Phase transition of the FCC Ising ferromagnet with competing interactions

International Nuclear Information System (INIS)

Oh, J.H.; Lee, J.Y.; Kim, D.C.

1984-01-01

A molecular field theory with correlation and Monte Carlo simulations are utilized to determine the zero field phase diagram of a fcc Ising model with ferromagnetic nearest neighbor(-J) and antiferromagnetic next neighbor (*aJ) interactions. The correlated molecular field theory predicts a fluctuation induced first order phase transition for 0.87<*a<1.31. Monte Carlo analysis indicates that the first order transition occurs for a somewhat wider range of *a. The transition temperatures obtained by the two methods are in good agreement especially near *a=1 where the fluctuation effect is expected to be large. (Author)

Scalable Nearest Neighbor Algorithms for High Dimensional Data.

Science.gov (United States)

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.

Lectures on the nearest neighbor method

CERN Document Server

Biau, Gérard

2015-01-01

This text presents a wide-ranging and rigorous overview of nearest neighbor methods, one of the most important paradigms in machine learning. Now in one self-contained volume, this book systematically covers key statistical, probabilistic, combinatorial and geometric ideas for understanding, analyzing and developing nearest neighbor methods. Gérard Biau is a professor at Université Pierre et Marie Curie (Paris). Luc Devroye is a professor at the School of Computer Science at McGill University (Montreal).   .

Dimensional testing for reverse k-nearest neighbor search

DEFF Research Database (Denmark)

Casanova, Guillaume; Englmeier, Elias; Houle, Michael E.

2017-01-01

Given a query object q, reverse k-nearest neighbor (RkNN) search aims to locate those objects of the database that have q among their k-nearest neighbors. In this paper, we propose an approximation method for solving RkNN queries, where the pruning operations and termination tests are guided...... by a characterization of the intrinsic dimensionality of the data. The method can accommodate any index structure supporting incremental (forward) nearest-neighbor search for the generation and verification of candidates, while avoiding impractically-high preprocessing costs. We also provide experimental evidence...

Dimensionality reduction with unsupervised nearest neighbors

CERN Document Server

Kramer, Oliver

2013-01-01

This book is devoted to a novel approach for dimensionality reduction based on the famous nearest neighbor method that is a powerful classification and regression approach. It starts with an introduction to machine learning concepts and a real-world application from the energy domain. Then, unsupervised nearest neighbors (UNN) is introduced as efficient iterative method for dimensionality reduction. Various UNN models are developed step by step, reaching from a simple iterative strategy for discrete latent spaces to a stochastic kernel-based algorithm for learning submanifolds with independent parameterizations. Extensions that allow the embedding of incomplete and noisy patterns are introduced. Various optimization approaches are compared, from evolutionary to swarm-based heuristics. Experimental comparisons to related methodologies taking into account artificial test data sets and also real-world data demonstrate the behavior of UNN in practical scenarios. The book contains numerous color figures to illustr...

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Phase transitions in an Ising model for monolayers of coadsorbed atoms

International Nuclear Information System (INIS)

Lee, H.H.; Landau, D.P.

1979-01-01

A Monte Carlo method is used to study a simple S=1 Ising (lattice-gas) model appropriate for monolayers composed of two kinds of atoms on cubic metal substrates H = K/sub nn/ Σ/sub nn/ S 2 /sub i/zS 2 /sub j/z + J/sub nnn/ Σ/sub nnn/ S/sub i/zS/sub j/z + Δ Σ/sub i/ S 2 /sub i/z (where nn denotes nearest-neighbor and nnn next-nearest-neighbor pairs). The phase diagram is determined over a wide range of Δ and T for K/sub nn//J/sub nnn/=1/4. For small (or negative) Δ we find an antiferromagnetic 2 x 1 ordered phase separated from the disordered state by a line of second-order phase transitions. The 2 x 1 phase is separated by a line of first-order transitions from a c (2 x 2) phase which appears for larger Δ. The 2 x 1 and c (2 x 2) phases become simultaneously critical at a bicritical point and the phase boundary of the c (2 x 2) → disordered transition shows a tricritical point

Diagnostic tools for nearest neighbors techniques when used with satellite imagery

Science.gov (United States)

Ronald E. McRoberts

2009-01-01

Nearest neighbors techniques are non-parametric approaches to multivariate prediction that are useful for predicting both continuous and categorical forest attribute variables. Although some assumptions underlying nearest neighbor techniques are common to other prediction techniques such as regression, other assumptions are unique to nearest neighbor techniques....

Secure Nearest Neighbor Query on Crowd-Sensing Data

Directory of Open Access Journals (Sweden)

Ke Cheng

2016-09-01

Full Text Available 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.

Interacting-fermion approximation in the two-dimensional ANNNI model

International Nuclear Information System (INIS)

Grynberg, M.D.; Ceva, H.

1990-12-01

We investigate the effect of including domain-walls interactions in the two-dimensional axial next-nearest-neighbor Ising or ANNNI model. At low temperatures this problem is reduced to a one-dimensional system of interacting fermions which can be treated exactly. It is found that the critical boundaries of the low-temperature phases are in good agreement with those obtained using a free-fermion approximation. In contrast with the monotonic behavior derived from the free-fermion approach, the wall density or wave number displays reentrant phenomena when the ratio of the next-nearest-neighbor and nearest-neighbor interactions is greater than one-half. (author). 17 refs, 2 figs

Effective-field theory of the Ising model with three alternative layers on the honeycomb and square lattices

Energy Technology Data Exchange (ETDEWEB)

Deviren, Bayram [Institute of Science, Erciyes University, Kayseri 38039 (Turkey); Canko, Osman [Department of Physics, Erciyes University, Kayseri 38039 (Turkey); Keskin, Mustafa [Department of Physics, Erciyes University, Kayseri 38039 (Turkey)], E-mail: keskin@erciyes.edu.tr

2008-09-15

The Ising model with three alternative layers on the honeycomb and square lattices is studied by using the effective-field theory with correlations. We consider that the nearest-neighbor spins of each layer are coupled ferromagnetically and the adjacent spins of the nearest-neighbor layers are coupled either ferromagnetically or anti-ferromagnetically depending on the sign of the bilinear exchange interactions. We investigate the thermal variations of the magnetizations and present the phase diagrams. The phase diagrams contain the paramagnetic, ferromagnetic and anti-ferromagnetic phases, and the system also exhibits a tricritical behavior.

Effective-field theory of the Ising model with three alternative layers on the honeycomb and square lattices

International Nuclear Information System (INIS)

Deviren, Bayram; Canko, Osman; Keskin, Mustafa

2008-01-01

The Ising model with three alternative layers on the honeycomb and square lattices is studied by using the effective-field theory with correlations. We consider that the nearest-neighbor spins of each layer are coupled ferromagnetically and the adjacent spins of the nearest-neighbor layers are coupled either ferromagnetically or anti-ferromagnetically depending on the sign of the bilinear exchange interactions. We investigate the thermal variations of the magnetizations and present the phase diagrams. The phase diagrams contain the paramagnetic, ferromagnetic and anti-ferromagnetic phases, and the system also exhibits a tricritical behavior

On Competitiveness of Nearest-Neighbor-Based Music Classification: A Methodological Critique

DEFF Research Database (Denmark)

Pálmason, Haukur; Jónsson, Björn Thór; Amsaleg, Laurent

2017-01-01

The traditional role of nearest-neighbor classification in music classification research is that of a straw man opponent for the learning approach of the hour. Recent work in high-dimensional indexing has shown that approximate nearest-neighbor algorithms are extremely scalable, yielding results...... of reasonable quality from billions of high-dimensional features. With such efficient large-scale classifiers, the traditional music classification methodology of aggregating and compressing the audio features is incorrect; instead the approximate nearest-neighbor classifier should be given an extensive data...... collection to work with. We present a case study, using a well-known MIR classification benchmark with well-known music features, which shows that a simple nearest-neighbor classifier performs very competitively when given ample data. In this position paper, we therefore argue that nearest...

The Islands Approach to Nearest Neighbor Querying in Spatial Networks

DEFF Research Database (Denmark)

Huang, Xuegang; Jensen, Christian Søndergaard; Saltenis, Simonas

2005-01-01

, and versatile approach to k nearest neighbor computation that obviates the need for using several k nearest neighbor approaches for supporting a single service scenario. The experimental comparison with the existing techniques uses real-world road network data and considers both I/O and CPU performance...

The transverse spin-1 Ising model with random interactions

Energy Technology Data Exchange (ETDEWEB)

Bouziane, Touria [Department of Physics, Faculty of Sciences, University of Moulay Ismail, B.P. 11201 Meknes (Morocco)], E-mail: touria582004@yahoo.fr; Saber, Mohammed [Department of Physics, Faculty of Sciences, University of Moulay Ismail, B.P. 11201 Meknes (Morocco); Dpto. Fisica Aplicada I, EUPDS (EUPDS), Plaza Europa, 1, San Sebastian 20018 (Spain)

2009-01-15

The phase diagrams of the transverse spin-1 Ising model with random interactions are investigated using a new technique in the effective field theory that employs a probability distribution within the framework of the single-site cluster theory based on the use of exact Ising spin identities. A model is adopted in which the nearest-neighbor exchange couplings are independent random variables distributed according to the law P(J{sub ij})=p{delta}(J{sub ij}-J)+(1-p){delta}(J{sub ij}-{alpha}J). General formulae, applicable to lattices with coordination number N, are given. Numerical results are presented for a simple cubic lattice. The possible reentrant phenomenon displayed by the system due to the competitive effects between exchange interactions occurs for the appropriate range of the parameter {alpha}.

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

Science.gov (United States)

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.

On Ising - Onsager problem in external magnetic field

International Nuclear Information System (INIS)

Kochmanski, M.S.

1997-01-01

In this paper a new approach to solving the Ising - Onsager problem in external magnetic field is investigated. The expression for free energy on one Ising spin in external field both for the two dimensional and three dimensional Ising model with interaction of the nearest neighbors are derived. The representations of free energy being expressed by multidimensional integrals of Gauss type with the appropriate dimensionality are shown. Possibility of calculating the integrals and the critical indices on the base of the derived representations for free energy is investigated

Multiple k Nearest Neighbor Query Processing in Spatial Network Databases

DEFF Research Database (Denmark)

Xuegang, Huang; Jensen, Christian Søndergaard; Saltenis, Simonas

2006-01-01

This paper concerns the efficient processing of multiple k nearest neighbor queries in a road-network setting. The assumed setting covers a range of scenarios such as the one where a large population of mobile service users that are constrained to a road network issue nearest-neighbor queries...... for points of interest that are accessible via the road network. Given multiple k nearest neighbor queries, the paper proposes progressive techniques that selectively cache query results in main memory and subsequently reuse these for query processing. The paper initially proposes techniques for the case...... where an upper bound on k is known a priori and then extends the techniques to the case where this is not so. Based on empirical studies with real-world data, the paper offers insight into the circumstances under which the different proposed techniques can be used with advantage for multiple k nearest...

The influence of further-neighbor spin-spin interaction on a ground state of 2D coupled spin-electron model in a magnetic field

Science.gov (United States)

Čenčariková, Hana; Strečka, Jozef; Gendiar, Andrej; Tomašovičová, Natália

2018-05-01

An exhaustive ground-state analysis of extended two-dimensional (2D) correlated spin-electron model consisting of the Ising spins localized on nodal lattice sites and mobile electrons delocalized over pairs of decorating sites is performed within the framework of rigorous analytical calculations. The investigated model, defined on an arbitrary 2D doubly decorated lattice, takes into account the kinetic energy of mobile electrons, the nearest-neighbor Ising coupling between the localized spins and mobile electrons, the further-neighbor Ising coupling between the localized spins and the Zeeman energy. The ground-state phase diagrams are examined for a wide range of model parameters for both ferromagnetic as well as antiferromagnetic interaction between the nodal Ising spins and non-zero value of external magnetic field. It is found that non-zero values of further-neighbor interaction leads to a formation of new quantum states as a consequence of competition between all considered interaction terms. Moreover, the new quantum states are accompanied with different magnetic features and thus, several kinds of field-driven phase transitions are observed.

Nearest neighbors by neighborhood counting.

Science.gov (United States)

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.

Stimulated wave of polarization in a one-dimensional Ising chain

International Nuclear Information System (INIS)

Lee, Jae-Seung; Khitrin, A.K.

2005-01-01

It is demonstrated that in a one-dimensional Ising chain with nearest-neighbor interactions, irradiated by a weak resonant transverse field, a stimulated wave of flipped spins can be triggered by a flip of a single spin. This analytically solvable model illustrates mechanisms of quantum amplification and quantum measurement

Nearest Neighbor Search in the Metric Space of a Complex Network for Community Detection

Directory of Open Access Journals (Sweden)

Suman Saha

2016-03-01

Full Text Available The objective of this article is to bridge the gap between two important research directions: (1 nearest neighbor search, which is a fundamental computational tool for large data analysis; and (2 complex network analysis, which deals with large real graphs but is generally studied via graph theoretic analysis or spectral analysis. In this article, we have studied the nearest neighbor search problem in a complex network by the development of a suitable notion of nearness. The computation of efficient nearest neighbor search among the nodes of a complex network using the metric tree and locality sensitive hashing (LSH are also studied and experimented. For evaluation of the proposed nearest neighbor search in a complex network, we applied it to a network community detection problem. Experiments are performed to verify the usefulness of nearness measures for the complex networks, the role of metric tree and LSH to compute fast and approximate node nearness and the the efficiency of community detection using nearest neighbor search. We observed that nearest neighbor between network nodes is a very efficient tool to explore better the community structure of the real networks. Several efficient approximation schemes are very useful for large networks, which hardly made any degradation of results, whereas they save lot of computational times, and nearest neighbor based community detection approach is very competitive in terms of efficiency and time.

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

Science.gov (United States)

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.

A Novel Hybrid Model Based on Extreme Learning Machine, k-Nearest Neighbor Regression and Wavelet Denoising Applied to Short-Term Electric Load Forecasting

Directory of Open Access Journals (Sweden)

Weide Li

2017-05-01

Full Text Available Electric load forecasting plays an important role in electricity markets and power systems. Because electric load time series are complicated and nonlinear, it is very difficult to achieve a satisfactory forecasting accuracy. In this paper, a hybrid model, Wavelet Denoising-Extreme Learning Machine optimized by k-Nearest Neighbor Regression (EWKM, which combines k-Nearest Neighbor (KNN and Extreme Learning Machine (ELM based on a wavelet denoising technique is proposed for short-term load forecasting. The proposed hybrid model decomposes the time series into a low frequency-associated main signal and some detailed signals associated with high frequencies at first, then uses KNN to determine the independent and dependent variables from the low-frequency signal. Finally, the ELM is used to get the non-linear relationship between these variables to get the final prediction result for the electric load. Compared with three other models, Extreme Learning Machine optimized by k-Nearest Neighbor Regression (EKM, Wavelet Denoising-Extreme Learning Machine (WKM and Wavelet Denoising-Back Propagation Neural Network optimized by k-Nearest Neighbor Regression (WNNM, the model proposed in this paper can improve the accuracy efficiently. New South Wales is the economic powerhouse of Australia, so we use the proposed model to predict electric demand for that region. The accurate prediction has a significant meaning.

Introduction to machine learning: k-nearest neighbors.

Science.gov (United States)

Zhang, Zhongheng

2016-06-01

Machine learning techniques have been widely used in many scientific fields, but its use in medical literature is limited partly because of technical difficulties. k-nearest neighbors (kNN) is a simple method of machine learning. The article introduces some basic ideas underlying the kNN algorithm, and then focuses on how to perform kNN modeling with R. The dataset should be prepared before running the knn() function in R. After prediction of outcome with kNN algorithm, the diagnostic performance of the model should be checked. Average accuracy is the mostly widely used statistic to reflect the kNN algorithm. Factors such as k value, distance calculation and choice of appropriate predictors all have significant impact on the model performance.

Nearest Neighbor Networks: clustering expression data based on gene neighborhoods

Directory of Open Access Journals (Sweden)

Olszewski Kellen L

2007-07-01

Full Text Available Abstract Background The availability of microarrays measuring thousands of genes simultaneously across hundreds of biological conditions represents an opportunity to understand both individual biological pathways and the integrated workings of the cell. However, translating this amount of data into biological insight remains a daunting task. An important initial step in the analysis of microarray data is clustering of genes with similar behavior. A number of classical techniques are commonly used to perform this task, particularly hierarchical and K-means clustering, and many novel approaches have been suggested recently. While these approaches are useful, they are not without drawbacks; these methods can find clusters in purely random data, and even clusters enriched for biological functions can be skewed towards a small number of processes (e.g. ribosomes. Results We developed Nearest Neighbor Networks (NNN, a graph-based algorithm to generate clusters of genes with similar expression profiles. This method produces clusters based on overlapping cliques within an interaction network generated from mutual nearest neighborhoods. This focus on nearest neighbors rather than on absolute distance measures allows us to capture clusters with high connectivity even when they are spatially separated, and requiring mutual nearest neighbors allows genes with no sufficiently similar partners to remain unclustered. We compared the clusters generated by NNN with those generated by eight other clustering methods. NNN was particularly successful at generating functionally coherent clusters with high precision, and these clusters generally represented a much broader selection of biological processes than those recovered by other methods. Conclusion The Nearest Neighbor Networks algorithm is a valuable clustering method that effectively groups genes that are likely to be functionally related. It is particularly attractive due to its simplicity, its success in the

Text Categorization Using Weight Adjusted k-Nearest Neighbor Classification

National Research Council Canada - National Science Library

Han, Euihong; Karypis, George; Kumar, Vipin

1999-01-01

.... The authors present a nearest neighbor classification scheme for text categorization in which the importance of discriminating words is learned using mutual information and weight adjustment techniques...

Classification of EEG Signals using adaptive weighted distance nearest neighbor algorithm

Directory of Open Access Journals (Sweden)

E. Parvinnia

2014-01-01

Full Text Available Electroencephalogram (EEG signals are often used to diagnose diseases such as seizure, alzheimer, and schizophrenia. One main problem with the recorded EEG samples is that they are not equally reliable due to the artifacts at the time of recording. EEG signal classification algorithms should have a mechanism to handle this issue. It seems that using adaptive classifiers can be useful for the biological signals such as EEG. In this paper, a general adaptive method named weighted distance nearest neighbor (WDNN is applied for EEG signal classification to tackle this problem. This classification algorithm assigns a weight to each training sample to control its influence in classifying test samples. The weights of training samples are used to find the nearest neighbor of an input query pattern. To assess the performance of this scheme, EEG signals of thirteen schizophrenic patients and eighteen normal subjects are analyzed for the classification of these two groups. Several features including, fractal dimension, band power and autoregressive (AR model are extracted from EEG signals. The classification results are evaluated using Leave one (subject out cross validation for reliable estimation. The results indicate that combination of WDNN and selected features can significantly outperform the basic nearest-neighbor and the other methods proposed in the past for the classification of these two groups. Therefore, this method can be a complementary tool for specialists to distinguish schizophrenia disorder.

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

Science.gov (United States)

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,...

The ground-state phase diagrams of the spin-3/2 Ising model

International Nuclear Information System (INIS)

Canko, Osman; Keskin, Mustafa

2003-01-01

The ground-state spin configurations are obtained for the spin-3/2 Ising model Hamiltonian with bilinear and biquadratic exchange interactions and a single-ion crystal field. The interactions are assumed to be only between nearest-neighbors. The calculated ground-state phase diagrams are presented on diatomic lattices, such as the square, honeycomb and sc lattices, and triangular lattice in the (Δ/z vertical bar J vertical bar ,K/ vertical bar J vertical bar) and (H/z vertical bar J vertical bar, K/ vertical bar J vertical bar) planes

Magnetic properties of Fe–Al for quenched diluted spin-1 Ising model

Energy Technology Data Exchange (ETDEWEB)

Freitas, A.S. [Departamento de Física, Universidade Federal de Sergipe, 49100-000, São Cristovão, SE (Brazil); Coordenadoria de Física, Instituto Federal de Sergipe, 49400-000 Lagarto, SE (Brazil); Albuquerque, Douglas F. de, E-mail: douglas@ufs.br [Departamento de Física, Universidade Federal de Sergipe, 49100-000, São Cristovão, SE (Brazil); Departamento de Matemática, Universidade Federal de Sergipe, 49100-000, São Cristovão, SE (Brazil); Fittipaldi, I.P. [Representação Regional do Ministério da Ciência, Tecnologia e Inovação no Nordeste - ReNE, 50740-540 Recife, PE (Brazil); Moreno, N.O. [Departamento de Física, Universidade Federal de Sergipe, 49100-000, São Cristovão, SE (Brazil)

2014-08-01

We study the phase diagram of Fe{sub 1−q}Al{sub q} alloys via the quenched site diluted spin-1 ferromagnetic Ising model by employing effective field theory. One suggests a new approach to exchange interaction between nearest neighbors of Fe that depends on the powers of the Al (q) instead of the linear dependence proposed in other papers. In such model we propose the same kind of the exchange interaction in which the iron–nickel alloys obtain an excellent theoretical description of the experimental data of the T–q phase diagram for all Al concentration q. - Highlights: • We apply the quenched Ising model spin-1 to study the properties of Fe–Al. • We employ the EFT and suggest a new approach to ferromagnetic coupling. • The new probability distribution is considered. • The phase diagram is obtained for all values of q in T–q plane.

Magnetic properties of Fe–Al for quenched diluted spin-1 Ising model

International Nuclear Information System (INIS)

Freitas, A.S.; Albuquerque, Douglas F. de; Fittipaldi, I.P.; Moreno, N.O.

2014-01-01

We study the phase diagram of Fe 1−q Al q alloys via the quenched site diluted spin-1 ferromagnetic Ising model by employing effective field theory. One suggests a new approach to exchange interaction between nearest neighbors of Fe that depends on the powers of the Al (q) instead of the linear dependence proposed in other papers. In such model we propose the same kind of the exchange interaction in which the iron–nickel alloys obtain an excellent theoretical description of the experimental data of the T–q phase diagram for all Al concentration q. - Highlights: • We apply the quenched Ising model spin-1 to study the properties of Fe–Al. • We employ the EFT and suggest a new approach to ferromagnetic coupling. • The new probability distribution is considered. • The phase diagram is obtained for all values of q in T–q plane

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

Science.gov (United States)

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.

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

Science.gov (United States)

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....

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

Science.gov (United States)

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.

Ising model with competing axial interactions in the presence of a field

International Nuclear Information System (INIS)

Yokoi, C.S.O.; Salinas, S.R.A.; Coutinho Filho, M.D.

1980-09-01

A layered Ising model is studied with competing interactions between nearest and next-nearest layers in the presence of a magnetic field. The analysis is carried out in the mean-field approximation with one effective field for each layer. The high-temperature region is studied analytically. The low-temperature region is studied numerically. T-H phase diagrams are constructed, which exhibit a variety of modulated phases, for various values of the ratio of the strength of the competing interactions. Numerical evidence of the devil's staircase behavior is found either as a function of temperature or applied magnetic field. (Author) [pt

River Flow Prediction Using the Nearest Neighbor Probabilistic Ensemble Method

Directory of Open Access Journals (Sweden)

H. Sanikhani

2016-02-01

Full Text Available Introduction: In the recent years, researchers interested on probabilistic forecasting of hydrologic variables such river flow.A probabilistic approach aims at quantifying the prediction reliability through a probability distribution function or a prediction interval for the unknown future value. The evaluation of the uncertainty associated to the forecast is seen as a fundamental information, not only to correctly assess the prediction, but also to compare forecasts from different methods and to evaluate actions and decisions conditionally on the expected values. Several probabilistic approaches have been proposed in the literature, including (1 methods that use resampling techniques to assess parameter and model uncertainty, such as the Metropolis algorithm or the Generalized Likelihood Uncertainty Estimation (GLUE methodology for an application to runoff prediction, (2 methods based on processing the forecast errors of past data to produce the probability distributions of future values and (3 methods that evaluate how the uncertainty propagates from the rainfall forecast to the river discharge prediction, as the Bayesian forecasting system. Materials and Methods: In this study, two different probabilistic methods are used for river flow prediction.Then the uncertainty related to the forecast is quantified. One approach is based on linear predictors and in the other, nearest neighbor was used. The nonlinear probabilistic ensemble can be used for nonlinear time series analysis using locally linear predictors, while NNPE utilize a method adapted for one step ahead nearest neighbor methods. In this regard, daily river discharge (twelve years of Dizaj and Mashin Stations on Baranduz-Chay basin in west Azerbijan and Zard-River basin in Khouzestan provinces were used, respectively. The first six years of data was applied for fitting the model. The next three years was used to calibration and the remained three yeas utilized for testing the models

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

Science.gov (United States)

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%.

