-Tsutsumi and Grünrock to the modulation space setting and exploits polynomial growth of the free Schrödinger group on modulation spaces.

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

NASA Astrophysics Data System (ADS)

Dušek, Zdeněk; Kowalski, Oldřich

2007-09-01

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

Exhaustive geographic search with mobile robots along space-filling curves

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

Spires, S.V.; Goldsmith, S.Y.

1998-03-01

Swarms of mobile robots can be tasked with searching a geographic region for targets of interest, such as buried land mines. The authors assume that the individual robots are equipped with sensors tuned to the targets of interest, that these sensors have limited range, and that the robots can communicate with one another to enable cooperation. How can a swarm of cooperating sensate robots efficiently search a given geographic region for targets in the absence of a priori information about the target`s locations? Many of the obvious approaches are inefficient or lack robustness. One efficient approach is to have themore » robots traverse a space-filling curve. For many geographic search applications, this method is energy-frugal, highly robust, and provides guaranteed coverage in a finite time that decreases as the reciprocal of the number of robots sharing the search task. Furthermore, it minimizes the amount of robot-to-robot communication needed for the robots to organize their movements. This report presents some preliminary results from applying the Hilbert space-filling curve to geographic search by mobile robots.« less

On infinite-dimensional state spaces

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

Fritz, Tobias

It is well known that the canonical commutation relation [x, p]=i can be realized only on an infinite-dimensional Hilbert space. While any finite set of experimental data can also be explained in terms of a finite-dimensional Hilbert space by approximating the commutation relation, Occam's razor prefers the infinite-dimensional model in which [x, p]=i holds on the nose. This reasoning one will necessarily have to make in any approach which tries to detect the infinite-dimensionality. One drawback of using the canonical commutation relation for this purpose is that it has unclear operational meaning. Here, we identify an operationally well-defined context frommore » which an analogous conclusion can be drawn: if two unitary transformations U, V on a quantum system satisfy the relation V{sup -1}U{sup 2}V=U{sup 3}, then finite-dimensionality entails the relation UV{sup -1}UV=V{sup -1}UVU; this implication strongly fails in some infinite-dimensional realizations. This is a result from combinatorial group theory for which we give a new proof. This proof adapts to the consideration of cases where the assumed relation V{sup -1}U{sup 2}V=U{sup 3} holds only up to {epsilon} and then yields a lower bound on the dimension.« less

Gravitational wave searches using the DSN (Deep Space Network)

NASA Technical Reports Server (NTRS)

Nelson, S. J.; Armstrong, J. W.

1988-01-01

The Deep Space Network Doppler spacecraft link is currently the only method available for broadband gravitational wave searches in the 0.01 to 0.001 Hz frequency range. The DSN's role in the worldwide search for gravitational waves is described by first summarizing from the literature current theoretical estimates of gravitational wave strengths and time scales from various astrophysical sources. Current and future detection schemes for ground based and space based detectors are then discussed. Past, present, and future planned or proposed gravitational wave experiments using DSN Doppler tracking are described. Lastly, some major technical challenges to improve gravitational wave sensitivities using the DSN are discussed.

Space debris measurement program at Phillips Laboratory

NASA Technical Reports Server (NTRS)

Dao, Phan D.; Mcnutt, Ross T.

1992-01-01

Ground-based optical sensing was identified as a technique for measuring space debris complementary to radar in the critical debris size range of 1 to 10 cm. The Phillips Laboratory is building a staring optical sensor for space debris measurement and considering search and track optical measurement at additional sites. The staring sensor is implemented in collaboration with Wright Laboratory using the 2.5 m telescope at Wright Patterson AFB, Dayton, Ohio. The search and track sensor is designed to detect and track orbital debris in tasked orbits. A progress report and a discussion of sensor performance and search and track strategies will be given.

