An orthogonal oriented quadrature hexagonal image pyramid

NASA Technical Reports Server (NTRS)

Watson, Andrew B.; Ahumada, Albert J., Jr.

1987-01-01

An image pyramid has been developed with basis functions that are orthogonal, self-similar, and localized in space, spatial frequency, orientation, and phase. The pyramid operates on a hexagonal sample lattice. The set of seven basis functions consist of three even high-pass kernels, three odd high-pass kernels, and one low-pass kernel. The three even kernels are identified when rotated by 60 or 120 deg, and likewise for the odd. The seven basis functions occupy a point and a hexagon of six nearest neighbors on a hexagonal sample lattice. At the lowest level of the pyramid, the input lattice is the image sample lattice. At each higher level, the input lattice is provided by the low-pass coefficients computed at the previous level. At each level, the output is subsampled in such a way as to yield a new hexagonal lattice with a spacing sq rt 7 larger than the previous level, so that the number of coefficients is reduced by a factor of 7 at each level. The relationship between this image code and the processing architecture of the primate visual cortex is discussed.

Constructing a logical, regular axis topology from an irregular topology

DOEpatents

Faraj, Daniel A.

2014-07-22

Constructing a logical regular topology from an irregular topology including, for each axial dimension and recursively, for each compute node in a subcommunicator until returning to a first node: adding to a logical line of the axial dimension a neighbor specified in a nearest neighbor list; calling the added compute node; determining, by the called node, whether any neighbor in the node's nearest neighbor list is available to add to the logical line; if a neighbor in the called compute node's nearest neighbor list is available to add to the logical line, adding, by the called compute node to the logical line, any neighbor in the called compute node's nearest neighbor list for the axial dimension not already added to the logical line; and, if no neighbor in the called compute node's nearest neighbor list is available to add to the logical line, returning to the calling compute node.

Constructing a logical, regular axis topology from an irregular topology

DOEpatents

Faraj, Daniel A.

2014-07-01

Constructing a logical regular topology from an irregular topology including, for each axial dimension and recursively, for each compute node in a subcommunicator until returning to a first node: adding to a logical line of the axial dimension a neighbor specified in a nearest neighbor list; calling the added compute node; determining, by the called node, whether any neighbor in the node's nearest neighbor list is available to add to the logical line; if a neighbor in the called compute node's nearest neighbor list is available to add to the logical line, adding, by the called compute node to the logical line, any neighbor in the called compute node's nearest neighbor list for the axial dimension not already added to the logical line; and, if no neighbor in the called compute node's nearest neighbor list is available to add to the logical line, returning to the calling compute node.

Frog sound identification using extended k-nearest neighbor classifier

NASA Astrophysics Data System (ADS)

Mukahar, Nordiana; Affendi Rosdi, Bakhtiar; Athiar Ramli, Dzati; Jaafar, Haryati

2017-09-01

Frog sound identification based on the vocalization becomes important for biological research and environmental monitoring. As a result, different types of feature extractions and classifiers have been employed to evaluate the accuracy of frog sound identification. This paper presents a frog sound identification with Extended k-Nearest Neighbor (EKNN) classifier. The EKNN classifier integrates the nearest neighbors and mutual sharing of neighborhood concepts, with the aims of improving the classification performance. It makes a prediction based on who are the nearest neighbors of the testing sample and who consider the testing sample as their nearest neighbors. In order to evaluate the classification performance in frog sound identification, the EKNN classifier is compared with competing classifier, k -Nearest Neighbor (KNN), Fuzzy k -Nearest Neighbor (FKNN) k - General Nearest Neighbor (KGNN)and Mutual k -Nearest Neighbor (MKNN) on the recorded sounds of 15 frog species obtained in Malaysia forest. The recorded sounds have been segmented using Short Time Energy and Short Time Average Zero Crossing Rate (STE+STAZCR), sinusoidal modeling (SM), manual and the combination of Energy (E) and Zero Crossing Rate (ZCR) (E+ZCR) while the features are extracted by Mel Frequency Cepstrum Coefficient (MFCC). The experimental results have shown that the EKNCN classifier exhibits the best performance in terms of accuracy compared to the competing classifiers, KNN, FKNN, GKNN and MKNN for all cases.

Comparative decision models for anticipating shortage of food grain production in India

NASA Astrophysics Data System (ADS)

Chattopadhyay, Manojit; Mitra, Subrata Kumar

2018-01-01

This paper attempts to predict food shortages in advance from the analysis of rainfall during the monsoon months along with other inputs used for crop production, such as land used for cereal production, percentage of area covered under irrigation and fertiliser use. We used six binary classification data mining models viz., logistic regression, Multilayer Perceptron, kernel lab-Support Vector Machines, linear discriminant analysis, quadratic discriminant analysis and k-Nearest Neighbors Network, and found that linear discriminant analysis and kernel lab-Support Vector Machines are equally suitable for predicting per capita food shortage with 89.69 % accuracy in overall prediction and 92.06 % accuracy in predicting food shortage ( true negative rate). Advance information of food shortage can help policy makers to take remedial measures in order to prevent devastating consequences arising out of food non-availability.

Local Subspace Classifier with Transform-Invariance for Image Classification

NASA Astrophysics Data System (ADS)

Hotta, Seiji

A family of linear subspace classifiers called local subspace classifier (LSC) outperforms the k-nearest neighbor rule (kNN) and conventional subspace classifiers in handwritten digit classification. However, LSC suffers very high sensitivity to image transformations because it uses projection and the Euclidean distances for classification. In this paper, I present a combination of a local subspace classifier (LSC) and a tangent distance (TD) for improving accuracy of handwritten digit recognition. In this classification rule, we can deal with transform-invariance easily because we are able to use tangent vectors for approximation of transformations. However, we cannot use tangent vectors in other type of images such as color images. Hence, kernel LSC (KLSC) is proposed for incorporating transform-invariance into LSC via kernel mapping. The performance of the proposed methods is verified with the experiments on handwritten digit and color image classification.

Quantum Correlation in the XY Spin Model with Anisotropic Three-Site Interaction

NASA Astrophysics Data System (ADS)

Wang, Yao; Chai, Bing-Bing; Guo, Jin-Liang

2018-05-01

We investigate pairwise entanglement and quantum discord (QD) in the XY spin model with anisotropic three-site interaction at zero and finite temperatures. For both the nearest-neighbor spins and the next nearest-neighbor spins, special attention is paid to the dependence of entanglement and QD on the anisotropic parameter δ induced by the next nearest-neighbor spins. We show that the behavior of QD differs in many ways from entanglement under the influences of the anisotropic three-site interaction at finite temperatures. More important, comparing the effects of δ on the entanglement and QD, we find the anisotropic three-site interaction plays an important role in the quantum correlations at zero and finite temperatures. It is found that δ can strengthen the quantum correlation for both the nearest-neighbor spins and the next nearest-neighbor spins, especially for the nearest-neighbor spins at low temperature.

Performing a scatterv operation on a hierarchical tree network optimized for collective operations

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

Archer, Charles J; Blocksome, Michael A; Ratterman, Joseph D

Performing a scatterv operation on a hierarchical tree network optimized for collective operations including receiving, by the scatterv module installed on the node, from a nearest neighbor parent above the node a chunk of data having at least a portion of data for the node; maintaining, by the scatterv module installed on the node, the portion of the data for the node; determining, by the scatterv module installed on the node, whether any portions of the data are for a particular nearest neighbor child below the node or one or more other nodes below the particular nearest neighbor child; andmore » sending, by the scatterv module installed on the node, those portions of data to the nearest neighbor child if any portions of the data are for a particular nearest neighbor child below the node or one or more other nodes below the particular nearest neighbor child.« less

The Application of Determining Students’ Graduation Status of STMIK Palangkaraya Using K-Nearest Neighbors Method

NASA Astrophysics Data System (ADS)

Rusdiana, Lili; Marfuah

2017-12-01

K-Nearest Neighbors method is one of methods used for classification which calculate a value to find out the closest in distance. It is used to group a set of data such as students’ graduation status that are got from the amount of course credits taken by them, the grade point average (AVG), and the mini-thesis grade. The study is conducted to know the results of using K-Nearest Neighbors method on the application of determining students’ graduation status, so it can be analyzed from the method used, the data, and the application constructed. The aim of this study is to find out the application results by using K-Nearest Neighbors concept to determine students’ graduation status using the data of STMIK Palangkaraya students. The development of the software used Extreme Programming, since it was appropriate and precise for this study which was to quickly finish the project. The application was created using Microsoft Office Excel 2007 for the training data and Matlab 7 to implement the application. The result of K-Nearest Neighbors method on the application of determining students’ graduation status was 92.5%. It could determine the predicate graduation of 94 data used from the initial data before the processing as many as 136 data which the maximal training data was 50data. The K-Nearest Neighbors method is one of methods used to group a set of data based on the closest value, so that using K-Nearest Neighbors method agreed with this study. The results of K-Nearest Neighbors method on the application of determining students’ graduation status was 92.5% could determine the predicate graduation which is the maximal training data. The K-Nearest Neighbors method is one of methods used to group a set of data based on the closest value, so that using K-Nearest Neighbors method agreed with this study.

GPU-accelerated Kernel Regression Reconstruction for Freehand 3D Ultrasound Imaging.

PubMed

Wen, Tiexiang; Li, Ling; Zhu, Qingsong; Qin, Wenjian; Gu, Jia; Yang, Feng; Xie, Yaoqin

2017-07-01

Volume reconstruction method plays an important role in improving reconstructed volumetric image quality for freehand three-dimensional (3D) ultrasound imaging. By utilizing the capability of programmable graphics processing unit (GPU), we can achieve a real-time incremental volume reconstruction at a speed of 25-50 frames per second (fps). After incremental reconstruction and visualization, hole-filling is performed on GPU to fill remaining empty voxels. However, traditional pixel nearest neighbor-based hole-filling fails to reconstruct volume with high image quality. On the contrary, the kernel regression provides an accurate volume reconstruction method for 3D ultrasound imaging but with the cost of heavy computational complexity. In this paper, a GPU-based fast kernel regression method is proposed for high-quality volume after the incremental reconstruction of freehand ultrasound. The experimental results show that improved image quality for speckle reduction and details preservation can be obtained with the parameter setting of kernel window size of [Formula: see text] and kernel bandwidth of 1.0. The computational performance of the proposed GPU-based method can be over 200 times faster than that on central processing unit (CPU), and the volume with size of 50 million voxels in our experiment can be reconstructed within 10 seconds.

K-Nearest Neighbor Algorithm Optimization in Text Categorization

NASA Astrophysics Data System (ADS)

Chen, Shufeng

2018-01-01

K-Nearest Neighbor (KNN) classification algorithm is one of the simplest methods of data mining. It has been widely used in classification, regression and pattern recognition. The traditional KNN method has some shortcomings such as large amount of sample computation and strong dependence on the sample library capacity. In this paper, a method of representative sample optimization based on CURE algorithm is proposed. On the basis of this, presenting a quick algorithm QKNN (Quick k-nearest neighbor) to find the nearest k neighbor samples, which greatly reduces the similarity calculation. The experimental results show that this algorithm can effectively reduce the number of samples and speed up the search for the k nearest neighbor samples to improve the performance of the algorithm.

Relationship between neighbor number and vibrational spectra in disordered colloidal clusters with attractive interactions

NASA Astrophysics Data System (ADS)

Yunker, Peter J.; Zhang, Zexin; Gratale, Matthew; Chen, Ke; Yodh, A. G.

2013-03-01

We study connections between vibrational spectra and average nearest neighbor number in disordered clusters of colloidal particles with attractive interactions. Measurements of displacement covariances between particles in each cluster permit calculation of the stiffness matrix, which contains effective spring constants linking pairs of particles. From the cluster stiffness matrix, we derive vibrational properties of corresponding "shadow" glassy clusters, with the same geometric configuration and interactions as the "source" cluster but without damping. Here, we investigate the stiffness matrix to elucidate the origin of the correlations between the median frequency of cluster vibrational modes and average number of nearest neighbors in the cluster. We find that the mean confining stiffness of particles in a cluster, i.e., the ensemble-averaged sum of nearest neighbor spring constants, correlates strongly with average nearest neighbor number, and even more strongly with median frequency. Further, we find that the average oscillation frequency of an individual particle is set by the total stiffness of its nearest neighbor bonds; this average frequency increases as the square root of the nearest neighbor bond stiffness, in a manner similar to the simple harmonic oscillator.

Signal dependence of inter-pixel capacitance in hybridized HgCdTe H2RG arrays for use in James Webb space telescope's NIRcam

NASA Astrophysics Data System (ADS)

Donlon, Kevan; Ninkov, Zoran; Baum, Stefi

2016-08-01

Interpixel capacitance (IPC) is a deterministic electronic coupling by which signal generated in one pixel is measured in neighboring pixels. Examination of dark frames from test NIRcam arrays corroborates earlier results and simulations illustrating a signal dependent coupling. When the signal on an individual pixel is larger, the fractional coupling to nearest neighbors is lesser than when the signal is lower. Frames from test arrays indicate a drop in average coupling from approximately 1.0% at low signals down to approximately 0.65% at high signals depending on the particular array in question. The photometric ramifications for this non-uniformity are not fully understood. This non-uniformity intro-duces a non-linearity in the current mathematical model for IPC coupling. IPC coupling has been mathematically formalized as convolution by a blur kernel. Signal dependence requires that the blur kernel be locally defined as a function of signal intensity. Through application of a signal dependent coupling kernel, the IPC coupling can be modeled computationally. This method allows for simultaneous knowledge of the intrinsic parameters of the image scene, the result of applying a constant IPC, and the result of a signal dependent IPC. In the age of sub-pixel precision in astronomy these effects must be properly understood and accounted for in order for the data to accurately represent the object of observation. Implementation of this method is done through python scripted processing of images. The introduction of IPC into simulated frames is accomplished through convolution of the image with a blur kernel whose parameters are themselves locally defined functions of the image. These techniques can be used to enhance the data processing pipeline for NIRcam.

Unconventional quantum antiferromagnetism with a fourfold symmetry breaking in a spin-1/2 Ising-Heisenberg pentagonal chain

NASA Astrophysics Data System (ADS)

Karľová, Katarína; Strečka, Jozef; Lyra, Marcelo L.

2018-03-01

The spin-1/2 Ising-Heisenberg pentagonal chain is investigated with use of the star-triangle transformation, which establishes a rigorous mapping equivalence with the effective spin-1/2 Ising zigzag ladder. The investigated model has a rich ground-state phase diagram including two spectacular quantum antiferromagnetic ground states with a fourfold broken symmetry. It is demonstrated that these long-period quantum ground states arise due to a competition between the effective next-nearest-neighbor and nearest-neighbor interactions of the corresponding spin-1/2 Ising zigzag ladder. The concurrence is used to quantify the bipartite entanglement between the nearest-neighbor Heisenberg spin pairs, which are quantum-mechanically entangled in two quantum ground states with or without spontaneously broken symmetry. The pair correlation functions between the nearest-neighbor Heisenberg spins as well as the next-nearest-neighbor and nearest-neighbor Ising spins were investigated with the aim to bring insight into how a relevant short-range order manifests itself at low enough temperatures. It is shown that the specific heat displays temperature dependencies with either one or two separate round maxima.

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

NASA Astrophysics Data System (ADS)

Kohno, Masanori

2018-05-01

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

Phase transitions in the antiferromagnetic Ising model on a body-centered cubic lattice with interactions between next-to-nearest neighbors

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

Murtazaev, A. K.; Ramazanov, M. K., E-mail: sheikh77@mail.ru; Kassan-Ogly, F. A.

2015-01-15

Phase transitions in the antiferromagnetic Ising model on a body-centered cubic lattice are studied on the basis of the replica algorithm by the Monte Carlo method and histogram analysis taking into account the interaction of next-to-nearest neighbors. The phase diagram of the dependence of the critical temperature on the intensity of interaction of the next-to-nearest neighbors is constructed. It is found that a second-order phase transition is realized in this model in the investigated interval of the intensities of interaction of next-to-nearest neighbors.

Finger vein identification using fuzzy-based k-nearest centroid neighbor classifier

NASA Astrophysics Data System (ADS)

Rosdi, Bakhtiar Affendi; Jaafar, Haryati; Ramli, Dzati Athiar

2015-02-01

In this paper, a new approach for personal identification using finger vein image is presented. Finger vein is an emerging type of biometrics that attracts attention of researchers in biometrics area. As compared to other biometric traits such as face, fingerprint and iris, finger vein is more secured and hard to counterfeit since the features are inside the human body. So far, most of the researchers focus on how to extract robust features from the captured vein images. Not much research was conducted on the classification of the extracted features. In this paper, a new classifier called fuzzy-based k-nearest centroid neighbor (FkNCN) is applied to classify the finger vein image. The proposed FkNCN employs a surrounding rule to obtain the k-nearest centroid neighbors based on the spatial distributions of the training images and their distance to the test image. Then, the fuzzy membership function is utilized to assign the test image to the class which is frequently represented by the k-nearest centroid neighbors. Experimental evaluation using our own database which was collected from 492 fingers shows that the proposed FkNCN has better performance than the k-nearest neighbor, k-nearest-centroid neighbor and fuzzy-based-k-nearest neighbor classifiers. This shows that the proposed classifier is able to identify the finger vein image effectively.

Scalable Nearest Neighbor Algorithms for High Dimensional Data.

PubMed

Muja, Marius; Lowe, David G

2014-11-01

For many computer vision and machine learning problems, large training sets are key for good performance. However, the most computationally expensive part of many computer vision and machine learning algorithms consists of finding nearest neighbor matches to high dimensional vectors that represent the training data. We propose new algorithms for approximate nearest neighbor matching and evaluate and compare them with previous algorithms. For matching high dimensional features, we find two algorithms to be the most efficient: the randomized k-d forest and a new algorithm proposed in this paper, the priority search k-means tree. We also propose a new algorithm for matching binary features by searching multiple hierarchical clustering trees and show it outperforms methods typically used in the literature. We show that the optimal nearest neighbor algorithm and its parameters depend on the data set characteristics and describe an automated configuration procedure for finding the best algorithm to search a particular data set. In order to scale to very large data sets that would otherwise not fit in the memory of a single machine, we propose a distributed nearest neighbor matching framework that can be used with any of the algorithms described in the paper. All this research has been released as an open source library called fast library for approximate nearest neighbors (FLANN), which has been incorporated into OpenCV and is now one of the most popular libraries for nearest neighbor matching.

yaImpute: An R package for kNN imputation

Treesearch

Nicholas L. Crookston; Andrew O. Finley

2008-01-01

This article introduces yaImpute, an R package for nearest neighbor search and imputation. Although nearest neighbor imputation is used in a host of disciplines, the methods implemented in the yaImpute package are tailored to imputation-based forest attribute estimation and mapping. The impetus to writing the yaImpute is a growing interest in nearest neighbor...

Social aggregation in pea aphids: experiment and random walk modeling.

PubMed

Nilsen, Christa; Paige, John; Warner, Olivia; Mayhew, Benjamin; Sutley, Ryan; Lam, Matthew; Bernoff, Andrew J; Topaz, Chad M

2013-01-01

From bird flocks to fish schools and ungulate herds to insect swarms, social biological aggregations are found across the natural world. An ongoing challenge in the mathematical modeling of aggregations is to strengthen the connection between models and biological data by quantifying the rules that individuals follow. We model aggregation of the pea aphid, Acyrthosiphon pisum. Specifically, we conduct experiments to track the motion of aphids walking in a featureless circular arena in order to deduce individual-level rules. We observe that each aphid transitions stochastically between a moving and a stationary state. Moving aphids follow a correlated random walk. The probabilities of motion state transitions, as well as the random walk parameters, depend strongly on distance to an aphid's nearest neighbor. For large nearest neighbor distances, when an aphid is essentially isolated, its motion is ballistic with aphids moving faster, turning less, and being less likely to stop. In contrast, for short nearest neighbor distances, aphids move more slowly, turn more, and are more likely to become stationary; this behavior constitutes an aggregation mechanism. From the experimental data, we estimate the state transition probabilities and correlated random walk parameters as a function of nearest neighbor distance. With the individual-level model established, we assess whether it reproduces the macroscopic patterns of movement at the group level. To do so, we consider three distributions, namely distance to nearest neighbor, angle to nearest neighbor, and percentage of population moving at any given time. For each of these three distributions, we compare our experimental data to the output of numerical simulations of our nearest neighbor model, and of a control model in which aphids do not interact socially. Our stochastic, social nearest neighbor model reproduces salient features of the experimental data that are not captured by the control.

Quantum realization of the nearest-neighbor interpolation method for FRQI and NEQR

NASA Astrophysics Data System (ADS)

Sang, Jianzhi; Wang, Shen; Niu, Xiamu

2016-01-01

This paper is concerned with the feasibility of the classical nearest-neighbor interpolation based on flexible representation of quantum images (FRQI) and novel enhanced quantum representation (NEQR). Firstly, the feasibility of the classical image nearest-neighbor interpolation for quantum images of FRQI and NEQR is proven. Then, by defining the halving operation and by making use of quantum rotation gates, the concrete quantum circuit of the nearest-neighbor interpolation for FRQI is designed for the first time. Furthermore, quantum circuit of the nearest-neighbor interpolation for NEQR is given. The merit of the proposed NEQR circuit lies in their low complexity, which is achieved by utilizing the halving operation and the quantum oracle operator. Finally, in order to further improve the performance of the former circuits, new interpolation circuits for FRQI and NEQR are presented by using Control-NOT gates instead of a halving operation. Simulation results show the effectiveness of the proposed circuits.

A Feature-based Approach to Big Data Analysis of Medical Images

PubMed Central

Toews, Matthew; Wachinger, Christian; Estepar, Raul San Jose; Wells, William M.

2015-01-01

This paper proposes an inference method well-suited to large sets of medical images. The method is based upon a framework where distinctive 3D scale-invariant features are indexed efficiently to identify approximate nearest-neighbor (NN) feature matches in O(log N) computational complexity in the number of images N. It thus scales well to large data sets, in contrast to methods based on pair-wise image registration or feature matching requiring O(N) complexity. Our theoretical contribution is a density estimator based on a generative model that generalizes kernel density estimation and K-nearest neighbor (KNN) methods. The estimator can be used for on-the-fly queries, without requiring explicit parametric models or an off-line training phase. The method is validated on a large multi-site data set of 95,000,000 features extracted from 19,000 lung CT scans. Subject-level classification identifies all images of the same subjects across the entire data set despite deformation due to breathing state, including unintentional duplicate scans. State-of-the-art performance is achieved in predicting chronic pulmonary obstructive disorder (COPD) severity across the 5-category GOLD clinical rating, with an accuracy of 89% if both exact and one-off predictions are considered correct. PMID:26221685

A Feature-Based Approach to Big Data Analysis of Medical Images.

PubMed

Toews, Matthew; Wachinger, Christian; Estepar, Raul San Jose; Wells, William M

2015-01-01

This paper proposes an inference method well-suited to large sets of medical images. The method is based upon a framework where distinctive 3D scale-invariant features are indexed efficiently to identify approximate nearest-neighbor (NN) feature matches-in O (log N) computational complexity in the number of images N. It thus scales well to large data sets, in contrast to methods based on pair-wise image registration or feature matching requiring O(N) complexity. Our theoretical contribution is a density estimator based on a generative model that generalizes kernel density estimation and K-nearest neighbor (KNN) methods.. The estimator can be used for on-the-fly queries, without requiring explicit parametric models or an off-line training phase. The method is validated on a large multi-site data set of 95,000,000 features extracted from 19,000 lung CT scans. Subject-level classification identifies all images of the same subjects across the entire data set despite deformation due to breathing state, including unintentional duplicate scans. State-of-the-art performance is achieved in predicting chronic pulmonary obstructive disorder (COPD) severity across the 5-category GOLD clinical rating, with an accuracy of 89% if both exact and one-off predictions are considered correct.

Prediction of Human Intestinal Absorption of Compounds Using Artificial Intelligence Techniques.

PubMed

Kumar, Rajnish; Sharma, Anju; Siddiqui, Mohammed Haris; Tiwari, Rajesh Kumar

2017-01-01

Information about Pharmacokinetics of compounds is an essential component of drug design and development. Modeling the pharmacokinetic properties require identification of the factors effecting absorption, distribution, metabolism and excretion of compounds. There have been continuous attempts in the prediction of intestinal absorption of compounds using various Artificial intelligence methods in the effort to reduce the attrition rate of drug candidates entering to preclinical and clinical trials. Currently, there are large numbers of individual predictive models available for absorption using machine learning approaches. Six Artificial intelligence methods namely, Support vector machine, k- nearest neighbor, Probabilistic neural network, Artificial neural network, Partial least square and Linear discriminant analysis were used for prediction of absorption of compounds. Prediction accuracy of Support vector machine, k- nearest neighbor, Probabilistic neural network, Artificial neural network, Partial least square and Linear discriminant analysis for prediction of intestinal absorption of compounds was found to be 91.54%, 88.33%, 84.30%, 86.51%, 79.07% and 80.08% respectively. Comparative analysis of all the six prediction models suggested that Support vector machine with Radial basis function based kernel is comparatively better for binary classification of compounds using human intestinal absorption and may be useful at preliminary stages of drug design and development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

A shock-capturing SPH scheme based on adaptive kernel estimation

NASA Astrophysics Data System (ADS)

Sigalotti, Leonardo Di G.; López, Hender; Donoso, Arnaldo; Sira, Eloy; Klapp, Jaime

2006-02-01

Here we report a method that converts standard smoothed particle hydrodynamics (SPH) into a working shock-capturing scheme without relying on solutions to the Riemann problem. Unlike existing adaptive SPH simulations, the present scheme is based on an adaptive kernel estimation of the density, which combines intrinsic features of both the kernel and nearest neighbor approaches in a way that the amount of smoothing required in low-density regions is effectively controlled. Symmetrized SPH representations of the gas dynamic equations along with the usual kernel summation for the density are used to guarantee variational consistency. Implementation of the adaptive kernel estimation involves a very simple procedure and allows for a unique scheme that handles strong shocks and rarefactions the same way. Since it represents a general improvement of the integral interpolation on scattered data, it is also applicable to other fluid-dynamic models. When the method is applied to supersonic compressible flows with sharp discontinuities, as in the classical one-dimensional shock-tube problem and its variants, the accuracy of the results is comparable, and in most cases superior, to that obtained from high quality Godunov-type methods and SPH formulations based on Riemann solutions. The extension of the method to two- and three-space dimensions is straightforward. In particular, for the two-dimensional cylindrical Noh's shock implosion and Sedov point explosion problems the present scheme produces much better results than those obtained with conventional SPH codes.

A two-step nearest neighbors algorithm using satellite imagery for predicting forest structure within species composition classes

Treesearch

Ronald E. McRoberts

2009-01-01

Nearest neighbors techniques have been shown to be useful for predicting multiple forest attributes from forest inventory and Landsat satellite image data. However, in regions lacking good digital land cover information, nearest neighbors selected to predict continuous variables such as tree volume must be selected without regard to relevant categorical variables such...

Earthquake Declustering via a Nearest-Neighbor Approach in Space-Time-Magnitude Domain

NASA Astrophysics Data System (ADS)

Zaliapin, I. V.; Ben-Zion, Y.

2016-12-01

We propose a new method for earthquake declustering based on nearest-neighbor analysis of earthquakes in space-time-magnitude domain. The nearest-neighbor approach was recently applied to a variety of seismological problems that validate the general utility of the technique and reveal the existence of several different robust types of earthquake clusters. Notably, it was demonstrated that clustering associated with the largest earthquakes is statistically different from that of small-to-medium events. In particular, the characteristic bimodality of the nearest-neighbor distances that helps separating clustered and background events is often violated after the largest earthquakes in their vicinity, which is dominated by triggered events. This prevents using a simple threshold between the two modes of the nearest-neighbor distance distribution for declustering. The current study resolves this problem hence extending the nearest-neighbor approach to the problem of earthquake declustering. The proposed technique is applied to seismicity of different areas in California (San Jacinto, Coso, Salton Sea, Parkfield, Ventura, Mojave, etc.), as well as to the global seismicity, to demonstrate its stability and efficiency in treating various clustering types. The results are compared with those of alternative declustering methods.

Smart BIT/TSMD Integration

DTIC Science & Technology

1991-12-01

user using the ’: knn ’ option in the do-scenario command line). An instance of the K-Nearest Neighbor object is first created and initialized before...Navigation Computer HF High Frequency ILS Instrument Landing System KNN K - Nearest Neighbor LRU Line Replaceable Unit MC Mission Computer MTCA...approaches have been investigated here, K-nearest Neighbors ( KNN ) and neural networks (NN). Both approaches require that previously classified examples of

G-Hash: Towards Fast Kernel-based Similarity Search in Large Graph Databases.

PubMed

Wang, Xiaohong; Smalter, Aaron; Huan, Jun; Lushington, Gerald H

2009-01-01

Structured data including sets, sequences, trees and graphs, pose significant challenges to fundamental aspects of data management such as efficient storage, indexing, and similarity search. With the fast accumulation of graph databases, similarity search in graph databases has emerged as an important research topic. Graph similarity search has applications in a wide range of domains including cheminformatics, bioinformatics, sensor network management, social network management, and XML documents, among others.Most of the current graph indexing methods focus on subgraph query processing, i.e. determining the set of database graphs that contains the query graph and hence do not directly support similarity search. In data mining and machine learning, various graph kernel functions have been designed to capture the intrinsic similarity of graphs. Though successful in constructing accurate predictive and classification models for supervised learning, graph kernel functions have (i) high computational complexity and (ii) non-trivial difficulty to be indexed in a graph database.Our objective is to bridge graph kernel function and similarity search in graph databases by proposing (i) a novel kernel-based similarity measurement and (ii) an efficient indexing structure for graph data management. Our method of similarity measurement builds upon local features extracted from each node and their neighboring nodes in graphs. A hash table is utilized to support efficient storage and fast search of the extracted local features. Using the hash table, a graph kernel function is defined to capture the intrinsic similarity of graphs and for fast similarity query processing. We have implemented our method, which we have named G-hash, and have demonstrated its utility on large chemical graph databases. Our results show that the G-hash method achieves state-of-the-art performance for k-nearest neighbor (k-NN) classification. Most importantly, the new similarity measurement and the index structure is scalable to large database with smaller indexing size, faster indexing construction time, and faster query processing time as compared to state-of-the-art indexing methods such as C-tree, gIndex, and GraphGrep.

Feature extraction based on semi-supervised kernel Marginal Fisher analysis and its application in bearing fault diagnosis

NASA Astrophysics Data System (ADS)

Jiang, Li; Xuan, Jianping; Shi, Tielin

2013-12-01

Generally, the vibration signals of faulty machinery are non-stationary and nonlinear under complicated operating conditions. Therefore, it is a big challenge for machinery fault diagnosis to extract optimal features for improving classification accuracy. This paper proposes semi-supervised kernel Marginal Fisher analysis (SSKMFA) for feature extraction, which can discover the intrinsic manifold structure of dataset, and simultaneously consider the intra-class compactness and the inter-class separability. Based on SSKMFA, a novel approach to fault diagnosis is put forward and applied to fault recognition of rolling bearings. SSKMFA directly extracts the low-dimensional characteristics from the raw high-dimensional vibration signals, by exploiting the inherent manifold structure of both labeled and unlabeled samples. Subsequently, the optimal low-dimensional features are fed into the simplest K-nearest neighbor (KNN) classifier to recognize different fault categories and severities of bearings. The experimental results demonstrate that the proposed approach improves the fault recognition performance and outperforms the other four feature extraction methods.

Online Feature Transformation Learning for Cross-Domain Object Category Recognition.

PubMed

Zhang, Xuesong; Zhuang, Yan; Wang, Wei; Pedrycz, Witold

2017-06-09

In this paper, we introduce a new research problem termed online feature transformation learning in the context of multiclass object category recognition. The learning of a feature transformation is viewed as learning a global similarity metric function in an online manner. We first consider the problem of online learning a feature transformation matrix expressed in the original feature space and propose an online passive aggressive feature transformation algorithm. Then these original features are mapped to kernel space and an online single kernel feature transformation (OSKFT) algorithm is developed to learn a nonlinear feature transformation. Based on the OSKFT and the existing Hedge algorithm, a novel online multiple kernel feature transformation algorithm is also proposed, which can further improve the performance of online feature transformation learning in large-scale application. The classifier is trained with k nearest neighbor algorithm together with the learned similarity metric function. Finally, we experimentally examined the effect of setting different parameter values in the proposed algorithms and evaluate the model performance on several multiclass object recognition data sets. The experimental results demonstrate the validity and good performance of our methods on cross-domain and multiclass object recognition application.

A locally adaptive kernel regression method for facies delineation

NASA Astrophysics Data System (ADS)

Fernàndez-Garcia, D.; Barahona-Palomo, M.; Henri, C. V.; Sanchez-Vila, X.

2015-12-01

Facies delineation is defined as the separation of geological units with distinct intrinsic characteristics (grain size, hydraulic conductivity, mineralogical composition). A major challenge in this area stems from the fact that only a few scattered pieces of hydrogeological information are available to delineate geological facies. Several methods to delineate facies are available in the literature, ranging from those based only on existing hard data, to those including secondary data or external knowledge about sedimentological patterns. This paper describes a methodology to use kernel regression methods as an effective tool for facies delineation. The method uses both the spatial and the actual sampled values to produce, for each individual hard data point, a locally adaptive steering kernel function, self-adjusting the principal directions of the local anisotropic kernels to the direction of highest local spatial correlation. The method is shown to outperform the nearest neighbor classification method in a number of synthetic aquifers whenever the available number of hard data is small and randomly distributed in space. In the case of exhaustive sampling, the steering kernel regression method converges to the true solution. Simulations ran in a suite of synthetic examples are used to explore the selection of kernel parameters in typical field settings. It is shown that, in practice, a rule of thumb can be used to obtain suboptimal results. The performance of the method is demonstrated to significantly improve when external information regarding facies proportions is incorporated. Remarkably, the method allows for a reasonable reconstruction of the facies connectivity patterns, shown in terms of breakthrough curves performance.

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.

Secure Nearest Neighbor Query on Crowd-Sensing Data

PubMed Central

Cheng, Ke; Wang, Liangmin; Zhong, Hong

2016-01-01

Nearest neighbor queries are fundamental in location-based services, and secure nearest neighbor queries mainly focus on how to securely and quickly retrieve the nearest neighbor in the outsourced cloud server. However, the previous big data system structure has changed because of the crowd-sensing data. On the one hand, sensing data terminals as the data owner are numerous and mistrustful, while, on the other hand, in most cases, the terminals find it difficult to finish many safety operation due to computation and storage capability constraints. In light of they Multi Owners and Multi Users (MOMU) situation in the crowd-sensing data cloud environment, this paper presents a secure nearest neighbor query scheme based on the proxy server architecture, which is constructed by protocols of secure two-party computation and secure Voronoi diagram algorithm. It not only preserves the data confidentiality and query privacy but also effectively resists the collusion between the cloud server and the data owners or users. Finally, extensive theoretical and experimental evaluations are presented to show that our proposed scheme achieves a superior balance between the security and query performance compared to other schemes. PMID:27669253

Secure Nearest Neighbor Query on Crowd-Sensing Data.

PubMed

Cheng, Ke; Wang, Liangmin; Zhong, Hong

2016-09-22

Nearest neighbor queries are fundamental in location-based services, and secure nearest neighbor queries mainly focus on how to securely and quickly retrieve the nearest neighbor in the outsourced cloud server. However, the previous big data system structure has changed because of the crowd-sensing data. On the one hand, sensing data terminals as the data owner are numerous and mistrustful, while, on the other hand, in most cases, the terminals find it difficult to finish many safety operation due to computation and storage capability constraints. In light of they Multi Owners and Multi Users (MOMU) situation in the crowd-sensing data cloud environment, this paper presents a secure nearest neighbor query scheme based on the proxy server architecture, which is constructed by protocols of secure two-party computation and secure Voronoi diagram algorithm. It not only preserves the data confidentiality and query privacy but also effectively resists the collusion between the cloud server and the data owners or users. Finally, extensive theoretical and experimental evaluations are presented to show that our proposed scheme achieves a superior balance between the security and query performance compared to other schemes.

Improving RNA nearest neighbor parameters for helices by going beyond the two-state model.

PubMed

Spasic, Aleksandar; Berger, Kyle D; Chen, Jonathan L; Seetin, Matthew G; Turner, Douglas H; Mathews, David H

2018-06-01

RNA folding free energy change nearest neighbor parameters are widely used to predict folding stabilities of secondary structures. They were determined by linear regression to datasets of optical melting experiments on small model systems. Traditionally, the optical melting experiments are analyzed assuming a two-state model, i.e. a structure is either complete or denatured. Experimental evidence, however, shows that structures exist in an ensemble of conformations. Partition functions calculated with existing nearest neighbor parameters predict that secondary structures can be partially denatured, which also directly conflicts with the two-state model. Here, a new approach for determining RNA nearest neighbor parameters is presented. Available optical melting data for 34 Watson-Crick helices were fit directly to a partition function model that allows an ensemble of conformations. Fitting parameters were the enthalpy and entropy changes for helix initiation, terminal AU pairs, stacks of Watson-Crick pairs and disordered internal loops. The resulting set of nearest neighbor parameters shows a 38.5% improvement in the sum of residuals in fitting the experimental melting curves compared to the current literature set.

Nearest neighbor density ratio estimation for large-scale applications in astronomy

NASA Astrophysics Data System (ADS)

Kremer, J.; Gieseke, F.; Steenstrup Pedersen, K.; Igel, C.

2015-09-01

In astronomical applications of machine learning, the distribution of objects used for building a model is often different from the distribution of the objects the model is later applied to. This is known as sample selection bias, which is a major challenge for statistical inference as one can no longer assume that the labeled training data are representative. To address this issue, one can re-weight the labeled training patterns to match the distribution of unlabeled data that are available already in the training phase. There are many examples in practice where this strategy yielded good results, but estimating the weights reliably from a finite sample is challenging. We consider an efficient nearest neighbor density ratio estimator that can exploit large samples to increase the accuracy of the weight estimates. To solve the problem of choosing the right neighborhood size, we propose to use cross-validation on a model selection criterion that is unbiased under covariate shift. The resulting algorithm is our method of choice for density ratio estimation when the feature space dimensionality is small and sample sizes are large. The approach is simple and, because of the model selection, robust. We empirically find that it is on a par with established kernel-based methods on relatively small regression benchmark datasets. However, when applied to large-scale photometric redshift estimation, our approach outperforms the state-of-the-art.

Control of coherence among the spins of a single electron and the three nearest neighbor {sup 13}C nuclei of a nitrogen-vacancy center in diamond

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

Shimo-Oka, T.; Miwa, S.; Suzuki, Y.

2015-04-13

Individual nuclear spins in diamond can be optically detected through hyperfine couplings with the electron spin of a single nitrogen-vacancy (NV) center; such nuclear spins have outstandingly long coherence times. Among the hyperfine couplings in the NV center, the nearest neighbor {sup 13}C nuclear spins have the largest coupling strength. Nearest neighbor {sup 13}C nuclear spins have the potential to perform fastest gate operations, providing highest fidelity in quantum computing. Herein, we report on the control of coherences in the NV center where all three nearest neighbor carbons are of the {sup 13}C isotope. Coherence among the three and fourmore » qubits are generated and analyzed at room temperature.« less

Efficiency of encounter-controlled reaction between diffusing reactants in a finite lattice: Non-nearest-neighbor effects

NASA Astrophysics Data System (ADS)

Bentz, Jonathan L.; Kozak, John J.; Nicolis, Gregoire

2005-08-01

The influence of non-nearest-neighbor displacements on the efficiency of diffusion-reaction processes involving one and two mobile diffusing reactants is studied. An exact analytic result is given for dimension d=1 from which, for large lattices, one can recover the asymptotic estimate reported 30 years ago by Lakatos-Lindenberg and Shuler. For dimensions d=2,3 we present numerically exact values for the mean time to reaction, as gauged by the mean walklength before reactive encounter, obtained via the theory of finite Markov processes and supported by Monte Carlo simulations. Qualitatively different results are found between processes occurring on d=1 versus d>1 lattices, and between results obtained assuming nearest-neighbor (only) versus non-nearest-neighbor displacements.

Technique for fast and efficient hierarchical clustering

DOEpatents

Stork, Christopher

2013-10-08

A fast and efficient technique for hierarchical clustering of samples in a dataset includes compressing the dataset to reduce a number of variables within each of the samples of the dataset. A nearest neighbor matrix is generated to identify nearest neighbor pairs between the samples based on differences between the variables of the samples. The samples are arranged into a hierarchy that groups the samples based on the nearest neighbor matrix. The hierarchy is rendered to a display to graphically illustrate similarities or differences between the samples.

Quantum realization of the nearest neighbor value interpolation method for INEQR

NASA Astrophysics Data System (ADS)

Zhou, RiGui; Hu, WenWen; Luo, GaoFeng; Liu, XingAo; Fan, Ping

2018-07-01

This paper presents the nearest neighbor value (NNV) interpolation algorithm for the improved novel enhanced quantum representation of digital images (INEQR). It is necessary to use interpolation in image scaling because there is an increase or a decrease in the number of pixels. The difference between the proposed scheme and nearest neighbor interpolation is that the concept applied, to estimate the missing pixel value, is guided by the nearest value rather than the distance. Firstly, a sequence of quantum operations is predefined, such as cyclic shift transformations and the basic arithmetic operations. Then, the feasibility of the nearest neighbor value interpolation method for quantum image of INEQR is proven using the previously designed quantum operations. Furthermore, quantum image scaling algorithm in the form of circuits of the NNV interpolation for INEQR is constructed for the first time. The merit of the proposed INEQR circuit lies in their low complexity, which is achieved by utilizing the unique properties of quantum superposition and entanglement. Finally, simulation-based experimental results involving different classical images and ratios (i.e., conventional or non-quantum) are simulated based on the classical computer's MATLAB 2014b software, which demonstrates that the proposed interpolation method has higher performances in terms of high resolution compared to the nearest neighbor and bilinear interpolation.

The Effective Resistance of the -Cycle Graph with Four Nearest Neighbors

NASA Astrophysics Data System (ADS)

Chair, Noureddine

2014-02-01

The exact expression for the effective resistance between any two vertices of the -cycle graph with four nearest neighbors , is given. It turns out that this expression is written in terms of the effective resistance of the -cycle graph , the square of the Fibonacci numbers, and the bisected Fibonacci numbers. As a consequence closed form formulas for the total effective resistance, the first passage time, and the mean first passage time for the simple random walk on the the -cycle graph with four nearest neighbors are obtained. Finally, a closed form formula for the effective resistance of with all first neighbors removed is obtained.

Estimating forest attribute parameters for small areas using nearest neighbors techniques

Treesearch

Ronald E. McRoberts

2012-01-01

Nearest neighbors techniques have become extremely popular, particularly for use with forest inventory data. With these techniques, a population unit prediction is calculated as a linear combination of observations for a selected number of population units in a sample that are most similar, or nearest, in a space of ancillary variables to the population unit requiring...

[The impact of subsidized healthcare insurance on access to cervical cytology in Medellin, Colombia].

PubMed

Atehortúa, Sara C; Palacio-Mejía, Lina S

2014-01-01

Assessing the impact of subsidized healthcare insurance on access to cervical cytology in Medellin, Colombia. Propensity score matching (PSM) was used with 2008 Life Quality Survey in Colombia figures to obtain a control group comparable to a treatment group. This involved using stratification estimates, the k-nearest-neighbor algorithm and kernel density for calculating impact size Access to cytology for 19 to 49 year-old women having subsidized healthcare insurance were 2.2 % to 2.9 % lower compared to women who did not have any healthcare insurance. Estimates were not statistically significant for women over 50 years-old. Women lacking healthcare insurance having increased access to cytology could be explained by charities or social programs aiding the population lacking healthcare insurance.

LoCoH: Non-parameteric kernel methods for constructing home ranges and utilization distributions

USGS Publications Warehouse

Getz, Wayne M.; Fortmann-Roe, Scott; Cross, Paul C.; Lyons, Andrew J.; Ryan, Sadie J.; Wilmers, Christopher C.

2007-01-01

Parametric kernel methods currently dominate the literature regarding the construction of animal home ranges (HRs) and utilization distributions (UDs). These methods frequently fail to capture the kinds of hard boundaries common to many natural systems. Recently a local convex hull (LoCoH) nonparametric kernel method, which generalizes the minimum convex polygon (MCP) method, was shown to be more appropriate than parametric kernel methods for constructing HRs and UDs, because of its ability to identify hard boundaries (e.g., rivers, cliff edges) and convergence to the true distribution as sample size increases. Here we extend the LoCoH in two ways: ‘‘fixed sphere-of-influence,’’ or r -LoCoH (kernels constructed from all points within a fixed radius r of each reference point), and an ‘‘adaptive sphere-of-influence,’’ or a -LoCoH (kernels constructed from all points within a radius a such that the distances of all points within the radius to the reference point sum to a value less than or equal to a ), and compare them to the original ‘‘fixed-number-of-points,’’ or k -LoCoH (all kernels constructed from k -1 nearest neighbors of root points). We also compare these nonparametric LoCoH to parametric kernel methods using manufactured data and data collected from GPS collars on African buffalo in the Kruger National Park, South Africa. Our results demonstrate that LoCoH methods are superior to parametric kernel methods in estimating areas used by animals, excluding unused areas (holes) and, generally, in constructing UDs and HRs arising from the movement of animals influenced by hard boundaries and irregular structures (e.g., rocky outcrops). We also demonstrate that a -LoCoH is generally superior to k - and r -LoCoH (with software for all three methods available at http://locoh.cnr.berkeley.edu).

Nearest neighbor, bilinear interpolation and bicubic interpolation geographic correction effects on LANDSAT imagery

NASA Technical Reports Server (NTRS)

Jayroe, R. R., Jr.

1976-01-01

Geographical correction effects on LANDSAT image data are identified, using the nearest neighbor, bilinear interpolation and bicubic interpolation techniques. Potential impacts of registration on image compression and classification are explored.

On the consistency between nearest-neighbor peridynamic discretizations and discretized classical elasticity models

DOE PAGES

Seleson, Pablo; Du, Qiang; Parks, Michael L.

2016-08-16

The peridynamic theory of solid mechanics is a nonlocal reformulation of the classical continuum mechanics theory. At the continuum level, it has been demonstrated that classical (local) elasticity is a special case of peridynamics. Such a connection between these theories has not been extensively explored at the discrete level. This paper investigates the consistency between nearest-neighbor discretizations of linear elastic peridynamic models and finite difference discretizations of the Navier–Cauchy equation of classical elasticity. While nearest-neighbor discretizations in peridynamics have been numerically observed to present grid-dependent crack paths or spurious microcracks, this paper focuses on a different, analytical aspect of suchmore » discretizations. We demonstrate that, even in the absence of cracks, such discretizations may be problematic unless a proper selection of weights is used. Specifically, we demonstrate that using the standard meshfree approach in peridynamics, nearest-neighbor discretizations do not reduce, in general, to discretizations of corresponding classical models. We study nodal-based quadratures for the discretization of peridynamic models, and we derive quadrature weights that result in consistency between nearest-neighbor discretizations of peridynamic models and discretized classical models. The quadrature weights that lead to such consistency are, however, model-/discretization-dependent. We motivate the choice of those quadrature weights through a quadratic approximation of displacement fields. The stability of nearest-neighbor peridynamic schemes is demonstrated through a Fourier mode analysis. Finally, an approach based on a normalization of peridynamic constitutive constants at the discrete level is explored. This approach results in the desired consistency for one-dimensional models, but does not work in higher dimensions. The results of the work presented in this paper suggest that even though nearest-neighbor discretizations should be avoided in peridynamic simulations involving cracks, such discretizations are viable, for example for verification or validation purposes, in problems characterized by smooth deformations. Furthermore, we demonstrate that better quadrature rules in peridynamics can be obtained based on the functional form of solutions.« less

Classification and recognition of dynamical models: the role of phase, independent components, kernels and optimal transport.

PubMed

Bissacco, Alessandro; Chiuso, Alessandro; Soatto, Stefano

2007-11-01

We address the problem of performing decision tasks, and in particular classification and recognition, in the space of dynamical models in order to compare time series of data. Motivated by the application of recognition of human motion in image sequences, we consider a class of models that include linear dynamics, both stable and marginally stable (periodic), both minimum and non-minimum phase, driven by non-Gaussian processes. This requires extending existing learning and system identification algorithms to handle periodic modes and nonminimum phase behavior, while taking into account higher-order statistics of the data. Once a model is identified, we define a kernel-based cord distance between models that includes their dynamics, their initial conditions as well as input distribution. This is made possible by a novel kernel defined between two arbitrary (non-Gaussian) distributions, which is computed by efficiently solving an optimal transport problem. We validate our choice of models, inference algorithm, and distance on the tasks of human motion synthesis (sample paths of the learned models), and recognition (nearest-neighbor classification in the computed distance). However, our work can be applied more broadly where one needs to compare historical data while taking into account periodic trends, non-minimum phase behavior, and non-Gaussian input distributions.

Competitive code-based fast palmprint identification using a set of cover trees

NASA Astrophysics Data System (ADS)

Yue, Feng; Zuo, Wangmeng; Zhang, David; Wang, Kuanquan

2009-06-01

A palmprint identification system recognizes a query palmprint image by searching for its nearest neighbor from among all the templates in a database. When applied on a large-scale identification system, it is often necessary to speed up the nearest-neighbor searching process. We use competitive code, which has very fast feature extraction and matching speed, for palmprint identification. To speed up the identification process, we extend the cover tree method and propose to use a set of cover trees to facilitate the fast and accurate nearest-neighbor searching. We can use the cover tree method because, as we show, the angular distance used in competitive code can be decomposed into a set of metrics. Using the Hong Kong PolyU palmprint database (version 2) and a large-scale palmprint database, our experimental results show that the proposed method searches for nearest neighbors faster than brute force searching.

Application of kernel functions for accurate similarity search in large chemical databases.

PubMed

Wang, Xiaohong; Huan, Jun; Smalter, Aaron; Lushington, Gerald H

2010-04-29

Similarity search in chemical structure databases is an important problem with many applications in chemical genomics, drug design, and efficient chemical probe screening among others. It is widely believed that structure based methods provide an efficient way to do the query. Recently various graph kernel functions have been designed to capture the intrinsic similarity of graphs. Though successful in constructing accurate predictive and classification models, graph kernel functions can not be applied to large chemical compound database due to the high computational complexity and the difficulties in indexing similarity search for large databases. To bridge graph kernel function and similarity search in chemical databases, we applied a novel kernel-based similarity measurement, developed in our team, to measure similarity of graph represented chemicals. In our method, we utilize a hash table to support new graph kernel function definition, efficient storage and fast search. We have applied our method, named G-hash, to large chemical databases. Our results show that the G-hash method achieves state-of-the-art performance for k-nearest neighbor (k-NN) classification. Moreover, the similarity measurement and the index structure is scalable to large chemical databases with smaller indexing size, and faster query processing time as compared to state-of-the-art indexing methods such as Daylight fingerprints, C-tree and GraphGrep. Efficient similarity query processing method for large chemical databases is challenging since we need to balance running time efficiency and similarity search accuracy. Our previous similarity search method, G-hash, provides a new way to perform similarity search in chemical databases. Experimental study validates the utility of G-hash in chemical databases.

An intelligent fault diagnosis method of rolling bearings based on regularized kernel Marginal Fisher analysis

NASA Astrophysics Data System (ADS)

Jiang, Li; Shi, Tielin; Xuan, Jianping

2012-05-01

Generally, the vibration signals of fault bearings are non-stationary and highly nonlinear under complicated operating conditions. Thus, it's a big challenge to extract optimal features for improving classification and simultaneously decreasing feature dimension. Kernel Marginal Fisher analysis (KMFA) is a novel supervised manifold learning algorithm for feature extraction and dimensionality reduction. In order to avoid the small sample size problem in KMFA, we propose regularized KMFA (RKMFA). A simple and efficient intelligent fault diagnosis method based on RKMFA is put forward and applied to fault recognition of rolling bearings. So as to directly excavate nonlinear features from the original high-dimensional vibration signals, RKMFA constructs two graphs describing the intra-class compactness and the inter-class separability, by combining traditional manifold learning algorithm with fisher criteria. Therefore, the optimal low-dimensional features are obtained for better classification and finally fed into the simplest K-nearest neighbor (KNN) classifier to recognize different fault categories of bearings. The experimental results demonstrate that the proposed approach improves the fault classification performance and outperforms the other conventional approaches.

Phase transitions and thermodynamic properties of antiferromagnetic Ising model with next-nearest-neighbor interactions on the Kagomé lattice

NASA Astrophysics Data System (ADS)

Ramazanov, M. K.; Murtazaev, A. K.; Magomedov, M. A.; Badiev, M. K.

2018-06-01

We study phase transitions and thermodynamic properties in the two-dimensional antiferromagnetic Ising model with next-nearest-neighbor interaction on a Kagomé lattice by Monte Carlo simulations. A histogram data analysis shows that a second-order transition occurs in the model. From the analysis of obtained data, we can assume that next-nearest-neighbor ferromagnetic interactions in two-dimensional antiferromagnetic Ising model on a Kagomé lattice excite the occurrence of a second-order transition and unusual behavior of thermodynamic properties on the temperature dependence.

Elliptic Painlevé equations from next-nearest-neighbor translations on the E_8^{(1)} lattice

NASA Astrophysics Data System (ADS)

Joshi, Nalini; Nakazono, Nobutaka

2017-07-01

The well known elliptic discrete Painlevé equation of Sakai is constructed by a standard translation on the E_8(1) lattice, given by nearest neighbor vectors. In this paper, we give a new elliptic discrete Painlevé equation obtained by translations along next-nearest-neighbor vectors. This equation is a generic (8-parameter) version of a 2-parameter elliptic difference equation found by reduction from Adler’s partial difference equation, the so-called Q4 equation. We also provide a projective reduction of the well known equation of Sakai.

Missing value imputation for gene expression data by tailored nearest neighbors.

PubMed

Faisal, Shahla; Tutz, Gerhard

2017-04-25

High dimensional data like gene expression and RNA-sequences often contain missing values. The subsequent analysis and results based on these incomplete data can suffer strongly from the presence of these missing values. Several approaches to imputation of missing values in gene expression data have been developed but the task is difficult due to the high dimensionality (number of genes) of the data. Here an imputation procedure is proposed that uses weighted nearest neighbors. Instead of using nearest neighbors defined by a distance that includes all genes the distance is computed for genes that are apt to contribute to the accuracy of imputed values. The method aims at avoiding the curse of dimensionality, which typically occurs if local methods as nearest neighbors are applied in high dimensional settings. The proposed weighted nearest neighbors algorithm is compared to existing missing value imputation techniques like mean imputation, KNNimpute and the recently proposed imputation by random forests. We use RNA-sequence and microarray data from studies on human cancer to compare the performance of the methods. The results from simulations as well as real studies show that the weighted distance procedure can successfully handle missing values for high dimensional data structures where the number of predictors is larger than the number of samples. The method typically outperforms the considered competitors.

Estimation of Carcinogenicity using Hierarchical Clustering and Nearest Neighbor Methodologies

EPA Science Inventory

Previously a hierarchical clustering (HC) approach and a nearest neighbor (NN) approach were developed to model acute aquatic toxicity end points. These approaches were developed to correlate the toxicity for large, noncongeneric data sets. In this study these approaches applie...

Atomically informed nonlocal semi-discrete variational Peierls-Nabarro model for planar core dislocations

PubMed Central

Liu, Guisen; Cheng, Xi; Wang, Jian; Chen, Kaiguo; Shen, Yao

2017-01-01

Prediction of Peierls stress associated with dislocation glide is of fundamental concern in understanding and designing the plasticity and mechanical properties of crystalline materials. Here, we develop a nonlocal semi-discrete variational Peierls-Nabarro (SVPN) model by incorporating the nonlocal atomic interactions into the semi-discrete variational Peierls framework. The nonlocal kernel is simplified by limiting the nonlocal atomic interaction in the nearest neighbor region, and the nonlocal coefficient is directly computed from the dislocation core structure. Our model is capable of accurately predicting the displacement profile, and the Peierls stress, of planar-extended core dislocations in face-centered cubic structures. Our model could be extended to study more complicated planar-extended core dislocations, such as <110> {111} dislocations in Al-based and Ti-based intermetallic compounds. PMID:28252102

Fast Demand Forecast of Electric Vehicle Charging Stations for Cell Phone Application

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

Majidpour, Mostafa; Qiu, Charlie; Chung, Ching-Yen

This paper describes the core cellphone application algorithm which has been implemented for the prediction of energy consumption at Electric Vehicle (EV) Charging Stations at UCLA. For this interactive user application, the total time of accessing database, processing the data and making the prediction, needs to be within a few seconds. We analyze four relatively fast Machine Learning based time series prediction algorithms for our prediction engine: Historical Average, kNearest Neighbor, Weighted k-Nearest Neighbor, and Lazy Learning. The Nearest Neighbor algorithm (k Nearest Neighbor with k=1) shows better performance and is selected to be the prediction algorithm implemented for themore » cellphone application. Two applications have been designed on top of the prediction algorithm: one predicts the expected available energy at the station and the other one predicts the expected charging finishing time. The total time, including accessing the database, data processing, and prediction is about one second for both applications.« less

K-nearest neighbors based methods for identification of different gear crack levels under different motor speeds and loads: Revisited

NASA Astrophysics Data System (ADS)

Wang, Dong

2016-03-01

Gears are the most commonly used components in mechanical transmission systems. Their failures may cause transmission system breakdown and result in economic loss. Identification of different gear crack levels is important to prevent any unexpected gear failure because gear cracks lead to gear tooth breakage. Signal processing based methods mainly require expertize to explain gear fault signatures which is usually not easy to be achieved by ordinary users. In order to automatically identify different gear crack levels, intelligent gear crack identification methods should be developed. The previous case studies experimentally proved that K-nearest neighbors based methods exhibit high prediction accuracies for identification of 3 different gear crack levels under different motor speeds and loads. In this short communication, to further enhance prediction accuracies of existing K-nearest neighbors based methods and extend identification of 3 different gear crack levels to identification of 5 different gear crack levels, redundant statistical features are constructed by using Daubechies 44 (db44) binary wavelet packet transform at different wavelet decomposition levels, prior to the use of a K-nearest neighbors method. The dimensionality of redundant statistical features is 620, which provides richer gear fault signatures. Since many of these statistical features are redundant and highly correlated with each other, dimensionality reduction of redundant statistical features is conducted to obtain new significant statistical features. At last, the K-nearest neighbors method is used to identify 5 different gear crack levels under different motor speeds and loads. A case study including 3 experiments is investigated to demonstrate that the developed method provides higher prediction accuracies than the existing K-nearest neighbors based methods for recognizing different gear crack levels under different motor speeds and loads. Based on the new significant statistical features, some other popular statistical models including linear discriminant analysis, quadratic discriminant analysis, classification and regression tree and naive Bayes classifier, are compared with the developed method. The results show that the developed method has the highest prediction accuracies among these statistical models. Additionally, selection of the number of new significant features and parameter selection of K-nearest neighbors are thoroughly investigated.

Nearest unlike neighbor (NUN): an aid to decision confidence estimation

NASA Astrophysics Data System (ADS)

Dasarathy, Belur V.

1995-09-01

The concept of nearest unlike neighbor (NUN), proposed and explored previously in the design of nearest neighbor (NN) based decision systems, is further exploited in this study to develop a measure of confidence in the decisions made by NN-based decision systems. This measure of confidence, on the basis of comparison with a user-defined threshold, may be used to determine the acceptability of the decision provided by the NN-based decision system. The concepts, associated methodology, and some illustrative numerical examples using the now classical Iris data to bring out the ease of implementation and effectiveness of the proposed innovations are presented.

Creating peer groups for assessing and comparing nursing home performance.

PubMed

Byrne, Margaret M; Daw, Christina; Pietz, Ken; Reis, Brian; Petersen, Laura A

2013-11-01

Publicly reported performance data for hospitals and nursing homes are becoming ubiquitous. For such comparisons to be fair, facilities must be compared with their peers. To adapt a previously published methodology for developing hospital peer groupings so that it is applicable to nursing homes and to explore the characteristics of "nearest-neighbor" peer groupings. Analysis of Department of Veterans Affairs administrative databases and nursing home facility characteristics. The nearest-neighbor methodology for developing peer groupings involves calculating the Euclidean distance between facilities based on facility characteristics. We describe our steps in selection of facility characteristics, describe the characteristics of nearest-neighbor peer groups, and compare them with peer groups derived through classical cluster analysis. The facility characteristics most pertinent to nursing home groupings were found to be different from those that were most relevant for hospitals. Unlike classical cluster groups, nearest neighbor groups are not mutually exclusive, and the nearest-neighbor methodology resulted in nursing home peer groupings that were substantially less diffuse than nursing home peer groups created using traditional cluster analysis. It is essential that healthcare policy makers and administrators have a means of fairly grouping facilities for the purposes of quality, cost, or efficiency comparisons. In this research, we show that a previously published methodology can be successfully applied to a nursing home setting. The same approach could be applied in other clinical settings such as primary care.

Error minimizing algorithms for nearest eighbor classifiers

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

Porter, Reid B; Hush, Don; Zimmer, G. Beate

2011-01-03

Stack Filters define a large class of discrete nonlinear filter first introd uced in image and signal processing for noise removal. In recent years we have suggested their application to classification problems, and investigated their relationship to other types of discrete classifiers such as Decision Trees. In this paper we focus on a continuous domain version of Stack Filter Classifiers which we call Ordered Hypothesis Machines (OHM), and investigate their relationship to Nearest Neighbor classifiers. We show that OHM classifiers provide a novel framework in which to train Nearest Neighbor type classifiers by minimizing empirical error based loss functions. Wemore » use the framework to investigate a new cost sensitive loss function that allows us to train a Nearest Neighbor type classifier for low false alarm rate applications. We report results on both synthetic data and real-world image data.« less

Colorectal Cancer and Colitis Diagnosis Using Fourier Transform Infrared Spectroscopy and an Improved K-Nearest-Neighbour Classifier.

PubMed

Li, Qingbo; Hao, Can; Kang, Xue; Zhang, Jialin; Sun, Xuejun; Wang, Wenbo; Zeng, Haishan

2017-11-27

Combining Fourier transform infrared spectroscopy (FTIR) with endoscopy, it is expected that noninvasive, rapid detection of colorectal cancer can be performed in vivo in the future. In this study, Fourier transform infrared spectra were collected from 88 endoscopic biopsy colorectal tissue samples (41 colitis and 47 cancers). A new method, viz., entropy weight local-hyperplane k-nearest-neighbor (EWHK), which is an improved version of K-local hyperplane distance nearest-neighbor (HKNN), is proposed for tissue classification. In order to avoid limiting high dimensions and small values of the nearest neighbor, the new EWHK method calculates feature weights based on information entropy. The average results of the random classification showed that the EWHK classifier for differentiating cancer from colitis samples produced a sensitivity of 81.38% and a specificity of 92.69%.