Using K-Nearest Neighbor in Optical Character Recognition

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Veronica Ong

2016-03-01

Full Text Available The growth in computer vision technology has aided society with various kinds of tasks. One of these tasks is the ability of recognizing text contained in an image, or usually referred to as Optical Character Recognition (OCR. There are many kinds of algorithms that can be implemented into an OCR. The K-Nearest Neighbor is one such algorithm. This research aims to find out the process behind the OCR mechanism by using K-Nearest Neighbor algorithm; one of the most influential machine learning algorithms. It also aims to find out how precise the algorithm is in an OCR program. To do that, a simple OCR program to classify alphabets of capital letters is made to produce and compare real results. The result of this research yielded a maximum of 76.9% accuracy with 200 training samples per alphabet. A set of reasons are also given as to why the program is able to reach said level of accuracy.

Studying nearest neighbor correlations by atom probe tomography (APT) in metallic glasses as exemplified for Fe40Ni40B20 glassy ribbons

KAUST Repository

Shariq, Ahmed

2012-01-01

A next nearest neighbor evaluation procedure of atom probe tomography data provides distributions of the distances between atoms. The width of these distributions for metallic glasses studied so far is a few Angstrom reflecting the spatial resolution of the analytical technique. However, fitting Gaussian distributions to the distribution of atomic distances yields average distances with statistical uncertainties of 2 to 3 hundredth of an Angstrom. Fe 40Ni40B20 metallic glass ribbons are characterized this way in the as quenched state and for a state heat treated at 350 °C for 1 h revealing a change in the structure on the sub-nanometer scale. By applying the statistical tool of the χ2 test a slight deviation from a random distribution of B-atoms in the as quenched sample is perceived, whereas a pronounced elemental inhomogeneity of boron is detected for the annealed state. In addition, the distance distribution of the first fifteen atomic neighbors is determined by using this algorithm for both annealed and as quenched states. The next neighbor evaluation algorithm evinces a steric periodicity of the atoms when the next neighbor distances are normalized by the first next neighbor distance. A comparison of the nearest neighbor atomic distribution for as quenched and annealed state shows accumulation of Ni and B. Moreover, it also reveals the tendency of Fe and B to move slightly away from each other, an incipient step to Ni rich boride formation. © 2011 Elsevier B.V.

Emergent 1d Ising Behavior in AN Elementary Cellular Automaton Model

Science.gov (United States)

Kassebaum, Paul G.; Iannacchione, Germano S.

The fundamental nature of an evolving one-dimensional (1D) Ising model is investigated with an elementary cellular automaton (CA) simulation. The emergent CA simulation employs an ensemble of cells in one spatial dimension, each cell capable of two microstates interacting with simple nearest-neighbor rules and incorporating an external field. The behavior of the CA model provides insight into the dynamics of coupled two-state systems not expressible by exact analytical solutions. For instance, state progression graphs show the causal dynamics of a system through time in relation to the system's entropy. Unique graphical analysis techniques are introduced through difference patterns, diffusion patterns, and state progression graphs of the 1D ensemble visualizing the evolution. All analyses are consistent with the known behavior of the 1D Ising system. The CA simulation and new pattern recognition techniques are scalable (in both dimension, complexity, and size) and have many potential applications such as complex design of materials, control of agent systems, and evolutionary mechanism design.

A Novel Preferential Diffusion Recommendation Algorithm Based on User’s Nearest Neighbors

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Fuguo Zhang

2017-01-01

Full Text Available Recommender system is a very efficient way to deal with the problem of information overload for online users. In recent years, network based recommendation algorithms have demonstrated much better performance than the standard collaborative filtering methods. However, most of network based algorithms do not give a high enough weight to the influence of the target user’s nearest neighbors in the resource diffusion process, while a user or an object with high degree will obtain larger influence in the standard mass diffusion algorithm. In this paper, we propose a novel preferential diffusion recommendation algorithm considering the significance of the target user’s nearest neighbors and evaluate it in the three real-world data sets: MovieLens 100k, MovieLens 1M, and Epinions. Experiments results demonstrate that the novel preferential diffusion recommendation algorithm based on user’s nearest neighbors can significantly improve the recommendation accuracy and diversity.

Effective field study of ising model on a double perovskite structure

Energy Technology Data Exchange (ETDEWEB)

Ngantso, G. Dimitri; El Amraoui, Y. [LMPHE, (URAC 12), Faculté des Sciences, Université Mohammed V, Rabat (Morocco); Benyoussef, A. [LMPHE, (URAC 12), Faculté des Sciences, Université Mohammed V, Rabat (Morocco); Center of Materials and Nanomaterials, MAScIR, Rabat (Morocco); Hassan II Academy of Science and Technology, Rabat (Morocco); El Kenz, A., E-mail: elkenz@fsr.ac.ma [LMPHE, (URAC 12), Faculté des Sciences, Université Mohammed V, Rabat (Morocco)

2017-02-01

By using the effective field theory (EFT), the mixed spin-1/2 and spin-3/2 Ising ferrimagnetic model adapted to a double perovskite structure has been studied. The EFT calculations have been carried out from Ising Hamiltonian by taking into account first and second nearest-neighbors interactions and the crystal and external magnetic fields. Both first- and second-order phase transitions have been found in phase diagrams of interest. Depending on crystal-field values, the thermodynamic behavior of total magnetization indicated the compensation phenomenon existence. The hysteresis behaviors are studied by investigating the reduced magnetic field dependence of total magnetization and a series of hysteresis loops are shown for different reduced temperatures around the critical one. - Highlights: • Magnetic properties of double perovskite Structure have been studied. • Compensation temperature has been observed below the critical temperature. • Hysteresis behaviors have been studied.

Effective field study of ising model on a double perovskite structure

International Nuclear Information System (INIS)

Ngantso, G. Dimitri; El Amraoui, Y.; Benyoussef, A.; El Kenz, A.

2017-01-01

By using the effective field theory (EFT), the mixed spin-1/2 and spin-3/2 Ising ferrimagnetic model adapted to a double perovskite structure has been studied. The EFT calculations have been carried out from Ising Hamiltonian by taking into account first and second nearest-neighbors interactions and the crystal and external magnetic fields. Both first- and second-order phase transitions have been found in phase diagrams of interest. Depending on crystal-field values, the thermodynamic behavior of total magnetization indicated the compensation phenomenon existence. The hysteresis behaviors are studied by investigating the reduced magnetic field dependence of total magnetization and a series of hysteresis loops are shown for different reduced temperatures around the critical one. - Highlights: • Magnetic properties of double perovskite Structure have been studied. • Compensation temperature has been observed below the critical temperature. • Hysteresis behaviors have been studied.

A new approach to very short term wind speed prediction using k-nearest neighbor classification

International Nuclear Information System (INIS)

Yesilbudak, Mehmet; Sagiroglu, Seref; Colak, Ilhami

2013-01-01

Highlights: ► Wind speed parameter was predicted in an n-tupled inputs using k-NN classification. ► The effects of input parameters, nearest neighbors and distance metrics were analyzed. ► Many useful and reasonable inferences were uncovered using the developed model. - Abstract: Wind energy is an inexhaustible energy source and wind power production has been growing rapidly in recent years. However, wind power has a non-schedulable nature due to wind speed variations. Hence, wind speed prediction is an indispensable requirement for power system operators. This paper predicts wind speed parameter in an n-tupled inputs using k-nearest neighbor (k-NN) classification and analyzes the effects of input parameters, nearest neighbors and distance metrics on wind speed prediction. The k-NN classification model was developed using the object oriented programming techniques and includes Manhattan and Minkowski distance metrics except from Euclidean distance metric on the contrary of literature. The k-NN classification model which uses wind direction, air temperature, atmospheric pressure and relative humidity parameters in a 4-tupled space achieved the best wind speed prediction for k = 5 in the Manhattan distance metric. Differently, the k-NN classification model which uses wind direction, air temperature and atmospheric pressure parameters in a 3-tupled inputs gave the worst wind speed prediction for k = 1 in the Minkowski distance metric

Estimating forest attribute parameters for small areas using nearest neighbors techniques

Science.gov (United States)

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...

Recursive nearest neighbor search in a sparse and multiscale domain for comparing audio signals

DEFF Research Database (Denmark)

Sturm, Bob L.; Daudet, Laurent

2011-01-01

We investigate recursive nearest neighbor search in a sparse domain at the scale of audio signals. Essentially, to approximate the cosine distance between the signals we make pairwise comparisons between the elements of localized sparse models built from large and redundant multiscale dictionaries...

ANN-Benchmarks: A Benchmarking Tool for Approximate Nearest Neighbor Algorithms

DEFF Research Database (Denmark)

Aumüller, Martin; Bernhardsson, Erik; Faithfull, Alexander

2017-01-01

This paper describes ANN-Benchmarks, a tool for evaluating the performance of in-memory approximate nearest neighbor algorithms. It provides a standard interface for measuring the performance and quality achieved by nearest neighbor algorithms on different standard data sets. It supports several...... visualise these as images, Open image in new window plots, and websites with interactive plots. ANN-Benchmarks aims to provide a constantly updated overview of the current state of the art of k-NN algorithms. In the short term, this overview allows users to choose the correct k-NN algorithm and parameters...... for their similarity search task; in the longer term, algorithm designers will be able to use this overview to test and refine automatic parameter tuning. The paper gives an overview of the system, evaluates the results of the benchmark, and points out directions for future work. Interestingly, very different...

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Third nearest neighbor parameterized tight binding model for graphene nano-ribbons

Directory of Open Access Journals (Sweden)

Van-Truong Tran

2017-07-01

Full Text Available The existing tight binding models can very well reproduce the ab initio band structure of a 2D graphene sheet. For graphene nano-ribbons (GNRs, the current sets of tight binding parameters can successfully describe the semi-conducting behavior of all armchair GNRs. However, they are still failing in reproducing accurately the slope of the bands that is directly associated with the group velocity and the effective mass of electrons. In this work, both density functional theory and tight binding calculations were performed and a new set of tight binding parameters up to the third nearest neighbors including overlap terms is introduced. The results obtained with this model offer excellent agreement with the predictions of the density functional theory in most cases of ribbon structures, even in the high-energy region. Moreover, this set can induce electron-hole asymmetry as manifested in results from density functional theory. Relevant outcomes are also achieved for armchair ribbons of various widths as well as for zigzag structures, thus opening a route for multi-scale atomistic simulation of large systems that cannot be considered using density functional theory.

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

Science.gov (United States)

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.

A Hybrid Instance Selection Using Nearest-Neighbor for Cross-Project Defect Prediction

Institute of Scientific and Technical Information of China (English)

Duksan Ryu; Jong-In Jang; Jongmoon Baik; Member; ACM; IEEE

2015-01-01

Software defect prediction (SDP) is an active research field in software engineering to identify defect-prone modules. Thanks to SDP, limited testing resources can be effectively allocated to defect-prone modules. Although SDP requires suffcient local data within a company, there are cases where local data are not available, e.g., pilot projects. Companies without local data can employ cross-project defect prediction (CPDP) using external data to build classifiers. The major challenge of CPDP is different distributions between training and test data. To tackle this, instances of source data similar to target data are selected to build classifiers. Software datasets have a class imbalance problem meaning the ratio of defective class to clean class is far low. It usually lowers the performance of classifiers. We propose a Hybrid Instance Selection Using Nearest-Neighbor (HISNN) method that performs a hybrid classification selectively learning local knowledge (via k-nearest neighbor) and global knowledge (via na¨ıve Bayes). Instances having strong local knowledge are identified via nearest-neighbors with the same class label. Previous studies showed low PD (probability of detection) or high PF (probability of false alarm) which is impractical to use. The experimental results show that HISNN produces high overall performance as well as high PD and low PF.

Distance-Constraint k-Nearest Neighbor Searching in Mobile Sensor Networks.

Science.gov (United States)

Han, Yongkoo; Park, Kisung; Hong, Jihye; Ulamin, Noor; Lee, Young-Koo

2015-07-27

The κ-Nearest Neighbors ( κNN) query is an important spatial query in mobile sensor networks. In this work we extend κNN to include a distance constraint, calling it a l-distant κ-nearest-neighbors (l-κNN) query, which finds the κ sensor nodes nearest to a query point that are also at or greater distance from each other. The query results indicate the objects nearest to the area of interest that are scattered from each other by at least distance l. The l-κNN query can be used in most κNN applications for the case of well distributed query results. To process an l-κNN query, we must discover all sets of κNN sensor nodes and then find all pairs of sensor nodes in each set that are separated by at least a distance l. Given the limited battery and computing power of sensor nodes, this l-κNN query processing is problematically expensive in terms of energy consumption. In this paper, we propose a greedy approach for l-κNN query processing in mobile sensor networks. The key idea of the proposed approach is to divide the search space into subspaces whose all sides are l. By selecting κ sensor nodes from the other subspaces near the query point, we guarantee accurate query results for l-κNN. In our experiments, we show that the proposed method exhibits superior performance compared with a post-processing based method using the κNN query in terms of energy efficiency, query latency, and accuracy.

Sistem Rekomendasi Pada E-Commerce Menggunakan K-Nearest Neighbor

Directory of Open Access Journals (Sweden)

Chandra Saha Dewa Prasetya

2017-09-01

The growing number of product information available on the internet brings challenges to both customer and online businesses in the e-commerce environment. Customer often have difficulty when looking for products on the internet because of the number of products sold on the internet. In addition, online businessman often experience difficulties because they has much data about products, customers and transactions, thus causing online businessman have difficulty to promote the right product to a particular customer target. A recommendation system was developed to address those problem with various methods such as Collaborative Filtering, ContentBased, and Hybrid. Collaborative filtering method uses customer’s rating data, content based using product content such as title or description, and hybrid using both as the basis of the recommendation. In this research, the k-nearest neighbor algorithm is used to determine the top-n product recommendations for each buyer. The result of this research method Content Based outperforms other methods because the sparse data, that is the condition where the number of rating given by the customers is relatively little compared the number of products available in e-commerce. Keywords: recomendation system, k-nearest neighbor, collaborative filtering, content based.

Nonequilibrium dynamics of a mixed spin-1/2 and spin-3/2 Ising ferrimagnetic system with a time dependent oscillating magnetic field source

Energy Technology Data Exchange (ETDEWEB)

Vatansever, Erol [Dokuz Eylül University, Graduate School of Natural and Applied Sciences, TR-35160 Izmir (Turkey); Polat, Hamza, E-mail: hamza.polat@deu.edu.tr [Department of Physics, Dokuz Eylül University, TR-35160 Izmir (Turkey)

2015-10-15

Nonequilibrium phase transition properties of a mixed Ising ferrimagnetic model consisting of spin-1/2 and spin-3/2 on a square lattice under the existence of a time dependent oscillating magnetic field have been investigated by making use of Monte Carlo simulations with a single-spin flip Metropolis algorithm. A complete picture of dynamic phase boundary and magnetization profiles have been illustrated and the conditions of a dynamic compensation behavior have been discussed in detail. According to our simulation results, the considered system does not point out a dynamic compensation behavior, when it only includes the nearest-neighbor interaction, single-ion anisotropy and an oscillating magnetic field source. As the next-nearest-neighbor interaction between the spins-1/2 takes into account and exceeds a characteristic value which sensitively depends upon values of single-ion anisotropy and only of amplitude of external magnetic field, a dynamic compensation behavior occurs in the system. Finally, it is reported that it has not been found any evidence of dynamically first-order phase transition between dynamically ordered and disordered phases, which conflicts with the recently published molecular field investigation, for a wide range of selected system parameters. - Highlights: • Spin-1/2 and spin-3/2 Ising ferrimagnetic model is examined. • The system is exposed to time-dependent magnetic field. • Kinetic Monte Carlo simulation technique is used. • Any evidence of first-order phase transition has not been found.

Coarsening in 3D nonconserved Ising model at zero temperature: Anomaly in structure and slow relaxation of order-parameter autocorrelation

Science.gov (United States)

Chakraborty, Saikat; Das, Subir K.

2017-09-01

Via Monte Carlo simulations we study pattern and aging during coarsening in a nonconserved nearest-neighbor Ising model, following quenches from infinite to zero temperature, in space dimension d = 3. The decay of the order-parameter autocorrelation function appears to obey a power-law behavior, as a function of the ratio between the observation and waiting times, in the large ratio limit. However, the exponent of the power law, estimated accurately via a state-of-the-art method, violates a well-known lower bound. This surprising fact has been discussed in connection with a quantitative picture of the structural anomaly that the 3D Ising model exhibits during coarsening at zero temperature. These results are compared with those for quenches to a temperature above that of the roughening transition.

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

Science.gov (United States)

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

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

Science.gov (United States)

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...

Sampling algorithms for validation of supervised learning models for Ising-like systems

Science.gov (United States)

Portman, Nataliya; Tamblyn, Isaac

2017-12-01

In this paper, we build and explore supervised learning models of ferromagnetic system behavior, using Monte-Carlo sampling of the spin configuration space generated by the 2D Ising model. Given the enormous size of the space of all possible Ising model realizations, the question arises as to how to choose a reasonable number of samples that will form physically meaningful and non-intersecting training and testing datasets. Here, we propose a sampling technique called ;ID-MH; that uses the Metropolis-Hastings algorithm creating Markov process across energy levels within the predefined configuration subspace. We show that application of this method retains phase transitions in both training and testing datasets and serves the purpose of validation of a machine learning algorithm. For larger lattice dimensions, ID-MH is not feasible as it requires knowledge of the complete configuration space. As such, we develop a new ;block-ID; sampling strategy: it decomposes the given structure into square blocks with lattice dimension N ≤ 5 and uses ID-MH sampling of candidate blocks. Further comparison of the performance of commonly used machine learning methods such as random forests, decision trees, k nearest neighbors and artificial neural networks shows that the PCA-based Decision Tree regressor is the most accurate predictor of magnetizations of the Ising model. For energies, however, the accuracy of prediction is not satisfactory, highlighting the need to consider more algorithmically complex methods (e.g., deep learning).

Kinetic Models for Topological Nearest-Neighbor Interactions

Science.gov (United States)

Blanchet, Adrien; Degond, Pierre

2017-12-01

We consider systems of agents interacting through topological interactions. These have been shown to play an important part in animal and human behavior. Precisely, the system consists of a finite number of particles characterized by their positions and velocities. At random times a randomly chosen particle, the follower, adopts the velocity of its closest neighbor, the leader. We study the limit of a system size going to infinity and, under the assumption of propagation of chaos, show that the limit kinetic equation is a non-standard spatial diffusion equation for the particle distribution function. We also study the case wherein the particles interact with their K closest neighbors and show that the corresponding kinetic equation is the same. Finally, we prove that these models can be seen as a singular limit of the smooth rank-based model previously studied in Blanchet and Degond (J Stat Phys 163:41-60, 2016). The proofs are based on a combinatorial interpretation of the rank as well as some concentration of measure arguments.

Attribute Weighting Based K-Nearest Neighbor Using Gain Ratio

Science.gov (United States)

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%.

The nearest neighbor and the bayes error rates.

Science.gov (United States)

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.

Novel qsar combination forecast model for insect repellent coupling support vector regression and k-nearest-neighbor

International Nuclear Information System (INIS)

Wang, L.F.; Bai, L.Y.

2013-01-01

To improve the precision of quantitative structure-activity relationship (QSAR) modeling for aromatic carboxylic acid derivatives insect repellent, a novel nonlinear combination forecast model was proposed integrating support vector regression (SVR) and K-nearest neighbor (KNN): Firstly, search optimal kernel function and nonlinearly select molecular descriptors by the rule of minimum MSE value using SVR. Secondly, illuminate the effects of all descriptors on biological activity by multi-round enforcement resistance-selection. Thirdly, construct the sub-models with predicted values of different KNN. Then, get the optimal kernel and corresponding retained sub-models through subtle selection. Finally, make prediction with leave-one-out (LOO) method in the basis of reserved sub-models. Compared with previous widely used models, our work shows significant improvement in modeling performance, which demonstrates the superiority of the present combination forecast model. (author)

Predicting Audience Location on the Basis of the k-Nearest Neighbor Multilabel Classification

Directory of Open Access Journals (Sweden)

Haitao Wu

2014-01-01

Full Text Available Understanding audience location information in online social networks is important in designing recommendation systems, improving information dissemination, and so on. In this paper, we focus on predicting the location distribution of audiences on YouTube. And we transform this problem to a multilabel classification problem, while we find there exist three problems when the classical k-nearest neighbor based algorithm for multilabel classification (ML-kNN is used to predict location distribution. Firstly, the feature weights are not considered in measuring the similarity degree. Secondly, it consumes considerable computing time in finding similar items by traversing all the training set. Thirdly, the goal of ML-kNN is to find relevant labels for every sample which is different from audience location prediction. To solve these problems, we propose the methods of measuring similarity based on weight, quickly finding similar items, and ranking a specific number of labels. On the basis of these methods and the ML-kNN, the k-nearest neighbor based model for audience location prediction (AL-kNN is proposed for predicting audience location. The experiments based on massive YouTube data show that the proposed model can more accurately predict the location of YouTube video audience than the ML-kNN, MLNB, and Rank-SVM methods.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

An effective correlated mean-field theory applied in the spin-1/2 Ising ferromagnetic model

Energy Technology Data Exchange (ETDEWEB)

Roberto Viana, J.; Salmon, Octávio R. [Universidade Federal do Amazonas – UFAM, Manaus 69077-000, AM (Brazil); Ricardo de Sousa, J. [Universidade Federal do Amazonas – UFAM, Manaus 69077-000, AM (Brazil); National Institute of Science and Technology for Complex Systems, Universidade Federal do Amazonas, 3000, Japiim, 69077-000 Manaus, AM (Brazil); Neto, Minos A.; Padilha, Igor T. [Universidade Federal do Amazonas – UFAM, Manaus 69077-000, AM (Brazil)

2014-11-15

We developed a new treatment for mean-field theory applied in spins systems, denominated effective correlated mean-field (ECMF). We apply this theory to study the spin-1/2 Ising ferromagnetic model with nearest-neighbor interactions on a square lattice. We use clusters of finite sizes and study the criticality of the ferromagnetic system, where we obtain a convergence of critical temperature for the value k{sub B}T{sub c}/J≃2.27905±0.00141. Also the behavior of magnetic and thermodynamic properties, using the condition of minimum energy of the physical system is obtained. - Highlights: • We developed spin models to study real magnetic systems. • We study the thermodynamic and magnetic properties of the ferromagnetism. • We enhanced a mean-field theory applied in spins models.

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

Science.gov (United States)

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...

Quantum phase transition of the transverse-field quantum Ising model on scale-free networks.

Science.gov (United States)

Yi, Hangmo

2015-01-01

I investigate the quantum phase transition of the transverse-field quantum Ising model in which nearest neighbors are defined according to the connectivity of scale-free networks. Using a continuous-time quantum Monte Carlo simulation method and the finite-size scaling analysis, I identify the quantum critical point and study its scaling characteristics. For the degree exponent λ=6, I obtain results that are consistent with the mean-field theory. For λ=4.5 and 4, however, the results suggest that the quantum critical point belongs to a non-mean-field universality class. Further simulations indicate that the quantum critical point remains mean-field-like if λ>5, but it continuously deviates from the mean-field theory as λ becomes smaller.