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

NASA Astrophysics Data System (ADS)

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

2012-02-01

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

Hyper-spectral image segmentation using spectral clustering with covariance descriptors

NASA Astrophysics Data System (ADS)

Kursun, Olcay; Karabiber, Fethullah; Koc, Cemalettin; Bal, Abdullah

2009-02-01

Image segmentation is an important and difficult computer vision problem. Hyper-spectral images pose even more difficulty due to their high-dimensionality. Spectral clustering (SC) is a recently popular clustering/segmentation algorithm. In general, SC lifts the data to a high dimensional space, also known as the kernel trick, then derive eigenvectors in this new space, and finally using these new dimensions partition the data into clusters. We demonstrate that SC works efficiently when combined with covariance descriptors that can be used to assess pixelwise similarities rather than in the high-dimensional Euclidean space. We present the formulations and some preliminary results of the proposed hybrid image segmentation method for hyper-spectral images.

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

Krause, Josua; Dasgupta, Aritra; Fekete, Jean-Daniel

Dealing with the curse of dimensionality is a key challenge in high-dimensional data visualization. We present SeekAView to address three main gaps in the existing research literature. First, automated methods like dimensionality reduction or clustering suffer from a lack of transparency in letting analysts interact with their outputs in real-time to suit their exploration strategies. The results often suffer from a lack of interpretability, especially for domain experts not trained in statistics and machine learning. Second, exploratory visualization techniques like scatter plots or parallel coordinates suffer from a lack of visual scalability: it is difficult to present a coherent overviewmore » of interesting combinations of dimensions. Third, the existing techniques do not provide a flexible workflow that allows for multiple perspectives into the analysis process by automatically detecting and suggesting potentially interesting subspaces. In SeekAView we address these issues using suggestion based visual exploration of interesting patterns for building and refining multidimensional subspaces. Compared to the state-of-the-art in subspace search and visualization methods, we achieve higher transparency in showing not only the results of the algorithms, but also interesting dimensions calibrated against different metrics. We integrate a visually scalable design space with an iterative workflow guiding the analysts by choosing the starting points and letting them slice and dice through the data to find interesting subspaces and detect correlations, clusters, and outliers. We present two usage scenarios for demonstrating how SeekAView can be applied in real-world data analysis scenarios.« less

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

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

PubMed

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

2017-10-01

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

Adjoint Methods for Adjusting Three-Dimensional Atmosphere and Surface Properties to Fit Multi-Angle Multi-Pixel Polarimetric Measurements

NASA Technical Reports Server (NTRS)

Martin, William G.; Cairns, Brian; Bal, Guillaume

2014-01-01

This paper derives an efficient procedure for using the three-dimensional (3D) vector radiative transfer equation (VRTE) to adjust atmosphere and surface properties and improve their fit with multi-angle/multi-pixel radiometric and polarimetric measurements of scattered sunlight. The proposed adjoint method uses the 3D VRTE to compute the measurement misfit function and the adjoint 3D VRTE to compute its gradient with respect to all unknown parameters. In the remote sensing problems of interest, the scalar-valued misfit function quantifies agreement with data as a function of atmosphere and surface properties, and its gradient guides the search through this parameter space. Remote sensing of the atmosphere and surface in a three-dimensional region may require thousands of unknown parameters and millions of data points. Many approaches would require calls to the 3D VRTE solver in proportion to the number of unknown parameters or measurements. To avoid this issue of scale, we focus on computing the gradient of the misfit function as an alternative to the Jacobian of the measurement operator. The resulting adjoint method provides a way to adjust 3D atmosphere and surface properties with only two calls to the 3D VRTE solver for each spectral channel, regardless of the number of retrieval parameters, measurement view angles or pixels. This gives a procedure for adjusting atmosphere and surface parameters that will scale to the large problems of 3D remote sensing. For certain types of multi-angle/multi-pixel polarimetric measurements, this encourages the development of a new class of three-dimensional retrieval algorithms with more flexible parametrizations of spatial heterogeneity, less reliance on data screening procedures, and improved coverage in terms of the resolved physical processes in the Earth?s atmosphere.