Classification of matrix-product ground states corresponding to one-dimensional chains of two-state sites of nearest neighbor interactions

NASA Astrophysics Data System (ADS)

Fatollahi, Amir H.; Khorrami, Mohammad; Shariati, Ahmad; Aghamohammadi, Amir

2011-04-01

A complete classification is given for one-dimensional chains with nearest-neighbor interactions having two states in each site, for which a matrix product ground state exists. The Hamiltonians and their corresponding matrix product ground states are explicitly obtained.

TACOA – Taxonomic classification of environmental genomic fragments using a kernelized nearest neighbor approach

PubMed Central

Diaz, Naryttza N; Krause, Lutz; Goesmann, Alexander; Niehaus, Karsten; Nattkemper, Tim W

2009-01-01

Background Metagenomics, or the sequencing and analysis of collective genomes (metagenomes) of microorganisms isolated from an environment, promises direct access to the "unculturable majority". This emerging field offers the potential to lay solid basis on our understanding of the entire living world. However, the taxonomic classification is an essential task in the analysis of metagenomics data sets that it is still far from being solved. We present a novel strategy to predict the taxonomic origin of environmental genomic fragments. The proposed classifier combines the idea of the k-nearest neighbor with strategies from kernel-based learning. Results Our novel strategy was extensively evaluated using the leave-one-out cross validation strategy on fragments of variable length (800 bp – 50 Kbp) from 373 completely sequenced genomes. TACOA is able to classify genomic fragments of length 800 bp and 1 Kbp with high accuracy until rank class. For longer fragments ≥ 3 Kbp accurate predictions are made at even deeper taxonomic ranks (order and genus). Remarkably, TACOA also produces reliable results when the taxonomic origin of a fragment is not represented in the reference set, thus classifying such fragments to its known broader taxonomic class or simply as "unknown". We compared the classification accuracy of TACOA with the latest intrinsic classifier PhyloPythia using 63 recently published complete genomes. For fragments of length 800 bp and 1 Kbp the overall accuracy of TACOA is higher than that obtained by PhyloPythia at all taxonomic ranks. For all fragment lengths, both methods achieved comparable high specificity results up to rank class and low false negative rates are also obtained. Conclusion An accurate multi-class taxonomic classifier was developed for environmental genomic fragments. TACOA can predict with high reliability the taxonomic origin of genomic fragments as short as 800 bp. The proposed method is transparent, fast, accurate and the reference set can be easily updated as newly sequenced genomes become available. Moreover, the method demonstrated to be competitive when compared to the most current classifier PhyloPythia and has the advantage that it can be locally installed and the reference set can be kept up-to-date. PMID:19210774

Distribution of Steps with Finite-Range Interactions: Analytic Approximations and Numerical Results

NASA Astrophysics Data System (ADS)

GonzáLez, Diego Luis; Jaramillo, Diego Felipe; TéLlez, Gabriel; Einstein, T. L.

2013-03-01

While most Monte Carlo simulations assume only nearest-neighbor steps interact elastically, most analytic frameworks (especially the generalized Wigner distribution) posit that each step elastically repels all others. In addition to the elastic repulsions, we allow for possible surface-state-mediated interactions. We investigate analytically and numerically how next-nearest neighbor (NNN) interactions and, more generally, interactions out to q'th nearest neighbor alter the form of the terrace-width distribution and of pair correlation functions (i.e. the sum over n'th neighbor distribution functions, which we investigated recently.[2] For physically plausible interactions, we find modest changes when NNN interactions are included and generally negligible changes when more distant interactions are allowed. We discuss methods for extracting from simulated experimental data the characteristic scale-setting terms in assumed potential forms.

Dynamical phases in a one-dimensional chain of heterospecies Rydberg atoms with next-nearest-neighbor interactions

NASA Astrophysics Data System (ADS)

Qian, Jing; Zhang, Lu; Zhai, Jingjing; Zhang, Weiping

2015-12-01

We theoretically investigate the dynamical phase diagram of a one-dimensional chain of laser-excited two-species Rydberg atoms. The existence of a variety of unique dynamical phases in the experimentally achievable parameter region is predicted under the mean-field approximation, and the change in those phases when the effect of the next-nearest-neighbor interaction is included is further discussed. In particular, we find that the com-petition of the strong Rydberg-Rydberg interactions and the optical excitation imbalance can lead to the presence of complex multiple chaotic phases, which are highly sensitive to the initial Rydberg-state population and the strength of the next-nearest-neighbor interactions.

Matrix-valued Boltzmann equation for the nonintegrable Hubbard chain.

PubMed

Fürst, Martin L R; Mendl, Christian B; Spohn, Herbert

2013-07-01

The standard Fermi-Hubbard chain becomes nonintegrable by adding to the nearest neighbor hopping additional longer range hopping amplitudes. We assume that the quartic interaction is weak and investigate numerically the dynamics of the chain on the level of the Boltzmann type kinetic equation. Only the spatially homogeneous case is considered. We observe that the huge degeneracy of stationary states in the case of nearest neighbor hopping is lost and the convergence to the thermal Fermi-Dirac distribution is restored. The convergence to equilibrium is exponentially fast. However for small next-nearest neighbor hopping amplitudes one has a rapid relaxation towards the manifold of quasistationary states and slow relaxation to the final equilibrium state.

A dynamical mean-field study of orbital-selective Mott phase enhanced by next-nearest neighbor hopping

NASA Astrophysics Data System (ADS)

Niu, Yuekun; Sun, Jian; Ni, Yu; Song, Yun

2018-06-01

The dynamical mean-field theory is employed to study the orbital-selective Mott transition (OSMT) of the two-orbital Hubbard model with nearest neighbor hopping and next-nearest neighbor (NNN) hopping. The NNN hopping breaks the particle-hole symmetry at half filling and gives rise to an asymmetric density of states (DOS). Our calculations show that the broken symmetry of DOS benefits the OSMT, where the region of the orbital-selective Mott phase significantly extends with the increasing NNN hopping integral. We also find that Hund's rule coupling promotes OSMT by blocking the orbital fluctuations, but the influence of NNN hopping is more remarkable.

Next nearest neighbors sites and the reactivity of the CO NO surface reaction

NASA Astrophysics Data System (ADS)

Cortés, Joaquín.; Valencia, Eliana

1998-04-01

Using Monte Carlo experiments of the reduction of NO by CO, a study is made of the effect on reactivity due to the formation of N 2O and to the increased coordination of the sites considering the next nearest neighbors sites (nnn) in a square lattice of superficial sites.

Nearest Neighbor Searching in Binary Search Trees: Simulation of a Multiprocessor System.

ERIC Educational Resources Information Center

Stewart, Mark; Willett, Peter

1987-01-01

Describes the simulation of a nearest neighbor searching algorithm for document retrieval using a pool of microprocessors. Three techniques are described which allow parallel searching of a binary search tree as well as a PASCAL-based system, PASSIM, which can simulate these techniques. Fifty-six references are provided. (Author/LRW)

K-Nearest Neighbor Estimation of Forest Attributes: Improving Mapping Efficiency

Treesearch

Andrew O. Finley; Alan R. Ek; Yun Bai; Marvin E. Bauer

2005-01-01

This paper describes our efforts in refining k-nearest neighbor forest attributes classification using U.S. Department of Agriculture Forest Service Forest Inventory and Analysis plot data and Landsat 7 Enhanced Thematic Mapper Plus imagery. The analysis focuses on FIA-defined forest type classification across St. Louis County in northeastern Minnesota. We outline...

Solitary wave for a nonintegrable discrete nonlinear Schrödinger equation in nonlinear optical waveguide arrays

NASA Astrophysics Data System (ADS)

Ma, Li-Yuan; Ji, Jia-Liang; Xu, Zong-Wei; Zhu, Zuo-Nong

2018-03-01

We study a nonintegrable discrete nonlinear Schrödinger (dNLS) equation with the term of nonlinear nearest-neighbor interaction occurred in nonlinear optical waveguide arrays. By using discrete Fourier transformation, we obtain numerical approximations of stationary and travelling solitary wave solutions of the nonintegrable dNLS equation. The analysis of stability of stationary solitary waves is performed. It is shown that the nonlinear nearest-neighbor interaction term has great influence on the form of solitary wave. The shape of solitary wave is important in the electric field propagating. If we neglect the nonlinear nearest-neighbor interaction term, much important information in the electric field propagating may be missed. Our numerical simulation also demonstrates the difference of chaos phenomenon between the nonintegrable dNLS equation with nonlinear nearest-neighbor interaction and another nonintegrable dNLS equation without the term. Project supported by the National Natural Science Foundation of China (Grant Nos. 11671255 and 11701510), the Ministry of Economy and Competitiveness of Spain (Grant No. MTM2016-80276-P (AEI/FEDER, EU)), and the China Postdoctoral Science Foundation (Grant No. 2017M621964).

Large margin nearest neighbor classifiers.

PubMed

Domeniconi, Carlotta; Gunopulos, Dimitrios; Peng, Jing

2005-07-01

The nearest neighbor technique is a simple and appealing approach to addressing classification problems. It relies on the assumption of locally constant class conditional probabilities. This assumption becomes invalid in high dimensions with a finite number of examples due to the curse of dimensionality. Severe bias can be introduced under these conditions when using the nearest neighbor rule. The employment of a locally adaptive metric becomes crucial in order to keep class conditional probabilities close to uniform, thereby minimizing the bias of estimates. We propose a technique that computes a locally flexible metric by means of support vector machines (SVMs). The decision function constructed by SVMs is used to determine the most discriminant direction in a neighborhood around the query. Such a direction provides a local feature weighting scheme. We formally show that our method increases the margin in the weighted space where classification takes place. Moreover, our method has the important advantage of online computational efficiency over competing locally adaptive techniques for nearest neighbor classification. We demonstrate the efficacy of our method using both real and simulated data.

Classification of multispectral image data by the Binary Diamond neural network and by nonparametric, pixel-by-pixel methods

NASA Technical Reports Server (NTRS)

Salu, Yehuda; Tilton, James

1993-01-01

The classification of multispectral image data obtained from satellites has become an important tool for generating ground cover maps. This study deals with the application of nonparametric pixel-by-pixel classification methods in the classification of pixels, based on their multispectral data. A new neural network, the Binary Diamond, is introduced, and its performance is compared with a nearest neighbor algorithm and a back-propagation network. The Binary Diamond is a multilayer, feed-forward neural network, which learns from examples in unsupervised, 'one-shot' mode. It recruits its neurons according to the actual training set, as it learns. The comparisons of the algorithms were done by using a realistic data base, consisting of approximately 90,000 Landsat 4 Thematic Mapper pixels. The Binary Diamond and the nearest neighbor performances were close, with some advantages to the Binary Diamond. The performance of the back-propagation network lagged behind. An efficient nearest neighbor algorithm, the binned nearest neighbor, is described. Ways for improving the performances, such as merging categories, and analyzing nonboundary pixels, are addressed and evaluated.

A range of complex probabilistic models for RNA secondary structure prediction that includes the nearest-neighbor model and more.

PubMed

Rivas, Elena; Lang, Raymond; Eddy, Sean R

2012-02-01

The standard approach for single-sequence RNA secondary structure prediction uses a nearest-neighbor thermodynamic model with several thousand experimentally determined energy parameters. An attractive alternative is to use statistical approaches with parameters estimated from growing databases of structural RNAs. Good results have been reported for discriminative statistical methods using complex nearest-neighbor models, including CONTRAfold, Simfold, and ContextFold. Little work has been reported on generative probabilistic models (stochastic context-free grammars [SCFGs]) of comparable complexity, although probabilistic models are generally easier to train and to use. To explore a range of probabilistic models of increasing complexity, and to directly compare probabilistic, thermodynamic, and discriminative approaches, we created TORNADO, a computational tool that can parse a wide spectrum of RNA grammar architectures (including the standard nearest-neighbor model and more) using a generalized super-grammar that can be parameterized with probabilities, energies, or arbitrary scores. By using TORNADO, we find that probabilistic nearest-neighbor models perform comparably to (but not significantly better than) discriminative methods. We find that complex statistical models are prone to overfitting RNA structure and that evaluations should use structurally nonhomologous training and test data sets. Overfitting has affected at least one published method (ContextFold). The most important barrier to improving statistical approaches for RNA secondary structure prediction is the lack of diversity of well-curated single-sequence RNA secondary structures in current RNA databases.

A range of complex probabilistic models for RNA secondary structure prediction that includes the nearest-neighbor model and more

PubMed Central

Rivas, Elena; Lang, Raymond; Eddy, Sean R.

2012-01-01

The standard approach for single-sequence RNA secondary structure prediction uses a nearest-neighbor thermodynamic model with several thousand experimentally determined energy parameters. An attractive alternative is to use statistical approaches with parameters estimated from growing databases of structural RNAs. Good results have been reported for discriminative statistical methods using complex nearest-neighbor models, including CONTRAfold, Simfold, and ContextFold. Little work has been reported on generative probabilistic models (stochastic context-free grammars [SCFGs]) of comparable complexity, although probabilistic models are generally easier to train and to use. To explore a range of probabilistic models of increasing complexity, and to directly compare probabilistic, thermodynamic, and discriminative approaches, we created TORNADO, a computational tool that can parse a wide spectrum of RNA grammar architectures (including the standard nearest-neighbor model and more) using a generalized super-grammar that can be parameterized with probabilities, energies, or arbitrary scores. By using TORNADO, we find that probabilistic nearest-neighbor models perform comparably to (but not significantly better than) discriminative methods. We find that complex statistical models are prone to overfitting RNA structure and that evaluations should use structurally nonhomologous training and test data sets. Overfitting has affected at least one published method (ContextFold). The most important barrier to improving statistical approaches for RNA secondary structure prediction is the lack of diversity of well-curated single-sequence RNA secondary structures in current RNA databases. PMID:22194308

An Improvement To The k-Nearest Neighbor Classifier For ECG Database

NASA Astrophysics Data System (ADS)

Jaafar, Haryati; Hidayah Ramli, Nur; Nasir, Aimi Salihah Abdul

2018-03-01

The k nearest neighbor (kNN) is a non-parametric classifier and has been widely used for pattern classification. However, in practice, the performance of kNN often tends to fail due to the lack of information on how the samples are distributed among them. Moreover, kNN is no longer optimal when the training samples are limited. Another problem observed in kNN is regarding the weighting issues in assigning the class label before classification. Thus, to solve these limitations, a new classifier called Mahalanobis fuzzy k-nearest centroid neighbor (MFkNCN) is proposed in this study. Here, a Mahalanobis distance is applied to avoid the imbalance of samples distribition. Then, a surrounding rule is employed to obtain the nearest centroid neighbor based on the distributions of training samples and its distance to the query point. Consequently, the fuzzy membership function is employed to assign the query point to the class label which is frequently represented by the nearest centroid neighbor Experimental studies from electrocardiogram (ECG) signal is applied in this study. The classification performances are evaluated in two experimental steps i.e. different values of k and different sizes of feature dimensions. Subsequently, a comparative study of kNN, kNCN, FkNN and MFkCNN classifier is conducted to evaluate the performances of the proposed classifier. The results show that the performance of MFkNCN consistently exceeds the kNN, kNCN and FkNN with the best classification rates of 96.5%.

Quantitative analysis and comparative study of four cities green pattern in API system on the background of big data

NASA Astrophysics Data System (ADS)

Xin, YANG; Si-qi, WU; Qi, ZHANG

2018-05-01

Beijing, London, Paris, New York are typical cities in the world, so comparative study of four cities green pattern is very important to find out gap and advantage and to learn from each other. The paper will provide basis and new ideas for development of metropolises in China. On the background of big data, API (Application Programming Interface) system can provide extensive and accurate basic data to study urban green pattern in different geographical environment in domestic and foreign. On the basis of this, Average nearest neighbor tool, Kernel density tool and Standard Ellipse tool in ArcGIS platform can process and summarize data and realize quantitative analysis of green pattern. The paper summarized uniqueness of four cities green pattern and reasons of formation on basis of numerical comparison.

Model Based Predictive Control of Multivariable Hammerstein Processes with Fuzzy Logic Hypercube Interpolated Models

PubMed Central

Coelho, Antonio Augusto Rodrigues

2016-01-01

This paper introduces the Fuzzy Logic Hypercube Interpolator (FLHI) and demonstrates applications in control of multiple-input single-output (MISO) and multiple-input multiple-output (MIMO) processes with Hammerstein nonlinearities. FLHI consists of a Takagi-Sugeno fuzzy inference system where membership functions act as kernel functions of an interpolator. Conjunction of membership functions in an unitary hypercube space enables multivariable interpolation of N-dimensions. Membership functions act as interpolation kernels, such that choice of membership functions determines interpolation characteristics, allowing FLHI to behave as a nearest-neighbor, linear, cubic, spline or Lanczos interpolator, to name a few. The proposed interpolator is presented as a solution to the modeling problem of static nonlinearities since it is capable of modeling both a function and its inverse function. Three study cases from literature are presented, a single-input single-output (SISO) system, a MISO and a MIMO system. Good results are obtained regarding performance metrics such as set-point tracking, control variation and robustness. Results demonstrate applicability of the proposed method in modeling Hammerstein nonlinearities and their inverse functions for implementation of an output compensator with Model Based Predictive Control (MBPC), in particular Dynamic Matrix Control (DMC). PMID:27657723

Collective Behaviors of Mobile Robots Beyond the Nearest Neighbor Rules With Switching Topology.

PubMed

Ning, Boda; Han, Qing-Long; Zuo, Zongyu; Jin, Jiong; Zheng, Jinchuan

2018-05-01

This paper is concerned with the collective behaviors of robots beyond the nearest neighbor rules, i.e., dispersion and flocking, when robots interact with others by applying an acute angle test (AAT)-based interaction rule. Different from a conventional nearest neighbor rule or its variations, the AAT-based interaction rule allows interactions with some far-neighbors and excludes unnecessary nearest neighbors. The resulting dispersion and flocking hold the advantages of scalability, connectivity, robustness, and effective area coverage. For the dispersion, a spring-like controller is proposed to achieve collision-free coordination. With switching topology, a new fixed-time consensus-based energy function is developed to guarantee the system stability. An upper bound of settling time for energy consensus is obtained, and a uniform time interval is accordingly set so that energy distribution is conducted in a fair manner. For the flocking, based on a class of generalized potential functions taking nonsmooth switching into account, a new controller is proposed to ensure that the same velocity for all robots is eventually reached. A co-optimizing problem is further investigated to accomplish additional tasks, such as enhancing communication performance, while maintaining the collective behaviors of mobile robots. Simulation results are presented to show the effectiveness of the theoretical results.

Analysis of miRNA expression profile based on SVM algorithm

NASA Astrophysics Data System (ADS)

Ting-ting, Dai; Chang-ji, Shan; Yan-shou, Dong; Yi-duo, Bian

2018-05-01

Based on mirna expression spectrum data set, a new data mining algorithm - tSVM - KNN (t statistic with support vector machine - k nearest neighbor) is proposed. the idea of the algorithm is: firstly, the feature selection of the data set is carried out by the unified measurement method; Secondly, SVM - KNN algorithm, which combines support vector machine (SVM) and k - nearest neighbor (k - nearest neighbor) is used as classifier. Simulation results show that SVM - KNN algorithm has better classification ability than SVM and KNN alone. Tsvm - KNN algorithm only needs 5 mirnas to obtain 96.08 % classification accuracy in terms of the number of mirna " tags" and recognition accuracy. compared with similar algorithms, tsvm - KNN algorithm has obvious advantages.

Multi-strategy based quantum cost reduction of linear nearest-neighbor quantum circuit

NASA Astrophysics Data System (ADS)

Tan, Ying-ying; Cheng, Xue-yun; Guan, Zhi-jin; Liu, Yang; Ma, Haiying

2018-03-01

With the development of reversible and quantum computing, study of reversible and quantum circuits has also developed rapidly. Due to physical constraints, most quantum circuits require quantum gates to interact on adjacent quantum bits. However, many existing quantum circuits nearest-neighbor have large quantum cost. Therefore, how to effectively reduce quantum cost is becoming a popular research topic. In this paper, we proposed multiple optimization strategies to reduce the quantum cost of the circuit, that is, we reduce quantum cost from MCT gates decomposition, nearest neighbor and circuit simplification, respectively. The experimental results show that the proposed strategies can effectively reduce the quantum cost, and the maximum optimization rate is 30.61% compared to the corresponding results.

Nearest Neighbor Interactions Affect the Conformational Distribution in the Unfolded State of Peptides

NASA Astrophysics Data System (ADS)

Toal, Siobhan; Schweitzer-Stenner, Reinhard; Rybka, Karin; Schwalbe, Hardol

2013-03-01

In order to enable structural predictions of intrinsically disordered proteins (IDPs) the intrinsic conformational propensities of amino acids must be complimented by information on nearest-neighbor interactions. To explore the influence of nearest-neighbors on conformational distributions, we preformed a joint vibrational (Infrared, Vibrational Circular Dichroism (VCD), polarized Raman) and 2D-NMR study of selected GxyG host-guest peptides: GDyG, GSyG, GxLG, GxVG, where x/y ={A,K,LV}. D and S (L and V) were chosen at the x (y) position due to their observance to drastically change the distribution of alanine in xAy tripeptide sequences in truncated coil libraries. The conformationally sensitive amide' profiles of the respective spectra were analyzed in terms of a statistical ensemble described as a superposition of 2D-Gaussian functions in Ramachandran space representing sub-ensembles of pPII-, β-strand-, helical-, and turn-like conformations. Our analysis and simulation of the amide I' band profiles exploits excitonic coupling between the local amide I' vibrational modes in the tetra-peptides. The resulting distributions reveal that D and S, which themselves have high propensities for turn-structures, strongly affect the conformational distribution of their downstream neighbor. Taken together, our results indicate that Dx and Sx motifs might act as conformational randomizers in proteins, attenuating intrinsic propensities of neighboring residues. Overall, our results show that nearest neighbor interactions contribute significantly to the Gibbs energy landscape of disordered peptides and proteins.

Localization in one-dimensional lattices with non-nearest-neighbor hopping: Generalized Anderson and Aubry-Andre models

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

Biddle, J.; Priour, D. J. Jr.; Wang, B.

We study the quantum localization phenomena of noninteracting particles in one-dimensional lattices based on tight-binding models with various forms of hopping terms beyond the nearest neighbor, which are generalizations of the famous Aubry-Andre and noninteracting Anderson models. For the case with deterministic disordered potential induced by a secondary incommensurate lattice (i.e., the Aubry-Andre model), we identify a class of self-dual models, for which the boundary between localized and extended eigenstates are determined analytically by employing a generalized Aubry-Andre transformation. We also numerically investigate the localization properties of nondual models with next-nearest-neighbor hopping, Gaussian, and power-law decay hopping terms. We findmore » that even for these nondual models, the numerically obtained mobility edges can be well approximated by the analytically obtained condition for localization transition in the self-dual models, as long as the decay of the hopping rate with respect to distance is sufficiently fast. For the disordered potential with genuinely random character, we examine scenarios with next-nearest-neighbor hopping, exponential, Gaussian, and power-law decay hopping terms numerically. We find that the higher-order hopping terms can remove the symmetry in the localization length about the energy band center compared to the Anderson model. Furthermore, our results demonstrate that for the power-law decay case, there exists a critical exponent below which mobility edges can be found. Our theoretical results could, in principle, be directly tested in shallow atomic optical lattice systems enabling non-nearest-neighbor hopping.« less

A comparison of 12 algorithms for matching on the propensity score.

PubMed

Austin, Peter C

2014-03-15

Propensity-score matching is increasingly being used to reduce the confounding that can occur in observational studies examining the effects of treatments or interventions on outcomes. We used Monte Carlo simulations to examine the following algorithms for forming matched pairs of treated and untreated subjects: optimal matching, greedy nearest neighbor matching without replacement, and greedy nearest neighbor matching without replacement within specified caliper widths. For each of the latter two algorithms, we examined four different sub-algorithms defined by the order in which treated subjects were selected for matching to an untreated subject: lowest to highest propensity score, highest to lowest propensity score, best match first, and random order. We also examined matching with replacement. We found that (i) nearest neighbor matching induced the same balance in baseline covariates as did optimal matching; (ii) when at least some of the covariates were continuous, caliper matching tended to induce balance on baseline covariates that was at least as good as the other algorithms; (iii) caliper matching tended to result in estimates of treatment effect with less bias compared with optimal and nearest neighbor matching; (iv) optimal and nearest neighbor matching resulted in estimates of treatment effect with negligibly less variability than did caliper matching; (v) caliper matching had amongst the best performance when assessed using mean squared error; (vi) the order in which treated subjects were selected for matching had at most a modest effect on estimation; and (vii) matching with replacement did not have superior performance compared with caliper matching without replacement. © 2013 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd.

A comparison of 12 algorithms for matching on the propensity score

PubMed Central

Austin, Peter C

2014-01-01

Propensity-score matching is increasingly being used to reduce the confounding that can occur in observational studies examining the effects of treatments or interventions on outcomes. We used Monte Carlo simulations to examine the following algorithms for forming matched pairs of treated and untreated subjects: optimal matching, greedy nearest neighbor matching without replacement, and greedy nearest neighbor matching without replacement within specified caliper widths. For each of the latter two algorithms, we examined four different sub-algorithms defined by the order in which treated subjects were selected for matching to an untreated subject: lowest to highest propensity score, highest to lowest propensity score, best match first, and random order. We also examined matching with replacement. We found that (i) nearest neighbor matching induced the same balance in baseline covariates as did optimal matching; (ii) when at least some of the covariates were continuous, caliper matching tended to induce balance on baseline covariates that was at least as good as the other algorithms; (iii) caliper matching tended to result in estimates of treatment effect with less bias compared with optimal and nearest neighbor matching; (iv) optimal and nearest neighbor matching resulted in estimates of treatment effect with negligibly less variability than did caliper matching; (v) caliper matching had amongst the best performance when assessed using mean squared error; (vi) the order in which treated subjects were selected for matching had at most a modest effect on estimation; and (vii) matching with replacement did not have superior performance compared with caliper matching without replacement. © 2013 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd. PMID:24123228

Fidelity study of superconductivity in extended Hubbard models

NASA Astrophysics Data System (ADS)

Plonka, N.; Jia, C. J.; Wang, Y.; Moritz, B.; Devereaux, T. P.

2015-07-01

The Hubbard model with local on-site repulsion is generally thought to possess a superconducting ground state for appropriate parameters, but the effects of more realistic long-range Coulomb interactions have not been studied extensively. We study the influence of these interactions on superconductivity by including nearest- and next-nearest-neighbor extended Hubbard interactions in addition to the usual on-site terms. Utilizing numerical exact diagonalization, we analyze the signatures of superconductivity in the ground states through the fidelity metric of quantum information theory. We find that nearest and next-nearest neighbor interactions have thresholds above which they destabilize superconductivity regardless of whether they are attractive or repulsive, seemingly due to competing charge fluctuations.

Using genetic algorithms to optimize k-Nearest Neighbors configurations for use with airborne laser scanning data

Treesearch

Ronald E. McRoberts; Grant M. Domke; Qi Chen; Erik Næsset; Terje Gobakken

2016-01-01

The relatively small sampling intensities used by national forest inventories are often insufficient to produce the desired precision for estimates of population parameters unless the estimation process is augmented with auxiliary information, usually in the form of remotely sensed data. The k-Nearest Neighbors (k-NN) technique is a non-parametric,multivariate approach...

Estimating areal means and variances of forest attributes using the k-Nearest Neighbors technique and satellite imagery

Treesearch

Ronald E. McRoberts; Erkki O. Tomppo; Andrew O. Finley; Heikkinen Juha

2007-01-01

The k-Nearest Neighbor (k-NN) technique has become extremely popular for a variety of forest inventory mapping and estimation applications. Much of this popularity may be attributed to the non-parametric, multivariate features of the technique, its intuitiveness, and its ease of use. When used with satellite imagery and forest...

A Comparison of the Spatial Linear Model to Nearest Neighbor (k-NN) Methods for Forestry Applications

Treesearch

Jay M. Ver Hoef; Hailemariam Temesgen; Sergio Gómez

2013-01-01

Forest surveys provide critical information for many diverse interests. Data are often collected from samples, and from these samples, maps of resources and estimates of aerial totals or averages are required. In this paper, two approaches for mapping and estimating totals; the spatial linear model (SLM) and k-NN (k-Nearest Neighbor) are compared, theoretically,...

Applying an efficient K-nearest neighbor search to forest attribute imputation

Treesearch

Andrew O. Finley; Ronald E. McRoberts; Alan R. Ek

2006-01-01

This paper explores the utility of an efficient nearest neighbor (NN) search algorithm for applications in multi-source kNN forest attribute imputation. The search algorithm reduces the number of distance calculations between a given target vector and each reference vector, thereby, decreasing the time needed to discover the NN subset. Results of five trials show gains...

Mapping wildland fuels and forest structure for land management: a comparison of nearest neighbor imputation and other methods

Treesearch

Kenneth B. Pierce; Janet L. Ohmann; Michael C. Wimberly; Matthew J. Gregory; Jeremy S. Fried

2009-01-01

Land managers need consistent information about the geographic distribution of wildland fuels and forest structure over large areas to evaluate fire risk and plan fuel treatments. We compared spatial predictions for 12 fuel and forest structure variables across three regions in the western United States using gradient nearest neighbor (GNN) imputation, linear models (...

K-nearest neighbor imputation of forest inventory variables in New Hampshire

Treesearch

Andrew Lister; Michael Hoppus; Raymond L. Czaplewski

2005-01-01

The k-nearest neighbor (kNN) method was used to map stand volume for a mosaic of 4 Landsat scenes covering the state of New Hampshire. Data for gross cubic foot volume and trees per acre were summarized from USDA Forest Service Forest Inventory and Analysis (FIA) plots and used as training for kNN. Six bands of...

Mapping change of older forest with nearest-neighbor imputation and Landsat time-series

Treesearch

Janet L. Ohmann; Matthew J. Gregory; Heather M. Roberts; Warren B. Cohen; Robert E. Kennedy; Zhiqiang Yang

2012-01-01

The Northwest Forest Plan (NWFP), which aims to conserve late-successional and old-growth forests (older forests) and associated species, established new policies on federal lands in the Pacific Northwest USA. As part of monitoring for the NWFP, we tested nearest-neighbor imputation for mapping change in older forest, defined by threshold values for forest attributes...

Landscape-scale parameterization of a tree-level forest growth model: a k-nearest neighbor imputation approach incorporating LiDAR data

Treesearch

Michael J. Falkowski; Andrew T. Hudak; Nicholas L. Crookston; Paul E. Gessler; Edward H. Uebler; Alistair M. S. Smith

2010-01-01

Sustainable forest management requires timely, detailed forest inventory data across large areas, which is difficult to obtain via traditional forest inventory techniques. This study evaluated k-nearest neighbor imputation models incorporating LiDAR data to predict tree-level inventory data (individual tree height, diameter at breast height, and...

Nearest-neighbor thermodynamics of deoxyinosine pairs in DNA duplexes

PubMed Central

Watkins, Norman E.; SantaLucia, John

2005-01-01

Nearest-neighbor thermodynamic parameters of the ‘universal pairing base’ deoxyinosine were determined for the pairs I·C, I·A, I·T, I·G and I·I adjacent to G·C and A·T pairs. Ultraviolet absorbance melting curves were measured and non-linear regression performed on 84 oligonucleotide duplexes with 9 or 12 bp lengths. These data were combined with data for 13 inosine containing duplexes from the literature. Multiple linear regression was used to solve for the 32 nearest-neighbor unknowns. The parameters predict the Tm for all sequences within 1.2°C on average. The general trend in decreasing stability is I·C > I·A > I·T ≈ I· G > I·I. The stability trend for the base pair 5′ of the I·X pair is G·C > C·G > A·T > T·A. The stability trend for the base pair 3′ of I·X is the same. These trends indicate a complex interplay between H-bonding, nearest-neighbor stacking, and mismatch geometry. A survey of 14 tandem inosine pairs and 8 tandem self-complementary inosine pairs is also provided. These results may be used in the design of degenerate PCR primers and for degenerate microarray probes. PMID:16264087

Nearest Neighbor Algorithms for Pattern Classification

NASA Technical Reports Server (NTRS)

Barrios, J. O.

1972-01-01

A solution of the discrimination problem is considered by means of the minimum distance classifier, commonly referred to as the nearest neighbor (NN) rule. The NN rule is nonparametric, or distribution free, in the sense that it does not depend on any assumptions about the underlying statistics for its application. The k-NN rule is a procedure that assigns an observation vector z to a category F if most of the k nearby observations x sub i are elements of F. The condensed nearest neighbor (CNN) rule may be used to reduce the size of the training set required categorize The Bayes risk serves merely as a reference-the limit of excellence beyond which it is not possible to go. The NN rule is bounded below by the Bayes risk and above by twice the Bayes risk.

Clustering, randomness and regularity in cloud fields. I - Theoretical considerations. II - Cumulus cloud fields

NASA Technical Reports Server (NTRS)

Weger, R. C.; Lee, J.; Zhu, Tianri; Welch, R. M.

1992-01-01

The current controversy existing in reference to the regularity vs. clustering in cloud fields is examined by means of analysis and simulation studies based upon nearest-neighbor cumulative distribution statistics. It is shown that the Poisson representation of random point processes is superior to pseudorandom-number-generated models and that pseudorandom-number-generated models bias the observed nearest-neighbor statistics towards regularity. Interpretation of this nearest-neighbor statistics is discussed for many cases of superpositions of clustering, randomness, and regularity. A detailed analysis is carried out of cumulus cloud field spatial distributions based upon Landsat, AVHRR, and Skylab data, showing that, when both large and small clouds are included in the cloud field distributions, the cloud field always has a strong clustering signal.

Phase transition and monopole densities in a nearest neighbor two-dimensional spin ice model

NASA Astrophysics Data System (ADS)

Morais, C. W.; de Freitas, D. N.; Mota, A. L.; Bastone, E. C.

2017-12-01

In this work, we show that, due to the alternating orientation of the spins in the ground state of the artificial square spin ice, the influence of a set of spins at a certain distance of a reference spin decreases faster than the expected result for the long range dipolar interaction, justifying the use of the nearest neighbor two-dimensional square spin ice model as an effective model. Using an extension of the model presented in Y. L. Xie et al., Sci. Rep. 5, 15875 (2015), considering the influence of the eight nearest neighbors of each spin on the lattice, we analyze the thermodynamics of the model and study the dependence of monopoles and string densities as a function of the temperature.

Velocity statistics for interacting edge dislocations in one dimension from Dyson's Coulomb gas model.

PubMed

Jafarpour, Farshid; Angheluta, Luiza; Goldenfeld, Nigel

2013-10-01

The dynamics of edge dislocations with parallel Burgers vectors, moving in the same slip plane, is mapped onto Dyson's model of a two-dimensional Coulomb gas confined in one dimension. We show that the tail distribution of the velocity of dislocations is power law in form, as a consequence of the pair interaction of nearest neighbors in one dimension. In two dimensions, we show the presence of a pairing phase transition in a system of interacting dislocations with parallel Burgers vectors. The scaling exponent of the velocity distribution at effective temperatures well below this pairing transition temperature can be derived from the nearest-neighbor interaction, while near the transition temperature, the distribution deviates from the form predicted by the nearest-neighbor interaction, suggesting the presence of collective effects.

Thermal rectification in mass-graded next-nearest-neighbor Fermi-Pasta-Ulam lattices

NASA Astrophysics Data System (ADS)

Romero-Bastida, M.; Miranda-Peña, Jorge-Orlando; López, Juan M.

2017-03-01

We study the thermal rectification efficiency, i.e., quantification of asymmetric heat flow, of a one-dimensional mass-graded anharmonic oscillator Fermi-Pasta-Ulam lattice both with nearest-neighbor (NN) and next-nearest-neighbor (NNN) interactions. The system presents a maximum rectification efficiency for a very precise value of the parameter that controls the coupling strength of the NNN interactions, which also optimizes the rectification figure when its dependence on mass asymmetry and temperature differences is considered. The origin of the enhanced rectification is the asymmetric local heat flow response as the heat reservoirs are swapped when a finely tuned NNN contribution is taken into account. A simple theoretical analysis gives an estimate of the optimal NNN coupling in excellent agreement with our simulation results.

Importance of interatomic spacing in catalytic reduction of oxygen in phosphoric acid

NASA Technical Reports Server (NTRS)

Jalan, V.; Taylor, E. J.

1983-01-01

A correlation between the nearest-neighbor distance and the oxygen reduction activity of various platinum alloys is reported. It is proposed that the distance between nearest-neighbor Pt atoms on the surface of a supported catalyst is not ideal for dual site absorption of O2 or 'HO2' and that the introduction of foreign atoms which reduce the Pt nearest-neighbor spacing would result in higher oxygen reduction activity. This may allow the critical 0-0 bond interatomic distance and hence the optimum Pt-Pt separation for bond rupture to be determined from quantum chemical calculations. A composite analysis shows that the data on supported Pt alloys are consistent with Appleby's (1970) data on bulk metals with respect to specific activity, activation energy, preexponential factor, and percent d-band character.

Monte Carlo study of a ferrimagnetic mixed-spin (2, 5/2) system with the nearest and next-nearest neighbors exchange couplings

NASA Astrophysics Data System (ADS)

Bi, Jiang-lin; Wang, Wei; Li, Qi

2017-07-01

In this paper, the effects of the next-nearest neighbors exchange couplings on the magnetic and thermal properties of the ferrimagnetic mixed-spin (2, 5/2) Ising model on a 3D honeycomb lattice have been investigated by the use of Monte Carlo simulation. In particular, the influences of exchange couplings (Ja, Jb, Jan) and the single-ion anisotropy(Da) on the phase diagrams, the total magnetization, the sublattice magnetization, the total susceptibility, the internal energy and the specific heat have been discussed in detail. The results clearly show that the system can express the critical and compensation behavior within the next-nearest neighbors exchange coupling. Great deals of the M curves such as N-, Q-, P- and L-types have been discovered, owing to the competition between the exchange coupling and the temperature. Compared with other theoretical and experimental works, our results have an excellent consistency with theirs.

The probability of misassociation between neighboring targets

NASA Astrophysics Data System (ADS)

Areta, Javier A.; Bar-Shalom, Yaakov; Rothrock, Ronald

2008-04-01

This paper presents procedures to calculate the probability that the measurement originating from an extraneous target will be (mis)associated with a target of interest for the cases of Nearest Neighbor and Global association. It is shown that these misassociation probabilities depend, under certain assumptions, on a particular - covariance weighted - norm of the difference between the targets' predicted measurements. For the Nearest Neighbor association, the exact solution, obtained for the case of equal innovation covariances, is based on a noncentral chi-square distribution. An approximate solution is also presented for the case of unequal innovation covariances. For the Global case an approximation is presented for the case of "similar" innovation covariances. In the general case of unequal innovation covariances where this approximation fails, an exact method based on the inversion of the characteristic function is presented. The theoretical results, confirmed by Monte Carlo simulations, quantify the benefit of Global vs. Nearest Neighbor association. These results are applied to problems of single sensor as well as centralized fusion architecture multiple sensor tracking.

Moderate-resolution data and gradient nearest neighbor imputation for regional-national risk assessment

Treesearch

Kenneth B. Jr. Pierce; C. Kenneth Brewer; Janet L. Ohmann

2010-01-01

This study was designed to test the feasibility of combining a method designed to populate pixels with inventory plot data at the 30-m scale with a new national predictor data set. The new national predictor data set was developed by the USDA Forest Service Remote Sensing Applications Center (hereafter RSAC) at the 250-m scale. Gradient Nearest Neighbor (GNN)...

Predictive mapping of forest composition and structure with direct gradient analysis and nearest neighbor imputation in coastal Oregon, U.S.A.

Treesearch

Janet L. Ohmann; Matthew J. Gregory

2002-01-01

Spatially explicit information on the species composition and structure of forest vegetation is needed at broad spatial scales for natural resource policy analysis and ecological research. We present a method for predictive vegetation mapping that applies direct gradient analysis and nearest-neighbor imputation to ascribe detailed ground attributes of vegetation to...

The roles of nearest neighbor methods in imputing missing data in forest inventory and monitoring databases

Treesearch

Bianca N. I. Eskelson; Hailemariam Temesgen; Valerie Lemay; Tara M. Barrett; Nicholas L. Crookston; Andrew T. Hudak

2009-01-01

Almost universally, forest inventory and monitoring databases are incomplete, ranging from missing data for only a few records and a few variables, common for small land areas, to missing data for many observations and many variables, common for large land areas. For a wide variety of applications, nearest neighbor (NN) imputation methods have been developed to fill in...

Estimating cavity tree and snag abundance using negative binomial regression models and nearest neighbor imputation methods

Treesearch

Bianca N.I. Eskelson; Hailemariam Temesgen; Tara M. Barrett

2009-01-01

Cavity tree and snag abundance data are highly variable and contain many zero observations. We predict cavity tree and snag abundance from variables that are readily available from forest cover maps or remotely sensed data using negative binomial (NB), zero-inflated NB, and zero-altered NB (ZANB) regression models as well as nearest neighbor (NN) imputation methods....

Influence of lidar, Landsat imagery, disturbance history, plot location accuracy, and plot size on accuracy of imputation maps of forest composition and structure

Treesearch

Harold S.J. Zald; Janet L. Ohmann; Heather M. Roberts; Matthew J. Gregory; Emilie B. Henderson; Robert J. McGaughey; Justin Braaten

2014-01-01

This study investigated how lidar-derived vegetation indices, disturbance history from Landsat time series (LTS) imagery, plot location accuracy, and plot size influenced accuracy of statistical spatial models (nearest-neighbor imputation maps) of forest vegetation composition and structure. Nearest-neighbor (NN) imputation maps were developed for 539,000 ha in the...

Conditional parametric models for storm sewer runoff

NASA Astrophysics Data System (ADS)

Jonsdottir, H.; Nielsen, H. Aa; Madsen, H.; Eliasson, J.; Palsson, O. P.; Nielsen, M. K.

2007-05-01

The method of conditional parametric modeling is introduced for flow prediction in a sewage system. It is a well-known fact that in hydrological modeling the response (runoff) to input (precipitation) varies depending on soil moisture and several other factors. Consequently, nonlinear input-output models are needed. The model formulation described in this paper is similar to the traditional linear models like final impulse response (FIR) and autoregressive exogenous (ARX) except that the parameters vary as a function of some external variables. The parameter variation is modeled by local lines, using kernels for local linear regression. As such, the method might be referred to as a nearest neighbor method. The results achieved in this study were compared to results from the conventional linear methods, FIR and ARX. The increase in the coefficient of determination is substantial. Furthermore, the new approach conserves the mass balance better. Hence this new approach looks promising for various hydrological models and analysis.

Seismic hazard in the Nation's breadbasket

USGS Publications Warehouse

Boyd, Oliver; Haller, Kathleen; Luco, Nicolas; Moschetti, Morgan P.; Mueller, Charles; Petersen, Mark D.; Rezaeian, Sanaz; Rubinstein, Justin L.

2015-01-01

The USGS National Seismic Hazard Maps were updated in 2014 and included several important changes for the central United States (CUS). Background seismicity sources were improved using a new moment-magnitude-based catalog; a new adaptive, nearest-neighbor smoothing kernel was implemented; and maximum magnitudes for background sources were updated. Areal source zones developed by the Central and Eastern United States Seismic Source Characterization for Nuclear Facilities project were simplified and adopted. The weighting scheme for ground motion models was updated, giving more weight to models with a faster attenuation with distance compared to the previous maps. Overall, hazard changes (2% probability of exceedance in 50 years, across a range of ground-motion frequencies) were smaller than 10% in most of the CUS relative to the 2008 USGS maps despite new ground motion models and their assigned logic tree weights that reduced the probabilistic ground motions by 5–20%.

Pair and triplet approximation of a spatial lattice population model with multiscale dispersal using Markov chains for estimating spatial autocorrelation.

PubMed

Hiebeler, David E; Millett, Nicholas E

2011-06-21

We investigate a spatial lattice model of a population employing dispersal to nearest and second-nearest neighbors, as well as long-distance dispersal across the landscape. The model is studied via stochastic spatial simulations, ordinary pair approximation, and triplet approximation. The latter method, which uses the probabilities of state configurations of contiguous blocks of three sites as its state variables, is demonstrated to be greatly superior to pair approximations for estimating spatial correlation information at various scales. Correlations between pairs of sites separated by arbitrary distances are estimated by constructing spatial Markov processes using the information from both approximations. These correlations demonstrate why pair approximation misses basic qualitative features of the model, such as decreasing population density as a large proportion of offspring are dropped on second-nearest neighbors, and why triplet approximation is able to include them. Analytical and numerical results show that, excluding long-distance dispersal, the initial growth rate of an invading population is maximized and the equilibrium population density is also roughly maximized when the population spreads its offspring evenly over nearest and second-nearest neighboring sites. Copyright © 2011 Elsevier Ltd. All rights reserved.

Ground-state ordering of the J1-J2 model on the simple cubic and body-centered cubic lattices

NASA Astrophysics Data System (ADS)

Farnell, D. J. J.; Götze, O.; Richter, J.

2016-06-01

The J1-J2 Heisenberg model is a "canonical" model in the field of quantum magnetism in order to study the interplay between frustration and quantum fluctuations as well as quantum phase transitions driven by frustration. Here we apply the coupled cluster method (CCM) to study the spin-half J1-J2 model with antiferromagnetic nearest-neighbor bonds J1>0 and next-nearest-neighbor bonds J2>0 for the simple cubic (sc) and body-centered cubic (bcc) lattices. In particular, we wish to study the ground-state ordering of these systems as a function of the frustration parameter p =z2J2/z1J1 , where z1 (z2) is the number of nearest (next-nearest) neighbors. We wish to determine the positions of the phase transitions using the CCM and we aim to resolve the nature of the phase transition points. We consider the ground-state energy, order parameters, spin-spin correlation functions, as well as the spin stiffness in order to determine the ground-state phase diagrams of these models. We find a direct first-order phase transition at a value of p =0.528 from a state of nearest-neighbor Néel order to next-nearest-neighbor Néel order for the bcc lattice. For the sc lattice the situation is more subtle. CCM results for the energy, the order parameter, the spin-spin correlation functions, and the spin stiffness indicate that there is no direct first-order transition between ground-state phases with magnetic long-range order, rather it is more likely that two phases with antiferromagnetic long range are separated by a narrow region of a spin-liquid-like quantum phase around p =0.55 . Thus the strong frustration present in the J1-J2 Heisenberg model on the sc lattice may open a window for an unconventional quantum ground state in this three-dimensional spin model.

Fidelity study of superconductivity in extended Hubbard models

DOE PAGES

Plonka, N.; Jia, C. J.; Wang, Y.; ...

2015-07-08

The Hubbard model with local on-site repulsion is generally thought to possess a superconducting ground state for appropriate parameters, but the effects of more realistic long-range Coulomb interactions have not been studied extensively. We study the influence of these interactions on superconductivity by including nearest- and next-nearest-neighbor extended Hubbard interactions in addition to the usual on-site terms. Utilizing numerical exact diagonalization, we analyze the signatures of superconductivity in the ground states through the fidelity metric of quantum information theory. Finally, we find that nearest and next-nearest neighbor interactions have thresholds above which they destabilize superconductivity regardless of whether they aremore » attractive or repulsive, seemingly due to competing charge fluctuations.« less

Pivot methods for global optimization

NASA Astrophysics Data System (ADS)

Stanton, Aaron Fletcher

A new algorithm is presented for the location of the global minimum of a multiple minima problem. It begins with a series of randomly placed probes in phase space, and then uses an iterative redistribution of the worst probes into better regions of phase space until a chosen convergence criterion is fulfilled. The method quickly converges, does not require derivatives, and is resistant to becoming trapped in local minima. Comparison of this algorithm with others using a standard test suite demonstrates that the number of function calls has been decreased conservatively by a factor of about three with the same degrees of accuracy. Two major variations of the method are presented, differing primarily in the method of choosing the probes that act as the basis for the new probes. The first variation, termed the lowest energy pivot method, ranks all probes by their energy and keeps the best probes. The probes being discarded select from those being kept as the basis for the new cycle. In the second variation, the nearest neighbor pivot method, all probes are paired with their nearest neighbor. The member of each pair with the higher energy is relocated in the vicinity of its neighbor. Both methods are tested against a standard test suite of functions to determine their relative efficiency, and the nearest neighbor pivot method is found to be the more efficient. A series of Lennard-Jones clusters is optimized with the nearest neighbor method, and a scaling law is found for cpu time versus the number of particles in the system. The two methods are then compared more explicitly, and finally a study in the use of the pivot method for solving the Schroedinger equation is presented. The nearest neighbor method is found to be able to solve the ground state of the quantum harmonic oscillator from a pure random initialization of the wavefunction.

Study of parameters of the nearest neighbour shared algorithm on clustering documents

NASA Astrophysics Data System (ADS)

Mustika Rukmi, Alvida; Budi Utomo, Daryono; Imro’atus Sholikhah, Neni

2018-03-01

Document clustering is one way of automatically managing documents, extracting of document topics and fastly filtering information. Preprocess of clustering documents processed by textmining consists of: keyword extraction using Rapid Automatic Keyphrase Extraction (RAKE) and making the document as concept vector using Latent Semantic Analysis (LSA). Furthermore, the clustering process is done so that the documents with the similarity of the topic are in the same cluster, based on the preprocesing by textmining performed. Shared Nearest Neighbour (SNN) algorithm is a clustering method based on the number of "nearest neighbors" shared. The parameters in the SNN Algorithm consist of: k nearest neighbor documents, ɛ shared nearest neighbor documents and MinT minimum number of similar documents, which can form a cluster. Characteristics The SNN algorithm is based on shared ‘neighbor’ properties. Each cluster is formed by keywords that are shared by the documents. SNN algorithm allows a cluster can be built more than one keyword, if the value of the frequency of appearing keywords in document is also high. Determination of parameter values on SNN algorithm affects document clustering results. The higher parameter value k, will increase the number of neighbor documents from each document, cause similarity of neighboring documents are lower. The accuracy of each cluster is also low. The higher parameter value ε, caused each document catch only neighbor documents that have a high similarity to build a cluster. It also causes more unclassified documents (noise). The higher the MinT parameter value cause the number of clusters will decrease, since the number of similar documents can not form clusters if less than MinT. Parameter in the SNN Algorithm determine performance of clustering result and the amount of noise (unclustered documents ). The Silhouette coeffisient shows almost the same result in many experiments, above 0.9, which means that SNN algorithm works well with different parameter values.

Scalable Machine Learning for Massive Astronomical Datasets

NASA Astrophysics Data System (ADS)

Ball, Nicholas M.; Gray, A.

2014-04-01

We present the ability to perform data mining and machine learning operations on a catalog of half a billion astronomical objects. This is the result of the combination of robust, highly accurate machine learning algorithms with linear scalability that renders the applications of these algorithms to massive astronomical data tractable. We demonstrate the core algorithms kernel density estimation, K-means clustering, linear regression, nearest neighbors, random forest and gradient-boosted decision tree, singular value decomposition, support vector machine, and two-point correlation function. Each of these is relevant for astronomical applications such as finding novel astrophysical objects, characterizing artifacts in data, object classification (including for rare objects), object distances, finding the important features describing objects, density estimation of distributions, probabilistic quantities, and exploring the unknown structure of new data. The software, Skytree Server, runs on any UNIX-based machine, a virtual machine, or cloud-based and distributed systems including Hadoop. We have integrated it on the cloud computing system of the Canadian Astronomical Data Centre, the Canadian Advanced Network for Astronomical Research (CANFAR), creating the world's first cloud computing data mining system for astronomy. We demonstrate results showing the scaling of each of our major algorithms on large astronomical datasets, including the full 470,992,970 objects of the 2 Micron All-Sky Survey (2MASS) Point Source Catalog. We demonstrate the ability to find outliers in the full 2MASS dataset utilizing multiple methods, e.g., nearest neighbors. This is likely of particular interest to the radio astronomy community given, for example, that survey projects contain groups dedicated to this topic. 2MASS is used as a proof-of-concept dataset due to its convenience and availability. These results are of interest to any astronomical project with large and/or complex datasets that wishes to extract the full scientific value from its data.

Scalable Machine Learning for Massive Astronomical Datasets

NASA Astrophysics Data System (ADS)

Ball, Nicholas M.; Astronomy Data Centre, Canadian

2014-01-01

We present the ability to perform data mining and machine learning operations on a catalog of half a billion astronomical objects. This is the result of the combination of robust, highly accurate machine learning algorithms with linear scalability that renders the applications of these algorithms to massive astronomical data tractable. We demonstrate the core algorithms kernel density estimation, K-means clustering, linear regression, nearest neighbors, random forest and gradient-boosted decision tree, singular value decomposition, support vector machine, and two-point correlation function. Each of these is relevant for astronomical applications such as finding novel astrophysical objects, characterizing artifacts in data, object classification (including for rare objects), object distances, finding the important features describing objects, density estimation of distributions, probabilistic quantities, and exploring the unknown structure of new data. The software, Skytree Server, runs on any UNIX-based machine, a virtual machine, or cloud-based and distributed systems including Hadoop. We have integrated it on the cloud computing system of the Canadian Astronomical Data Centre, the Canadian Advanced Network for Astronomical Research (CANFAR), creating the world's first cloud computing data mining system for astronomy. We demonstrate results showing the scaling of each of our major algorithms on large astronomical datasets, including the full 470,992,970 objects of the 2 Micron All-Sky Survey (2MASS) Point Source Catalog. We demonstrate the ability to find outliers in the full 2MASS dataset utilizing multiple methods, e.g., nearest neighbors, and the local outlier factor. 2MASS is used as a proof-of-concept dataset due to its convenience and availability. These results are of interest to any astronomical project with large and/or complex datasets that wishes to extract the full scientific value from its data.

[Classification of Children with Attention-Deficit/Hyperactivity Disorder and Typically Developing Children Based on Electroencephalogram Principal Component Analysis and k-Nearest Neighbor].

PubMed

Yang, Jiaojiao; Guo, Qian; Li, Wenjie; Wang, Suhong; Zou, Ling

2016-04-01

This paper aims to assist the individual clinical diagnosis of children with attention-deficit/hyperactivity disorder using electroencephalogram signal detection method.Firstly,in our experiments,we obtained and studied the electroencephalogram signals from fourteen attention-deficit/hyperactivity disorder children and sixteen typically developing children during the classic interference control task of Simon-spatial Stroop,and we completed electroencephalogram data preprocessing including filtering,segmentation,removal of artifacts and so on.Secondly,we selected the subset electroencephalogram electrodes using principal component analysis(PCA)method,and we collected the common channels of the optimal electrodes which occurrence rates were more than 90%in each kind of stimulation.We then extracted the latency(200~450ms)mean amplitude features of the common electrodes.Finally,we used the k-nearest neighbor(KNN)classifier based on Euclidean distance and the support vector machine(SVM)classifier based on radial basis kernel function to classify.From the experiment,at the same kind of interference control task,the attention-deficit/hyperactivity disorder children showed lower correct response rates and longer reaction time.The N2 emerged in prefrontal cortex while P2 presented in the inferior parietal area when all kinds of stimuli demonstrated.Meanwhile,the children with attention-deficit/hyperactivity disorder exhibited markedly reduced N2 and P2amplitude compared to typically developing children.KNN resulted in better classification accuracy than SVM classifier,and the best classification rate was 89.29%in StI task.The results showed that the electroencephalogram signals were different in the brain regions of prefrontal cortex and inferior parietal cortex between attention-deficit/hyperactivity disorder and typically developing children during the interference control task,which provided a scientific basis for the clinical diagnosis of attention-deficit/hyperactivity disorder individuals.

Discrimination of soft tissues using laser-induced breakdown spectroscopy in combination with k nearest neighbors (kNN) and support vector machine (SVM) classifiers

NASA Astrophysics Data System (ADS)

Li, Xiaohui; Yang, Sibo; Fan, Rongwei; Yu, Xin; Chen, Deying

2018-06-01

In this paper, discrimination of soft tissues using laser-induced breakdown spectroscopy (LIBS) in combination with multivariate statistical methods is presented. Fresh pork fat, skin, ham, loin and tenderloin muscle tissues are manually cut into slices and ablated using a 1064 nm pulsed Nd:YAG laser. Discrimination analyses between fat, skin and muscle tissues, and further between highly similar ham, loin and tenderloin muscle tissues, are performed based on the LIBS spectra in combination with multivariate statistical methods, including principal component analysis (PCA), k nearest neighbors (kNN) classification, and support vector machine (SVM) classification. Performances of the discrimination models, including accuracy, sensitivity and specificity, are evaluated using 10-fold cross validation. The classification models are optimized to achieve best discrimination performances. The fat, skin and muscle tissues can be definitely discriminated using both kNN and SVM classifiers, with accuracy of over 99.83%, sensitivity of over 0.995 and specificity of over 0.998. The highly similar ham, loin and tenderloin muscle tissues can also be discriminated with acceptable performances. The best performances are achieved with SVM classifier using Gaussian kernel function, with accuracy of 76.84%, sensitivity of over 0.742 and specificity of over 0.869. The results show that the LIBS technique assisted with multivariate statistical methods could be a powerful tool for online discrimination of soft tissues, even for tissues of high similarity, such as muscles from different parts of the animal body. This technique could be used for discrimination of tissues suffering minor clinical changes, thus may advance the diagnosis of early lesions and abnormalities.

Structure and Bonding in Noncrystalline Solids Abstracts

DTIC Science & Technology

1983-06-02

displacement cascades are unlikely. Related damage studies as diffuse X- ray scattering, magnetic susceptibility and positron - annihilation lifetime...the positron annihilation lifetime data; diffuse X-ray scattering studies give evidence for "amorphized" clusters in neutron but not in elec-ron...feldspar glasses and glasses in the system CaO- MgO -SiO 2 . These results indicate that the nearest-neighbor and next- nearest-neighbor environments are very

Parametric, bootstrap, and jackknife variance estimators for the k-Nearest Neighbors technique with illustrations using forest inventory and satellite image data

Treesearch

Ronald E. McRoberts; Steen Magnussen; Erkki O. Tomppo; Gherardo Chirici

2011-01-01

Nearest neighbors techniques have been shown to be useful for estimating forest attributes, particularly when used with forest inventory and satellite image data. Published reports of positive results have been truly international in scope. However, for these techniques to be more useful, they must be able to contribute to scientific inference which, for sample-based...

A systematic molecular dynamics study of nearest-neighbor effects on base pair and base pair step conformations and fluctuations in B-DNA

PubMed Central

Lavery, Richard; Zakrzewska, Krystyna; Beveridge, David; Bishop, Thomas C.; Case, David A.; Cheatham, Thomas; Dixit, Surjit; Jayaram, B.; Lankas, Filip; Laughton, Charles; Maddocks, John H.; Michon, Alexis; Osman, Roman; Orozco, Modesto; Perez, Alberto; Singh, Tanya; Spackova, Nada; Sponer, Jiri

2010-01-01

It is well recognized that base sequence exerts a significant influence on the properties of DNA and plays a significant role in protein–DNA interactions vital for cellular processes. Understanding and predicting base sequence effects requires an extensive structural and dynamic dataset which is currently unavailable from experiment. A consortium of laboratories was consequently formed to obtain this information using molecular simulations. This article describes results providing information not only on all 10 unique base pair steps, but also on all possible nearest-neighbor effects on these steps. These results are derived from simulations of 50–100 ns on 39 different DNA oligomers in explicit solvent and using a physiological salt concentration. We demonstrate that the simulations are converged in terms of helical and backbone parameters. The results show that nearest-neighbor effects on base pair steps are very significant, implying that dinucleotide models are insufficient for predicting sequence-dependent behavior. Flanking base sequences can notably lead to base pair step parameters in dynamic equilibrium between two conformational sub-states. Although this study only provides limited data on next-nearest-neighbor effects, we suggest that such effects should be analyzed before attempting to predict the sequence-dependent behavior of DNA. PMID:19850719

Improving the accuracy of k-nearest neighbor using local mean based and distance weight

NASA Astrophysics Data System (ADS)

Syaliman, K. U.; Nababan, E. B.; Sitompul, O. S.

2018-03-01

In k-nearest neighbor (kNN), the determination of classes for new data is normally performed by a simple majority vote system, which may ignore the similarities among data, as well as allowing the occurrence of a double majority class that can lead to misclassification. In this research, we propose an approach to resolve the majority vote issues by calculating the distance weight using a combination of local mean based k-nearest neighbor (LMKNN) and distance weight k-nearest neighbor (DWKNN). The accuracy of results is compared to the accuracy acquired from the original k-NN method using several datasets from the UCI Machine Learning repository, Kaggle and Keel, such as ionosphare, iris, voice genre, lower back pain, and thyroid. In addition, the proposed method is also tested using real data from a public senior high school in city of Tualang, Indonesia. Results shows that the combination of LMKNN and DWKNN was able to increase the classification accuracy of kNN, whereby the average accuracy on test data is 2.45% with the highest increase in accuracy of 3.71% occurring on the lower back pain symptoms dataset. For the real data, the increase in accuracy is obtained as high as 5.16%.

Computer Simulation of Energy Parameters and Magnetic Effects in Fe-Si-C Ternary Alloys

NASA Astrophysics Data System (ADS)

Ridnyi, Ya. M.; Mirzoev, A. A.; Mirzaev, D. A.

2018-06-01

The paper presents ab initio simulation with the WIEN2k software package of the equilibrium structure and properties of silicon and carbon atoms dissolved in iron with the body-centered cubic crystal system of the lattice. Silicon and carbon atoms manifest a repulsive interaction in the first two nearest neighbors, in the second neighbor the repulsion being stronger than in the first. In the third and next-nearest neighbors a very weak repulsive interaction occurs and tends to zero with increasing distance between atoms. Silicon and carbon dissolution reduces the magnetic moment of iron atoms.

The advantages of the surface Laplacian in brain-computer interface research.

PubMed

McFarland, Dennis J

2015-09-01

Brain-computer interface (BCI) systems frequently use signal processing methods, such as spatial filtering, to enhance performance. The surface Laplacian can reduce spatial noise and aid in identification of sources. In BCI research, these two functions of the surface Laplacian correspond to prediction accuracy and signal orthogonality. In the present study, an off-line analysis of data from a sensorimotor rhythm-based BCI task dissociated these functions of the surface Laplacian by comparing nearest-neighbor and next-nearest neighbor Laplacian algorithms. The nearest-neighbor Laplacian produced signals that were more orthogonal while the next-nearest Laplacian produced signals that resulted in better accuracy. Both prediction and signal identification are important for BCI research. Better prediction of user's intent produces increased speed and accuracy of communication and control. Signal identification is important for ruling out the possibility of control by artifacts. Identifying the nature of the control signal is relevant both to understanding exactly what is being studied and in terms of usability for individuals with limited motor control. Copyright © 2014 Elsevier B.V. All rights reserved.