Efficient and accurate nearest neighbor and closest pair search in high-dimensional space

KAUST Repository

Tao, Yufei; Yi, Ke; Sheng, Cheng; Kalnis, Panos

2010-01-01

Nearest Neighbor (NN) search in high-dimensional space is an important problem in many applications. From the database perspective, a good solution needs to have two properties: (i) it can be easily incorporated in a relational database, and (ii

Double transitions, non-Ising criticality and the critical absorbing phase in an interacting monomer–dimer model on a square lattice

International Nuclear Information System (INIS)

Nam, Keekwon; Kim, Bongsoo; Park, Sangwoong; Lee, Sung Jong

2011-01-01

We present a numerical study on an interacting monomer–dimer model with nearest neighbor repulsion on a square lattice, which possesses two symmetric absorbing states. The model is observed to exhibit two nearby continuous transitions: the Z 2 symmetry-breaking order–disorder transition and the absorbing transition with directed percolation criticality. We find that the symmetry-breaking transition shows a non-Ising critical behavior, and that the absorbing phase becomes critical, in the sense that the critical decay of the dimer density observed at the absorbing transition persists even within the absorbing phase. Our findings call for further studies on microscopic models and the corresponding continuum description belonging to the generalized voter university class. (letter)

Credit scoring analysis using weighted k nearest neighbor

Science.gov (United States)

Mukid, M. A.; Widiharih, T.; Rusgiyono, A.; Prahutama, A.

2018-05-01

Credit scoring is a quatitative method to evaluate the credit risk of loan applications. Both statistical methods and artificial intelligence are often used by credit analysts to help them decide whether the applicants are worthy of credit. These methods aim to predict future behavior in terms of credit risk based on past experience of customers with similar characteristics. This paper reviews the weighted k nearest neighbor (WKNN) method for credit assessment by considering the use of some kernels. We use credit data from a private bank in Indonesia. The result shows that the Gaussian kernel and rectangular kernel have a better performance based on the value of percentage corrected classified whose value is 82.4% respectively.

Nearest neighbor 3D segmentation with context features

Science.gov (United States)

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.

k-Nearest Neighbors Algorithm in Profiling Power Analysis Attacks

Directory of Open Access Journals (Sweden)

Z. Martinasek

2016-06-01

Full Text Available Power analysis presents the typical example of successful attacks against trusted cryptographic devices such as RFID (Radio-Frequency IDentifications and contact smart cards. In recent years, the cryptographic community has explored new approaches in power analysis based on machine learning models such as Support Vector Machine (SVM, RF (Random Forest and Multi-Layer Perceptron (MLP. In this paper, we made an extensive comparison of machine learning algorithms in the power analysis. For this purpose, we implemented a verification program that always chooses the optimal settings of individual machine learning models in order to obtain the best classification accuracy. In our research, we used three datasets, the first containing the power traces of an unprotected AES (Advanced Encryption Standard implementation. The second and third datasets are created independently from public available power traces corresponding to a masked AES implementation (DPA Contest v4. The obtained results revealed some interesting facts, namely, an elementary k-NN (k-Nearest Neighbors algorithm, which has not been commonly used in power analysis yet, shows great application potential in practice.

The influence of As/III pressure ratio on nitrogen nearest-neighbor environments in as-grown GaInNAs quantum wells

International Nuclear Information System (INIS)

Kudrawiec, R.; Poloczek, P.; Misiewicz, J.; Korpijaervi, V.-M.; Laukkanen, P.; Pakarinen, J.; Dumitrescu, M.; Guina, M.; Pessa, M.

2009-01-01

The energy fine structure, corresponding to different nitrogen nearest-neighbor environments, was observed in contactless electroreflectance (CER) spectra of as-grown GaInNAs quantum wells (QWs) obtained at various As/III pressure ratios. In the spectral range of the fundamental transition, two CER resonances were detected for samples grown at low As pressures whereas only one CER resonance was observed for samples obtained at higher As pressures. This resonance corresponds to the most favorable nitrogen nearest-neighbor environment in terms of the total crystal energy. It means that the nitrogen nearest-neighbor environment in GaInNAs QWs can be controlled in molecular beam epitaxy process by As/III pressure ratio.

Ising percolation in a three-state majority vote model

Energy Technology Data Exchange (ETDEWEB)

Balankin, Alexander S., E-mail: abalankin@ipn.mx [Grupo Mecánica Fractal, ESIME, Instituto Politécnico Nacional, México D.F., 07738 (Mexico); Martínez-Cruz, M.A.; Gayosso Martínez, Felipe [Grupo Mecánica Fractal, ESIME, Instituto Politécnico Nacional, México D.F., 07738 (Mexico); Mena, Baltasar [Laboratorio de Ingeniería y Procesos Costeros, Instituto de Ingeniería, Universidad Nacional Autónoma de México, Sisal, Yucatán, 97355 (Mexico); Tobon, Atalo; Patiño-Ortiz, Julián; Patiño-Ortiz, Miguel; Samayoa, Didier [Grupo Mecánica Fractal, ESIME, Instituto Politécnico Nacional, México D.F., 07738 (Mexico)

2017-02-05

Highlights: • Three-state non-consensus majority voter model is introduced. • Phase transition in the absorbing state non-consensus is revealed. • The percolation transition belongs to the universality class of Ising percolation. • The effect of an updating rule for a tie between voter neighbors is highlighted. - Abstract: In this Letter, we introduce a three-state majority vote model in which each voter adopts a state of a majority of its active neighbors, if exist, but the voter becomes uncommitted if its active neighbors are in a tie, or all neighbors are the uncommitted. Numerical simulations were performed on square lattices of different linear size with periodic boundary conditions. Starting from a random distribution of active voters, the model leads to a stable non-consensus state in which three opinions coexist. We found that the “magnetization” of the non-consensus state and the concentration of uncommitted voters in it are governed by an initial composition of system and are independent of the lattice size. Furthermore, we found that a configuration of the stable non-consensus state undergoes a second order percolation transition at a critical concentration of voters holding the same opinion. Numerical simulations suggest that this transition belongs to the same universality class as the Ising percolation. These findings highlight the effect of an updating rule for a tie between voter neighbors on the critical behavior of models obeying the majority vote rule whenever a strict majority exists.

Ising percolation in a three-state majority vote model

International Nuclear Information System (INIS)

Balankin, Alexander S.; Martínez-Cruz, M.A.; Gayosso Martínez, Felipe; Mena, Baltasar; Tobon, Atalo; Patiño-Ortiz, Julián; Patiño-Ortiz, Miguel; Samayoa, Didier

2017-01-01

Highlights: • Three-state non-consensus majority voter model is introduced. • Phase transition in the absorbing state non-consensus is revealed. • The percolation transition belongs to the universality class of Ising percolation. • The effect of an updating rule for a tie between voter neighbors is highlighted. - Abstract: In this Letter, we introduce a three-state majority vote model in which each voter adopts a state of a majority of its active neighbors, if exist, but the voter becomes uncommitted if its active neighbors are in a tie, or all neighbors are the uncommitted. Numerical simulations were performed on square lattices of different linear size with periodic boundary conditions. Starting from a random distribution of active voters, the model leads to a stable non-consensus state in which three opinions coexist. We found that the “magnetization” of the non-consensus state and the concentration of uncommitted voters in it are governed by an initial composition of system and are independent of the lattice size. Furthermore, we found that a configuration of the stable non-consensus state undergoes a second order percolation transition at a critical concentration of voters holding the same opinion. Numerical simulations suggest that this transition belongs to the same universality class as the Ising percolation. These findings highlight the effect of an updating rule for a tie between voter neighbors on the critical behavior of models obeying the majority vote rule whenever a strict majority exists.

Diagnosis of Diabetes Diseases Using an Artificial Immune Recognition System2 (AIRS2) with Fuzzy K-nearest Neighbor

OpenAIRE

CHIKH, Mohamed Amine; SAIDI, Meryem; SETTOUTI, Nesma

2012-01-01

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 disea...

Nearest neighbors EPR superhyperfine interaction in divalent iridium complexes in alkali halide host lattice

International Nuclear Information System (INIS)

Pinhal, N.M.; Vugman, N.V.

1983-01-01

Further splitting of chlorine superhyperfine lines on the EPR spectrum of the [Ir (CN) 4 Cl 2 ] 4 - molecular species in NaCl latice indicates a super-superhyperfine interaction with the nearest neighbors sodium atoms. (Author) [pt

Chaotic Synchronization in Nearest-Neighbor Coupled Networks of 3D CNNs

OpenAIRE

Serrano-Guerrero, H.; Cruz-Hernández, C.; López-Gutiérrez, R.M.; Cardoza-Avendaño, L.; Chávez-Pérez, R.A.

2013-01-01

In this paper, a synchronization of Cellular Neural Networks (CNNs) in nearest-neighbor coupled arrays, is numerically studied. Synchronization of multiple chaotic CNNs is achieved by appealing to complex systems theory. In particular, we consider dynamical networks composed by 3D CNNs, as interconnected nodes, where the interactions in the networks are defined by coupling the first state of each node. Four cases of interest are considered: i) synchronization without chaotic master, ii) maste...

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

Science.gov (United States)

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 (...

Improved Fuzzy K-Nearest Neighbor Using Modified Particle Swarm Optimization

Science.gov (United States)

Jamaluddin; Siringoringo, Rimbun

2017-12-01

Fuzzy k-Nearest Neighbor (FkNN) is one of the most powerful classification methods. The presence of fuzzy concepts in this method successfully improves its performance on almost all classification issues. The main drawbackof FKNN is that it is difficult to determine the parameters. These parameters are the number of neighbors (k) and fuzzy strength (m). Both parameters are very sensitive. This makes it difficult to determine the values of ‘m’ and ‘k’, thus making FKNN difficult to control because no theories or guides can deduce how proper ‘m’ and ‘k’ should be. This study uses Modified Particle Swarm Optimization (MPSO) to determine the best value of ‘k’ and ‘m’. MPSO is focused on the Constriction Factor Method. Constriction Factor Method is an improvement of PSO in order to avoid local circumstances optima. The model proposed in this study was tested on the German Credit Dataset. The test of the data/The data test has been standardized by UCI Machine Learning Repository which is widely applied to classification problems. The application of MPSO to the determination of FKNN parameters is expected to increase the value of classification performance. Based on the experiments that have been done indicating that the model offered in this research results in a better classification performance compared to the Fk-NN model only. The model offered in this study has an accuracy rate of 81%, while. With using Fk-NN model, it has the accuracy of 70%. At the end is done comparison of research model superiority with 2 other classification models;such as Naive Bayes and Decision Tree. This research model has a better performance level, where Naive Bayes has accuracy 75%, and the decision tree model has 70%

Implementation of Nearest Neighbor using HSV to Identify Skin Disease

Science.gov (United States)

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.

Multi spin-flip dynamics: a solution of the one-dimensional Ising model

International Nuclear Information System (INIS)

Novak, I.

1990-01-01

The Glauber dynamics of interacting Ising spins (the single spin-flip dynamics) is generalized to p spin-flip dynamics with a simultaneous flip of up to p spins in a single configuration move. The p spin-flip dynamics is studied of the one-dimensional Ising model with uniform nearest-neighbour interaction. For this case, an exact relation is given for the time dependence of magnetization. It was found that the critical slowing down in this model could be avoided when p spin-flip dynamics with p>2 was considered. (author). 17 refs

Common Nearest Neighbor Clustering—A Benchmark

Directory of Open Access Journals (Sweden)

Oliver Lemke

2018-02-01

Full Text Available Cluster analyses are often conducted with the goal to characterize an underlying probability density, for which the data-point density serves as an estimate for this probability density. We here test and benchmark the common nearest neighbor (CNN cluster algorithm. This algorithm assigns a spherical neighborhood R to each data point and estimates the data-point density between two data points as the number of data points N in the overlapping region of their neighborhoods (step 1. The main principle in the CNN cluster algorithm is cluster growing. This grows the clusters by sequentially adding data points and thereby effectively positions the border of the clusters along an iso-surface of the underlying probability density. This yields a strict partitioning with outliers, for which the cluster represents peaks in the underlying probability density—termed core sets (step 2. The removal of the outliers on the basis of a threshold criterion is optional (step 3. The benchmark datasets address a series of typical challenges, including datasets with a very high dimensional state space and datasets in which the cluster centroids are aligned along an underlying structure (Birch sets. The performance of the CNN algorithm is evaluated with respect to these challenges. The results indicate that the CNN cluster algorithm can be useful in a wide range of settings. Cluster algorithms are particularly important for the analysis of molecular dynamics (MD simulations. We demonstrate how the CNN cluster results can be used as a discretization of the molecular state space for the construction of a core-set model of the MD improving the accuracy compared to conventional full-partitioning models. The software for the CNN clustering is available on GitHub.

Thermal contact through a two-temperature kinetic Ising chain

Science.gov (United States)

Bauer, M.; Cornu, F.

2018-05-01

We consider a model for thermal contact through a diathermal interface between two macroscopic bodies at different temperatures: an Ising spin chain with nearest neighbor interactions is endowed with a Glauber dynamics with different temperatures and kinetic parameters on alternating sites. The inhomogeneity of the kinetic parameter is a novelty with respect to the model of Racz and Zia (1994 Phys. Rev. E 49 139), and we exhibit its influence upon the stationary non equilibrium values of the two-spin correlations at any distance. By mapping to the dynamics of spin domain walls and using free fermion techniques, we determine the scaled generating function for the cumulants of the exchanged heat amounts per unit of time in the long time limit.

Probabilistic image processing by means of the Bethe approximation for the Q-Ising model

International Nuclear Information System (INIS)

Tanaka, Kazuyuki; Inoue, Jun-ichi; Titterington, D M

2003-01-01

The framework of Bayesian image restoration for multi-valued images by means of the Q-Ising model with nearest-neighbour interactions is presented. Hyperparameters in the probabilistic model are determined so as to maximize the marginal likelihood. A practical algorithm is described for multi-valued image restoration based on the Bethe approximation. The algorithm corresponds to loopy belief propagation in artificial intelligence. We conclude that, in real world grey-level images, the Q-Ising model can give us good results

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

Science.gov (United States)

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...

Search for the non-canonical Ising spin glass on rewired square lattices

Science.gov (United States)

Surungan, Tasrief

2018-03-01

A spin glass (SG) of non-canonical type is a purely antiferromagnetic (AF) system, exemplified by the AF Ising model on a scale free network (SFN), studied by Bartolozzi et al. [ Phys. Rev. B73, 224419 (2006)]. Frustration in this new type of SG is rendered by topological factor and its randomness is caused by random connectivity. As an SFN corresponds to a large dimensional lattice, finding non-canonical SG in lattice with physical dimension is desireable. However, a regular lattice can not have random connectivity. In order to obtain lattices with random connection and preserving the notion of finite dimension, we costructed rewired lattices. We added some extra bonds randomly connecting each site of a regular lattice to its next-nearest neighbors. Very recently, Surungan et al., studied AF Heisenberg system on rewired square lattice and found no SG behavior [AIP Conf. Proc. 1719, 030006 (2016)]. Due to the importance of discrete symmetry for phase transition, here we study similar structure for the Ising model (Z 2 symmetry). We used Monte Carlo simulation with Replica Exchange algorithm. Two types of structures were studied, firstly, the rewired square lattices with one extra bonds added to each site, and secondly, two bonds added to each site. We calculated the Edwards-Anderson paremeter, the commonly used parameter in searching for SG phase. The non-canonical SG is clearly observed in the rewired square lattice with two extra bonds added.

Penerapan Metode K-nearest Neighbor pada Penentuan Grade Dealer Sepeda Motor

OpenAIRE

Leidiyana, Henny

2017-01-01

The mutually beneficial cooperation is a very important thing for a leasing and dealer. Incentives for marketing is given in order to get consumers as much as possible. But sometimes the surveyor objectivity is lost due to the conspiracy on the field of marketing and surveyors. To overcome this, leasing a variety of ways one of them is doing ranking against the dealer. In this study the application of the k-Nearest Neighbor method and Euclidean distance measurement to determine the grade deal...

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

Science.gov (United States)

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)...

Phase Transition and Critical Values of a Nearest-Neighbor System with Uncountable Local State Space on Cayley Trees

International Nuclear Information System (INIS)

Jahnel, Benedikt; Külske, Christof; Botirov, Golibjon I.

2014-01-01

We consider a ferromagnetic nearest-neighbor model on a Cayley tree of degree k ⩾ 2 with uncountable local state space [0,1] where the energy function depends on a parameter θ ∊[0, 1). We show that for 0 ⩽ θ ⩽ 5 3 k the model has a unique translation-invariant Gibbs measure. If 5 3 k < θ < 1 , there is a phase transition, in particular there are three translation-invariant Gibbs measures

Morphological type correlation between nearest neighbor pairs of galaxies

Science.gov (United States)

Yamagata, Tomohiko

1990-01-01

Although the morphological type of galaxies is one of the most fundamental properties of galaxies, its origin and evolutionary processes, if any, are not yet fully understood. It has been established that the galaxy morphology strongly depends on the environment in which the galaxy resides (e.g., Dressler 1980). Galaxy pairs correspond to the smallest scales of galaxy clustering and may provide important clues to how the environment influences the formation and evolution of galaxies. Several investigators pointed out that there is a tendency for pair galaxies to have similar morphological types (Karachentsev and Karachentseva 1974, Page 1975, Noerdlinger 1979). Here, researchers analyze morphological type correlation for 18,364 nearest neighbor pairs of galaxies identified in the magnetic tape version of the Center for Astrophysics Redshift Catalogue.

Designing lattice structures with maximal nearest-neighbor entanglement

Energy Technology Data Exchange (ETDEWEB)

Navarro-Munoz, J C; Lopez-Sandoval, R [Instituto Potosino de Investigacion CientIfica y Tecnologica, Camino a la presa San Jose 2055, 78216 San Luis Potosi (Mexico); Garcia, M E [Theoretische Physik, FB 18, Universitaet Kassel and Center for Interdisciplinary Nanostructure Science and Technology (CINSaT), Heinrich-Plett-Str.40, 34132 Kassel (Germany)

2009-08-07

In this paper, we study the numerical optimization of nearest-neighbor concurrence of bipartite one- and two-dimensional lattices, as well as non-bipartite two-dimensional lattices. These systems are described in the framework of a tight-binding Hamiltonian while the optimization of concurrence was performed using genetic algorithms. Our results show that the concurrence of the optimized lattice structures is considerably higher than that of non-optimized systems. In the case of one-dimensional chains, the concurrence increases dramatically when the system begins to dimerize, i.e., it undergoes a structural phase transition (Peierls distortion). This result is consistent with the idea that entanglement is maximal or shows a singularity near quantum phase transitions. Moreover, the optimization of concurrence in two-dimensional bipartite and non-bipartite lattices is achieved when the structures break into smaller subsystems, which are arranged in geometrically distinguishable configurations.

Lifshitz-Allen-Cahn domain-growth kinetics of Ising models with conserved density

DEFF Research Database (Denmark)

Fogedby, Hans C.; Mouritsen, Ole G.

1988-01-01

The domain-growth kinetics of p=fourfold degenerate (2×1) ordering in two-dimensional Ising models with conserved density is studied as a function of temperature and range of Kawasaki spin exchange. It is found by computer simulations that the zero-temperature freezing-in behavior for nearest-nei...

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

Science.gov (United States)

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.

Exact solutions to plaquette Ising models with free and periodic boundaries

International Nuclear Information System (INIS)

Mueller, Marco; Johnston, Desmond A.; Janke, Wolfhard

2017-01-01

An anisotropic limit of the 3d plaquette Ising model, in which the plaquette couplings in one direction were set to zero, was solved for free boundary conditions by Suzuki (1972) , who later dubbed it the fuki-nuke, or “no-ceiling”, model. Defining new spin variables as the product of nearest-neighbour spins transforms the Hamiltonian into that of a stack of (standard) 2d Ising models and reveals the planar nature of the magnetic order, which is also present in the fully isotropic 3d plaquette model. More recently, the solution of the fuki-nuke model was discussed for periodic boundary conditions, which require a different approach to defining the product spin transformation, by Castelnovo et al. (2010) . We clarify the exact relation between partition functions with free and periodic boundary conditions expressed in terms of original and product spin variables for the 2d plaquette and 3d fuki-nuke models, noting that the differences are already present in the 1d Ising model. In addition, we solve the 2d plaquette Ising model with helical boundary conditions. The various exactly solved examples illustrate how correlations can be induced in finite systems as a consequence of the choice of boundary conditions.

Enhanced Approximate Nearest Neighbor via Local Area Focused Search.

Energy Technology Data Exchange (ETDEWEB)

Gonzales, Antonio [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Blazier, Nicholas Paul [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2017-02-01

Approximate Nearest Neighbor (ANN) algorithms are increasingly important in machine learning, data mining, and image processing applications. There is a large family of space- partitioning ANN algorithms, such as randomized KD-Trees, that work well in practice but are limited by an exponential increase in similarity comparisons required to optimize recall. Additionally, they only support a small set of similarity metrics. We present Local Area Fo- cused Search (LAFS), a method that enhances the way queries are performed using an existing ANN index. Instead of a single query, LAFS performs a number of smaller (fewer similarity comparisons) queries and focuses on a local neighborhood which is refined as candidates are identified. We show that our technique improves performance on several well known datasets and is easily extended to general similarity metrics using kernel projection techniques.

The critical 1-arm exponent for the ferromagnetic Ising model on the Bethe lattice

Science.gov (United States)

Heydenreich, Markus; Kolesnikov, Leonid

2018-04-01

We consider the ferromagnetic nearest-neighbor Ising model on regular trees (Bethe lattice), which is well-known to undergo a phase transition in the absence of an external magnetic field. The behavior of the model at critical temperature can be described in terms of various critical exponents; one of them is the critical 1-arm exponent ρ which characterizes the rate of decay of the (root) magnetization as a function of the distance to the boundary. The crucial quantity we analyze in this work is the thermal expectation of the root spin on a finite subtree, where the expected value is taken with respect to a probability measure related to the corresponding finite-volume Hamiltonian with a fixed boundary condition. The spontaneous magnetization, which is the limit of this thermal expectation in the distance between the root and the boundary (i.e., in the height of the subtree), is known to vanish at criticality. We are interested in a quantitative analysis of the rate of this convergence in terms of the critical 1-arm exponent ρ. Therefore, we rigorously prove that ⟨σ0⟩ n +, the thermal expectation of the root spin at the critical temperature and in the presence of the positive boundary condition, decays as ⟨σ0 ⟩ n +≈n-1/2 (in a rather sharp sense), where n is the height of the tree. This establishes the 1-arm critical exponent for the Ising model on regular trees (ρ =1/2 ).

Quality and efficiency in high dimensional Nearest neighbor search

KAUST Repository

Tao, Yufei; Yi, Ke; Sheng, Cheng; Kalnis, Panos

2009-01-01

Nearest neighbor (NN) search in high dimensional space is an important problem in many applications. Ideally, a practical solution (i) should be implementable in a relational database, and (ii) its query cost should grow sub-linearly with the dataset size, regardless of the data and query distributions. Despite the bulk of NN literature, no solution fulfills both requirements, except locality sensitive hashing (LSH). The existing LSH implementations are either rigorous or adhoc. Rigorous-LSH ensures good quality of query results, but requires expensive space and query cost. Although adhoc-LSH is more efficient, it abandons quality control, i.e., the neighbor it outputs can be arbitrarily bad. As a result, currently no method is able to ensure both quality and efficiency simultaneously in practice. Motivated by this, we propose a new access method called the locality sensitive B-tree (LSB-tree) that enables fast highdimensional NN search with excellent quality. The combination of several LSB-trees leads to a structure called the LSB-forest that ensures the same result quality as rigorous-LSH, but reduces its space and query cost dramatically. The LSB-forest also outperforms adhoc-LSH, even though the latter has no quality guarantee. Besides its appealing theoretical properties, the LSB-tree itself also serves as an effective index that consumes linear space, and supports efficient updates. Our extensive experiments confirm that the LSB-tree is faster than (i) the state of the art of exact NN search by two orders of magnitude, and (ii) the best (linear-space) method of approximate retrieval by an order of magnitude, and at the same time, returns neighbors with much better quality. © 2009 ACM.