HSTLBO: A hybrid algorithm based on Harmony Search and Teaching-Learning-Based Optimization for complex high-dimensional optimization problems

PubMed Central

Tuo, Shouheng; Yong, Longquan; Deng, Fang’an; Li, Yanhai; Lin, Yong; Lu, Qiuju

2017-01-01

Harmony Search (HS) and Teaching-Learning-Based Optimization (TLBO) as new swarm intelligent optimization algorithms have received much attention in recent years. Both of them have shown outstanding performance for solving NP-Hard optimization problems. However, they also suffer dramatic performance degradation for some complex high-dimensional optimization problems. Through a lot of experiments, we find that the HS and TLBO have strong complementarity each other. The HS has strong global exploration power but low convergence speed. Reversely, the TLBO has much fast convergence speed but it is easily trapped into local search. In this work, we propose a hybrid search algorithm named HSTLBO that merges the two algorithms together for synergistically solving complex optimization problems using a self-adaptive selection strategy. In the HSTLBO, both HS and TLBO are modified with the aim of balancing the global exploration and exploitation abilities, where the HS aims mainly to explore the unknown regions and the TLBO aims to rapidly exploit high-precision solutions in the known regions. Our experimental results demonstrate better performance and faster speed than five state-of-the-art HS variants and show better exploration power than five good TLBO variants with similar run time, which illustrates that our method is promising in solving complex high-dimensional optimization problems. The experiment on portfolio optimization problems also demonstrate that the HSTLBO is effective in solving complex read-world application. PMID:28403224

HSTLBO: A hybrid algorithm based on Harmony Search and Teaching-Learning-Based Optimization for complex high-dimensional optimization problems.

PubMed

Tuo, Shouheng; Yong, Longquan; Deng, Fang'an; Li, Yanhai; Lin, Yong; Lu, Qiuju

2017-01-01

Harmony Search (HS) and Teaching-Learning-Based Optimization (TLBO) as new swarm intelligent optimization algorithms have received much attention in recent years. Both of them have shown outstanding performance for solving NP-Hard optimization problems. However, they also suffer dramatic performance degradation for some complex high-dimensional optimization problems. Through a lot of experiments, we find that the HS and TLBO have strong complementarity each other. The HS has strong global exploration power but low convergence speed. Reversely, the TLBO has much fast convergence speed but it is easily trapped into local search. In this work, we propose a hybrid search algorithm named HSTLBO that merges the two algorithms together for synergistically solving complex optimization problems using a self-adaptive selection strategy. In the HSTLBO, both HS and TLBO are modified with the aim of balancing the global exploration and exploitation abilities, where the HS aims mainly to explore the unknown regions and the TLBO aims to rapidly exploit high-precision solutions in the known regions. Our experimental results demonstrate better performance and faster speed than five state-of-the-art HS variants and show better exploration power than five good TLBO variants with similar run time, which illustrates that our method is promising in solving complex high-dimensional optimization problems. The experiment on portfolio optimization problems also demonstrate that the HSTLBO is effective in solving complex read-world application.

A Systematic Review to Uncover a Universal Protocol for Accuracy Assessment of 3-Dimensional Virtually Planned Orthognathic Surgery.

PubMed

Gaber, Ramy M; Shaheen, Eman; Falter, Bart; Araya, Sebastian; Politis, Constantinus; Swennen, Gwen R J; Jacobs, Reinhilde

2017-11-01

The aim of this study was to systematically review methods used for assessing the accuracy of 3-dimensional virtually planned orthognathic surgery in an attempt to reach an objective assessment protocol that could be universally used. A systematic review of the currently available literature, published until September 12, 2016, was conducted using PubMed as the primary search engine. We performed secondary searches using the Cochrane Database, clinical trial registries, Google Scholar, and Embase, as well as a bibliography search. Included articles were required to have stated clearly that 3-dimensional virtual planning was used and accuracy assessment performed, along with validation of the planning and/or assessment method. Descriptive statistics and quality assessment of included articles were performed. The initial search yielded 1,461 studies. Only 7 studies were included in our review. An important variability was found regarding methods used for 1) accuracy assessment of virtually planned orthognathic surgery or 2) validation of the tools used. Included studies were of moderate quality; reviewers' agreement regarding quality was calculated to be 0.5 using the Cohen κ test. On the basis of the findings of this review, it is evident that the literature lacks consensus regarding accuracy assessment. Hence, a protocol is suggested for accuracy assessment of virtually planned orthognathic surgery with the lowest margin of error. Copyright © 2017 American Association of Oral and Maxillofacial Surgeons. Published by Elsevier Inc. All rights reserved.

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.