Minimizers with Bounded Action for the High-Dimensional Frenkel-Kontorova Model

NASA Astrophysics Data System (ADS)

Miao, Xue-Qing; Wang, Ya-Nan; Qin, Wen-Xin

In Aubry-Mather theory for monotone twist maps or for one-dimensional Frenkel-Kontorova (FK) model with nearest neighbor interactions, each global minimizer (minimal energy configuration) is naturally Birkhoff. However, this is not true for the one-dimensional FK model with non-nearest neighbor interactions or for the high-dimensional FK model. In this paper, we study the Birkhoff property of minimizers with bounded action for the high-dimensional FK model.

Weighted Parzen Windows for Pattern Classification

DTIC Science & Technology

1994-05-01

Nearest-Neighbor Rule The k-Nearest-Neighbor ( kNN ) technique is nonparametric, assuming nothing about the distribution of the data. Stated succinctly...probabilities P(wj I x) from samples." Raudys and Jain [20:255] advance this interpretation by pointing out that the kNN technique can be viewed as the...34Parzen window classifier with a hyper- rectangular window function." As with the Parzen-window technique, the kNN classifier is more accurate as the

Analytical approach for collective diffusion: One-dimensional lattice with the nearest neighbor and the next nearest neighbor lateral interactions

NASA Astrophysics Data System (ADS)

Tarasenko, Alexander

2018-01-01

Diffusion of particles adsorbed on a homogeneous one-dimensional lattice is investigated using a theoretical approach and MC simulations. The analytical dependencies calculated in the framework of approach are tested using the numerical data. The perfect coincidence of the data obtained by these different methods demonstrates that the correctness of the approach based on the theory of the non-equilibrium statistical operator.

General formulation of long-range degree correlations in complex networks

NASA Astrophysics Data System (ADS)

Fujiki, Yuka; Takaguchi, Taro; Yakubo, Kousuke

2018-06-01

We provide a general framework for analyzing degree correlations between nodes separated by more than one step (i.e., beyond nearest neighbors) in complex networks. One joint and four conditional probability distributions are introduced to fully describe long-range degree correlations with respect to degrees k and k' of two nodes and shortest path length l between them. We present general relations among these probability distributions and clarify the relevance to nearest-neighbor degree correlations. Unlike nearest-neighbor correlations, some of these probability distributions are meaningful only in finite-size networks. Furthermore, as a baseline to determine the existence of intrinsic long-range degree correlations in a network other than inevitable correlations caused by the finite-size effect, the functional forms of these probability distributions for random networks are analytically evaluated within a mean-field approximation. The utility of our argument is demonstrated by applying it to real-world networks.

Effect of nearest-neighbor ions on excited ionic states, emission spectra, and line profiles in hot and dense plasmas

NASA Technical Reports Server (NTRS)

Salzmann, D.; Stein, J.; Goldberg, I. B.; Pratt, R. H.

1991-01-01

The effect of the cylindrical symmetry imposed by the nearest-neighbor ions on the ionic levels and the emission spectra of a Li-like Kr ion immersed in hot and dense plasmas is investigated using the Stein et al. (1989) two-centered model extended to include computations of the line profiles, shifts, and widths, as well as the energy-level mixing and the forbidden transition probabilities. It is shown that the cylindrical symmetry mixes states with different orbital quantum numbers l, particularly for highly excited states, and, thereby, gives rise to forbidden transitions in the emission spectrum. Results are obtained for the variation of the ionic level shifts and mixing coefficients with the distance to the nearest neighbor. Also obtained are representative computed spectra that show the density effects on the spectral line profiles, shifts, and widths, and the forbidden components in the spectrum.

Diagnosis of diabetes diseases using an Artificial Immune Recognition System2 (AIRS2) with fuzzy K-nearest neighbor.

PubMed

Chikh, Mohamed Amine; Saidi, Meryem; Settouti, Nesma

2012-10-01

The use of expert systems and artificial intelligence techniques in disease diagnosis has been increasing gradually. Artificial Immune Recognition System (AIRS) is one of the methods used in medical classification problems. AIRS2 is a more efficient version of the AIRS algorithm. In this paper, we used a modified AIRS2 called MAIRS2 where we replace the K- nearest neighbors algorithm with the fuzzy K-nearest neighbors to improve the diagnostic accuracy of diabetes diseases. The diabetes disease dataset used in our work is retrieved from UCI machine learning repository. The performances of the AIRS2 and MAIRS2 are evaluated regarding classification accuracy, sensitivity and specificity values. The highest classification accuracy obtained when applying the AIRS2 and MAIRS2 using 10-fold cross-validation was, respectively 82.69% and 89.10%.

Acoustic localization of triggered lightning

NASA Astrophysics Data System (ADS)

Arechiga, Rene O.; Johnson, Jeffrey B.; Edens, Harald E.; Thomas, Ronald J.; Rison, William

2011-05-01

We use acoustic (3.3-500 Hz) arrays to locate local (<20 km) thunder produced by triggered lightning in the Magdalena Mountains of central New Mexico. The locations of the thunder sources are determined by the array back azimuth and the elapsed time since discharge of the lightning flash. We compare the acoustic source locations with those obtained by the Lightning Mapping Array (LMA) from Langmuir Laboratory, which is capable of accurately locating the lightning channels. To estimate the location accuracy of the acoustic array we performed Monte Carlo simulations and measured the distance (nearest neighbors) between acoustic and LMA sources. For close sources (<5 km) the mean nearest-neighbors distance was 185 m compared to 100 m predicted by the Monte Carlo analysis. For far distances (>6 km) the error increases to 800 m for the nearest neighbors and 650 m for the Monte Carlo analysis. This work shows that thunder sources can be accurately located using acoustic signals.

Geographical traceability of Marsdenia tenacissima by Fourier transform infrared spectroscopy and chemometrics

NASA Astrophysics Data System (ADS)

Li, Chao; Yang, Sheng-Chao; Guo, Qiao-Sheng; Zheng, Kai-Yan; Wang, Ping-Li; Meng, Zhen-Gui

2016-01-01

A combination of Fourier transform infrared spectroscopy with chemometrics tools provided an approach for studying Marsdenia tenacissima according to its geographical origin. A total of 128 M. tenacissima samples from four provinces in China were analyzed with FTIR spectroscopy. Six pattern recognition methods were used to construct the discrimination models: support vector machine-genetic algorithms, support vector machine-particle swarm optimization, K-nearest neighbors, radial basis function neural network, random forest and support vector machine-grid search. Experimental results showed that K-nearest neighbors was superior to other mathematical algorithms after data were preprocessed with wavelet de-noising, with a discrimination rate of 100% in both the training and prediction sets. This study demonstrated that FTIR spectroscopy coupled with K-nearest neighbors could be successfully applied to determine the geographical origins of M. tenacissima samples, thereby providing reliable authentication in a rapid, cheap and noninvasive way.

Ab initio calculation of atomic interactions on Al(110): implications for epitaxial growth

NASA Astrophysics Data System (ADS)

Fichthorn, Kristen; Tiwary, Yogesh

2007-03-01

Using first-principles calculations based on density-functional theory, we resolved atomic interactions between adsorbed Al atoms on Al(110). Relevant pair and trio interactions were quantified. We find that pair interactions extend to the third in-channel and second cross-channel neighbor on the anisotropic (110) surface. Beyond these distances, pair interactions are negligible. The nearest-neighbor interaction in the in-channel direction is attractive, but nearest-neighbor cross-channel interaction is repulsive. While nearest-neighbor, cross-channel repulsion does not support the experimental observation of 3D hut formation in Al/Al(110) homoepitaxial growth [1], we find that trio interactions can be significant and attractive and they support cross-channel bonding. The pair and trio interactions have direct and indirect components. We have quantified the electronic and elastic components of the indirect, substrate-mediated interactions. We also probe the influence of these interactions on the energy barriers for adatom hopping. [1] F. Buatier de Mongeot, W. Zhu, A. Molle, R. Buzio, C. Boragno, U. Valbusa, E. Wang, and Z. Zhang, Phys. Rev. Lett. 91, 016102 (2003).

Rectangular Array Of Digital Processors For Planning Paths

NASA Technical Reports Server (NTRS)

Kemeny, Sabrina E.; Fossum, Eric R.; Nixon, Robert H.

1993-01-01

Prototype 24 x 25 rectangular array of asynchronous parallel digital processors rapidly finds best path across two-dimensional field, which could be patch of terrain traversed by robotic or military vehicle. Implemented as single-chip very-large-scale integrated circuit. Excepting processors on edges, each processor communicates with four nearest neighbors along paths representing travel to north, south, east, and west. Each processor contains delay generator in form of 8-bit ripple counter, preset to 1 of 256 possible values. Operation begins with choice of processor representing starting point. Transmits signals to nearest neighbor processors, which retransmits to other neighboring processors, and process repeats until signals propagated across entire field.

Interruption of Hydrogen Bonding Networks of Water in Carbon Nanotubes Due to Strong Hydration Shell Formation.

PubMed

Oya, Yoshifumi; Hata, Kenji; Ohba, Tomonori

2017-10-24

We present the structures of NaCl aqueous solution in carbon nanotubes with diameters of 1, 2, and 3 nm based on an analysis performed using X-ray diffraction and canonical ensemble Monte Carlo simulations. Anomalously longer nearest-neighbor distances were observed in the electrolyte for the 1-nm-diameter carbon nanotubes; in contrast, in the 2 and 3 nm carbon nanotubes, the nearest-neighbor distances were shorter than those in the bulk electrolyte. We also observed similar properties for water in carbon nanotubes, which was expected because the main component of the electrolyte was water. However, the nearest-neighbor distances of the electrolyte were longer than those of water in all of the carbon nanotubes; the difference was especially pronounced in the 2-nm-diameter carbon nanotubes. Thus, small numbers of ions affected the entire structure of the electrolyte in the nanopores of the carbon nanotubes. The formation of strong hydration shells between ions and water molecules considerably interrupted the hydrogen bonding between water molecules in the nanopores of the carbon nanotubes. The hydration shell had a diameter of approximately 1 nm, and hydration shells were thus adopted for the nanopores of the 2-nm-diameter carbon nanotubes, providing an explanation for the large difference in the nearest-neighbor distances between the electrolyte and water in these nanopores.

Probability machines: consistent probability estimation using nonparametric learning machines.

PubMed

Malley, J D; Kruppa, J; Dasgupta, A; Malley, K G; Ziegler, A

2012-01-01

Most machine learning approaches only provide a classification for binary responses. However, probabilities are required for risk estimation using individual patient characteristics. It has been shown recently that every statistical learning machine known to be consistent for a nonparametric regression problem is a probability machine that is provably consistent for this estimation problem. The aim of this paper is to show how random forests and nearest neighbors can be used for consistent estimation of individual probabilities. Two random forest algorithms and two nearest neighbor algorithms are described in detail for estimation of individual probabilities. We discuss the consistency of random forests, nearest neighbors and other learning machines in detail. We conduct a simulation study to illustrate the validity of the methods. We exemplify the algorithms by analyzing two well-known data sets on the diagnosis of appendicitis and the diagnosis of diabetes in Pima Indians. Simulations demonstrate the validity of the method. With the real data application, we show the accuracy and practicality of this approach. We provide sample code from R packages in which the probability estimation is already available. This means that all calculations can be performed using existing software. Random forest algorithms as well as nearest neighbor approaches are valid machine learning methods for estimating individual probabilities for binary responses. Freely available implementations are available in R and may be used for applications.

Multidimensional k-nearest neighbor model based on EEMD for financial time series forecasting

NASA Astrophysics Data System (ADS)

Zhang, Ningning; Lin, Aijing; Shang, Pengjian

2017-07-01

In this paper, we propose a new two-stage methodology that combines the ensemble empirical mode decomposition (EEMD) with multidimensional k-nearest neighbor model (MKNN) in order to forecast the closing price and high price of the stocks simultaneously. The modified algorithm of k-nearest neighbors (KNN) has an increasingly wide application in the prediction of all fields. Empirical mode decomposition (EMD) decomposes a nonlinear and non-stationary signal into a series of intrinsic mode functions (IMFs), however, it cannot reveal characteristic information of the signal with much accuracy as a result of mode mixing. So ensemble empirical mode decomposition (EEMD), an improved method of EMD, is presented to resolve the weaknesses of EMD by adding white noise to the original data. With EEMD, the components with true physical meaning can be extracted from the time series. Utilizing the advantage of EEMD and MKNN, the new proposed ensemble empirical mode decomposition combined with multidimensional k-nearest neighbor model (EEMD-MKNN) has high predictive precision for short-term forecasting. Moreover, we extend this methodology to the case of two-dimensions to forecast the closing price and high price of the four stocks (NAS, S&P500, DJI and STI stock indices) at the same time. The results indicate that the proposed EEMD-MKNN model has a higher forecast precision than EMD-KNN, KNN method and ARIMA.

Thermodynamics of alternating spin chains with competing nearest- and next-nearest-neighbor interactions: Ising model

NASA Astrophysics Data System (ADS)

Pini, Maria Gloria; Rettori, Angelo

1993-08-01

The thermodynamical properties of an alternating spin (S,s) one-dimensional (1D) Ising model with competing nearest- and next-nearest-neighbor interactions are exactly calculated using a transfer-matrix technique. In contrast to the case S=s=1/2, previously investigated by Harada, the alternation of different spins (S≠s) along the chain is found to give rise to two-peaked static structure factors, signaling the coexistence of different short-range-order configurations. The relevance of our calculations with regard to recent experimental data by Gatteschi et al. in quasi-1D molecular magnetic materials, R (hfac)3 NITEt (R=Gd, Tb, Dy, Ho, Er, . . .), is discussed; hfac is hexafluoro-acetylacetonate and NlTEt is 2-Ethyl-4,4,5,5-tetramethyl-4,5-dihydro-1H-imidazolyl-1-oxyl-3-oxide.

Highly Anisotropic Magnon Dispersion in Ca_{2}RuO_{4}: Evidence for Strong Spin Orbit Coupling.

PubMed

Kunkemöller, S; Khomskii, D; Steffens, P; Piovano, A; Nugroho, A A; Braden, M

2015-12-11

The magnon dispersion in Ca_{2}RuO_{4} has been determined by inelastic neutron scattering on single crytals containing 1% of Ti. The dispersion is well described by a conventional Heisenberg model suggesting a local moment model with nearest neighbor interaction of J=8  meV. Nearest and next-nearest neighbor interaction as well as interlayer coupling parameters are required to properly describe the entire dispersion. Spin-orbit coupling induces a very large anisotropy gap in the magnetic excitations in apparent contrast with a simple planar magnetic model. Orbital ordering breaking tetragonal symmetry, and strong spin-orbit coupling can thus be identified as important factors in this system.

Second-Nearest-Neighbor Effects upon N NMR Shieldings in Models for Solid Si 3N 4and C 3N 4

NASA Astrophysics Data System (ADS)

Tossell, J. A.

1997-07-01

NMR shifts are generally determined mainly by the nearest-neighbor environment of an atom, with fairly small changes in the shift arising from differences in the second-nearest-neighbor environment. Previous calculations on the (SiH3)3N molecule used as a model for the local environment of N in crystalline α- and β-Si3N4gave N NMR shieldings much larger than those measured in the solids and gave the wrong order for the shifts of the inequivalent N sites (e.g., N1 and N2 in β-Si3N4). We have now calculated the N NMR shieldings in larger molecular models for the N2 site of β-Si3N4and have found that the N2 shielding is greatly reduced when additional N1 atoms (second-nearest-neighbors to the central N2) are included. The calculated N2 shieldings (using the GIAO method with the 6-31G* basis set and 6-31G* SCF optimized geometries) are 288.1, 244.7, and 206.0 ppm for the molecules (SiH3)3N, Si6N5H15, and Si9N9H21(central N2), respectively, while the experimental shielding of N2 in β-Si3N4is about 155 ppm. Second-nearest-neighbor effects of only slightly smaller magnitude are calculated for the analog C molecules. At the same time, the effects of molecule size upon Si NMR shieldings and N electric field gradients are small. The local geometries at the N2-like Ns in C6N5H15and C9N9H21are calculated to be planar, consistent with the planar local geometry recently calculated for N in crystalline C3N4using density functional theory.

Quantum phase transitions of the one-dimensional Peierls-Hubbard model with next-nearest-neighbor hopping integrals

NASA Astrophysics Data System (ADS)

Otsuka, Hiromi

1998-06-01

We investigate two kinds of quantum phase transitions observed in the one-dimensional half-filled Peierls-Hubbard model with the next-nearest-neighbor hopping integral in the strong-coupling region U>>t, t' [t (t'), nearest- (next-nearest-) neighbor hopping; U, on-site Coulomb repulsion]. In the uniform case, with the help of the conformal field theory prediction, we numerically determine a phase boundary t'c(U/t) between the spin-fluid and the dimer states, where a bare coupling of the marginal operator vanishes and the low-energy and long-distance behaviors of the spin part are described by a free-boson model. To exhibit the conformal invariance of the systems on the phase boundary, a multiplet structure of the excitation spectrum of finite-size systems and a value of the central charge are also examined. The critical phenomenological aspect of the spin-Peierls transitions accompanied by the lattice dimerization is then argued for the systems on the phase boundary; the existence of logarithmic corrections to the power-law behaviors of the energy gain and the spin gap (i.e., the Cross-Fisher scaling law) are discussed.

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.

Multipartite quantum correlations in the extended J1-J2 Heisenberg model

NASA Astrophysics Data System (ADS)

Batle, J.; Tarawneh, O.; Nagata, Koji; Nakamura, Tadao; Abdalla, S.; Farouk, Ahmed

2017-11-01

Multipartite entanglement and the maximum violation of Bell inequalities are studied in finite clusters of spins in an extended J1-J2 Heisenberg model at zero temperature. The ensuing highly frustrated states will unveil a rich structure for different values of the corresponding spin-spin interaction strengths. The interplay between nearest-neighbors, next-nearest neighbors and further couplings will be explored using multipartite correlations. The model is relevant to certain quantum annealing computation architectures where an all-to-all connectivity is considered.

Missing value imputation in DNA microarrays based on conjugate gradient method.

PubMed

Dorri, Fatemeh; Azmi, Paeiz; Dorri, Faezeh

2012-02-01

Analysis of gene expression profiles needs a complete matrix of gene array values; consequently, imputation methods have been suggested. In this paper, an algorithm that is based on conjugate gradient (CG) method is proposed to estimate missing values. k-nearest neighbors of the missed entry are first selected based on absolute values of their Pearson correlation coefficient. Then a subset of genes among the k-nearest neighbors is labeled as the best similar ones. CG algorithm with this subset as its input is then used to estimate the missing values. Our proposed CG based algorithm (CGimpute) is evaluated on different data sets. The results are compared with sequential local least squares (SLLSimpute), Bayesian principle component analysis (BPCAimpute), local least squares imputation (LLSimpute), iterated local least squares imputation (ILLSimpute) and adaptive k-nearest neighbors imputation (KNNKimpute) methods. The average of normalized root mean squares error (NRMSE) and relative NRMSE in different data sets with various missing rates shows CGimpute outperforms other methods. Copyright © 2011 Elsevier Ltd. All rights reserved.

Nearest-neighbor Kitaev exchange blocked by charge order in electron-doped α -RuCl3

NASA Astrophysics Data System (ADS)

Koitzsch, A.; Habenicht, C.; Müller, E.; Knupfer, M.; Büchner, B.; Kretschmer, S.; Richter, M.; van den Brink, J.; Börrnert, F.; Nowak, D.; Isaeva, A.; Doert, Th.

2017-10-01

A quantum spin liquid might be realized in α -RuCl3 , a honeycomb-lattice magnetic material with substantial spin-orbit coupling. Moreover, α -RuCl3 is a Mott insulator, which implies the possibility that novel exotic phases occur upon doping. Here, we study the electronic structure of this material when intercalated with potassium by photoemission spectroscopy, electron energy loss spectroscopy, and density functional theory calculations. We obtain a stable stoichiometry at K0.5RuCl3 . This gives rise to a peculiar charge disproportionation into formally Ru2 + (4 d6 ) and Ru3 + (4 d5 ). Every Ru 4 d5 site with one hole in the t2 g shell is surrounded by nearest neighbors of 4 d6 character, where the t2 g level is full and magnetically inert. Thus, each type of Ru site forms a triangular lattice, and nearest-neighbor interactions of the original honeycomb are blocked.

Collective coherence in nearest neighbor coupled metamaterials: A metasurface ruler equation

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

Xu, Ningning; Zhang, Weili, E-mail: weili.zhang@okstate.edu; Singh, Ranjan, E-mail: ranjans@ntu.edu.sg

The collective coherent interactions in a meta-atom lattice are the key to myriad applications and functionalities offered by metasurfaces. We demonstrate a collective coherent response of the nearest neighbor coupled split-ring resonators whose resonance shift decays exponentially in the strong near-field coupled regime. This occurs due to the dominant magnetic coupling between the nearest neighbors which leads to the decay of the electromagnetic near fields. Based on the size scaling behavior of the different periodicity metasurfaces, we identified a collective coherent metasurface ruler equation. From the coherent behavior, we also show that the near-field coupling in a metasurface lattice existsmore » even when the periodicity exceeds the resonator size. The identification of a universal coherence in metasurfaces and their scaling behavior would enable the design of novel metadevices whose spectral tuning response based on near-field effects could be calibrated across microwave, terahertz, infrared, and the optical parts of the electromagnetic spectrum.« less

Terahertz metasurfaces with a high refractive index enhanced by the strong nearest neighbor coupling.

PubMed

Tan, Siyu; Yan, Fengping; Singh, Leena; Cao, Wei; Xu, Ningning; Hu, Xiang; Singh, Ranjan; Wang, Mingwei; Zhang, Weili

2015-11-02

The realization of high refractive index is of significant interest in optical imaging with enhanced resolution. Strongly coupled subwavelength resonators were proposed and demonstrated at both optical and terahertz frequencies to enhance the refractive index due to large induced dipole moment in meta-atoms. Here, we report an alternative design for flexible free-standing terahertz metasurface in the strong coupling regime where we experimentally achieve a peak refractive index value of 14.36. We also investigate the impact of the nearest neighbor coupling in the form of frequency tuning and enhancement of the peak refractive index. We provide an analytical circuit model to explain the impact of geometrical parameters and coupling on the effective refractive index of the metasurface. The proposed meta-atom structure enables tailoring of the peak refractive index based on nearest neighbor coupling and this property offers tremendous design flexibility for transformation optics and other index-gradient devices at terahertz frequencies.

Examining change detection approaches for tropical mangrove monitoring

USGS Publications Warehouse

Myint, Soe W.; Franklin, Janet; Buenemann, Michaela; Kim, Won; Giri, Chandra

2014-01-01

This study evaluated the effectiveness of different band combinations and classifiers (unsupervised, supervised, object-oriented nearest neighbor, and object-oriented decision rule) for quantifying mangrove forest change using multitemporal Landsat data. A discriminant analysis using spectra of different vegetation types determined that bands 2 (0.52 to 0.6 μm), 5 (1.55 to 1.75 μm), and 7 (2.08 to 2.35 μm) were the most effective bands for differentiating mangrove forests from surrounding land cover types. A ranking of thirty-six change maps, produced by comparing the classification accuracy of twelve change detection approaches, was used. The object-based Nearest Neighbor classifier produced the highest mean overall accuracy (84 percent) regardless of band combinations. The automated decision rule-based approach (mean overall accuracy of 88 percent) as well as a composite of bands 2, 5, and 7 used with the unsupervised classifier and the same composite or all band difference with the object-oriented Nearest Neighbor classifier were the most effective approaches.

Phase transitions and critical properties in the antiferromagnetic Ising model on a layered triangular lattice with allowance for intralayer next-nearest-neighbor interactions

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

Badiev, M. K., E-mail: m-zagir@mail.ru; Murtazaev, A. K.; Ramazanov, M. K.

2016-10-15

The phase transitions (PTs) and critical properties of the antiferromagnetic Ising model on a layered (stacked) triangular lattice have been studied by the Monte Carlo method using a replica algorithm with allowance for the next-nearest-neighbor interactions. The character of PTs is analyzed using the histogram technique and the method of Binder cumulants. It is established that the transition from the disordered to paramagnetic phase in the adopted model is a second-order PT. Static critical exponents of the heat capacity (α), susceptibility (γ), order parameter (β), and correlation radius (ν) and the Fischer exponent η are calculated using the finite-size scalingmore » theory. It is shown that (i) the antiferromagnetic Ising model on a layered triangular lattice belongs to the XY universality class of critical behavior and (ii) allowance for the intralayer interactions of next-nearest neighbors in the adopted model leads to a change in the universality class of critical behavior.« less

Maximum unbiased validation (MUV) data sets for virtual screening based on PubChem bioactivity data.

PubMed

Rohrer, Sebastian G; Baumann, Knut

2009-02-01

Refined nearest neighbor analysis was recently introduced for the analysis of virtual screening benchmark data sets. It constitutes a technique from the field of spatial statistics and provides a mathematical framework for the nonparametric analysis of mapped point patterns. Here, refined nearest neighbor analysis is used to design benchmark data sets for virtual screening based on PubChem bioactivity data. A workflow is devised that purges data sets of compounds active against pharmaceutically relevant targets from unselective hits. Topological optimization using experimental design strategies monitored by refined nearest neighbor analysis functions is applied to generate corresponding data sets of actives and decoys that are unbiased with regard to analogue bias and artificial enrichment. These data sets provide a tool for Maximum Unbiased Validation (MUV) of virtual screening methods. The data sets and a software package implementing the MUV design workflow are freely available at http://www.pharmchem.tu-bs.de/lehre/baumann/MUV.html.

[Galaxy/quasar classification based on nearest neighbor method].

PubMed

Li, Xiang-Ru; Lu, Yu; Zhou, Jian-Ming; Wang, Yong-Jun

2011-09-01

With the wide application of high-quality CCD in celestial spectrum imagery and the implementation of many large sky survey programs (e. g., Sloan Digital Sky Survey (SDSS), Two-degree-Field Galaxy Redshift Survey (2dF), Spectroscopic Survey Telescope (SST), Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) program and Large Synoptic Survey Telescope (LSST) program, etc.), celestial observational data are coming into the world like torrential rain. Therefore, to utilize them effectively and fully, research on automated processing methods for celestial data is imperative. In the present work, we investigated how to recognizing galaxies and quasars from spectra based on nearest neighbor method. Galaxies and quasars are extragalactic objects, they are far away from earth, and their spectra are usually contaminated by various noise. Therefore, it is a typical problem to recognize these two types of spectra in automatic spectra classification. Furthermore, the utilized method, nearest neighbor, is one of the most typical, classic, mature algorithms in pattern recognition and data mining, and often is used as a benchmark in developing novel algorithm. For applicability in practice, it is shown that the recognition ratio of nearest neighbor method (NN) is comparable to the best results reported in the literature based on more complicated methods, and the superiority of NN is that this method does not need to be trained, which is useful in incremental learning and parallel computation in mass spectral data processing. In conclusion, the results in this work are helpful for studying galaxies and quasars spectra classification.

Ultracold fermions in a one-dimensional bipartite optical lattice: Metal-insulator transitions driven by shaking

NASA Astrophysics Data System (ADS)

Di Liberto, M.; Malpetti, D.; Japaridze, G. I.; Morais Smith, C.

2014-08-01

We theoretically investigate the behavior of a system of fermionic atoms loaded in a bipartite one-dimensional optical lattice that is under the action of an external time-periodic driving force. By using Floquet theory, an effective model is derived. The bare hopping coefficients are renormalized by zeroth-order Bessel functions of the first kind with different arguments for the nearest-neighbor and next-nearest-neighbor hopping. The insulating behavior characterizing the system at half filling in the absence of driving is dynamically suppressed, and for particular values of the driving parameter the system becomes either a standard metal or an unconventional metal with four Fermi points. The existence of the four-Fermi-point metal relies on the fact that, as a consequence of the shaking procedure, the next-nearest-neighbor hopping coefficients become significant compared to the nearest-neighbor ones. We use the bosonization technique to investigate the effect of on-site Hubbard interactions on the four-Fermi-point metal-insulator phase transition. Attractive interactions are expected to enlarge the regime of parameters where the unconventional metallic phase arises, whereas repulsive interactions reduce it. This metallic phase is known to be a Luther-Emery liquid (spin-gapped metal) for both repulsive and attractive interactions, contrary to the usual Hubbard model, which exhibits a Mott-insulator phase for repulsive interactions. Ultracold fermions in driven one-dimensional bipartite optical lattices provide an interesting platform for the realization of this long-studied four-Fermi-point unconventional metal.

Quantum Correlation Properties in Composite Parity-Conserved Matrix Product States

NASA Astrophysics Data System (ADS)

Zhu, Jing-Min

2016-09-01

We give a new thought for constructing long-range quantum correlation in quantum many-body systems. Our proposed composite parity-conserved matrix product state has long-range quantum correlation only for two spin blocks where their spin-block length larger than 1 compared to any subsystem only having short-range quantum correlation, and we investigate quantum correlation properties of two spin blocks varying with environment parameter and spacing spin number. We also find that the geometry quantum discords of two nearest-neighbor spin blocks and two next-nearest-neighbor spin blocks become smaller and for other conditions the geometry quantum discord becomes larger than that in any subcomponent, i.e., the increase or the production of the long-range quantum correlation is at the cost of reducing the short-range quantum correlation compared to the corresponding classical correlation and total correlation having no any characteristic of regulation. For nearest-neighbor and next-nearest-neighbor all the correlations take their maximal values at the same points, while for other conditions no whether for spacing same spin number or for different spacing spin numbers all the correlations taking their maximal values are respectively at different points which are very close. We believe that our work is helpful to comprehensively and deeply understand the organization and structure of quantum correlation especially for long-range quantum correlation of quantum many-body systems; and further helpful for the classification, the depiction and the measure of quantum correlation of quantum many-body systems.

Interacting steps with finite-range interactions: Analytical approximation and numerical results

NASA Astrophysics Data System (ADS)

Jaramillo, Diego Felipe; Téllez, Gabriel; González, Diego Luis; Einstein, T. L.

2013-05-01

We calculate an analytical expression for the terrace-width distribution P(s) for an interacting step system with nearest- and next-nearest-neighbor interactions. Our model is derived by mapping the step system onto a statistically equivalent one-dimensional system of classical particles. The validity of the model is tested with several numerical simulations and experimental results. We explore the effect of the range of interactions q on the functional form of the terrace-width distribution and pair correlation functions. For physically plausible interactions, we find modest changes when next-nearest neighbor interactions are included and generally negligible changes when more distant interactions are allowed. We discuss methods for extracting from simulated experimental data the characteristic scale-setting terms in assumed potential forms.

[Searching for Rare Celestial Objects Automatically from Stellar Spectra of the Sloan Digital Sky Survey Data Release Eight].

PubMed

Si, Jian-min; Luo, A-li; Wu, Fu-zhao; Wu, Yi-hong

2015-03-01

There are many valuable rare and unusual objects in spectra dataset of Sloan Digital Sky Survey (SDSS) Data Release eight (DR8), such as special white dwarfs (DZ, DQ, DC), carbon stars, white dwarf main-sequence binaries (WDMS), cataclysmic variable (CV) stars and so on, so it is extremely significant to search for rare and unusual celestial objects from massive spectra dataset. A novel algorithm based on Kernel dense estimation and K-nearest neighborhoods (KNN) has been presented, and applied to search for rare and unusual celestial objects from 546 383 stellar spectra of SDSS DR8. Their densities are estimated using Gaussian kernel density estimation, the top 5 000 spectra in descend order by their densities are selected as rare objects, and the top 300 000 spectra in ascend order by their densities are selected as normal objects. Then, KNN were used to classify the rest objects, and simultaneously K nearest neighbors of the 5 000 rare spectra are also selected as rare objects. As a result, there are totally 21 193 spectra selected as initial rare spectra, which include error spectra caused by deletion, redden, bad calibration, spectra consisting of different physically irrelevant components, planetary nebulas, QSOs, special white dwarfs (DZ, DQ, DC), carbon stars, white dwarf main-sequence binaries (WDMS), cataclysmic variable (CV) stars and so on. By cross identification with SIMBAD, NED, ADS and major literature, it is found that three DZ white dwarfs, one WDMS, two CVs with company of G-type star, three CVs candidates, six DC white dwarfs, one DC white dwarf candidate and one BL Lacertae (BL lac) candidate are our new findings. We also have found one special DA white dwarf with emission lines of Ca II triple and Mg I, and one unknown object whose spectrum looks like a late M star with emission lines and its image looks like a galaxy or nebula.

A Global Covariance Descriptor for Nuclear Atypia Scoring in Breast Histopathology Images.

PubMed

Khan, Adnan Mujahid; Sirinukunwattana, Korsuk; Rajpoot, Nasir

2015-09-01

Nuclear atypia scoring is a diagnostic measure commonly used to assess tumor grade of various cancers, including breast cancer. It provides a quantitative measure of deviation in visual appearance of cell nuclei from those in normal epithelial cells. In this paper, we present a novel image-level descriptor for nuclear atypia scoring in breast cancer histopathology images. The method is based on the region covariance descriptor that has recently become a popular method in various computer vision applications. The descriptor in its original form is not suitable for classification of histopathology images as cancerous histopathology images tend to possess diversely heterogeneous regions in a single field of view. Our proposed image-level descriptor, which we term as the geodesic mean of region covariance descriptors, possesses all the attractive properties of covariance descriptors lending itself to tractable geodesic-distance-based k-nearest neighbor classification using efficient kernels. The experimental results suggest that the proposed image descriptor yields high classification accuracy compared to a variety of widely used image-level descriptors.

Fuzzy support vector machine: an efficient rule-based classification technique for microarrays.

PubMed

Hajiloo, Mohsen; Rabiee, Hamid R; Anooshahpour, Mahdi

2013-01-01

The abundance of gene expression microarray data has led to the development of machine learning algorithms applicable for tackling disease diagnosis, disease prognosis, and treatment selection problems. However, these algorithms often produce classifiers with weaknesses in terms of accuracy, robustness, and interpretability. This paper introduces fuzzy support vector machine which is a learning algorithm based on combination of fuzzy classifiers and kernel machines for microarray classification. Experimental results on public leukemia, prostate, and colon cancer datasets show that fuzzy support vector machine applied in combination with filter or wrapper feature selection methods develops a robust model with higher accuracy than the conventional microarray classification models such as support vector machine, artificial neural network, decision trees, k nearest neighbors, and diagonal linear discriminant analysis. Furthermore, the interpretable rule-base inferred from fuzzy support vector machine helps extracting biological knowledge from microarray data. Fuzzy support vector machine as a new classification model with high generalization power, robustness, and good interpretability seems to be a promising tool for gene expression microarray classification.

Hyperspectral Image Classification via Kernel Sparse Representation

DTIC Science & Technology

2013-01-01

classification algorithms. Moreover, the spatial coherency across neighboring pixels is also incorporated through a kernelized joint sparsity model , where...joint sparsity model , where all of the pixels within a small neighborhood are jointly represented in the feature space by selecting a few common training...hyperspectral imagery, joint spar- sity model , kernel methods, sparse representation. I. INTRODUCTION HYPERSPECTRAL imaging sensors capture images

The nearest neighbor and the bayes error rates.

PubMed

Loizou, G; Maybank, S J

1987-02-01

The (k, l) nearest neighbor method of pattern classification is compared to the Bayes method. If the two acceptance rates are equal then the asymptotic error rates satisfy the inequalities Ek,l + 1 ¿ E*(¿) ¿ Ek,l dE*(¿), where d is a function of k, l, and the number of pattern classes, and ¿ is the reject threshold for the Bayes method. An explicit expression for d is given which is optimal in the sense that for some probability distributions Ek,l and dE* (¿) are equal.

A Novel Quantum Solution to Privacy-Preserving Nearest Neighbor Query in Location-Based Services

NASA Astrophysics Data System (ADS)

Luo, Zhen-yu; Shi, Run-hua; Xu, Min; Zhang, Shun

2018-04-01

We present a cheating-sensitive quantum protocol for Privacy-Preserving Nearest Neighbor Query based on Oblivious Quantum Key Distribution and Quantum Encryption. Compared with the classical related protocols, our proposed protocol has higher security, because the security of our protocol is based on basic physical principles of quantum mechanics, instead of difficulty assumptions. Especially, our protocol takes single photons as quantum resources and only needs to perform single-photon projective measurement. Therefore, it is feasible to implement this protocol with the present technologies.

Comparison of spatiotemporal interpolators for 4D image reconstruction from 2D transesophageal ultrasound

NASA Astrophysics Data System (ADS)

Haak, Alexander; van Stralen, Marijn; van Burken, Gerard; Klein, Stefan; Pluim, Josien P. W.; de Jong, Nico; van der Steen, Antonius F. W.; Bosch, Johan G.

2012-03-01

°For electrophysiology intervention monitoring, we intend to reconstruct 4D ultrasound (US) of structures in the beating heart from 2D transesophageal US by scanplane rotation. The image acquisition is continuous but unsynchronized to the heart rate, which results in a sparsely and irregularly sampled dataset and a spatiotemporal interpolation method is desired. Previously, we showed the potential of normalized convolution (NC) for interpolating such datasets. We explored 4D interpolation by 3 different methods: NC, nearest neighbor (NN), and temporal binning followed by linear interpolation (LTB). The test datasets were derived by slicing three 4D echocardiography datasets at random rotation angles (θ, range: 0-180) and random normalized cardiac phase (τ, range: 0-1). Four different distributions of rotated 2D images with 600, 900, 1350, and 1800 2D input images were created from all TEE sets. A 2D Gaussian kernel was used for NC and optimal kernel sizes (σθ and στ) were found by performing an exhaustive search. The RMS gray value error (RMSE) of the reconstructed images was computed for all interpolation methods. The estimated optimal kernels were in the range of σθ = 3.24 - 3.69°/ στ = 0.045 - 0.048, σθ = 2.79°/ στ = 0.031 - 0.038, σθ = 2.34°/ στ = 0.023 - 0.026, and σθ = 1.89°/ στ = 0.021 - 0.023 for 600, 900, 1350, and 1800 input images respectively. We showed that NC outperforms NN and LTB. For a small number of input images the advantage of NC is more pronounced.

Seismic clusters analysis in Northeastern Italy by the nearest-neighbor approach

NASA Astrophysics Data System (ADS)

Peresan, Antonella; Gentili, Stefania

2018-01-01

The main features of earthquake clusters in Northeastern Italy are explored, with the aim to get new insights on local scale patterns of seismicity in the area. The study is based on a systematic analysis of robustly and uniformly detected seismic clusters, which are identified by a statistical method, based on nearest-neighbor distances of events in the space-time-energy domain. The method permits us to highlight and investigate the internal structure of earthquake sequences, and to differentiate the spatial properties of seismicity according to the different topological features of the clusters structure. To analyze seismicity of Northeastern Italy, we use information from local OGS bulletins, compiled at the National Institute of Oceanography and Experimental Geophysics since 1977. A preliminary reappraisal of the earthquake bulletins is carried out and the area of sufficient completeness is outlined. Various techniques are considered to estimate the scaling parameters that characterize earthquakes occurrence in the region, namely the b-value and the fractal dimension of epicenters distribution, required for the application of the nearest-neighbor technique. Specifically, average robust estimates of the parameters of the Unified Scaling Law for Earthquakes, USLE, are assessed for the whole outlined region and are used to compute the nearest-neighbor distances. Clusters identification by the nearest-neighbor method turn out quite reliable and robust with respect to the minimum magnitude cutoff of the input catalog; the identified clusters are well consistent with those obtained from manual aftershocks identification of selected sequences. We demonstrate that the earthquake clusters have distinct preferred geographic locations, and we identify two areas that differ substantially in the examined clustering properties. Specifically, burst-like sequences are associated with the north-western part and swarm-like sequences with the south-eastern part of the study region. The territorial heterogeneity of earthquakes clustering is in good agreement with spatial variability of scaling parameters identified by the USLE. In particular, the fractal dimension is higher to the west (about 1.2-1.4), suggesting a spatially more distributed seismicity, compared to the eastern parte of the investigated territory, where fractal dimension is very low (about 0.8-1.0).

Weak doping dependence of the antiferromagnetic coupling between nearest-neighbor Mn2 + spins in (Ba1 -xKx) (Zn1-yMny) 2As2

NASA Astrophysics Data System (ADS)

Surmach, M. A.; Chen, B. J.; Deng, Z.; Jin, C. Q.; Glasbrenner, J. K.; Mazin, I. I.; Ivanov, A.; Inosov, D. S.

2018-03-01

Dilute magnetic semiconductors (DMS) are nonmagnetic semiconductors doped with magnetic transition metals. The recently discovered DMS material (Ba1 -xKx) (Zn1-yMny) 2As2 offers a unique and versatile control of the Curie temperature TC by decoupling the spin (Mn2 +, S =5 /2 ) and charge (K+) doping in different crystallographic layers. In an attempt to describe from first-principles calculations the role of hole doping in stabilizing ferromagnetic order, it was recently suggested that the antiferromagnetic exchange coupling J between the nearest-neighbor Mn ions would experience a nearly twofold suppression upon doping 20% of holes by potassium substitution. At the same time, further-neighbor interactions become increasingly ferromagnetic upon doping, leading to a rapid increase of TC. Using inelastic neutron scattering, we have observed a localized magnetic excitation at about 13 meV associated with the destruction of the nearest-neighbor Mn-Mn singlet ground state. Hole doping results in a notable broadening of this peak, evidencing significant particle-hole damping, but with only a minor change in the peak position. We argue that this unexpected result can be explained by a combined effect of superexchange and double-exchange interactions.

nth-Nearest-neighbor distribution functions of an interacting fluid from the pair correlation function: a hierarchical approach.

PubMed

Bhattacharjee, Biplab

2003-04-01

The paper presents a general formalism for the nth-nearest-neighbor distribution (NND) of identical interacting particles in a fluid confined in a nu-dimensional space. The nth-NND functions, W(n,r) (for n=1,2,3, em leader) in a fluid are obtained hierarchically in terms of the pair correlation function and W(n-1,r) alone. The radial distribution function (RDF) profiles obtained from the molecular dynamics (MD) simulation of Lennard-Jones (LJ) fluid is used to illustrate the results. It is demonstrated that the collective structural information contained in the maxima and minima of the RDF profiles being resolved in terms of individual NND functions may provide more insights about the microscopic neighborhood structure around a reference particle in a fluid. Representative comparison between the results obtained from the formalism and the MD simulation data shows good agreement. Apart from the quantities such as nth-NND functions and nth-nearest-neighbor distances, the average neighbor population number is defined. These quantities are evaluated for the LJ model system and interesting density dependence of the microscopic neighborhood shell structures are discussed in terms of them. The relevance of the NND functions in various phenomena is also pointed out.

nth-nearest-neighbor distribution functions of an interacting fluid from the pair correlation function: A hierarchical approach

NASA Astrophysics Data System (ADS)

Bhattacharjee, Biplab

2003-04-01

The paper presents a general formalism for the nth-nearest-neighbor distribution (NND) of identical interacting particles in a fluid confined in a ν-dimensional space. The nth-NND functions, W(n,r¯) (for n=1,2,3,…) in a fluid are obtained hierarchically in terms of the pair correlation function and W(n-1,r¯) alone. The radial distribution function (RDF) profiles obtained from the molecular dynamics (MD) simulation of Lennard-Jones (LJ) fluid is used to illustrate the results. It is demonstrated that the collective structural information contained in the maxima and minima of the RDF profiles being resolved in terms of individual NND functions may provide more insights about the microscopic neighborhood structure around a reference particle in a fluid. Representative comparison between the results obtained from the formalism and the MD simulation data shows good agreement. Apart from the quantities such as nth-NND functions and nth-nearest-neighbor distances, the average neighbor population number is defined. These quantities are evaluated for the LJ model system and interesting density dependence of the microscopic neighborhood shell structures are discussed in terms of them. The relevance of the NND functions in various phenomena is also pointed out.

Mapping growing stock volume and forest live biomass: a case study of the Polissya region of Ukraine

NASA Astrophysics Data System (ADS)

Bilous, Andrii; Myroniuk, Viktor; Holiaka, Dmytrii; Bilous, Svitlana; See, Linda; Schepaschenko, Dmitry

2017-10-01

Forest inventory and biomass mapping are important tasks that require inputs from multiple data sources. In this paper we implement two methods for the Ukrainian region of Polissya: random forest (RF) for tree species prediction and k-nearest neighbors (k-NN) for growing stock volume and biomass mapping. We examined the suitability of the five-band RapidEye satellite image to predict the distribution of six tree species. The accuracy of RF is quite high: ~99% for forest/non-forest mask and 89% for tree species prediction. Our results demonstrate that inclusion of elevation as a predictor variable in the RF model improved the performance of tree species classification. We evaluated different distance metrics for the k-NN method, including Euclidean or Mahalanobis distance, most similar neighbor (MSN), gradient nearest neighbor, and independent component analysis. The MSN with the four nearest neighbors (k = 4) is the most precise (according to the root-mean-square deviation) for predicting forest attributes across the study area. The k-NN method allowed us to estimate growing stock volume with an accuracy of 3 m3 ha-1 and for live biomass of about 2 t ha-1 over the study area.

A Novel Graph Constructor for Semisupervised Discriminant Analysis: Combined Low-Rank and k-Nearest Neighbor Graph

PubMed Central

Pan, Yongke; Niu, Wenjia

2017-01-01

Semisupervised Discriminant Analysis (SDA) is a semisupervised dimensionality reduction algorithm, which can easily resolve the out-of-sample problem. Relative works usually focus on the geometric relationships of data points, which are not obvious, to enhance the performance of SDA. Different from these relative works, the regularized graph construction is researched here, which is important in the graph-based semisupervised learning methods. In this paper, we propose a novel graph for Semisupervised Discriminant Analysis, which is called combined low-rank and k-nearest neighbor (LRKNN) graph. In our LRKNN graph, we map the data to the LR feature space and then the kNN is adopted to satisfy the algorithmic requirements of SDA. Since the low-rank representation can capture the global structure and the k-nearest neighbor algorithm can maximally preserve the local geometrical structure of the data, the LRKNN graph can significantly improve the performance of SDA. Extensive experiments on several real-world databases show that the proposed LRKNN graph is an efficient graph constructor, which can largely outperform other commonly used baselines. PMID:28316616

Reverse Nearest Neighbor Search on a Protein-Protein Interaction Network to Infer Protein-Disease Associations.

PubMed

Suratanee, Apichat; Plaimas, Kitiporn

2017-01-01

The associations between proteins and diseases are crucial information for investigating pathological mechanisms. However, the number of known and reliable protein-disease associations is quite small. In this study, an analysis framework to infer associations between proteins and diseases was developed based on a large data set of a human protein-protein interaction network integrating an effective network search, namely, the reverse k -nearest neighbor (R k NN) search. The R k NN search was used to identify an impact of a protein on other proteins. Then, associations between proteins and diseases were inferred statistically. The method using the R k NN search yielded a much higher precision than a random selection, standard nearest neighbor search, or when applying the method to a random protein-protein interaction network. All protein-disease pair candidates were verified by a literature search. Supporting evidence for 596 pairs was identified. In addition, cluster analysis of these candidates revealed 10 promising groups of diseases to be further investigated experimentally. This method can be used to identify novel associations to better understand complex relationships between proteins and diseases.

Evaluation of nearest-neighbor methods for detection of chimeric small-subunit rRNA sequences

NASA Technical Reports Server (NTRS)

Robison-Cox, J. F.; Bateson, M. M.; Ward, D. M.

1995-01-01

Detection of chimeric artifacts formed when PCR is used to retrieve naturally occurring small-subunit (SSU) rRNA sequences may rely on demonstrating that different sequence domains have different phylogenetic affiliations. We evaluated the CHECK_CHIMERA method of the Ribosomal Database Project and another method which we developed, both based on determining nearest neighbors of different sequence domains, for their ability to discern artificially generated SSU rRNA chimeras from authentic Ribosomal Database Project sequences. The reliability of both methods decreases when the parental sequences which contribute to chimera formation are more than 82 to 84% similar. Detection is also complicated by the occurrence of authentic SSU rRNA sequences that behave like chimeras. We developed a naive statistical test based on CHECK_CHIMERA output and used it to evaluate previously reported SSU rRNA chimeras. Application of this test also suggests that chimeras might be formed by retrieving SSU rRNAs as cDNA. The amount of uncertainty associated with nearest-neighbor analyses indicates that such tests alone are insufficient and that better methods are needed.

An incremental knowledge assimilation system (IKAS) for mine detection

NASA Astrophysics Data System (ADS)

Porway, Jake; Raju, Chaitanya; Varadarajan, Karthik Mahesh; Nguyen, Hieu; Yadegar, Joseph

2010-04-01

In this paper we present an adaptive incremental learning system for underwater mine detection and classification that utilizes statistical models of seabed texture and an adaptive nearest-neighbor classifier to identify varied underwater targets in many different environments. The first stage of processing uses our Background Adaptive ANomaly detector (BAAN), which identifies statistically likely target regions using Gabor filter responses over the image. Using this information, BAAN classifies the background type and updates its detection using background-specific parameters. To perform classification, a Fully Adaptive Nearest Neighbor (FAAN) determines the best label for each detection. FAAN uses an extremely fast version of Nearest Neighbor to find the most likely label for the target. The classifier perpetually assimilates new and relevant information into its existing knowledge database in an incremental fashion, allowing improved classification accuracy and capturing concept drift in the target classes. Experiments show that the system achieves >90% classification accuracy on underwater mine detection tasks performed on synthesized datasets provided by the Office of Naval Research. We have also demonstrated that the system can incrementally improve its detection accuracy by constantly learning from new samples.

Heat perturbation spreading in the Fermi-Pasta-Ulam-β system with next-nearest-neighbor coupling: Competition between phonon dispersion and nonlinearity

NASA Astrophysics Data System (ADS)

Xiong, Daxing

2017-06-01

We employ the heat perturbation correlation function to study thermal transport in the one-dimensional Fermi-Pasta-Ulam-β lattice with both nearest-neighbor and next-nearest-neighbor couplings. We find that such a system bears a peculiar phonon dispersion relation, and thus there exists a competition between phonon dispersion and nonlinearity that can strongly affect the heat correlation function's shape and scaling property. Specifically, for small and large anharmoncities, the scaling laws are ballistic and superdiffusive types, respectively, which are in good agreement with the recent theoretical predictions; whereas in the intermediate range of the nonlinearity, we observe an unusual multiscaling property characterized by a nonmonotonic delocalization process of the central peak of the heat correlation function. To understand these multiscaling laws, we also examine the momentum perturbation correlation function and find a transition process with the same turning point of the anharmonicity as that shown in the heat correlation function. This suggests coupling between the momentum transport and the heat transport, in agreement with the theoretical arguments of mode cascade theory.

Aftershock identification problem via the nearest-neighbor analysis for marked point processes

NASA Astrophysics Data System (ADS)

Gabrielov, A.; Zaliapin, I.; Wong, H.; Keilis-Borok, V.

2007-12-01

The centennial observations on the world seismicity have revealed a wide variety of clustering phenomena that unfold in the space-time-energy domain and provide most reliable information about the earthquake dynamics. However, there is neither a unifying theory nor a convenient statistical apparatus that would naturally account for the different types of seismic clustering. In this talk we present a theoretical framework for nearest-neighbor analysis of marked processes and obtain new results on hierarchical approach to studying seismic clustering introduced by Baiesi and Paczuski (2004). Recall that under this approach one defines an asymmetric distance D in space-time-energy domain such that the nearest-neighbor spanning graph with respect to D becomes a time- oriented tree. We demonstrate how this approach can be used to detect earthquake clustering. We apply our analysis to the observed seismicity of California and synthetic catalogs from ETAS model and show that the earthquake clustering part is statistically different from the homogeneous part. This finding may serve as a basis for an objective aftershock identification procedure.

Lattice-dynamical model for the filled skutterudite LaFe4Sb12: Harmonic and anharmonic couplings

NASA Astrophysics Data System (ADS)

Feldman, J. L.; Singh, D. J.; Bernstein, N.

2014-06-01

The filled skutterudite LaFe4Sb12 shows greatly reduced thermal conductivity compared to that of the related unfilled compound CoSb3, although the microscopic reasons for this are unclear. We calculate harmonic and anharmonic force constants for the interaction of the La filler atom with the framework atoms. We find that force constants show a general trend of decaying rapidly with distance and are very small for the interaction of the La with its next-nearest-neighbor Sb and nearest-neighbor La. However, a few rather long-range interactions, such as with the next-nearest-neighbor La and with the third neighbor Sb, are surprisingly strong, although still small. We test the central-force approximation and find significant deviations from it. Using our force constants we calculate a bare La mode Gruneisen parameter and find a value of 3-4, substantially higher than values associated with cage atom anharmonicity, i.e., a value of about 1 for CoSb3 but much smaller than a previous estimate [Bernstein et al., Phys. Rev. B 81, 134301 (2010), 10.1103/PhysRevB.81.134301]. This latter difference is primarily due to the previously used overestimate of the La-Fe cubic force constants. We also find a substantial negative contribution to this bare La Gruneisen parameter from the aforementioned third-neighbor La-Sb interaction. Our results underscore the need for rather long-range interactions in describing the role of anharmonicity on the dynamics in this material.

Jastrow-like ground states for quantum many-body potentials with near-neighbors interactions

NASA Astrophysics Data System (ADS)

Baradaran, Marzieh; Carrasco, José A.; Finkel, Federico; González-López, Artemio

2018-01-01

We completely solve the problem of classifying all one-dimensional quantum potentials with nearest- and next-to-nearest-neighbors interactions whose ground state is Jastrow-like, i.e., of Jastrow type but depending only on differences of consecutive particles. In particular, we show that these models must necessarily contain a three-body interaction term, as was the case with all previously known examples. We discuss several particular instances of the general solution, including a new hyperbolic potential and a model with elliptic interactions which reduces to the known rational and trigonometric ones in appropriate limits.

Fast trimers in a one-dimensional extended Fermi-Hubbard model

NASA Astrophysics Data System (ADS)

Dhar, A.; Törmä, P.; Kinnunen, J. J.

2018-04-01

We consider a one-dimensional two-component extended Fermi-Hubbard model with nearest-neighbor interactions and mass imbalance between the two species. We study the binding energy of trimers, various observables for detecting them, and expansion dynamics. We generalize the definition of the trimer gap to include the formation of different types of clusters originating from nearest-neighbor interactions. Expansion dynamics reveal rapidly propagating trimers, with speeds exceeding doublon propagation in the strongly interacting regime. We present a simple model for understanding this unique feature of the movement of the trimers, and we discuss the potential for experimental realization.

Evidence for a novel chemisorption bond: Formate (HCO/sub 2/) on Cu(100)

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

Stoehr, J.; Outka, D.A.; Madix, R.J.

1985-03-25

Surface extended-x-ray-absorption fine-structure measurements reveal that formate (HCO/sub 2/) groups on Cu(100) chemisorb via the two oxygen atoms in adjacent fourfold hollow sites with an average O-Cu nearest-neighbor bond length of 2.38 +- 0.03 A. This distance is sig- nificantly (approx.0.4 A) longer than typical O-Cu bonds in bulk compounds and all known surface complexes. The unusually large O-Cu distance is attributed to a steric effect involving the C atom in HCO/sub 2/ and the nearest-neighbor Cu surface atoms.

Analysis of radiation-induced small Cu particle cluster formation in aqueous CuCl2

USGS Publications Warehouse

Jayanetti, Sumedha; Mayanovic, Robert A.; Anderson, Alan J.; Bassett, William A.; Chou, I.-Ming

2001-01-01

Radition-induced small Cu particle cluster formation in aqueous CuCl2 was analyzed. It was noticed that nearest neighbor distance increased with the increase in the time of irradiation. This showed that the clusters approached the lattice dimension of bulk copper. As the average cluster size approached its bulk dimensions, an increase in the nearest neighbor coordination number was found with the decrease in the surface to volume ratio. Radiolysis of water by incident x-ray beam led to the reduction of copper ions in the solution to themetallic state.

AVNM: A Voting based Novel Mathematical Rule for Image Classification.

PubMed

Vidyarthi, Ankit; Mittal, Namita

2016-12-01

In machine learning, the accuracy of the system depends upon classification result. Classification accuracy plays an imperative role in various domains. Non-parametric classifier like K-Nearest Neighbor (KNN) is the most widely used classifier for pattern analysis. Besides its easiness, simplicity and effectiveness characteristics, the main problem associated with KNN classifier is the selection of a number of nearest neighbors i.e. "k" for computation. At present, it is hard to find the optimal value of "k" using any statistical algorithm, which gives perfect accuracy in terms of low misclassification error rate. Motivated by the prescribed problem, a new sample space reduction weighted voting mathematical rule (AVNM) is proposed for classification in machine learning. The proposed AVNM rule is also non-parametric in nature like KNN. AVNM uses the weighted voting mechanism with sample space reduction to learn and examine the predicted class label for unidentified sample. AVNM is free from any initial selection of predefined variable and neighbor selection as found in KNN algorithm. The proposed classifier also reduces the effect of outliers. To verify the performance of the proposed AVNM classifier, experiments are made on 10 standard datasets taken from UCI database and one manually created dataset. The experimental result shows that the proposed AVNM rule outperforms the KNN classifier and its variants. Experimentation results based on confusion matrix accuracy parameter proves higher accuracy value with AVNM rule. The proposed AVNM rule is based on sample space reduction mechanism for identification of an optimal number of nearest neighbor selections. AVNM results in better classification accuracy and minimum error rate as compared with the state-of-art algorithm, KNN, and its variants. The proposed rule automates the selection of nearest neighbor selection and improves classification rate for UCI dataset and manually created dataset. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Super-resolution fusion of complementary panoramic images based on cross-selection kernel regression interpolation.

PubMed

Chen, Lidong; Basu, Anup; Zhang, Maojun; Wang, Wei; Liu, Yu

2014-03-20

A complementary catadioptric imaging technique was proposed to solve the problem of low and nonuniform resolution in omnidirectional imaging. To enhance this research, our paper focuses on how to generate a high-resolution panoramic image from the captured omnidirectional image. To avoid the interference between the inner and outer images while fusing the two complementary views, a cross-selection kernel regression method is proposed. First, in view of the complementarity of sampling resolution in the tangential and radial directions between the inner and the outer images, respectively, the horizontal gradients in the expected panoramic image are estimated based on the scattered neighboring pixels mapped from the outer, while the vertical gradients are estimated using the inner image. Then, the size and shape of the regression kernel are adaptively steered based on the local gradients. Furthermore, the neighboring pixels in the next interpolation step of kernel regression are also selected based on the comparison between the horizontal and vertical gradients. In simulation and real-image experiments, the proposed method outperforms existing kernel regression methods and our previous wavelet-based fusion method in terms of both visual quality and objective evaluation.

Are hospitals "keeping up with the Joneses"?: Assessing the spatial and temporal diffusion of the surgical robot.

PubMed

Li, Huilin; Gail, Mitchell H; Braithwaite, R Scott; Gold, Heather T; Walter, Dawn; Liu, Mengling; Gross, Cary P; Makarov, Danil V

2014-07-01

The surgical robot has been widely adopted in the United States in spite of its high cost and controversy surrounding its benefit. Some have suggested that a "medical arms race" influences technology adoption. We wanted to determine whether a hospital would acquire a surgical robot if its nearest neighboring hospital already owned one. We identified 554 hospitals performing radical prostatectomy from the Healthcare Cost and Utilization Project Statewide Inpatient Databases for seven states. We used publicly available data from the website of the surgical robot's sole manufacturer (Intuitive Surgical, Sunnyvale, CA) combined with data collected from the hospitals to ascertain the timing of robot acquisition during year 2001 to 2008. One hundred thirty four hospitals (24%) had acquired a surgical robot by the end of 2008. We geocoded the address of each hospital and determined a hospital's likelihood to acquire a surgical robot based on whether its nearest neighbor owned a surgical robot . We developed a Markov chain method to model the acquisition process spatially and temporally and quantified the "neighborhood effect" on the acquisition of the surgical robot while adjusting simultaneously for known confounders. After adjusting for hospital teaching status, surgical volume, urban status and number of hospital beds, the Markov chain analysis demonstrated that a hospital whose nearest neighbor had acquired a surgical robot had a higher likelihood itself acquiring a surgical robot. (OR=1.71, 95% CI: 1.07-2.72 , p=0.02). There is a significant spatial and temporal association for hospitals acquiring surgical robots during the study period. Hospitals were more likely to acquire a surgical robot during the robot's early adoption phase if their nearest neighbor had already done so.

Dipole-dipole resonance line shapes in a cold Rydberg gas

NASA Astrophysics Data System (ADS)

Richards, B. G.; Jones, R. R.

2016-04-01

We have explored the dipole-dipole mediated, resonant energy transfer reaction, 32 p3 /2+32 p3 /2→32 s +33 s , in an ensemble of cold 85Rb Rydberg atoms. Stark tuning is employed to measure the population transfer probability as a function of the total electronic energy difference between the initial and final atom-pair states over a range of Rydberg densities, 2 ×108≤ρ ≤3 ×109 cm-3. The observed line shapes provide information on the role of beyond nearest-neighbor interactions, the range of Rydberg atom separations, and the electric field inhomogeneity in the sample. The widths of the resonance line shapes increase approximately linearly with the Rydberg density and are only a factor of 2 larger than expected for two-body, nearest-neighbor interactions alone. These results are in agreement with the prediction [B. Sun and F. Robicheaux, Phys. Rev. A 78, 040701(R) (2008), 10.1103/PhysRevA.78.040701] that beyond nearest-neighbor exchange interactions should not influence the population transfer process to the degree once thought. At low densities, Gaussian rather than Lorentzian line shapes are observed due to electric field inhomogeneities, allowing us to set an upper limit for the field variation across the Rydberg sample. At higher densities, non-Lorentzian, cusplike line shapes characterized by sharp central peaks and broad wings reflect the random distribution of interatomic distances within the magneto-optical trap (MOT). These line shapes are well reproduced by an analytic expression derived from a nearest-neighbor interaction model and may serve as a useful fingerprint for characterizing the position correlation function for atoms within the MOT.

Effective model with strong Kitaev interactions for α -RuCl3

NASA Astrophysics Data System (ADS)

Suzuki, Takafumi; Suga, Sei-ichiro

2018-04-01

We use an exact numerical diagonalization method to calculate the dynamical spin structure factors of three ab initio models and one ab initio guided model for a honeycomb-lattice magnet α -RuCl3 . We also use thermal pure quantum states to calculate the temperature dependence of the heat capacity, the nearest-neighbor spin-spin correlation function, and the static spin structure factor. From the results obtained from these four effective models, we find that, even when the magnetic order is stabilized at low temperature, the intensity at the Γ point in the dynamical spin structure factors increases with increasing nearest-neighbor spin correlation. In addition, we find that the four models fail to explain heat-capacity measurements whereas two of the four models succeed in explaining inelastic-neutron-scattering experiments. In the four models, when temperature decreases, the heat capacity shows a prominent peak at a high temperature where the nearest-neighbor spin-spin correlation function increases. However, the peak temperature in heat capacity is too low in comparison with that observed experimentally. To address these discrepancies, we propose an effective model that includes strong ferromagnetic Kitaev coupling, and we show that this model quantitatively reproduces both inelastic-neutron-scattering experiments and heat-capacity measurements. To further examine the adequacy of the proposed model, we calculate the field dependence of the polarized terahertz spectra, which reproduces the experimental results: the spin-gapped excitation survives up to an onset field where the magnetic order disappears and the response in the high-field region is almost linear. Based on these numerical results, we argue that the low-energy magnetic excitation in α -RuCl3 is mainly characterized by interactions such as off-diagonal interactions and weak Heisenberg interactions between nearest-neighbor pairs, rather than by the strong Kitaev interactions.