Entanglement in a simple quantum phase transition

International Nuclear Information System (INIS)

Osborne, Tobias J.; Nielsen, Michael A.

2002-01-01

What entanglement is present in naturally occurring physical systems at thermal equilibrium? Most such systems are intractable and it is desirable to study simple but realistic systems that can be solved. An example of such a system is the one-dimensional infinite-lattice anisotropic XY model. This model is exactly solvable using the Jordan-Wigner transform, and it is possible to calculate the two-site reduced density matrix for all pairs of sites. Using the two-site density matrix, the entanglement of formation between any two sites is calculated for all parameter values and temperatures. We also study the entanglement in the transverse Ising model, a special case of the XY model, which exhibits a quantum phase transition. It is found that the next-nearest-neighbor entanglement (though not the nearest-neighbor entanglement) is a maximum at the critical point. Furthermore, we show that the critical point in the transverse Ising model corresponds to a transition in the behavior of the entanglement between a single site and the remainder of the lattice

Critical behavior of ferromagnetic Ising thin films

International Nuclear Information System (INIS)

Cossio, P.; Mazo-Zuluaga, J.; Restrepo, J.

2006-01-01

In the present work, we study the magnetic properties and critical behavior of simple cubic ferromagnetic thin films. We simulate LxLxd films with semifree boundary conditions on the basis of the Monte Carlo method and the Ising model with nearest neighbor interactions. A Metropolis dynamics was implemented to carry out the energy minimization process. For different film thickness, in the nanometer range, we compute the temperature dependence of the magnetization, the magnetic susceptibility and the fourth order Binder's cumulant. Bulk and surface contributions of these quantities are computed in a differentiated fashion. Additionally, according to finite size scaling theory, we estimate the critical exponents for the correlation length, magnetic susceptibility, and magnetization. Results reveal a strong dependence of critical temperature and critical exponents on the film thickness. The obtained critical exponents are finally compared to those reported in literature for thin films

Influence of geometry on light harvesting in dendrimeric systems. II. nth-nearest neighbor effects and the onset of percolation

International Nuclear Information System (INIS)

Bentz, Jonathan L.; Kozak, John J.

2006-01-01

We explore the effect of imposing different constraints (biases, boundary conditions) on the mean time to trapping (or mean walklength) for a particle (excitation) migrating on a finite dendrimer lattice with a centrally positioned trap. By mobilizing the theory of finite Markov processes, we are able to obtain exact analytic expressions for site-specific walklengths as well as the overall walklength for both nearest-neighbor and second-nearest-neighbor displacements. This allows the comparison with and generalization of earlier results [A. Bar-Haim, J. Klafter, J. Phys. Chem. B 102 (1998) 1662; A. Bar-Haim, J. Klafter, J. Lumin. 76, 77 (1998) 197; O. Flomenbom, R.J. Amir, D. Shabat, J. Klafter, J. Lumin. 111 (2005) 315; J.L. Bentz, F.N. Hosseini, J.J. Kozak, Chem. Phys. Lett. 370 (2003) 319]. A novel feature of this work is the establishment of a connection between the random walk models studied here and percolation theory. The full dynamical behavior was also determined via solution of the stochastic master equation, and the results obtained compared with recent spectroscopic experiments

Influence of geometry on light harvesting in dendrimeric systems. II. nth-nearest neighbor effects and the onset of percolation

Energy Technology Data Exchange (ETDEWEB)

Bentz, Jonathan L. [Department of Chemistry, Iowa State University, Ames, IA, 50011 (United States)]. E-mail: jnbntz@iastate.edu; Kozak, John J. [Beckman Institute, California Institute of Technology, 1200 E. California Boulevard, Pasadena, CA 91125-7400 (United States)

2006-11-15

We explore the effect of imposing different constraints (biases, boundary conditions) on the mean time to trapping (or mean walklength) for a particle (excitation) migrating on a finite dendrimer lattice with a centrally positioned trap. By mobilizing the theory of finite Markov processes, we are able to obtain exact analytic expressions for site-specific walklengths as well as the overall walklength for both nearest-neighbor and second-nearest-neighbor displacements. This allows the comparison with and generalization of earlier results [A. Bar-Haim, J. Klafter, J. Phys. Chem. B 102 (1998) 1662; A. Bar-Haim, J. Klafter, J. Lumin. 76, 77 (1998) 197; O. Flomenbom, R.J. Amir, D. Shabat, J. Klafter, J. Lumin. 111 (2005) 315; J.L. Bentz, F.N. Hosseini, J.J. Kozak, Chem. Phys. Lett. 370 (2003) 319]. A novel feature of this work is the establishment of a connection between the random walk models studied here and percolation theory. The full dynamical behavior was also determined via solution of the stochastic master equation, and the results obtained compared with recent spectroscopic experiments.

A γ dose distribution evaluation technique using the k-d tree for nearest neighbor searching

International Nuclear Information System (INIS)

Yuan Jiankui; Chen Weimin

2010-01-01

Purpose: The authors propose an algorithm based on the k-d tree for nearest neighbor searching to improve the γ calculation time for 2D and 3D dose distributions. Methods: The γ calculation method has been widely used for comparisons of dose distributions in clinical treatment plans and quality assurances. By specifying the acceptable dose and distance-to-agreement criteria, the method provides quantitative measurement of the agreement between the reference and evaluation dose distributions. The γ value indicates the acceptability. In regions where γ≤1, the predefined criterion is satisfied and thus the agreement is acceptable; otherwise, the agreement fails. Although the concept of the method is not complicated and a quick naieve implementation is straightforward, an efficient and robust implementation is not trivial. Recent algorithms based on exhaustive searching within a maximum radius, the geometric Euclidean distance, and the table lookup method have been proposed to improve the computational time for multidimensional dose distributions. Motivated by the fact that the least searching time for finding a nearest neighbor can be an O(log N) operation with a k-d tree, where N is the total number of the dose points, the authors propose an algorithm based on the k-d tree for the γ evaluation in this work. Results: In the experiment, the authors found that the average k-d tree construction time per reference point is O(log N), while the nearest neighbor searching time per evaluation point is proportional to O(N 1/k ), where k is between 2 and 3 for two-dimensional and three-dimensional dose distributions, respectively. Conclusions: Comparing with other algorithms such as exhaustive search and sorted list O(N), the k-d tree algorithm for γ evaluation is much more efficient.

Phase diagram of the Ising model on a Cayley tree in the presence of competing interactions and magnetic field

International Nuclear Information System (INIS)

Mariz, A.M.; Tsallis, C.; Albuquerque, E.L. de.

1984-01-01

The phae diagram for the Ising Model on a Cayley tree with competing nearest-neighbour interactions J 1 and next-nearest-neighbour interactions J 2 and J 3 in the presence of an external magnetic field is studied. To perform this study, an iterative scheme similar to that appearing in real space renormalization group frameworks is established; it recovers, as particular cases, previous works by Vannimenus and by Inawashiro et al. At vanishing temperature, the phase diagram is fully determined, for all values and signs of J 2 /J 1 and J 3 /J 2 ; in particular, it is verified that values of J 3 /J 2 high enough favour the paramagnetic phase. At finite temperatures, several interesting features (evolution of re-entrances, separation of the modulated region in two disconnected pieces, etc.) are exhibited for typical values of J 2 /J 1 and J 3 /J 2 . (Author) [pt

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

Science.gov (United States)

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%.

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

Science.gov (United States)

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.

Fracton topological order from nearest-neighbor two-spin interactions and dualities

Science.gov (United States)

Slagle, Kevin; Kim, Yong Baek

2017-10-01

Fracton topological order describes a remarkable phase of matter, which can be characterized by fracton excitations with constrained dynamics and a ground-state degeneracy that increases exponentially with the length of the system on a three-dimensional torus. However, previous models exhibiting this order require many-spin interactions, which may be very difficult to realize in a real material or cold atom system. In this work, we present a more physically realistic model which has the so-called X-cube fracton topological order [Vijay, Haah, and Fu, Phys. Rev. B 94, 235157 (2016), 10.1103/PhysRevB.94.235157] but only requires nearest-neighbor two-spin interactions. The model lives on a three-dimensional honeycomb-based lattice with one to two spin-1/2 degrees of freedom on each site and a unit cell of six sites. The model is constructed from two orthogonal stacks of Z2 topologically ordered Kitaev honeycomb layers [Kitaev, Ann. Phys. 321, 2 (2006), 10.1016/j.aop.2005.10.005], which are coupled together by a two-spin interaction. It is also shown that a four-spin interaction can be included to instead stabilize 3+1D Z2 topological order. We also find dual descriptions of four quantum phase transitions in our model, all of which appear to be discontinuous first-order transitions.

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

International Nuclear Information System (INIS)

Rhodes, C.; Morari, M.

1997-01-01

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. copyright 1997 The American Physical Society

Nearest Neighbor Estimates of Entropy for Multivariate Circular Distributions

Directory of Open Access Journals (Sweden)

Neeraj Misra

2010-05-01

Full Text Available In molecular sciences, the estimation of entropies of molecules is important for the understanding of many chemical and biological processes. Motivated by these applications, we consider the problem of estimating the entropies of circular random vectors and introduce non-parametric estimators based on circular distances between n sample points and their k th nearest neighbors (NN, where k (≤ n – 1 is a fixed positive integer. The proposed NN estimators are based on two different circular distances, and are proven to be asymptotically unbiased and consistent. The performance of one of the circular-distance estimators is investigated and compared with that of the already established Euclidean-distance NN estimator using Monte Carlo samples from an analytic distribution of six circular variables of an exactly known entropy and a large sample of seven internal-rotation angles in the molecule of tartaric acid, obtained by a realistic molecular-dynamics simulation.

Sequential nearest-neighbor effects on computed {sup 13}C{sup {alpha}} chemical shifts

Energy Technology Data Exchange (ETDEWEB)

Vila, Jorge A. [Cornell University, Baker Laboratory of Chemistry and Chemical Biology (United States); Serrano, Pedro; Wuethrich, Kurt [The Scripps Research Institute, Department of Molecular Biology (United States); Scheraga, Harold A., E-mail: has5@cornell.ed [Cornell University, Baker Laboratory of Chemistry and Chemical Biology (United States)

2010-09-15

To evaluate sequential nearest-neighbor effects on quantum-chemical calculations of {sup 13}C{sup {alpha}} chemical shifts, we selected the structure of the nucleic acid binding (NAB) protein from the SARS coronavirus determined by NMR in solution (PDB id 2K87). NAB is a 116-residue {alpha}/{beta} protein, which contains 9 prolines and has 50% of its residues located in loops and turns. Overall, the results presented here show that sizeable nearest-neighbor effects are seen only for residues preceding proline, where Pro introduces an overestimation, on average, of 1.73 ppm in the computed {sup 13}C{sup {alpha}} chemical shifts. A new ensemble of 20 conformers representing the NMR structure of the NAB, which was calculated with an input containing backbone torsion angle constraints derived from the theoretical {sup 13}C{sup {alpha}} chemical shifts as supplementary data to the NOE distance constraints, exhibits very similar topology and comparable agreement with the NOE constraints as the published NMR structure. However, the two structures differ in the patterns of differences between observed and computed {sup 13}C{sup {alpha}} chemical shifts, {Delta}{sub ca,i}, for the individual residues along the sequence. This indicates that the {Delta}{sub ca,i} -values for the NAB protein are primarily a consequence of the limited sampling by the bundles of 20 conformers used, as in common practice, to represent the two NMR structures, rather than of local flaws in the structures.

The anisotropic Ising superantiferromagnet on a simple cubic lattice in the presence of a magnetic field: Effective-field theory analysis

Energy Technology Data Exchange (ETDEWEB)

Ricardo de Sousa, J. [Universidade Federal do Amazonas, Departamento de Física, 3000, Japiim, 69077-000 Manaus, AM (Brazil); National Institute of Science and Technology for Complex Systems, 3000, Japiim, 69077-000 Manaus, AM (Brazil); Neto, Minos A., E-mail: minos@pq.cnpq.br [Universidade Federal do Amazonas, Departamento de Física, 3000, Japiim, 69077-000 Manaus, AM (Brazil); Padilha, Igor T.; Salmon, Octavio D.R.; Viana, J. Roberto [Universidade Federal do Amazonas, Departamento de Física, 3000, Japiim, 69077-000 Manaus, AM (Brazil)

2013-12-15

We have studied the anisotropic three-dimensional nearest-neighbor Ising model with competitive interactions in an uniform longitudinal magnetic field H. The model consists of ferromagnetic interactions J{sub z}=λ{sub 2}J{sub x} in the x(z) direction and antiferromagnetic interactions J{sub y}=λ{sub 1}J{sub x} in the y direction (Ising superantiferromagnet). For the particular case λ{sub 1}=λ{sub 2}=1 we obtain the phase diagram in the H−T plane, using the framework of the differential operator technique in the effective-field theory with finite cluster of N=4 spins (EFT-4). It was observed first- and second-order transitions in the low and high temperature limits, respectively, with the presence of a tricritical point and a reentrant behavior is observed at low temperature. The critical curve in the classical approach is also obtained and the results are compared.

FCNN-MR: A Parallel Instance Selection Method Based on Fast Condensed Nearest Neighbor Rule

OpenAIRE

Lu Si; Jie Yu; Shasha Li; Jun Ma; Lei Luo; Qingbo Wu; Yongqi Ma; Zhengji Liu

2017-01-01

Instance selection (IS) technique is used to reduce the data size to improve the performance of data mining methods. Recently, to process very large data set, several proposed methods divide the training set into some disjoint subsets and apply IS algorithms independently to each subset. In this paper, we analyze the limitation of these methods and give our viewpoint about how to divide and conquer in IS procedure. Then, based on fast condensed nearest neighbor (FCNN) rul...

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

Science.gov (United States)

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).

Ising percolation in a three-state majority vote model

Science.gov (United States)

Balankin, Alexander S.; Martínez-Cruz, M. A.; Gayosso Martínez, Felipe; Mena, Baltasar; Tobon, Atalo; Patiño-Ortiz, Julián; Patiño-Ortiz, Miguel; Samayoa, Didier

2017-02-01

In this Letter, we introduce a three-state majority vote model in which each voter adopts a state of a majority of its active neighbors, if exist, but the voter becomes uncommitted if its active neighbors are in a tie, or all neighbors are the uncommitted. Numerical simulations were performed on square lattices of different linear size with periodic boundary conditions. Starting from a random distribution of active voters, the model leads to a stable non-consensus state in which three opinions coexist. We found that the "magnetization" of the non-consensus state and the concentration of uncommitted voters in it are governed by an initial composition of system and are independent of the lattice size. Furthermore, we found that a configuration of the stable non-consensus state undergoes a second order percolation transition at a critical concentration of voters holding the same opinion. Numerical simulations suggest that this transition belongs to the same universality class as the Ising percolation. These findings highlight the effect of an updating rule for a tie between voter neighbors on the critical behavior of models obeying the majority vote rule whenever a strict majority exists.

Correction of dental artifacts within the anatomical surface in PET/MRI using active shape models and k-nearest-neighbors

DEFF Research Database (Denmark)

Ladefoged, Claes N.; Andersen, Flemming L.; Keller, Sune H.

2014-01-01

n combined PET/MR, attenuation correction (AC) is performed indirectly based on the available MR image information. Metal implant-induced susceptibility artifacts and subsequent signal voids challenge MR-based AC. Several papers acknowledge the problem in PET attenuation correction when dental...... artifacts are ignored, but none of them attempts to solve the problem. We propose a clinically feasible correction method which combines Active Shape Models (ASM) and k- Nearest-Neighbors (kNN) into a simple approach which finds and corrects the dental artifacts within the surface boundaries of the patient...... anatomy. ASM is used to locate a number of landmarks in the T1-weighted MR-image of a new patient. We calculate a vector of offsets from each voxel within a signal void to each of the landmarks. We then use kNN to classify each voxel as belonging to an artifact or an actual signal void using this offset...

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

Science.gov (United States)

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.

Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN classification method

Directory of Open Access Journals (Sweden)

D.A. Adeniyi

2016-01-01

Full Text Available The major problem of many on-line web sites is the presentation of many choices to the client at a time; this usually results to strenuous and time consuming task in finding the right product or information on the site. In this work, we present a study of automatic web usage data mining and recommendation system based on current user behavior through his/her click stream data on the newly developed Really Simple Syndication (RSS reader website, in order to provide relevant information to the individual without explicitly asking for it. The K-Nearest-Neighbor (KNN classification method has been trained to be used on-line and in Real-Time to identify clients/visitors click stream data, matching it to a particular user group and recommend a tailored browsing option that meet the need of the specific user at a particular time. To achieve this, web users RSS address file was extracted, cleansed, formatted and grouped into meaningful session and data mart was developed. Our result shows that the K-Nearest Neighbor classifier is transparent, consistent, straightforward, simple to understand, high tendency to possess desirable qualities and easy to implement than most other machine learning techniques specifically when there is little or no prior knowledge about data distribution.

Deep neural networks for direct, featureless learning through observation: The case of two-dimensional spin models

Science.gov (United States)

Mills, Kyle; Tamblyn, Isaac

2018-03-01

We demonstrate the capability of a convolutional deep neural network in predicting the nearest-neighbor energy of the 4 ×4 Ising model. Using its success at this task, we motivate the study of the larger 8 ×8 Ising model, showing that the deep neural network can learn the nearest-neighbor Ising Hamiltonian after only seeing a vanishingly small fraction of configuration space. Additionally, we show that the neural network has learned both the energy and magnetization operators with sufficient accuracy to replicate the low-temperature Ising phase transition. We then demonstrate the ability of the neural network to learn other spin models, teaching the convolutional deep neural network to accurately predict the long-range interaction of a screened Coulomb Hamiltonian, a sinusoidally attenuated screened Coulomb Hamiltonian, and a modified Potts model Hamiltonian. In the case of the long-range interaction, we demonstrate the ability of the neural network to recover the phase transition with equivalent accuracy to the numerically exact method. Furthermore, in the case of the long-range interaction, the benefits of the neural network become apparent; it is able to make predictions with a high degree of accuracy, and do so 1600 times faster than a CUDA-optimized exact calculation. Additionally, we demonstrate how the neural network succeeds at these tasks by looking at the weights learned in a simplified demonstration.

The diluted tri-dimensional spin-one Ising model with crystal field interactions

International Nuclear Information System (INIS)

Saber, M.

1988-09-01

3D spin-one Ising models with nearest-neighbour ferromagnetic interactions with crystal-field exhibit tricritical behaviour. A new method that applies to a wide class of random systems is used to study the influence of site and bond dilution on this behaviour. We have calculated temperature-crystal-field-concentration phase diagrams and determined, in particular, the influence of dilution on the zero temperature tricritical temperature. (author). 10 refs, 8 figs

Spatiotemporal distribution of Oklahoma earthquakes: Exploring relationships using a nearest-neighbor approach

Science.gov (United States)

Vasylkivska, Veronika S.; Huerta, Nicolas J.

2017-07-01

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 with 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.

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

International Nuclear Information System (INIS)

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

2011-01-01

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.

Phase diagram of the quantum Ising model with long-range interactions on an infinite-cylinder triangular lattice

Science.gov (United States)

Saadatmand, S. N.; Bartlett, S. D.; McCulloch, I. P.

2018-04-01

Obtaining quantitative ground-state behavior for geometrically-frustrated quantum magnets with long-range interactions is challenging for numerical methods. Here, we demonstrate that the ground states of these systems on two-dimensional lattices can be efficiently obtained using state-of-the-art translation-invariant variants of matrix product states and density-matrix renormalization-group algorithms. We use these methods to calculate the fully-quantitative ground-state phase diagram of the long-range interacting triangular Ising model with a transverse field on six-leg infinite-length cylinders and scrutinize the properties of the detected phases. We compare these results with those of the corresponding nearest neighbor model. Our results suggest that, for such long-range Hamiltonians, the long-range quantum fluctuations always lead to long-range correlations, where correlators exhibit power-law decays instead of the conventional exponential drops observed for short-range correlated gapped phases. Our results are relevant for comparisons with recent ion-trap quantum simulator experiments that demonstrate highly-controllable long-range spin couplings for several hundred ions.

Fast and Accuracy Control Chart Pattern Recognition using a New cluster-k-Nearest Neighbor

OpenAIRE

Samir Brahim Belhaouari

2009-01-01

By taking advantage of both k-NN which is highly accurate and K-means cluster which is able to reduce the time of classification, we can introduce Cluster-k-Nearest Neighbor as "variable k"-NN dealing with the centroid or mean point of all subclasses generated by clustering algorithm. In general the algorithm of K-means cluster is not stable, in term of accuracy, for that reason we develop another algorithm for clustering our space which gives a higher accuracy than K-means cluster, less ...

Nearest neighbor spacing distributions of low-lying levels of vibrational nuclei

International Nuclear Information System (INIS)

Abul-Magd, A.Y.; Simbel, M.H.

1996-01-01

Energy-level statistics are considered for nuclei whose Hamiltonian is divided into intrinsic and collective-vibrational terms. The levels are described as a random superposition of independent sequences, each corresponding to a given number of phonons. The intrinsic motion is assumed chaotic. The level spacing distribution is found to be intermediate between the Wigner and Poisson distributions and similar in form to the spacing distribution of a system with classical phase space divided into separate regular and chaotic domains. We have obtained approximate expressions for the nearest neighbor spacing and cumulative spacing distribution valid when the level density is described by a constant-temperature formula and not involving additional free parameters. These expressions have been able to achieve good agreement with the experimental spacing distributions. copyright 1996 The American Physical Society

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

Directory of Open Access Journals (Sweden)

Zhen Liu

2017-11-01

Full Text Available 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.

Evaluation of the suitability of free-energy minimization using nearest-neighbor energy parameters for RNA secondary structure prediction

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Cobaugh Christian W

2004-08-01

Full Text Available Abstract Background A detailed understanding of an RNA's correct secondary and tertiary structure is crucial to understanding its function and mechanism in the cell. Free energy minimization with energy parameters based on the nearest-neighbor model and comparative analysis are the primary methods for predicting an RNA's secondary structure from its sequence. Version 3.1 of Mfold has been available since 1999. This version contains an expanded sequence dependence of energy parameters and the ability to incorporate coaxial stacking into free energy calculations. We test Mfold 3.1 by performing the largest and most phylogenetically diverse comparison of rRNA and tRNA structures predicted by comparative analysis and Mfold, and we use the results of our tests on 16S and 23S rRNA sequences to assess the improvement between Mfold 2.3 and Mfold 3.1. Results The average prediction accuracy for a 16S or 23S rRNA sequence with Mfold 3.1 is 41%, while the prediction accuracies for the majority of 16S and 23S rRNA structures tested are between 20% and 60%, with some having less than 20% prediction accuracy. The average prediction accuracy was 71% for 5S rRNA and 69% for tRNA. The majority of the 5S rRNA and tRNA sequences have prediction accuracies greater than 60%. The prediction accuracy of 16S rRNA base-pairs decreases exponentially as the number of nucleotides intervening between the 5' and 3' halves of the base-pair increases. Conclusion Our analysis indicates that the current set of nearest-neighbor energy parameters in conjunction with the Mfold folding algorithm are unable to consistently and reliably predict an RNA's correct secondary structure. For 16S or 23S rRNA structure prediction, Mfold 3.1 offers little improvement over Mfold 2.3. However, the nearest-neighbor energy parameters do work well for shorter RNA sequences such as tRNA or 5S rRNA, or for larger rRNAs when the contact distance between the base-pairs is less than 100 nucleotides.