Rarefied gas flow through two-dimensional nozzles

NASA Technical Reports Server (NTRS)

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

1989-01-01

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

Building the 3D Geological Model of Wall Rock of Salt Caverns Based on Integration Method of Multi-source data

NASA Astrophysics Data System (ADS)

Yongzhi, WANG; hui, WANG; Lixia, LIAO; Dongsen, LI

2017-02-01

In order to analyse the geological characteristics of salt rock and stability of salt caverns, rough three-dimensional (3D) models of salt rock stratum and the 3D models of salt caverns on study areas are built by 3D GIS spatial modeling technique. During implementing, multi-source data, such as basic geographic data, DEM, geological plane map, geological section map, engineering geological data, and sonar data are used. In this study, the 3D spatial analyzing and calculation methods, such as 3D GIS intersection detection method in three-dimensional space, Boolean operations between three-dimensional space entities, three-dimensional space grid discretization, are used to build 3D models on wall rock of salt caverns. Our methods can provide effective calculation models for numerical simulation and analysis of the creep characteristics of wall rock in salt caverns.

Space shuttle search and rescue experiment using synthetic aperture radar

NASA Technical Reports Server (NTRS)

Sivertson, W. E., Jr.; Larson, R. W.; Zelenka, J. S.

1977-01-01

The feasibility of a synthetic aperture radar for search and rescue applications was demonstrated with aircraft experiments. One experiment was conducted using the ERIM four-channel radar and several test sites in the Michigan area. In this test simple corner-reflector targets were successfully imaged. Results from this investigation were positive and indicate that the concept can be used to investigate new approaches focused on the development of a global search and rescue system. An orbital experiment to demonstrate the application of synthetic aperture radar to search and rescue is proposed using the space shuttle.

A one-dimensional with three-dimensional velocity space hybrid-PIC model of the discharge plasma in a Hall thruster

NASA Astrophysics Data System (ADS)

Shashkov, Andrey; Lovtsov, Alexander; Tomilin, Dmitry

2017-04-01

According to present knowledge, countless numerical simulations of the discharge plasma in Hall thrusters were conducted. However, on the one hand, adequate two-dimensional (2D) models require a lot of time to carry out numerical research of the breathing mode oscillations or the discharge structure. On the other hand, existing one-dimensional (1D) models are usually too simplistic and do not take into consideration such important phenomena as neutral-wall collisions, magnetic field induced by Hall current and double, secondary, and stepwise ionizations together. In this paper a one-dimensional with three-dimensional velocity space (1D3V) hybrid-PIC model is presented. The model is able to incorporate all the phenomena mentioned above. A new method of neutral-wall collisions simulation in described space was developed and validated. Simulation results obtained for KM-88 and KM-60 thrusters are in a good agreement with experimental data. The Bohm collision coefficient was the same for both thrusters. Neutral-wall collisions, doubly charged ions, and induced magnetic field were proved to stabilize the breathing mode oscillations in a Hall thruster under some circumstances.

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

NASA Astrophysics Data System (ADS)

Visinescu, Mihai

2012-12-01

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

Some blackhole and compactification solutions of noncanonical global monopole in 4-dimensional spacetime

NASA Astrophysics Data System (ADS)

Prasetyo, I.; Ramadhan, H. S.

2017-07-01

Here we present some solutions with noncanonical global monopole in nonlinear sigma model in 4-dimensional spacetime. We discuss some blackhole solutions and its horizons. We also obtain some compactification solutions. We list some possible compactification channels from 4-space to 2 × 2-spaces of constant curvatures.

Group-theoretical approach to the construction of bases in 2{sup n}-dimensional Hilbert space

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

Garcia, A.; Romero, J. L.; Klimov, A. B., E-mail: klimov@cencar.udg.mx

2011-06-15

We propose a systematic procedure to construct all the possible bases with definite factorization structure in 2{sup n}-dimensional Hilbert space and discuss an algorithm for the determination of basis separability. The results are applied for classification of bases for an n-qubit system.