Minimum Expected Risk Estimation for Near-neighbor Classification

DTIC Science & Technology

2006-04-01

We consider the problems of class probability estimation and classification when using near-neighbor classifiers, such as k-nearest neighbors ( kNN ...estimate for weighted kNN classifiers with different prior information, for a broad class of risk functions. Theory and simulations show how significant...the difference is compared to the standard maximum likelihood weighted kNN estimates. Comparisons are made with uniform weights, symmetric weights

Quantum Algorithm for K-Nearest Neighbors Classification Based on the Metric of Hamming Distance

NASA Astrophysics Data System (ADS)

Ruan, Yue; Xue, Xiling; Liu, Heng; Tan, Jianing; Li, Xi

2017-11-01

K-nearest neighbors (KNN) algorithm is a common algorithm used for classification, and also a sub-routine in various complicated machine learning tasks. In this paper, we presented a quantum algorithm (QKNN) for implementing this algorithm based on the metric of Hamming distance. We put forward a quantum circuit for computing Hamming distance between testing sample and each feature vector in the training set. Taking advantage of this method, we realized a good analog for classical KNN algorithm by setting a distance threshold value t to select k - n e a r e s t neighbors. As a result, QKNN achieves O( n 3) performance which is only relevant to the dimension of feature vectors and high classification accuracy, outperforms Llyod's algorithm (Lloyd et al. 2013) and Wiebe's algorithm (Wiebe et al. 2014).

Structure of the first- and second-neighbor shells of simulated water: Quantitative relation to translational and orientational order

NASA Astrophysics Data System (ADS)

Yan, Zhenyu; Buldyrev, Sergey V.; Kumar, Pradeep; Giovambattista, Nicolas; Debenedetti, Pablo G.; Stanley, H. Eugene

2007-11-01

We perform molecular dynamics simulations of water using the five-site transferable interaction potential (TIP5P) model to quantify structural order in both the first shell (defined by four nearest neighbors) and second shell (defined by twelve next-nearest neighbors) of a central water molecule. We find that the anomalous decrease of orientational order upon compression occurs in both shells, but the anomalous decrease of translational order upon compression occurs mainly in the second shell. The decreases of translational order and orientational order upon compression (called the “structural anomaly”) are thus correlated only in the second shell. Our findings quantitatively confirm the qualitative idea that the thermodynamic, structural, and hence dynamic anomalies of water are related to changes upon compression in the second shell.

Rumor has it...: relay communication of stress cues in plants.

PubMed

Falik, Omer; Mordoch, Yonat; Quansah, Lydia; Fait, Aaron; Novoplansky, Ariel

2011-01-01

Recent evidence demonstrates that plants are able not only to perceive and adaptively respond to external information but also to anticipate forthcoming hazards and stresses. Here, we tested the hypothesis that unstressed plants are able to respond to stress cues emitted from their abiotically-stressed neighbors and in turn induce stress responses in additional unstressed plants located further away from the stressed plants. Pisum sativum plants were subjected to drought while neighboring rows of five unstressed plants on both sides, with which they could exchange different cue combinations. On one side, the stressed plant and its unstressed neighbors did not share their rooting volumes (UNSHARED) and thus were limited to shoot communication. On its other side, the stressed plant shared one of its rooting volumes with its nearest unstressed neighbor and all plants shared their rooting volumes with their immediate neighbors (SHARED), allowing both root and shoot communication. Fifteen minutes following drought induction, significant stomatal closure was observed in both the stressed plants and their nearest unstressed SHARED neighbors, and within one hour, all SHARED neighbors closed their stomata. Stomatal closure was not observed in the UNSHARED neighbors. The results demonstrate that unstressed plants are able to perceive and respond to stress cues emitted by the roots of their drought-stressed neighbors and, via 'relay cuing', elicit stress responses in further unstressed plants. Further work is underway to study the underlying mechanisms of this new mode of plant communication and its possible adaptive implications for the anticipation of forthcoming abiotic stresses by plants.

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

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

Thermal Entanglement Between Atoms in the Four-Cavity Linear Chain Coupled by Single-Mode Fibers

NASA Astrophysics Data System (ADS)

Wang, Jun-Biao; Zhang, Guo-Feng

2018-05-01

Natural thermal entanglement between atoms of a linear arranged four coupled cavities system is studied. The results show that there is no thermal pairwise entanglement between atoms if atom-field interaction strength f or fiber-cavity coupling constant J equals to zero, both f and J can induce thermal pairwise entanglement in a certain range. Numerical simulations show that the nearest neighbor concurrence C A B is always greater than alternate concurrence C A C in the same condition. In addition, the effect of temperature T on the entanglement of alternate qubits is much stronger than the nearest neighbor qubits.

Rotationally Invariant Image Representation for Viewing Direction Classification in Cryo-EM

PubMed Central

Zhao, Zhizhen; Singer, Amit

2014-01-01

We introduce a new rotationally invariant viewing angle classification method for identifying, among a large number of cryo-EM projection images, similar views without prior knowledge of the molecule. Our rotationally invariant features are based on the bispectrum. Each image is denoised and compressed using steerable principal component analysis (PCA) such that rotating an image is equivalent to phase shifting the expansion coefficients. Thus we are able to extend the theory of bispectrum of 1D periodic signals to 2D images. The randomized PCA algorithm is then used to efficiently reduce the dimensionality of the bispectrum coefficients, enabling fast computation of the similarity between any pair of images. The nearest neighbors provide an initial classification of similar viewing angles. In this way, rotational alignment is only performed for images with their nearest neighbors. The initial nearest neighbor classification and alignment are further improved by a new classification method called vector diffusion maps. Our pipeline for viewing angle classification and alignment is experimentally shown to be faster and more accurate than reference-free alignment with rotationally invariant K-means clustering, MSA/MRA 2D classification, and their modern approximations. PMID:24631969

Heterogeneity and nearest-neighbor coupling can explain small-worldness and wave properties in pancreatic islets

NASA Astrophysics Data System (ADS)

Cappon, Giacomo; Pedersen, Morten Gram

2016-05-01

Many multicellular systems consist of coupled cells that work as a syncytium. The pancreatic islet of Langerhans is a well-studied example of such a microorgan. The islets are responsible for secretion of glucose-regulating hormones, mainly glucagon and insulin, which are released in distinct pulses. In order to observe pulsatile insulin secretion from the β-cells within the islets, the cellular responses must be synchronized. It is now well established that gap junctions provide the electrical nearest-neighbor coupling that allows excitation waves to spread across islets to synchronize the β-cell population. Surprisingly, functional coupling analysis of calcium responses in β-cells shows small-world properties, i.e., a high degree of local coupling with a few long-range "short-cut" connections that reduce the average path-length greatly. Here, we investigate how such long-range functional coupling can appear as a result of heterogeneity, nearest-neighbor coupling, and wave propagation. Heterogeneity is also able to explain a set of experimentally observed synchronization and wave properties without introducing all-or-none cell coupling and percolation theory. Our theoretical results highlight how local biological coupling can give rise to functional small-world properties via heterogeneity and wave propagation.

Spatio-temporal distribution of Oklahoma earthquakes: Exploring relationships using a nearest-neighbor approach: Nearest-neighbor analysis of Oklahoma

DOE PAGES

Vasylkivska, Veronika S.; Huerta, Nicolas J.

2017-06-24

Determining the spatiotemporal characteristics of natural and induced seismic events holds the opportunity to gain new insights into why these events occur. Linking the seismicity characteristics with other geologic, geographic, natural, or anthropogenic factors could help to identify the causes and suggest mitigation strategies that reduce the risk associated with such events. The nearest-neighbor approach utilized in this work represents a practical first step toward identifying statistically correlated clusters of recorded earthquake events. Detailed study of the Oklahoma earthquake catalog’s inherent errors, empirical model parameters, and model assumptions is presented. We found that the cluster analysis results are stable withmore » respect to empirical parameters (e.g., fractal dimension) but were sensitive to epicenter location errors and seismicity rates. Most critically, we show that the patterns in the distribution of earthquake clusters in Oklahoma are primarily defined by spatial relationships between events. This observation is a stark contrast to California (also known for induced seismicity) where a comparable cluster distribution is defined by both spatial and temporal interactions between events. These results highlight the difficulty in understanding the mechanisms and behavior of induced seismicity but provide insights for future work.« less

Effects of hydrophobic and dipole-dipole interactions on the conformational transitions of a model polypeptide

NASA Astrophysics Data System (ADS)

Mu, Yan; Gao, Yi Qin

2007-09-01

We studied the effects of hydrophobicity and dipole-dipole interactions between the nearest-neighbor amide planes on the secondary structures of a model polypeptide by calculating the free energy differences between different peptide structures. The free energy calculations were performed with low computational costs using the accelerated Monte Carlo simulation (umbrella sampling) method, with a bias-potential method used earlier in our accelerated molecular dynamics simulations. It was found that the hydrophobic interaction enhances the stability of α helices at both low and high temperatures but stabilizes β structures only at high temperatures at which α helices are not stable. The nearest-neighbor dipole-dipole interaction stabilizes β structures under all conditions, especially in the low temperature region where α helices are the stable structures. Our results indicate clearly that the dipole-dipole interaction between the nearest neighboring amide planes plays an important role in determining the peptide structures. Current research provides a more unified and quantitative picture for understanding the effects of different forms of interactions on polypeptide structures. In addition, the present model can be extended to describe DNA/RNA, polymer, copolymer, and other chain systems.

Spatio-temporal distribution of Oklahoma earthquakes: Exploring relationships using a nearest-neighbor approach: Nearest-neighbor analysis of Oklahoma

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

Vasylkivska, Veronika S.; Huerta, Nicolas J.

Determining the spatiotemporal characteristics of natural and induced seismic events holds the opportunity to gain new insights into why these events occur. Linking the seismicity characteristics with other geologic, geographic, natural, or anthropogenic factors could help to identify the causes and suggest mitigation strategies that reduce the risk associated with such events. The nearest-neighbor approach utilized in this work represents a practical first step toward identifying statistically correlated clusters of recorded earthquake events. Detailed study of the Oklahoma earthquake catalog’s inherent errors, empirical model parameters, and model assumptions is presented. We found that the cluster analysis results are stable withmore » respect to empirical parameters (e.g., fractal dimension) but were sensitive to epicenter location errors and seismicity rates. Most critically, we show that the patterns in the distribution of earthquake clusters in Oklahoma are primarily defined by spatial relationships between events. This observation is a stark contrast to California (also known for induced seismicity) where a comparable cluster distribution is defined by both spatial and temporal interactions between events. These results highlight the difficulty in understanding the mechanisms and behavior of induced seismicity but provide insights for future work.« less

Implicit kernel sparse shape representation: a sparse-neighbors-based objection segmentation framework.

PubMed

Yao, Jincao; Yu, Huimin; Hu, Roland

2017-01-01

This paper introduces a new implicit-kernel-sparse-shape-representation-based object segmentation framework. Given an input object whose shape is similar to some of the elements in the training set, the proposed model can automatically find a cluster of implicit kernel sparse neighbors to approximately represent the input shape and guide the segmentation. A distance-constrained probabilistic definition together with a dualization energy term is developed to connect high-level shape representation and low-level image information. We theoretically prove that our model not only derives from two projected convex sets but is also equivalent to a sparse-reconstruction-error-based representation in the Hilbert space. Finally, a "wake-sleep"-based segmentation framework is applied to drive the evolutionary curve to recover the original shape of the object. We test our model on two public datasets. Numerical experiments on both synthetic images and real applications show the superior capabilities of the proposed framework.

Integrated Sensing and Processing (ISP) Phase II: Demonstration and Evaluation for Distributed Sensor Netowrks and Missile Seeker Systems

DTIC Science & Technology

2007-02-28

Shah, D. Waagen, H. Schmitt, S. Bellofiore, A. Spanias, and D. Cochran, 32nd International Conference on Acoustics, Speech , and Signal Processing...Information Exploitation Office kNN k-Nearest Neighbor LEAN Laplacian Eigenmap Adaptive Neighbor LIP Linear Integer Programming ISP

Improving medical diagnosis reliability using Boosted C5.0 decision tree empowered by Particle Swarm Optimization.

PubMed

Pashaei, Elnaz; Ozen, Mustafa; Aydin, Nizamettin

2015-08-01

Improving accuracy of supervised classification algorithms in biomedical applications is one of active area of research. In this study, we improve the performance of Particle Swarm Optimization (PSO) combined with C4.5 decision tree (PSO+C4.5) classifier by applying Boosted C5.0 decision tree as the fitness function. To evaluate the effectiveness of our proposed method, it is implemented on 1 microarray dataset and 5 different medical data sets obtained from UCI machine learning databases. Moreover, the results of PSO + Boosted C5.0 implementation are compared to eight well-known benchmark classification methods (PSO+C4.5, support vector machine under the kernel of Radial Basis Function, Classification And Regression Tree (CART), C4.5 decision tree, C5.0 decision tree, Boosted C5.0 decision tree, Naive Bayes and Weighted K-Nearest neighbor). Repeated five-fold cross-validation method was used to justify the performance of classifiers. Experimental results show that our proposed method not only improve the performance of PSO+C4.5 but also obtains higher classification accuracy compared to the other classification methods.

Non-invasive health status detection system using Gabor filters based on facial block texture features.

PubMed

Shu, Ting; Zhang, Bob

2015-04-01

Blood tests allow doctors to check for certain diseases and conditions. However, using a syringe to extract the blood can be deemed invasive, slightly painful, and its analysis time consuming. In this paper, we propose a new non-invasive system to detect the health status (Healthy or Diseased) of an individual based on facial block texture features extracted using the Gabor filter. Our system first uses a non-invasive capture device to collect facial images. Next, four facial blocks are located on these images to represent them. Afterwards, each facial block is convolved with a Gabor filter bank to calculate its texture value. Classification is finally performed using K-Nearest Neighbor and Support Vector Machines via a Library for Support Vector Machines (with four kernel functions). The system was tested on a dataset consisting of 100 Healthy and 100 Diseased (with 13 forms of illnesses) samples. Experimental results show that the proposed system can detect the health status with an accuracy of 93 %, a sensitivity of 94 %, a specificity of 92 %, using a combination of the Gabor filters and facial blocks.

Developing a radiomics framework for classifying non-small cell lung carcinoma subtypes

NASA Astrophysics Data System (ADS)

Yu, Dongdong; Zang, Yali; Dong, Di; Zhou, Mu; Gevaert, Olivier; Fang, Mengjie; Shi, Jingyun; Tian, Jie

2017-03-01

Patient-targeted treatment of non-small cell lung carcinoma (NSCLC) has been well documented according to the histologic subtypes over the past decade. In parallel, recent development of quantitative image biomarkers has recently been highlighted as important diagnostic tools to facilitate histological subtype classification. In this study, we present a radiomics analysis that classifies the adenocarcinoma (ADC) and squamous cell carcinoma (SqCC). We extract 52-dimensional, CT-based features (7 statistical features and 45 image texture features) to represent each nodule. We evaluate our approach on a clinical dataset including 324 ADCs and 110 SqCCs patients with CT image scans. Classification of these features is performed with four different machine-learning classifiers including Support Vector Machines with Radial Basis Function kernel (RBF-SVM), Random forest (RF), K-nearest neighbor (KNN), and RUSBoost algorithms. To improve the classifiers' performance, optimal feature subset is selected from the original feature set by using an iterative forward inclusion and backward eliminating algorithm. Extensive experimental results demonstrate that radiomics features achieve encouraging classification results on both complete feature set (AUC=0.89) and optimal feature subset (AUC=0.91).

Automated diagnosis of epilepsy using CWT, HOS and texture parameters.

PubMed

Acharya, U Rajendra; Yanti, Ratna; Zheng, Jia Wei; Krishnan, M Muthu Rama; Tan, Jen Hong; Martis, Roshan Joy; Lim, Choo Min

2013-06-01

Epilepsy is a chronic brain disorder which manifests as recurrent seizures. Electroencephalogram (EEG) signals are generally analyzed to study the characteristics of epileptic seizures. In this work, we propose a method for the automated classification of EEG signals into normal, interictal and ictal classes using Continuous Wavelet Transform (CWT), Higher Order Spectra (HOS) and textures. First the CWT plot was obtained for the EEG signals and then the HOS and texture features were extracted from these plots. Then the statistically significant features were fed to four classifiers namely Decision Tree (DT), K-Nearest Neighbor (KNN), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM) to select the best classifier. We observed that the SVM classifier with Radial Basis Function (RBF) kernel function yielded the best results with an average accuracy of 96%, average sensitivity of 96.9% and average specificity of 97% for 23.6 s duration of EEG data. Our proposed technique can be used as an automatic seizure monitoring software. It can also assist the doctors to cross check the efficacy of their prescribed drugs.

Band nesting, massive Dirac fermions, and valley Landé and Zeeman effects in transition metal dichalcogenides: A tight-binding model

NASA Astrophysics Data System (ADS)

Bieniek, Maciej; Korkusiński, Marek; Szulakowska, Ludmiła; Potasz, Paweł; Ozfidan, Isil; Hawrylak, Paweł

2018-02-01

We present here the minimal tight-binding model for a single layer of transition metal dichalcogenides (TMDCs) MX 2(M , metal; X , chalcogen) which illuminates the physics and captures band nesting, massive Dirac fermions, and valley Landé and Zeeman magnetic field effects. TMDCs share the hexagonal lattice with graphene but their electronic bands require much more complex atomic orbitals. Using symmetry arguments, a minimal basis consisting of three metal d orbitals and three chalcogen dimer p orbitals is constructed. The tunneling matrix elements between nearest-neighbor metal and chalcogen orbitals are explicitly derived at K ,-K , and Γ points of the Brillouin zone. The nearest-neighbor tunneling matrix elements connect specific metal and sulfur orbitals yielding an effective 6 ×6 Hamiltonian giving correct composition of metal and chalcogen orbitals but not the direct gap at K points. The direct gap at K , correct masses, and conduction band minima at Q points responsible for band nesting are obtained by inclusion of next-neighbor Mo-Mo tunneling. The parameters of the next-nearest-neighbor model are successfully fitted to MX 2(M =Mo ; X =S ) density functional ab initio calculations of the highest valence and lowest conduction band dispersion along K -Γ line in the Brillouin zone. The effective two-band massive Dirac Hamiltonian for MoS2, Landé g factors, and valley Zeeman splitting are obtained.

Strong coupling between adenine nucleobases in DNA single strands revealed by circular dichroism using synchrotron radiation

NASA Astrophysics Data System (ADS)

Kadhane, Umesh; Holm, Anne I. S.; Hoffmann, Søren Vrønning; Nielsen, Steen Brøndsted

2008-02-01

Circular dichroism (CD) experiments on DNA single strands (dAn) at the ASTRID synchrotron radiation facility reveal that eight adenine (A) bases electronically couple upon 190nm excitation. After n=8 , the CD signal increases linearly with n with a slope equal to the sum of the coupling terms. Nearest neighbor interactions account for only 24% of the CD signal whereas electronic communication is limited to nearest neighbors for two other exciton bands observed at 218 and 251nm (i.e., dimer excited states). Electronic coupling between bases in DNA is important for nonradiative deexcitation of electronically excited states since the hazardous energy is spread over a larger spatial region.

Anomalous magnon Nernst effect of topological magnonic materials

NASA Astrophysics Data System (ADS)

Wang, X. S.; Wang, X. R.

2018-05-01

The magnon transport driven by a thermal gradient in a perpendicularly magnetized honeycomb lattice is studied. The system with the nearest-neighbor pseudodipolar interaction and the next-nearest-neighbor Dzyaloshinskii–Moriya interaction has various topologically nontrivial phases. When an in-plane thermal gradient is applied, a transverse in-plane magnon current is generated. This phenomenon is termed as the anomalous magnon Nernst effect that closely resembles the anomalous Nernst effect for an electronic system. The anomalous magnon Nernst coefficient and its sign are determined by the magnon Berry curvature distributions in the momentum space and magnon populations in the magnon bands. We predict a temperature-induced sign reversal in anomalous magnon Nernst effect under certain conditions.

Gapless Spin-Liquid Ground State in the S =1 /2 Kagome Antiferromagnet

NASA Astrophysics Data System (ADS)

Liao, H. J.; Xie, Z. Y.; Chen, J.; Liu, Z. Y.; Xie, H. D.; Huang, R. Z.; Normand, B.; Xiang, T.

2017-03-01

The defining problem in frustrated quantum magnetism, the ground state of the nearest-neighbor S =1 /2 antiferromagnetic Heisenberg model on the kagome lattice, has defied all theoretical and numerical methods employed to date. We apply the formalism of tensor-network states, specifically the method of projected entangled simplex states, which combines infinite system size with a correct accounting for multipartite entanglement. By studying the ground-state energy, the finite magnetic order appearing at finite tensor bond dimensions, and the effects of a next-nearest-neighbor coupling, we demonstrate that the ground state is a gapless spin liquid. We discuss the comparison with other numerical studies and the physical interpretation of this result.

Real-time Interpolation for True 3-Dimensional Ultrasound Image Volumes

PubMed Central

Ji, Songbai; Roberts, David W.; Hartov, Alex; Paulsen, Keith D.

2013-01-01

We compared trilinear interpolation to voxel nearest neighbor and distance-weighted algorithms for fast and accurate processing of true 3-dimensional ultrasound (3DUS) image volumes. In this study, the computational efficiency and interpolation accuracy of the 3 methods were compared on the basis of a simulated 3DUS image volume, 34 clinical 3DUS image volumes from 5 patients, and 2 experimental phantom image volumes. We show that trilinear interpolation improves interpolation accuracy over both the voxel nearest neighbor and distance-weighted algorithms yet achieves real-time computational performance that is comparable to the voxel nearest neighbor algrorithm (1–2 orders of magnitude faster than the distance-weighted algorithm) as well as the fastest pixel-based algorithms for processing tracked 2-dimensional ultrasound images (0.035 seconds per 2-dimesional cross-sectional image [76,800 pixels interpolated, or 0.46 ms/1000 pixels] and 1.05 seconds per full volume with a 1-mm3 voxel size [4.6 million voxels interpolated, or 0.23 ms/1000 voxels]). On the basis of these results, trilinear interpolation is recommended as a fast and accurate interpolation method for rectilinear sampling of 3DUS image acquisitions, which is required to facilitate subsequent processing and display during operating room procedures such as image-guided neurosurgery. PMID:21266563

Real-time interpolation for true 3-dimensional ultrasound image volumes.

PubMed

Ji, Songbai; Roberts, David W; Hartov, Alex; Paulsen, Keith D

2011-02-01

We compared trilinear interpolation to voxel nearest neighbor and distance-weighted algorithms for fast and accurate processing of true 3-dimensional ultrasound (3DUS) image volumes. In this study, the computational efficiency and interpolation accuracy of the 3 methods were compared on the basis of a simulated 3DUS image volume, 34 clinical 3DUS image volumes from 5 patients, and 2 experimental phantom image volumes. We show that trilinear interpolation improves interpolation accuracy over both the voxel nearest neighbor and distance-weighted algorithms yet achieves real-time computational performance that is comparable to the voxel nearest neighbor algrorithm (1-2 orders of magnitude faster than the distance-weighted algorithm) as well as the fastest pixel-based algorithms for processing tracked 2-dimensional ultrasound images (0.035 seconds per 2-dimesional cross-sectional image [76,800 pixels interpolated, or 0.46 ms/1000 pixels] and 1.05 seconds per full volume with a 1-mm(3) voxel size [4.6 million voxels interpolated, or 0.23 ms/1000 voxels]). On the basis of these results, trilinear interpolation is recommended as a fast and accurate interpolation method for rectilinear sampling of 3DUS image acquisitions, which is required to facilitate subsequent processing and display during operating room procedures such as image-guided neurosurgery.

Segregation and trapping of oxygen vacancies near the SrTiO 3Σ3 (112) [110] tilt grain boundary

DOE PAGES

Liu, Bin; Cooper, Valentino R.; Zhang, Yanwen; ...

2015-03-21

In nanocrystalline materials, structural discontinuities at grain boundaries (GBs) and the segregation of point defects to these GBs play a key role in defining the structural stability of a material, as well as its macroscopic electrical/mechanical properties. In this study, the segregation of oxygen vacancies near the Σ3 (1 1 2) [¯110] tilt GB in SrTiO 3 is explored using density functional theory. We find that oxygen vacancies segregate toward the GB, preferring to reside within the next nearest-neighbor layer. This oxygen vacancy segregation is found to be crucial for stabilizing this tilt GB. Furthermore, we find that the migrationmore » barriers of oxygen vacancies diffusing toward the first nearest-neighbor layer of the GB are low, while those away from this layer are very high. Furthermore, the segregation and trapping of the oxygen vacancies in the first nearest-neighbor layer of GBs are attributed to the large local distortions, which can now accommodate the preferred sixfold coordination of Ti. These results suggest that the electronic, transport, and capacitive properties of SrTiO 3 can be engineered through the control of GB structure and grain size or layer thickness.« less

Simulating ensembles of source water quality using a K-nearest neighbor resampling approach.

PubMed

Towler, Erin; Rajagopalan, Balaji; Seidel, Chad; Summers, R Scott

2009-03-01

Climatological, geological, and water management factors can cause significant variability in surface water quality. As drinking water quality standards become more stringent, the ability to quantify the variability of source water quality becomes more important for decision-making and planning in water treatment for regulatory compliance. However, paucity of long-term water quality data makes it challenging to apply traditional simulation techniques. To overcome this limitation, we have developed and applied a robust nonparametric K-nearest neighbor (K-nn) bootstrap approach utilizing the United States Environmental Protection Agency's Information Collection Rule (ICR) data. In this technique, first an appropriate "feature vector" is formed from the best available explanatory variables. The nearest neighbors to the feature vector are identified from the ICR data and are resampled using a weight function. Repetition of this results in water quality ensembles, and consequently the distribution and the quantification of the variability. The main strengths of the approach are its flexibility, simplicity, and the ability to use a large amount of spatial data with limited temporal extent to provide water quality ensembles for any given location. We demonstrate this approach by applying it to simulate monthly ensembles of total organic carbon for two utilities in the U.S. with very different watersheds and to alkalinity and bromide at two other U.S. utilities.

Attribute Weighting Based K-Nearest Neighbor Using Gain Ratio

NASA Astrophysics Data System (ADS)

Nababan, A. A.; Sitompul, O. S.; Tulus

2018-04-01

K- Nearest Neighbor (KNN) is a good classifier, but from several studies, the result performance accuracy of KNN still lower than other methods. One of the causes of the low accuracy produced, because each attribute has the same effect on the classification process, while some less relevant characteristics lead to miss-classification of the class assignment for new data. In this research, we proposed Attribute Weighting Based K-Nearest Neighbor Using Gain Ratio as a parameter to see the correlation between each attribute in the data and the Gain Ratio also will be used as the basis for weighting each attribute of the dataset. The accuracy of results is compared to the accuracy acquired from the original KNN method using 10-fold Cross-Validation with several datasets from the UCI Machine Learning repository and KEEL-Dataset Repository, such as abalone, glass identification, haberman, hayes-roth and water quality status. Based on the result of the test, the proposed method was able to increase the classification accuracy of KNN, where the highest difference of accuracy obtained hayes-roth dataset is worth 12.73%, and the lowest difference of accuracy obtained in the abalone dataset of 0.07%. The average result of the accuracy of all dataset increases the accuracy by 5.33%.

New insights into the negative thermal expansion: Direct experimental evidence for the “guitar-string” effect in cubic ScF 3

DOE PAGES

Hu, Lei; Chen, Jun; Sanson, Andrea; ...

2016-06-23

The understanding of the negative thermal expansion (NTE) mechanism remains challenging but critical for the development of NTE materials. This study sheds light on NTE of ScF 3, one of the most outstanding materials with NTE. The local dynamics of ScF 3 has been investigated by a combined analysis of synchrotron-based X-ray total scattering, ex-tended X-ray absorption fine structure and neutron powder diffraction. Very interestingly, we observe that i) the Sc-F nearest-neighbor distance strongly expands with increasing temperature while the Sc-Sc next-nearest-neighbor distance contracts, ii) the thermal ellipsoids of relative vibrations be-tween Sc-F nearest-neighbors are highly elongated in the directionmore » perpendicular to the Sc-F bond, indicating that the Sc-F bond is much softer to bend than to stretch, and iii) there is mainly dynamically transverse motion of fluorine atoms, rather than static shifts. Here, these results are the direct experimental evidence for the NTE mechanism, in which the rigid unit is not necessary for the occurrence of NTE, and the key role is played by the transverse thermal vibrations of fluorine atoms through the “guitar-string” effect.« less

Competing growth processes induced by next-nearest-neighbor interactions: Effects on meandering wavelength and stiffness

NASA Astrophysics Data System (ADS)

Blel, Sonia; Hamouda, Ajmi BH.; Mahjoub, B.; Einstein, T. L.

2017-02-01

In this paper we explore the meandering instability of vicinal steps with a kinetic Monte Carlo simulations (kMC) model including the attractive next-nearest-neighbor (NNN) interactions. kMC simulations show that increase of the NNN interaction strength leads to considerable reduction of the meandering wavelength and to weaker dependence of the wavelength on the deposition rate F. The dependences of the meandering wavelength on the temperature and the deposition rate obtained with simulations are in good quantitative agreement with the experimental result on the meandering instability of Cu(0 2 24) [T. Maroutian et al., Phys. Rev. B 64, 165401 (2001), 10.1103/PhysRevB.64.165401]. The effective step stiffness is found to depend not only on the strength of NNN interactions and the Ehrlich-Schwoebel barrier, but also on F. We argue that attractive NNN interactions intensify the incorporation of adatoms at step edges and enhance step roughening. Competition between NNN and nearest-neighbor interactions results in an alternative form of meandering instability which we call "roughening-limited" growth, rather than attachment-detachment-limited growth that governs the Bales-Zangwill instability. The computed effective wavelength and the effective stiffness behave as λeff˜F-q and β˜eff˜F-p , respectively, with q ≈p /2 .

Half-magnetization plateau in a Heisenberg antiferromagnet on a triangular lattice

NASA Astrophysics Data System (ADS)

Ye, Mengxing; Chubukov, Andrey V.

2017-10-01

We present the phase diagram of a 2D isotropic triangular Heisenberg antiferromagnet in a magnetic field. We consider spin-S model with nearest-neighbor (J1) and next-nearest-neighbor (J2) interactions. We focus on the range of 1 /8

Extended magnetic exchange interactions in the high-temperature ferromagnet MnBi

DOE PAGES

Christianson, Andrew D.; Hahn, Steven E.; Fishman, Randy Scott; ...

2016-05-09

Here, the high-temperature ferromagnet MnBi continues to receive attention as a candidate to replace rare-earth-containing permanent magnets in applications above room temperature. This is due to a high Curie temperature, large magnetic moments, and a coercivity that increases with temperature. The synthesis of MnBi also allows for crystals that are free of interstitial Mn, enabling more direct access to the key interactions underlying the physical properties of binary Mn-based ferromagnets. In this work, we use inelastic neutron scattering to measure the spin waves of MnBi in order to characterize the magnetic exchange at low temperature. Consistent with the spin reorientationmore » that occurs below 140~K, we do not observe a spin gap in this system above our experimental resolution. A Heisenberg model was fit to the spin wave data in order to characterize the long-range nature of the exchange. It was found that interactions up to sixth nearest neighbor are required to fully parameterize the spin waves. Surprisingly, the nearest-neighbor term is antiferromagnetic, and the realization of a ferromagnetic ground state relies on the more numerous ferromagnetic terms beyond nearest neighbor, suggesting that the ferromagnetic ground state arises as a consequence of the long-ranged interactions in the system.« less

Deriving Flood-Mediated Connectivity between River Channels and Floodplains: Data-Driven Approaches

NASA Astrophysics Data System (ADS)

Zhao, Tongtiegang; Shao, Quanxi; Zhang, Yongyong

2017-03-01

The flood-mediated connectivity between river channels and floodplains plays a fundamental role in flood hazard mapping and exerts profound ecological effects. The classic nearest neighbor search (NNS) fails to derive this connectivity because of spatial heterogeneity and continuity. We develop two novel data-driven connectivity-deriving approaches, namely, progressive nearest neighbor search (PNNS) and progressive iterative nearest neighbor search (PiNNS). These approaches are illustrated through a case study in Northern Australia. First, PNNS and PiNNS are employed to identify flood pathways on floodplains through forward tracking. That is, progressive search is performed to associate newly inundated cells in each time step to previously inundated cells. In particular, iterations in PiNNS ensure that the connectivity is continuous - the connection between any two cells along the pathway is built through intermediate inundated cells. Second, inundated floodplain cells are collectively connected to river channel cells through backward tracing. Certain river channel sections are identified to connect to a large number of inundated floodplain cells. That is, the floodwater from these sections causes widespread floodplain inundation. Our proposed approaches take advantage of spatial-temporal data. They can be applied to achieve connectivity from hydro-dynamic and remote sensing data and assist in river basin planning and management.

Theoretical studies of the defect structures for the two Cr3+ centers in KCl

NASA Astrophysics Data System (ADS)

Liu, Xu-Sheng; Wu, Shao-Yi; Wu, Li-Na; Zhang, Li-Juan; Guo, Jia-Xing; Dong, Hui-Ning

2017-06-01

The spin Hamiltonian (SH) parameters (i.e. the zero-field splitting parameters (ZFSPs) and g factors) and local structures of the two Cr3+ centers I and II at room temperature in KCl single crystals are theoretically investigated from the perturbation calculations for a rhombically distorted octahedral 3d3 cluster. The impurity systems are attributed to the doped Cr(CN)63- groups into KCl replacing the host KCl65- ones, associated with two nearest neighbor potassium vacancies VK in [011] and [ 0 1 bar 1 bar ] axes in center I and one nearest neighbor VK along [ 0 1 bar 1 ] and another next-nearest neighbor VK along [100] axis in center II, respectively. In center I, the four coplanar and two axial ligands CN- undergo the shifts ∆R1 (≈0.0044 nm) away from the VK and ∆R2‧ (≈0.0144 nm) away from the central ion along Z axis, respectively, because of the electrostatic interactions. In center II, the impurity Cr3+ is found to undergo the shift ∆RC (≈0.0063 nm) towards the nearest neighbor VK along [ 0 1 bar 1 ] axis, while the two ligands in [001] and [ 0 1 bar 0 ] axes closest to the VK undergo the shifts ∆R1 (≈0.0081 nm) away from the respective VK, and the ligand intervening in the VK and the central ion experiences the shift ∆R2 (≈0.0238 nm) away from the VK along [100] axis. The charge-transfer (CT) contributions to g-shifts are found to be opposite in sign and more than half (characterized by the ratios |ΔgCT/ΔgCF|>50%) in magnitude compared with the CF ones for both centers. The local structures and the microscopic mechanisms of the relevant impurity and ligand shifts are discussed for the two centers.

RVB states in doped band insulators from Coulomb forces: theory and a case study of superconductivity in BiS2 layers

NASA Astrophysics Data System (ADS)

Baskaran, G.

2016-12-01

Doped band insulators, HfNCl, WO3, diamond, Bi2Se3, BiS2 families, STO/LAO interface, gate doped SrTiO3, MoS2 and so on are unusual superconductors. With an aim to build a general theory for superconductivity in doped band insulators, we focus on the BiS2 family which was discovered by Mizuguchi et al in 2012. While maximum Tc is only ˜11 K in {{LaO}}1-{{x}}{{{F}}}{{x}}{{BiS}}2, a number of experimental results are puzzling and anomalous in the sense that they resemble high T c and unconventional superconductors. Using a two orbital model of Usui, Suzuki and Kuroki, we show that the uniform low density free Fermi sea in {{LaO}}{0,5}{{{F}}}0.5{{BiS}}2 is unstable towards formation of the next nearest neighbor Bi-S-Bi diagonal valence bond (charged -2e Cooper pair) and their Wigner crystallization. Instability to this novel state of matter is caused by unscreened nearest neighbor coulomb repulsions (V ˜ 1 eV) and a hopping pattern with sulfur mediated diagonal next nearest neighbor Bi-S-Bi hopping t’ ˜ 0.88 eV, as well as larger than nearest neighbor Bi-Bi hopping, t ˜ 0.16 eV. Wigner crystals of Cooper pairs quantum melt for doping around x = 0.5 and stabilize certain resonating valence bond states and superconductivity. We study a few variational RVB states and suggest that BiS2 family members are latent high Tc superconductors, but challenged by competing orders and the fragile nature of many body states sustained by unscreened Coulomb forces. One of our superconducting states has d XY symmetry and a gap. We also predict a 2d Bose metal or vortex liquid normal state, as charged -2e valence bonds survive in the normal state.

The characterization of organic monolayers at gold surfaces using scanning tunneling microscopy and atomic force microscopy correlation with macrostructural properties

NASA Astrophysics Data System (ADS)

Alves, C. A.

1992-09-01

Monolayer films formed by self-assembly of organothiols at epitaxially grown Au(111) films at mica were examined in air using scanning tunneling (STM) and atomic force microscopies (AFM). n-Alkanethiolate monolayers exhibit a hexagonal packing arrangement with nearest-neighbor and next-nearest-neighbor spacings of 0.50 and 0.87 nm. This arrangement is consistent with (the square root of 3 x the square root of 3)R30 deg adlayer structure at Au(111). STM reveals the structure of the Au-bound sulfur, while AFM details the structure at the monolayer/air interface, revealing that the order at the Au-S interface is retained up to the monolayer/air interface. The investigation of the self-assembled (CF3CF2)7(CH2)2SH monolayer at Au(111) by AFM reveals a (2 x 2) adlayer structure, with nearest-neighbor and next-nearest-neighbor spacings of 0.58 plus or minus 0.02 nm and 1.0 plus or minus 0.02 nm, respectively. This is consistent with the larger van der Waals diameter of the fluorinated chain. Coverage of this fluorinated thiolate monolayer is (6.3 plus or minus 0.8) x 10(exp -10) mol/cm(sup 2), consistent with the expected 0.25 monolayer coverage of the (2 x 2) adlayer structure at Au(111). Infrared reflection spectroscopy also confirmed this. Upon prolonged exposure to air, the thiolate species is oxidized to elemental sulfur in the forms of cyclooctasulfur (cyclo-S8) and other allotropes. STM reveals square structures on aged thiolate monolayers. Dimensions of these squares (0.40-0.50 nm per side) are close to those of cyclo-S8. Electrochemical reductive desorption experiments also reveal a change in the surface species with time, with a second desorption wave.

Mutual information estimation for irregularly sampled time series

NASA Astrophysics Data System (ADS)

Rehfeld, K.; Marwan, N.; Heitzig, J.; Kurths, J.

2012-04-01

For the automated, objective and joint analysis of time series, similarity measures are crucial. Used in the analysis of climate records, they allow for a complimentary, unbiased view onto sparse datasets. The irregular sampling of many of these time series, however, makes it necessary to either perform signal reconstruction (e.g. interpolation) or to develop and use adapted measures. Standard linear interpolation comes with an inevitable loss of information and bias effects. We have recently developed a Gaussian kernel-based correlation algorithm with which the interpolation error can be substantially lowered, but this would not work should the functional relationship in a bivariate setting be non-linear. We therefore propose an algorithm to estimate lagged auto and cross mutual information from irregularly sampled time series. We have extended the standard and adaptive binning histogram estimators and use Gaussian distributed weights in the estimation of the (joint) probabilities. To test our method we have simulated linear and nonlinear auto-regressive processes with Gamma-distributed inter-sampling intervals. We have then performed a sensitivity analysis for the estimation of actual coupling length, the lag of coupling and the decorrelation time in the synthetic time series and contrast our results to the performance of a signal reconstruction scheme. Finally we applied our estimator to speleothem records. We compare the estimated memory (or decorrelation time) to that from a least-squares estimator based on fitting an auto-regressive process of order 1. The calculated (cross) mutual information results are compared for the different estimators (standard or adaptive binning) and contrasted with results from signal reconstruction. We find that the kernel-based estimator has a significantly lower root mean square error and less systematic sampling bias than the interpolation-based method. It is possible that these encouraging results could be further improved by using non-histogram mutual information estimators, like k-Nearest Neighbor or Kernel-Density estimators, but for short (<1000 points) and irregularly sampled datasets the proposed algorithm is already a great improvement.

Density-dependent nest predation in waterfowl: the relative importance of nest density versus nest dispersion

USGS Publications Warehouse

Ackerman, Joshua T.; Ringelman, Kevin M.; Eadie, J.M.

2012-01-01

When nest predation levels are very high or very low, the absolute range of observable nest success is constrained (a floor/ceiling effect), and it may be more difficult to detect density-dependent nest predation. Density-dependent nest predation may be more detectable in years with moderate predation rates, simply because there can be a greater absolute difference in nest success between sites. To test this, we replicated a predation experiment 10 years after the original study, using both natural and artificial nests, comparing a year when overall rates of nest predation were high (2000) to a year with moderate nest predation (2010). We found no evidence for density-dependent predation on artificial nests in either year, indicating that nest predation is not density-dependent at the spatial scale of our experimental replicates (1-ha patches). Using nearest-neighbor distances as a measure of nest dispersion, we also found little evidence for “dispersion-dependent” predation on artificial nests. However, when we tested for dispersion-dependent predation using natural nests, we found that nest survival increased with shorter nearest-neighbor distances, and that neighboring nests were more likely to share the same nest fate than non-adjacent nests. Thus, at small spatial scales, density-dependence appears to operate in the opposite direction as predicted: closer nearest neighbors are more likely to be successful. We suggest that local nest dispersion, rather than larger-scale measures of nest density per se, may play a more important role in density-dependent nest predation.

Phylogenetic turnover along local environmental gradients in tropical forest communities.

PubMed

Baldeck, C A; Kembel, S W; Harms, K E; Yavitt, J B; John, R; Turner, B L; Madawala, S; Gunatilleke, N; Gunatilleke, S; Bunyavejchewin, S; Kiratiprayoon, S; Yaacob, A; Supardi, M N N; Valencia, R; Navarrete, H; Davies, S J; Chuyong, G B; Kenfack, D; Thomas, D W; Dalling, J W

2016-10-01

While the importance of local-scale habitat niches in shaping tree species turnover along environmental gradients in tropical forests is well appreciated, relatively little is known about the influence of phylogenetic signal in species' habitat niches in shaping local community structure. We used detailed maps of the soil resource and topographic variation within eight 24-50 ha tropical forest plots combined with species phylogenies created from the APG III phylogeny to examine how phylogenetic beta diversity (indicating the degree of phylogenetic similarity of two communities) was related to environmental gradients within tropical tree communities. Using distance-based redundancy analysis we found that phylogenetic beta diversity, expressed as either nearest neighbor distance or mean pairwise distance, was significantly related to both soil and topographic variation in all study sites. In general, more phylogenetic beta diversity within a forest plot was explained by environmental variables this was expressed as nearest neighbor distance versus mean pairwise distance (3.0-10.3 % and 0.4-8.8 % of variation explained among plots, respectively), and more variation was explained by soil resource variables than topographic variables using either phylogenetic beta diversity metric. We also found that patterns of phylogenetic beta diversity expressed as nearest neighbor distance were consistent with previously observed patterns of niche similarity among congeneric species pairs in these plots. These results indicate the importance of phylogenetic signal in local habitat niches in shaping the phylogenetic structure of tropical tree communities, especially at the level of close phylogenetic neighbors, where similarity in habitat niches is most strongly preserved.

Nearest neighbors by neighborhood counting.

PubMed

Wang, Hui

2006-06-01

Finding nearest neighbors is a general idea that underlies many artificial intelligence tasks, including machine learning, data mining, natural language understanding, and information retrieval. This idea is explicitly used in the k-nearest neighbors algorithm (kNN), a popular classification method. In this paper, this idea is adopted in the development of a general methodology, neighborhood counting, for devising similarity functions. We turn our focus from neighbors to neighborhoods, a region in the data space covering the data point in question. To measure the similarity between two data points, we consider all neighborhoods that cover both data points. We propose to use the number of such neighborhoods as a measure of similarity. Neighborhood can be defined for different types of data in different ways. Here, we consider one definition of neighborhood for multivariate data and derive a formula for such similarity, called neighborhood counting measure or NCM. NCM was tested experimentally in the framework of kNN. Experiments show that NCM is generally comparable to VDM and its variants, the state-of-the-art distance functions for multivariate data, and, at the same time, is consistently better for relatively large k values. Additionally, NCM consistently outperforms HEOM (a mixture of Euclidean and Hamming distances), the "standard" and most widely used distance function for multivariate data. NCM has a computational complexity in the same order as the standard Euclidean distance function and NCM is task independent and works for numerical and categorical data in a conceptually uniform way. The neighborhood counting methodology is proven sound for multivariate data experimentally. We hope it will work for other types of data.

Spatial correlations in polydisperse, frictionless, two-dimensional packings

NASA Astrophysics Data System (ADS)

O'Donovan, C. B.; Möbius, M. E.

2011-08-01

We investigate next-nearest-neighbor correlations of the contact number in simulations of polydisperse, frictionless packings in two dimensions. We find that disks with few contacting neighbors are predominantly in contact with disks that have many neighbors and vice versa at all packing fractions. This counterintuitive result can be explained by drawing a direct analogy to the Aboav-Weaire law in cellular structures. We find an empirical one parameter relation similar to the Aboav-Weaire law that satisfies an exact sum rule constraint. Surprisingly, there are no correlations in the radii between neighboring particles, despite correlations between contact number and radius.

Impact of nearest-neighbor repulsion on superconducting pairing in 2D extended Hubbard model

NASA Astrophysics Data System (ADS)

Jiang, Mi; Hahner, U. R.; Maier, T. A.; Schulthess, T. C.

Using dynamical cluster approximation (DCA) with an continuous-time QMC solver for the two-dimensional extended Hubbard model, we studied the impact of nearest-neighbor Coulomb repulsion V on d-wave superconducting pairing dynamics. By solving Bethe-Salpeter equation for particle-particle superconducting channel, we focused on the evolution of leading d-wave eigenvalue with V and the momentum and frequency dependence of the corresponding eigenfunction. The comparison with the evolution of both spin and charge susceptibilities versus V is presented showing the competition between spin and charge fluctuations. This research received generous support from the MARVEL NCCR and used resources of the Swiss National Supercomputing Center, as well as (INCITE) program in Oak Ridge Leadership Computing Facility.

Transferable tight-binding model for strained group IV and III-V materials and heterostructures

NASA Astrophysics Data System (ADS)

Tan, Yaohua; Povolotskyi, Michael; Kubis, Tillmann; Boykin, Timothy B.; Klimeck, Gerhard

2016-07-01

It is critical to capture the effect due to strain and material interface for device level transistor modeling. We introduce a transferable s p3d5s* tight-binding model with nearest-neighbor interactions for arbitrarily strained group IV and III-V materials. The tight-binding model is parametrized with respect to hybrid functional (HSE06) calculations for varieties of strained systems. The tight-binding calculations of ultrasmall superlattices formed by group IV and group III-V materials show good agreement with the corresponding HSE06 calculations. The application of the tight-binding model to superlattices demonstrates that the transferable tight-binding model with nearest-neighbor interactions can be obtained for group IV and III-V materials.

Spatial distribution of nuclei in progressive nucleation: Modeling and application

NASA Astrophysics Data System (ADS)

Tomellini, Massimo

2018-04-01

Phase transformations ruled by non-simultaneous nucleation and growth do not lead to random distribution of nuclei. Since nucleation is only allowed in the untransformed portion of space, positions of nuclei are correlated. In this article an analytical approach is presented for computing pair-correlation function of nuclei in progressive nucleation. This quantity is further employed for characterizing the spatial distribution of nuclei through the nearest neighbor distribution function. The modeling is developed for nucleation in 2D space with power growth law and it is applied to describe electrochemical nucleation where correlation effects are significant. Comparison with both computer simulations and experimental data lends support to the model which gives insights into the transition from Poissonian to correlated nearest neighbor probability density.

Determination of some pure compound ideal-gas enthalpies of formation

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

Steele, W. V.; Chirico, R. D.; Nguyen, A.

1989-06-01

The results of a study aimed at improvement of group-additivity methodology for estimation of thermodynamic properties of organic substances are reported. Specific weaknesses where ring corrections were unknown or next-nearest-neighbor interactions were only estimated because of lack of experimental data are addressed by experimental studies of enthalpies of combustion in the condensed- phase and vapor pressure measurements. Ideal-gas enthalpies of formation are reported for acrylamide, succinimide, ..gamma..-butyrolactone, 2-pyrrolidone, 2,3-dihydrofuran, 3,4-dihydro-2H-pyran, 1,3-cyclohexadiene, 1,4-cyclohexadiene, and 1-methyl-1-phenylhydrazine. Ring corrections, group terms, and next-nearest-neighbor interaction terms useful in the application of group additivity correlations are derived. 44 refs., 2 figs., 59 tabs.

Observation of Dipolar Spin-Exchange Interactions with Polar Molecules in a Lattice

DTIC Science & Technology

2013-01-01

extend beyond nearest neighbours. This allows coherent spin dynamics to persist even for gases with relatively high entropy and low lattice filling...dynamics to persist even for gases with relatively high entropy and low lat- tice filling. While measured effects of dipolar interactions in ultracold...limits superexchange to nearest-neighbor interactions and requires extremely low temperature and entropy . In contrast, long-range dipolar

Imputed forest structure uncertainty varies across elevational and longitudinal gradients in the western Cascade mountains, Oregon, USA

Treesearch

David M. Bell; Matthew J. Gregory; Janet L. Ohmann

2015-01-01

Imputation provides a useful method for mapping forest attributes across broad geographic areas based on field plot measurements and Landsat multi-spectral data, but the resulting map products may be of limited use without corresponding analyses of uncertainties in predictions. In the case of k-nearest neighbor (kNN) imputation with k = 1, such as the Gradient Nearest...

Ground-state entropy of the potts antiferromagnet with next-nearest-neighbor spin-spin couplings on strips of the square lattice

PubMed

Chang; Shrock

2000-10-01

We present exact calculations of the zero-temperature partition function (chromatic polynomial) and W(q), the exponent of the ground-state entropy, for the q-state Potts antiferromagnet with next-nearest-neighbor spin-spin couplings on square lattice strips, of width L(y)=3 and L(y)=4 vertices and arbitrarily great length Lx vertices, with both free and periodic boundary conditions. The resultant values of W for a range of physical q values are compared with each other and with the values for the full two-dimensional lattice. These results give insight into the effect of such nonnearest-neighbor couplings on the ground-state entropy. We show that the q=2 (Ising) and q=4 Potts antiferromagnets have zero-temperature critical points on the Lx-->infinity limits of the strips that we study. With the generalization of q from Z+ to C, we determine the analytic structure of W(q) in the q plane for the various cases.

Corrigendum to "Nearest neighbor imputation of species-level, plot-scale forest structure attributes from LiDAR data"

Treesearch

Andrew T. Hudak; Nicholas L. Crookston; Jeffrey S. Evans; David E. hall; Michael J. Falkowski

2009-01-01

The authors regret that an error was discovered in the code within the R software package, yaImpute (Crookston & Finley, 2008), which led to incorrect results reported in the above article. The Most Similar Neighbor (MSN) method computes the distance between reference observations and target observations in a projected space defined using canonical correlation...

Velocity correlations and spatial dependencies between neighbors in a unidirectional flow of pedestrians

NASA Astrophysics Data System (ADS)

Porzycki, Jakub; WÄ s, Jarosław; Hedayatifar, Leila; Hassanibesheli, Forough; Kułakowski, Krzysztof

2017-08-01

The aim of the paper is an analysis of self-organization patterns observed in the unidirectional flow of pedestrians. On the basis of experimental data from Zhang et al. [J. Zhang et al., J. Stat. Mech. (2011) P06004, 10.1088/1742-5468/2011/06/P06004], we analyze the mutual positions and velocity correlations between pedestrians when walking along a corridor. The angular and spatial dependencies of the mutual positions reveal a spatial structure that remains stable during the crowd motion. This structure differs depending on the value of n , for the consecutive n th -nearest-neighbor position set. The preferred position for the first-nearest neighbor is on the side of the pedestrian, while for further neighbors, this preference shifts to the axis of movement. The velocity correlations vary with the angle formed by the pair of neighboring pedestrians and the direction of motion and with the time delay between pedestrians' movements. The delay dependence of the correlations shows characteristic oscillations, produced by the velocity oscillations when striding; however, a filtering of the main frequency of individual striding out reduces the oscillations only partially. We conclude that pedestrians select their path directions so as to evade the necessity of continuously adjusting their speed to their neighbors'. They try to keep a given distance, but follow the person in front of them, as well as accepting and observing pedestrians on their sides. Additionally, we show an empirical example that illustrates the shape of a pedestrian's personal space during movement.

Local structure of ion pair interaction in lapatinib amorphous dispersions characterized by synchrotron x-ray diffraction and pair distribution function analysis

DOE PAGES

de Araujo, Gabriel L. B.; Benmore, Chris J.; Byrn, Stephen R.

2017-04-11

For many years, the idea of analyzing atom-atom contacts in amorphous drug-polymer systems has been of major interest, because this method has always had the potential to differentiate between amorphous systems with domains and amorphous systems which are molecular mixtures. In this study, local structure of ionic and noninonic interactions were studied by High-Energy X-ray Diffraction and Pair Distribution Function (PDF) analysis in amorphous solid dispersions of lapatinib in hypromellose phthalate (HPMCP) and hypromellose (HPMC-E3). The strategy of extracting lapatinib intermolecular drug interactions from the total PDF x-ray pattern was successfully applied allowing the detection of distinct nearest neighbor contactsmore » for the HPMC-E3 rich preparations showing that lapatinib molecules do not cluster in the same way as observed in HPMC-P, where ionic interactions are present. Orientational correlations up to nearest neighbor molecules at about 4.3 Å were observed for polymer rich samples; both observations showed strong correlation to the stability of the systems. Lasty, the superior physical stability of 1:3 LP:HPMCP was consistent with the absence of significant intermolecular interactions in (ΔD inter LP(r)) in the range of 3.0 to 6.0 Å, which are attributed to C-C, C-N and C-O nearest neighbor contacts present in drug-drug interactions.« less

Local structure of ion pair interaction in lapatinib amorphous dispersions characterized by synchrotron x-ray diffraction and pair distribution function analysis

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

de Araujo, Gabriel L. B.; Benmore, Chris J.; Byrn, Stephen R.

For many years, the idea of analyzing atom-atom contacts in amorphous drug-polymer systems has been of major interest, because this method has always had the potential to differentiate between amorphous systems with domains and amorphous systems which are molecular mixtures. In this study, local structure of ionic and noninonic interactions were studied by High-Energy X-ray Diffraction and Pair Distribution Function (PDF) analysis in amorphous solid dispersions of lapatinib in hypromellose phthalate (HPMCP) and hypromellose (HPMC-E3). The strategy of extracting lapatinib intermolecular drug interactions from the total PDF x-ray pattern was successfully applied allowing the detection of distinct nearest neighbor contactsmore » for the HPMC-E3 rich preparations showing that lapatinib molecules do not cluster in the same way as observed in HPMC-P, where ionic interactions are present. Orientational correlations up to nearest neighbor molecules at about 4.3 Å were observed for polymer rich samples; both observations showed strong correlation to the stability of the systems. Lasty, the superior physical stability of 1:3 LP:HPMCP was consistent with the absence of significant intermolecular interactions in (ΔD inter LP(r)) in the range of 3.0 to 6.0 Å, which are attributed to C-C, C-N and C-O nearest neighbor contacts present in drug-drug interactions.« less

Quantum Phase Transition and Local Entanglement in Extended Hubbard Model on Anisotropic Triangular Lattices

NASA Astrophysics Data System (ADS)

Gao, Ji-Ming; Tang, Rong-An; Zhang, Zheng-Mei; Xue, Ju-Kui

2016-11-01

Using a mean-field theory based upon Hartree—Fock approximation, we theoretically investigate the competition between the metallic conductivity, spin order and charge order phases in a two-dimensional half-filled extended Hubbard model on anisotropic triangular lattice. Bond order, double occupancy, spin and charge structure factor are calculated, and the phase diagram of the extended Hubbard model is presented. It is found that the interplay of strong interaction and geometric frustration leads to exotic phases, the charge fluctuation is enhanced and three kinds of charge orders appear with the introduction of the nearest-neighbor interaction. Moreover, for different frustrations, it is also found that the antiferromagnetic insulating phase and nonmagnetic insulating phase are rapidly suppressed, and eventually disappeared as the ratio between the nearest-neighbor interaction and on-site interaction increases. This indicates that spin order is also sensitive to the nearest-neighbor interaction. Finally, the single-site entanglement is calculated and it is found that a clear discontinuous of the single-site entanglement appears at the critical points of the phase transition. Supported by National Natural Science Foundation of China under Grant Nos.11274255, 11475027 and 11305132, Specialized Research Fund for the Doctoral Program of Higher Education of China under Grant No. 20136203110001, and Technology of Northwest Normal University, China under Grants No. NWNU-LKQN-11-26

Local Structure of Ion Pair Interaction in Lapatinib Amorphous Dispersions characterized by Synchrotron X-Ray diffraction and Pair Distribution Function Analysis

NASA Astrophysics Data System (ADS)

de Araujo, Gabriel L. B.; Benmore, Chris J.; Byrn, Stephen R.

2017-04-01

For many years, the idea of analyzing atom-atom contacts in amorphous drug-polymer systems has been of major interest, because this method has always had the potential to differentiate between amorphous systems with domains and amorphous systems which are molecular mixtures. In this study, local structure of ionic and noninonic interactions were studied by High-Energy X-ray Diffraction and Pair Distribution Function (PDF) analysis in amorphous solid dispersions of lapatinib in hypromellose phthalate (HPMCP) and hypromellose (HPMC-E3). The strategy of extracting lapatinib intermolecular drug interactions from the total PDF x-ray pattern was successfully applied allowing the detection of distinct nearest neighbor contacts for the HPMC-E3 rich preparations showing that lapatinib molecules do not cluster in the same way as observed in HPMC-P, where ionic interactions are present. Orientational correlations up to nearest neighbor molecules at about 4.3 Å were observed for polymer rich samples; both observations showed strong correlation to the stability of the systems. Finally, the superior physical stability of 1:3 LP:HPMCP was consistent with the absence of significant intermolecular interactions in (Δ) in the range of 3.0 to 6.0 Å, which are attributed to C-C, C-N and C-O nearest neighbor contacts present in drug-drug interactions.

Quantitative diagnosis of bladder cancer by morphometric analysis of HE images

NASA Astrophysics Data System (ADS)

Wu, Binlin; Nebylitsa, Samantha V.; Mukherjee, Sushmita; Jain, Manu

2015-02-01

In clinical practice, histopathological analysis of biopsied tissue is the main method for bladder cancer diagnosis and prognosis. The diagnosis is performed by a pathologist based on the morphological features in the image of a hematoxylin and eosin (HE) stained tissue sample. This manuscript proposes algorithms to perform morphometric analysis on the HE images, quantify the features in the images, and discriminate bladder cancers with different grades, i.e. high grade and low grade. The nuclei are separated from the background and other types of cells such as red blood cells (RBCs) and immune cells using manual outlining, color deconvolution and image segmentation. A mask of nuclei is generated for each image for quantitative morphometric analysis. The features of the nuclei in the mask image including size, shape, orientation, and their spatial distributions are measured. To quantify local clustering and alignment of nuclei, we propose a 1-nearest-neighbor (1-NN) algorithm which measures nearest neighbor distance and nearest neighbor parallelism. The global distributions of the features are measured using statistics of the proposed parameters. A linear support vector machine (SVM) algorithm is used to classify the high grade and low grade bladder cancers. The results show using a particular group of nuclei such as large ones, and combining multiple parameters can achieve better discrimination. This study shows the proposed approach can potentially help expedite pathological diagnosis by triaging potentially suspicious biopsies.

Absence of long-range order in the frustrated magnet SrDy2O4 due to trapped defects from a dimensionality crossover

NASA Astrophysics Data System (ADS)

Gauthier, N.; Fennell, A.; Prévost, B.; Uldry, A.-C.; Delley, B.; Sibille, R.; Désilets-Benoit, A.; Dabkowska, H. A.; Nilsen, G. J.; Regnault, L.-P.; White, J. S.; Niedermayer, C.; Pomjakushin, V.; Bianchi, A. D.; Kenzelmann, M.

2017-04-01

Magnetic frustration and low dimensionality can prevent long-range magnetic order and lead to exotic correlated ground states. SrDy2O4 consists of magnetic Dy3 + ions forming magnetically frustrated zigzag chains along the c axis and shows no long-range order to temperatures as low as T =60 mK. We carried out neutron scattering and ac magnetic susceptibility measurements using powder and single crystals of SrDy2O4 . Diffuse neutron scattering indicates strong one-dimensional (1D) magnetic correlations along the chain direction that can be qualitatively accounted for by the axial next-nearest-neighbor Ising model with nearest-neighbor and next-nearest-neighbor exchange J1=0.3 meV and J2=0.2 meV, respectively. Three-dimensional (3D) correlations become important below T*≈0.7 K. At T =60 mK, the short-range correlations are characterized by a putative propagation vector k1 /2=(0 ,1/2 ,1/2 ) . We argue that the absence of long-range order arises from the presence of slowly decaying 1D domain walls that are trapped due to 3D correlations. This stabilizes a low-temperature phase without long-range magnetic order, but with well-ordered chain segments separated by slowly moving domain walls.

An automated algorithm for determining photometric redshifts of quasars

NASA Astrophysics Data System (ADS)

Wang, Dan; Zhang, Yanxia; Zhao, Yongheng

2010-07-01

We employ k-nearest neighbor algorithm (KNN) for photometric redshift measurement of quasars with the Fifth Data Release (DR5) of the Sloan Digital Sky Survey (SDSS). KNN is an instance learning algorithm where the result of new instance query is predicted based on the closest training samples. The regressor do not use any model to fit and only based on memory. Given a query quasar, we find the known quasars or (training points) closest to the query point, whose redshift value is simply assigned to be the average of the values of its k nearest neighbors. Three kinds of different colors (PSF, Model or Fiber) and spectral redshifts are used as input parameters, separatively. The combination of the three kinds of colors is also taken as input. The experimental results indicate that the best input pattern is PSF + Model + Fiber colors in all experiments. With this pattern, 59.24%, 77.34% and 84.68% of photometric redshifts are obtained within ▵z < 0.1, 0.2 and 0.3, respectively. If only using one kind of colors as input, the model colors achieve the best performance. However, when using two kinds of colors, the best result is achieved by PSF + Fiber colors. In addition, nearest neighbor method (k = 1) shows its superiority compared to KNN (k ≠ 1) for the given sample.