A Local Weighted Nearest Neighbor Algorithm and a Weighted and Constrained Least-Squared Method for Mixed Odor Analysis by Electronic Nose Systems

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Jyuo-Min Shyu

2010-11-01

Full Text Available A great deal of work has been done to develop techniques for odor analysis by electronic nose systems. These analyses mostly focus on identifying a particular odor by comparing with a known odor dataset. However, in many situations, it would be more practical if each individual odorant could be determined directly. This paper proposes two methods for such odor components analysis for electronic nose systems. First, a K-nearest neighbor (KNN-based local weighted nearest neighbor (LWNN algorithm is proposed to determine the components of an odor. According to the component analysis, the odor training data is firstly categorized into several groups, each of which is represented by its centroid. The examined odor is then classified as the class of the nearest centroid. The distance between the examined odor and the centroid is calculated based on a weighting scheme, which captures the local structure of each predefined group. To further determine the concentration of each component, odor models are built by regressions. Then, a weighted and constrained least-squares (WCLS method is proposed to estimate the component concentrations. Experiments were carried out to assess the effectiveness of the proposed methods. The LWNN algorithm is able to classify mixed odors with different mixing ratios, while the WCLS method can provide good estimates on component concentrations.

Predicting the severity of nuclear power plant transients using nearest neighbors modeling optimized by genetic algorithms on a parallel computer

International Nuclear Information System (INIS)

Lin, J.; Bartal, Y.; Uhrig, R.E.

1995-01-01

The importance of automatic diagnostic systems for nuclear power plants (NPPs) has been discussed in numerous studies, and various such systems have been proposed. None of those systems were designed to predict the severity of the diagnosed scenario. A classification and severity prediction system for NPP transients is developed. The system is based on nearest neighbors modeling, which is optimized using genetic algorithms. The optimization process is used to determine the most important variables for each of the transient types analyzed. An enhanced version of the genetic algorithms is used in which a local downhill search is performed to further increase the accuracy achieved. The genetic algorithms search was implemented on a massively parallel supercomputer, the KSR1-64, to perform the analysis in a reasonable time. The data for this study were supplied by the high-fidelity simulator of the San Onofre unit 1 pressurized water reactor

K-Nearest Neighbor Intervals Based AP Clustering Algorithm for Large Incomplete Data

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Cheng Lu

2015-01-01

Full Text Available The Affinity Propagation (AP algorithm is an effective algorithm for clustering analysis, but it can not be directly applicable to the case of incomplete data. In view of the prevalence of missing data and the uncertainty of missing attributes, we put forward a modified AP clustering algorithm based on K-nearest neighbor intervals (KNNI for incomplete data. Based on an Improved Partial Data Strategy, the proposed algorithm estimates the KNNI representation of missing attributes by using the attribute distribution information of the available data. The similarity function can be changed by dealing with the interval data. Then the improved AP algorithm can be applicable to the case of incomplete data. Experiments on several UCI datasets show that the proposed algorithm achieves impressive clustering results.

Local Order in the Unfolded State: Conformational Biases and Nearest Neighbor Interactions

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Siobhan Toal

2014-07-01

Full Text Available The discovery of Intrinsically Disordered Proteins, which contain significant levels of disorder yet perform complex biologically functions, as well as unwanted aggregation, has motivated numerous experimental and theoretical studies aimed at describing residue-level conformational ensembles. Multiple lines of evidence gathered over the last 15 years strongly suggest that amino acids residues display unique and restricted conformational preferences in the unfolded state of peptides and proteins, contrary to one of the basic assumptions of the canonical random coil model. To fully understand residue level order/disorder, however, one has to gain a quantitative, experimentally based picture of conformational distributions and to determine the physical basis underlying residue-level conformational biases. Here, we review the experimental, computational and bioinformatic evidence for conformational preferences of amino acid residues in (mostly short peptides that can be utilized as suitable model systems for unfolded states of peptides and proteins. In this context particular attention is paid to the alleged high polyproline II preference of alanine. We discuss how these conformational propensities may be modulated by peptide solvent interactions and so called nearest-neighbor interactions. The relevance of conformational propensities for the protein folding problem and the understanding of IDPs is briefly discussed.

Renormalization-group studies of antiferromagnetic chains. I. Nearest-neighbor interactions

International Nuclear Information System (INIS)

Rabin, J.M.

1980-01-01

The real-space renormalization-group method introduced by workers at the Stanford Linear Accelerator Center (SLAC) is used to study one-dimensional antiferromagnetic chains at zero temperature. Calculations using three-site blocks (for the Heisenberg-Ising model) and two-site blocks (for the isotropic Heisenberg model) are compared with exact results. In connection with the two-site calculation a duality transformation is introduced under which the isotropic Heisenberg model is self-dual. Such duality transformations can be defined for models other than those considered here, and may be useful in various block-spin calculations

Automated analysis of long-term grooming behavior in Drosophila using a k-nearest neighbors classifier

Science.gov (United States)

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

Obstacle Detection for Intelligent Transportation Systems Using Deep Stacked Autoencoder and k-Nearest Neighbor Scheme

KAUST Repository

Dairi, Abdelkader; Harrou, Fouzi; Sun, Ying; Senouci, Mohamed

2018-01-01

Obstacle detection is an essential element for the development of intelligent transportation systems so that accidents can be avoided. In this study, we propose a stereovisionbased method for detecting obstacles in urban environment. The proposed method uses a deep stacked auto-encoders (DSA) model that combines the greedy learning features with the dimensionality reduction capacity and employs an unsupervised k-nearest neighbors algorithm (KNN) to accurately and reliably detect the presence of obstacles. We consider obstacle detection as an anomaly detection problem. We evaluated the proposed method by using practical data from three publicly available datasets, the Malaga stereovision urban dataset (MSVUD), the Daimler urban segmentation dataset (DUSD), and Bahnhof dataset. Also, we compared the efficiency of DSA-KNN approach to the deep belief network (DBN)-based clustering schemes. Results show that the DSA-KNN is suitable to visually monitor urban scenes.

Obstacle Detection for Intelligent Transportation Systems Using Deep Stacked Autoencoder and k-Nearest Neighbor Scheme

KAUST Repository

Dairi, Abdelkader

2018-04-30

Obstacle detection is an essential element for the development of intelligent transportation systems so that accidents can be avoided. In this study, we propose a stereovisionbased method for detecting obstacles in urban environment. The proposed method uses a deep stacked auto-encoders (DSA) model that combines the greedy learning features with the dimensionality reduction capacity and employs an unsupervised k-nearest neighbors algorithm (KNN) to accurately and reliably detect the presence of obstacles. We consider obstacle detection as an anomaly detection problem. We evaluated the proposed method by using practical data from three publicly available datasets, the Malaga stereovision urban dataset (MSVUD), the Daimler urban segmentation dataset (DUSD), and Bahnhof dataset. Also, we compared the efficiency of DSA-KNN approach to the deep belief network (DBN)-based clustering schemes. Results show that the DSA-KNN is suitable to visually monitor urban scenes.

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

Science.gov (United States)

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.

Geometric k-nearest neighbor estimation of entropy and mutual information

Science.gov (United States)

Lord, Warren M.; Sun, Jie; Bollt, Erik M.

2018-03-01

Nonparametric estimation of mutual information is used in a wide range of scientific problems to quantify dependence between variables. The k-nearest neighbor (knn) methods are consistent, and therefore expected to work well for a large sample size. These methods use geometrically regular local volume elements. This practice allows maximum localization of the volume elements, but can also induce a bias due to a poor description of the local geometry of the underlying probability measure. We introduce a new class of knn estimators that we call geometric knn estimators (g-knn), which use more complex local volume elements to better model the local geometry of the probability measures. As an example of this class of estimators, we develop a g-knn estimator of entropy and mutual information based on elliptical volume elements, capturing the local stretching and compression common to a wide range of dynamical system attractors. A series of numerical examples in which the thickness of the underlying distribution and the sample sizes are varied suggest that local geometry is a source of problems for knn methods such as the Kraskov-Stögbauer-Grassberger estimator when local geometric effects cannot be removed by global preprocessing of the data. The g-knn method performs well despite the manipulation of the local geometry. In addition, the examples suggest that the g-knn estimators can be of particular relevance to applications in which the system is large, but the data size is limited.

Fidelity study of superconductivity in extended Hubbard models

Science.gov (United States)

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.

Effect of the Hamiltonian parameters on the hysteresis properties of the kinetic mixed spin (1/2, 1) Ising ferrimagnetic model on a hexagonal lattice

Energy Technology Data Exchange (ETDEWEB)

Batı, Mehmet, E-mail: mehmet.bati@erdogan.edu.tr [Department of Physics, Recep Tayyip Erdoğan University, 53100 Rize (Turkey); Ertaş, Mehmet [Department of Physics, Erciyes University, 38039 Kayseri (Turkey)

2017-05-15

The hysteresis properties of a kinetic mixed spin (1/2, 1) Ising ferrimagnetic system on a hexagonal lattice are studied by means of the dynamic mean field theory. In the present study, the effects of the nearest-neighbor interaction, temperature, frequency of oscillating magnetic field and the exchange anisotropy on the hysteresis properties of the kinetic system are discussed in detail. A number of interesting phenomena such as the shape of hysteresis loops with one, two, three and inverted-hysteresis/proteresis (butterfly shape hysteresis) have been obtained. Finally, the obtained results are compared with some experimental and theoretical results and a qualitatively good agreement is found.

Prototype Generation Using Multiobjective Particle Swarm Optimization for Nearest Neighbor Classification.

Science.gov (United States)

Hu, Weiwei; Tan, Ying

2016-12-01

The nearest neighbor (NN) classifier suffers from high time complexity when classifying a test instance since the need of searching the whole training set. Prototype generation is a widely used approach to reduce the classification time, which generates a small set of prototypes to classify a test instance instead of using the whole training set. In this paper, particle swarm optimization is applied to prototype generation and two novel methods for improving the classification performance are presented: 1) a fitness function named error rank and 2) the multiobjective (MO) optimization strategy. Error rank is proposed to enhance the generation ability of the NN classifier, which takes the ranks of misclassified instances into consideration when designing the fitness function. The MO optimization strategy pursues the performance on multiple subsets of data simultaneously, in order to keep the classifier from overfitting the training set. Experimental results over 31 UCI data sets and 59 additional data sets show that the proposed algorithm outperforms nearly 30 existing prototype generation algorithms.

Chaotic synchronization of nearest-neighbor diffusive coupling Hindmarsh-Rose neural networks in noisy environments

International Nuclear Information System (INIS)

Fang Xiaoling; Yu Hongjie; Jiang Zonglai

2009-01-01

The chaotic synchronization of Hindmarsh-Rose neural networks linked by a nonlinear coupling function is discussed. The HR neural networks with nearest-neighbor diffusive coupling form are treated as numerical examples. By the construction of a special nonlinear-coupled term, the chaotic system is coupled symmetrically. For three and four neurons network, a certain region of coupling strength corresponding to full synchronization is given, and the effect of network structure and noise position are analyzed. For five and more neurons network, the full synchronization is very difficult to realize. All the results have been proved by the calculation of the maximum conditional Lyapunov exponent.

The Ising model for prediction of disordered residues from protein sequence alone

International Nuclear Information System (INIS)

Lobanov, Michail Yu; Galzitskaya, Oxana V

2011-01-01

Intrinsically disordered regions serve as molecular recognition elements, which play an important role in the control of many cellular processes and signaling pathways. It is useful to be able to predict positions of disordered residues and disordered regions in protein chains using protein sequence alone. A new method (IsUnstruct) based on the Ising model for prediction of disordered residues from protein sequence alone has been developed. According to this model, each residue can be in one of two states: ordered or disordered. The model is an approximation of the Ising model in which the interaction term between neighbors has been replaced by a penalty for changing between states (the energy of border). The IsUnstruct has been compared with other available methods and found to perform well. The method correctly finds 77% of disordered residues as well as 87% of ordered residues in the CASP8 database, and 72% of disordered residues as well as 85% of ordered residues in the DisProt database

Quasi-phases and pseudo-transitions in one-dimensional models with nearest neighbor interactions

Science.gov (United States)

de Souza, S. M.; Rojas, Onofre

2018-01-01

There are some particular one-dimensional models, such as the Ising-Heisenberg spin models with a variety of chain structures, which exhibit unexpected behaviors quite similar to the first and second order phase transition, which could be confused naively with an authentic phase transition. Through the analysis of the first derivative of free energy, such as entropy, magnetization, and internal energy, a "sudden" jump that closely resembles a first-order phase transition at finite temperature occurs. However, by analyzing the second derivative of free energy, such as specific heat and magnetic susceptibility at finite temperature, it behaves quite similarly to a second-order phase transition exhibiting an astonishingly sharp and fine peak. The correlation length also confirms the evidence of this pseudo-transition temperature, where a sharp peak occurs at the pseudo-critical temperature. We also present the necessary conditions for the emergence of these quasi-phases and pseudo-transitions.

Exact ground-state phase diagrams for the spin-3/2 Blume-Emery-Griffiths model

International Nuclear Information System (INIS)

Canko, Osman; Keskin, Mustafa; Deviren, Bayram

2008-01-01

We have calculated the exact ground-state phase diagrams of the spin-3/2 Ising model using the method that was proposed and applied to the spin-1 Ising model by Dublenych (2005 Phys. Rev. B 71 012411). The calculated, exact ground-state phase diagrams on the diatomic and triangular lattices with the nearest-neighbor (NN) interaction have been presented in this paper. We have obtained seven and 15 topologically different ground-state phase diagrams for J>0 and J 0 and J<0, respectively, the conditions for the existence of uniform and intermediate phases have also been found

Compensation phenomena of a mixed spin-2 and spin-12 Heisenberg ferrimagnetic model: Green function study

International Nuclear Information System (INIS)

Li Jun; Wei Guozhu; Du An

2005-01-01

The compensation and critical behaviors of a mixed spin-2 and spin-12 Heisenberg ferrimagnetic system on a square lattice are investigated theoretically by the two-time Green's function technique, which takes into account the quantum nature of Heisenberg spins. The model can be relevant for understanding the magnetic behavior of the new class of organometallic ferromagnetic materials that exhibit spontaneous magnetic properties at room temperature. We carry out the calculation of the sublattice magnetizations and the spin-wave spectra of the ground state. In particular, we have studied the effects of the nearest, next-nearest-neighbor interactions, the crystal field and the external magnetic field on the compensation temperature and the critical temperature. When only the nearest-neighbor interactions and the crystal field are included, no compensation temperature exists; when the next-nearest-neighbor interaction between spin-12 is taken into account and exceeds a minimum value, a compensation point appears and it is basically unchanged for other parameters in Hamiltonian fixed. The next-nearest-neighbor interactions between spin-2 and the external magnetic field have the effects of changing the compensation temperature and there is a narrow range of parameters of the Hamiltonian for which the model has the compensation temperatures and compensation temperature exists only for a small value of them

Algoritma Interpolasi Nearest-Neighbor untuk Pendeteksian Sampul Pulsa Oscilometri Menggunakan Mikrokontroler Berbiaya Rendah

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Firdaus Firdaus

2017-12-01

Full Text Available Non-invasive blood pressure measurement devices are widely available in the marketplace. Most of these devices use the oscillometric principle that store and analyze oscillometric waveforms during cuff deflation to obtain mean arterial pressure, systolic blood pressure and diastolic blood pressure. Those pressure values are determined from the oscillometric waveform envelope. Several methods to detect the envelope of oscillometric pulses utilize a complex algorithm that requires a large capacity memory and certainly difficult to process by a low memory capacity embedded system. A simple nearest-neighbor interpolation method is applied for oscillometric pulse envelope detection in non-invasive blood pressure measurement using microcontroller such ATmega328. The experiment yields 59 seconds average time to process the computation with 3.6% average percent error in blood pressure measurement.

Highway Travel Time Prediction Using Sparse Tensor Completion Tactics and K-Nearest Neighbor Pattern Matching Method

Directory of Open Access Journals (Sweden)

Jiandong Zhao

2018-01-01

Full Text Available Remote transportation microwave sensor (RTMS technology is being promoted for China’s highways. The distance is about 2 to 5 km between RTMSs, which leads to missing data and data sparseness problems. These two problems seriously restrict the accuracy of travel time prediction. Aiming at the data-missing problem, based on traffic multimode characteristics, a tensor completion method is proposed to recover the lost RTMS speed and volume data. Aiming at the data sparseness problem, virtual sensor nodes are set up between real RTMS nodes, and the two-dimensional linear interpolation and piecewise method are applied to estimate the average travel time between two nodes. Next, compared with the traditional K-nearest neighbor method, an optimal KNN method is proposed for travel time prediction. optimization is made in three aspects. Firstly, the three original state vectors, that is, speed, volume, and time of the day, are subdivided into seven periods. Secondly, the traffic congestion level is added as a new state vector. Thirdly, the cross-validation method is used to calibrate the K value to improve the adaptability of the KNN algorithm. Based on the data collected from Jinggangao highway, all the algorithms are validated. The results show that the proposed method can improve data quality and prediction precision of travel time.

Phosphorous vacancy nearest neighbor hopping induced instabilities in InP capacitors II. Computer simulation

International Nuclear Information System (INIS)

Juang, M.T.; Wager, J.F.; Van Vechten, J.A.

1988-01-01

Drain current drift in InP metal insulator semiconductor devices display distinct activation energies and pre-exponential factors. The authors have given evidence that these result from two physical mechanisms: thermionic tunneling of electrons into native oxide traps and phosphorous vacancy nearest neighbor hopping (PVNNH). They here present a computer simulation of the effect of the PVNHH mechanism on flatband voltage shift vs. bias stress time measurements. The simulation is based on an analysis of the kinetics of the PVNNH defect reaction sequence in which the electron concentration in the channel is related to the applied bias by a solution of the Poisson equation. The simulation demonstrates quantitatively that the temperature dependence of the flatband shift is associated with PVNNH for temperatures above room temperature

Two tree-formation methods for fast pattern search using nearest-neighbour and nearest-centroid matching

NARCIS (Netherlands)

Schomaker, Lambertus; Mangalagiu, D.; Vuurpijl, Louis; Weinfeld, M.; Schomaker, Lambert; Vuurpijl, Louis

2000-01-01

This paper describes tree­based classification of character images, comparing two methods of tree formation and two methods of matching: nearest neighbor and nearest centroid. The first method, Preprocess Using Relative Distances (PURD) is a tree­based reorganization of a flat list of patterns,

Forecasting of steel consumption with use of nearest neighbors method

Directory of Open Access Journals (Sweden)

Rogalewicz Michał

2017-01-01

Full Text Available In the process of building a steel construction, its design is usually commissioned to the design office. Then a quotation is made and the finished offer is delivered to the customer. Its final shape is influenced by steel consumption to a great extent. Correct determination of the potential consumption of this material most often determines the profitability of the project. Because of a long waiting time for a final project from the design office, it is worthwhile to pre-analyze the project’s profitability and feasibility using historical data on already realized orders. The paper presents an innovative approach to decision-making support in one of the Polish construction companies. The authors have defined and prioritized the most important factors that differentiate the executed orders and have the greatest impact on steel consumption. These are, among others: height and width of steel structure, number of aisles, type of roof, etc. Then they applied and adapted the method of k-nearest neighbors to the specificity of the discussed problem. The goal was to search a set of historical orders and find the most similar to the analyzed one. On this basis, consumption of steel can be estimated. The method was programmed within the EXPLOR application.

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

Science.gov (United States)

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.

Error minimizing algorithms for nearest eighbor classifiers

Energy Technology Data Exchange (ETDEWEB)

Porter, Reid B [Los Alamos National Laboratory; Hush, Don [Los Alamos National Laboratory; Zimmer, G. Beate [TEXAS A& M

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. We 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.

A Distributed Approach to Continuous Monitoring of Constrained k-Nearest Neighbor Queries in Road Networks

Directory of Open Access Journals (Sweden)

Hyung-Ju Cho

2012-01-01

Full Text Available Given two positive parameters k and r, a constrained k-nearest neighbor (CkNN query returns the k closest objects within a network distance r of the query location in road networks. In terms of the scalability of monitoring these CkNN queries, existing solutions based on central processing at a server suffer from a sudden and sharp rise in server load as well as messaging cost as the number of queries increases. In this paper, we propose a distributed and scalable scheme called DAEMON for the continuous monitoring of CkNN queries in road networks. Our query processing is distributed among clients (query objects and server. Specifically, the server evaluates CkNN queries issued at intersections of road segments, retrieves the objects on the road segments between neighboring intersections, and sends responses to the query objects. Finally, each client makes its own query result using this server response. As a result, our distributed scheme achieves close-to-optimal communication costs and scales well to large numbers of monitoring queries. Exhaustive experimental results demonstrate that our scheme substantially outperforms its competitor in terms of query processing time and messaging cost.

Transverse Ising spin-glass model

International Nuclear Information System (INIS)

Santos, Raimundo R. dos; Santos, R.M.Z. dos.

1984-01-01

The zero temperature behavior of the Transverse Ising spin-glass (+-J 0 ) model is discussed. The d-dimensional quantum model is shown to be equivalent to a classical (d + 1)- dimensional Ising spin-glass with correlated disorder. An exact Renormalization Group treatment of the one-dimensional quantum model indicates the existence of a spin-glass phase. The Migdal-Kadanoff approximation is used to obtain the phase diagram of the quantum spin-glass in two-dimensions. (Author) [pt

Ordering kinetics in quasi-one-dimensional Ising-like systems

International Nuclear Information System (INIS)

Mueller, M.; Paul, W.

1993-01-01

Results are presented of a Monte Carlo simulation of the kinetics of ordering in the two-dimensional nearest-neighbor Ising model in an L x M geometry with two free boundaries of length M much-gt L. This model can be viewed as representing an adsorbant on a stepped surface with mean terrace width L. The authors follow the ordering kinetics after quenches to temperatures 0.25 ≤T/T c ≤1 starting from a random initial configuration at a coverage of Θ=0.5 in the corresponding lattice gas picture. The systems evolve in time according to a Glauber kinetics with nonconserved order parameter. The equilibrium structure is given by a one-dimensional sequence of ordered domains. The ordering process evolves from a short initial two-dimensional ordering process through a crossover region to a quasi-one-dimensional behavior. The whole process is diffusive (inverse half-width of the structure factor peak 1/Δq parallel ∝ √t), in contrast to a model proposed by Kawasaki et al., where an intermediate logarithmic growth law is expected. All results are completely describable in the picture of an annihilating random walk (ARW) of domain walls. 36 refs., 16 figs

An Ising model for metal-organic frameworks

Science.gov (United States)

Höft, Nicolas; Horbach, Jürgen; Martín-Mayor, Victor; Seoane, Beatriz

2017-08-01

We present a three-dimensional Ising model where lines of equal spins are frozen such that they form an ordered framework structure. The frame spins impose an external field on the rest of the spins (active spins). We demonstrate that this "porous Ising model" can be seen as a minimal model for condensation transitions of gas molecules in metal-organic frameworks. Using Monte Carlo simulation techniques, we compare the phase behavior of a porous Ising model with that of a particle-based model for the condensation of methane (CH4) in the isoreticular metal-organic framework IRMOF-16. For both models, we find a line of first-order phase transitions that end in a critical point. We show that the critical behavior in both cases belongs to the 3D Ising universality class, in contrast to other phase transitions in confinement such as capillary condensation.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

'Devil's Staircase'-Type Phase Transition in NaV2O5 under High Pressure

International Nuclear Information System (INIS)

Ohwada, K.; Fujii, Y.; Takesue, N.; Isobe, M.; Ueda, Y.; Nakao, H.; Wakabayashi, Y.; Murakami, Y.; Ito, K.; Amemiya, Y.