Efficient Stochastic Inversion Using Adjoint Models and Kernel-PCA

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

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

2017-10-18

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

Exploring Replica-Exchange Wang-Landau sampling in higher-dimensional parameter space

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

Valentim, Alexandra; Rocha, Julio C. S.; Tsai, Shan-Ho

We considered a higher-dimensional extension for the replica-exchange Wang-Landau algorithm to perform a random walk in the energy and magnetization space of the two-dimensional Ising model. This hybrid scheme combines the advantages of Wang-Landau and Replica-Exchange algorithms, and the one-dimensional version of this approach has been shown to be very efficient and to scale well, up to several thousands of computing cores. This approach allows us to split the parameter space of the system to be simulated into several pieces and still perform a random walk over the entire parameter range, ensuring the ergodicity of the simulation. Previous work, inmore » which a similar scheme of parallel simulation was implemented without using replica exchange and with a different way to combine the result from the pieces, led to discontinuities in the final density of states over the entire range of parameters. From our simulations, it appears that the replica-exchange Wang-Landau algorithm is able to overcome this diculty, allowing exploration of higher parameter phase space by keeping track of the joint density of states.« less

Computational genetic neuroanatomy of the developing mouse brain: dimensionality reduction, visualization, and clustering

PubMed Central

2013-01-01

Background The structured organization of cells in the brain plays a key role in its functional efficiency. This delicate organization is the consequence of unique molecular identity of each cell gradually established by precise spatiotemporal gene expression control during development. Currently, studies on the molecular-structural association are beginning to reveal how the spatiotemporal gene expression patterns are related to cellular differentiation and structural development. Results In this article, we aim at a global, data-driven study of the relationship between gene expressions and neuroanatomy in the developing mouse brain. To enable visual explorations of the high-dimensional data, we map the in situ hybridization gene expression data to a two-dimensional space by preserving both the global and the local structures. Our results show that the developing brain anatomy is largely preserved in the reduced gene expression space. To provide a quantitative analysis, we cluster the reduced data into groups and measure the consistency with neuroanatomy at multiple levels. Our results show that the clusters in the low-dimensional space are more consistent with neuroanatomy than those in the original space. Conclusions Gene expression patterns and developing brain anatomy are closely related. Dimensionality reduction and visual exploration facilitate the study of this relationship. PMID:23845024

Machine Learning Based Dimensionality Reduction Facilitates Ligand Diffusion Paths Assessment: A Case of Cytochrome P450cam.

PubMed

Rydzewski, J; Nowak, W

2016-04-12

In this work we propose an application of a nonlinear dimensionality reduction method to represent the high-dimensional configuration space of the ligand-protein dissociation process in a manner facilitating interpretation. Rugged ligand expulsion paths are mapped into 2-dimensional space. The mapping retains the main structural changes occurring during the dissociation. The topological similarity of the reduced paths may be easily studied using the Fréchet distances, and we show that this measure facilitates machine learning classification of the diffusion pathways. Further, low-dimensional configuration space allows for identification of residues active in transport during the ligand diffusion from a protein. The utility of this approach is illustrated by examination of the configuration space of cytochrome P450cam involved in expulsing camphor by means of enhanced all-atom molecular dynamics simulations. The expulsion trajectories are sampled and constructed on-the-fly during molecular dynamics simulations using the recently developed memetic algorithms [ Rydzewski, J.; Nowak, W. J. Chem. Phys. 2015 , 143 ( 12 ), 124101 ]. We show that the memetic algorithms are effective for enforcing the ligand diffusion and cavity exploration in the P450cam-camphor complex. Furthermore, we demonstrate that machine learning techniques are helpful in inspecting ligand diffusion landscapes and provide useful tools to examine structural changes accompanying rare events.

TALE proteins search DNA using a rotationally decoupled mechanism.

PubMed

Cuculis, Luke; Abil, Zhanar; Zhao, Huimin; Schroeder, Charles M

2016-10-01

Transcription activator-like effector (TALE) proteins are a class of programmable DNA-binding proteins used extensively for gene editing. Despite recent progress, however, little is known about their sequence search mechanism. Here, we use single-molecule experiments to study TALE search along DNA. Our results show that TALEs utilize a rotationally decoupled mechanism for nonspecific search, despite remaining associated with DNA templates during the search process. Our results suggest that the protein helical structure enables TALEs to adopt a loosely wrapped conformation around DNA templates during nonspecific search, facilitating rapid one-dimensional (1D) diffusion under a range of solution conditions. Furthermore, this model is consistent with a previously reported two-state mechanism for TALE search that allows these proteins to overcome the search speed-stability paradox. Taken together, our results suggest that TALE search is unique among the broad class of sequence-specific DNA-binding proteins and supports efficient 1D search along DNA.