Practice makes proficient: pigeons (Columba livia) learn efficient routes on full-circuit navigational traveling salesperson problems.

PubMed

Baron, Danielle M; Ramirez, Alejandro J; Bulitko, Vadim; Madan, Christopher R; Greiner, Ariel; Hurd, Peter L; Spetch, Marcia L

2015-01-01

Visiting multiple locations and returning to the start via the shortest route, referred to as the traveling salesman (or salesperson) problem (TSP), is a valuable skill for both humans and non-humans. In the current study, pigeons were trained with increasing set sizes of up to six goals, with each set size presented in three distinct configurations, until consistency in route selection emerged. After training at each set size, the pigeons were tested with two novel configurations. All pigeons acquired routes that were significantly more efficient (i.e., shorter in length) than expected by chance selection of the goals. On average, the pigeons also selected routes that were more efficient than expected based on a local nearest-neighbor strategy and were as efficient as the average route generated by a crossing-avoidance strategy. Analysis of the routes taken indicated that they conformed to both a nearest-neighbor and a crossing-avoidance strategy significantly more often than expected by chance. Both the time taken to visit all goals and the actual distance traveled decreased from the first to the last trials of training in each set size. On the first trial with novel configurations, average efficiency was higher than chance, but was not higher than expected from a nearest-neighbor or crossing-avoidance strategy. These results indicate that pigeons can learn to select efficient routes on a TSP problem.

Prediction of Drug-Plasma Protein Binding Using Artificial Intelligence Based Algorithms.

PubMed

Kumar, Rajnish; Sharma, Anju; Siddiqui, Mohammed Haris; Tiwari, Rajesh Kumar

2018-01-01

Plasma protein binding (PPB) has vital importance in the characterization of drug distribution in the systemic circulation. Unfavorable PPB can pose a negative effect on clinical development of promising drug candidates. The drug distribution properties should be considered at the initial phases of the drug design and development. Therefore, PPB prediction models are receiving an increased attention. In the current study, we present a systematic approach using Support vector machine, Artificial neural network, k- nearest neighbor, Probabilistic neural network, Partial least square and Linear discriminant analysis to relate various in vitro and in silico molecular descriptors to a diverse dataset of 736 drugs/drug-like compounds. The overall accuracy of Support vector machine with Radial basis function kernel came out to be comparatively better than the rest of the applied algorithms. The training set accuracy, validation set accuracy, precision, sensitivity, specificity and F1 score for the Suprort vector machine was found to be 89.73%, 89.97%, 92.56%, 87.26%, 91.97% and 0.898, respectively. This model can potentially be useful in screening of relevant drug candidates at the preliminary stages of drug design and development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

In silico prediction of Tetrahymena pyriformis toxicity for diverse industrial chemicals with substructure pattern recognition and machine learning methods.

PubMed

Cheng, Feixiong; Shen, Jie; Yu, Yue; Li, Weihua; Liu, Guixia; Lee, Philip W; Tang, Yun

2011-03-01

There is an increasing need for the rapid safety assessment of chemicals by both industries and regulatory agencies throughout the world. In silico techniques are practical alternatives in the environmental hazard assessment. It is especially true to address the persistence, bioaccumulative and toxicity potentials of organic chemicals. Tetrahymena pyriformis toxicity is often used as a toxic endpoint. In this study, 1571 diverse unique chemicals were collected from the literature and composed of the largest diverse data set for T. pyriformis toxicity. Classification predictive models of T. pyriformis toxicity were developed by substructure pattern recognition and different machine learning methods, including support vector machine (SVM), C4.5 decision tree, k-nearest neighbors and random forest. The results of a 5-fold cross-validation showed that the SVM method performed better than other algorithms. The overall predictive accuracies of the SVM classification model with radial basis functions kernel was 92.2% for the 5-fold cross-validation and 92.6% for the external validation set, respectively. Furthermore, several representative substructure patterns for characterizing T. pyriformis toxicity were also identified via the information gain analysis methods. Copyright © 2010 Elsevier Ltd. All rights reserved.

A Machine Learning Approach to the Detection of Pilot's Reaction to Unexpected Events Based on EEG Signals

PubMed Central

Cyran, Krzysztof A.

2018-01-01

This work considers the problem of utilizing electroencephalographic signals for use in systems designed for monitoring and enhancing the performance of aircraft pilots. Systems with such capabilities are generally referred to as cognitive cockpits. This article provides a description of the potential that is carried by such systems, especially in terms of increasing flight safety. Additionally, a neuropsychological background of the problem is presented. Conducted research was focused mainly on the problem of discrimination between states of brain activity related to idle but focused anticipation of visual cue and reaction to it. Especially, a problem of selecting a proper classification algorithm for such problems is being examined. For that purpose an experiment involving 10 subjects was planned and conducted. Experimental electroencephalographic data was acquired using an Emotiv EPOC+ headset. Proposed methodology involved use of a popular method in biomedical signal processing, the Common Spatial Pattern, extraction of bandpower features, and an extensive test of different classification algorithms, such as Linear Discriminant Analysis, k-nearest neighbors, and Support Vector Machines with linear and radial basis function kernels, Random Forests, and Artificial Neural Networks. PMID:29849544

Food environment of fruits and vegetables in the territory of the Health Academy Program.

PubMed

Costa, Bruna Vieira de Lima; Oliveira, Cláudia Di Lorenzo; Lopes, Aline Cristine Souza

2015-11-01

This study provides a spatial analysis of distribution and access to commercial fruit and vegetable establishments within the territory of a representative sample of public fitness facilities known as the Health Academy Program (HAP) in Belo Horizonte, Minas Gerais State, Brazil. The study evaluated commercial food establishments within a buffer area based on a radius of 1,600 meters around each of 18 randomly selected fitness facilities. Quality of access to fruits and vegetables was assessed by the Healthy Food Store Index (HFSI), consisting of the variables availability, variety, and advertising of fruits, vegetables and ultra-processed foods. The analysis was based on calculation of the Kernel intensity estimator, nearest neighbor method, and Ripley K-function. Of the 336 food establishments, 61.3% were green grocers and open-air markets, with a median HFSI of 11 (5 to 16). In only 17% of the territories, the majority of the "hot area" establishments displayed better access to healthy foods, and only three areas showed a clustering pattern. The study showed limited access to commercial establishments supplying healthy fruits and vegetables within the territory of the public fitness program.

Pattern Recognition for a Flight Dynamics Monte Carlo Simulation

NASA Technical Reports Server (NTRS)

Restrepo, Carolina; Hurtado, John E.

2011-01-01

The design, analysis, and verification and validation of a spacecraft relies heavily on Monte Carlo simulations. Modern computational techniques are able to generate large amounts of Monte Carlo data but flight dynamics engineers lack the time and resources to analyze it all. The growing amounts of data combined with the diminished available time of engineers motivates the need to automate the analysis process. Pattern recognition algorithms are an innovative way of analyzing flight dynamics data efficiently. They can search large data sets for specific patterns and highlight critical variables so analysts can focus their analysis efforts. This work combines a few tractable pattern recognition algorithms with basic flight dynamics concepts to build a practical analysis tool for Monte Carlo simulations. Current results show that this tool can quickly and automatically identify individual design parameters, and most importantly, specific combinations of parameters that should be avoided in order to prevent specific system failures. The current version uses a kernel density estimation algorithm and a sequential feature selection algorithm combined with a k-nearest neighbor classifier to find and rank important design parameters. This provides an increased level of confidence in the analysis and saves a significant amount of time.

DichroMatch at the protein circular dichroism data bank (DM@PCDDB): A web-based tool for identifying protein nearest neighbors using circular dichroism spectroscopy.

PubMed

Whitmore, Lee; Mavridis, Lazaros; Wallace, B A; Janes, Robert W

2018-01-01

Circular dichroism spectroscopy is a well-used, but simple method in structural biology for providing information on the secondary structure and folds of proteins. DichroMatch (DM@PCDDB) is an online tool that is newly available in the Protein Circular Dichroism Data Bank (PCDDB), which takes advantage of the wealth of spectral and metadata deposited therein, to enable identification of spectral nearest neighbors of a query protein based on four different methods of spectral matching. DM@PCDDB can potentially provide novel information about structural relationships between proteins and can be used in comparison studies of protein homologs and orthologs. © 2017 The Authors Protein Science published by Wiley Periodicals, Inc. on behalf of The Protein Society.

d -wave superconductivity in the presence of nearest-neighbor Coulomb repulsion

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

Jiang, M.; Hahner, U. R.; Schulthess, T. C.

Dynamic cluster quantum Monte Carlo calculations for a doped two-dimensional extended Hubbard model are used to study the stability and dynamics of d-wave pairing when a nearest-neighbor Coulomb repulsion V is present in addition to the on-site Coulomb repulsion U. We find that d-wave pairing and the superconducting transition temperature Tc are only weakly suppressed as long as V does not exceed U/2. This stability is traced to the strongly retarded nature of pairing that allows the d-wave pairs to minimize the repulsive effect of V. When V approaches U/2, large momentum charge fluctuations are found to become important andmore » to give rise to a more rapid suppression of d-wave pairing and T c than for smaller V.« less

Exploitation of RF-DNA for Device Classification and Verification Using GRLVQI Processing

DTIC Science & Technology

2012-12-01

5 FLD Fisher’s Linear Discriminant . . . . . . . . . . . . . . . . . . . 6 kNN K-Nearest Neighbor...Neighbor ( kNN ), Support Vector Machine (SVM), and simple cross-correlation techniques [40, 57, 82, 88, 94, 95]. The RF-DNA fingerprinting research in...Expansion and the Dis- crete Gabor Transform on a Non-Separable Lattice”. 2000 IEEE Int’l Conf on Acoustics, Speech , and Signal Processing (ICASSP00

A Fast Implementation of the ISOCLUS Algorithm

NASA Technical Reports Server (NTRS)

Memarsadeghi, Nargess; Mount, David M.; Netanyahu, Nathan S.; LeMoigne, Jacqueline

2003-01-01

Unsupervised clustering is a fundamental building block in numerous image processing applications. One of the most popular and widely used clustering schemes for remote sensing applications is the ISOCLUS algorithm, which is based on the ISODATA method. The algorithm is given a set of n data points in d-dimensional space, an integer k indicating the initial number of clusters, and a number of additional parameters. The general goal is to compute the coordinates of a set of cluster centers in d-space, such that those centers minimize the mean squared distance from each data point to its nearest center. This clustering algorithm is similar to another well-known clustering method, called k-means. One significant feature of ISOCLUS over k-means is that the actual number of clusters reported might be fewer or more than the number supplied as part of the input. The algorithm uses different heuristics to determine whether to merge lor split clusters. As ISOCLUS can run very slowly, particularly on large data sets, there has been a growing .interest in the remote sensing community in computing it efficiently. We have developed a faster implementation of the ISOCLUS algorithm. Our improvement is based on a recent acceleration to the k-means algorithm of Kanungo, et al. They showed that, by using a kd-tree data structure for storing the data, it is possible to reduce the running time of k-means. We have adapted this method for the ISOCLUS algorithm, and we show that it is possible to achieve essentially the same results as ISOCLUS on large data sets, but with significantly lower running times. This adaptation involves computing a number of cluster statistics that are needed for ISOCLUS but not for k-means. Both the k-means and ISOCLUS algorithms are based on iterative schemes, in which nearest neighbors are calculated until some convergence criterion is satisfied. Each iteration requires that the nearest center for each data point be computed. Naively, this requires O(kn) time, where k denotes the current number of centers. Traditional techniques for accelerating nearest neighbor searching involve storing the k centers in a data structure. However, because of the iterative nature of the algorithm, this data structure would need to be rebuilt with each new iteration. Our approach is to store the data points in a kd-tree data structure. The assignment of points to nearest neighbors is carried out by a filtering process, which successively eliminates centers that can not possibly be the nearest neighbor for a given region of space. This algorithm is significantly faster, because large groups of data points can be assigned to their nearest center in a single operation. Preliminary results on a number of real Landsat datasets show that our revised ISOCLUS-like scheme runs about twice as fast.

Nearest neighbor-density-based clustering methods for large hyperspectral images

NASA Astrophysics Data System (ADS)

Cariou, Claude; Chehdi, Kacem

2017-10-01

We address the problem of hyperspectral image (HSI) pixel partitioning using nearest neighbor - density-based (NN-DB) clustering methods. NN-DB methods are able to cluster objects without specifying the number of clusters to be found. Within the NN-DB approach, we focus on deterministic methods, e.g. ModeSeek, knnClust, and GWENN (standing for Graph WatershEd using Nearest Neighbors). These methods only require the availability of a k-nearest neighbor (kNN) graph based on a given distance metric. Recently, a new DB clustering method, called Density Peak Clustering (DPC), has received much attention, and kNN versions of it have quickly followed and showed their efficiency. However, NN-DB methods still suffer from the difficulty of obtaining the kNN graph due to the quadratic complexity with respect to the number of pixels. This is why GWENN was embedded into a multiresolution (MR) scheme to bypass the computation of the full kNN graph over the image pixels. In this communication, we propose to extent the MR-GWENN scheme on three aspects. Firstly, similarly to knnClust, the original labeling rule of GWENN is modified to account for local density values, in addition to the labels of previously processed objects. Secondly, we set up a modified NN search procedure within the MR scheme, in order to stabilize of the number of clusters found from the coarsest to the finest spatial resolution. Finally, we show that these extensions can be easily adapted to the three other NN-DB methods (ModeSeek, knnClust, knnDPC) for pixel clustering in large HSIs. Experiments are conducted to compare the four NN-DB methods for pixel clustering in HSIs. We show that NN-DB methods can outperform a classical clustering method such as fuzzy c-means (FCM), in terms of classification accuracy, relevance of found clusters, and clustering speed. Finally, we demonstrate the feasibility and evaluate the performances of NN-DB methods on a very large image acquired by our AISA Eagle hyperspectral imaging sensor.

Misregistration in Adaptive Optics Systems

DTIC Science & Technology

2009-03-01

introduces a new factor called the influence function , or the amount of slope that is introduced in neighboring subapertures by pushing one actuator...nearest neighbor is unity. The actuator influence function Akl, is the phase caused by poking an individual actuator. It is assumed that Akl = 1 at the...The square bracket indicates actua- tor indices, and the round brackets are subaperture indices. 28 influence function is given by A(x, y

Three Dimensional Object Recognition Using a Complex Autoregressive Model

DTIC Science & Technology

1993-12-01

3.4.2 Template Matching Algorithm ...................... 3-16 3.4.3 K-Nearest-Neighbor ( KNN ) Techniques ................. 3-25 3.4.4 Hidden Markov Model...Neighbor ( KNN ) Test Results ...................... 4-13 4.2.1 Single-Look 1-NN Testing .......................... 4-14 4.2.2 Multiple-Look 1-NN Testing...4-15 4.2.3 Discussion of KNN Test Results ...................... 4-15 4.3 Hidden Markov Model (HMM) Test Results

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

Chatterjee, Anupam; Department of Chemistry, Indian Institute of Technology Bombay, Powai, Mumbai 400076; Higham, Jonathan

A range of methods are presented to calculate a solute’s hydration shell from computer simulations of dilute solutions of monatomic ions and noble gas atoms. The methods are designed to be parameter-free and instantaneous so as to make them more general, accurate, and consequently applicable to disordered systems. One method is a modified nearest-neighbor method, another considers solute-water Lennard-Jones overlap followed by hydrogen-bond rearrangement, while three methods compare various combinations of water-solute and water-water forces. The methods are tested on a series of monatomic ions and solutes and compared with the values from cutoffs in the radial distribution function, themore » nearest-neighbor distribution functions, and the strongest-acceptor hydrogen bond definition for anions. The Lennard-Jones overlap method and one of the force-comparison methods are found to give a hydration shell for cations which is in reasonable agreement with that using a cutoff in the radial distribution function. Further modifications would be required, though, to make them capture the neighboring water molecules of noble-gas solutes if these weakly interacting molecules are considered to constitute the hydration shell.« less

Equilibrium, metastability, and hysteresis in a model spin-crossover material with nearest-neighbor antiferromagnetic-like and long-range ferromagnetic-like interactions

NASA Astrophysics Data System (ADS)

Rikvold, Per Arne; Brown, Gregory; Miyashita, Seiji; Omand, Conor; Nishino, Masamichi

2016-02-01

Phase diagrams and hysteresis loops were obtained by Monte Carlo simulations and a mean-field method for a simplified model of a spin-crossover material with a two-step transition between the high-spin and low-spin states. This model is a mapping onto a square-lattice S =1 /2 Ising model with antiferromagnetic nearest-neighbor and ferromagnetic Husimi-Temperley (equivalent-neighbor) long-range interactions. Phase diagrams obtained by the two methods for weak and strong long-range interactions are found to be similar. However, for intermediate-strength long-range interactions, the Monte Carlo simulations show that tricritical points decompose into pairs of critical end points and mean-field critical points surrounded by horn-shaped regions of metastability. Hysteresis loops along paths traversing the horn regions are strongly reminiscent of thermal two-step transition loops with hysteresis, recently observed experimentally in several spin-crossover materials. We believe analogous phenomena should be observable in experiments and simulations for many systems that exhibit competition between local antiferromagnetic-like interactions and long-range ferromagnetic-like interactions caused by elastic distortions.

SVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition

PubMed Central

Melvin, Iain; Ie, Eugene; Kuang, Rui; Weston, Jason; Stafford, William Noble; Leslie, Christina

2007-01-01

Background Predicting a protein's structural class from its amino acid sequence is a fundamental problem in computational biology. Much recent work has focused on developing new representations for protein sequences, called string kernels, for use with support vector machine (SVM) classifiers. However, while some of these approaches exhibit state-of-the-art performance at the binary protein classification problem, i.e. discriminating between a particular protein class and all other classes, few of these studies have addressed the real problem of multi-class superfamily or fold recognition. Moreover, there are only limited software tools and systems for SVM-based protein classification available to the bioinformatics community. Results We present a new multi-class SVM-based protein fold and superfamily recognition system and web server called SVM-Fold, which can be found at . Our system uses an efficient implementation of a state-of-the-art string kernel for sequence profiles, called the profile kernel, where the underlying feature representation is a histogram of inexact matching k-mer frequencies. We also employ a novel machine learning approach to solve the difficult multi-class problem of classifying a sequence of amino acids into one of many known protein structural classes. Binary one-vs-the-rest SVM classifiers that are trained to recognize individual structural classes yield prediction scores that are not comparable, so that standard "one-vs-all" classification fails to perform well. Moreover, SVMs for classes at different levels of the protein structural hierarchy may make useful predictions, but one-vs-all does not try to combine these multiple predictions. To deal with these problems, our method learns relative weights between one-vs-the-rest classifiers and encodes information about the protein structural hierarchy for multi-class prediction. In large-scale benchmark results based on the SCOP database, our code weighting approach significantly improves on the standard one-vs-all method for both the superfamily and fold prediction in the remote homology setting and on the fold recognition problem. Moreover, our code weight learning algorithm strongly outperforms nearest-neighbor methods based on PSI-BLAST in terms of prediction accuracy on every structure classification problem we consider. Conclusion By combining state-of-the-art SVM kernel methods with a novel multi-class algorithm, the SVM-Fold system delivers efficient and accurate protein fold and superfamily recognition. PMID:17570145

Realization of the axial next-nearest-neighbor Ising model in U 3 Al 2 Ge 3

DOE PAGES

Fobes, David M.; Lin, Shi-Zeng; Ghimire, Nirmal J.; ...

2017-11-09

Inmore » this paper, we report small-angle neutron scattering (SANS) measurements and theoretical modeling of U 3 Al 2 Ge 3 . Analysis of the SANS data reveals a phase transition to sinusoidally modulated magnetic order at T N = 63 K to be second order and a first-order phase transition to ferromagnetic order at T c = 48 K. Within the sinusoidally modulated magnetic phase (T c < T < T N), we uncover a dramatic change, by a factor of 3, in the ordering wave vector as a function of temperature. Finally, these observations all indicate that U 3 Al 2 Ge 3 is a close realization of the three-dimensional axial next-nearest-neighbor Ising model, a prototypical framework for describing commensurate to incommensurate phase transitions in frustrated magnets.« less

Realization of the axial next-nearest-neighbor Ising model in U 3 Al 2 Ge 3

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

Fobes, David M.; Lin, Shi-Zeng; Ghimire, Nirmal J.

Inmore » this paper, we report small-angle neutron scattering (SANS) measurements and theoretical modeling of U 3 Al 2 Ge 3 . Analysis of the SANS data reveals a phase transition to sinusoidally modulated magnetic order at T N = 63 K to be second order and a first-order phase transition to ferromagnetic order at T c = 48 K. Within the sinusoidally modulated magnetic phase (T c < T < T N), we uncover a dramatic change, by a factor of 3, in the ordering wave vector as a function of temperature. Finally, these observations all indicate that U 3 Al 2 Ge 3 is a close realization of the three-dimensional axial next-nearest-neighbor Ising model, a prototypical framework for describing commensurate to incommensurate phase transitions in frustrated magnets.« less

Meat and Fish Freshness Inspection System Based on Odor Sensing

PubMed Central

Hasan, Najam ul; Ejaz, Naveed; Ejaz, Waleed; Kim, Hyung Seok

2012-01-01

We propose a method for building a simple electronic nose based on commercially available sensors used to sniff in the market and identify spoiled/contaminated meat stocked for sale in butcher shops. Using a metal oxide semiconductor-based electronic nose, we measured the smell signature from two of the most common meat foods (beef and fish) stored at room temperature. Food samples were divided into two groups: fresh beef with decayed fish and fresh fish with decayed beef. The prime objective was to identify the decayed item using the developed electronic nose. Additionally, we tested the electronic nose using three pattern classification algorithms (artificial neural network, support vector machine and k-nearest neighbor), and compared them based on accuracy, sensitivity, and specificity. The results demonstrate that the k-nearest neighbor algorithm has the highest accuracy. PMID:23202222

Categorizing document by fuzzy C-Means and K-nearest neighbors approach

NASA Astrophysics Data System (ADS)

Priandini, Novita; Zaman, Badrus; Purwanti, Endah

2017-08-01

Increasing of technology had made categorizing documents become important. It caused by increasing of number of documents itself. Managing some documents by categorizing is one of Information Retrieval application, because it involve text mining on its process. Whereas, categorization technique could be done both Fuzzy C-Means (FCM) and K-Nearest Neighbors (KNN) method. This experiment would consolidate both methods. The aim of the experiment is increasing performance of document categorize. First, FCM is in order to clustering training documents. Second, KNN is in order to categorize testing document until the output of categorization is shown. Result of the experiment is 14 testing documents retrieve relevantly to its category. Meanwhile 6 of 20 testing documents retrieve irrelevant to its category. Result of system evaluation shows that both precision and recall are 0,7.

Truncated Calogero-Sutherland models

NASA Astrophysics Data System (ADS)

Pittman, S. M.; Beau, M.; Olshanii, M.; del Campo, A.

2017-05-01

A one-dimensional quantum many-body system consisting of particles confined in a harmonic potential and subject to finite-range two-body and three-body inverse-square interactions is introduced. The range of the interactions is set by truncation beyond a number of neighbors and can be tuned to interpolate between the Calogero-Sutherland model and a system with nearest and next-nearest neighbors interactions discussed by Jain and Khare. The model also includes the Tonks-Girardeau gas describing impenetrable bosons as well as an extension with truncated interactions. While the ground state wave function takes a truncated Bijl-Jastrow form, collective modes of the system are found in terms of multivariable symmetric polynomials. We numerically compute the density profile, one-body reduced density matrix, and momentum distribution of the ground state as a function of the range r and the interaction strength.

EPR investigation of the trivalent chromium complexes in SrTiO3

NASA Astrophysics Data System (ADS)

Azamat, D. V.; Dejneka, A.; Lančok, J.; Jastrabik, L.; Trepakov, V. A.; Bryknar, Z.; Neverova, E. V.; Badalyan, A. G.

2014-02-01

The trivalent chromium centers were investigated by means of electron paramagnetic resonance (EPR) in SrTiO3 single crystals grown using the Verneuil technique. It was shown that the charge compensation of the Cr3+-VO dominant centers in octahedral environment is due to the remote oxygen vacancy located on the axial axis of the center. In order to provide insight into spin-phonon relaxation processes the studies of axial distortion of Cr3+-VO centers have been performed as function of temperature. The analysis of the trigonal Cr3+ centers found in SrTiO3 indicates the presence of the nearest-neighbor strontium vacancy. The next-nearest-neighbor exchange-coupled pairs of Cr3+ in SrTiO3 has been analyzed from the angular variation of the total electron spin of S=2 resonance lines.

Medical Image Retrieval Using Multi-Texton Assignment.

PubMed

Tang, Qiling; Yang, Jirong; Xia, Xianfu

2018-02-01

In this paper, we present a multi-texton representation method for medical image retrieval, which utilizes the locality constraint to encode each filter bank response within its local-coordinate system consisting of the k nearest neighbors in texton dictionary and subsequently employs spatial pyramid matching technique to implement feature vector representation. Comparison with the traditional nearest neighbor assignment followed by texton histogram statistics method, our strategies reduce the quantization errors in mapping process and add information about the spatial layout of texton distributions and, thus, increase the descriptive power of the image representation. We investigate the effects of different parameters on system performance in order to choose the appropriate ones for our datasets and carry out experiments on the IRMA-2009 medical collection and the mammographic patch dataset. The extensive experimental results demonstrate that the proposed method has superior performance.

Neural Network and Nearest Neighbor Algorithms for Enhancing Sampling of Molecular Dynamics.

PubMed

Galvelis, Raimondas; Sugita, Yuji

2017-06-13

The free energy calculations of complex chemical and biological systems with molecular dynamics (MD) are inefficient due to multiple local minima separated by high-energy barriers. The minima can be escaped using an enhanced sampling method such as metadynamics, which apply bias (i.e., importance sampling) along a set of collective variables (CV), but the maximum number of CVs (or dimensions) is severely limited. We propose a high-dimensional bias potential method (NN2B) based on two machine learning algorithms: the nearest neighbor density estimator (NNDE) and the artificial neural network (ANN) for the bias potential approximation. The bias potential is constructed iteratively from short biased MD simulations accounting for correlation among CVs. Our method is capable of achieving ergodic sampling and calculating free energy of polypeptides with up to 8-dimensional bias potential.

Accurate modeling of defects in graphene transport calculations

NASA Astrophysics Data System (ADS)

Linhart, Lukas; Burgdörfer, Joachim; Libisch, Florian

2018-01-01

We present an approach for embedding defect structures modeled by density functional theory into large-scale tight-binding simulations. We extract local tight-binding parameters for the vicinity of the defect site using Wannier functions. In the transition region between the bulk lattice and the defect the tight-binding parameters are continuously adjusted to approach the bulk limit far away from the defect. This embedding approach allows for an accurate high-level treatment of the defect orbitals using as many as ten nearest neighbors while keeping a small number of nearest neighbors in the bulk to render the overall computational cost reasonable. As an example of our approach, we consider an extended graphene lattice decorated with Stone-Wales defects, flower defects, double vacancies, or silicon substitutes. We predict distinct scattering patterns mirroring the defect symmetries and magnitude that should be experimentally accessible.

Diversity of charge orderings in correlated systems

NASA Astrophysics Data System (ADS)

Kapcia, Konrad Jerzy; Barański, Jan; Ptok, Andrzej

2017-10-01

The phenomenon associated with inhomogeneous distribution of electron density is known as a charge ordering. In this work, we study the zero-bandwidth limit of the extended Hubbard model, which can be considered as a simple effective model of charge ordered insulators. It consists of the on-site interaction U and the intersite density-density interactions W1 and W2 between nearest neighbors and next-nearest neighbors, respectively. We derived the exact ground state diagrams for different lattice dimensionalities and discuss effects of small finite temperatures in the limit of high dimensions. In particular, we estimated the critical interactions for which new ordered phases emerge (laminar or stripe and four-sublattice-type). Our analysis show that the ground state of the model is highly degenerated. One of the most intriguing finding is that the nonzero temperature removes these degenerations.

Multidimensional Optimization of Signal Space Distance Parameters in WLAN Positioning

PubMed Central

Brković, Milenko; Simić, Mirjana

2014-01-01

Accurate indoor localization of mobile users is one of the challenging problems of the last decade. Besides delivering high speed Internet, Wireless Local Area Network (WLAN) can be used as an effective indoor positioning system, being competitive both in terms of accuracy and cost. Among the localization algorithms, nearest neighbor fingerprinting algorithms based on Received Signal Strength (RSS) parameter have been extensively studied as an inexpensive solution for delivering indoor Location Based Services (LBS). In this paper, we propose the optimization of the signal space distance parameters in order to improve precision of WLAN indoor positioning, based on nearest neighbor fingerprinting algorithms. Experiments in a real WLAN environment indicate that proposed optimization leads to substantial improvements of the localization accuracy. Our approach is conceptually simple, is easy to implement, and does not require any additional hardware. PMID:24757443

Ground state of a Heisenberg chain with next-nearest-neighbor bond alternation

NASA Astrophysics Data System (ADS)

Capriotti, Luca; Becca, Federico; Sorella, Sandro; Parola, Alberto

2003-05-01

We investigate the ground-state properties of the spin-half J1-J2 Heisenberg chain with a next-nearest-neighbor spin-Peierls dimerization using conformal field theory and Lanczos exact diagonalizations. In agreement with the results of a recent bosonization analysis by Sarkar and Sen [Phys. Rev. B 65, 172408 (2002)], we find that for small frustration (J2/J1) the system is in a Luttinger spin-fluid phase, with gapless excitations, and a finite spin-wave velocity. In the regime of strong frustration the ground state is spontaneously dimerized and the bond alternation reduces the triplet gap, leading to a slight enhancement of the critical point separating the Luttinger phase from the gapped one. An accurate determination of the phase boundary is obtained numerically from the study of the excitation spectrum.

Analysis of the seismicity preceding large earthquakes

NASA Astrophysics Data System (ADS)

Stallone, Angela; Marzocchi, Warner

2017-04-01

The most common earthquake forecasting models assume that the magnitude of the next earthquake is independent from the past. This feature is probably one of the most severe limitations of the capability to forecast large earthquakes. In this work, we investigate empirically on this specific aspect, exploring whether variations in seismicity in the space-time-magnitude domain encode some information on the size of the future earthquakes. For this purpose, and to verify the stability of the findings, we consider seismic catalogs covering quite different space-time-magnitude windows, such as the Alto Tiberina Near Fault Observatory (TABOO) catalogue, the California and Japanese seismic catalog. Our method is inspired by the statistical methodology proposed by Baiesi & Paczuski (2004) and elaborated by Zaliapin et al. (2008) to distinguish between triggered and background earthquakes, based on a pairwise nearest-neighbor metric defined by properly rescaled temporal and spatial distances. We generalize the method to a metric based on the k-nearest-neighbors that allows us to consider the overall space-time-magnitude distribution of k-earthquakes, which are the strongly correlated ancestors of a target event. Finally, we analyze the statistical properties of the clusters composed by the target event and its k-nearest-neighbors. In essence, the main goal of this study is to verify if different classes of target event magnitudes are characterized by distinctive "k-foreshocks" distributions. The final step is to show how the findings of this work may (or not) improve the skill of existing earthquake forecasting models.

Analysis of the Seismicity Preceding Large Earthquakes

NASA Astrophysics Data System (ADS)

Stallone, A.; Marzocchi, W.

2016-12-01

The most common earthquake forecasting models assume that the magnitude of the next earthquake is independent from the past. This feature is probably one of the most severe limitations of the capability to forecast large earthquakes.In this work, we investigate empirically on this specific aspect, exploring whether spatial-temporal variations in seismicity encode some information on the magnitude of the future earthquakes. For this purpose, and to verify the universality of the findings, we consider seismic catalogs covering quite different space-time-magnitude windows, such as the Alto Tiberina Near Fault Observatory (TABOO) catalogue, and the California and Japanese seismic catalog. Our method is inspired by the statistical methodology proposed by Zaliapin (2013) to distinguish triggered and background earthquakes, using the nearest-neighbor clustering analysis in a two-dimension plan defined by rescaled time and space. In particular, we generalize the metric based on the nearest-neighbor to a metric based on the k-nearest-neighbors clustering analysis that allows us to consider the overall space-time-magnitude distribution of k-earthquakes (k-foreshocks) which anticipate one target event (the mainshock); then we analyze the statistical properties of the clusters identified in this rescaled space. In essence, the main goal of this study is to verify if different classes of mainshock magnitudes are characterized by distinctive k-foreshocks distribution. The final step is to show how the findings of this work may (or not) improve the skill of existing earthquake forecasting models.

Mean and Fluctuating Force Distribution in a Random Array of Spheres

NASA Astrophysics Data System (ADS)

Akiki, Georges; Jackson, Thomas; Balachandar, Sivaramakrishnan

2015-11-01

This study presents a numerical study of the force distribution within a cluster of mono-disperse spherical particles. A direct forcing immersed boundary method is used to calculate the forces on individual particles for a volume fraction range of [0.1, 0.4] and a Reynolds number range of [10, 625]. The overall drag is compared to several drag laws found in the literature. As for the fluctuation of the hydrodynamic streamwise force among individual particles, it is shown to have a normal distribution with a standard deviation that varies with the volume fraction only. The standard deviation remains approximately 25% of the mean streamwise force on a single sphere. The force distribution shows a good correlation between the location of two to three nearest upstream and downstream neighbors and the magnitude of the forces. A detailed analysis of the pressure and shear forces contributions calculated on a ghost sphere in the vicinity of a single particle in a uniform flow reveals a mapping of those contributions. The combination of the mapping and number of nearest neighbors leads to a first order correction of the force distribution within a cluster which can be used in Lagrangian-Eulerian techniques. We also explore the possibility of a binary force model that systematically accounts for the effect of the nearest neighbors. This work was supported by the National Science Foundation (NSF OISE-0968313) under Partnership for International Research and Education (PIRE) in Multiphase Flows at the University of Florida.

High-temperature dynamic behavior in bulk liquid water: A molecular dynamics simulation study using the OPC and TIP4P-Ew potentials

NASA Astrophysics Data System (ADS)

Gabrieli, Andrea; Sant, Marco; Izadi, Saeed; Shabane, Parviz Seifpanahi; Onufriev, Alexey V.; Suffritti, Giuseppe B.

2018-02-01

Classical molecular dynamics simulations were performed to study the high-temperature (above 300 K) dynamic behavior of bulk water, specifically the behavior of the diffusion coefficient, hydrogen bond, and nearest-neighbor lifetimes. Two water potentials were compared: the recently proposed "globally optimal" point charge (OPC) model and the well-known TIP4P-Ew model. By considering the Arrhenius plots of the computed inverse diffusion coefficient and rotational relaxation constants, a crossover from Vogel-Fulcher-Tammann behavior to a linear trend with increasing temperature was detected at T* ≈ 309 and T* ≈ 285 K for the OPC and TIP4P-Ew models, respectively. Experimentally, the crossover point was previously observed at T* ± 315-5 K. We also verified that for the coefficient of thermal expansion α P ( T, P), the isobaric α P ( T) curves cross at about the same T* as in the experiment. The lifetimes of water hydrogen bonds and of the nearest neighbors were evaluated and were found to cross near T*, where the lifetimes are about 1 ps. For T < T*, hydrogen bonds persist longer than nearest neighbors, suggesting that the hydrogen bonding network dominates the water structure at T < T*, whereas for T > T*, water behaves more like a simple liquid. The fact that T* falls within the biologically relevant temperature range is a strong motivation for further analysis of the phenomenon and its possible consequences for biomolecular systems.

Truncated Calogero-Sutherland models on a circle

NASA Astrophysics Data System (ADS)

Tummuru, Tarun R.; Jain, Sudhir R.; Khare, Avinash

2017-12-01

We investigate a quantum many-body system with particles moving in a circle and subject to two-body and three-body potentials. This class of models, in which the range of interaction r can be set to a certain number of neighbors, extrapolates from a system with interactions up to next-to-nearest neighbors and the celebrated Calogero-Sutherland model. The exact ground state energy and a part of the excitation spectrum have been obtained.

Relative resolution: A hybrid formalism for fluid mixtures.

PubMed

Chaimovich, Aviel; Peter, Christine; Kremer, Kurt

2015-12-28

We show here that molecular resolution is inherently hybrid in terms of relative separation. While nearest neighbors are characterized by a fine-grained (geometrically detailed) model, other neighbors are characterized by a coarse-grained (isotropically simplified) model. We notably present an analytical expression for relating the two models via energy conservation. This hybrid framework is correspondingly capable of retrieving the structural and thermal behavior of various multi-component and multi-phase fluids across state space.

Relative resolution: A hybrid formalism for fluid mixtures

NASA Astrophysics Data System (ADS)

Chaimovich, Aviel; Peter, Christine; Kremer, Kurt

2015-12-01

We show here that molecular resolution is inherently hybrid in terms of relative separation. While nearest neighbors are characterized by a fine-grained (geometrically detailed) model, other neighbors are characterized by a coarse-grained (isotropically simplified) model. We notably present an analytical expression for relating the two models via energy conservation. This hybrid framework is correspondingly capable of retrieving the structural and thermal behavior of various multi-component and multi-phase fluids across state space.

From pairwise to group interactions in games of cyclic dominance.

PubMed

Szolnoki, Attila; Vukov, Jeromos; Perc, Matjaž

2014-06-01

We study the rock-paper-scissors game in structured populations, where the invasion rates determine individual payoffs that govern the process of strategy change. The traditional version of the game is recovered if the payoffs for each potential invasion stem from a single pairwise interaction. However, the transformation of invasion rates to payoffs also allows the usage of larger interaction ranges. In addition to the traditional pairwise interaction, we therefore consider simultaneous interactions with all nearest neighbors, as well as with all nearest and next-nearest neighbors, thus effectively going from single pair to group interactions in games of cyclic dominance. We show that differences in the interaction range affect not only the stationary fractions of strategies but also their relations of dominance. The transition from pairwise to group interactions can thus decelerate and even revert the direction of the invasion between the competing strategies. Like in evolutionary social dilemmas, in games of cyclic dominance, too, the indirect multipoint interactions that are due to group interactions hence play a pivotal role. Our results indicate that, in addition to the invasion rates, the interaction range is at least as important for the maintenance of biodiversity among cyclically competing strategies.

Nation-Building Modeling and Resource Allocation Via Dynamic Programming

DTIC Science & Technology

2014-09-01

Figure 2. RAND Study Models[59:98,115] (WMA) and used both the k-Nearest Neighbor ( KNN ) and Nearest Centroid (NC) algorithms to classify future features...The study found that KNN performed bet- ter than NC with 85% or greater accuracy in all test cases. The methodology was adopted for use under the...analysis feature of the model. 3.7.1 The No Surge Alternative. On the 10th of January 2007, President George W. Bush delivered a speech to the American

Finite element computation on nearest neighbor connected machines

NASA Technical Reports Server (NTRS)

Mcaulay, A. D.

1984-01-01

Research aimed at faster, more cost effective parallel machines and algorithms for improving designer productivity with finite element computations is discussed. A set of 8 boards, containing 4 nearest neighbor connected arrays of commercially available floating point chips and substantial memory, are inserted into a commercially available machine. One-tenth Mflop (64 bit operation) processors provide an 89% efficiency when solving the equations arising in a finite element problem for a single variable regular grid of size 40 by 40 by 40. This is approximately 15 to 20 times faster than a much more expensive machine such as a VAX 11/780 used in double precision. The efficiency falls off as faster or more processors are envisaged because communication times become dominant. A novel successive overrelaxation algorithm which uses cyclic reduction in order to permit data transfer and computation to overlap in time is proposed.

Liquid li structure and dynamics: A comparison between OFDFT and second nearest-neighbor embedded-atom method

DOE PAGES

Chen, Mohan; Vella, Joseph R.; Panagiotopoulos, Athanassios Z.; ...

2015-04-08

The structure and dynamics of liquid lithium are studied using two simulation methods: orbital-free (OF) first-principles molecular dynamics (MD), which employs OF density functional theory (DFT), and classical MD utilizing a second nearest-neighbor embedded-atom method potential. The properties we studied include the dynamic structure factor, the self-diffusion coefficient, the dispersion relation, the viscosity, and the bond angle distribution function. Our simulation results were compared to available experimental data when possible. Each method has distinct advantages and disadvantages. For example, OFDFT gives better agreement with experimental dynamic structure factors, yet is more computationally demanding than classical simulations. Classical simulations can accessmore » a broader temperature range and longer time scales. The combination of first-principles and classical simulations is a powerful tool for studying properties of liquid lithium.« less

Quantum phases in circuit QED with a superconducting qubit array

PubMed Central

Zhang, Yuanwei; Yu, Lixian; Liang, J. -Q; Chen, Gang; Jia, Suotang; Nori, Franco

2014-01-01

Circuit QED on a chip has become a powerful platform for simulating complex many-body physics. In this report, we realize a Dicke-Ising model with an antiferromagnetic nearest-neighbor spin-spin interaction in circuit QED with a superconducting qubit array. We show that this system exhibits a competition between the collective spin-photon interaction and the antiferromagnetic nearest-neighbor spin-spin interaction, and then predict four quantum phases, including: a paramagnetic normal phase, an antiferromagnetic normal phase, a paramagnetic superradiant phase, and an antiferromagnetic superradiant phase. The antiferromagnetic normal phase and the antiferromagnetic superradiant phase are new phases in many-body quantum optics. In the antiferromagnetic superradiant phase, both the antiferromagnetic and superradiant orders can coexist, and thus the system possesses symmetry. Moreover, we find an unconventional photon signature in this phase. In future experiments, these predicted quantum phases could be distinguished by detecting both the mean-photon number and the magnetization. PMID:24522250

Field-induced States and Excitations in the Quasicritical Spin-1 /2 Chain Linarite

NASA Astrophysics Data System (ADS)

Cemal, Eron; Enderle, Mechthild; Kremer, Reinhard K.; Fâk, Björn; Ressouche, Eric; Goff, Jon P.; Gvozdikova, Mariya V.; Zhitomirsky, Mike E.; Ziman, Tim

2018-02-01

The mineral linarite, PbCuSO4(OH )2 , is a spin-1 /2 chain with frustrating nearest-neighbor ferromagnetic and next-nearest-neighbor antiferromagnetic exchange interactions. Our inelastic neutron scattering experiments performed above the saturation field establish that the ratio between these exchanges is such that linarite is extremely close to the quantum critical point between spin-multipolar phases and the ferromagnetic state. We show that the predicted quantum multipolar phases are fragile and actually suppressed by a tiny orthorhombic exchange anisotropy and weak interchain interactions in favor of a dipolar fan phase. Including this anisotropy in classical simulations of a nearly critical model explains the field-dependent phase sequence of the phase diagram of linarite, its strong dependence of the magnetic field direction, and the measured variations of the wave vector as well as the staggered and the uniform magnetizations in an applied field.

``Glue" approximation for the pairing interaction in the Hubbard model with next nearest neighbor hopping

NASA Astrophysics Data System (ADS)

Khatami, Ehsan; Macridin, Alexandru; Jarrell, Mark

2008-03-01

Recently, several authors have employed the ``glue" approximation for the Cuprates in which the full pairing vertex is approximated by the spin susceptibility. We study this approximation using Quantum Monte Carlo Dynamical Cluster Approximation methods on a 2D Hubbard model. By considering a reasonable finite value for the next nearest neighbor hopping, we find that this ``glue" approximation, in the current form, does not capture the correct pairing symmetry. Here, d-wave is not the leading pairing symmetry while it is the dominant symmetry using the ``exact" QMC results. We argue that the sensitivity of this approximation to the band structure changes leads to this inconsistency and that this form of interaction may not be the appropriate description of the pairing mechanism in Cuprates. We suggest improvements to this approximation which help to capture the the essential features of the QMC data.

Experimental investigation of vector static magnetic field detection using an NV center with a single first-shell 13C nuclear spin in diamond

NASA Astrophysics Data System (ADS)

Jiang, Feng-Jian; Ye, Jian-Feng; Jiao, Zheng; Jiang, Jun; Ma, Kun; Yan, Xin-Hu; Lv, Hai-Jiang

2018-05-01

We perform a proof-of-principle experiment that uses a single negatively charged nitrogen–vacancy (NV) color center with a nearest neighbor 13C nuclear spin in diamond to detect the strength and direction (including both polar and azimuth angles) of a static vector magnetic field by optical detection magnetic resonance (ODMR) technique. With the known hyperfine coupling tensor between an NV center and a nearest neighbor 13C nuclear spin, we show that the information of static vector magnetic field could be extracted by observing the pulsed continuous wave (CW) spectrum. Project supported by the National Natural Science Foundation of China (Grant Nos. 11305074, 11135002, and 11275083), the Key Program of the Education Department Outstanding Youth Foundation of Anhui Province, China (Grant No. gxyqZD2017080), and the Education Department Natural Science Foundation of Anhui Province, China (Grant No. KJHS2015B09).

A collaborative filtering recommendation algorithm based on weighted SimRank and social trust

NASA Astrophysics Data System (ADS)

Su, Chang; Zhang, Butao

2017-05-01

Collaborative filtering is one of the most widely used recommendation technologies, but the data sparsity and cold start problem of collaborative filtering algorithms are difficult to solve effectively. In order to alleviate the problem of data sparsity in collaborative filtering algorithm, firstly, a weighted improved SimRank algorithm is proposed to compute the rating similarity between users in rating data set. The improved SimRank can find more nearest neighbors for target users according to the transmissibility of rating similarity. Then, we build trust network and introduce the calculation of trust degree in the trust relationship data set. Finally, we combine rating similarity and trust to build a comprehensive similarity in order to find more appropriate nearest neighbors for target user. Experimental results show that the algorithm proposed in this paper improves the recommendation precision of the Collaborative algorithm effectively.

Integrating instance selection, instance weighting, and feature weighting for nearest neighbor classifiers by coevolutionary algorithms.

PubMed

Derrac, Joaquín; Triguero, Isaac; Garcia, Salvador; Herrera, Francisco

2012-10-01

Cooperative coevolution is a successful trend of evolutionary computation which allows us to define partitions of the domain of a given problem, or to integrate several related techniques into one, by the use of evolutionary algorithms. It is possible to apply it to the development of advanced classification methods, which integrate several machine learning techniques into a single proposal. A novel approach integrating instance selection, instance weighting, and feature weighting into the framework of a coevolutionary model is presented in this paper. We compare it with a wide range of evolutionary and nonevolutionary related methods, in order to show the benefits of the employment of coevolution to apply the techniques considered simultaneously. The results obtained, contrasted through nonparametric statistical tests, show that our proposal outperforms other methods in the comparison, thus becoming a suitable tool in the task of enhancing the nearest neighbor classifier.

Incommensurate phase of a triangular frustrated Heisenberg model studied via Schwinger-boson mean-field theory

NASA Astrophysics Data System (ADS)

Li, Peng; Su, Haibin; Dong, Hui-Ning; Shen, Shun-Qing

2009-08-01

We study a triangular frustrated antiferromagnetic Heisenberg model with nearest-neighbor interactions J1 and third-nearest-neighbor interactions J3 by means of Schwinger-boson mean-field theory. By setting an antiferromagnetic J3 and varying J1 from positive to negative values, we disclose the low-temperature features of its interesting incommensurate phase. The gapless dispersion of quasiparticles leads to the intrinsic T2 law of specific heat. The magnetic susceptibility is linear in temperature. The local magnetization is significantly reduced by quantum fluctuations. We address possible relevance of these results to the low-temperature properties of NiGa2S4. From a careful analysis of the incommensurate spin wavevector, the interaction parameters are estimated as J1≈-3.8755 K and J3≈14.0628 K, in order to account for the experimental data.

Construction of phase diagrams for nanoscaled Ising thin films on the honeycomb lattice using cellular automata simulation approach

NASA Astrophysics Data System (ADS)

Ghaemi, Mehrdad; Javadi, Nabi

2017-11-01

The phase diagrams of the three-layer Ising model on the honeycomb lattice with a diluted surface have been constructed using the probabilistic cellular automata based on Glauber algorithm. The effects of the exchange interactions on the phase diagrams have been investigated. A general mathematical expression for the critical temperature is obtained in terms of relative coupling r = J1/J and Δs = (Js/J) - 1, where J and Js represent the nearest neighbor coupling within inner- and surface-layers, respectively, and each magnetic site in the surface-layer is coupled with the nearest neighbor site in the inner-layer via the exchange coupling J1. In the case of antiferromagnetic coupling between surface-layer and inner-layer, system reveals many interesting phenomena, such as the possibility of existence of compensation line before the critical temperature.

Vehicle Classification Using an Imbalanced Dataset Based on a Single Magnetic Sensor.

PubMed

Xu, Chang; Wang, Yingguan; Bao, Xinghe; Li, Fengrong

2018-05-24

This paper aims to improve the accuracy of automatic vehicle classifiers for imbalanced datasets. Classification is made through utilizing a single anisotropic magnetoresistive sensor, with the models of vehicles involved being classified into hatchbacks, sedans, buses, and multi-purpose vehicles (MPVs). Using time domain and frequency domain features in combination with three common classification algorithms in pattern recognition, we develop a novel feature extraction method for vehicle classification. These three common classification algorithms are the k-nearest neighbor, the support vector machine, and the back-propagation neural network. Nevertheless, a problem remains with the original vehicle magnetic dataset collected being imbalanced, and may lead to inaccurate classification results. With this in mind, we propose an approach called SMOTE, which can further boost the performance of classifiers. Experimental results show that the k-nearest neighbor (KNN) classifier with the SMOTE algorithm can reach a classification accuracy of 95.46%, thus minimizing the effect of the imbalance.

False-nearest-neighbors algorithm and noise-corrupted time series

NASA Astrophysics Data System (ADS)

Rhodes, Carl; Morari, Manfred

1997-05-01

The false-nearest-neighbors (FNN) algorithm was originally developed to determine the embedding dimension for autonomous time series. For noise-free computer-generated time series, the algorithm does a good job in predicting the embedding dimension. However, the problem of predicting the embedding dimension when the time-series data are corrupted by noise was not fully examined in the original studies of the FNN algorithm. Here it is shown that with large data sets, even small amounts of noise can lead to incorrect prediction of the embedding dimension. Surprisingly, as the length of the time series analyzed by FNN grows larger, the cause of incorrect prediction becomes more pronounced. An analysis of the effect of noise on the FNN algorithm and a solution for dealing with the effects of noise are given here. Some results on the theoretically correct choice of the FNN threshold are also presented.

Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors.

PubMed

Haghverdi, Laleh; Lun, Aaron T L; Morgan, Michael D; Marioni, John C

2018-06-01

Large-scale single-cell RNA sequencing (scRNA-seq) data sets that are produced in different laboratories and at different times contain batch effects that may compromise the integration and interpretation of the data. Existing scRNA-seq analysis methods incorrectly assume that the composition of cell populations is either known or identical across batches. We present a strategy for batch correction based on the detection of mutual nearest neighbors (MNNs) in the high-dimensional expression space. Our approach does not rely on predefined or equal population compositions across batches; instead, it requires only that a subset of the population be shared between batches. We demonstrate the superiority of our approach compared with existing methods by using both simulated and real scRNA-seq data sets. Using multiple droplet-based scRNA-seq data sets, we demonstrate that our MNN batch-effect-correction method can be scaled to large numbers of cells.

Implementation of Nearest Neighbor using HSV to Identify Skin Disease

NASA Astrophysics Data System (ADS)

Gerhana, Y. A.; Zulfikar, W. B.; Ramdani, A. H.; Ramdhani, M. A.

2018-01-01

Today, Android is one of the most widely used operating system in the world. Most of android device has a camera that could capture an image, this feature could be optimized to identify skin disease. The disease is one of health problem caused by bacterium, fungi, and virus. The symptoms of skin disease usually visible. In this work, the symptoms that captured as image contains HSV in every pixel of the image. HSV can extracted and then calculate to earn euclidean value. The value compared using nearest neighbor algorithm to discover closer value between image testing and image training to get highest value that decide class label or type of skin disease. The testing result show that 166 of 200 or about 80% is accurate. There are some reasons that influence the result of classification model like number of image training and quality of android device’s camera.

Predicting Flavonoid UGT Regioselectivity

PubMed Central

Jackson, Rhydon; Knisley, Debra; McIntosh, Cecilia; Pfeiffer, Phillip

2011-01-01

Machine learning was applied to a challenging and biologically significant protein classification problem: the prediction of avonoid UGT acceptor regioselectivity from primary sequence. Novel indices characterizing graphical models of residues were proposed and found to be widely distributed among existing amino acid indices and to cluster residues appropriately. UGT subsequences biochemically linked to regioselectivity were modeled as sets of index sequences. Several learning techniques incorporating these UGT models were compared with classifications based on standard sequence alignment scores. These techniques included an application of time series distance functions to protein classification. Time series distances defined on the index sequences were used in nearest neighbor and support vector machine classifiers. Additionally, Bayesian neural network classifiers were applied to the index sequences. The experiments identified improvements over the nearest neighbor and support vector machine classifications relying on standard alignment similarity scores, as well as strong correlations between specific subsequences and regioselectivities. PMID:21747849

Rb-NMR study of the quasi-one-dimensional competing spin-chain compound R b2C u2M o3O12

NASA Astrophysics Data System (ADS)

Matsui, Kazuki; Yagi, Ayato; Hoshino, Yukihiro; Atarashi, Sochiro; Hase, Masashi; Sasaki, Takahiko; Goto, Takayuki

2017-12-01

A Rb-NMR study has been performed on the quasi-one-dimensional competing spin chain R b2C u2M o3O12 with ferromagnetic and antiferromagnetic exchange interactions on nearest-neighboring and next-nearest neighboring spins, respectively. The system changes from a gapped ground state at zero field to a gapless state at HC≃2 T , where the existence of magnetic order below 1 K was demonstrated by a broadening of the NMR spectrum, associated with a critical divergence of 1 /T1 . In the higher-temperature region, T1-1 showed a power-law-type temperature dependence, from which the field dependence of the Luttinger parameter K was obtained and compared with theoretical calculations based on the spin nematic Tomonaga-Luttinger liquid (TLL) state.

Active learning for solving the incomplete data problem in facial age classification by the furthest nearest-neighbor criterion.

PubMed

Wang, Jian-Gang; Sung, Eric; Yau, Wei-Yun

2011-07-01

Facial age classification is an approach to classify face images into one of several predefined age groups. One of the difficulties in applying learning techniques to the age classification problem is the large amount of labeled training data required. Acquiring such training data is very costly in terms of age progress, privacy, human time, and effort. Although unlabeled face images can be obtained easily, it would be expensive to manually label them on a large scale and getting the ground truth. The frugal selection of the unlabeled data for labeling to quickly reach high classification performance with minimal labeling efforts is a challenging problem. In this paper, we present an active learning approach based on an online incremental bilateral two-dimension linear discriminant analysis (IB2DLDA) which initially learns from a small pool of labeled data and then iteratively selects the most informative samples from the unlabeled set to increasingly improve the classifier. Specifically, we propose a novel data selection criterion called the furthest nearest-neighbor (FNN) that generalizes the margin-based uncertainty to the multiclass case and which is easy to compute, so that the proposed active learning algorithm can handle a large number of classes and large data sizes efficiently. Empirical experiments on FG-NET and Morph databases together with a large unlabeled data set for age categorization problems show that the proposed approach can achieve results comparable or even outperform a conventionally trained active classifier that requires much more labeling effort. Our IB2DLDA-FNN algorithm can achieve similar results much faster than random selection and with fewer samples for age categorization. It also can achieve comparable results with active SVM but is much faster than active SVM in terms of training because kernel methods are not needed. The results on the face recognition database and palmprint/palm vein database showed that our approach can handle problems with large number of classes. Our contributions in this paper are twofold. First, we proposed the IB2DLDA-FNN, the FNN being our novel idea, as a generic on-line or active learning paradigm. Second, we showed that it can be another viable tool for active learning of facial age range classification.

Heterogeneous Multi-Metric Learning for Multi-Sensor Fusion

DTIC Science & Technology

2011-07-01

distance”. One of the most widely used methods is the k-nearest neighbor ( KNN ) method [4], which labels an input data sample to be the class with majority...despite of its simplicity, it can be an effective candidate and can be easily extended to handle multiple sensors. Distance based method such as KNN relies...Neighbor (LMNN) method [21] which will be briefly reviewed in the sequel. LMNN method tries to learn an optimal metric specifically for KNN classifier. The

Adaptive local linear regression with application to printer color management.

PubMed

Gupta, Maya R; Garcia, Eric K; Chin, Erika

2008-06-01

Local learning methods, such as local linear regression and nearest neighbor classifiers, base estimates on nearby training samples, neighbors. Usually, the number of neighbors used in estimation is fixed to be a global "optimal" value, chosen by cross validation. This paper proposes adapting the number of neighbors used for estimation to the local geometry of the data, without need for cross validation. The term enclosing neighborhood is introduced to describe a set of neighbors whose convex hull contains the test point when possible. It is proven that enclosing neighborhoods yield bounded estimation variance under some assumptions. Three such enclosing neighborhood definitions are presented: natural neighbors, natural neighbors inclusive, and enclosing k-NN. The effectiveness of these neighborhood definitions with local linear regression is tested for estimating lookup tables for color management. Significant improvements in error metrics are shown, indicating that enclosing neighborhoods may be a promising adaptive neighborhood definition for other local learning tasks as well, depending on the density of training samples.

Method of Grassland Information Extraction Based on Multi-Level Segmentation and Cart Model

NASA Astrophysics Data System (ADS)

Qiao, Y.; Chen, T.; He, J.; Wen, Q.; Liu, F.; Wang, Z.

2018-04-01

It is difficult to extract grassland accurately by traditional classification methods, such as supervised method based on pixels or objects. This paper proposed a new method combing the multi-level segmentation with CART (classification and regression tree) model. The multi-level segmentation which combined the multi-resolution segmentation and the spectral difference segmentation could avoid the over and insufficient segmentation seen in the single segmentation mode. The CART model was established based on the spectral characteristics and texture feature which were excavated from training sample data. Xilinhaote City in Inner Mongolia Autonomous Region was chosen as the typical study area and the proposed method was verified by using visual interpretation results as approximate truth value. Meanwhile, the comparison with the nearest neighbor supervised classification method was obtained. The experimental results showed that the total precision of classification and the Kappa coefficient of the proposed method was 95 % and 0.9, respectively. However, the total precision of classification and the Kappa coefficient of the nearest neighbor supervised classification method was 80 % and 0.56, respectively. The result suggested that the accuracy of classification proposed in this paper was higher than the nearest neighbor supervised classification method. The experiment certificated that the proposed method was an effective extraction method of grassland information, which could enhance the boundary of grassland classification and avoid the restriction of grassland distribution scale. This method was also applicable to the extraction of grassland information in other regions with complicated spatial features, which could avoid the interference of woodland, arable land and water body effectively.

Remaining Useful Life Estimation of Insulated Gate Biploar Transistors (IGBTs) Based on a Novel Volterra k-Nearest Neighbor Optimally Pruned Extreme Learning Machine (VKOPP) Model Using Degradation Data

PubMed Central

Mei, Wenjuan; Zeng, Xianping; Yang, Chenglin; Zhou, Xiuyun

2017-01-01

The insulated gate bipolar transistor (IGBT) is a kind of excellent performance switching device used widely in power electronic systems. How to estimate the remaining useful life (RUL) of an IGBT to ensure the safety and reliability of the power electronics system is currently a challenging issue in the field of IGBT reliability. The aim of this paper is to develop a prognostic technique for estimating IGBTs’ RUL. There is a need for an efficient prognostic algorithm that is able to support in-situ decision-making. In this paper, a novel prediction model with a complete structure based on optimally pruned extreme learning machine (OPELM) and Volterra series is proposed to track the IGBT’s degradation trace and estimate its RUL; we refer to this model as Volterra k-nearest neighbor OPELM prediction (VKOPP) model. This model uses the minimum entropy rate method and Volterra series to reconstruct phase space for IGBTs’ ageing samples, and a new weight update algorithm, which can effectively reduce the influence of the outliers and noises, is utilized to establish the VKOPP network; then a combination of the k-nearest neighbor method (KNN) and least squares estimation (LSE) method is used to calculate the output weights of OPELM and predict the RUL of the IGBT. The prognostic results show that the proposed approach can predict the RUL of IGBT modules with small error and achieve higher prediction precision and lower time cost than some classic prediction approaches. PMID:29099811

Study of long-range orders of hard-core bosons coupled to cooperative normal modes in two-dimensional lattices

NASA Astrophysics Data System (ADS)

Ghosh, A.; Yarlagadda, S.

2017-09-01

Understanding the microscopic mechanism of coexisting long-range orders (such as lattice supersolidity) in strongly correlated systems is a subject of immense interest. We study the possible manifestations of long-range orders, including lattice-supersolid phases with differently broken symmetry, in a two-dimensional square lattice system of hard-core bosons (HCBs) coupled to archetypal cooperative/coherent normal-mode distortions such as those in perovskites. At strong HCB-phonon coupling, using a duality transformation to map the strong-coupling problem to a weak-coupling one, we obtain an effective Hamiltonian involving nearest-neighbor, next-nearest-neighbor, and next-to-next-nearest-neighbor hoppings and repulsions. Using stochastic series expansion quantum Monte Carlo, we construct the phase diagram of the system. As coupling strength is increased, we find that the system undergoes a first-order quantum phase transition from a superfluid to a checkerboard solid at half-filling and from a superfluid to a diagonal striped solid [with crystalline ordering wave vector Q ⃗=(2 π /3 ,2 π /3 ) or (2 π /3 ,4 π /3 )] at one-third filling without showing any evidence of supersolidity. On tuning the system away from these commensurate fillings, checkerboard supersolid is generated near half-filling whereas a rare diagonal striped supersolid is realized near one-third filling. Interestingly, there is an asymmetry in the extent of supersolidity about one-third filling. Within our framework, we also provide an explanation for the observed checkerboard and stripe formations in La2 -xSrxNiO4 at x =1 /2 and x =1 /3 .

Spin canting in a Dy-based single-chain magnet with dominant next-nearest-neighbor antiferromagnetic interactions

NASA Astrophysics Data System (ADS)

Bernot, K.; Luzon, J.; Caneschi, A.; Gatteschi, D.; Sessoli, R.; Bogani, L.; Vindigni, A.; Rettori, A.; Pini, M. G.