2001-01-01

The 'devil's staircase'-type phase transition in the quarter-filled spin-ladder compound NaV 2 O 5 has been discovered at low temperature and high pressure by synchrotron radiation x-ray diffraction. A large number of transitions are found to successively take place among higher-order commensurate phases with 2a x 2b x zc type superstructures. The observed temperature and pressure dependence of modulation wave number q c , defined by 1/z, is well reproduced by the axial next nearest neighbor Ising model. The q c is suggested to reflect atomic displacements presumably coupled with charge ordering in this system

Environment overwhelms both nature and nurture in a model spin glass

Science.gov (United States)

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).

The magnetic properties of a mixed spin-1/2 and spin-1 Heisenberg ferrimagnetic system on a two-dimensional square lattice

Energy Technology Data Exchange (ETDEWEB)

Hu, Ai-Yuan, E-mail: huaiyuanhuyuanai@126.com [School of Physics and Electronic Engineering, Chongqing Normal University, Chongqing 401331 (China); Zhang, A.-Jie [Military Operational Research Teaching Division of the 4th Department, PLA Academy of National Defense Information, Wuhan 430000 (China)

2016-02-01

The magnetic properties of a mixed spin-1/2 and spin-1 Heisenberg ferrimagnetic system on a two-dimensional square lattice are investigated by means of the double-time Green's function technique within the random phase decoupling approximation. The role of the nearest-, next-nearest-neighbors interactions and the exchange anisotropy in the Hamiltonian is explored. And their effects on the critical and compensation temperature are discussed in detail. Our investigation indicates that both the next-nearest-neighbor interactions and the anisotropy have a great effect on the phase diagram. - Highlights: • Spin-1/2 and spin-1 ferrimagnetic model is examined. • Green's function technique is used. • The role of the nearest-, next-nearest-neighbors interactions and the exchange anisotropy in the Hamiltonian is explored. • The next-nearest-neighbor interactions and the anisotropy have a great effect on the phase diagram.

Effective Hamiltonian for 2-dimensional arbitrary spin Ising model

International Nuclear Information System (INIS)

Sznajd, J.; Polska Akademia Nauk, Wroclaw. Inst. Niskich Temperatur i Badan Strukturalnych)

1983-08-01

The method of the reduction of the generalized arbitrary-spin 2-dimensional Ising model to spin-half Ising model is presented. The method is demonstrated in detail by calculating the effective interaction constants to the third order in cumulant expansion for the triangular spin-1 Ising model (the Blume-Emery-Griffiths model). (author)

Phase transitions and thermal entanglement of the distorted Ising-Heisenberg spin chain: topology of multiple-spin exchange interactions in spin ladders

Science.gov (United States)

Arian Zad, Hamid; Ananikian, Nerses

2017-11-01

We consider a symmetric spin-1/2 Ising-XXZ double sawtooth spin ladder obtained from distorting a spin chain, with the XXZ interaction between the interstitial Heisenberg dimers (which are connected to the spins based on the legs via an Ising-type interaction), the Ising coupling between nearest-neighbor spins of the legs and rungs spins, respectively, and additional cyclic four-spin exchange (ring exchange) in the square plaquette of each block. The presented analysis supplemented by results of the exact solution of the model with infinite periodic boundary implies a rich ground state phase diagram. As well as the quantum phase transitions, the characteristics of some of the thermodynamic parameters such as heat capacity, magnetization and magnetic susceptibility are investigated. We prove here that among the considered thermodynamic and thermal parameters, solely heat capacity is sensitive versus the changes of the cyclic four-spin exchange interaction. By using the heat capacity function, we obtain a singularity relation between the cyclic four-spin exchange interaction and the exchange coupling between pair spins on each rung of the spin ladder. All thermal and thermodynamic quantities under consideration should be investigated by regarding those points which satisfy the singularity relation. The thermal entanglement within the Heisenberg spin dimers is investigated by using the concurrence, which is calculated from a relevant reduced density operator in the thermodynamic limit.

Magnetization reversal in magnetic dot arrays: Nearest-neighbor interactions and global configurational anisotropy

Energy Technology Data Exchange (ETDEWEB)

Van de Wiele, Ben [Department of Electrical Energy, Systems and Automation, Ghent University, Technologiepark 913, B-9052 Ghent-Zwijnaarde (Belgium); Fin, Samuele [Dipartimento di Fisica e Scienze della Terra, Università degli Studi di Ferrara, 44122 Ferrara (Italy); Pancaldi, Matteo [CIC nanoGUNE, E-20018 Donostia-San Sebastian (Spain); Vavassori, Paolo [CIC nanoGUNE, E-20018 Donostia-San Sebastian (Spain); IKERBASQUE, Basque Foundation for Science, E-48013 Bilbao (Spain); Sarella, Anandakumar [Physics Department, Mount Holyoke College, 211 Kendade, 50 College St., South Hadley, Massachusetts 01075 (United States); Bisero, Diego [Dipartimento di Fisica e Scienze della Terra, Università degli Studi di Ferrara, 44122 Ferrara (Italy); CNISM, Unità di Ferrara, 44122 Ferrara (Italy)

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 sets 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.

Green function study of a mixed spin-((3)/(2)) and spin-((1)/(2)) Heisenberg ferrimagnetic model

International Nuclear Information System (INIS)

Li Jun; Wei Guozhu; Du An

2004-01-01

The magnetic properties of a mixed spin-((3)/(2)) and spin-((1)/(2)) Heisenberg ferrimagnetic system on a square lattice are investigated theoretically by a multisublattice Green-function technique which takes into account the quantum nature of Heisenberg spins. This model can be relevant for understanding the magnetic behavior of the new class of organometallic materials that exhibit spontaneous magnetic moments at room temperature. We discuss the spontaneous magnetic moments and the finite-temperature phase diagram. We find that there is no compensation point at finite temperature when only the nearest-neighbor interaction and the single-ion anisotropy are included. When the next-nearest-neighbor interaction between spin-((1)/(2)) is taken into account and exceeds a minimum value, a compensation point appears and it is basically unchanged for other values in Hamiltonian fixed. The next-nearest-neighbor interaction between spin-((3)/(2)) has the effect of changing the compensation temperature

Thue-Morse quantum Ising model

International Nuclear Information System (INIS)

Doria, M.M.; Nori, F.; Satija, I.I.

1989-01-01

We study the one-dimensional quantum Ising model in a transverse magnetic field where the exchange couplings are ordered according to the Thue-Morse (TM) sequence. At zero temperature, this model is equivalent to a two-dimensional classical Ising model in a magnetic field with TM aperiodicity along one direction. We compute the order parameter (magnetization) of the chain and the scaling behavior of the energy spectrum when the system undergoes a phase transition. Analogous to the quasiperiodic (QP) quantum Ising chain, the onset of long-range order is signaled by a nonanaliticity in the exponent δ which describes the scaling of the total bandwidth with the size of the chain. The critical spin-coupling can be computed analytically and it is found to be lower than the QP case. Furthermore, the energy bands are found to be narrower than the corresponding QP chain. The former and latter results are consistent with the fact that the present structure has a degree of ordering intermediate between QP and random

Spinon decay in the spin-1/2 Heisenberg chain with weak next nearest neighbour exchange

International Nuclear Information System (INIS)

Groha, Stefan; Essler, Fabian H L

2017-01-01

Integrable models support elementary excitations with infinite lifetimes. In the spin-1/2 Heisenberg chain these are known as spinons. We consider the stability of spinons when a weak integrability breaking perturbation is added to the Heisenberg chain in a magnetic field. We focus on the case where the perturbation is a next nearest neighbour exchange interaction. We calculate the spinon decay rate in leading order in perturbation theory using methods of integrability and identify the dominant decay channels. The decay rate is found to be small, which indicates that spinons remain well-defined excitations even though integrability is broken. (paper)

Using K-Nearest Neighbor Classification to Diagnose Abnormal Lung Sounds

Directory of Open Access Journals (Sweden)

Chin-Hsing Chen

2015-06-01

Full Text Available A reported 30% of people worldwide have abnormal lung sounds, including crackles, rhonchi, and wheezes. To date, the traditional stethoscope remains the most popular tool used by physicians to diagnose such abnormal lung sounds, however, many problems arise with the use of a stethoscope, including the effects of environmental noise, the inability to record and store lung sounds for follow-up or tracking, and the physician’s subjective diagnostic experience. This study has developed a digital stethoscope to help physicians overcome these problems when diagnosing abnormal lung sounds. In this digital system, mel-frequency cepstral coefficients (MFCCs were used to extract the features of lung sounds, and then the K-means algorithm was used for feature clustering, to reduce the amount of data for computation. Finally, the K-nearest neighbor method was used to classify the lung sounds. The proposed system can also be used for home care: if the percentage of abnormal lung sound frames is > 30% of the whole test signal, the system can automatically warn the user to visit a physician for diagnosis. We also used bend sensors together with an amplification circuit, Bluetooth, and a microcontroller to implement a respiration detector. The respiratory signal extracted by the bend sensors can be transmitted to the computer via Bluetooth to calculate the respiratory cycle, for real-time assessment. If an abnormal status is detected, the device will warn the user automatically. Experimental results indicated that the error in respiratory cycles between measured and actual values was only 6.8%, illustrating the potential of our detector for home care applications.

Fermions as generalized Ising models

Directory of Open Access Journals (Sweden)

C. Wetterich

2017-04-01

Full Text Available We establish a general map between Grassmann functionals for fermions and probability or weight distributions for Ising spins. The equivalence between the two formulations is based on identical transfer matrices and expectation values of products of observables. The map preserves locality properties and can be realized for arbitrary dimensions. We present a simple example where a quantum field theory for free massless Dirac fermions in two-dimensional Minkowski space is represented by an asymmetric Ising model on a euclidean square lattice.

Ising model for packet routing control

International Nuclear Information System (INIS)

Horiguchi, Tsuyoshi; Takahashi, Hideyuki; Hayashi, Keisuke; Yamaguchi, Chiaki

2004-01-01

For packet routing control in computer networks, we propose an Ising model which is defined in order to express competition among a queue length and a distance from a node with a packet to its destination node. By introducing a dynamics for a mean-field value of an Ising spin, we show by computer simulations that effective control of packet routing through priority links is possible

Analytic nearest neighbour model for FCC metals

International Nuclear Information System (INIS)

Idiodi, J.O.A.; Garba, E.J.D.; Akinlade, O.

1991-06-01

A recently proposed analytic nearest-neighbour model for fcc metals is criticised and two alternative nearest-neighbour models derived from the separable potential method (SPM) are recommended. Results for copper and aluminium illustrate the utility of the recommended models. (author). 20 refs, 5 tabs

The susceptibilities in the spin-S Ising model

International Nuclear Information System (INIS)

Ainane, A.; Saber, M.

1995-08-01

The susceptibilities of the spin-S Ising model are evaluated using the effective field theory introduced by Tucker et al. for studying general spin-S Ising model. The susceptibilities are studied for all spin values from S = 1/2 to S = 5/2. (author). 12 refs, 4 figs

Disordering scaling and generalized nearest-neighbor approach in the thermodynamics of Lennard-Jones systems

International Nuclear Information System (INIS)

Vorob'ev, V.S.

2003-01-01

We suggest a concept of multiple disordering scaling of the crystalline state. Such a scaling procedure applied to a crystal leads to the liquid and (in low density limit) gas states. This approach provides an explanation to a high value of configuration (common) entropy of liquefied noble gases, which can be deduced from experimental data. We use the generalized nearest-neighbor approach to calculate free energy and pressure of the Lennard-Jones systems after performing this scaling procedure. These thermodynamic functions depend on one parameter characterizing the disordering only. Condensed states of the system (liquid and solid) correspond to small values of this parameter. When this parameter tends to unity, we get an asymptotically exact equation of state for a gas involving the second virial coefficient. A reasonable choice of the values for the disordering parameter (ranging between zero and unity) allows us to find the lines of coexistence between different phase states in the Lennard-Jones systems, which are in a good agreement with the available experimental data

Truncated Calogero-Sutherland models on a circle

Science.gov (United States)

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.

An extended chain Ising model and its Glauber dynamics

International Nuclear Information System (INIS)

Zhao Xing-Yu; Fan Xiao-Hui; Huang Yi-Neng; Huang Xin-Ru

2012-01-01

It was first proposed that an extended chain Ising (ECI) model contains the Ising chain model, single spin double-well potentials and a pure phonon heat bath of a specific energy exchange with the spins. The extension method is easy to apply to high dimensional cases. Then the single spin-flip probability (rate) of the ECI model is deduced based on the Boltzmann principle and general statistical principles of independent events and the model is simplified to an extended chain Glauber—Ising (ECGI) model. Moreover, the relaxation dynamics of the ECGI model were simulated by the Monte Carlo method and a comparison with the predictions of the special chain Glauber—Ising (SCGI) model was presented. It was found that the results of the two models are consistent with each other when the Ising chain length is large enough and temperature is relative low, which is the most valuable case of the model applications. These show that the ECI model will provide a firm physical base for the widely used single spin-flip rate proposed by Glauber and a possible route to obtain the single spin-flip rate of other form and even the multi-spin-flip rate. (condensed matter: electronic structure, electrical, magnetic, and optical properties)

An extended approach for computing the critical properties in the two-and three-dimensional lattices within the effective-field renormalization group method

Science.gov (United States)

de Albuquerque, Douglas F.; Santos-Silva, Edimilson; Moreno, N. O.

2009-10-01

In this letter we employing the effective-field renormalization group (EFRG) to study the Ising model with nearest neighbors to obtain the reduced critical temperature and exponents ν for bi- and three-dimensional lattices by increasing cluster scheme by extending recent works. The technique follows up the same strategy of the mean field renormalization group (MFRG) by introducing an alternative way for constructing classical effective-field equations of state takes on rigorous Ising spin identities.

An extended approach for computing the critical properties in the two-and three-dimensional lattices within the effective-field renormalization group method

Energy Technology Data Exchange (ETDEWEB)

Albuquerque, Douglas F. de [Departamento de Matematica, Universidade Federal de Sergipe, 49100-000 Sao Cristovao, SE (Brazil)], E-mail: douglas@ufs.br; Santos-Silva, Edimilson [Departamento de Matematica, Universidade Federal de Sergipe, 49100-000 Sao Cristovao, SE (Brazil); Moreno, N.O. [Departamento de Fisica, Universidade Federal de Sergipe, 49100-000 Sao Cristovao, SE (Brazil)

2009-10-15

In this letter we employing the effective-field renormalization group (EFRG) to study the Ising model with nearest neighbors to obtain the reduced critical temperature and exponents {nu} for bi- and three-dimensional lattices by increasing cluster scheme by extending recent works. The technique follows up the same strategy of the mean field renormalization group (MFRG) by introducing an alternative way for constructing classical effective-field equations of state takes on rigorous Ising spin identities.

An extended approach for computing the critical properties in the two-and three-dimensional lattices within the effective-field renormalization group method

International Nuclear Information System (INIS)

Albuquerque, Douglas F. de; Santos-Silva, Edimilson; Moreno, N.O.

2009-01-01

In this letter we employing the effective-field renormalization group (EFRG) to study the Ising model with nearest neighbors to obtain the reduced critical temperature and exponents ν for bi- and three-dimensional lattices by increasing cluster scheme by extending recent works. The technique follows up the same strategy of the mean field renormalization group (MFRG) by introducing an alternative way for constructing classical effective-field equations of state takes on rigorous Ising spin identities.

Speeding up transmissions of unknown quantum information along Ising-type quantum channels

International Nuclear Information System (INIS)

Guo W J; Wei L F

2017-01-01

Quantum teleportation with entanglement channels and a series of two-qubit SWAP gates between the nearest-neighbor qubits are usually utilized to achieve the transfers of unknown quantum state from the sender to the distant receiver. In this paper, by simplifying the usual SWAP gates we propose an approach to speed up the transmissions of unknown quantum information, specifically including the single-qubit unknown state and two-qubit unknown entangled ones, by a series of entangling and disentangling operations between the remote qubits with distant interactions. The generic proposal is demonstrated specifically with experimentally-existing Ising-type quantum channels without transverse interaction; liquid NMR-molecules driven by global radio frequency electromagnetic pulses and capacitively-coupled Josephson circuits driven by local microwave pulses. The proposal should be particularly useful to set up the connections between the distant qubits in a chip of quantum computing. (paper)

Probability distribution of magnetization in the one-dimensional Ising model: effects of boundary conditions

Energy Technology Data Exchange (ETDEWEB)

Antal, T [Physics Department, Simon Fraser University, Burnaby, BC V5A 1S6 (Canada); Droz, M [Departement de Physique Theorique, Universite de Geneve, CH 1211 Geneva 4 (Switzerland); Racz, Z [Institute for Theoretical Physics, Eoetvoes University, 1117 Budapest, Pazmany setany 1/a (Hungary)

2004-02-06

Finite-size scaling functions are investigated both for the mean-square magnetization fluctuations and for the probability distribution of the magnetization in the one-dimensional Ising model. The scaling functions are evaluated in the limit of the temperature going to zero (T {yields} 0), the size of the system going to infinity (N {yields} {infinity}) while N[1 - tanh(J/k{sub B}T)] is kept finite (J being the nearest neighbour coupling). Exact calculations using various boundary conditions (periodic, antiperiodic, free, block) demonstrate explicitly how the scaling functions depend on the boundary conditions. We also show that the block (small part of a large system) magnetization distribution results are identical to those obtained for free boundary conditions.

A Regression-based K nearest neighbor algorithm for gene function prediction from heterogeneous data

Directory of Open Access Journals (Sweden)

Ruzzo Walter L

2006-03-01

Full Text Available Abstract Background As a variety of functional genomic and proteomic techniques become available, there is an increasing need for functional analysis methodologies that integrate heterogeneous data sources. Methods In this paper, we address this issue by proposing a general framework for gene function prediction based on the k-nearest-neighbor (KNN algorithm. The choice of KNN is motivated by its simplicity, flexibility to incorporate different data types and adaptability to irregular feature spaces. A weakness of traditional KNN methods, especially when handling heterogeneous data, is that performance is subject to the often ad hoc choice of similarity metric. To address this weakness, we apply regression methods to infer a similarity metric as a weighted combination of a set of base similarity measures, which helps to locate the neighbors that are most likely to be in the same class as the target gene. We also suggest a novel voting scheme to generate confidence scores that estimate the accuracy of predictions. The method gracefully extends to multi-way classification problems. Results We apply this technique to gene function prediction according to three well-known Escherichia coli classification schemes suggested by biologists, using information derived from microarray and genome sequencing data. We demonstrate that our algorithm dramatically outperforms the naive KNN methods and is competitive with support vector machine (SVM algorithms for integrating heterogenous data. We also show that by combining different data sources, prediction accuracy can improve significantly. Conclusion Our extension of KNN with automatic feature weighting, multi-class prediction, and probabilistic inference, enhance prediction accuracy significantly while remaining efficient, intuitive and flexible. This general framework can also be applied to similar classification problems involving heterogeneous datasets.

CATEGORIZATION OF GELAM, ACACIA AND TUALANG HONEY ODORPROFILE USING K-NEAREST NEIGHBORS

Directory of Open Access Journals (Sweden)

Nurdiyana Zahed

2018-02-01

Full Text Available Honey authenticity refer to honey types is of great importance issue and interest in agriculture. In current research, several documents of specific types of honey have their own usage in medical field. However, it is quite challenging task to classify different types of honey by simply using our naked eye. This work demostrated a successful an electronic nose (E-nose application as an instrument for identifying odor profile pattern of three common honey in Malaysia (Gelam, Acacia and Tualang honey. The applied E-nose has produced signal for odor measurement in form of numeric resistance (Ω. The data reading have been pre-processed using normalization technique for standardized scale of unique features. Mean features is extracted and boxplot used as the statistical tool to present the data pattern according to three types of honey. Mean features that have been extracted were employed into K-Nearest Neighbors classifier as an input features and evaluated using several splitting ratio. Excellent results were obtained by showing 100% rate of accuracy, sensitivity and specificity of classification from KNN using weigh (k=1, ratio 90:10 and Euclidean distance. The findings confirmed the ability of KNN classifier as intelligent classification to classify different honey types from E-nose calibration. Outperform of other classifier, KNN required less parameter optimization and achieved promising result.

Statistical mechanics of the cluster Ising model

International Nuclear Information System (INIS)

Smacchia, Pietro; Amico, Luigi; Facchi, Paolo; Fazio, Rosario; Florio, Giuseppe; Pascazio, Saverio; Vedral, Vlatko

2011-01-01

We study a Hamiltonian system describing a three-spin-1/2 clusterlike interaction competing with an Ising-like antiferromagnetic interaction. We compute free energy, spin-correlation functions, and entanglement both in the ground and in thermal states. The model undergoes a quantum phase transition between an Ising phase with a nonvanishing magnetization and a cluster phase characterized by a string order. Any two-spin entanglement is found to vanish in both quantum phases because of a nontrivial correlation pattern. Nevertheless, the residual multipartite entanglement is maximal in the cluster phase and dependent on the magnetization in the Ising phase. We study the block entropy at the critical point and calculate the central charge of the system, showing that the criticality of the system is beyond the Ising universality class.

Statistical mechanics of the cluster Ising model

Energy Technology Data Exchange (ETDEWEB)

Smacchia, Pietro [SISSA - via Bonomea 265, I-34136, Trieste (Italy); Amico, Luigi [CNR-MATIS-IMM and Dipartimento di Fisica e Astronomia Universita di Catania, C/O ed. 10, viale Andrea Doria 6, I-95125 Catania (Italy); Facchi, Paolo [Dipartimento di Matematica and MECENAS, Universita di Bari, I-70125 Bari (Italy); INFN, Sezione di Bari, I-70126 Bari (Italy); Fazio, Rosario [NEST, Scuola Normale Superiore and Istituto Nanoscienze - CNR, 56126 Pisa (Italy); Center for Quantum Technology, National University of Singapore, 117542 Singapore (Singapore); Florio, Giuseppe; Pascazio, Saverio [Dipartimento di Fisica and MECENAS, Universita di Bari, I-70126 Bari (Italy); INFN, Sezione di Bari, I-70126 Bari (Italy); Vedral, Vlatko [Center for Quantum Technology, National University of Singapore, 117542 Singapore (Singapore); Department of Physics, National University of Singapore, 2 Science Drive 3, Singapore 117542 (Singapore); Department of Physics, University of Oxford, Clarendon Laboratory, Oxford, OX1 3PU (United Kingdom)

2011-08-15

We study a Hamiltonian system describing a three-spin-1/2 clusterlike interaction competing with an Ising-like antiferromagnetic interaction. We compute free energy, spin-correlation functions, and entanglement both in the ground and in thermal states. The model undergoes a quantum phase transition between an Ising phase with a nonvanishing magnetization and a cluster phase characterized by a string order. Any two-spin entanglement is found to vanish in both quantum phases because of a nontrivial correlation pattern. Nevertheless, the residual multipartite entanglement is maximal in the cluster phase and dependent on the magnetization in the Ising phase. We study the block entropy at the critical point and calculate the central charge of the system, showing that the criticality of the system is beyond the Ising universality class.