Quadcopter control in three-dimensional space using a noninvasive motor imagery based brain-computer interface

PubMed Central

LaFleur, Karl; Cassady, Kaitlin; Doud, Alexander; Shades, Kaleb; Rogin, Eitan; He, Bin

2013-01-01

Objective At the balanced intersection of human and machine adaptation is found the optimally functioning brain-computer interface (BCI). In this study, we report a novel experiment of BCI controlling a robotic quadcopter in three-dimensional physical space using noninvasive scalp EEG in human subjects. We then quantify the performance of this system using metrics suitable for asynchronous BCI. Lastly, we examine the impact that operation of a real world device has on subjects’ control with comparison to a two-dimensional virtual cursor task. Approach Five human subjects were trained to modulate their sensorimotor rhythms to control an AR Drone navigating a three-dimensional physical space. Visual feedback was provided via a forward facing camera on the hull of the drone. Individual subjects were able to accurately acquire up to 90.5% of all valid targets presented while travelling at an average straight-line speed of 0.69 m/s. Significance Freely exploring and interacting with the world around us is a crucial element of autonomy that is lost in the context of neurodegenerative disease. Brain-computer interfaces are systems that aim to restore or enhance a user’s ability to interact with the environment via a computer and through the use of only thought. We demonstrate for the first time the ability to control a flying robot in the three-dimensional physical space using noninvasive scalp recorded EEG in humans. Our work indicates the potential of noninvasive EEG based BCI systems to accomplish complex control in three-dimensional physical space. The present study may serve as a framework for the investigation of multidimensional non-invasive brain-computer interface control in a physical environment using telepresence robotics. PMID:23735712

A computational model of visual marking using an inter-connected network of spiking neurons: the spiking search over time & space model (sSoTS).

PubMed

Mavritsaki, Eirini; Heinke, Dietmar; Humphreys, Glyn W; Deco, Gustavo

2006-01-01

In the real world, visual information is selected over time as well as space, when we prioritise new stimuli for attention. Watson and Humphreys [Watson, D., Humphreys, G.W., 1997. Visual marking: prioritizing selection for new objects by top-down attentional inhibition of old objects. Psychological Review 104, 90-122] presented evidence that new information in search tasks is prioritised by (amongst other processes) active ignoring of old items - a process they termed visual marking. In this paper we present, for the first time, an explicit computational model of visual marking using biologically plausible activation functions. The "spiking search over time and space" model (sSoTS) incorporates different synaptic components (NMDA, AMPA, GABA) and a frequency adaptation mechanism based on [Ca(2+)] sensitive K(+) current. This frequency adaptation current can act as a mechanism that suppresses the previously attended items. We show that, when coupled with a process of active inhibition applied to old items, frequency adaptation leads to old items being de-prioritised (and new items prioritised) across time in search. Furthermore, the time course of these processes mimics the time course of the preview effect in human search. The results indicate that the sSoTS model can provide a biologically plausible account of human search over time as well as space.

Reactive scattering with row-orthonormal hyperspherical coordinates. 4. Four-dimensional-space Wigner rotation function for pentaatomic systems.

PubMed

Kuppermann, Aron

2011-05-14

The row-orthonormal hyperspherical coordinate (ROHC) approach to calculating state-to-state reaction cross sections and bound state levels of N-atom systems requires the use of angular momentum tensors and Wigner rotation functions in a space of dimension N - 1. The properties of those tensors and functions are discussed for arbitrary N and determined for N = 5 in terms of the 6 Euler angles involved in 4-dimensional space.

High Redshift Supernova Search

Science.gov Websites

;on schedule." Before-and-after pictures (and Hubble Space Telescope picture) of a high-redshift High Redshift Supernova Search Home Page of the Supernova Cosmology Project This is the Lawrence Foretell Fate of the Universe." Pictures from the ground and from the Hubble Space Telescope: [PDF