2009-04-01

We investigate theoretically and experimentally the static magnetic properties of single crystals of the molecular-based single-chain magnet of formula [Dy(hfac)3NIT(C6H4OPh)]∞ comprising alternating Dy3+ and organic radicals. The magnetic molar susceptibility χM displays a strong angular variation for sample rotations around two directions perpendicular to the chain axis. A peculiar inversion between maxima and minima in the angular dependence of χM occurs on increasing temperature. Using information regarding the monomeric building block as well as an ab initio estimation of the magnetic anisotropy of the Dy3+ ion, this “anisotropy-inversion” phenomenon can be assigned to weak one-dimensional ferromagnetism along the chain axis. This indicates that antiferromagnetic next-nearest-neighbor interactions between Dy3+ ions dominate, despite the large Dy-Dy separation, over the nearest-neighbor interactions between the radicals and the Dy3+ ions. Measurements of the field dependence of the magnetization, both along and perpendicularly to the chain, and of the angular dependence of χM in a strong magnetic field confirm such an interpretation. Transfer-matrix simulations of the experimental measurements are performed using a classical one-dimensional spin model with antiferromagnetic Heisenberg exchange interaction and noncollinear uniaxial single-ion anisotropies favoring a canted antiferromagnetic spin arrangement, with a net magnetic moment along the chain axis. The fine agreement obtained with experimental data provides estimates of the Hamiltonian parameters, essential for further study of the dynamics of rare-earth-based molecular chains.

Self-Organizing Map Neural Network-Based Nearest Neighbor Position Estimation Scheme for Continuous Crystal PET Detectors

NASA Astrophysics Data System (ADS)

Wang, Yonggang; Li, Deng; Lu, Xiaoming; Cheng, Xinyi; Wang, Liwei

2014-10-01

Continuous crystal-based positron emission tomography (PET) detectors could be an ideal alternative for current high-resolution pixelated PET detectors if the issues of high performance γ interaction position estimation and its real-time implementation are solved. Unfortunately, existing position estimators are not very feasible for implementation on field-programmable gate array (FPGA). In this paper, we propose a new self-organizing map neural network-based nearest neighbor (SOM-NN) positioning scheme aiming not only at providing high performance, but also at being realistic for FPGA implementation. Benefitting from the SOM feature mapping mechanism, the large set of input reference events at each calibration position is approximated by a small set of prototypes, and the computation of the nearest neighbor searching for unknown events is largely reduced. Using our experimental data, the scheme was evaluated, optimized and compared with the smoothed k-NN method. The spatial resolutions of full-width-at-half-maximum (FWHM) of both methods averaged over the center axis of the detector were obtained as 1.87 ±0.17 mm and 1.92 ±0.09 mm, respectively. The test results show that the SOM-NN scheme has an equivalent positioning performance with the smoothed k-NN method, but the amount of computation is only about one-tenth of the smoothed k-NN method. In addition, the algorithm structure of the SOM-NN scheme is more feasible for implementation on FPGA. It has the potential to realize real-time position estimation on an FPGA with a high-event processing throughput.

Double propensity-score adjustment: A solution to design bias or bias due to incomplete matching.

PubMed

Austin, Peter C

2017-02-01

Propensity-score matching is frequently used to reduce the effects of confounding when using observational data to estimate the effects of treatments. Matching allows one to estimate the average effect of treatment in the treated. Rosenbaum and Rubin coined the term "bias due to incomplete matching" to describe the bias that can occur when some treated subjects are excluded from the matched sample because no appropriate control subject was available. The presence of incomplete matching raises important questions around the generalizability of estimated treatment effects to the entire population of treated subjects. We describe an analytic solution to address the bias due to incomplete matching. Our method is based on using optimal or nearest neighbor matching, rather than caliper matching (which frequently results in the exclusion of some treated subjects). Within the sample matched on the propensity score, covariate adjustment using the propensity score is then employed to impute missing potential outcomes under lack of treatment for each treated subject. Using Monte Carlo simulations, we found that the proposed method resulted in estimates of treatment effect that were essentially unbiased. This method resulted in decreased bias compared to caliper matching alone and compared to either optimal matching or nearest neighbor matching alone. Caliper matching alone resulted in design bias or bias due to incomplete matching, while optimal matching or nearest neighbor matching alone resulted in bias due to residual confounding. The proposed method also tended to result in estimates with decreased mean squared error compared to when caliper matching was used.

Double propensity-score adjustment: A solution to design bias or bias due to incomplete matching

PubMed Central

2016-01-01

Propensity-score matching is frequently used to reduce the effects of confounding when using observational data to estimate the effects of treatments. Matching allows one to estimate the average effect of treatment in the treated. Rosenbaum and Rubin coined the term “bias due to incomplete matching” to describe the bias that can occur when some treated subjects are excluded from the matched sample because no appropriate control subject was available. The presence of incomplete matching raises important questions around the generalizability of estimated treatment effects to the entire population of treated subjects. We describe an analytic solution to address the bias due to incomplete matching. Our method is based on using optimal or nearest neighbor matching, rather than caliper matching (which frequently results in the exclusion of some treated subjects). Within the sample matched on the propensity score, covariate adjustment using the propensity score is then employed to impute missing potential outcomes under lack of treatment for each treated subject. Using Monte Carlo simulations, we found that the proposed method resulted in estimates of treatment effect that were essentially unbiased. This method resulted in decreased bias compared to caliper matching alone and compared to either optimal matching or nearest neighbor matching alone. Caliper matching alone resulted in design bias or bias due to incomplete matching, while optimal matching or nearest neighbor matching alone resulted in bias due to residual confounding. The proposed method also tended to result in estimates with decreased mean squared error compared to when caliper matching was used. PMID:25038071

Equilibrium, metastability, and hysteresis in a model spin-crossover material with nearest-neighbor antiferromagnetic-like and long-range ferromagnetic-like interactions

DOE PAGES

Rikvold, Per Arne; Brown, Gregory; Miyashita, Seiji; ...

2016-02-16

Phase diagrams and hysteresis loops were obtained by Monte Carlo simulations and a mean- field method for a simplified model of a spin-crossovermaterialwith a two-step transition between the high-spin and low-spin states. This model is a mapping onto a square-lattice S = 1/2 Ising model with antiferromagnetic nearest-neighbor and ferromagnetic Husimi-Temperley ( equivalent-neighbor) long-range interactions. Phase diagrams obtained by the two methods for weak and strong long-range interactions are found to be similar. However, for intermediate-strength long-range interactions, the Monte Carlo simulations show that tricritical points decompose into pairs of critical end points and mean-field critical points surrounded by horn-shapedmore » regions of metastability. Hysteresis loops along paths traversing the horn regions are strongly reminiscent of thermal two-step transition loops with hysteresis, recently observed experimentally in several spin-crossover materials. As a result, we believe analogous phenomena should be observable in experiments and simulations for many systems that exhibit competition between local antiferromagnetic-like interactions and long-range ferromagnetic-like interactions caused by elastic distortions.« less

Streamflow variability and classification using false nearest neighbor method

NASA Astrophysics Data System (ADS)

Vignesh, R.; Jothiprakash, V.; Sivakumar, B.

2015-12-01

Understanding regional streamflow dynamics and patterns continues to be a challenging problem. The present study introduces the false nearest neighbor (FNN) algorithm, a nonlinear dynamic-based method, to examine the spatial variability of streamflow over a region. The FNN method is a dimensionality-based approach, where the dimension of the time series represents its variability. The method uses phase space reconstruction and nearest neighbor concepts, and identifies false neighbors in the reconstructed phase space. The FNN method is applied to monthly streamflow data monitored over a period of 53 years (1950-2002) in an extensive network of 639 stations in the contiguous United States (US). Since selection of delay time in phase space reconstruction may influence the FNN outcomes, analysis is carried out for five different delay time values: monthly, seasonal, and annual separation of data as well as delay time values obtained using autocorrelation function (ACF) and average mutual information (AMI) methods. The FNN dimensions for the 639 streamflow series are generally identified to range from 4 to 12 (with very few exceptional cases), indicating a wide range of variability in the dynamics of streamflow across the contiguous US. However, the FNN dimensions for a majority of the streamflow series are found to be low (less than or equal to 6), suggesting low level of complexity in streamflow dynamics in most of the individual stations and over many sub-regions. The FNN dimension estimates also reveal that streamflow dynamics in the western parts of the US (including far west, northwestern, and southwestern parts) generally exhibit much greater variability compared to that in the eastern parts of the US (including far east, northeastern, and southeastern parts), although there are also differences among 'pockets' within these regions. These results are useful for identification of appropriate model complexity at individual stations, patterns across regions and sub-regions, interpolation and extrapolation of data, and catchment classification. An attempt is also made to relate the FNN dimensions with catchment characteristics and streamflow statistical properties.

Query-Adaptive Reciprocal Hash Tables for Nearest Neighbor Search.

PubMed

Liu, Xianglong; Deng, Cheng; Lang, Bo; Tao, Dacheng; Li, Xuelong

2016-02-01

Recent years have witnessed the success of binary hashing techniques in approximate nearest neighbor search. In practice, multiple hash tables are usually built using hashing to cover more desired results in the hit buckets of each table. However, rare work studies the unified approach to constructing multiple informative hash tables using any type of hashing algorithms. Meanwhile, for multiple table search, it also lacks of a generic query-adaptive and fine-grained ranking scheme that can alleviate the binary quantization loss suffered in the standard hashing techniques. To solve the above problems, in this paper, we first regard the table construction as a selection problem over a set of candidate hash functions. With the graph representation of the function set, we propose an efficient solution that sequentially applies normalized dominant set to finding the most informative and independent hash functions for each table. To further reduce the redundancy between tables, we explore the reciprocal hash tables in a boosting manner, where the hash function graph is updated with high weights emphasized on the misclassified neighbor pairs of previous hash tables. To refine the ranking of the retrieved buckets within a certain Hamming radius from the query, we propose a query-adaptive bitwise weighting scheme to enable fine-grained bucket ranking in each hash table, exploiting the discriminative power of its hash functions and their complement for nearest neighbor search. Moreover, we integrate such scheme into the multiple table search using a fast, yet reciprocal table lookup algorithm within the adaptive weighted Hamming radius. In this paper, both the construction method and the query-adaptive search method are general and compatible with different types of hashing algorithms using different feature spaces and/or parameter settings. Our extensive experiments on several large-scale benchmarks demonstrate that the proposed techniques can significantly outperform both the naive construction methods and the state-of-the-art hashing algorithms.

Traffic flow forecasting using approximate nearest neighbor nonparametric regression

DOT National Transportation Integrated Search

2000-12-01

The purpose of this research is to enhance nonparametric regression (NPR) for use in real-time systems by first reducing execution time using advanced data structures and imprecise computations and then developing a methodology for applying NPR. Due ...

SU-F-T-315: Comparative Studies of Planar Dose with Different Spatial Resolution for Head and Neck IMRT QA

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

Hwang, T; Koo, T

Purpose: To quantitatively investigate the planar dose difference and the γ value between the reference fluence map with the 1 mm detector-to-detector distance and the other fluence maps with less spatial resolution for head and neck intensity modulated radiation (IMRT) therapy. Methods: For ten head and neck cancer patients, the IMRT quality assurance (QA) beams were generated using by the commercial radiation treatment planning system, Pinnacle3 (ver. 8.0.d Philips Medical System, Madison, WI). For each beam, ten fluence maps (detector-to-detector distance: 1 mm to 10 mm by 1 mm) were generated. The fluence maps with larger than 1 mm detector-todetectormore » distance were interpolated using MATLAB (R2014a, the Math Works,Natick, MA) by four different interpolation Methods: for the bilinear, the cubic spline, the bicubic, and the nearest neighbor interpolation, respectively. These interpolated fluence maps were compared with the reference one using the γ value (criteria: 3%, 3 mm) and the relative dose difference. Results: As the detector-to-detector distance increases, the dose difference between the two maps increases. For the fluence map with the same resolution, the cubic spline interpolation and the bicubic interpolation are almost equally best interpolation methods while the nearest neighbor interpolation is the worst.For example, for 5 mm distance fluence maps, γ≤1 are 98.12±2.28%, 99.48±0.66%, 99.45±0.65% and 82.23±0.48% for the bilinear, the cubic spline, the bicubic, and the nearest neighbor interpolation, respectively. For 7 mm distance fluence maps, γ≤1 are 90.87±5.91%, 90.22±6.95%, 91.79±5.97% and 71.93±4.92 for the bilinear, the cubic spline, the bicubic, and the nearest neighbor interpolation, respectively. Conclusion: We recommend that the 2-dimensional detector array with high spatial resolution should be used as an IMRT QA tool and that the measured fluence maps should be interpolated using by the cubic spline interpolation or the bicubic interpolation for head and neck IMRT delivery. This work was supported by Radiation Technology R&D program through the National Research Foundation of Korea funded by the Ministry of Science, ICT & Future Planning (No. 2013M2A2A7038291)« less

Deep Recurrent Neural Networks for Human Activity Recognition

PubMed Central

Murad, Abdulmajid

2017-01-01

Adopting deep learning methods for human activity recognition has been effective in extracting discriminative features from raw input sequences acquired from body-worn sensors. Although human movements are encoded in a sequence of successive samples in time, typical machine learning methods perform recognition tasks without exploiting the temporal correlations between input data samples. Convolutional neural networks (CNNs) address this issue by using convolutions across a one-dimensional temporal sequence to capture dependencies among input data. However, the size of convolutional kernels restricts the captured range of dependencies between data samples. As a result, typical models are unadaptable to a wide range of activity-recognition configurations and require fixed-length input windows. In this paper, we propose the use of deep recurrent neural networks (DRNNs) for building recognition models that are capable of capturing long-range dependencies in variable-length input sequences. We present unidirectional, bidirectional, and cascaded architectures based on long short-term memory (LSTM) DRNNs and evaluate their effectiveness on miscellaneous benchmark datasets. Experimental results show that our proposed models outperform methods employing conventional machine learning, such as support vector machine (SVM) and k-nearest neighbors (KNN). Additionally, the proposed models yield better performance than other deep learning techniques, such as deep believe networks (DBNs) and CNNs. PMID:29113103

Deep Recurrent Neural Networks for Human Activity Recognition.

PubMed

Murad, Abdulmajid; Pyun, Jae-Young

2017-11-06

Adopting deep learning methods for human activity recognition has been effective in extracting discriminative features from raw input sequences acquired from body-worn sensors. Although human movements are encoded in a sequence of successive samples in time, typical machine learning methods perform recognition tasks without exploiting the temporal correlations between input data samples. Convolutional neural networks (CNNs) address this issue by using convolutions across a one-dimensional temporal sequence to capture dependencies among input data. However, the size of convolutional kernels restricts the captured range of dependencies between data samples. As a result, typical models are unadaptable to a wide range of activity-recognition configurations and require fixed-length input windows. In this paper, we propose the use of deep recurrent neural networks (DRNNs) for building recognition models that are capable of capturing long-range dependencies in variable-length input sequences. We present unidirectional, bidirectional, and cascaded architectures based on long short-term memory (LSTM) DRNNs and evaluate their effectiveness on miscellaneous benchmark datasets. Experimental results show that our proposed models outperform methods employing conventional machine learning, such as support vector machine (SVM) and k-nearest neighbors (KNN). Additionally, the proposed models yield better performance than other deep learning techniques, such as deep believe networks (DBNs) and CNNs.

Mutual Information in Frequency and Its Application to Measure Cross-Frequency Coupling in Epilepsy

NASA Astrophysics Data System (ADS)

Malladi, Rakesh; Johnson, Don H.; Kalamangalam, Giridhar P.; Tandon, Nitin; Aazhang, Behnaam

2018-06-01

We define a metric, mutual information in frequency (MI-in-frequency), to detect and quantify the statistical dependence between different frequency components in the data, referred to as cross-frequency coupling and apply it to electrophysiological recordings from the brain to infer cross-frequency coupling. The current metrics used to quantify the cross-frequency coupling in neuroscience cannot detect if two frequency components in non-Gaussian brain recordings are statistically independent or not. Our MI-in-frequency metric, based on Shannon's mutual information between the Cramer's representation of stochastic processes, overcomes this shortcoming and can detect statistical dependence in frequency between non-Gaussian signals. We then describe two data-driven estimators of MI-in-frequency: one based on kernel density estimation and the other based on the nearest neighbor algorithm and validate their performance on simulated data. We then use MI-in-frequency to estimate mutual information between two data streams that are dependent across time, without making any parametric model assumptions. Finally, we use the MI-in- frequency metric to investigate the cross-frequency coupling in seizure onset zone from electrocorticographic recordings during seizures. The inferred cross-frequency coupling characteristics are essential to optimize the spatial and spectral parameters of electrical stimulation based treatments of epilepsy.

Automated diagnosis of dry eye using infrared thermography images

NASA Astrophysics Data System (ADS)

Acharya, U. Rajendra; Tan, Jen Hong; Koh, Joel E. W.; Sudarshan, Vidya K.; Yeo, Sharon; Too, Cheah Loon; Chua, Chua Kuang; Ng, E. Y. K.; Tong, Louis

2015-07-01

Dry Eye (DE) is a condition of either decreased tear production or increased tear film evaporation. Prolonged DE damages the cornea causing the corneal scarring, thinning and perforation. There is no single uniform diagnosis test available to date; combinations of diagnostic tests are to be performed to diagnose DE. The current diagnostic methods available are subjective, uncomfortable and invasive. Hence in this paper, we have developed an efficient, fast and non-invasive technique for the automated identification of normal and DE classes using infrared thermography images. The features are extracted from nonlinear method called Higher Order Spectra (HOS). Features are ranked using t-test ranking strategy. These ranked features are fed to various classifiers namely, K-Nearest Neighbor (KNN), Nave Bayesian Classifier (NBC), Decision Tree (DT), Probabilistic Neural Network (PNN), and Support Vector Machine (SVM) to select the best classifier using minimum number of features. Our proposed system is able to identify the DE and normal classes automatically with classification accuracy of 99.8%, sensitivity of 99.8%, and specificity if 99.8% for left eye using PNN and KNN classifiers. And we have reported classification accuracy of 99.8%, sensitivity of 99.9%, and specificity if 99.4% for right eye using SVM classifier with polynomial order 2 kernel.

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

NASA Astrophysics Data System (ADS)

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

2018-05-01

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

Four competing interactions for models with an uncountable set of spin values on a Cayley tree

NASA Astrophysics Data System (ADS)

Rozikov, U. A.; Haydarov, F. H.

2017-06-01

We consider models with four competing interactions ( external field, nearest neighbor, second neighbor, and three neighbors) and an uncountable set [0, 1] of spin values on the Cayley tree of order two. We reduce the problem of describing the splitting Gibbs measures of the model to the problem of analyzing solutions of a nonlinear integral equation and study some particular cases for Ising and Potts models. We also show that periodic Gibbs measures for the given models either are translation invariant or have the period two. We present examples where periodic Gibbs measures with the period two are not unique.

Understanding the Instruments of National Power through a System of Differential Equations in a Counterinsurgency

DTIC Science & Technology

2012-03-01

WMA) and used both the k-Nearest Neighbor ( KNN ) and Nearest Centroid 27 (a) Coalition and Regional (b) Indigenous Figure 3. RAND Study Models[32:98,115...NC) algorithms to classify future features. The study found that KNN performed better than NC with 85% or greater accuracy in all test cases. The...the model. 4.2.1 No Surge. On the 10th of January 2007, President George W. Bush delivered a speech to the American Public outlining a new strategy in

Diffusion reordering kinetics in lattice-gas systems: Time evolution of configurational entropy and internal energy

NASA Astrophysics Data System (ADS)

Weinketz, Sieghard

1998-07-01

The reordering kinetics of a diffusion lattice-gas system of adsorbates with nearest- and next-nearest-neighbor interactions on a square lattice is studied within a dynamic Monte Carlo simulation, as it evolves towards the equilibrium from a given initial configuration, at a constant temperature. The diffusion kinetics proceeds through adsorbate hoppings to empty nearest-neighboring sites (Kawasaki dynamics). The Monte Carlo procedure allows a ``real'' time definition from the local transition rates, and the configurational entropy and internal energy can be obtained from the lattice configuration at any instant t by counting the local clusters and using the C2 approximation of the cluster variation method. These state functions are then used in their nonequilibrium form as a direct measure of reordering along the time. Different reordering processes are analyzed within this approach, presenting a rich variety of behaviors. It can also be shown that the time derivative of entropy (times temperature) is always equal to or lower than the time derivative of energy, and that the reordering path is always strongly dependent on the initial order, presenting in some cases an ``invariance'' of the entropy function to the magnitude of the interactions as far as the final order is unaltered.

Propagation and Directional Scattering of Ocean Waves in the Marginal Ice Zone and Neighboring Seas

DTIC Science & Technology

2015-09-30

expected to be the average of the kernel for 10 s and 12 s. This means that we should be able to calculate empirical formulas for 2 the scattering kernel...floe packing. Thus, establish a way to incorporate what has been done by Squire and co-workers into the wave model paradigm (in which the phase of the...cases observed by Kohout et al. (2014) in Antarctica . vii. Validation: We are planning validation tests for wave-ice scattering / attenuation model by

Quantum phase diagram of distorted J 1 - J 2 Heisenberg S  =  1/2 antiferromagnet in honeycomb lattice: a modified spin wave study

NASA Astrophysics Data System (ADS)

Ghorbani, Elaheh; Shahbazi, Farhad; Mosadeq, Hamid

2016-10-01

Using the modified spin wave method, we study the {{J}1}-{{J}2} Heisenberg model with first and second neighbor antiferromagnetic exchange interactions. For a symmetric S  =  1/2 model, with the same couplings for all the equivalent neighbors, we find three phases in terms of the frustration parameter \\barα={{J}2}/{{J}1} : (1) a commensurate collinear ordering with staggered magnetization (Néel.I state) for 0≤slant \\barα≲ 0.207 , (2) a magnetically gapped disordered state for 0.207≲ \\barα≲ 0.369 , preserving all the symmetries of the Hamiltonian and lattice, which by definition is a quantum spin liquid (QSL) state and (3) a commensurate collinear ordering in which two out of the three nearest neighbor magnetizations are antiparallel and the remaining pair are parallel (Néel.II state), for 0.396≲ \\barα≤slant 1 . We also explore the phase diagram of a distorted {{J}1}-{{J}2} model with S  =  1/2. Distortion is introduced as an inequality of one nearest neighbor coupling with the other two. This yields a richer phase diagram by the appearance of a new gapped QSL, a gapless QSL and also a valence bond crystal phase in addition to the previous three phases found for the undistorted model.

Microscopic theory of the nearest-neighbor valence bond sector of the spin-1/2 kagome antiferromagnet

NASA Astrophysics Data System (ADS)

Ralko, Arnaud; Mila, Frédéric; Rousochatzakis, Ioannis

2018-03-01

The spin-1/2 Heisenberg model on the kagome lattice, which is closely realized in layered Mott insulators such as ZnCu3(OH) 6Cl2 , is one of the oldest and most enigmatic spin-1/2 lattice models. While the numerical evidence has accumulated in favor of a quantum spin liquid, the debate is still open as to whether it is a Z2 spin liquid with very short-range correlations (some kind of resonating valence bond spin liquid), or an algebraic spin liquid with power-law correlations. To address this issue, we have pushed the program started by Rokhsar and Kivelson in their derivation of the effective quantum dimer model description of Heisenberg models to unprecedented accuracy for the spin-1/2 kagome, by including all the most important virtual singlet contributions on top of the orthogonalization of the nearest-neighbor valence bond singlet basis. Quite remarkably, the resulting picture is a competition between a Z2 spin liquid and a diamond valence bond crystal with a 12-site unit cell, as in the density-matrix renormalization group simulations of Yan et al. Furthermore, we found that, on cylinders of finite diameter d , there is a transition between the Z2 spin liquid at small d and the diamond valence bond crystal at large d , the prediction of the present microscopic description for the two-dimensional lattice. These results show that, if the ground state of the spin-1/2 kagome antiferromagnet can be described by nearest-neighbor singlet dimers, it is a diamond valence bond crystal, and, a contrario, that, if the system is a quantum spin liquid, it has to involve long-range singlets, consistent with the algebraic spin liquid scenario.

Mismatch and G-Stack Modulated Probe Signals on SNP Microarrays

PubMed Central

Binder, Hans; Fasold, Mario; Glomb, Torsten

2009-01-01

Background Single nucleotide polymorphism (SNP) arrays are important tools widely used for genotyping and copy number estimation. This technology utilizes the specific affinity of fragmented DNA for binding to surface-attached oligonucleotide DNA probes. We analyze the variability of the probe signals of Affymetrix GeneChip SNP arrays as a function of the probe sequence to identify relevant sequence motifs which potentially cause systematic biases of genotyping and copy number estimates. Methodology/Principal Findings The probe design of GeneChip SNP arrays enables us to disentangle different sources of intensity modulations such as the number of mismatches per duplex, matched and mismatched base pairings including nearest and next-nearest neighbors and their position along the probe sequence. The effect of probe sequence was estimated in terms of triple-motifs with central matches and mismatches which include all 256 combinations of possible base pairings. The probe/target interactions on the chip can be decomposed into nearest neighbor contributions which correlate well with free energy terms of DNA/DNA-interactions in solution. The effect of mismatches is about twice as large as that of canonical pairings. Runs of guanines (G) and the particular type of mismatched pairings formed in cross-allelic probe/target duplexes constitute sources of systematic biases of the probe signals with consequences for genotyping and copy number estimates. The poly-G effect seems to be related to the crowded arrangement of probes which facilitates complex formation of neighboring probes with at minimum three adjacent G's in their sequence. Conclusions The applied method of “triple-averaging” represents a model-free approach to estimate the mean intensity contributions of different sequence motifs which can be applied in calibration algorithms to correct signal values for sequence effects. Rules for appropriate sequence corrections are suggested. PMID:19924253

Frustrated quantum magnetism in the Kondo lattice on the zigzag ladder

NASA Astrophysics Data System (ADS)

Peschke, Matthias; Rausch, Roman; Potthoff, Michael

2018-03-01

The interplay between the Kondo effect, indirect magnetic interaction, and geometrical frustration is studied in the Kondo lattice on the one-dimensional zigzag ladder. Using the density-matrix renormalization group, the ground-state and various short- and long-range spin- and density-correlation functions are calculated for the model at half filling as a function of the antiferromagnetic Kondo interaction down to J =0.3 t , where t is the nearest-neighbor hopping on the zigzag ladder. Geometrical frustration is shown to lead to at least two critical points: Starting from the strong-J limit, where almost local Kondo screening dominates and where the system is a nonmagnetic Kondo insulator, antiferromagnetic correlations between nearest-neighbor and next-nearest-neighbor local spins become stronger and stronger, until at Jcdim≈0.89 t frustration is alleviated by a spontaneous breaking of translational symmetry and a corresponding transition to a dimerized state. This is characterized by antiferromagnetic correlations along the legs and by alternating antiferro- and ferromagnetic correlations on the rungs of the ladder. A mechanism of partial Kondo screening that has been suggested for the Kondo lattice on the two-dimensional triangular lattice is not realized in the one-dimensional case. Furthermore, within the symmetry-broken dimerized state, there is a magnetic transition to a 90∘ quantum spin spiral with quasi-long-range order at Jcmag≈0.84 t . The quantum-critical point is characterized by a closure of the spin gap (with decreasing J ) and a divergence of the spin-correlation length and of the spin-structure factor S (q ) at wave vector q =π /2 . This is opposed to the model on the one-dimensional bipartite chain, which is known to have a finite spin gap for all J >0 at half filling.

Spatial patterns in vegetation fires in the Indian region.

PubMed

Vadrevu, Krishna Prasad; Badarinath, K V S; Anuradha, Eaturu

2008-12-01

In this study, we used fire count datasets derived from Along Track Scanning Radiometer (ATSR) satellite to characterize spatial patterns in fire occurrences across highly diverse geographical, vegetation and topographic gradients in the Indian region. For characterizing the spatial patterns of fire occurrences, observed fire point patterns were tested against the hypothesis of a complete spatial random (CSR) pattern using three different techniques, the quadrat analysis, nearest neighbor analysis and Ripley's K function. Hierarchical nearest neighboring technique was used to depict the 'hotspots' of fire incidents. Of the different states, highest fire counts were recorded in Madhya Pradesh (14.77%) followed by Gujarat (10.86%), Maharastra (9.92%), Mizoram (7.66%), Jharkhand (6.41%), etc. With respect to the vegetation categories, highest number of fires were recorded in agricultural regions (40.26%) followed by tropical moist deciduous vegetation (12.72), dry deciduous vegetation (11.40%), abandoned slash and burn secondary forests (9.04%), tropical montane forests (8.07%) followed by others. Analysis of fire counts based on elevation and slope range suggested that maximum number of fires occurred in low and medium elevation types and in very low to low-slope categories. Results from three different spatial techniques for spatial pattern suggested clustered pattern in fire events compared to CSR. Most importantly, results from Ripley's K statistic suggested that fire events are highly clustered at a lag-distance of 125 miles. Hierarchical nearest neighboring clustering technique identified significant clusters of fire 'hotspots' in different states in northeast and central India. The implications of these results in fire management and mitigation were discussed. Also, this study highlights the potential of spatial point pattern statistics in environmental monitoring and assessment studies with special reference to fire events in the Indian region.

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.

pKa shifting in double-stranded RNA is highly dependent upon nearest neighbors and bulge positioning.

PubMed

Wilcox, Jennifer L; Bevilacqua, Philip C

2013-10-22

Shifting of pKa's in RNA is important for many biological processes; however, the driving forces responsible for shifting are not well understood. Herein, we determine how structural environments surrounding protonated bases affect pKa shifting in double-stranded RNA (dsRNA). Using (31)P NMR, we determined the pKa of the adenine in an A(+)·C base pair in various sequence and structural environments. We found a significant dependence of pKa on the base pairing strength of nearest neighbors and the location of a nearby bulge. Increasing nearest neighbor base pairing strength shifted the pKa of the adenine in an A(+)·C base pair higher by an additional 1.6 pKa units, from 6.5 to 8.1, which is well above neutrality. The addition of a bulge two base pairs away from a protonated A(+)·C base pair shifted the pKa by only ~0.5 units less than a perfectly base paired hairpin; however, positioning the bulge just one base pair away from the A(+)·C base pair prohibited formation of the protonated base pair as well as several flanking base pairs. Comparison of data collected at 25 °C and 100 mM KCl to biological temperature and Mg(2+) concentration revealed only slight pKa changes, suggesting that similar sequence contexts in biological systems have the potential to be protonated at biological pH. We present a general model to aid in the determination of the roles protonated bases may play in various dsRNA-mediated processes including ADAR editing, miRNA processing, programmed ribosomal frameshifting, and general acid-base catalysis in ribozymes.

Structural properties and diffusion processes of the Cu 3Au (0 0 1) surface

NASA Astrophysics Data System (ADS)

Wang, Fang; Zhang, Jian-Min; Zhang, Yan; Ji, Vincent

2010-09-01

The surface relaxation and surface energy of both the mixed AuCu and pure Cu terminated Cu 3Au (0 0 1) surfaces are simulated and calculated by using the modified analytical embedded-atom method. We find that the mixed AuCu termination is energetically preferred over the pure Cu termination thereby the mono-vacancy diffusion is also investigated in the topmost few layers of the mixed AuCu terminated Cu 3Au (0 0 1) surface. In the mixed AuCu terminated surface the relaxed Au atoms are raised above Cu atoms for 0.13 Å in the topmost layer. All the surface atoms displace outwards, this effect occurs in the first three layers and changes the first two inter-layer spacing. For mono-vacancy migration in the first layer, the migration energies of Au and Cu mono-vacancy via two-type in-plane displace: the nearest neighbor jump (NNJ) and the second nearest neighbor jump (2NNJ), are calculated and the results show that the NNJ requires a much lower energy than 2NNJ. For the evolution of the energy requirements for successive nearest neighbor jumps (SNNJ) along three different paths: circularity, zigzag and beeline, we find that the circularity path is preferred over the other two paths due to its minimum energy barriers and final energies. In the second layer, the NN jumps in intra- and inter-layer of the Cu mono-vacancy are investigated. The calculated energy barriers and final energies show that the vacancy prefer jump up to a proximate Cu site. This replacement between the Cu vacancy in the second layer and Cu atom in the first layer is remunerative for the Au atoms enrichment in the topmost layer.

Band structure and orbital character of monolayer MoS2 with eleven-band tight-binding model

NASA Astrophysics Data System (ADS)

Shahriari, Majid; Ghalambor Dezfuli, Abdolmohammad; Sabaeian, Mohammad

2018-02-01

In this paper, based on a tight-binding (TB) model, first we present the calculations of eigenvalues as band structure and then present the eigenvectors as probability amplitude for finding electron in atomic orbitals for monolayer MoS2 in the first Brillouin zone. In these calculations we are considering hopping processes between the nearest-neighbor Mo-S, the next nearest-neighbor in-plan Mo-Mo, and the next nearest-neighbor in-plan and out-of-plan S-S atoms in a three-atom based unit cell of two-dimensional rhombic MoS2. The hopping integrals have been solved in terms of Slater-Koster and crystal field parameters. These parameters are calculated by comparing TB model with the density function theory (DFT) in the high-symmetry k-points (i.e. the K- and Γ-points). In our TB model all the 4d Mo orbitals and the 3p S orbitals are considered and detailed analysis of the orbital character of each energy level at the main high-symmetry points of the Brillouin zone is described. In comparison with DFT calculations, our results of TB model show a very good agreement for bands near the Fermi level. However for other bands which are far from the Fermi level, some discrepancies between our TB model and DFT calculations are observed. Upon the accuracy of Slater-Koster and crystal field parameters, on the contrary of DFT, our model provide enough accuracy to calculate all allowed transitions between energy bands that are very crucial for investigating the linear and nonlinear optical properties of monolayer MoS2.

Molecular dynamics analysis of transitions between rotational isomers in polymethylene

NASA Astrophysics Data System (ADS)

Zúñiga, Ignacio; Bahar, Ivet; Dodge, Robert; Mattice, Wayne L.

1991-10-01

Molecular dynamics trajectories have been computed and analyzed for linear chains, with sizes ranging from C10H22 to C100H202, and for cyclic C100H200. All hydrogen atoms are included discretely. All bond lengths, bond angles, and torsion angles are variable. Hazard plots show a tendency, at very short times, for correlations between rotational isomeric transitions at bond i and i±2, in much the same manner as in the Brownian dynamics simulations reported by Helfand and co-workers. This correlation of next nearest neighbor bonds in isolated polyethylene chains is much weaker than the correlation found for next nearest neighbor CH-CH2 bonds in poly(1,4-trans-butadiene) confined to the channel formed by crystalline perhydrotriphenylene [Dodge and Mattice, Macromolecules 24, 2709 (1991)]. Less than half of the rotational isomeric transitions observed in the entire trajectory for C50H102 can be described as strongly coupled next nearest neighbor transitions. If correlated motions are identified with successive transitions, which occur within a time interval of Δt≤1 ps, only 18% of the transitions occur through cooperative motion of bonds i and i±2. An analysis of the entire data set of 2482 rotational isomeric state transitions, observed in a 3.7 ns trajectory for C50H102 at 400 K, was performed using a formalism that treats the transitions at different bonds as being independent. On time scales of 0.1 ns or longer, the analysis based on independent bonds accounts reasonably well for the results from the molecular dynamics simulations. At shorter times the molecular dynamics simulation reveals a higher mobility than implied by the analysis assuming independent bonds, presumably due to the influence of correlations that are important at shorter times.

Contact processes with competitive dynamics in bipartite lattices: effects of distinct interactions

NASA Astrophysics Data System (ADS)

Pianegonda, Salete; Fiore, Carlos E.

2014-05-01

The two-dimensional contact process (CP) with a competitive dynamics proposed by Martins et al (2011 Phys. Rev. E 84 011125) leads to the appearance of an unusual active-asymmetric phase, in which the system sublattices are unequally populated. It differs from the usual CP only by the fact that particles also interact with their next-nearest neighbor sites via a distinct strength creation rate, and for the inclusion of an inhibition effect, proportional to the local density. Aimed at investigating the robustness of such an asymmetric phase, in this paper we study the influence of distinct interactions for two bidimensional CPs. In the first model, the interaction between first neighbors requires a minimal neighborhood of adjacent particles for creating new offspring, whereas second neighbors interact as usual (e.g. at least one neighboring particle is required). The second model takes the opposite situation, in which the restrictive dynamics is in the interaction between next-nearest neighbor sites. Both models are investigated under mean field theory (MFT) and Monte Carlo simulations. In similarity with results by Martins et al, the inclusion of distinct sublattice interactions maintains the occurrence of an asymmetric active phase and re-entrant transition lines. In contrast, remarkable differences are presented, such as discontinuous phase transitions (even between the active phases), the appearance of tricritical points and the stabilization of active phases under larger values of control parameters. Finally, we have shown that the critical behaviors are not altered due to the change of interactions, in which the absorbing transitions belong to the directed percolation (DP) universality class, whereas second-order active phase transitions belong to the Ising universality class.

cluster trials v. 1.0

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

Mitchell, John; Castillo, Andrew

2016-09-21

This software contains a set of python modules – input, search, cluster, analysis; these modules read input files containing spatial coordinates and associated attributes which can be used to perform nearest neighbor search (spatial indexing via kdtree), cluster analysis/identification, and calculation of spatial statistics for analysis.

Are human spontaneous otoacoustic emissions generated by a chain of coupled nonlinear oscillators?

PubMed

Wit, Hero P; van Dijk, Pim

2012-08-01

Spontaneous otoacoustic emissions (SOAEs) are generated by self-sustained cochlear oscillators. Properties of a computational model for a linear array of active oscillators with nearest neighbor coupling are investigated. The model can produce many experimentally well-established properties of SOAEs.

On the (Frequency) Modulation of Coupled Oscillator Arrays in Phased Array Beam Control

NASA Technical Reports Server (NTRS)

Pogorzelski, R.; Acorn, J.; Zawadzki, M.

2000-01-01

It has been shown that arrays of voltage controlled oscillators coupled to nearest neighbors can be used to produce useful aperture phase distributions for phased array antennas. However, placing information of the transmitted signal requires that the oscillations be modulated.

Heterogeneous autoregressive model with structural break using nearest neighbor truncation volatility estimators for DAX.

PubMed

Chin, Wen Cheong; Lee, Min Cherng; Yap, Grace Lee Ching

2016-01-01

High frequency financial data modelling has become one of the important research areas in the field of financial econometrics. However, the possible structural break in volatile financial time series often trigger inconsistency issue in volatility estimation. In this study, we propose a structural break heavy-tailed heterogeneous autoregressive (HAR) volatility econometric model with the enhancement of jump-robust estimators. The breakpoints in the volatility are captured by dummy variables after the detection by Bai-Perron sequential multi breakpoints procedure. In order to further deal with possible abrupt jump in the volatility, the jump-robust volatility estimators are composed by using the nearest neighbor truncation approach, namely the minimum and median realized volatility. Under the structural break improvements in both the models and volatility estimators, the empirical findings show that the modified HAR model provides the best performing in-sample and out-of-sample forecast evaluations as compared with the standard HAR models. Accurate volatility forecasts have direct influential to the application of risk management and investment portfolio analysis.

Distributed memory approaches for robotic neural controllers

NASA Technical Reports Server (NTRS)

Jorgensen, Charles C.

1990-01-01

The suitability is explored of two varieties of distributed memory neutral networks as trainable controllers for a simulated robotics task. The task requires that two cameras observe an arbitrary target point in space. Coordinates of the target on the camera image planes are passed to a neural controller which must learn to solve the inverse kinematics of a manipulator with one revolute and two prismatic joints. Two new network designs are evaluated. The first, radial basis sparse distributed memory (RBSDM), approximates functional mappings as sums of multivariate gaussians centered around previously learned patterns. The second network types involved variations of Adaptive Vector Quantizers or Self Organizing Maps. In these networks, random N dimensional points are given local connectivities. They are then exposed to training patterns and readjust their locations based on a nearest neighbor rule. Both approaches are tested based on their ability to interpolate manipulator joint coordinates for simulated arm movement while simultaneously performing stereo fusion of the camera data. Comparisons are made with classical k-nearest neighbor pattern recognition techniques.

A nearest neighbor approach for automated transporter prediction and categorization from protein sequences.

PubMed

Li, Haiquan; Dai, Xinbin; Zhao, Xuechun

2008-05-01

Membrane transport proteins play a crucial role in the import and export of ions, small molecules or macromolecules across biological membranes. Currently, there are a limited number of published computational tools which enable the systematic discovery and categorization of transporters prior to costly experimental validation. To approach this problem, we utilized a nearest neighbor method which seamlessly integrates homologous search and topological analysis into a machine-learning framework. Our approach satisfactorily distinguished 484 transporter families in the Transporter Classification Database, a curated and representative database for transporters. A five-fold cross-validation on the database achieved a positive classification rate of 72.3% on average. Furthermore, this method successfully detected transporters in seven model and four non-model organisms, ranging from archaean to mammalian species. A preliminary literature-based validation has cross-validated 65.8% of our predictions on the 11 organisms, including 55.9% of our predictions overlapping with 83.6% of the predicted transporters in TransportDB.

Atomistic models of vacancy-mediated diffusion in silicon

NASA Astrophysics Data System (ADS)

Dunham, Scott T.; Wu, Can Dong

1995-08-01

Vacancy-mediated diffusion of dopants in silicon is investigated using Monte Carlo simulations of hopping diffusion, as well as analytic approximations based on atomistic considerations. Dopant/vacancy interaction potentials are assumed to extend out to third-nearest neighbor distances, as required for pair diffusion theories. Analysis focusing on the third-nearest neighbor sites as bridging configurations for uncorrelated hops leads to an improved analytic model for vacancy-mediated dopant diffusion. The Monte Carlo simulations of vacancy motion on a doped silicon lattice verify the analytic results for moderate doping levels. For very high doping (≳2×1020 cm-3) the simulations show a very rapid increase in pair diffusivity due to interactions of vacancies with more than one dopant atom. This behavior has previously been observed experimentally for group IV and V atoms in silicon [Nylandsted Larsen et al., J. Appl. Phys. 73, 691 (1993)], and the simulations predict both the point of onset and doping dependence of the experimentally observed diffusivity enhancement.

Synchronization crossover of polariton condensates in weakly disordered lattices

NASA Astrophysics Data System (ADS)

Ohadi, H.; del Valle-Inclan Redondo, Y.; Ramsay, A. J.; Hatzopoulos, Z.; Liew, T. C. H.; Eastham, P. R.; Savvidis, P. G.; Baumberg, J. J.

2018-05-01

We demonstrate that the synchronization of a lattice of solid-state condensates when intersite tunneling is switched on depends strongly on the weak local disorder. This finding is vital for implementation of condensate arrays as computation devices. The condensates here are nonlinear bosonic fluids of exciton-polaritons trapped in a weakly disordered Bose-Hubbard potential, where the nearest-neighboring tunneling rate (Josephson coupling) can be dynamically tuned. The system can thus be tuned from a localized to a delocalized fluid as the number density or the Josephson coupling between nearest neighbors increases. The localized fluid is observed as a lattice of unsynchronized condensates emitting at different energies set by the disorder potential. In the delocalized phase, the condensates synchronize and long-range order appears, evidenced by narrowing of momentum and energy distributions, new diffraction peaks in momentum space, and spatial coherence between condensates. Our paper identifies similarities and differences of this nonequilibrium crossover to the traditional Bose-glass to superfluid transition in atomic condensates.

Scattering of charge and spin excitations and equilibration of a one-dimensional Wigner crystal

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

Matveev, K. A.; Andreev, A. V.; Klironomos, A. D.

2014-07-01

We study scattering of charge and spin excitations in a system of interacting electrons in one dimension. At low densities, electrons form a one-dimensional Wigner crystal. To a first approximation, the charge excitations are the phonons in the Wigner crystal, and the spin excitations are described by the Heisenberg model with nearest-neighbor exchange coupling. This model is integrable and thus incapable of describing some important phenomena, such as scattering of excitations off each other and the resulting equilibration of the system. We obtain the leading corrections to this model, including charge-spin coupling and the next-nearest-neighbor exchange in the spin subsystem.more » We apply the results to the problem of equilibration of the one-dimensional Wigner crystal and find that the leading contribution to the equilibration rate arises from scattering of spin excitations off each other. We discuss the implications of our results for the conductance of quantum wires at low electron densities« less

Effect of the next-nearest-neighbor hopping on the charge collective modes in the paramagnetic phase of the Hubbard model

NASA Astrophysics Data System (ADS)

Dao, Vu Hung; Frésard, Raymond

2017-10-01

The charge dynamical response function of the t-t'-U Hubbard model is investigated on the square lattice in the thermodynamical limit. The correlation function is calculated from Gaussian fluctuations around the paramagnetic saddle-point within the Kotliar and Ruckenstein slave-boson representation. The next-nearest-neighbor hopping only slightly affects the renormalization of the quasiparticle mass. In contrast a negative t'/t notably decreases (increases) their velocity, and hence the zero-sound velocity, at positive (negative) doping. For low (high) density n ≲ 0.5 (n ≳ 1.5) we find that it enhances (reduces) the damping of the zero-sound mode. Furthermore it softens (hardens) the upper-Hubbard-band collective mode at positive (negative) doping. It is also shown that our results differ markedly from the random-phase approximation in the strong-coupling limit, even at high doping, while they compare favorably with existing quantum Monte Carlo numerical simulations.

Eigenvalue density of cross-correlations in Sri Lankan financial market

NASA Astrophysics Data System (ADS)

Nilantha, K. G. D. R.; Ranasinghe; Malmini, P. K. C.

2007-05-01

We apply the universal properties with Gaussian orthogonal ensemble (GOE) of random matrices namely spectral properties, distribution of eigenvalues, eigenvalue spacing predicted by random matrix theory (RMT) to compare cross-correlation matrix estimators from emerging market data. The daily stock prices of the Sri Lankan All share price index and Milanka price index from August 2004 to March 2005 were analyzed. Most eigenvalues in the spectrum of the cross-correlation matrix of stock price changes agree with the universal predictions of RMT. We find that the cross-correlation matrix satisfies the universal properties of the GOE of real symmetric random matrices. The eigen distribution follows the RMT predictions in the bulk but there are some deviations at the large eigenvalues. The nearest-neighbor spacing and the next nearest-neighbor spacing of the eigenvalues were examined and found that they follow the universality of GOE. RMT with deterministic correlations found that each eigenvalue from deterministic correlations is observed at values, which are repelled from the bulk distribution.

Exchange interactions in CdMnTe/CdMgTe quantum wells under high magnetic fields

NASA Astrophysics Data System (ADS)

Yasuhira, T.; Uchida, K.; Matsuda, Y. H.; Miura, N.; Kuroda, S.; Takita, K.

2002-03-01

The sp-d exchange interaction Jsp-d and the exchange interaction between the nearest neighbor Mn ions JNN were studied by magneto-photoluminescence spectra of excitons in CdMnTe/CdMgTe quantum wells in pulsed high magnetic fields up to 45 T. The magnitude of Jsp-d estimated from the observed Zeeman splitting was found to decrease as the quantum well width was decreased. The decrease is partly due to the penetration of the electron and the hole wave functions into the non-magnetic CdMgTe barrier layers, and partly due to the k-dependence of the exchange interaction. It was found that the latter effect is much larger than theoretically predicted. The observed features are well explained by a model assuming the interface disorder within some thickness near the interface. In contrast to Jsp-d, the nearest neighbor interaction JNN estimated from the steps in the photoluminescence peak was found to be independent of the well width.

Discrimination among individual Watson–Crick base pairs at the termini of single DNA hairpin molecules

PubMed Central

Vercoutere, Wenonah A.; Winters-Hilt, Stephen; DeGuzman, Veronica S.; Deamer, David; Ridino, Sam E.; Rodgers, Joseph T.; Olsen, Hugh E.; Marziali, Andre; Akeson, Mark

2003-01-01

Nanoscale α-hemolysin pores can be used to analyze individual DNA or RNA molecules. Serial examination of hundreds to thousands of molecules per minute is possible using ionic current impedance as the measured property. In a recent report, we showed that a nanopore device coupled with machine learning algorithms could automatically discriminate among the four combinations of Watson–Crick base pairs and their orientations at the ends of individual DNA hairpin molecules. Here we use kinetic analysis to demonstrate that ionic current signatures caused by these hairpin molecules depend on the number of hydrogen bonds within the terminal base pair, stacking between the terminal base pair and its nearest neighbor, and 5′ versus 3′ orientation of the terminal bases independent of their nearest neighbors. This report constitutes evidence that single Watson–Crick base pairs can be identified within individual unmodified DNA hairpin molecules based on their dynamic behavior in a nanoscale pore. PMID:12582251

Randomized Approaches for Nearest Neighbor Search in Metric Space When Computing the Pairwise Distance Is Extremely Expensive

NASA Astrophysics Data System (ADS)

Wang, Lusheng; Yang, Yong; Lin, Guohui

Finding the closest object for a query in a database is a classical problem in computer science. For some modern biological applications, computing the similarity between two objects might be very time consuming. For example, it takes a long time to compute the edit distance between two whole chromosomes and the alignment cost of two 3D protein structures. In this paper, we study the nearest neighbor search problem in metric space, where the pair-wise distance between two objects in the database is known and we want to minimize the number of distances computed on-line between the query and objects in the database in order to find the closest object. We have designed two randomized approaches for indexing metric space databases, where objects are purely described by their distances with each other. Analysis and experiments show that our approaches only need to compute O(logn) objects in order to find the closest object, where n is the total number of objects in the database.

α-K2AgF4: Ferromagnetism induced by the weak superexchange of different eg orbitals from the nearest neighbor Ag ions

NASA Astrophysics Data System (ADS)

Zhang, Xiaoli; Zhang, Guoren; Jia, Ting; Zeng, Zhi; Lin, H. Q.

2016-05-01

We study the abnormal ferromagnetism in α-K2AgF4, which is very similar to high-TC parent material La2CuO4 in structure. We find out that the electron correlation is very important in determining the insulating property of α-K2AgF4. The Ag(II) 4d9 in the octahedron crystal field has the t2 g 6 eg 3 electron occupation with eg x2-y2 orbital fully occupied and 3z2-r2 orbital partially occupied. The two eg orbitals are very extended indicating both of them are active in superexchange. Using the Hubbard model combined with Nth-order muffin-tin orbital (NMTO) downfolding technique, it is concluded that the exchange interaction between eg 3z2-r2 and x2-y2 from the first nearest neighbor Ag ions leads to the anomalous ferromagnetism in α-K2AgF4.

Nearest neighbor 3D segmentation with context features

NASA Astrophysics Data System (ADS)

Hristova, Evelin; Schulz, Heinrich; Brosch, Tom; Heinrich, Mattias P.; Nickisch, Hannes

2018-03-01

Automated and fast multi-label segmentation of medical images is challenging and clinically important. This paper builds upon a supervised machine learning framework that uses training data sets with dense organ annotations and vantage point trees to classify voxels in unseen images based on similarity of binary feature vectors extracted from the data. Without explicit model knowledge, the algorithm is applicable to different modalities and organs, and achieves high accuracy. The method is successfully tested on 70 abdominal CT and 42 pelvic MR images. With respect to ground truth, an average Dice overlap score of 0.76 for the CT segmentation of liver, spleen and kidneys is achieved. The mean score for the MR delineation of bladder, bones, prostate and rectum is 0.65. Additionally, we benchmark several variations of the main components of the method and reduce the computation time by up to 47% without significant loss of accuracy. The segmentation results are - for a nearest neighbor method - surprisingly accurate, robust as well as data and time efficient.

Histogram Curve Matching Approaches for Object-based Image Classification of Land Cover and Land Use

PubMed Central

Toure, Sory I.; Stow, Douglas A.; Weeks, John R.; Kumar, Sunil

2013-01-01

The classification of image-objects is usually done using parametric statistical measures of central tendency and/or dispersion (e.g., mean or standard deviation). The objectives of this study were to analyze digital number histograms of image objects and evaluate classifications measures exploiting characteristic signatures of such histograms. Two histograms matching classifiers were evaluated and compared to the standard nearest neighbor to mean classifier. An ADS40 airborne multispectral image of San Diego, California was used for assessing the utility of curve matching classifiers in a geographic object-based image analysis (GEOBIA) approach. The classifications were performed with data sets having 0.5 m, 2.5 m, and 5 m spatial resolutions. Results show that histograms are reliable features for characterizing classes. Also, both histogram matching classifiers consistently performed better than the one based on the standard nearest neighbor to mean rule. The highest classification accuracies were produced with images having 2.5 m spatial resolution. PMID:24403648

Exact density functional theory for ideal polymer fluids with nearest neighbor bonding constraints.

PubMed

Woodward, Clifford E; Forsman, Jan

2008-08-07

We present a new density functional theory of ideal polymer fluids, assuming nearest-neighbor bonding constraints. The free energy functional is expressed in terms of end site densities of chain segments and thus has a simpler mathematical structure than previously used expressions using multipoint distributions. This work is based on a formalism proposed by Tripathi and Chapman [Phys. Rev. Lett. 94, 087801 (2005)]. Those authors obtain an approximate free energy functional for ideal polymers in terms of monomer site densities. Calculations on both repulsive and attractive surfaces show that their theory is reasonably accurate in some cases, but does differ significantly from the exact result for longer polymers with attractive surfaces. We suggest that segment end site densities, rather than monomer site densities, are the preferred choice of "site functions" for expressing the free energy functional of polymer fluids. We illustrate the application of our theory to derive an expression for the free energy of an ideal fluid of infinitely long polymers.

Composition Formulas of Inorganic Compounds in Terms of Cluster Plus Glue Atom Model.

PubMed

Ma, Yanping; Dong, Dandan; Wu, Aimin; Dong, Chuang

2018-01-16

The present paper attempts to identify the molecule-like structural units in inorganic compounds, by applying the so-called "cluster plus glue atom model". This model, originating from metallic glasses and quasi-crystals, describes any structure in terms of a nearest-neighbor cluster and a few outer-shell glue atoms, expressed in the cluster formula [cluster](glue atoms). Similar to the case for normal molecules where the charge transfer occurs within the molecule to meet the commonly known octet electron rule, the octet state is reached after matching the nearest-neighbor cluster with certain outer-shell glue atoms. These kinds of structural units contain information on local atomic configuration, chemical composition, and electron numbers, just as for normal molecules. It is shown that the formulas of typical inorganic compounds, such as fluorides, oxides, and nitrides, satisfy a similar octet electron rule, with the total number of valence electrons per unit formula being multiples of eight.

Environment overwhelms both nature and nurture in a model spin glass

NASA Astrophysics Data System (ADS)

Middleton, A. Alan; Yang, Jie

We are interested in exploring what information determines the particular history of the glassy long term dynamics in a disordered material. We study the effect of initial configurations and the realization of stochastic dynamics on the long time evolution of configurations in a two-dimensional Ising spin glass model. The evolution of nearest neighbor correlations is computed using patchwork dynamics, a coarse-grained numerical heuristic for temporal evolution. The dependence of the nearest neighbor spin correlations at long time on both initial spin configurations and noise histories are studied through cross-correlations of long-time configurations and the spin correlations are found to be independent of both. We investigate how effectively rigid bond clusters coarsen. Scaling laws are used to study the convergence of configurations and the distribution of sizes of nearly rigid clusters. The implications of the computational results on simulations and phenomenological models of spin glasses are discussed. We acknowledge NSF support under DMR-1410937 (CMMT program).

Virtual spring damper method for nonholonomic robotic swarm self-organization and leader following

NASA Astrophysics Data System (ADS)

Wiech, Jakub; Eremeyev, Victor A.; Giorgio, Ivan

2018-04-01

In this paper, we demonstrate a method for self-organization and leader following of nonholonomic robotic swarm based on spring damper mesh. By self-organization of swarm robots we mean the emergence of order in a swarm as the result of interactions among the single robots. In other words the self-organization of swarm robots mimics some natural behavior of social animals like ants among others. The dynamics of two-wheel robot is derived, and a relation between virtual forces and robot control inputs is defined in order to establish stable swarm formation. Two cases of swarm control are analyzed. In the first case the swarm cohesion is achieved by virtual spring damper mesh connecting nearest neighboring robots without designated leader. In the second case we introduce a swarm leader interacting with nearest and second neighbors allowing the swarm to follow the leader. The paper ends with numeric simulation for performance evaluation of the proposed control method.

Ground State of Quasi-One Dimensional Competing Spin Chain Cs2Cu2Mo3O12 at zero and Finite Fields

NASA Astrophysics Data System (ADS)

Matsui, Kazuki; Goto, Takayuki; Angel, Julia; Watanabe, Isao; Sasaki, Takahiko; Hase, Masashi

The ground state of competing-spin-chain Cs2Cu2Mo3O12 with the ferromagnetic exchange interaction J1 = -93 K on nearest-neighboring spins and the antiferromagnetic one J2 = +33 K on next-nearest-neighboring spins was investigated by ZF/LF-μSR and 133Cs-NMR in the 3He temperature range. The zero-field μSR relaxation rate λ shows a significant increase below 1.85 K, suggesting the existence of magnetic order, which is consistent with the recent report on the specific heat. However, LF decoupling data at the lowest temperature 0.3 K indicate that the spins fluctuate dynamically, suggesting that the system is in a quasi-static ordered state under zero field. This idea is further supported by the fact that the broadening in NMR spectra below TN is weakened at low field below 2 T.

An integrated classifier for computer-aided diagnosis of colorectal polyps based on random forest and location index strategies

NASA Astrophysics Data System (ADS)

Hu, Yifan; Han, Hao; Zhu, Wei; Li, Lihong; Pickhardt, Perry J.; Liang, Zhengrong

2016-03-01

Feature classification plays an important role in differentiation or computer-aided diagnosis (CADx) of suspicious lesions. As a widely used ensemble learning algorithm for classification, random forest (RF) has a distinguished performance for CADx. Our recent study has shown that the location index (LI), which is derived from the well-known kNN (k nearest neighbor) and wkNN (weighted k nearest neighbor) classifier [1], has also a distinguished role in the classification for CADx. Therefore, in this paper, based on the property that the LI will achieve a very high accuracy, we design an algorithm to integrate the LI into RF for improved or higher value of AUC (area under the curve of receiver operating characteristics -- ROC). Experiments were performed by the use of a database of 153 lesions (polyps), including 116 neoplastic lesions and 37 hyperplastic lesions, with comparison to the existing classifiers of RF and wkNN, respectively. A noticeable gain by the proposed integrated classifier was quantified by the AUC measure.

Digital system for structural dynamics simulation

NASA Technical Reports Server (NTRS)

Krauter, A. I.; Lagace, L. J.; Wojnar, M. K.; Glor, C.

1982-01-01

State-of-the-art digital hardware and software for the simulation of complex structural dynamic interactions, such as those which occur in rotating structures (engine systems). System were incorporated in a designed to use an array of processors in which the computation for each physical subelement or functional subsystem would be assigned to a single specific processor in the simulator. These node processors are microprogrammed bit-slice microcomputers which function autonomously and can communicate with each other and a central control minicomputer over parallel digital lines. Inter-processor nearest neighbor communications busses pass the constants which represent physical constraints and boundary conditions. The node processors are connected to the six nearest neighbor node processors to simulate the actual physical interface of real substructures. Computer generated finite element mesh and force models can be developed with the aid of the central control minicomputer. The control computer also oversees the animation of a graphics display system, disk-based mass storage along with the individual processing elements.

Estimating affective word covariates using word association data.

PubMed

Van Rensbergen, Bram; De Deyne, Simon; Storms, Gert

2016-12-01

Word ratings on affective dimensions are an important tool in psycholinguistic research. Traditionally, they are obtained by asking participants to rate words on each dimension, a time-consuming procedure. As such, there has been some interest in computationally generating norms, by extrapolating words' affective ratings using their semantic similarity to words for which these values are already known. So far, most attempts have derived similarity from word co-occurrence in text corpora. In the current paper, we obtain similarity from word association data. We use these similarity ratings to predict the valence, arousal, and dominance of 14,000 Dutch words with the help of two extrapolation methods: Orientation towards Paradigm Words and k-Nearest Neighbors. The resulting estimates show very high correlations with human ratings when using Orientation towards Paradigm Words, and even higher correlations when using k-Nearest Neighbors. We discuss possible theoretical accounts of our results and compare our findings with previous attempts at computationally generating affective norms.

Floating phase in the one-dimensional transverse axial next-nearest-neighbor Ising model.

PubMed

Chandra, Anjan Kumar; Dasgupta, Subinay

2007-02-01

To study the ground state of an axial next-nearest-neighbor Ising chain under transverse field as a function of frustration parameter kappa and field strength Gamma, we present here two different perturbative analyses. In one, we consider the (known) ground state at kappa=0.5 and Gamma=0 as the unperturbed state and treat an increase of the field from 0 to Gamma coupled with an increase of kappa from 0.5 to 0.5+rGamma/J as perturbation. The first-order perturbation correction to eigenvalue can be calculated exactly and we could conclude that there are only two phase-transition lines emanating from the point kappa=0.5, Gamma=0. In the second perturbation scheme, we consider the number of domains of length 1 as the perturbation and obtain the zeroth-order eigenfunction for the perturbed ground state. From the longitudinal spin-spin correlation, we conclude that floating phase exists for small values of transverse field over the entire region intermediate between the ferromagnetic phase and antiphase.

An improved coupled-states approximation including the nearest neighbor Coriolis couplings for diatom-diatom inelastic collision

NASA Astrophysics Data System (ADS)

Yang, Dongzheng; Hu, Xixi; Zhang, Dong H.; Xie, Daiqian

2018-02-01

Solving the time-independent close coupling equations of a diatom-diatom inelastic collision system by using the rigorous close-coupling approach is numerically difficult because of its expensive matrix manipulation. The coupled-states approximation decouples the centrifugal matrix by neglecting the important Coriolis couplings completely. In this work, a new approximation method based on the coupled-states approximation is presented and applied to time-independent quantum dynamic calculations. This approach only considers the most important Coriolis coupling with the nearest neighbors and ignores weaker Coriolis couplings with farther K channels. As a result, it reduces the computational costs without a significant loss of accuracy. Numerical tests for para-H2+ortho-H2 and para-H2+HD inelastic collision were carried out and the results showed that the improved method dramatically reduces the errors due to the neglect of the Coriolis couplings in the coupled-states approximation. This strategy should be useful in quantum dynamics of other systems.

Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets.

PubMed

Datta, Abhirup; Banerjee, Sudipto; Finley, Andrew O; Gelfand, Alan E

2016-01-01

Spatial process models for analyzing geostatistical data entail computations that become prohibitive as the number of spatial locations become large. This article develops a class of highly scalable nearest-neighbor Gaussian process (NNGP) models to provide fully model-based inference for large geostatistical datasets. We establish that the NNGP is a well-defined spatial process providing legitimate finite-dimensional Gaussian densities with sparse precision matrices. We embed the NNGP as a sparsity-inducing prior within a rich hierarchical modeling framework and outline how computationally efficient Markov chain Monte Carlo (MCMC) algorithms can be executed without storing or decomposing large matrices. The floating point operations (flops) per iteration of this algorithm is linear in the number of spatial locations, thereby rendering substantial scalability. We illustrate the computational and inferential benefits of the NNGP over competing methods using simulation studies and also analyze forest biomass from a massive U.S. Forest Inventory dataset at a scale that precludes alternative dimension-reducing methods. Supplementary materials for this article are available online.

Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets

PubMed Central

Datta, Abhirup; Banerjee, Sudipto; Finley, Andrew O.; Gelfand, Alan E.