Energetics and Dynamics of Cu(001)-c(2x2)Cl steps

NARCIS (Netherlands)

van Dijk, F.R.; Zandvliet, Henricus J.W.; Poelsema, Bene

2006-01-01

The energetics of the step faceting transition of Cu(001) [copper (001) surface] upon Cl (chloride) adsorption in contact with HCl (hydrogen chloride) solution is modeled in terms of a solid-on-solid model that incorporates both nearest-neighbor and next-nearest-neighbor interactions. It is shown

Quantum simulation of transverse Ising models with Rydberg atoms

Science.gov (United States)

Schauss, Peter

2018-04-01

Quantum Ising models are canonical models for the study of quantum phase transitions (Sachdev 1999 Quantum Phase Transitions (Cambridge: Cambridge University Press)) and are the underlying concept for many analogue quantum computing and quantum annealing ideas (Tanaka et al Quantum Spin Glasses, Annealing and Computation (Cambridge: Cambridge University Press)). Here we focus on the implementation of finite-range interacting Ising spin models, which are barely tractable numerically. Recent experiments with cold atoms have reached the interaction-dominated regime in quantum Ising magnets via optical coupling of trapped neutral atoms to Rydberg states. This approach allows for the tunability of all relevant terms in an Ising spin Hamiltonian with 1/{r}6 interactions in transverse and longitudinal fields. This review summarizes the recent progress of these implementations in Rydberg lattices with site-resolved detection. Strong correlations in quantum Ising models have been observed in several experiments, starting from a single excitation in the superatom regime up to the point of crystallization. The rapid progress in this field makes spin systems based on Rydberg atoms a promising platform for quantum simulation because of the unmatched flexibility and strength of interactions combined with high control and good isolation from the environment.

An RFID Indoor Positioning Algorithm Based on Bayesian Probability and K-Nearest Neighbor.

Science.gov (United States)

Xu, He; Ding, Ye; Li, Peng; Wang, Ruchuan; Li, Yizhu

2017-08-05

The Global Positioning System (GPS) is widely used in outdoor environmental positioning. However, GPS cannot support indoor positioning because there is no signal for positioning in an indoor environment. Nowadays, there are many situations which require indoor positioning, such as searching for a book in a library, looking for luggage in an airport, emergence navigation for fire alarms, robot location, etc. Many technologies, such as ultrasonic, sensors, Bluetooth, WiFi, magnetic field, Radio Frequency Identification (RFID), etc., are used to perform indoor positioning. Compared with other technologies, RFID used in indoor positioning is more cost and energy efficient. The Traditional RFID indoor positioning algorithm LANDMARC utilizes a Received Signal Strength (RSS) indicator to track objects. However, the RSS value is easily affected by environmental noise and other interference. In this paper, our purpose is to reduce the location fluctuation and error caused by multipath and environmental interference in LANDMARC. We propose a novel indoor positioning algorithm based on Bayesian probability and K -Nearest Neighbor (BKNN). The experimental results show that the Gaussian filter can filter some abnormal RSS values. The proposed BKNN algorithm has the smallest location error compared with the Gaussian-based algorithm, LANDMARC and an improved KNN algorithm. The average error in location estimation is about 15 cm using our method.

Self-dual cluster renormalization group approach for the square lattice Ising model specific heat and magnetization

International Nuclear Information System (INIS)

Martin, H.O.; Tsallis, C.

1981-01-01

A simple renormalization group approach based on self-dual clusters is proposed for two-dimensional nearest-neighbour 1/2 - spin Ising model on the square lattice; it reproduces the exact critical point. The internal energy and the specific heat for vanishing external magnetic field, spontaneous magnetization and the thermal (Y sub(T)) and magnetic (Y sub(H)) critical exponents are calculated. The results obtained from the first four smallest cluster sizes strongly suggest the convergence towards the exact values when the cluster sizes increases. Even for the smallest cluster, where the calculation is very simple, the results are quite accurate, particularly in the neighbourhood of the critical point. (Author) [pt

The Peierls argument for higher dimensional Ising models

International Nuclear Information System (INIS)

Bonati, Claudio

2014-01-01

The Peierls argument is a mathematically rigorous and intuitive method to show the presence of a non-vanishing spontaneous magnetization in some lattice models. This argument is typically explained for the D = 2 Ising model in a way which cannot be easily generalized to higher dimensions. The aim of this paper is to present an elementary discussion of the Peierls argument for the general D-dimensional Ising model. (paper)

Prediction of human breast and colon cancers from imbalanced data using nearest neighbor and support vector machines.

Science.gov (United States)

Majid, Abdul; Ali, Safdar; Iqbal, Mubashar; Kausar, Nabeela

2014-03-01

This study proposes a novel prediction approach for human breast and colon cancers using different feature spaces. The proposed scheme consists of two stages: the preprocessor and the predictor. In the preprocessor stage, the mega-trend diffusion (MTD) technique is employed to increase the samples of the minority class, thereby balancing the dataset. In the predictor stage, machine-learning approaches of K-nearest neighbor (KNN) and support vector machines (SVM) are used to develop hybrid MTD-SVM and MTD-KNN prediction models. MTD-SVM model has provided the best values of accuracy, G-mean and Matthew's correlation coefficient of 96.71%, 96.70% and 71.98% for cancer/non-cancer dataset, breast/non-breast cancer dataset and colon/non-colon cancer dataset, respectively. We found that hybrid MTD-SVM is the best with respect to prediction performance and computational cost. MTD-KNN model has achieved moderately better prediction as compared to hybrid MTD-NB (Naïve Bayes) but at the expense of higher computing cost. MTD-KNN model is faster than MTD-RF (random forest) but its prediction is not better than MTD-RF. To the best of our knowledge, the reported results are the best results, so far, for these datasets. The proposed scheme indicates that the developed models can be used as a tool for the prediction of cancer. This scheme may be useful for study of any sequential information such as protein sequence or any nucleic acid sequence. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

The Ising model coupled to 2d orders

Science.gov (United States)

Glaser, Lisa

2018-04-01

In this article we make first steps in coupling matter to causal set theory in the path integral. We explore the case of the Ising model coupled to the 2d discrete Einstein Hilbert action, restricted to the 2d orders. We probe the phase diagram in terms of the Wick rotation parameter β and the Ising coupling j and find that the matter and the causal sets together give rise to an interesting phase structure. The couplings give rise to five different phases. The causal sets take on random or crystalline characteristics as described in Surya (2012 Class. Quantum Grav. 29 132001) and the Ising model can be correlated or uncorrelated on the random orders and correlated, uncorrelated or anti-correlated on the crystalline orders. We find that at least one new phase transition arises, in which the Ising spins push the causal set into the crystalline phase.

Charge-regulation phase transition on surface lattices of titratable sites adjacent to electrolyte solutions: An analog of the Ising antiferromagnet in a magnetic field

Science.gov (United States)

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

An improved coupled-states approximation including the nearest neighbor Coriolis couplings for diatom-diatom inelastic collision

Science.gov (United States)

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.

Exact ground-state phase diagrams for the spin-3/2 Blume-Emery-Griffiths model

Energy Technology Data Exchange (ETDEWEB)

Canko, Osman; Keskin, Mustafa [Department of Physics, Erciyes University, 38039 Kayseri (Turkey); Deviren, Bayram [Institute of Science, Erciyes University, 38039 Kayseri (Turkey)], E-mail: keskin@erciyes.edu.tr

2008-05-15

We have calculated the exact ground-state phase diagrams of the spin-3/2 Ising model using the method that was proposed and applied to the spin-1 Ising model by Dublenych (2005 Phys. Rev. B 71 012411). The calculated, exact ground-state phase diagrams on the diatomic and triangular lattices with the nearest-neighbor (NN) interaction have been presented in this paper. We have obtained seven and 15 topologically different ground-state phase diagrams for J>0 and J<0, respectively, on the diatomic lattice and have found the conditions for the existence of uniform and intermediate or non-uniform phases. We have also constructed the exact ground-state phase diagrams of the model on the triangular lattice and found 20 and 59 fundamental phase diagrams for J>0 and J<0, respectively, the conditions for the existence of uniform and intermediate phases have also been found.

Dynamical correlation functions of the S=1/2 nearest-neighbor and Haldane-Shastry Heisenberg antiferromagnetic chains in zero and applied fields

DEFF Research Database (Denmark)

Lefmann, K.; Rischel, C.

1996-01-01

We present a numerical diagonalization study of two one-dimensional S=1/2 antiferromagnetic Heisenberg chains, having nearest-neighbor and Haldane-Shastry (1/r(2)) interactions, respectively. We have obtained the T=0 dynamical correlation function, S-alpha alpha(q,omega), for chains of length N=8......-28. We have studied S-zz(q,omega) for the Heisenberg chain in zero field, and from finite-size scaling we have obtained a limiting behavior that for large omega deviates from the conjecture proposed earlier by Muller ct al. For both chains we describe the behavior of S-zz(q,omega) and S...

Predicting persistence in the sediment compartment with a new automatic software based on the k-Nearest Neighbor (k-NN) algorithm.

Science.gov (United States)

Manganaro, Alberto; Pizzo, Fabiola; Lombardo, Anna; Pogliaghi, Alberto; Benfenati, Emilio

2016-02-01

The ability of a substance to resist degradation and persist in the environment needs to be readily identified in order to protect the environment and human health. Many regulations require the assessment of persistence for substances commonly manufactured and marketed. Besides laboratory-based testing methods, in silico tools may be used to obtain a computational prediction of persistence. We present a new program to develop k-Nearest Neighbor (k-NN) models. The k-NN algorithm is a similarity-based approach that predicts the property of a substance in relation to the experimental data for its most similar compounds. We employed this software to identify persistence in the sediment compartment. Data on half-life (HL) in sediment were obtained from different sources and, after careful data pruning the final dataset, containing 297 organic compounds, was divided into four experimental classes. We developed several models giving satisfactory performances, considering that both the training and test set accuracy ranged between 0.90 and 0.96. We finally selected one model which will be made available in the near future in the freely available software platform VEGA. This model offers a valuable in silico tool that may be really useful for fast and inexpensive screening. Copyright © 2015 Elsevier Ltd. All rights reserved.

Conformal invariance in the long-range Ising model

Directory of Open Access Journals (Sweden)

Miguel F. Paulos

2016-01-01

Full Text Available We consider the question of conformal invariance of the long-range Ising model at the critical point. The continuum description is given in terms of a nonlocal field theory, and the absence of a stress tensor invalidates all of the standard arguments for the enhancement of scale invariance to conformal invariance. We however show that several correlation functions, computed to second order in the epsilon expansion, are nontrivially consistent with conformal invariance. We proceed to give a proof of conformal invariance to all orders in the epsilon expansion, based on the description of the long-range Ising model as a defect theory in an auxiliary higher-dimensional space. A detailed review of conformal invariance in the d-dimensional short-range Ising model is also included and may be of independent interest.

Conformal Invariance in the Long-Range Ising Model

CERN Document Server

Paulos, Miguel F; van Rees, Balt C; Zan, Bernardo

2016-01-01

We consider the question of conformal invariance of the long-range Ising model at the critical point. The continuum description is given in terms of a nonlocal field theory, and the absence of a stress tensor invalidates all of the standard arguments for the enhancement of scale invariance to conformal invariance. We however show that several correlation functions, computed to second order in the epsilon expansion, are nontrivially consistent with conformal invariance. We proceed to give a proof of conformal invariance to all orders in the epsilon expansion, based on the description of the long-range Ising model as a defect theory in an auxiliary higher-dimensional space. A detailed review of conformal invariance in the d-dimensional short-range Ising model is also included and may be of independent interest.

Conformal invariance in the long-range Ising model

Energy Technology Data Exchange (ETDEWEB)

Paulos, Miguel F. [CERN, Theory Group, Geneva (Switzerland); Rychkov, Slava, E-mail: slava.rychkov@lpt.ens.fr [CERN, Theory Group, Geneva (Switzerland); Laboratoire de Physique Théorique de l' École Normale Supérieure (LPTENS), Paris (France); Faculté de Physique, Université Pierre et Marie Curie (UPMC), Paris (France); Rees, Balt C. van [CERN, Theory Group, Geneva (Switzerland); Zan, Bernardo [Institute of Physics, Universiteit van Amsterdam, Amsterdam (Netherlands)

2016-01-15

We consider the question of conformal invariance of the long-range Ising model at the critical point. The continuum description is given in terms of a nonlocal field theory, and the absence of a stress tensor invalidates all of the standard arguments for the enhancement of scale invariance to conformal invariance. We however show that several correlation functions, computed to second order in the epsilon expansion, are nontrivially consistent with conformal invariance. We proceed to give a proof of conformal invariance to all orders in the epsilon expansion, based on the description of the long-range Ising model as a defect theory in an auxiliary higher-dimensional space. A detailed review of conformal invariance in the d-dimensional short-range Ising model is also included and may be of independent interest.

New relation for critical exponents in the Ising model

International Nuclear Information System (INIS)

Pishtshev, A.

2007-01-01

The Ising model in a transverse field is considered at T=0. From the analysis of the power low behaviors of the energy gap and the order parameter as functions of the field a new relation between the respective critical exponents, β>=1/(8s 2 ), is derived. By using the Suzuki equivalence from this inequality a new relation for critical exponents in the Ising model, β>=1/(8ν 2 ), is obtained. A number of numerical examples for different cases illustrates the generality and validity of the relation. By applying this relation the estimation ν=(1/4) 1/3 ∼0.62996 for the 3D-Ising model is proposed

Analysis and Identification of Aptamer-Compound Interactions with a Maximum Relevance Minimum Redundancy and Nearest Neighbor Algorithm.

Science.gov (United States)

Wang, ShaoPeng; Zhang, Yu-Hang; Lu, Jing; Cui, Weiren; Hu, Jerry; Cai, Yu-Dong

2016-01-01

The development of biochemistry and molecular biology has revealed an increasingly important role of compounds in several biological processes. Like the aptamer-protein interaction, aptamer-compound interaction attracts increasing attention. However, it is time-consuming to select proper aptamers against compounds using traditional methods, such as exponential enrichment. Thus, there is an urgent need to design effective computational methods for searching effective aptamers against compounds. This study attempted to extract important features for aptamer-compound interactions using feature selection methods, such as Maximum Relevance Minimum Redundancy, as well as incremental feature selection. Each aptamer-compound pair was represented by properties derived from the aptamer and compound, including frequencies of single nucleotides and dinucleotides for the aptamer, as well as the constitutional, electrostatic, quantum-chemical, and space conformational descriptors of the compounds. As a result, some important features were obtained. To confirm the importance of the obtained features, we further discussed the associations between them and aptamer-compound interactions. Simultaneously, an optimal prediction model based on the nearest neighbor algorithm was built to identify aptamer-compound interactions, which has the potential to be a useful tool for the identification of novel aptamer-compound interactions. The program is available upon the request.

One-dimensional Ising model with multispin interactions

Science.gov (United States)

Turban, Loïc

2016-09-01

We study the spin-1/2 Ising chain with multispin interactions K involving the product of m successive spins, for general values of m. Using a change of spin variables the zero-field partition function of a finite chain is obtained for free and periodic boundary conditions and we calculate the two-spin correlation function. When placed in an external field H the system is shown to be self-dual. Using another change of spin variables the one-dimensional Ising model with multispin interactions in a field is mapped onto a zero-field rectangular Ising model with first-neighbour interactions K and H. The 2D system, with size m × N/m, has the topology of a cylinder with helical BC. In the thermodynamic limit N/m\\to ∞ , m\\to ∞ , a 2D critical singularity develops on the self-duality line, \\sinh 2K\\sinh 2H=1.

An RFID Indoor Positioning Algorithm Based on Bayesian Probability and K-Nearest Neighbor

Directory of Open Access Journals (Sweden)

He Xu

2017-08-01

Full Text Available The Global Positioning System (GPS is widely used in outdoor environmental positioning. However, GPS cannot support indoor positioning because there is no signal for positioning in an indoor environment. Nowadays, there are many situations which require indoor positioning, such as searching for a book in a library, looking for luggage in an airport, emergence navigation for fire alarms, robot location, etc. Many technologies, such as ultrasonic, sensors, Bluetooth, WiFi, magnetic field, Radio Frequency Identification (RFID, etc., are used to perform indoor positioning. Compared with other technologies, RFID used in indoor positioning is more cost and energy efficient. The Traditional RFID indoor positioning algorithm LANDMARC utilizes a Received Signal Strength (RSS indicator to track objects. However, the RSS value is easily affected by environmental noise and other interference. In this paper, our purpose is to reduce the location fluctuation and error caused by multipath and environmental interference in LANDMARC. We propose a novel indoor positioning algorithm based on Bayesian probability and K-Nearest Neighbor (BKNN. The experimental results show that the Gaussian filter can filter some abnormal RSS values. The proposed BKNN algorithm has the smallest location error compared with the Gaussian-based algorithm, LANDMARC and an improved KNN algorithm. The average error in location estimation is about 15 cm using our method.

Truncated Calogero-Sutherland models

Science.gov (United States)

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.

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

Science.gov (United States)

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

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

Science.gov (United States)

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.

A Diagnosis Method for Rotation Machinery Faults Based on Dimensionless Indexes Combined with K-Nearest Neighbor Algorithm

Directory of Open Access Journals (Sweden)

Jianbin Xiong

2015-01-01

Full Text Available It is difficult to well distinguish the dimensionless indexes between normal petrochemical rotating machinery equipment and those with complex faults. When the conflict of evidence is too big, it will result in uncertainty of diagnosis. This paper presents a diagnosis method for rotation machinery fault based on dimensionless indexes combined with K-nearest neighbor (KNN algorithm. This method uses a KNN algorithm and an evidence fusion theoretical formula to process fuzzy data, incomplete data, and accurate data. This method can transfer the signals from the petrochemical rotating machinery sensors to the reliability manners using dimensionless indexes and KNN algorithm. The input information is further integrated by an evidence synthesis formula to get the final data. The type of fault will be decided based on these data. The experimental results show that the proposed method can integrate data to provide a more reliable and reasonable result, thereby reducing the decision risk.

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

Science.gov (United States)

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.

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

Directory of Open Access Journals (Sweden)

Xiaoli Zhang

2016-05-01

Full Text Available 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 t 2 g 6 e g 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.

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

Science.gov (United States)

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.

A statistical-thermodynamic model for ordering phenomena in thin film intermetallic structures

International Nuclear Information System (INIS)

Semenova, Olga; Krachler, Regina

2008-01-01

Ordering phenomena in bcc (110) binary thin film intermetallics are studied by a statistical-thermodynamic model. The system is modeled by an Ising approach that includes only nearest-neighbor chemical interactions and is solved in a mean-field approximation. Vacancies and anti-structure atoms are considered on both sublattices. The model describes long-range ordering and simultaneously short-range ordering in the thin film. It is applied to NiAl thin films with B2 structure. Vacancy concentrations, thermodynamic activity profiles and the virtual critical temperature of order-disorder as a function of film composition and thickness are presented. The results point to an important role of vacancies in near-stoichiometric and Ni-rich NiAl thin films

Clustered K nearest neighbor algorithm for daily inflow forecasting

NARCIS (Netherlands)

Akbari, M.; Van Overloop, P.J.A.T.M.; Afshar, A.

2010-01-01

Instance based learning (IBL) algorithms are a common choice among data driven algorithms for inflow forecasting. They are based on the similarity principle and prediction is made by the finite number of similar neighbors. In this sense, the similarity of a query instance is estimated according to

Ising critical behaviour in the one-dimensional frustrated quantum XY model

International Nuclear Information System (INIS)

Granato, E.

1993-06-01

A generalization of the one-dimensional frustrated quantum XY model is considered in which the inter and intra-chain coupling constants of the two infinite XY (planar rotor) chains have different strengths. The model can describe the superconductor-insulator transition due to charging effects in a ladder of Josephson junctions in a magnetic field with half a flux quantum per plaquette. From a fluctuation-effective action, this transition is expected to be in the universality class of the two-dimensional classical XY-Ising model. The critical behaviour is studied using a Monte Carlo transfer matrix applied to the path-integral representation of the model and a finite-size-scaling analysis of data on small system sizes. It is found that, unlike the previous studied case of equal inter and intra-chain coupling constants, the XY and Ising-like excitations of the quantum model decouple for large interchain coupling, giving rise to pure Ising model critical behaviour for the chirality order parameter in good agreement with the results for the XY-Ising model. (author). 18 refs, 4 figs

Quantum kinetic Ising models

International Nuclear Information System (INIS)

Augusiak, R; Cucchietti, F M; Lewenstein, M; Haake, F

2010-01-01

In this paper, we introduce a quantum generalization of classical kinetic Ising models (KIM), described by a certain class of quantum many-body master equations. Similarly to KIMs with detailed balance that are equivalent to certain Hamiltonian systems, our models reduce to a set of Hamiltonian systems determining the dynamics of the elements of the many-body density matrix. The ground states of these Hamiltonians are well described by the matrix product, or pair entangled projected states. We discuss critical properties of such Hamiltonians, as well as entanglement properties of their low-energy states.

Nearest neighbor imputation using spatial-temporal correlations in wireless sensor networks.

Science.gov (United States)

Li, YuanYuan; Parker, Lynne E

2014-01-01

Missing data is common in Wireless Sensor Networks (WSNs), especially with multi-hop communications. There are many reasons for this phenomenon, such as unstable wireless communications, synchronization issues, and unreliable sensors. Unfortunately, missing data creates a number of problems for WSNs. First, since most sensor nodes in the network are battery-powered, it is too expensive to have the nodes retransmit missing data across the network. Data re-transmission may also cause time delays when detecting abnormal changes in an environment. Furthermore, localized reasoning techniques on sensor nodes (such as machine learning algorithms to classify states of the environment) are generally not robust enough to handle missing data. Since sensor data collected by a WSN is generally correlated in time and space, we illustrate how replacing missing sensor values with spatially and temporally correlated sensor values can significantly improve the network's performance. However, our studies show that it is important to determine which nodes are spatially and temporally correlated with each other. Simple techniques based on Euclidean distance are not sufficient for complex environmental deployments. Thus, we have developed a novel Nearest Neighbor (NN) imputation method that estimates missing data in WSNs by learning spatial and temporal correlations between sensor nodes. To improve the search time, we utilize a k d-tree data structure, which is a non-parametric, data-driven binary search tree. Instead of using traditional mean and variance of each dimension for k d-tree construction, and Euclidean distance for k d-tree search, we use weighted variances and weighted Euclidean distances based on measured percentages of missing data. We have evaluated this approach through experiments on sensor data from a volcano dataset collected by a network of Crossbow motes, as well as experiments using sensor data from a highway traffic monitoring application. Our experimental

ESTIMATING PHOTOMETRIC REDSHIFTS OF QUASARS VIA THE k-NEAREST NEIGHBOR APPROACH BASED ON LARGE SURVEY DATABASES

Energy Technology Data Exchange (ETDEWEB)

Zhang Yanxia; Ma He; Peng Nanbo; Zhao Yongheng [Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, 100012 Beijing (China); Wu Xuebing, E-mail: zyx@bao.ac.cn [Department of Astronomy, Peking University, 100871 Beijing (China)

2013-08-01

We apply one of the lazy learning methods, the k-nearest neighbor (kNN) algorithm, to estimate the photometric redshifts of quasars based on various data sets from the Sloan Digital Sky Survey (SDSS), the UKIRT Infrared Deep Sky Survey (UKIDSS), and the Wide-field Infrared Survey Explorer (WISE; the SDSS sample, the SDSS-UKIDSS sample, the SDSS-WISE sample, and the SDSS-UKIDSS-WISE sample). The influence of the k value and different input patterns on the performance of kNN is discussed. kNN performs best when k is different with a special input pattern for a special data set. The best result belongs to the SDSS-UKIDSS-WISE sample. The experimental results generally show that the more information from more bands, the better performance of photometric redshift estimation with kNN. The results also demonstrate that kNN using multiband data can effectively solve the catastrophic failure of photometric redshift estimation, which is met by many machine learning methods. Compared with the performance of various other methods of estimating the photometric redshifts of quasars, kNN based on KD-Tree shows superiority, exhibiting the best accuracy.