2018-01-01

Spatial process models for analyzing geostatistical data entail computations that become prohibitive as the number of spatial locations become large. This article develops a class of highly scalable nearest-neighbor Gaussian process (NNGP) models to provide fully model-based inference for large geostatistical datasets. We establish that the NNGP is a well-defined spatial process providing legitimate finite-dimensional Gaussian densities with sparse precision matrices. We embed the NNGP as a sparsity-inducing prior within a rich hierarchical modeling framework and outline how computationally efficient Markov chain Monte Carlo (MCMC) algorithms can be executed without storing or decomposing large matrices. The floating point operations (flops) per iteration of this algorithm is linear in the number of spatial locations, thereby rendering substantial scalability. We illustrate the computational and inferential benefits of the NNGP over competing methods using simulation studies and also analyze forest biomass from a massive U.S. Forest Inventory dataset at a scale that precludes alternative dimension-reducing methods. Supplementary materials for this article are available online. PMID:29720777

Evaluation of the maximum-likelihood adaptive neural system (MLANS) applications to noncooperative IFF

NASA Astrophysics Data System (ADS)

Chernick, Julian A.; Perlovsky, Leonid I.; Tye, David M.

1994-06-01

This paper describes applications of maximum likelihood adaptive neural system (MLANS) to the characterization of clutter in IR images and to the identification of targets. The characterization of image clutter is needed to improve target detection and to enhance the ability to compare performance of different algorithms using diverse imagery data. Enhanced unambiguous IFF is important for fratricide reduction while automatic cueing and targeting is becoming an ever increasing part of operations. We utilized MLANS which is a parametric neural network that combines optimal statistical techniques with a model-based approach. This paper shows that MLANS outperforms classical classifiers, the quadratic classifier and the nearest neighbor classifier, because on the one hand it is not limited to the usual Gaussian distribution assumption and can adapt in real time to the image clutter distribution; on the other hand MLANS learns from fewer samples and is more robust than the nearest neighbor classifiers. Future research will address uncooperative IFF using fused IR and MMW data.

Reentrant behavior in the nearest-neighbor Ising antiferromagnet in a magnetic field

NASA Astrophysics Data System (ADS)

Neto, Minos A.; de Sousa, J. Ricardo

2004-12-01

Motived by the H-T phase diagram in the bcc Ising antiferromagnetic with nearest-neighbor interactions obtained by Monte Carlo simulation [Landau, Phys. Rev. B 16, 4164 (1977)] that shows a reentrant behavior at low temperature, with two critical temperatures in magnetic field about 2% greater than the critical value Hc=8J , we apply the effective field renormalization group (EFRG) approach in this model on three-dimensional lattices (simple cubic-sc and body centered cubic-bcc). We find that the critical curve TN(H) exhibits a maximum point around of H≃Hc only in the bcc lattice case. We also discuss the critical behavior by the effective field theory in clusters with one (EFT-1) and two (EFT-2) spins, and a reentrant behavior is observed for the sc and bcc lattices. We have compared our results of EFRG in the bcc lattice with Monte Carlo and series expansion, and we observe a good accordance between the methods.

Automated analysis of long-term grooming behavior in Drosophila using a k-nearest neighbors classifier

PubMed Central

Allen, Victoria W; Shirasu-Hiza, Mimi

2018-01-01

Despite being pervasive, the control of programmed grooming is poorly understood. We addressed this gap by developing a high-throughput platform that allows long-term detection of grooming in Drosophila melanogaster. In our method, a k-nearest neighbors algorithm automatically classifies fly behavior and finds grooming events with over 90% accuracy in diverse genotypes. Our data show that flies spend ~13% of their waking time grooming, driven largely by two major internal programs. One of these programs regulates the timing of grooming and involves the core circadian clock components cycle, clock, and period. The second program regulates the duration of grooming and, while dependent on cycle and clock, appears to be independent of period. This emerging dual control model in which one program controls timing and another controls duration, resembles the two-process regulatory model of sleep. Together, our quantitative approach presents the opportunity for further dissection of mechanisms controlling long-term grooming in Drosophila. PMID:29485401

Differentiating induced and natural seismicity using space-time-magnitude statistics applied to the Coso Geothermal field

USGS Publications Warehouse

Schoenball, Martin; Davatzes, Nicholas C.; Glen, Jonathan M. G.

2015-01-01

A remarkable characteristic of earthquakes is their clustering in time and space, displaying their self-similarity. It remains to be tested if natural and induced earthquakes share the same behavior. We study natural and induced earthquakes comparatively in the same tectonic setting at the Coso Geothermal Field. Covering the preproduction and coproduction periods from 1981 to 2013, we analyze interevent times, spatial dimension, and frequency-size distributions for natural and induced earthquakes. Individually, these distributions are statistically indistinguishable. Determining the distribution of nearest neighbor distances in a combined space-time-magnitude metric, lets us identify clear differences between both kinds of seismicity. Compared to natural earthquakes, induced earthquakes feature a larger population of background seismicity and nearest neighbors at large magnitude rescaled times and small magnitude rescaled distances. Local stress perturbations induced by field operations appear to be strong enough to drive local faults through several seismic cycles and reactivate them after time periods on the order of a year.

Activity Recognition in Egocentric video using SVM, kNN and Combined SVMkNN Classifiers

NASA Astrophysics Data System (ADS)

Sanal Kumar, K. P.; Bhavani, R., Dr.

2017-08-01

Egocentric vision is a unique perspective in computer vision which is human centric. The recognition of egocentric actions is a challenging task which helps in assisting elderly people, disabled patients and so on. In this work, life logging activity videos are taken as input. There are 2 categories, first one is the top level and second one is second level. Here, the recognition is done using the features like Histogram of Oriented Gradients (HOG), Motion Boundary Histogram (MBH) and Trajectory. The features are fused together and it acts as a single feature. The extracted features are reduced using Principal Component Analysis (PCA). The features that are reduced are provided as input to the classifiers like Support Vector Machine (SVM), k nearest neighbor (kNN) and combined Support Vector Machine (SVM) and k Nearest Neighbor (kNN) (combined SVMkNN). These classifiers are evaluated and the combined SVMkNN provided better results than other classifiers in the literature.

Nearest Neighbor Averaging and its Effect on the Critical Level and Minimum Detectable Concentration for Scanning Radiological Survey Instruments that Perform Facility Release Surveys.

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

Fournier, Sean Donovan; Beall, Patrick S; Miller, Mark L

2014-08-01

Through the SNL New Mexico Small Business Assistance (NMSBA) program, several Sandia engineers worked with the Environmental Restoration Group (ERG) Inc. to verify and validate a novel algorithm used to determine the scanning Critical Level (L c ) and Minimum Detectable Concentration (MDC) (or Minimum Detectable Areal Activity) for the 102F scanning system. Through the use of Monte Carlo statistical simulations the algorithm mathematically demonstrates accuracy in determining the L c and MDC when a nearest-neighbor averaging (NNA) technique was used. To empirically validate this approach, SNL prepared several spiked sources and ran a test with the ERG 102F instrumentmore » on a bare concrete floor known to have no radiological contamination other than background naturally occurring radioactive material (NORM). The tests conclude that the NNA technique increases the sensitivity (decreases the L c and MDC) for high-density data maps that are obtained by scanning radiological survey instruments.« less

The roles of the convex hull and the number of potential intersections in performance on visually presented traveling salesperson problems.

PubMed

Vickers, Douglas; Lee, Michael D; Dry, Matthew; Hughes, Peter

2003-10-01

The planar Euclidean version of the traveling salesperson problem requires finding the shortest tour through a two-dimensional array of points. MacGregor and Ormerod (1996) have suggested that people solve such problems by using a global-to-local perceptual organizing process based on the convex hull of the array. We review evidence for and against this idea, before considering an alternative, local-to-global perceptual process, based on the rapid automatic identification of nearest neighbors. We compare these approaches in an experiment in which the effects of number of convex hull points and number of potential intersections on solution performance are measured. Performance worsened with more points on the convex hull and with fewer potential intersections. A measure of response uncertainty was unaffected by the number of convex hull points but increased with fewer potential intersections. We discuss a possible interpretation of these results in terms of a hierarchical solution process based on linking nearest neighbor clusters.

RRAM-based parallel computing architecture using k-nearest neighbor classification for pattern recognition

NASA Astrophysics Data System (ADS)

Jiang, Yuning; Kang, Jinfeng; Wang, Xinan

2017-03-01

Resistive switching memory (RRAM) is considered as one of the most promising devices for parallel computing solutions that may overcome the von Neumann bottleneck of today’s electronic systems. However, the existing RRAM-based parallel computing architectures suffer from practical problems such as device variations and extra computing circuits. In this work, we propose a novel parallel computing architecture for pattern recognition by implementing k-nearest neighbor classification on metal-oxide RRAM crossbar arrays. Metal-oxide RRAM with gradual RESET behaviors is chosen as both the storage and computing components. The proposed architecture is tested by the MNIST database. High speed (~100 ns per example) and high recognition accuracy (97.05%) are obtained. The influence of several non-ideal device properties is also discussed, and it turns out that the proposed architecture shows great tolerance to device variations. This work paves a new way to achieve RRAM-based parallel computing hardware systems with high performance.

Anomalous magnetic structure and spin dynamics in magnetoelectric LiFePO 4

DOE PAGES

Toft-Petersen, Rasmus; Reehuis, Manfred; Jensen, Thomas B. S.; ...

2015-07-06

We report significant details of the magnetic structure and spin dynamics of LiFePO 4 obtained by single-crystal neutron scattering. Our results confirm a previously reported collinear rotation of the spins away from the principal b axis, and they determine that the rotation is toward the a axis. In addition, we find a significant spin-canting component along c. Furthermore, the possible causes of these components are discussed, and their significance for the magnetoelectric effect is analyzed. Inelastic neutron scattering along the three principal directions reveals a highly anisotropic hard plane consistent with earlier susceptibility measurements. While using a spin Hamiltonian, wemore » show that the spin dimensionality is intermediate between XY- and Ising-like, with an easy b axis and a hard c axis. As a result, it is shown that both next-nearest neighbor exchange couplings in the bc plane are in competition with the strongest nearest neighbor coupling.« less

Providing nearest neighbor point-to-point communications among compute nodes of an operational group in a global combining network of a parallel computer

DOEpatents

Archer, Charles J.; Faraj, Ahmad A.; Inglett, Todd A.; Ratterman, Joseph D.

2012-10-23

Methods, apparatus, and products are disclosed for providing nearest neighbor point-to-point communications among compute nodes of an operational group in a global combining network of a parallel computer, each compute node connected to each adjacent compute node in the global combining network through a link, that include: identifying each link in the global combining network for each compute node of the operational group; designating one of a plurality of point-to-point class routing identifiers for each link such that no compute node in the operational group is connected to two adjacent compute nodes in the operational group with links designated for the same class routing identifiers; and configuring each compute node of the operational group for point-to-point communications with each adjacent compute node in the global combining network through the link between that compute node and that adjacent compute node using that link's designated class routing identifier.

Environment Dependence of Disk Morphology of Spiral Galaxies

NASA Astrophysics Data System (ADS)

Ann, Hong Bae

2014-02-01

We analyze the dependence of disk morphology (arm class, Hubble type, bar type) of nearby spiral galaxies on the galaxy environment by using local background density (Σ_{n}), project distance (r_{p}), and tidal index (TI) as measures of the environment. There is a strong dependence of arm class and Hubble type on the galaxy environment, while the bar type exhibits a weak dependence with a high frequency of SB galaxies in high density regions. Grand design fractions and early-type fractions increase with increasing Σ_{n}, 1/r_{p}, and TI, while fractions of flocculent spirals and late-type spirals decrease. Multiple-arm and intermediate-type spirals exhibit nearly constant fractions with weak trends similar to grand design and early-type spirals. While bar types show only a marginal dependence on Σ_{n}, they show a fairly clear dependence on r_{p} with a high frequency of SB galaxies at small r_{p}. The arm class also exhibits a stronger correlation with r_{p} than Σ_{n} and TI, whereas the Hubble type exhibits similar correlations with Σ_{n} and r_{p}. This suggests that the arm class is mostly affected by the nearest neighbor while the Hubble type is affected by the local densities contributed by neighboring galaxies as well as the nearest neighbor.

Comprehensive thermodynamic analysis of 3′ double-nucleotide overhangs neighboring Watson–Crick terminal base pairs

PubMed Central

O'Toole, Amanda S.; Miller, Stacy; Haines, Nathan; Zink, M. Coleen; Serra, Martin J.

2006-01-01

Thermodynamic parameters are reported for duplex formation of 48 self-complementary RNA duplexes containing Watson–Crick terminal base pairs (GC, AU and UA) with all 16 possible 3′ double-nucleotide overhangs; mimicking the structures of short interfering RNAs (siRNA) and microRNAs (miRNA). Based on nearest-neighbor analysis, the addition of a second dangling nucleotide to a single 3′ dangling nucleotide increases stability of duplex formation up to 0.8 kcal/mol in a sequence dependent manner. Results from this study in conjunction with data from a previous study [A. S. O'Toole, S. Miller and M. J. Serra (2005) RNA, 11, 512.] allows for the development of a refined nearest-neighbor model to predict the influence of 3′ double-nucleotide overhangs on the stability of duplex formation. The model improves the prediction of free energy and melting temperature when tested against five oligomers with various core duplex sequences. Phylogenetic analysis of naturally occurring miRNAs was performed to support our results. Selection of the effector miR strand of the mature miRNA duplex appears to be dependent upon the identity of the 3′ double-nucleotide overhang. Thermodynamic parameters for 3′ single terminal overhangs adjacent to a UA pair are also presented. PMID:16820533

Correlations and analytical approaches to co-evolving voter models

NASA Astrophysics Data System (ADS)

Ji, M.; Xu, C.; Choi, C. W.; Hui, P. M.

2013-11-01

The difficulty in formulating analytical treatments in co-evolving networks is studied in light of the Vazquez-Eguíluz-San Miguel voter model (VM) and a modified VM (MVM) that introduces a random mutation of the opinion as a noise in the VM. The density of active links, which are links that connect the nodes of opposite opinions, is shown to be highly sensitive to both the degree k of a node and the active links n among the neighbors of a node. We test the validity in the formalism of analytical approaches and show explicitly that the assumptions behind the commonly used homogeneous pair approximation scheme in formulating a mean-field theory are the source of the theory's failure due to the strong correlations between k, n and n2. An improved approach that incorporates spatial correlation to the nearest-neighbors explicitly and a random approximation for the next-nearest neighbors is formulated for the VM and the MVM, and it gives better agreement with the simulation results. We introduce an empirical approach that quantifies the correlations more accurately and gives results in good agreement with the simulation results. The work clarifies why simply mean-field theory fails and sheds light on how to analyze the correlations in the dynamic equations that are often generated in co-evolving processes.

Spin excitations used to probe the nature of exchange coupling in the magnetically ordered ground state of Pr 0.5 Ca 0.5 MnO 3

DOE PAGES

Ewings, R. A.; Perring, T. G.; Sikora, O.; ...

2016-07-06

We have used time-of-flight inelastic neutron scattering to measure the spin wave spectrum of the canonical half-doped manganite Pr 0.5Ca 0.5MnO 3 in its magnetic and orbitally ordered phase. Comparison of the data, which cover multiple Brillouin zones and the entire energy range of the excitations, with several different models shows that only the CE-type ordered state provides an adequate description of the magnetic ground state, provided interactions beyond nearest neighbor are included. We are able to rule out a ground state in which there exist pairs of dimerized spins which interact only with their nearest neighbors. The Zener polaronmore » ground state, which comprises strongly bound magnetic dimers, can be ruled out on the basis of gross features of the observed spin wave spectrum. A model with weaker dimerization reproduces the observed dispersion but can be ruled out on the basis of subtle discrepancies between the calculated and observed structure factors at certain positions in reciprocal space. Adding further neighbor interactions results in almost no dimerization, i.e. interpolating back to the CE model. These results are consistent with theoretical analysis of the degenerate double exchange model for half-doping.« less

Effect of available space and previous contact in the social integration of Saint Croix and Suffolk ewes.

PubMed

Orihuela, A; Averós, X; Solano, J; Clemente, N; Estevez, I

2016-03-01

Reproduction in tropical sheep is not affected by season, whereas the reproductive cycle of temperate-climate breeds such as Suffolk depends on the photoperiod. Close contact with tropical ewes during the anestrous period might induce Suffolk ewes to cycle, making the use of artificial light or hormonal treatments unnecessary. However, the integration of both breeds within the social group would be necessary to trigger this effect, and so the aim of the experiment was to determine the speed of integration of 2 groups of Saint Croix and Suffolk ewes into a single flock, according to space allowance and previous experience. For this, 6 groups of 10 ewes (half from each breed) from both breeds, housed at 2 or 4 m/ewe (3 groups/treatment) and with or without previous contact with the other breed, were monitored for 3 d. Each observation day, the behavior, movement, and use of space of ewes were collected during 10 min at 1-h intervals between 0900 and 1400 h. Generalized linear mixed models were used to test the effects of breed, space allowance, and previous experience on behavior, movement, and use of space. Net distances, interbreed farthest neighbor distance, mean interbreed distance, and walking frequencies were greater at 4 m/ewe ( < 0.05). Intrabreed nearest neighbor, mean intrabreed neighbor, and interbreed nearest neighbor distances and minimum convex polygons at 4 m/ewe were greatest for Saint Croix ewes, whereas the opposite was found for lying down ( < 0.05). Experienced ewes showed larger intrabreed nearest neighbor distances, minimum convex polygons, and home range overlapping ( < 0.05). Experienced ewes at 4 m/ewe showed longest total distances and step lengths and greatest movement activity ( < 0.05). Experienced ewes walked longer total distances during Day 1 and 2 ( < 0.05). Lying down frequency was greater for Day 3 than Day 1 ( < 0.05), and Suffolk ewes kept longer interindividual distances during Day 1 ( < 0.05). After 3 d of cohabitation, Suffolk and Saint Croix ewes did not fully integrate into a cohesive flock, with each breed displaying specific behavioral patterns. Decreasing space allowance and previous experience resulted in limited benefits for the successful group cohesion. Longer cohabitation periods might result in complete integration, although practical implementation might be difficult.

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

PubMed Central

Peterson, Leif E

2002-01-01

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

A Bootstrap Procedure of Propensity Score Estimation

ERIC Educational Resources Information Center

Bai, Haiyan

2013-01-01

Propensity score estimation plays a fundamental role in propensity score matching for reducing group selection bias in observational data. To increase the accuracy of propensity score estimation, the author developed a bootstrap propensity score. The commonly used propensity score matching methods: nearest neighbor matching, caliper matching, and…

K-Nearest Neighbors Relevance Annotation Model for Distance Education

ERIC Educational Resources Information Center

Ke, Xiao; Li, Shaozi; Cao, Donglin

2011-01-01

With the rapid development of Internet technologies, distance education has become a popular educational mode. In this paper, the authors propose an online image automatic annotation distance education system, which could effectively help children learn interrelations between image content and corresponding keywords. Image automatic annotation is…

Privacy-Preserving Location-Based Services

ERIC Educational Resources Information Center

Chow, Chi Yin

2010-01-01

Location-based services (LBS for short) providers require users' current locations to answer their location-based queries, e.g., range and nearest-neighbor queries. Revealing personal location information to potentially untrusted service providers could create privacy risks for users. To this end, our objective is to design a privacy-preserving…

Structure of Ordinary Ice Ih. Part 1: Ideal Structure of Ice

DTIC Science & Technology

1993-10-01

T., H . Onuki and R. Onaka (1977) Electronic structures of water and ice. Journal of the Physics Society of Japan, 42: 152-158. Shimaoka, K. (1960...nearest neighbors .................................................................................................................. 5 6. H -bond...8 12. Positions of oxygen atoms in the ice % h crystal

Hierarchical Freezing in a Lattice Model

NASA Astrophysics Data System (ADS)

Byington, Travis W.; Socolar, Joshua E. S.

2012-01-01

A certain two-dimensional lattice model with nearest and next-nearest neighbor interactions is known to have a limit-periodic ground state. We show that during a slow quench from the high temperature, disordered phase, the ground state emerges through an infinite sequence of phase transitions. We define appropriate order parameters and show that the transitions are related by renormalizations of the temperature scale. As the temperature is decreased, sublattices with increasingly large lattice constants become ordered. A rapid quench results in a glasslike state due to kinetic barriers created by simultaneous freezing on sublattices with different lattice constants.

Exotic states of matter with polariton chains

NASA Astrophysics Data System (ADS)

Kalinin, Kirill P.; Lagoudakis, Pavlos G.; Berloff, Natalia G.

2018-04-01

We consider linear periodic chains of exciton-polariton condensates formed by pumping polaritons nonresonantly into a linear network. To the leading order such a sequence of condensates establishes relative phases as to minimize a classical one-dimensional X Y Hamiltonian with nearest and next-to-nearest neighbors. We show that the low-energy states of polaritonic linear chains demonstrate various classical regimes: ferromagnetic, antiferromagnetic, and frustrated spiral phases where quantum or thermal fluctuations are expected to give rise to a spin-liquid state. At the same time nonlinear interactions at higher pumping intensities bring about phase chaos and novel exotic phases.

Surface morphology of a modified ballistic deposition model.

PubMed

Banerjee, Kasturi; Shamanna, J; Ray, Subhankar

2014-08-01

The surface and bulk properties of a modified ballistic deposition model are investigated. The deposition rule interpolates between nearest- and next-nearest-neighbor ballistic deposition and the random deposition models. The stickiness of the depositing particle is controlled by a parameter and the type of interparticle force. Two such forces are considered: Coulomb and van der Waals type. The interface width shows three distinct growth regions before eventual saturation. The rate of growth depends more strongly on the stickiness parameter than on the type of interparticle force. However, the porosity of the deposits is strongly influenced by the interparticle force.

X-ray absorption studies of chlorine valence and local environments in borosilicate waste glasses

NASA Astrophysics Data System (ADS)

McKeown, David A.; Gan, Hao; Pegg, Ian L.; Stolte, W. C.; Demchenko, I. N.

2011-01-01

Chlorine (Cl) is a constituent of certain types of nuclear wastes and its presence can affect the physical and chemical properties of silicate melts and glasses developed for the immobilization of such wastes. Cl K-edge X-ray absorption spectra (XAS) were collected and analyzed to characterize the unknown Cl environments in borosilicate waste glass formulations, ranging in Cl-content from 0.23 to 0.94 wt.%. Both X-ray absorption near edge structure (XANES) and extended X-ray absorption fine structure (EXAFS) data for the glasses show trends dependent on calcium (Ca) content. Near-edge data for the Ca-rich glasses are most similar to the Cl XANES of CaCl 2, where Cl - is coordinated to three Ca atoms, while the XANES for the Ca-poor glasses are more similar to the mineral davyne, where Cl is most commonly coordinated to two Ca in one site, as well as Cl and oxygen nearest-neighbors in other sites. With increasing Ca content in the glass, Cl XANES for the glasses approach that for CaCl 2, indicating more Ca nearest-neighbors around Cl. Reliable structural information obtained from the EXAFS data for the glasses is limited, however, to Cl sbnd Cl, Cl sbnd O, and Cl sbnd Na distances; Cl sbnd Ca contributions could not be fit to the glass data, due to the narrow k-space range available for analysis. Structural models that best fit the glass EXAFS data include Cl sbnd Cl, Cl sbnd O, and Cl sbnd Na correlations, where Cl sbnd O and Cl sbnd Na distances decrease by approximately 0.16 Å as glass Ca content increases. XAS for the glasses indicates Cl - is found in multiple sites where most Cl-sites have Ca neighbors, with oxygen, and possibly, Na second-nearest neighbors. EXAFS analyses suggest that Cl sbnd Cl environments may also exist in the glasses in minor amounts. These results are generally consistent with earlier findings for silicate glasses, where Cl - was associated with Ca 2+ and Na + in network modifier sites.

Phase transition in the spin- 3 / 2 Blume-Emery-Griffiths model with antiferromagnetic second neighbor interactions

NASA Astrophysics Data System (ADS)

Yezli, M.; Bekhechi, S.; Hontinfinde, F.; EZ-Zahraouy, H.

2016-04-01

Two nonperturbative methods such as Monte-Carlo simulation (MC) and Transfer-Matrix Finite-Size-Scaling calculations (TMFSS) have been used to study the phase transition of the spin- 3 / 2 ​Blume-Emery-Griffiths model (BEG) with quadrupolar and antiferromagnetic next-nearest-neighbor exchange interactions. Ground state and finite temperature phase diagrams are obtained by means of these two methods. New degenerate phases are found and only second order phase transitions occur for all values of the parameter interactions. No sign of the intermediate phase is found from both methods. Critical exponents are also obtained from TMFSS calculations. Ising criticality and nonuniversal behaviors are observed depending on the strength of the second neighbor interaction.

I See Your Smart Phone and Raise You Smart Bacteria

Science.gov Websites

understanding how bacteria sense their nearest neighbors (including pathogens), a DTRA CB/JSTO-funded research ;wild type" E. coli was tested via reverse transcription quantitative polymerase chain reactions Director of Research, Dale Ormond, kicks off #MATHCOUNTS #NationalCompetition2018 Countdown Round

Random walk in generalized quantum theory

NASA Astrophysics Data System (ADS)

Martin, Xavier; O'Connor, Denjoe; Sorkin, Rafael D.

2005-01-01

One can view quantum mechanics as a generalization of classical probability theory that provides for pairwise interference among alternatives. Adopting this perspective, we “quantize” the classical random walk by finding, subject to a certain condition of “strong positivity”, the most general Markovian, translationally invariant “decoherence functional” with nearest neighbor transitions.

A Coupled k-Nearest Neighbor Algorithm for Multi-Label Classification

DTIC Science & Technology

2015-05-22

classification, an image may contain several concepts simultaneously, such as beach, sunset and kangaroo . Such tasks are usually denoted as multi-label...informatics, a gene can belong to both metabolism and transcription classes; and in music categorization, a song may labeled as Mozart and sad. In the

Identification of jasmine flower (Jasminum sp.) based on the shape of the flower using sobel edge and k-nearest neighbour

NASA Astrophysics Data System (ADS)

Qur’ania, A.; Sarinah, I.

2018-03-01

People often wrong in knowing the type of jasmine by just looking at the white color of the jasmine, while not all white flowers including jasmine and not all jasmine flowers have white. There is a jasmine that is yellow and there is a jasmine that is white and purple.The aim of this research is to identify Jasmine flower (Jasminum sp.) based on the shape of the flower image-based using Sobel edge detection and k-Nearest Neighbor. Edge detection is used to detect the type of flower from the flower shape. Edge detection aims to improve the appearance of the border of a digital image. While k-Nearest Neighbor method is used to classify the classification of test objects into classes that have neighbouring properties closest to the object of training. The data used in this study are three types of jasmine namely jasmine white (Jasminum sambac), jasmine gambir (Jasminum pubescens), and jasmine japan (Pseuderanthemum reticulatum). Testing of jasmine flower image resized 50 × 50 pixels, 100 × 100 pixels, 150 × 150 pixels yields an accuracy of 84%. Tests on distance values of the k-NN method with spacing 5, 10 and 15 resulted in different accuracy rates for 5 and 10 closest distances yielding the same accuracy rate of 84%, for the 15 shortest distance resulted in a small accuracy of 65.2%.

Sequence specificity, statistical potentials, and three-dimensional structure prediction with self-correcting distance geometry calculations of beta-sheet formation in proteins.

PubMed Central

Zhu, H.; Braun, W.

1999-01-01

A statistical analysis of a representative data set of 169 known protein structures was used to analyze the specificity of residue interactions between spatial neighboring strands in beta-sheets. Pairwise potentials were derived from the frequency of residue pairs in nearest contact, second nearest and third nearest contacts across neighboring beta-strands compared to the expected frequency of residue pairs in a random model. A pseudo-energy function based on these statistical pairwise potentials recognized native beta-sheets among possible alternative pairings. The native pairing was found within the three lowest energies in 73% of the cases in the training data set and in 63% of beta-sheets in a test data set of 67 proteins, which were not part of the training set. The energy function was also used to detect tripeptides, which occur frequently in beta-sheets of native proteins. The majority of native partners of tripeptides were distributed in a low energy range. Self-correcting distance geometry (SECODG) calculations using distance constraints sets derived from possible low energy pairing of beta-strands uniquely identified the native pairing of the beta-sheet in pancreatic trypsin inhibitor (BPTI). These results will be useful for predicting the structure of proteins from their amino acid sequence as well as for the design of proteins containing beta-sheets. PMID:10048326

Evaluating Descriptive Metrics of the Human Cone Mosaic

PubMed Central

Cooper, Robert F.; Wilk, Melissa A.; Tarima, Sergey; Carroll, Joseph

2016-01-01

Purpose To evaluate how metrics used to describe the cone mosaic change in response to simulated photoreceptor undersampling (i.e., cell loss or misidentification). Methods Using an adaptive optics ophthalmoscope, we acquired images of the cone mosaic from the center of fixation to 10° along the temporal, superior, inferior, and nasal meridians in 20 healthy subjects. Regions of interest (n = 1780) were extracted at regular intervals along each meridian. Cone mosaic geometry was assessed using a variety of metrics − density, density recovery profile distance (DRPD), nearest neighbor distance (NND), intercell distance (ICD), farthest neighbor distance (FND), percentage of six-sided Voronoi cells, nearest neighbor regularity (NNR), number of neighbors regularity (NoNR), and Voronoi cell area regularity (VCAR). The “performance” of each metric was evaluated by determining the level of simulated loss necessary to obtain 80% statistical power. Results Of the metrics assessed, NND and DRPD were the least sensitive to undersampling, classifying mosaics that lost 50% of their coordinates as indistinguishable from normal. The NoNR was the most sensitive, detecting a significant deviation from normal with only a 10% cell loss. Conclusions The robustness of cone spacing metrics makes them unsuitable for reliably detecting small deviations from normal or for tracking small changes in the mosaic over time. In contrast, regularity metrics are more sensitive to diffuse loss and, therefore, better suited for detecting such changes, provided the fraction of misidentified cells is minimal. Combining metrics with a variety of sensitivities may provide a more complete picture of the integrity of the photoreceptor mosaic. PMID:27273598

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.

Estimation of the climate change impact on a catchment water balance using an ensemble of GCMs

NASA Astrophysics Data System (ADS)

Reshmidevi, T. V.; Nagesh Kumar, D.; Mehrotra, R.; Sharma, A.

2018-01-01

This work evaluates the impact of climate change on the water balance of a catchment in India. Rainfall and hydro-meteorological variables for current (20C3M scenario, 1981-2000) and two future time periods: mid of the 21st century (2046-2065) and end of the century (2081-2100) are simulated using Modified Markov Model-Kernel Density Estimation (MMM-KDE) and k-nearest neighbor downscaling models. Climate projections from an ensemble of 5 GCMs (MPI-ECHAM5, BCCR-BCM2.0, CSIRO-mk3.5, IPSL-CM4, and MRI-CGCM2) are used in this study. Hydrologic simulations for the current as well as future climate scenarios are carried out using Soil and Water Assessment Tool (SWAT) integrated with ArcGIS (ArcSWAT v.2009). The results show marginal reduction in runoff ratio, annual streamflow and groundwater recharge towards the end of the century. Increased temperature and evapotranspiration project an increase in the irrigation demand towards the end of the century. Rainfall projections for the future shows marginal increase in the annual average rainfall. Short and moderate wet spells are projected to decrease, whereas short and moderate dry spells are projected to increase in the future. Projected reduction in streamflow and groundwater recharge along with the increase in irrigation demand is likely to aggravate the water stress in the region under the future scenario.

The probabilistic neural network architecture for high speed classification of remotely sensed imagery

NASA Technical Reports Server (NTRS)

Chettri, Samir R.; Cromp, Robert F.

1993-01-01

In this paper we discuss a neural network architecture (the Probabilistic Neural Net or the PNN) that, to the best of our knowledge, has not previously been applied to remotely sensed data. The PNN is a supervised non-parametric classification algorithm as opposed to the Gaussian maximum likelihood classifier (GMLC). The PNN works by fitting a Gaussian kernel to each training point. The width of the Gaussian is controlled by a tuning parameter called the window width. If very small widths are used, the method is equivalent to the nearest neighbor method. For large windows, the PNN behaves like the GMLC. The basic implementation of the PNN requires no training time at all. In this respect it is far better than the commonly used backpropagation neural network which can be shown to take O(N6) time for training where N is the dimensionality of the input vector. In addition the PNN can be implemented in a feed forward mode in hardware. The disadvantage of the PNN is that it requires all the training data to be stored. Some solutions to this problem are discussed in the paper. Finally, we discuss the accuracy of the PNN with respect to the GMLC and the backpropagation neural network (BPNN). The PNN is shown to be better than GMLC and not as good as the BPNN with regards to classification accuracy.

Comparative evaluation of support vector machine classification for computer aided detection of breast masses in mammography

NASA Astrophysics Data System (ADS)

Lesniak, J. M.; Hupse, R.; Blanc, R.; Karssemeijer, N.; Székely, G.

2012-08-01

False positive (FP) marks represent an obstacle for effective use of computer-aided detection (CADe) of breast masses in mammography. Typically, the problem can be approached either by developing more discriminative features or by employing different classifier designs. In this paper, the usage of support vector machine (SVM) classification for FP reduction in CADe is investigated, presenting a systematic quantitative evaluation against neural networks, k-nearest neighbor classification, linear discriminant analysis and random forests. A large database of 2516 film mammography examinations and 73 input features was used to train the classifiers and evaluate for their performance on correctly diagnosed exams as well as false negatives. Further, classifier robustness was investigated using varying training data and feature sets as input. The evaluation was based on the mean exam sensitivity in 0.05-1 FPs on normals on the free-response receiver operating characteristic curve (FROC), incorporated into a tenfold cross validation framework. It was found that SVM classification using a Gaussian kernel offered significantly increased detection performance (P = 0.0002) compared to the reference methods. Varying training data and input features, SVMs showed improved exploitation of large feature sets. It is concluded that with the SVM-based CADe a significant reduction of FPs is possible outperforming other state-of-the-art approaches for breast mass CADe.

First-principles study of structure, electronic properties and stability of tungsten adsorption on TiC(111) surface with disordered vacancies

NASA Astrophysics Data System (ADS)

Ilyasov, Victor V.; Pham, Khang D.; Zhdanova, Tatiana P.; Phuc, Huynh V.; Hieu, Nguyen N.; Nguyen, Chuong V.

2017-12-01

In this paper, we systematically investigate the atomic structure, electronic and thermodynamic properties of adsorbed W atoms on the polar Ti-terminated TixCy (111) surface with different configurations of adsorptions using first principle calculations. The bond length, adsorption energy, and formation energy for different reconstructions of the atomic structure of the W/TixCy (111) systems were established. The effect of the tungsten coverage on the electronic structure and the adsorption mechanism of tungsten atom on the TixCy (111) are also investigated. We also suggest the possible mechanisms of W nucleation on the TixCy (111) surface. The effective charges on W atoms and nearest-neighbor atoms in the examined reconstructions were identified. Additionally, we have established the charge transfer from titanium atom to tungsten and carbon atoms which determine by the reconstruction of the local atomic and electronic structures. Our calculations showed that the charge transfer correlates with the electronegativity of tungsten and nearest-neighbor atoms. We also determined the effective charge per atom of titanium, carbon atoms, and neighboring adsorbed tungsten atom in different binding configurations. We found that, with reduction of the lattice symmetry associated with titanium and carbon vacancies, the adsorption energy increases by 1.2 times in the binding site A of W/TixCy systems.

Predicting drug-target interactions by dual-network integrated logistic matrix factorization

NASA Astrophysics Data System (ADS)

Hao, Ming; Bryant, Stephen H.; Wang, Yanli

2017-01-01

In this work, we propose a dual-network integrated logistic matrix factorization (DNILMF) algorithm to predict potential drug-target interactions (DTI). The prediction procedure consists of four steps: (1) inferring new drug/target profiles and constructing profile kernel matrix; (2) diffusing drug profile kernel matrix with drug structure kernel matrix; (3) diffusing target profile kernel matrix with target sequence kernel matrix; and (4) building DNILMF model and smoothing new drug/target predictions based on their neighbors. We compare our algorithm with the state-of-the-art method based on the benchmark dataset. Results indicate that the DNILMF algorithm outperforms the previously reported approaches in terms of AUPR (area under precision-recall curve) and AUC (area under curve of receiver operating characteristic) based on the 5 trials of 10-fold cross-validation. We conclude that the performance improvement depends on not only the proposed objective function, but also the used nonlinear diffusion technique which is important but under studied in the DTI prediction field. In addition, we also compile a new DTI dataset for increasing the diversity of currently available benchmark datasets. The top prediction results for the new dataset are confirmed by experimental studies or supported by other computational research.

Fidelity Study of Superconductivity in Extended Hubbard Models

NASA Astrophysics Data System (ADS)

Plonka, Nachum; Jia, Chunjing; Moritz, Brian; Wang, Yao; Devereaux, Thomas

2015-03-01

The role of strong electronic correlations on unconventional superconductivity remains an important open question. Here, we explore the influence of long-range Coulomb interactions, present in real material systems, through nearest and next-nearest neighbor extended Hubbard interactions in addition to the usual on-site terms. Utilizing large scale, numerical exact diagonalization, we analyze the signatures of superconductivity in the ground states through the fidelity metric of quantum information theory. We find that these extended interactions enhance charge fluctuations with various wave vectors. These suppress superconductivity in general, but in certain parameter regimes superconductivity is sustained. This has implications for tuning extended interactions in real materials.

Predicting protein submitochondrial locations using a K-Nearest neighbor method based on the Bit-Score weighted euclidean distance

USDA-ARS?s Scientific Manuscript database

Mitochondria are essential subcellular organelles found in eukaryotic cells. Knowing information on a protein’s subcellular or sub subcellular location provides in-depth insights about the microenvironment where it interacts with other molecules and is crucial for inferring the protein’s function. T...

String mediated phase transitions

NASA Technical Reports Server (NTRS)

Copeland, ED; Haws, D.; Rivers, R.; Holbraad, S.

1988-01-01

It is demonstrated from first principles how the existence of string-like structures can cause a system to undergo a phase transition. In particular, the role of topologically stable cosmic string in the restoration of spontaneously broken symmetries is emphasized. How the thermodynamic properties of strings alter when stiffness and nearest neighbor string-string interactions are included is discussed.

Nearest Neighbor Classification Using a Density Sensitive Distance Measurement

DTIC Science & Technology

2009-09-01

both the proposed density sensitive distance measurement and Euclidean distance are compared on the Wisconsin Diagnostic Breast Cancer dataset and...proposed density sensitive distance measurement and Euclidean distance are compared on the Wisconsin Diagnostic Breast Cancer dataset and the MNIST...35 1. The Wisconsin Diagnostic Breast Cancer (WDBC) Dataset..........35 2. The

Metastability of Reversible Random Walks in Potential Fields

NASA Astrophysics Data System (ADS)

Landim, C.; Misturini, R.; Tsunoda, K.

2015-09-01

Let be an open and bounded subset of , and let be a twice continuously differentiable function. Denote by the discretization of , , and denote by the continuous-time, nearest-neighbor, random walk on which jumps from to at rate . We examine in this article the metastable behavior of among the wells of the potential F.

Spin-1 Heisenberg ferromagnet using pair approximation method

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

Mert, Murat; Mert, Gülistan; Kılıç, Ahmet

2016-06-08

Thermodynamic properties for Heisenberg ferromagnet with spin-1 on the simple cubic lattice have been calculated using pair approximation method. We introduce the single-ion anisotropy and the next-nearest-neighbor exchange interaction. We found that for negative single-ion anisotropy parameter, the internal energy is positive and heat capacity has two peaks.

Mining Mineral Aggregates in Urban Areas.

ERIC Educational Resources Information Center

Thomson, Robert D.

This study can be used in a geographic research methods course to show how nearest-neighbor analysis and regression analysis can be used to study various aspects of land use. An analysis of the sand, gravel, and crushed stone industry in three urban areas of Pennsylvania, Massachusetts, and Florida illustrates the locational problems faced by…

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

Barber, M.N.; Derrida, B.

We study the phase diagram of the two-dimensional anisotropic next-nearest neighbor Ising (ANNNI) model by comparing the time evolution of two distinct spin configurations submitted to the same thermal noise. We clearly se several dynamical transitions between ferromagnetic, paramagnetic, antiphase, and floating phases. These dynamical transitions seem to occur rather close to the transition lines determined previously in the literature.

In situ XANES and EXAFS Analysis of Redox Active Fe Center Ionic Liquids

DOE PAGES

Apblett, Christopher A.; Stewart, David M.; Fryer, Robert T.; ...

2015-10-23

We apply in situ X-Ray Absorption Near Edge Spectroscopy (XANES) and Extended X-Ray Absorption Fine Structure (EXAFS) techniques to a metal center ionic liquid undergoing oxidation and reduction in a three electrode spectroscopic cell. Furthermore, the determination of the extent of reduction under negative bias on the working electrode and the extent of oxidation are determined after pulse voltammetry to quiescence. While the ionic liquid undergoes full oxidation, it undergoes only partial reduction, likely due to transport issues on the timescale of the experiment. Nearest neighbor Fe-O distances in the fully oxidized state match well to expected values for similarlymore » coordinated solids, but reduction does not result in an extension of the Fe-O bond length, as would be expected from comparisons to the solid phase. Instead, little change in bond length is observed. Finally, we suggest that this may be due to a more complex interaction between the monodentate ligands of the metal center anion and the surrounding charge cloud, rather than straightforward electrostatics between the metal center and the nearest neighbor grouping.« less

Geometry-based populated chessboard recognition

NASA Astrophysics Data System (ADS)

Xie, Youye; Tang, Gongguo; Hoff, William

2018-04-01

Chessboards are commonly used to calibrate cameras, and many robust methods have been developed to recognize the unoccupied boards. However, when the chessboard is populated with chess pieces, such as during an actual game, the problem of recognizing the board is much harder. Challenges include occlusion caused by the chess pieces, the presence of outlier lines and low viewing angles of the chessboard. In this paper, we present a novel approach to address the above challenges and recognize the chessboard. The Canny edge detector and Hough transform are used to capture all possible lines in the scene. The k-means clustering and a k-nearest-neighbors inspired algorithm are applied to cluster and reject the outlier lines based on their Euclidean distances to the nearest neighbors in a scaled Hough transform space. Finally, based on prior knowledge of the chessboard structure, a geometric constraint is used to find the correspondences between image lines and the lines on the chessboard through the homography transformation. The proposed algorithm works for a wide range of the operating angles and achieves high accuracy in experiments.

Orthogonal Polynomials on the Unit Circle with Fibonacci Verblunsky Coefficients, II. Applications

NASA Astrophysics Data System (ADS)

Damanik, David; Munger, Paul; Yessen, William N.

2013-10-01

We consider CMV matrices with Verblunsky coefficients determined in an appropriate way by the Fibonacci sequence and present two applications of the spectral theory of such matrices to problems in mathematical physics. In our first application we estimate the spreading rates of quantum walks on the line with time-independent coins following the Fibonacci sequence. The estimates we obtain are explicit in terms of the parameters of the system. In our second application, we establish a connection between the classical nearest neighbor Ising model on the one-dimensional lattice in the complex magnetic field regime, and CMV operators. In particular, given a sequence of nearest-neighbor interaction couplings, we construct a sequence of Verblunsky coefficients, such that the support of the Lee-Yang zeros of the partition function for the Ising model in the thermodynamic limit coincides with the essential spectrum of the CMV matrix with the constructed Verblunsky coefficients. Under certain technical conditions, we also show that the zeros distribution measure coincides with the density of states measure for the CMV matrix.

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

Hudson, W.G.

Scapteriscus vicinus is the most important pest of turf and pasture grasses in Florida. This study develops a method of correlating sample results with true population density and provides the first quantitative information on spatial distribution and movement patterns of mole crickets. Three basic techniques for sampling mole crickets were compared: soil flushes, soil corer, and pitfall trapping. No statistical difference was found between the soil corer and soil flushing. Soil flushing was shown to be more sensitive to changes in population density than pitfall trapping. No technique was effective for sampling adults. Regression analysis provided a means of adjustingmore » for the effects of soil moisture and showed soil temperature to be unimportant in predicting efficiency of flush sampling. Cesium-137 was used to label females for subsequent location underground. Comparison of mean distance to nearest neighbor with the distance predicted by a random distribution model showed that the observed distance in the spring was significantly greater than hypothesized (Student's T-test, p < 0.05). Fall adult nearest neighbor distance was not different than predicted by the random distribution hypothesis.« less

A novel method for the detection of R-peaks in ECG based on K-Nearest Neighbors and Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

He, Runnan; Wang, Kuanquan; Li, Qince; Yuan, Yongfeng; Zhao, Na; Liu, Yang; Zhang, Henggui

2017-12-01

Cardiovascular diseases are associated with high morbidity and mortality. However, it is still a challenge to diagnose them accurately and efficiently. Electrocardiogram (ECG), a bioelectrical signal of the heart, provides crucial information about the dynamical functions of the heart, playing an important role in cardiac diagnosis. As the QRS complex in ECG is associated with ventricular depolarization, therefore, accurate QRS detection is vital for interpreting ECG features. In this paper, we proposed a real-time, accurate, and effective algorithm for QRS detection. In the algorithm, a proposed preprocessor with a band-pass filter was first applied to remove baseline wander and power-line interference from the signal. After denoising, a method combining K-Nearest Neighbor (KNN) and Particle Swarm Optimization (PSO) was used for accurate QRS detection in ECGs with different morphologies. The proposed algorithm was tested and validated using 48 ECG records from MIT-BIH arrhythmia database (MITDB), achieved a high averaged detection accuracy, sensitivity and positive predictivity of 99.43, 99.69, and 99.72%, respectively, indicating a notable improvement to extant algorithms as reported in literatures.

Emotion recognition from multichannel EEG signals using K-nearest neighbor classification.

PubMed

Li, Mi; Xu, Hongpei; Liu, Xingwang; Lu, Shengfu

2018-04-27

Many studies have been done on the emotion recognition based on multi-channel electroencephalogram (EEG) signals. This paper explores the influence of the emotion recognition accuracy of EEG signals in different frequency bands and different number of channels. We classified the emotional states in the valence and arousal dimensions using different combinations of EEG channels. Firstly, DEAP default preprocessed data were normalized. Next, EEG signals were divided into four frequency bands using discrete wavelet transform, and entropy and energy were calculated as features of K-nearest neighbor Classifier. The classification accuracies of the 10, 14, 18 and 32 EEG channels based on the Gamma frequency band were 89.54%, 92.28%, 93.72% and 95.70% in the valence dimension and 89.81%, 92.24%, 93.69% and 95.69% in the arousal dimension. As the number of channels increases, the classification accuracy of emotional states also increases, the classification accuracy of the gamma frequency band is greater than that of the beta frequency band followed by the alpha and theta frequency bands. This paper provided better frequency bands and channels reference for emotion recognition based on EEG.

Characterization of 3D Voronoi Tessellation Nearest Neighbor Lipid Shells Provides Atomistic Lipid Disruption Profile of Protein Containing Lipid Membranes

PubMed Central

Cheng, Sara Y.; Duong, Hai V.; Compton, Campbell; Vaughn, Mark W.; Nguyen, Hoa; Cheng, Kwan H.

2015-01-01

Quantifying protein-induced lipid disruptions at the atomistic level is a challenging problem in membrane biophysics. Here we propose a novel 3D Voronoi tessellation nearest-atom-neighbor shell method to classify and characterize lipid domains into discrete concentric lipid shells surrounding membrane proteins in structurally heterogeneous lipid membranes. This method needs only the coordinates of the system and is independent of force fields and simulation conditions. As a proof-of-principle, we use this multiple lipid shell method to analyze the lipid disruption profiles of three simulated membrane systems: phosphatidylcholine, phosphatidylcholine/cholesterol, and beta-amyloid/phosphatidylcholine/cholesterol. We observed different atomic volume disruption mechanisms due to cholesterol and beta-amyloid Additionally, several lipid fractional groups and lipid-interfacial water did not converge to their control values with increasing distance or shell order from the protein. This volume divergent behavior was confirmed by bilayer thickness and chain orientational order calculations. Our method can also be used to analyze high-resolution structural experimental data. PMID:25637891

Exact ground states for the nearest neighbor quantum XXZ model on the kagome and other lattices with triangular motifs at Jz /Jxy = - 1 / 2

NASA Astrophysics Data System (ADS)

Changlani, Hitesh; Kumar, Krishna; Kochkov, Dmitrii; Fradkin, Eduardo; Clark, Bryan

We report the existence of a quantum macroscopically degenerate ground state manifold on the nearest neighbor XXZ model on the kagome lattice at the point Jz /Jxy = - 1 / 2 . On many lattices with triangular motifs (including the kagome, sawtooth, icosidodecahedron and Shastry-Sutherland lattice for a certain choice of couplings) this Hamiltonian is found to be frustration-free with exact ground states which correspond to three-colorings of these lattices. Several results also generalize to the case of variable couplings and to other motifs (albeit with possibly more complex Hamiltonians). The degenerate manifold on the kagome lattice corresponds to a ''many-body flat band'' of interacting hard-core bosons; and for the one boson case our results also explain the well-known non-interacting flat band. On adding realistic perturbations, state selection in this manifold of quantum many-body states is discussed along with the implications for the phase diagram of the kagome lattice antiferromagnet. supported by DE-FG02-12ER46875, DMR 1408713, DE-FG02-08ER46544.

Optimization of Applications with Non-blocking Neighborhood Collectives via Multisends on the Blue Gene/P Supercomputer.

PubMed

Kumar, Sameer; Heidelberger, Philip; Chen, Dong; Hines, Michael

2010-04-19

We explore the multisend interface as a data mover interface to optimize applications with neighborhood collective communication operations. One of the limitations of the current MPI 2.1 standard is that the vector collective calls require counts and displacements (zero and nonzero bytes) to be specified for all the processors in the communicator. Further, all the collective calls in MPI 2.1 are blocking and do not permit overlap of communication with computation. We present the record replay persistent optimization to the multisend interface that minimizes the processor overhead of initiating the collective. We present four different case studies with the multisend API on Blue Gene/P (i) 3D-FFT, (ii) 4D nearest neighbor exchange as used in Quantum Chromodynamics, (iii) NAMD and (iv) neural network simulator NEURON. Performance results show 1.9× speedup with 32(3) 3D-FFTs, 1.9× speedup for 4D nearest neighbor exchange with the 2(4) problem, 1.6× speedup in NAMD and almost 3× speedup in NEURON with 256K cells and 1k connections/cell.

Magnetization reversal in magnetic dot arrays: Nearest-neighbor interactions and global configurational anisotropy

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

Van de Wiele, Ben; Fin, Samuele; Pancaldi, Matteo

2016-05-28

Various proposals for future magnetic memories, data processing devices, and sensors rely on a precise control of the magnetization ground state and magnetization reversal process in periodically patterned media. In finite dot arrays, such control is hampered by the magnetostatic interactions between the nanomagnets, leading to the non-uniform magnetization state distributions throughout the sample while reversing. In this paper, we evidence how during reversal typical geometric arrangements of dots in an identical magnetization state appear that originate in the dominance of either Global Configurational Anisotropy or Nearest-Neighbor Magnetostatic interactions, which depends on the fields at which the magnetization reversal setsmore » in. Based on our findings, we propose design rules to obtain the uniform magnetization state distributions throughout the array, and also suggest future research directions to achieve non-uniform state distributions of interest, e.g., when aiming at guiding spin wave edge-modes through dot arrays. Our insights are based on the Magneto-Optical Kerr Effect and Magnetic Force Microscopy measurements as well as the extensive micromagnetic simulations.« less

Kinetic Monte Carlo Investigation of the Effects of Vacancy Pairing on Oxygen Diffusivity in Yttria-Stabilized Zirconia

NASA Technical Reports Server (NTRS)

Good, Brian S.

2011-01-01

Yttria-stabilized zirconia s high oxygen diffusivity and corresponding high ionic conductivity, and its structural stability over a broad range of temperatures, have made the material of interest for use in a number of applications, for example, as solid electrolytes in fuel cells. At low concentrations, the stabilizing yttria also serves to increase the oxygen diffusivity through the presence of corresponding oxygen vacancies, needed to maintain charge neutrality. At higher yttria concentration, however, diffusivity is impeded by the larger number of relatively high energy migration barriers associated with yttrium cations. In addition, there is evidence that oxygen vacancies preferentially occupy nearest-neighbor sites around either dopant or Zr cations, further affecting vacancy diffusion. We present the results of ab initio calculations that indicate that it is energetically favorable for oxygen vacancies to occupy nearest-neighbor sites adjacent to Y ions, and that the presence of vacancies near either species of cation lowers the migration barriers. Kinetic Monte Carlo results from simulations incorporating this effect are presented and compared with results from simulations in which the effect is not present.

Comment on ``Performance of different synchronization measures in real data: A case study on electroencephalographic signals''

NASA Astrophysics Data System (ADS)

Nicolaou, N.; Nasuto, S. J.

2005-12-01

We agree with Duckrow and Albano [Phys. Rev. E 67, 063901 (2003)] and Quian Quiroga [Phys. Rev. E 67, 063902 (2003)] that mutual information (MI) is a useful measure of dependence for electroencephalogram (EEG) data, but we show that the improvement seen in the performance of MI on extracting dependence trends from EEG is more dependent on the type of MI estimator rather than any embedding technique used. In an independent study we conducted in search for an optimal MI estimator, and in particular for EEG applications, we examined the performance of a number of MI estimators on the data set used by Quian Quiroga in their original study, where the performance of different dependence measures on real data was investigated [Phys. Rev. E 65, 041903 (2002)]. We show that for EEG applications the best performance among the investigated estimators is achieved by k -nearest neighbors, which supports the conjecture by Quian Quiroga in Phys. Rev. E 67, 063902 (2003) that the nearest neighbor estimator is the most precise method for estimating MI.

Conformal Prediction Based on K-Nearest Neighbors for Discrimination of Ginsengs by a Home-Made Electronic Nose

PubMed Central

Sun, Xiyang; Miao, Jiacheng; Wang, You; Luo, Zhiyuan; Li, Guang

2017-01-01

An estimate on the reliability of prediction in the applications of electronic nose is essential, which has not been paid enough attention. An algorithm framework called conformal prediction is introduced in this work for discriminating different kinds of ginsengs with a home-made electronic nose instrument. Nonconformity measure based on k-nearest neighbors (KNN) is implemented separately as underlying algorithm of conformal prediction. In offline mode, the conformal predictor achieves a classification rate of 84.44% based on 1NN and 80.63% based on 3NN, which is better than that of simple KNN. In addition, it provides an estimate of reliability for each prediction. In online mode, the validity of predictions is guaranteed, which means that the error rate of region predictions never exceeds the significance level set by a user. The potential of this framework for detecting borderline examples and outliers in the application of E-nose is also investigated. The result shows that conformal prediction is a promising framework for the application of electronic nose to make predictions with reliability and validity. PMID:28805721

Structure and Magnetic Properties in Ruthenium-Based Full-Heusler Alloys: AB INITIO Calculations

NASA Astrophysics Data System (ADS)

Bahlouli, S.; Aarizou, Z.; Elchikh, M.

2013-12-01

In this paper, we present ab initio calculations within density functional theory (DFT) to investigate structure, electronic and magnetic properties of Ru2CrZ (Z = Si, Ge and Sn) full-Heusler alloys. We have used the developed full-potential linearized muffin tin orbitals (FP-LMTO) based on the local spin density approximation (LSDA) with the PLane Wave expansion (PLW). In particular, we found that these Ruthenium-based Heusler alloys have the antiferromagnetic (AFM) type II as ground state. Then, we studied and discussed the magnetic properties belonging to our different magnetic structures: AFM type II, AFM type I and ferromagnetic (FM) phase. We also found that Ru2CrSi and Ru2CrGe exhibit a semiconducting behavior whereas Ru2CrSn has a semimetallic-like behavior as it is experimentally found. We made an estimation of Néel temperatures (TN) in the framework of the mean-field theory and used the energy differences approach to deduce the relevant short-range nearest-neighbor (J1) and next-nearest-neighbor (J2) interactions. The calculated TN are somewhat overestimated to the available experimental ones.

Modeling adsorption properties of structurally deformed metal–organic frameworks using structure–property map

PubMed Central

Lim, Dae-Woon; Kim, Sungjune; Harale, Aadesh; Yoon, Minyoung; Suh, Myunghyun Paik; Kim, Jihan

2017-01-01

Structural deformation and collapse in metal-organic frameworks (MOFs) can lead to loss of long-range order, making it a challenge to model these amorphous materials using conventional computational methods. In this work, we show that a structure–property map consisting of simulated data for crystalline MOFs can be used to indirectly obtain adsorption properties of structurally deformed MOFs. The structure–property map (with dimensions such as Henry coefficient, heat of adsorption, and pore volume) was constructed using a large data set of over 12000 crystalline MOFs from molecular simulations. By mapping the experimental data points of deformed SNU-200, MOF-5, and Ni-MOF-74 onto this structure–property map, we show that the experimentally deformed MOFs share similar adsorption properties with their nearest neighbor crystalline structures. Once the nearest neighbor crystalline MOFs for a deformed MOF are selected from a structure–property map at a specific condition, then the adsorption properties of these MOFs can be successfully transformed onto the degraded MOFs, leading to a new way to obtain properties of materials whose structural information is lost. PMID:28696307

Control of Synchronization Regimes in Networks of Mobile Interacting Agents

NASA Astrophysics Data System (ADS)

Perez-Diaz, Fernando; Zillmer, Ruediger; Groß, Roderich

2017-05-01

We investigate synchronization in a population of mobile pulse-coupled agents with a view towards implementations in swarm-robotics systems and mobile sensor networks. Previous theoretical approaches dealt with range and nearest-neighbor interactions. In the latter case, a synchronization-hindering regime for intermediate agent mobility is found. We investigate the robustness of this intermediate regime under practical scenarios. We show that synchronization in the intermediate regime can be predicted by means of a suitable metric of the phase response curve. Furthermore, we study more-realistic K -nearest-neighbor and cone-of-vision interactions, showing that it is possible to control the extent of the synchronization-hindering region by appropriately tuning the size of the neighborhood. To assess the effect of noise, we analyze the propagation of perturbations over the network and draw an analogy between the response in the hindering regime and stable chaos. Our findings reveal the conditions for the control of clock or activity synchronization of agents with intermediate mobility. In addition, the emergence of the intermediate regime is validated experimentally using a swarm of physical robots interacting with cone-of-vision interactions.

Structural and magnetic phase diagram of CrAs and its relationship with pressure-induced superconductivity

DOE PAGES

Shen, Yao; Wang, Qisi; Hao, Yiqing; ...

2016-02-01

In this paper, we use neutron diffraction to study the structure and magnetic phase diagram of the newly discovered pressure-induced superconductor CrAs. Unlike most magnetic unconventional superconductors where the magnetic moment direction barely changes upon doping, here we show that CrAs exhibits a spin reorientation from the ab plane to the ac plane, along with an abrupt drop of the magnetic propagation vector at a critical pressure (P c ≈ 0.6 GPa). This magnetic phase transition, accompanied by a lattice anomaly, coincides with the emergence of bulk superconductivity. With further increasing pressure, the magnetic order completely disappears near the optimalmore » T c regime (P ≈ 0.94 GPa). Moreover, the Cr magnetic moments tend to be aligned antiparallel between nearest neighbors with increasing pressure toward the optimal superconductivity regime. Finally, our findings suggest that the noncollinear helimagnetic order is strongly coupled to structural and electronic degrees of freedom, and that the antiferromagnetic correlations between nearest neighbors might be essential for superconductivity.« less

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

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

A sequence-dependent rigid-base model of DNA

NASA Astrophysics Data System (ADS)

Gonzalez, O.; Petkevičiutė, D.; Maddocks, J. H.

2013-02-01

A novel hierarchy of coarse-grain, sequence-dependent, rigid-base models of B-form DNA in solution is introduced. The hierarchy depends on both the assumed range of energetic couplings, and the extent of sequence dependence of the model parameters. A significant feature of the models is that they exhibit the phenomenon of frustration: each base cannot simultaneously minimize the energy of all of its interactions. As a consequence, an arbitrary DNA oligomer has an intrinsic or pre-existing stress, with the level of this frustration dependent on the particular sequence of the oligomer. Attention is focussed on the particular model in the hierarchy that has nearest-neighbor interactions and dimer sequence dependence of the model parameters. For a Gaussian version of this model, a complete coarse-grain parameter set is estimated. The parameterized model allows, for an oligomer of arbitrary length and sequence, a simple and explicit construction of an approximation to the configuration-space equilibrium probability density function for the oligomer in solution. The training set leading to the coarse-grain parameter set is itself extracted from a recent and extensive database of a large number of independent, atomic-resolution molecular dynamics (MD) simulations of short DNA oligomers immersed in explicit solvent. The Kullback-Leibler divergence between probability density functions is used to make several quantitative assessments of our nearest-neighbor, dimer-dependent model, which is compared against others in the hierarchy to assess various assumptions pertaining both to the locality of the energetic couplings and to the level of sequence dependence of its parameters. It is also compared directly against all-atom MD simulation to assess its predictive capabilities. The results show that the nearest-neighbor, dimer-dependent model can successfully resolve sequence effects both within and between oligomers. For example, due to the presence of frustration, the model can successfully predict the nonlocal changes in the minimum energy configuration of an oligomer that are consequent upon a local change of sequence at the level of a single point mutation.

A Novel Hybrid Classification Model of Genetic Algorithms, Modified k-Nearest Neighbor and Developed Backpropagation Neural Network

PubMed Central

Salari, Nader; Shohaimi, Shamarina; Najafi, Farid; Nallappan, Meenakshii; Karishnarajah, Isthrinayagy

2014-01-01

Among numerous artificial intelligence approaches, k-Nearest Neighbor algorithms, genetic algorithms, and artificial neural networks are considered as the most common and effective methods in classification problems in numerous studies. In the present study, the results of the implementation of a novel hybrid feature selection-classification model using the above mentioned methods are presented. The purpose is benefitting from the synergies obtained from combining these technologies for the development of classification models. Such a combination creates an opportunity to invest in the strength of each algorithm, and is an approach to make up for their deficiencies. To develop proposed model, with the aim of obtaining the best array of features, first, feature ranking techniques such as the Fisher's discriminant ratio and class separability criteria were used to prioritize features. Second, the obtained results that included arrays of the top-ranked features were used as the initial population of a genetic algorithm to produce optimum arrays of features. Third, using a modified k-Nearest Neighbor method as well as an improved method of backpropagation neural networks, the classification process was advanced based on optimum arrays of the features selected by genetic algorithms. The performance of the proposed model was compared with thirteen well-known classification models based on seven datasets. Furthermore, the statistical analysis was performed using the Friedman test followed by post-hoc tests. The experimental findings indicated that the novel proposed hybrid model resulted in significantly better classification performance compared with all 13 classification methods. Finally, the performance results of the proposed model was benchmarked against the best ones reported as the state-of-the-art classifiers in terms of classification accuracy for the same data sets. The substantial findings of the comprehensive comparative study revealed that performance of the proposed model in terms of classification accuracy is desirable, promising, and competitive to the existing state-of-the-art classification models. PMID:25419659

Whole Brain Functional Connectivity Pattern Homogeneity Mapping.

PubMed

Wang, Lijie; Xu, Jinping; Wang, Chao; Wang, Jiaojian

2018-01-01

Mounting studies have demonstrated that brain functions are determined by its external functional connectivity patterns. However, how to characterize the voxel-wise similarity of whole brain functional connectivity pattern is still largely unknown. In this study, we introduced a new method called functional connectivity homogeneity (FcHo) to delineate the voxel-wise similarity of whole brain functional connectivity patterns. FcHo was defined by measuring the whole brain functional connectivity patterns similarity of a given voxel with its nearest 26 neighbors using Kendall's coefficient concordance (KCC). The robustness of this method was tested in four independent datasets selected from a large repository of MRI. Furthermore, FcHo mapping results were further validated using the nearest 18 and six neighbors and intra-subject reproducibility with each subject scanned two times. We also compared FcHo distribution patterns with local regional homogeneity (ReHo) to identify the similarity and differences of the two methods. Finally, FcHo method was used to identify the differences of whole brain functional connectivity patterns between professional Chinese chess players and novices to test its application. FcHo mapping consistently revealed that the high FcHo was mainly distributed in association cortex including parietal lobe, frontal lobe, occipital lobe and default mode network (DMN) related areas, whereas the low FcHo was mainly found in unimodal cortex including primary visual cortex, sensorimotor cortex, paracentral lobule and supplementary motor area. These results were further supported by analyses of the nearest 18 and six neighbors and intra-subject similarity. Moreover, FcHo showed both similar and different whole brain distribution patterns compared to ReHo. Finally, we demonstrated that FcHo can effectively identify the whole brain functional connectivity pattern differences between professional Chinese chess players and novices. Our findings indicated that FcHo is a reliable method to delineate the whole brain functional connectivity pattern similarity and may provide a new way to study the functional organization and to reveal neuropathological basis for brain disorders.

A Prognostic Methodology for Precipitation Phase Detection using GPM Microwave Observations —With Focus on Snow Cover

NASA Astrophysics Data System (ADS)

Takbiri, Z.; Ebtehaj, A.; Foufoula-Georgiou, E.; Kirstetter, P.

2017-12-01

Improving satellite retrieval of precipitation requires increased understanding of its passive microwave signature over different land surfaces. Passive microwave signals over snow-covered surfaces are notoriously difficult to interpret because they record both emission from the land below and absorption/scattering from the liquid/ice crystals. Using data from the Global Precipitation Measurement (GPM) core satellite, we demonstrate that the microwave brightness temperatures of rain and snowfall shifts from a scattering to an emission regime from summer to winter, due to expansion of the less emissive snow cover underneath. We present evidence that the combination of low- (10-19 GHz) and high-frequency (89-166 GHz) channels provides the maximum amount of information for snowfall detection. The study also examines a prognostic nearest neighbor matching method for the detection of precipitation and its phase from passive microwave observations using GPM data. The nearest neighbor uses the weighted Euclidean distance metric to search through an a priori database that is populated with coincident GPM radiometer and radar data as well as ancillary snow cover fraction. The results demonstrate prognostic capabilities of the proposed method in detection of terrestrial snowfall. At the global scale, the average probability of hit and false alarm reaches to 0.80 and remains below 0.10, respectively. Surprisingly, the results show that the snow cover may help to better detect precipitation as the detection rate of terrestrial precipitation is increased from 0.75 (no snow cover) to 0.84 (snow-covered surfaces). For solid precipitation, this increased rate of detection is larger than its liquid counterpart by almost 8%. The main reasons are found to be related to the multi-frequency capabilities of the nearest neighbor matching that can properly isolate the atmospheric signal from the background emission and the fact that the precipitation can exhibit an emission-like (warmer than surface) signature over fresh snow cover.

A new EEG measure using the 1D cluster variation method

NASA Astrophysics Data System (ADS)

Maren, Alianna J.; Szu, Harold H.

2015-05-01

A new information measure, drawing on the 1-D Cluster Variation Method (CVM), describes local pattern distributions (nearest-neighbor and next-nearest neighbor) in a binary 1-D vector in terms of a single interaction enthalpy parameter h for the specific case where the fractions of elements in each of two states are the same (x1=x2=0.5). An example application of this method would be for EEG interpretation in Brain-Computer Interfaces (BCIs), especially in the frontier of invariant biometrics based on distinctive and invariant individual responses to stimuli containing an image of a person with whom there is a strong affiliative response (e.g., to a person's grandmother). This measure is obtained by mapping EEG observed configuration variables (z1, z2, z3 for next-nearest neighbor triplets) to h using the analytic function giving h in terms of these variables at equilibrium. This mapping results in a small phase space region of resulting h values, which characterizes local pattern distributions in the source data. The 1-D vector with equal fractions of units in each of the two states can be obtained using the method for transforming natural images into a binarized equi-probability ensemble (Saremi & Sejnowski, 2014; Stephens et al., 2013). An intrinsically 2-D data configuration can be mapped to 1-D using the 1-D Peano-Hilbert space-filling curve, which has demonstrated a 20 dB lower baseline using the method compared with other approaches (cf. SPIE ICA etc. by Hsu & Szu, 2014). This CVM-based method has multiple potential applications; one near-term one is optimizing classification of the EEG signals from a COTS 1-D BCI baseball hat. This can result in a convenient 3-D lab-tethered EEG, configured in a 1-D CVM equiprobable binary vector, and potentially useful for Smartphone wireless display. Longer-range applications include interpreting neural assembly activations via high-density implanted soft, cellular-scale electrodes.

A sequence-dependent rigid-base model of DNA.

PubMed

Gonzalez, O; Petkevičiūtė, D; Maddocks, J H

2013-02-07

A novel hierarchy of coarse-grain, sequence-dependent, rigid-base models of B-form DNA in solution is introduced. The hierarchy depends on both the assumed range of energetic couplings, and the extent of sequence dependence of the model parameters. A significant feature of the models is that they exhibit the phenomenon of frustration: each base cannot simultaneously minimize the energy of all of its interactions. As a consequence, an arbitrary DNA oligomer has an intrinsic or pre-existing stress, with the level of this frustration dependent on the particular sequence of the oligomer. Attention is focussed on the particular model in the hierarchy that has nearest-neighbor interactions and dimer sequence dependence of the model parameters. For a Gaussian version of this model, a complete coarse-grain parameter set is estimated. The parameterized model allows, for an oligomer of arbitrary length and sequence, a simple and explicit construction of an approximation to the configuration-space equilibrium probability density function for the oligomer in solution. The training set leading to the coarse-grain parameter set is itself extracted from a recent and extensive database of a large number of independent, atomic-resolution molecular dynamics (MD) simulations of short DNA oligomers immersed in explicit solvent. The Kullback-Leibler divergence between probability density functions is used to make several quantitative assessments of our nearest-neighbor, dimer-dependent model, which is compared against others in the hierarchy to assess various assumptions pertaining both to the locality of the energetic couplings and to the level of sequence dependence of its parameters. It is also compared directly against all-atom MD simulation to assess its predictive capabilities. The results show that the nearest-neighbor, dimer-dependent model can successfully resolve sequence effects both within and between oligomers. For example, due to the presence of frustration, the model can successfully predict the nonlocal changes in the minimum energy configuration of an oligomer that are consequent upon a local change of sequence at the level of a single point mutation.

A potential-energy scaling model to simulate the initial stages of thin-film growth

NASA Technical Reports Server (NTRS)

Heinbockel, J. H.; Outlaw, R. A.; Walker, G. H.

1983-01-01

A solid on solid (SOS) Monte Carlo computer simulation employing a potential energy scaling technique was used to model the initial stages of thin film growth. The model monitors variations in the vertical interaction potential that occur due to the arrival or departure of selected adatoms or impurities at all sites in the 400 sq. ft. array. Boltzmann ordered statistics are used to simulate fluctuations in vibrational energy at each site in the array, and the resulting site energy is compared with threshold levels of possible atomic events. In addition to adsorption, desorption, and surface migration, adatom incorporation and diffusion of a substrate atom to the surface are also included. The lateral interaction of nearest, second nearest, and third nearest neighbors is also considered. A series of computer experiments are conducted to illustrate the behavior of the model.

Exploring neighborhoods in the metagenome universe.

PubMed

Aßhauer, Kathrin P; Klingenberg, Heiner; Lingner, Thomas; Meinicke, Peter

2014-07-14

The variety of metagenomes in current databases provides a rapidly growing source of information for comparative studies. However, the quantity and quality of supplementary metadata is still lagging behind. It is therefore important to be able to identify related metagenomes by means of the available sequence data alone. We have studied efficient sequence-based methods for large-scale identification of similar metagenomes within a database retrieval context. In a broad comparison of different profiling methods we found that vector-based distance measures are well-suitable for the detection of metagenomic neighbors. Our evaluation on more than 1700 publicly available metagenomes indicates that for a query metagenome from a particular habitat on average nine out of ten nearest neighbors represent the same habitat category independent of the utilized profiling method or distance measure. While for well-defined labels a neighborhood accuracy of 100% can be achieved, in general the neighbor detection is severely affected by a natural overlap of manually annotated categories. In addition, we present results of a novel visualization method that is able to reflect the similarity of metagenomes in a 2D scatter plot. The visualization method shows a similarly high accuracy in the reduced space as compared with the high-dimensional profile space. Our study suggests that for inspection of metagenome neighborhoods the profiling methods and distance measures can be chosen to provide a convenient interpretation of results in terms of the underlying features. Furthermore, supplementary metadata of metagenome samples in the future needs to comply with readily available ontologies for fine-grained and standardized annotation. To make profile-based k-nearest-neighbor search and the 2D-visualization of the metagenome universe available to the research community, we included the proposed methods in our CoMet-Universe server for comparative metagenome analysis.

Exploring Neighborhoods in the Metagenome Universe

PubMed Central

Aßhauer, Kathrin P.; Klingenberg, Heiner; Lingner, Thomas; Meinicke, Peter

2014-01-01

The variety of metagenomes in current databases provides a rapidly growing source of information for comparative studies. However, the quantity and quality of supplementary metadata is still lagging behind. It is therefore important to be able to identify related metagenomes by means of the available sequence data alone. We have studied efficient sequence-based methods for large-scale identification of similar metagenomes within a database retrieval context. In a broad comparison of different profiling methods we found that vector-based distance measures are well-suitable for the detection of metagenomic neighbors. Our evaluation on more than 1700 publicly available metagenomes indicates that for a query metagenome from a particular habitat on average nine out of ten nearest neighbors represent the same habitat category independent of the utilized profiling method or distance measure. While for well-defined labels a neighborhood accuracy of 100% can be achieved, in general the neighbor detection is severely affected by a natural overlap of manually annotated categories. In addition, we present results of a novel visualization method that is able to reflect the similarity of metagenomes in a 2D scatter plot. The visualization method shows a similarly high accuracy in the reduced space as compared with the high-dimensional profile space. Our study suggests that for inspection of metagenome neighborhoods the profiling methods and distance measures can be chosen to provide a convenient interpretation of results in terms of the underlying features. Furthermore, supplementary metadata of metagenome samples in the future needs to comply with readily available ontologies for fine-grained and standardized annotation. To make profile-based k-nearest-neighbor search and the 2D-visualization of the metagenome universe available to the research community, we included the proposed methods in our CoMet-Universe server for comparative metagenome analysis. PMID:25026170

Intra-specific competition (crowding) of giant sequoias (Sequoiadendron giganteum)

USGS Publications Warehouse

Stohlgren, Thomas J.

1993-01-01

Information on the size and location of 1916 giant sequoias (Sequoiadendron giganteum (Lindl.) Buchholz) in Muir Grove, Sequoia National Park, in the southern Sierra Nevada of California was used to assess intra-specific crowding. Study objectives were to: (1) determine which parameters associated with intra-specific competition (i.e. size and distance to nearest neighbor, crowding/root system area overlap, or number of neighbors) might be important in spatial pattern development, growth, and survivorship of established giant sequoias; (2) quantify the level of intra-specific crowding of different sized live sequoias based on a model of estimated overlapping root system areas (i.e. an index of relative crowding); (3) compare the level of intra-specific crowding of similarly sized live and dead giant sequoias (less than 30 cm diameter at breast height (dbh) at the time of inventory (1969). Mean distances to the nearest live giant sequoia neighbor were not significantly different (at α = 0.05) for live and dead sequoias in similar size classes. A zone of influence competition model (i.e. index of crowding) based on horizontal overlap of estimated root system areas was developed for 1753 live sequoias. The model, based only on the spatial arrangement of live sequoias, was then tested on dead sequoias of less than 30 cm dbh (n = 163 trees; also recorded in 1969). The dead sequoias had a significantly higher crowding index than 561 live trees of similar diameter. Results showed that dead sequoias of less than 16.6 cm dbh had a significantly greater mean number of live neighbors and mean crowding index than live sequoias of similar size. Intra-specific crowding may be an important mechanism in determining the spatial distribution of sequoias in old-growth forests.

Double-stage nematic bond ordering above double stripe magnetism: Application to BaTi 2 Sb 2 O

DOE PAGES

Zhang, G.; Glasbrenner, J. K.; Flint, R.; ...

2017-05-01

Spin-driven nemore » maticity, or the breaking of the point-group symmetry of the lattice without long-range magnetic order, is clearly quite important in iron-based superconductors. From a symmetry point of view, nematic order can be described as a coherent locking of spin fluctuations in two interpenetrating Néel sublattices with ensuing nearest-neighbor bond order and an absence of static magnetism. In this paper, we argue that the low-temperature state of the recently discovered superconductor BaTi 2 Sb 2 O is a strong candidate for a more exotic form of spin-driven nematic order, in which fluctuations occurring in four Néel sublattices promote both nearest- and next-nearest-neighbor bond order. We develop a low-energy field theory of this state and show that it can have, as a function of temperature, up to two separate bond-order phase transitions, namely, one that breaks rotation symmetry and one that breaks reflection and translation symmetries of the lattice. The resulting state has an orthorhombic lattice distortion, an intra-unit-cell charge density wave, and no long-range magnetic order, all consistent with reported measurements of the low-temperature phase of BaTi 2 Sb 2 O . Finally, we then use density functional theory calculations to extract exchange parameters to confirm that the model is applicable to BaTi 2 Sb 2 O .« less

Double-stage nematic bond ordering above double stripe magnetism: Application to BaTi 2 Sb 2 O

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

Zhang, G.; Glasbrenner, J. K.; Flint, R.

Spin-driven nemore » maticity, or the breaking of the point-group symmetry of the lattice without long-range magnetic order, is clearly quite important in iron-based superconductors. From a symmetry point of view, nematic order can be described as a coherent locking of spin fluctuations in two interpenetrating Néel sublattices with ensuing nearest-neighbor bond order and an absence of static magnetism. In this paper, we argue that the low-temperature state of the recently discovered superconductor BaTi 2 Sb 2 O is a strong candidate for a more exotic form of spin-driven nematic order, in which fluctuations occurring in four Néel sublattices promote both nearest- and next-nearest-neighbor bond order. We develop a low-energy field theory of this state and show that it can have, as a function of temperature, up to two separate bond-order phase transitions, namely, one that breaks rotation symmetry and one that breaks reflection and translation symmetries of the lattice. The resulting state has an orthorhombic lattice distortion, an intra-unit-cell charge density wave, and no long-range magnetic order, all consistent with reported measurements of the low-temperature phase of BaTi 2 Sb 2 O . Finally, we then use density functional theory calculations to extract exchange parameters to confirm that the model is applicable to BaTi 2 Sb 2 O .« less

Post-fire saguaro community: impacts on associated vegetation still apparent 10 years later

Treesearch

Marcia Narog; Ruth Wilson

2005-01-01

Fire impacts on saguaro (Carnegiea gigantea) associated vegetation were studied in unburned and burned areas over a 10 year post-fire period after the 1993 Vista View fire, Tonto National Forest, Arizona. Many associated species, crucial for saguaro survival, regenerate by vegetative growth after fire. Bushes were the most common nearest-neighbor,...

Nearest neighbor imputation of species-level, plot-scale forest structure attributes from LiDAR data

Treesearch

Andrew T. Hudak; Nicholas L. Crookston; Jeffrey S. Evans; David E. Hall; Michael J. Falkowski

2008-01-01

Meaningful relationships between forest structure attributes measured in representative field plots on the ground and remotely sensed data measured comprehensively across the same forested landscape facilitate the production of maps of forest attributes such as basal area (BA) and tree density (TD). Because imputation methods can efficiently predict multiple response...

Creating Profiles from User Network Behavior

DTIC Science & Technology

2013-09-01

We varied the m-estimate in Naïve Bayes, m for pruning in Learning Tree, and how many k nearest neighbors to select from in KNN, before settling on the...N. Taft, “The cubicle vs. the coffee shop: behavioral modes in enterprise end-users,” in Proc. of the 9th Int. Conf. on Passive and Active Network

Abundance, distribution, and colony size estimates for Reticulitermes spp. (Isoptera: Rhinotermitidae) in Southern Mississippi

Treesearch

Ralph W. Howard; Susan C. Jones; Joe K. Mauldin; Raymond H. Beal

1982-01-01

A census of 24 1-ha plots indicated an average abundance per ha of 4.42 colonies of Reticulitermes flavipes (Kollar) and 2.38 colonies of R. virginicus (Banks). Nearest neighbor analysis indicated the mean distance between colonies of R. Flavipes to be 22.48 M, between colonies of R. virginicus...

Comparing Forest/Nonforest Classifications of Landsat TM Imagery for Stratifying FIA Estimates of Forest Land Area

Treesearch

Mark D. Nelson; Ronald E. McRoberts; Greg C. Liknes; Geoffrey R. Holden

2005-01-01

Landsat Thematic Mapper (TM) satellite imagery and Forest Inventory and Analysis (FIA) plot data were used to construct forest/nonforest maps of Mapping Zone 41, National Land Cover Dataset 2000 (NLCD 2000). Stratification approaches resulting from Maximum Likelihood, Fuzzy Convolution, Logistic Regression, and k-Nearest Neighbors classification/prediction methods were...

An imputed forest composition map for New England screened by species range boundaries

Treesearch

Matthew J. Duveneck; Jonathan R. Thompson; B. Tyler Wilson

2015-01-01

Initializing forest landscape models (FLMs) to simulate changes in tree species composition requires accurate fine-scale forest attribute information mapped continuously over large areas. Nearest-neighbor imputation maps, maps developed from multivariate imputation of field plots, have high potential for use as the initial condition within FLMs, but the tendency for...

Landscape scale mapping of forest inventory data by nearest neighbor classification

Treesearch

Andrew Lister

2009-01-01

One of the goals of the Forest Service, U.S. Department of Agriculture's Forest Inventory and Analysis (FIA) program is large-area mapping. FIA scientists have tried many methods in the past, including geostatistical methods, linear modeling, nonlinear modeling, and simple choropleth and dot maps. Mapping methods that require individual model-based maps to be...

The Statistical Power of the Cluster Randomized Block Design with Matched Pairs--A Simulation Study

ERIC Educational Resources Information Center

Dong, Nianbo; Lipsey, Mark

2010-01-01

This study uses simulation techniques to examine the statistical power of the group- randomized design and the matched-pair (MP) randomized block design under various parameter combinations. Both nearest neighbor matching and random matching are used for the MP design. The power of each design for any parameter combination was calculated from…

The Limitations of Term Co-Occurrence Data for Query Expansion in Document Retrieval Systems.

ERIC Educational Resources Information Center

Peat, Helen J.; Willett, Peter

1991-01-01

Identifies limitations in the use of term co-occurrence data as a basis for automatic query expansion in natural language document retrieval systems. The use of similarity coefficients to calculate the degree of similarity between pairs of terms is explained, and frequency and discriminatory characteristics for nearest neighbors of query terms are…

Charge-regulation phase transition on surface lattices of titratable sites adjacent to electrolyte solutions: An analog of the Ising antiferromagnet in a magnetic field

PubMed Central

Shore, Joel D.; Thurston, George M.

2018-01-01

We report a charge-patterning phase transition on two-dimensional square lattices of titratable sites, here regarded as protonation sites, placed in a low-dielectric medium just below the planar interface between this medium and a salt solution. We calculate the work-of-charging matrix of the lattice with use of a linear Debye-Hückel model, as input to a grand-canonical partition function for the distribution of occupancy patterns. For a large range of parameter values, this model exhibits an approximate inverse cubic power-law decrease of the voltage produced by an individual charge, as a function of its in-lattice separation from neighboring titratable sites. Thus, the charge coupling voltage biases the local probabilities of proton binding as a function of the occupancy of sites for many neighbors beyond the nearest ones. We find that even in the presence of these longer-range interactions, the site couplings give rise to a phase transition in which the site occupancies exhibit an alternating, checkerboard pattern that is an analog of antiferromagnetic ordering. The overall strength W of this canonical charge coupling voltage, per unit charge, is a function of the Debye length, the charge depth, the Bjerrum length, and the dielectric coefficients of the medium and the solvent. The alternating occupancy transition occurs above a curve of thermodynamic critical points in the (pH-pK,W) plane, the curve representing a charge-regulation analog of variation of the Néel temperature of an Ising antiferromagnet as a function of an applied, uniform magnetic field. The analog of a uniform magnetic field in the antiferromagnet problem is a combination of pH-pK and W, and 1/W is the analog of the temperature in the antiferromagnet problem. We use Monte Carlo simulations to study the occupancy patterns of the titratable sites, including interactions out to the 37th nearest-neighbor category (a distance of 74 lattice constants), first validating simulations through comparison with exact and approximate results for the nearest-neighbor case. We then use the simulations to map the charge-patterning phase boundary in the (pH-pK,W) plane. The physical parameters that determine W provide a framework for identifying and designing real surfaces that could exhibit charge-patterning phase transitions. PMID:26764648

Charge-regulation phase transition on surface lattices of titratable sites adjacent to electrolyte solutions: An analog of the Ising antiferromagnet in a magnetic field.

PubMed

Shore, Joel D; Thurston, George M

2015-12-01

We report a charge-patterning phase transition on two-dimensional square lattices of titratable sites, here regarded as protonation sites, placed in a low-dielectric medium just below the planar interface between this medium and a salt solution. We calculate the work-of-charging matrix of the lattice with use of a linear Debye-Hückel model, as input to a grand-canonical partition function for the distribution of occupancy patterns. For a large range of parameter values, this model exhibits an approximate inverse cubic power-law decrease of the voltage produced by an individual charge, as a function of its in-lattice separation from neighboring titratable sites. Thus, the charge coupling voltage biases the local probabilities of proton binding as a function of the occupancy of sites for many neighbors beyond the nearest ones. We find that even in the presence of these longer-range interactions, the site couplings give rise to a phase transition in which the site occupancies exhibit an alternating, checkerboard pattern that is an analog of antiferromagnetic ordering. The overall strength W of this canonical charge coupling voltage, per unit charge, is a function of the Debye length, the charge depth, the Bjerrum length, and the dielectric coefficients of the medium and the solvent. The alternating occupancy transition occurs above a curve of thermodynamic critical points in the (pH-pK,W) plane, the curve representing a charge-regulation analog of variation of the Néel temperature of an Ising antiferromagnet as a function of an applied, uniform magnetic field. The analog of a uniform magnetic field in the antiferromagnet problem is a combination of pH-pK and W, and 1/W is the analog of the temperature in the antiferromagnet problem. We use Monte Carlo simulations to study the occupancy patterns of the titratable sites, including interactions out to the 37th nearest-neighbor category (a distance of √74 lattice constants), first validating simulations through comparison with exact and approximate results for the nearest-neighbor case. We then use the simulations to map the charge-patterning phase boundary in the (pH-pK,W) plane. The physical parameters that determine W provide a framework for identifying and designing real surfaces that could exhibit charge-patterning phase transitions.

Charge-regulation phase transition on surface lattices of titratable sites adjacent to electrolyte solutions: An analog of the Ising antiferromagnet in a magnetic field

NASA Astrophysics Data System (ADS)

Shore, Joel D.; Thurston, George M.

2015-12-01

We report a charge-patterning phase transition on two-dimensional square lattices of titratable sites, here regarded as protonation sites, placed in a low-dielectric medium just below the planar interface between this medium and a salt solution. We calculate the work-of-charging matrix of the lattice with use of a linear Debye-Hückel model, as input to a grand-canonical partition function for the distribution of occupancy patterns. For a large range of parameter values, this model exhibits an approximate inverse cubic power-law decrease of the voltage produced by an individual charge, as a function of its in-lattice separation from neighboring titratable sites. Thus, the charge coupling voltage biases the local probabilities of proton binding as a function of the occupancy of sites for many neighbors beyond the nearest ones. We find that even in the presence of these longer-range interactions, the site couplings give rise to a phase transition in which the site occupancies exhibit an alternating, checkerboard pattern that is an analog of antiferromagnetic ordering. The overall strength W of this canonical charge coupling voltage, per unit charge, is a function of the Debye length, the charge depth, the Bjerrum length, and the dielectric coefficients of the medium and the solvent. The alternating occupancy transition occurs above a curve of thermodynamic critical points in the (p H-p K ,W ) plane, the curve representing a charge-regulation analog of variation of the Néel temperature of an Ising antiferromagnet as a function of an applied, uniform magnetic field. The analog of a uniform magnetic field in the antiferromagnet problem is a combination of p H-p K and W , and 1 /W is the analog of the temperature in the antiferromagnet problem. We use Monte Carlo simulations to study the occupancy patterns of the titratable sites, including interactions out to the 37th nearest-neighbor category (a distance of √{74 } lattice constants), first validating simulations through comparison with exact and approximate results for the nearest-neighbor case. We then use the simulations to map the charge-patterning phase boundary in the (p H-p K ,W ) plane. The physical parameters that determine W provide a framework for identifying and designing real surfaces that could exhibit charge-patterning phase transitions.

State-space prediction model for chaotic time series

NASA Astrophysics Data System (ADS)

Alparslan, A. K.; Sayar, M.; Atilgan, A. R.

1998-08-01

A simple method for predicting the continuation of scalar chaotic time series ahead in time is proposed. The false nearest neighbors technique in connection with the time-delayed embedding is employed so as to reconstruct the state space. A local forecasting model based upon the time evolution of the topological neighboring in the reconstructed phase space is suggested. A moving root-mean-square error is utilized in order to monitor the error along the prediction horizon. The model is tested for the convection amplitude of the Lorenz model. The results indicate that for approximately 100 cycles of the training data, the prediction follows the actual continuation very closely about six cycles. The proposed model, like other state-space forecasting models, captures the long-term behavior of the system due to the use of spatial neighbors in the state space.

Origin of Noncubic Scaling Law in Disordered Granular Packing.

PubMed

Xia, Chengjie; Li, Jindong; Kou, Binquan; Cao, Yixin; Li, Zhifeng; Xiao, Xianghui; Fu, Yanan; Xiao, Tiqiao; Hong, Liang; Zhang, Jie; Kob, Walter; Wang, Yujie

2017-06-09

Recent diffraction experiments on metallic glasses have unveiled an unexpected noncubic scaling law between density and average interatomic distance, which led to the speculation of the presence of fractal glass order. Using x-ray tomography we identify here a similar noncubic scaling law in disordered granular packing of spherical particles. We find that the scaling law is directly related to the contact neighbors within the first nearest neighbor shell, and, therefore, is closely connected to the phenomenon of jamming. The seemingly universal scaling exponent around 2.5 arises due to the isostatic condition with a contact number around 6, and we argue that the exponent should not be universal.

The application of k-Nearest Neighbour in the identification of high potential archers based on relative psychological coping skills variables

NASA Astrophysics Data System (ADS)

Taha, Zahari; Muazu Musa, Rabiu; Majeed, Anwar P. P. Abdul; Razali Abdullah, Mohamad; Muaz Alim, Muhammad; Nasir, Ahmad Fakhri Ab

2018-04-01

The present study aims at classifying and predicting high and low potential archers from a collection of psychological coping skills variables trained on different k-Nearest Neighbour (k-NN) kernels. 50 youth archers with the average age and standard deviation of (17.0 ±.056) gathered from various archery programmes completed a one end shooting score test. Psychological coping skills inventory which evaluates the archers level of related coping skills were filled out by the archers prior to their shooting tests. k-means cluster analysis was applied to cluster the archers based on their scores on variables assessed k-NN models, i.e. fine, medium, coarse, cosine, cubic and weighted kernel functions, were trained on the psychological variables. The k-means clustered the archers into high psychologically prepared archers (HPPA) and low psychologically prepared archers (LPPA), respectively. It was demonstrated that the cosine k-NN model exhibited good accuracy and precision throughout the exercise with an accuracy of 94% and considerably fewer error rate for the prediction of the HPPA and the LPPA as compared to the rest of the models. The findings of this investigation can be valuable to coaches and sports managers to recognise high potential athletes from the selected psychological coping skills variables examined which would consequently save time and energy during talent identification and development programme.

A molecular dynamics study of the relaxation of an excited molecule in crystalline nitromethane

NASA Astrophysics Data System (ADS)

Rivera-Rivera, Luis A.; Siavosh-Haghighi, Ali; Sewell, Thomas D.; Thompson, Donald L.

2014-07-01

Classical molecular dynamics simulations were used to study the relaxation of an excited nitromethane molecule in perfect crystalline nitromethane at 250 K and 1 atm pressure. The molecule was instantaneously excited by statistically distributing energy E∗ between 25.0 kcal/mol and 125.0 kcal/mol among the 21 degrees of freedom of the molecule. The relaxation occurs exponentially with time constants between 11.58 ps and 13.57 ps. Energy transfer from the excited molecule to surrounding quasi-spherical shells of molecules occurs concurrently to both the nearest and next-nearest neighbor shells, but with more energy per molecule transferred more rapidly to the first shell.

Random close packing of polydisperse jammed emulsions

NASA Astrophysics Data System (ADS)

Brujic, Jasna

2010-03-01

Packing problems are everywhere, ranging from oil extraction through porous rocks to grain storage in silos and the compaction of pharmaceutical powders into tablets. At a given density, particulate systems pack into a mechanically stable and amorphous jammed state. Theoretical frameworks have proposed a connection between this jammed state and the glass transition, a thermodynamics of jamming, as well as geometric modeling of random packings. Nevertheless, a simple underlying mechanism for the random assembly of athermal particles, analogous to crystalline ordering, remains unknown. Here we use 3D measurements of polydisperse packings of emulsion droplets to build a simple statistical model in which the complexity of the global packing is distilled into a local stochastic process. From the perspective of a single particle the packing problem is reduced to the random formation of nearest neighbors, followed by a choice of contacts among them. The two key parameters in the model, the available space around a particle and the ratio of contacts to neighbors, are directly obtained from experiments. Remarkably, we demonstrate that this ``granocentric'' view captures the properties of the polydisperse emulsion packing, ranging from the microscopic distributions of nearest neighbors and contacts to local density fluctuations and all the way to the global packing density. Further applications to monodisperse and bidisperse systems quantitatively agree with previously measured trends in global density. This model therefore reveals a general principle of organization for random packing and lays the foundations for a theory of jammed matter.

Combination of mass spectrometry-based targeted lipidomics and supervised machine learning algorithms in detecting adulterated admixtures of white rice.

PubMed

Lim, Dong Kyu; Long, Nguyen Phuoc; Mo, Changyeun; Dong, Ziyuan; Cui, Lingmei; Kim, Giyoung; Kwon, Sung Won

2017-10-01

The mixing of extraneous ingredients with original products is a common adulteration practice in food and herbal medicines. In particular, authenticity of white rice and its corresponding blended products has become a key issue in food industry. Accordingly, our current study aimed to develop and evaluate a novel discrimination method by combining targeted lipidomics with powerful supervised learning methods, and eventually introduce a platform to verify the authenticity of white rice. A total of 30 cultivars were collected, and 330 representative samples of white rice from Korea and China as well as seven mixing ratios were examined. Random forests (RF), support vector machines (SVM) with a radial basis function kernel, C5.0, model averaged neural network, and k-nearest neighbor classifiers were used for the classification. We achieved desired results, and the classifiers effectively differentiated white rice from Korea to blended samples with high prediction accuracy for the contamination ratio as low as five percent. In addition, RF and SVM classifiers were generally superior to and more robust than the other techniques. Our approach demonstrated that the relative differences in lysoGPLs can be successfully utilized to detect the adulterated mixing of white rice originating from different countries. In conclusion, the present study introduces a novel and high-throughput platform that can be applied to authenticate adulterated admixtures from original white rice samples. Copyright © 2017 Elsevier Ltd. All rights reserved.

Breast cancer Ki67 expression preoperative discrimination by DCE-MRI radiomics features

NASA Astrophysics Data System (ADS)

Ma, Wenjuan; Ji, Yu; Qin, Zhuanping; Guo, Xinpeng; Jian, Xiqi; Liu, Peifang

2018-02-01

To investigate whether quantitative radiomics features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) are associated with Ki67 expression of breast cancer. In this institutional review board approved retrospective study, we collected 377 cases Chinese women who were diagnosed with invasive breast cancer in 2015. This cohort included 53 low-Ki67 expression (Ki67 proliferation index less than 14%) and 324 cases with high-Ki67 expression (Ki67 proliferation index more than 14%). A binary-classification of low- vs. high- Ki67 expression was performed. A set of 52 quantitative radiomics features, including morphological, gray scale statistic, and texture features, were extracted from the segmented lesion area. Three most common machine learning classification methods, including Naive Bayes, k-Nearest Neighbor and support vector machine with Gaussian kernel, were employed for the classification and the least absolute shrink age and selection operator (LASSO) method was used to select most predictive features set for the classifiers. Classification performance was evaluated by the area under receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity. The model that used Naive Bayes classification method achieved the best performance than the other two methods, yielding 0.773 AUC value, 0.757 accuracy, 0.777 sensitivity and 0.769 specificity. Our study showed that quantitative radiomics imaging features of breast tumor extracted from DCE-MRI are associated with breast cancer Ki67 expression. Future larger studies are needed in order to further evaluate the findings.

Vector dissimilarity and clustering.

PubMed

Lefkovitch, L P

1991-04-01

Based on the description of objects by m attributes, an m-element vector dissimilarity function is defined that, unlike scalar functions, retains the distinction among attributes. This function, which satisfies the conditions for a metric, allows the definition of betweenness, which can then be used for clustering. Applications to the subset-generation phase of conditional clustering and to nearest-neighbor-type algorithms are described.

Estimating cavity tree abundance using nearest neighbor imputation methods for western Oregon and Washington forests

Treesearch

Hailemariam Temesgen; Tara M. Barrett; Greg Latta

2008-01-01

Cavity trees contribute to diverse forest structure and wildlife habitat. For a given stand, the size and density of cavity trees indicate its diversity, complexity, and suitability for wildlife habitat. Size and density of cavity trees vary with stand age, density, and structure. Using Forest Inventory and Analysis (FIA) data collected in western Oregon and western...

An Analysis of Document Category Prediction Responses to Classifier Model Parameter Treatment Permutations within the Software Design Patterns Subject Domain

ERIC Educational Resources Information Center

Pankau, Brian L.

2009-01-01

This empirical study evaluates the document category prediction effectiveness of Naive Bayes (NB) and K-Nearest Neighbor (KNN) classifier treatments built from different feature selection and machine learning settings and trained and tested against textual corpora of 2300 Gang-Of-Four (GOF) design pattern documents. Analysis of the experiment's…

Non-parametric analysis of LANDSAT maps using neural nets and parallel computers

NASA Technical Reports Server (NTRS)

Salu, Yehuda; Tilton, James

1991-01-01

Nearest neighbor approaches and a new neural network, the Binary Diamond, are used for the classification of images of ground pixels obtained by LANDSAT satellite. The performances are evaluated by comparing classifications of a scene in the vicinity of Washington DC. The problem of optimal selection of categories is addressed as a step in the classification process.

A Sun-Earth-Moon Activity to Develop Student Understanding of Lunar Phases and Frames of Reference

ERIC Educational Resources Information Center

Ashmann, Scott

2012-01-01

The Moon is an ever-present subject of observation, and it is a recurring topic in the science curriculum from kindergarten's basic observations through graduate courses' mathematical analyses of its orbit. How do students come to comprehend Earth's nearest neighbor? What is needed for them to understand the lunar phases and other phenomena and…

A Comparison of Rule-Based, K-Nearest Neighbor, and Neural Net Classifiers for Automated

Treesearch

Tai-Hoon Cho; Richard W. Conners; Philip A. Araman

1991-01-01

Over the last few years the authors have been involved in research aimed at developing a machine vision system for locating and identifying surface defects on materials. The particular problem being studied involves locating surface defects on hardwood lumber in a species independent manner. Obviously, the accurate location and identification of defects is of paramount...

Text Classification for Intelligent Portfolio Management

DTIC Science & Technology

2002-05-01

years including nearest neighbor classification [15], naive Bayes with EM (Ex- pectation Maximization) [11] [13], Winnow with active learning [10... Active Learning and Expectation Maximization (EM). In particular, active learning is used to actively select documents for labeling, then EM assigns...generalization with active learning . Machine Learning, 15(2):201–221, 1994. [3] I. Dagan and P. Engelson. Committee-based sampling for training

Applying Ancestry and Sex Computation as a Quality Control Tool in Targeted Next-Generation Sequencing.

PubMed

Mathias, Patrick C; Turner, Emily H; Scroggins, Sheena M; Salipante, Stephen J; Hoffman, Noah G; Pritchard, Colin C; Shirts, Brian H

2016-03-01

To apply techniques for ancestry and sex computation from next-generation sequencing (NGS) data as an approach to confirm sample identity and detect sample processing errors. We combined a principal component analysis method with k-nearest neighbors classification to compute the ancestry of patients undergoing NGS testing. By combining this calculation with X chromosome copy number data, we determined the sex and ancestry of patients for comparison with self-report. We also modeled the sensitivity of this technique in detecting sample processing errors. We applied this technique to 859 patient samples with reliable self-report data. Our k-nearest neighbors ancestry screen had an accuracy of 98.7% for patients reporting a single ancestry. Visual inspection of principal component plots was consistent with self-report in 99.6% of single-ancestry and mixed-ancestry patients. Our model demonstrates that approximately two-thirds of potential sample swaps could be detected in our patient population using this technique. Patient ancestry can be estimated from NGS data incidentally sequenced in targeted panels, enabling an inexpensive quality control method when coupled with patient self-report. © American Society for Clinical Pathology, 2016. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Improving GPU-accelerated adaptive IDW interpolation algorithm using fast kNN search.

PubMed

Mei, Gang; Xu, Nengxiong; Xu, Liangliang

2016-01-01

This paper presents an efficient parallel Adaptive Inverse Distance Weighting (AIDW) interpolation algorithm on modern Graphics Processing Unit (GPU). The presented algorithm is an improvement of our previous GPU-accelerated AIDW algorithm by adopting fast k-nearest neighbors (kNN) search. In AIDW, it needs to find several nearest neighboring data points for each interpolated point to adaptively determine the power parameter; and then the desired prediction value of the interpolated point is obtained by weighted interpolating using the power parameter. In this work, we develop a fast kNN search approach based on the space-partitioning data structure, even grid, to improve the previous GPU-accelerated AIDW algorithm. The improved algorithm is composed of the stages of kNN search and weighted interpolating. To evaluate the performance of the improved algorithm, we perform five groups of experimental tests. The experimental results indicate: (1) the improved algorithm can achieve a speedup of up to 1017 over the corresponding serial algorithm; (2) the improved algorithm is at least two times faster than our previous GPU-accelerated AIDW algorithm; and (3) the utilization of fast kNN search can significantly improve the computational efficiency of the entire GPU-accelerated AIDW algorithm.

Estimation of Full-Body Poses Using Only Five Inertial Sensors: An Eager or Lazy Learning Approach?

PubMed Central

Wouda, Frank J.; Giuberti, Matteo; Bellusci, Giovanni; Veltink, Peter H.

2016-01-01

Human movement analysis has become easier with the wide availability of motion capture systems. Inertial sensing has made it possible to capture human motion without external infrastructure, therefore allowing measurements in any environment. As high-quality motion capture data is available in large quantities, this creates possibilities to further simplify hardware setups, by use of data-driven methods to decrease the number of body-worn sensors. In this work, we contribute to this field by analyzing the capabilities of using either artificial neural networks (eager learning) or nearest neighbor search (lazy learning) for such a problem. Sparse orientation features, resulting from sensor fusion of only five inertial measurement units with magnetometers, are mapped to full-body poses. Both eager and lazy learning algorithms are shown to be capable of constructing this mapping. The full-body output poses are visually plausible with an average joint position error of approximately 7 cm, and average joint angle error of 7∘. Additionally, the effects of magnetic disturbances typical in orientation tracking on the estimation of full-body poses was also investigated, where nearest neighbor search showed better performance for such disturbances. PMID:27983676

Optimal Superpositioning of Flexible Molecule Ensembles

PubMed Central

Gapsys, Vytautas; de Groot, Bert L.

2013-01-01

Analysis of the internal dynamics of a biological molecule requires the successful removal of overall translation and rotation. Particularly for flexible or intrinsically disordered peptides, this is a challenging task due to the absence of a well-defined reference structure that could be used for superpositioning. In this work, we started the analysis with a widely known formulation of an objective for the problem of superimposing a set of multiple molecules as variance minimization over an ensemble. A negative effect of this superpositioning method is the introduction of ambiguous rotations, where different rotation matrices may be applied to structurally similar molecules. We developed two algorithms to resolve the suboptimal rotations. The first approach minimizes the variance together with the distance of a structure to a preceding molecule in the ensemble. The second algorithm seeks for minimal variance together with the distance to the nearest neighbors of each structure. The newly developed methods were applied to molecular-dynamics trajectories and normal-mode ensembles of the Aβ peptide, RS peptide, and lysozyme. These new (to our knowledge) superpositioning methods combine the benefits of variance and distance between nearest-neighbor(s) minimization, providing a solution for the analysis of intrinsic motions of flexible molecules and resolving ambiguous rotations. PMID:23332072

Intrinsic anharmonic effects on the phonon frequencies and effective spin-spin interactions in a quantum simulator made from trapped ions in a linear Paul trap

NASA Astrophysics Data System (ADS)

McAneny, M.; Freericks, J. K.

2014-11-01

The Coulomb repulsion between ions in a linear Paul trap gives rise to anharmonic terms in the potential energy when expanded about the equilibrium positions. We examine the effect of these anharmonic terms on the accuracy of a quantum simulator made from trapped ions. To be concrete, we consider a linear chain of Yb171+ ions stabilized close to the zigzag transition. We find that for typical experimental temperatures, frequencies change by no more than a factor of 0.01 % due to the anharmonic couplings. Furthermore, shifts in the effective spin-spin interactions (driven by a spin-dependent optical dipole force) are also, in general, less than 0.01 % for detunings to the blue of the transverse center-of-mass frequency. However, detuning the spin interactions near other frequencies can lead to non-negligible anharmonic contributions to the effective spin-spin interactions. We also examine an odd behavior exhibited by the harmonic spin-spin interactions for a range of intermediate detunings, where nearest-neighbor spins with a larger spatial separation on the ion chain interact more strongly than nearest neighbors with a smaller spatial separation.

Frustration by competing interactions in the highly-distorted double perovskites La2NaRuO6 and La2NaOsO6

NASA Astrophysics Data System (ADS)

Aczel, A. A.; Bugaris, D. E.; Li, L.; Yan, J.-Q.; de La Cruz, C.; Zur Loye, H.-C.; Nagler, S. E.

2013-03-01

The usual classical behavior of S = 3/2, B-site ordered double perovskites results in simple, commensurate magnetic ground states. In contrast, heat capacity and neutron powder diffraction measurements for the S = 3/2 systems La2NaB'O6 (B' = Ru, Os) reveal an incommensurate magnetic ground state for La2NaRuO6 and a drastically suppressed ordered moment for La2NaOsO6. This behavior is attributed to the large monoclinic structural distortions of these double perovskites. The distortions have the effect of weakening the nearest neighbor superexchange interactions, presumably to an energy scale that is comparable to the next nearest neighbor superexchange. The exotic ground states in these materials can then arise from a competition between these two types of antiferromagnetic interactions, providing a novel mechanism for achieving frustration in the double perovskite family. Work at ORNL is supported by the Division of Scientific User Facilities and the Materials Science and Engineering Division, DOE Basic Energy Sciences. Work at the University of South Carolina is supported by the Heterogeneous Functional Materials Research Center, funded by DOE under award number de-sc0001061.

A Sensor Data Fusion System Based on k-Nearest Neighbor Pattern Classification for Structural Health Monitoring Applications

PubMed Central

Vitola, Jaime; Pozo, Francesc; Tibaduiza, Diego A.; Anaya, Maribel

2017-01-01

Civil and military structures are susceptible and vulnerable to damage due to the environmental and operational conditions. Therefore, the implementation of technology to provide robust solutions in damage identification (by using signals acquired directly from the structure) is a requirement to reduce operational and maintenance costs. In this sense, the use of sensors permanently attached to the structures has demonstrated a great versatility and benefit since the inspection system can be automated. This automation is carried out with signal processing tasks with the aim of a pattern recognition analysis. This work presents the detailed description of a structural health monitoring (SHM) system based on the use of a piezoelectric (PZT) active system. The SHM system includes: (i) the use of a piezoelectric sensor network to excite the structure and collect the measured dynamic response, in several actuation phases; (ii) data organization; (iii) advanced signal processing techniques to define the feature vectors; and finally; (iv) the nearest neighbor algorithm as a machine learning approach to classify different kinds of damage. A description of the experimental setup, the experimental validation and a discussion of the results from two different structures are included and analyzed. PMID:28230796

Absorbing states in a catalysis model with anti-Arrhenius behavior.

PubMed

de Andrade, M F; Figueiredo, W

2012-04-28

We study a model of heterogeneous catalysis with competitive reactions between two monomers A and B. We assume that reactions are dependent on temperature and follow an anti-Arrhenius mechanism. In this model, a monomer A can react with a nearest neighbor monomer A or B, however, reactions between monomers of type B are not allowed. We assume attractive interactions between nearest neighbor monomers as well as between monomers and the catalyst. Through mean-field calculations, at the level of site and pair approximations, and extensive Monte Carlo simulations, we determine the phase diagram of the model in the plane y(A) versus temperature, where y(A) is the probability that a monomer A reaches the catalyst. The model exhibits absorbing and active phases separated by lines of continuous phase transitions. We calculate the static, dynamic, and spreading exponents of the model, and despite the absorbing state be represented by many different microscopic configurations, the model belongs to the directed percolation universality class in two dimensions. Both reaction mechanisms, Arrhenius and anti-Arrhenius, give the same set of critical exponents and do not change the nature of the universality class of the catalytic models.

A Sensor Data Fusion System Based on k-Nearest Neighbor Pattern Classification for Structural Health Monitoring Applications.

PubMed

Vitola, Jaime; Pozo, Francesc; Tibaduiza, Diego A; Anaya, Maribel

2017-02-21

Civil and military structures are susceptible and vulnerable to damage due to the environmental and operational conditions. Therefore, the implementation of technology to provide robust solutions in damage identification (by using signals acquired directly from the structure) is a requirement to reduce operational and maintenance costs. In this sense, the use of sensors permanently attached to the structures has demonstrated a great versatility and benefit since the inspection system can be automated. This automation is carried out with signal processing tasks with the aim of a pattern recognition analysis. This work presents the detailed description of a structural health monitoring (SHM) system based on the use of a piezoelectric (PZT) active system. The SHM system includes: (i) the use of a piezoelectric sensor network to excite the structure and collect the measured dynamic response, in several actuation phases; (ii) data organization; (iii) advanced signal processing techniques to define the feature vectors; and finally; (iv) the nearest neighbor algorithm as a machine learning approach to classify different kinds of damage. A description of the experimental setup, the experimental validation and a discussion of the results from two different structures are included and analyzed.

Hybrid local-order mechanism for inversion symmetry breaking

NASA Astrophysics Data System (ADS)

Wolpert, Emma H.; Overy, Alistair R.; Thygesen, Peter M. M.; Simonov, Arkadiy; Senn, Mark S.; Goodwin, Andrew L.

2018-04-01

Using classical Monte Carlo simulations, we study a simple statistical mechanical model of relevance to the emergence of polarization from local displacements on the square and cubic lattices. Our model contains two key ingredients: a Kitaev-like orientation-dependent interaction between nearest neighbors and a steric term that acts between next-nearest neighbors. Taken by themselves, each of these two ingredients is incapable of driving long-range symmetry breaking, despite the presence of a broad feature in the corresponding heat-capacity functions. Instead, each component results in a "hidden" transition on cooling to a manifold of degenerate states; the two manifolds are different in the sense that they reflect distinct types of local order. Remarkably, their intersection, i.e., the ground state when both interaction terms are included in the Hamiltonian, supports a spontaneous polarization. In this way, our study demonstrates how local-order mechanisms might be combined to break global inversion symmetry in a manner conceptually similar to that operating in the "hybrid" improper ferroelectrics. We discuss the relevance of our analysis to the emergence of spontaneous polarization in well-studied ferroelectrics such as BaTiO3 and KNbO3.

Order-disorder effects in structure and color relation of photonic-crystal-type nanostructures in butterfly wing scales.

PubMed

Márk, Géza I; Vértesy, Zofia; Kertész, Krisztián; Bálint, Zsolt; Biró, László P

2009-11-01

In order to study local and global order in butterfly wing scales possessing structural colors, we have developed a direct space algorithm, based on averaging the local environment of the repetitive units building up the structure. The method provides the statistical distribution of the local environments, including the histogram of the nearest-neighbor distance and the number of nearest neighbors. We have analyzed how the different kinds of randomness present in the direct space structure influence the reciprocal space structure. It was found that the Fourier method is useful in the case of a structure randomly deviating from an ordered lattice. The direct space averaging method remains applicable even for structures lacking long-range order. Based on the first Born approximation, a link is established between the reciprocal space image and the optical reflectance spectrum. Results calculated within this framework agree well with measured reflectance spectra because of the small width and moderate refractive index contrast of butterfly scales. By the analysis of the wing scales of Cyanophrys remus and Albulina metallica butterflies, we tested the methods for structures having long-range order, medium-range order, and short-range order.

Estimation of Full-Body Poses Using Only Five Inertial Sensors: An Eager or Lazy Learning Approach?

PubMed

Wouda, Frank J; Giuberti, Matteo; Bellusci, Giovanni; Veltink, Peter H

2016-12-15

Human movement analysis has become easier with the wide availability of motion capture systems. Inertial sensing has made it possible to capture human motion without external infrastructure, therefore allowing measurements in any environment. As high-quality motion capture data is available in large quantities, this creates possibilities to further simplify hardware setups, by use of data-driven methods to decrease the number of body-worn sensors. In this work, we contribute to this field by analyzing the capabilities of using either artificial neural networks (eager learning) or nearest neighbor search (lazy learning) for such a problem. Sparse orientation features, resulting from sensor fusion of only five inertial measurement units with magnetometers, are mapped to full-body poses. Both eager and lazy learning algorithms are shown to be capable of constructing this mapping. The full-body output poses are visually plausible with an average joint position error of approximately 7 cm, and average joint angle error of 7 ∘ . Additionally, the effects of magnetic disturbances typical in orientation tracking on the estimation of full-body poses was also investigated, where nearest neighbor search showed better performance for such disturbances.

Spatial and Alignment Analyses for a Field of Small Volcanic Vents South of Pavonis Mons and Implications for the Tharsis Province, Mars

NASA Technical Reports Server (NTRS)

Bleacher, Jacob E.; Glaze, Lori S.; Greeley, Ronald; Hauber, Ernst; Baloga, Stephen; Sakimoto, Susan E. H.; Williams, David A.; Glotch, Timothy D.

2009-01-01

A field of small volcanic vents south of Pavonis Mons was mapped with each vent assigned a two-dimensional data point. Nearest neighbor and two-point azimuth analyses were applied to the resulting location data. Nearest neighbor results show that vents within this field are spatially random in a Poisson sense, suggesting that the vents formed independently of each other without sharing a centralized magma source at shallow depth. Two-point azimuth results show that the vents display north-trending alignment relationships between one another. This trend corresponds to the trends of faults and fractures of the Noachian-aged Claritas Fossae, which might extend into our study area buried beneath more recently emplaced lava flows. However, individual elongate vent summit structures do not consistently display the same trend. The development of the volcanic field appears to display tectonic control from buried Noachian-aged structural patterns on small, ascending magma bodies while the surface orientations of the linear vents might reflect different, younger tectonic patterns. These results suggest a complex interaction between magma ascension through the crust, and multiple, older, buried Tharsis-related tectonic structures.

Streamflow Prediction based on Chaos Theory

NASA Astrophysics Data System (ADS)

Li, X.; Wang, X.; Babovic, V. M.

2015-12-01

Chaos theory is a popular method in hydrologic time series prediction. Local model (LM) based on this theory utilizes time-delay embedding to reconstruct the phase-space diagram. For this method, its efficacy is dependent on the embedding parameters, i.e. embedding dimension, time lag, and nearest neighbor number. The optimal estimation of these parameters is thus critical to the application of Local model. However, these embedding parameters are conventionally estimated using Average Mutual Information (AMI) and False Nearest Neighbors (FNN) separately. This may leads to local optimization and thus has limitation to its prediction accuracy. Considering about these limitation, this paper applies a local model combined with simulated annealing (SA) to find the global optimization of embedding parameters. It is also compared with another global optimization approach of Genetic Algorithm (GA). These proposed hybrid methods are applied in daily and monthly streamflow time series for examination. The results show that global optimization can contribute to the local model to provide more accurate prediction results compared with local optimization. The LM combined with SA shows more advantages in terms of its computational efficiency. The proposed scheme here can also be applied to other fields such as prediction of hydro-climatic time series, error correction, etc.

Order-disorder effects in structure and color relation of photonic-crystal-type nanostructures in butterfly wing scales

NASA Astrophysics Data System (ADS)

Márk, Géza I.; Vértesy, Zofia; Kertész, Krisztián; Bálint, Zsolt; Biró, László P.

2009-11-01

In order to study local and global order in butterfly wing scales possessing structural colors, we have developed a direct space algorithm, based on averaging the local environment of the repetitive units building up the structure. The method provides the statistical distribution of the local environments, including the histogram of the nearest-neighbor distance and the number of nearest neighbors. We have analyzed how the different kinds of randomness present in the direct space structure influence the reciprocal space structure. It was found that the Fourier method is useful in the case of a structure randomly deviating from an ordered lattice. The direct space averaging method remains applicable even for structures lacking long-range order. Based on the first Born approximation, a link is established between the reciprocal space image and the optical reflectance spectrum. Results calculated within this framework agree well with measured reflectance spectra because of the small width and moderate refractive index contrast of butterfly scales. By the analysis of the wing scales of Cyanophrys remus and Albulina metallica butterflies, we tested the methods for structures having long-range order, medium-range order, and short-range order.

Origins and implications of the ordering of oxygen vacancies and localized electrons on partially reduced CeO 2(111)

DOE PAGES

Sutton, Jonathan E.; Beste, Ariana; Steven H. Overbury

2015-10-12

In this study, we use density functional theory to explain the preferred structure of partially reduced CeO 2(111). Low-energy ordered structures are formed when the vacancies are isolated (maximized intervacancy separation) and the size of the Ce 3+ ions is minimized. Both conditions help minimize disruptions to the lattice around the vacancy. The stability of the ordered structures suggests that isolated vacancies are adequate for modeling more complex (e.g., catalytic) systems. Oxygen diffusion barriers are predicted to be low enough that O diffusion between vacancies is thermodynamically controlled at room temperature. The O-diffusion-reaction energies and barriers are decreased when onemore » Ce f electron hops from a nearest-neighbor Ce cation to a next-nearest-neighbor Ce cation, with a barrier that has been estimated to be slightly less than the barrier to O diffusion in the absence of polaron hopping. In conculsion, this indicates that polaron hopping plays a key role in facilitating the overall O diffusion process, and depending on the relative magnitudes of the polaron hopping and O diffusion barriers, polaron hopping may be the kinetically limiting process.« less

Predicting Drug-Target Interactions for New Drug Compounds Using a Weighted Nearest Neighbor Profile.

PubMed

van Laarhoven, Twan; Marchiori, Elena

2013-01-01

In silico discovery of interactions between drug compounds and target proteins is of core importance for improving the efficiency of the laborious and costly experimental determination of drug-target interaction. Drug-target interaction data are available for many classes of pharmaceutically useful target proteins including enzymes, ion channels, GPCRs and nuclear receptors. However, current drug-target interaction databases contain a small number of drug-target pairs which are experimentally validated interactions. In particular, for some drug compounds (or targets) there is no available interaction. This motivates the need for developing methods that predict interacting pairs with high accuracy also for these 'new' drug compounds (or targets). We show that a simple weighted nearest neighbor procedure is highly effective for this task. We integrate this procedure into a recent machine learning method for drug-target interaction we developed in previous work. Results of experiments indicate that the resulting method predicts true interactions with high accuracy also for new drug compounds and achieves results comparable or better than those of recent state-of-the-art algorithms. Software is publicly available at http://cs.ru.nl/~tvanlaarhoven/drugtarget2013/.

Autonomous target recognition using remotely sensed surface vibration measurements

NASA Astrophysics Data System (ADS)

Geurts, James; Ruck, Dennis W.; Rogers, Steven K.; Oxley, Mark E.; Barr, Dallas N.

1993-09-01

The remotely measured surface vibration signatures of tactical military ground vehicles are investigated for use in target classification and identification friend or foe (IFF) systems. The use of remote surface vibration sensing by a laser radar reduces the effects of partial occlusion, concealment, and camouflage experienced by automatic target recognition systems using traditional imagery in a tactical battlefield environment. Linear Predictive Coding (LPC) efficiently represents the vibration signatures and nearest neighbor classifiers exploit the LPC feature set using a variety of distortion metrics. Nearest neighbor classifiers achieve an 88 percent classification rate in an eight class problem, representing a classification performance increase of thirty percent from previous efforts. A novel confidence figure of merit is implemented to attain a 100 percent classification rate with less than 60 percent rejection. The high classification rates are achieved on a target set which would pose significant problems to traditional image-based recognition systems. The targets are presented to the sensor in a variety of aspects and engine speeds at a range of 1 kilometer. The classification rates achieved demonstrate the benefits of using remote vibration measurement in a ground IFF system. The signature modeling and classification system can also be used to identify rotary and fixed-wing targets.

Studying the hopping parameters of half-Heusler NaAuS using maximally localized Wannier function

NASA Astrophysics Data System (ADS)

Sihi, Antik; Lal, Sohan; Pandey, Sudhir K.

2018-04-01

Here, the electronic behavior of half-Heusler NaAuS is studied using PBEsol exchange correlation functional by plotting the band structure curve. These bands are reproduced using maximally localized Wannier function using WANNIER90. Tight-binding bands are nicely matched with density functional theory bands. By fitting the tight-binding model, hopping parameter for NaAuS is obtained by including Na 2s, 2p, Au 6s, 5p, 5d and S 3s, 3p orbitals within the energy interval of -5 to 16 eV around the Fermi level. In present study, hopping integrals for NaAuS are computed for the first primitive unit cell atoms as well as the first nearest neighbor primitive unit cell. The most dominating hopping integrals are found for Na (3s) - S (3s), Na (2px) - S (2px), Au (6s) - S (3px), Au (6s) - S (3py) and Au (6s) - S (3pz) orbitals. The hopping integrals for the first nearest neighbor primitive unit cell are also discussed in this manuscript. In future, these hopping integrals are very important to find the topological invariant for NaAuS compound.

Novel Hyperspectral Anomaly Detection Methods Based on Unsupervised Nearest Regularized Subspace

NASA Astrophysics Data System (ADS)

Hou, Z.; Chen, Y.; Tan, K.; Du, P.

2018-04-01

Anomaly detection has been of great interest in hyperspectral imagery analysis. Most conventional anomaly detectors merely take advantage of spectral and spatial information within neighboring pixels. In this paper, two methods of Unsupervised Nearest Regularized Subspace-based with Outlier Removal Anomaly Detector (UNRSORAD) and Local Summation UNRSORAD (LSUNRSORAD) are proposed, which are based on the concept that each pixel in background can be approximately represented by its spatial neighborhoods, while anomalies cannot. Using a dual window, an approximation of each testing pixel is a representation of surrounding data via a linear combination. The existence of outliers in the dual window will affect detection accuracy. Proposed detectors remove outlier pixels that are significantly different from majority of pixels. In order to make full use of various local spatial distributions information with the neighboring pixels of the pixels under test, we take the local summation dual-window sliding strategy. The residual image is constituted by subtracting the predicted background from the original hyperspectral imagery, and anomalies can be detected in the residual image. Experimental results show that the proposed methods have greatly improved the detection accuracy compared with other traditional detection method.

Anomaly Detection Based on Local Nearest Neighbor Distance Descriptor in Crowded Scenes

PubMed Central

Hu, Shiqiang; Zhang, Huanlong; Luo, Lingkun

2014-01-01

We propose a novel local nearest neighbor distance (LNND) descriptor for anomaly detection in crowded scenes. Comparing with the commonly used low-level feature descriptors in previous works, LNND descriptor has two major advantages. First, LNND descriptor efficiently incorporates spatial and temporal contextual information around the video event that is important for detecting anomalous interaction among multiple events, while most existing feature descriptors only contain the information of single event. Second, LNND descriptor is a compact representation and its dimensionality is typically much lower than the low-level feature descriptor. Therefore, not only the computation time and storage requirement can be accordingly saved by using LNND descriptor for the anomaly detection method with offline training fashion, but also the negative aspects caused by using high-dimensional feature descriptor can be avoided. We validate the effectiveness of LNND descriptor by conducting extensive experiments on different benchmark datasets. Experimental results show the promising performance of LNND-based method against the state-of-the-art methods. It is worthwhile to notice that the LNND-based approach requires less intermediate processing steps without any subsequent processing such as smoothing but achieves comparable event better performance. PMID:25105164

Resonant inelastic X-ray scattering study of spin-wave excitations in the cuprate parent compound Ca 2CuO 2Cl 2

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

Lebert, B. W.; Dean, M.; Nicolaou, A.

By means of resonant inelastic x-ray scattering at the Cu L 3 edge, we measured the spin wave dispersion along <100> and <110> in the undoped cuprate Ca 2CuO 2Cl 2. The data yields a reliable estimate of the superexchange parameter J = 135 ± 4 meV using a classical spin-1/2 2D Heisenberg model with nearest-neighbor interactions and including quantum fluctuations. Including further exchange interactions increases the estimate to J = 141 meV. The 40 meV dispersion between the magnetic Brillouin zone boundary points (1/2, 0) and (1/4, 1/4) indicates that next-nearest neighbor interactions in this compound are intermediate betweenmore » the values found in La 2CuO 4 and Sr 2CuO 2Cl 2. Here by owing to the low- Z elements composing Ca 2CuOCl 2, the present results may enable a reliable comparison with the predictions of quantum many-body calculations, which would improve our understanding of the role of magnetic excitations and of electronic correlations in cuprates.« less