ESTIMATING PHOTOMETRIC REDSHIFTS OF QUASARS VIA THE k-NEAREST NEIGHBOR APPROACH BASED ON LARGE SURVEY DATABASES

International Nuclear Information System (INIS)

Zhang Yanxia; Ma He; Peng Nanbo; Zhao Yongheng; Wu Xuebing

2013-01-01

We apply one of the lazy learning methods, the k-nearest neighbor (kNN) algorithm, to estimate the photometric redshifts of quasars based on various data sets from the Sloan Digital Sky Survey (SDSS), the UKIRT Infrared Deep Sky Survey (UKIDSS), and the Wide-field Infrared Survey Explorer (WISE; the SDSS sample, the SDSS-UKIDSS sample, the SDSS-WISE sample, and the SDSS-UKIDSS-WISE sample). The influence of the k value and different input patterns on the performance of kNN is discussed. kNN performs best when k is different with a special input pattern for a special data set. The best result belongs to the SDSS-UKIDSS-WISE sample. The experimental results generally show that the more information from more bands, the better performance of photometric redshift estimation with kNN. The results also demonstrate that kNN using multiband data can effectively solve the catastrophic failure of photometric redshift estimation, which is met by many machine learning methods. Compared with the performance of various other methods of estimating the photometric redshifts of quasars, kNN based on KD-Tree shows superiority, exhibiting the best accuracy.

Correlation functions of the Ising model and the eight-vertex model

International Nuclear Information System (INIS)

Ko, L.F.

1986-01-01

Calculations for the two-point correlation functions in the scaling limit for two statistical models are presented. In Part I, the Ising model with a linear defect is studied for T T/sub c/. The transfer matrix method of Onsager and Kaufman is used. The energy-density correlation is given by functions related to the modified Bessel functions. The dispersion expansion for the spin-spin correlation functions are derived. The dominant behavior for large separations at T not equal to T/sub c/ is extracted. It is shown that these expansions lead to systems of Fredholm integral equations. In Part II, the electric correlation function of the eight-vertex model for T < T/sub c/ is studied. The eight vertex model decouples to two independent Ising models when the four spin coupling vanishes. To first order in the four-spin coupling, the electric correlation function is related to a three-point function of the Ising model. This relation is systematically investigated and the full dispersion expansion (to first order in four-spin coupling) is obtained. The results is a new kind of structure which, unlike those of many solvable models, is apparently not expressible in terms of linear integral equations

Localized endomorphisms of the chiral Ising model

International Nuclear Information System (INIS)

Boeckenhauer, J.

1994-07-01

In the frame of the treatment of the chiral Ising model by Mack and Schomerus, examples of localized endomorphisms ρ 1 loc and ρ 1/2 loc are presented. It is shown that they lead to the same superselection sectors as the global ones in the sense that π 0 oρ 1 log ≅π 1 and π 0 pρ 1/2 loc ≅π 1/2 holds. For proving the latter unitary equivalence, Arakis formalism of the selfdual CAR algebra is used. Further it is shown that the localized endomorphisms obey the Ising fusion rules. (orig.)

Testing Lorentz Invariance Emergence in the Ising Model using Monte Carlo simulations

CERN Document Server

Dias Astros, Maria Isabel

2017-01-01

In the context of the Lorentz invariance as an emergent phenomenon at low energy scales to study quantum gravity a system composed by two 3D interacting Ising models (one with an anisotropy in one direction) was proposed. Two Monte Carlo simulations were run: one for the 2D Ising model and one for the target model. In both cases the observables (energy, magnetization, heat capacity and magnetic susceptibility) were computed for different lattice sizes and a Binder cumulant introduced in order to estimate the critical temperature of the systems. Moreover, the correlation function was calculated for the 2D Ising model.

Applying Ancestry and Sex Computation as a Quality Control Tool in Targeted Next-Generation Sequencing.

Science.gov (United States)

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.

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

Science.gov (United States)

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%.

Quantum decoration transformation for spin models

Energy Technology Data Exchange (ETDEWEB)

Braz, F.F.; Rodrigues, F.C.; Souza, S.M. de; Rojas, Onofre, E-mail: ors@dfi.ufla.br

2016-09-15

It is quite relevant the extension of decoration transformation for quantum spin models since most of the real materials could be well described by Heisenberg type models. Here we propose an exact quantum decoration transformation and also showing interesting properties such as the persistence of symmetry and the symmetry breaking during this transformation. Although the proposed transformation, in principle, cannot be used to map exactly a quantum spin lattice model into another quantum spin lattice model, since the operators are non-commutative. However, it is possible the mapping in the “classical” limit, establishing an equivalence between both quantum spin lattice models. To study the validity of this approach for quantum spin lattice model, we use the Zassenhaus formula, and we verify how the correction could influence the decoration transformation. But this correction could be useless to improve the quantum decoration transformation because it involves the second-nearest-neighbor and further nearest neighbor couplings, which leads into a cumbersome task to establish the equivalence between both lattice models. This correction also gives us valuable information about its contribution, for most of the Heisenberg type models, this correction could be irrelevant at least up to the third order term of Zassenhaus formula. This transformation is applied to a finite size Heisenberg chain, comparing with the exact numerical results, our result is consistent for weak xy-anisotropy coupling. We also apply to bond-alternating Ising–Heisenberg chain model, obtaining an accurate result in the limit of the quasi-Ising chain.

Quantum decoration transformation for spin models

International Nuclear Information System (INIS)

Braz, F.F.; Rodrigues, F.C.; Souza, S.M. de; Rojas, Onofre

2016-01-01

It is quite relevant the extension of decoration transformation for quantum spin models since most of the real materials could be well described by Heisenberg type models. Here we propose an exact quantum decoration transformation and also showing interesting properties such as the persistence of symmetry and the symmetry breaking during this transformation. Although the proposed transformation, in principle, cannot be used to map exactly a quantum spin lattice model into another quantum spin lattice model, since the operators are non-commutative. However, it is possible the mapping in the “classical” limit, establishing an equivalence between both quantum spin lattice models. To study the validity of this approach for quantum spin lattice model, we use the Zassenhaus formula, and we verify how the correction could influence the decoration transformation. But this correction could be useless to improve the quantum decoration transformation because it involves the second-nearest-neighbor and further nearest neighbor couplings, which leads into a cumbersome task to establish the equivalence between both lattice models. This correction also gives us valuable information about its contribution, for most of the Heisenberg type models, this correction could be irrelevant at least up to the third order term of Zassenhaus formula. This transformation is applied to a finite size Heisenberg chain, comparing with the exact numerical results, our result is consistent for weak xy-anisotropy coupling. We also apply to bond-alternating Ising–Heisenberg chain model, obtaining an accurate result in the limit of the quasi-Ising chain.

Alpha centauri unveiling the secrets of our nearest stellar neighbor

CERN Document Server

Beech, Martin

2015-01-01

As our closest stellar companion and composed of two Sun-like stars and a third small dwarf star, Alpha Centauri is an ideal testing ground of astrophysical models and has played a central role in the history and development of modern astronomy—from the first guesses at stellar distances to understanding how our own star, the Sun, might have evolved. It is also the host of the nearest known exoplanet, an ultra-hot, Earth-like planet recently discovered. Just 4.4 light years away Alpha Centauri is also the most obvious target for humanity’s first directed interstellar space probe. Such a mission could reveal the small-scale structure of a new planetary system and also represent the first step in what must surely be humanity’s greatest future adventure—exploration of the Milky Way Galaxy itself. For all of its closeness, α Centauri continues to tantalize astronomers with many unresolved mysteries, such as how did it form, how many planets does it contain and where are they, and how might we view its ex...

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

Directory of Open Access Journals (Sweden)

Christa Nilsen

Full Text Available 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.

Bayesian Modeling of ChIP-chip Data Through a High-Order Ising Model

KAUST Repository

Mo, Qianxing

2010-01-29

ChIP-chip experiments are procedures that combine chromatin immunoprecipitation (ChIP) and DNA microarray (chip) technology to study a variety of biological problems, including protein-DNA interaction, histone modification, and DNA methylation. The most important feature of ChIP-chip data is that the intensity measurements of probes are spatially correlated because the DNA fragments are hybridized to neighboring probes in the experiments. We propose a simple, but powerful Bayesian hierarchical approach to ChIP-chip data through an Ising model with high-order interactions. The proposed method naturally takes into account the intrinsic spatial structure of the data and can be used to analyze data from multiple platforms with different genomic resolutions. The model parameters are estimated using the Gibbs sampler. The proposed method is illustrated using two publicly available data sets from Affymetrix and Agilent platforms, and compared with three alternative Bayesian methods, namely, Bayesian hierarchical model, hierarchical gamma mixture model, and Tilemap hidden Markov model. The numerical results indicate that the proposed method performs as well as the other three methods for the data from Affymetrix tiling arrays, but significantly outperforms the other three methods for the data from Agilent promoter arrays. In addition, we find that the proposed method has better operating characteristics in terms of sensitivities and false discovery rates under various scenarios. © 2010, The International Biometric Society.

Effective field treatment of the annealed bond-dilute transverse Ising model

International Nuclear Information System (INIS)

Silva, P.R.; Sa Barreto, F.C. de

1983-01-01

The dilution of the spin-1/2 transverse Ising Model is studied by means of an effective field type treatment based on an extension of Callen's relation to the present model. The thermodynamics of the diluted model is obtained and the results are shown to be an improvement over the standard mean field treatment. The results are also compared with the Monte Carlo calculation for the spin-infinite transverse Ising Model. (Author) [pt

Zeros of the partition function for some generalized Ising models

International Nuclear Information System (INIS)

Dunlop, F.

1981-01-01

The author considers generalized Ising Models with two and four body interactions in a complex external field h such that Re h>=mod(Im h) + C, where C is an explicit function of the interaction parameters. The partition function Z(h) is then shown to satisfy mod(Z(h))>=Z(c), so that the pressure is analytic in h inside the given region. The method is applied to specific examples: the gauge invariant Ising Model, and the Widom Rowlinson model on the lattice. (Auth.)

Dynamics of the two-dimensional directed Ising model in the paramagnetic phase

Science.gov (United States)

Godrèche, C.; Pleimling, M.

2014-05-01

We consider the nonconserved dynamics of the Ising model on the two-dimensional square lattice, where each spin is influenced preferentially by its east and north neighbours. The single-spin flip rates are such that the stationary state is Gibbsian with respect to the usual ferromagnetic Ising Hamiltonian. We show the existence, in the paramagnetic phase, of a dynamical transition between two regimes of violation of the fluctuation-dissipation theorem in the nonequilibrium stationary state: a regime of weak violation where the stationary fluctuation-dissipation ratio is finite, when the asymmetry parameter is less than a threshold value, and a regime of strong violation where this ratio vanishes asymptotically above the threshold. This study suggests that this novel kind of dynamical transition in nonequilibrium stationary states, already found for the directed Ising chain and the spherical model with asymmetric dynamics, might be quite general. In contrast with the latter models, the equal-time correlation function for the two-dimensional directed Ising model depends on the asymmetry.

Evidence of codon usage in the nearest neighbor spacing distribution of bases in bacterial genomes

Science.gov (United States)

Higareda, M. F.; Geiger, O.; Mendoza, L.; Méndez-Sánchez, R. A.

2012-02-01

Statistical analysis of whole genomic sequences usually assumes a homogeneous nucleotide density throughout the genome, an assumption that has been proved incorrect for several organisms since the nucleotide density is only locally homogeneous. To avoid giving a single numerical value to this variable property, we propose the use of spectral statistics, which characterizes the density of nucleotides as a function of its position in the genome. We show that the cumulative density of bases in bacterial genomes can be separated into an average (or secular) plus a fluctuating part. Bacterial genomes can be divided into two groups according to the qualitative description of their secular part: linear and piecewise linear. These two groups of genomes show different properties when their nucleotide spacing distribution is studied. In order to analyze genomes having a variable nucleotide density, statistically, the use of unfolding is necessary, i.e., to get a separation between the secular part and the fluctuations. The unfolding allows an adequate comparison with the statistical properties of other genomes. With this methodology, four genomes were analyzed Burkholderia, Bacillus, Clostridium and Corynebacterium. Interestingly, the nearest neighbor spacing distributions or detrended distance distributions are very similar for species within the same genus but they are very different for species from different genera. This difference can be attributed to the difference in the codon usage.

Restoration of dimensional reduction in the random-field Ising model at five dimensions

Science.gov (United States)

Fytas, Nikolaos G.; Martín-Mayor, Víctor; Picco, Marco; Sourlas, Nicolas

2017-04-01

The random-field Ising model is one of the few disordered systems where the perturbative renormalization group can be carried out to all orders of perturbation theory. This analysis predicts dimensional reduction, i.e., that the critical properties of the random-field Ising model in D dimensions are identical to those of the pure Ising ferromagnet in D -2 dimensions. It is well known that dimensional reduction is not true in three dimensions, thus invalidating the perturbative renormalization group prediction. Here, we report high-precision numerical simulations of the 5D random-field Ising model at zero temperature. We illustrate universality by comparing different probability distributions for the random fields. We compute all the relevant critical exponents (including the critical slowing down exponent for the ground-state finding algorithm), as well as several other renormalization-group invariants. The estimated values of the critical exponents of the 5D random-field Ising model are statistically compatible to those of the pure 3D Ising ferromagnet. These results support the restoration of dimensional reduction at D =5 . We thus conclude that the failure of the perturbative renormalization group is a low-dimensional phenomenon. We close our contribution by comparing universal quantities for the random-field problem at dimensions 3 ≤D equality at all studied dimensions.

Universal amplitude ratios in the 3D Ising model

International Nuclear Information System (INIS)

Caselle, M.; Hasenbusch, M.

1998-01-01

We present a high precision Monte Carlo study of various universal amplitude ratios of the three dimensional Ising spin model. Using state of the art simulation techniques we studied the model close to criticality in both phases. Great care was taken to control systematic errors due to finite size effects and correction to scaling terms. We obtain C + /C - =4.75(3), f +,2nd /f -,2nd =1.95(2) and u * =14.3(1). Our results are compatible with those obtained by field theoretic methods applied to the φ 4 theory and high and low temperature series expansions of the Ising model. (orig.)

Phase diagrams of a nonequilibrium mixed spin-3/2 and spin-2 Ising system in an oscillating magnetic field

International Nuclear Information System (INIS)

Keskin, Mustafa; Polat, Yasin

2009-01-01

The phase diagrams of the nonequilibrium mixed spin-3/2 and spin-2 Ising ferrimagnetic system on square lattice under a time-dependent external magnetic field are presented by using the Glauber-type stochastic dynamics. The model system consists of two interpenetrating sublattices of spins σ=3/2 and S=2, and we take only nearest-neighbor interactions between pairs of spins. The system is in contact with a heat bath at absolute temperature T abs and the exchange of energy with the heat bath occurs via one-spin flip of the Glauber dynamics. First, we investigate the time variations of average order parameters to find the phases in the system and then the thermal behavior of the dynamic order parameters to obtain the dynamic phase transition (DPT) points as well as to characterize the nature (first- or second-order) phase transitions. The dynamic phase diagrams are presented in two different planes. Phase diagrams contain paramagnetic (p), ferrimagnetic (i 1 , i 2 , i 3 ) phases, and three coexistence or mixed phase regions, namely i 1 +p, i 2 +p and i 3 +p mixed phases that strongly depend on interaction parameters.

Phase diagrams of a nonequilibrium mixed spin-3/2 and spin-2 Ising system in an oscillating magnetic field

Energy Technology Data Exchange (ETDEWEB)

Keskin, Mustafa [Department of Physics, Erciyes University, 38039 Kayseri (Turkey)], E-mail: keskin@erciyes.edu.tr; Polat, Yasin [Institutes of Science, Erciyes University, 38039 Kayseri (Turkey)

2009-12-15

The phase diagrams of the nonequilibrium mixed spin-3/2 and spin-2 Ising ferrimagnetic system on square lattice under a time-dependent external magnetic field are presented by using the Glauber-type stochastic dynamics. The model system consists of two interpenetrating sublattices of spins {sigma}=3/2 and S=2, and we take only nearest-neighbor interactions between pairs of spins. The system is in contact with a heat bath at absolute temperature T{sub abs} and the exchange of energy with the heat bath occurs via one-spin flip of the Glauber dynamics. First, we investigate the time variations of average order parameters to find the phases in the system and then the thermal behavior of the dynamic order parameters to obtain the dynamic phase transition (DPT) points as well as to characterize the nature (first- or second-order) phase transitions. The dynamic phase diagrams are presented in two different planes. Phase diagrams contain paramagnetic (p), ferrimagnetic (i{sub 1}, i{sub 2}, i{sub 3}) phases, and three coexistence or mixed phase regions, namely i{sub 1}+p, i{sub 2}+p and i{sub 3}+p mixed phases that strongly depend on interaction parameters.

SISTEM PEMBAGIAN KELAS KULIAH MAHASISWA DENGAN METODE K-MEANS DAN K-NEAREST NEIGHBORS UNTUK MENINGKATKAN KUALITAS PEMBELAJARAN

Directory of Open Access Journals (Sweden)

Gede Aditra Pradnyana

2018-01-01

Full Text Available Permasalahan yang terjadi saat pembentukan atau pembagian kelas mahasiswa adalah perbedaan kemampuan yang dimiliki oleh mahasiswa di setiap kelasnya yang dapat berdampak pada tidak efektifnya proses pembelajaran yang berlangsung. Pengelompokkan mahasiswa dengan kemampuan yang sama merupakan hal yang sangat penting dalam rangka meningkatkan kualitas proses belajar mengajar yang dilakukan. Dengan pengelompokkan mahasiswa yang tepat, mereka akan dapat saling membantu dalam proses pembelajaran. Selain itu, membagi kelas mahasiswa sesuai dengan kemampuannya dapat mempermudah tenaga pendidik dalam menentukan metode atau strategi pembelajaran yang sesuai. Penggunaan metode dan strategi pembelajaran yang tepat akan meningkatkan efektifitas proses belajar mengajar. Pada penelitian ini dirancang sebuah metode baru untuk pembagian kelas kuliah mahasiswa dengan mengkombinasikan metode K-Means dan K-Nearest Neighbors (KNN. Metode K-means digunakan untuk pembagian kelas kuliah mahasiswa berdasarkan komponen penilaian dari mata kuliah prasyaratnya. Adapun fitur yang digunakan dalam pengelompokkan adalah nilai tugas, nilai ujian tengah semester, nilai ujian akhir semester, dan indeks prestasi kumulatif (IPK. Metode KNN digunakan untuk memprediksi kelulusan seoarang mahasiswa di sebuah matakuliah berdasarkan data sebelumnya. Hasil prediksi ini akan digunakan sebagai fitur tambahan yang digunakan dalam pembentukan kelas mahasiswa menggunakan metode K-means. Pendekatan yang digunakan dalam penelitian ini adalah Software Development Live Cycle (SDLC dengan model waterfall. Berdasarkan hasil pengujian yang dilakukan diperoleh kesimpulan bahwa jumlah cluster atau kelas dan jumlah data yang digunakan mempengaruhi dari kualitas cluster yang dibentuk oleh metode K-Means dan KNN yang digunakan. Nilai Silhouette Indeks tertinggi diperolah saat menggunakan 100 data dengan jumlah cluster 10 sebesar 0,534 yang tergolong kelas dengan kualitas medium structure.

A Sensor Data Fusion System Based on k-Nearest Neighbor Pattern Classification for Structural Health Monitoring Applications

Directory of Open Access Journals (Sweden)

Jaime Vitola

2017-02-01

Full Text Available 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.

Estimating Stand Height and Tree Density in Pinus taeda plantations using in-situ data, airborne LiDAR and k-Nearest Neighbor Imputation.

Science.gov (United States)

Silva, Carlos Alberto; Klauberg, Carine; Hudak, Andrew T; Vierling, Lee A; Liesenberg, Veraldo; Bernett, Luiz G; Scheraiber, Clewerson F; Schoeninger, Emerson R

2018-01-01

Accurate forest inventory is of great economic importance to optimize the entire supply chain management in pulp and paper companies. The aim of this study was to estimate stand dominate and mean heights (HD and HM) and tree density (TD) of Pinus taeda plantations located in South Brazil using in-situ measurements, airborne Light Detection and Ranging (LiDAR) data and the non- k-nearest neighbor (k-NN) imputation. Forest inventory attributes and LiDAR derived metrics were calculated at 53 regular sample plots and we used imputation models to retrieve the forest attributes at plot and landscape-levels. The best LiDAR-derived metrics to predict HD, HM and TD were H99TH, HSD, SKE and HMIN. The Imputation model using the selected metrics was more effective for retrieving height than tree density. The model coefficients of determination (adj.R2) and a root mean squared difference (RMSD) for HD, HM and TD were 0.90, 0.94, 0.38m and 6.99, 5.70, 12.92%, respectively. Our results show that LiDAR and k-NN imputation can be used to predict stand heights with high accuracy in Pinus taeda. However, furthers studies need to be realized to improve the accuracy prediction of TD and to evaluate and compare the cost of acquisition and processing of LiDAR data against the conventional inventory procedures.

Bona Fide Thermodynamic Temperature in Nonequilibrium Kinetic Ising Models

OpenAIRE

Sastre, Francisco; Dornic, Ivan; Chaté, Hugues

2003-01-01

We show that a nominal temperature can be consistently and uniquely defined everywhere in the phase diagram of large classes of nonequilibrium kinetic Ising spin models. In addition, we confirm the recent proposal that, at critical points, the large-time ``fluctuation-dissipation ratio'' $X_\\infty$ is a universal amplitude ratio and find in particular $X_\\infty \\approx 0.33(2)$ and $X_\\infty = 1/2$ for the magnetization in, respectively, the two-dimensional Ising and voter universality classes.

Statistical Mechanics of Coherent Ising Machine — The Case of Ferromagnetic and Finite-Loading Hopfield Models —

Science.gov (United States)

Aonishi, Toru; Mimura, Kazushi; Utsunomiya, Shoko; Okada, Masato; Yamamoto, Yoshihisa

2017-10-01

The coherent Ising machine (CIM) has attracted attention as one of the most effective Ising computing architectures for solving large scale optimization problems because of its scalability and high-speed computational ability. However, it is difficult to implement the Ising computation in the CIM because the theories and techniques of classical thermodynamic equilibrium Ising spin systems cannot be directly applied to the CIM. This means we have to adapt these theories and techniques to the CIM. Here we focus on a ferromagnetic model and a finite loading Hopfield model, which are canonical models sharing a common mathematical structure with almost all other Ising models. We derive macroscopic equations to capture nonequilibrium phase transitions in these models. The statistical mechanical methods developed here constitute a basis for constructing evaluation methods for other Ising computation models.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

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

Czech Academy of Sciences Publication Activity Database

Tarasenko, Alexander

2018-01-01

Roč. 95, Jan (2018), s. 37-40 ISSN 1386-9477 R&D Projects: GA MŠk LO1409; GA MŠk LM2015088 Institutional support: RVO:68378271 Keywords : lattice gas systems * kinetic Monte Carlo simulations * diffusion and migration Subject RIV: BE - Theoretical Physics OBOR OECD: Atomic, molecular and chemical physics (physics of atoms and molecules including collision, interaction with radiation, magnetic resonances, Mössbauer effect) Impact factor: 2.221, year: 2016

Degenerate Ising model for atomistic simulation of crystal-melt interfaces

Energy Technology Data Exchange (ETDEWEB)

Schebarchov, D., E-mail: Dmitri.Schebarchov@gmail.com [University Chemical Laboratories, Lensfield Road, Cambridge CB2 1EW (United Kingdom); Schulze, T. P., E-mail: schulze@math.utk.edu [Department of Mathematics, University of Tennessee, Knoxville, Tennessee 37996-1300 (United States); Hendy, S. C. [The MacDiarmid Institute for Advanced Materials and Nanotechnology, School of Chemical and Physical Sciences, Victoria University of Wellington, Wellington 6140 (New Zealand); Department of Physics, University of Auckland, Auckland 1010 (New Zealand)

2014-02-21

One of the simplest microscopic models for a thermally driven first-order phase transition is an Ising-type lattice system with nearest-neighbour interactions, an external field, and a degeneracy parameter. The underlying lattice and the interaction coupling constant control the anisotropic energy of the phase boundary, the field strength represents the bulk latent heat, and the degeneracy quantifies the difference in communal entropy between the two phases. We simulate the (stochastic) evolution of this minimal model by applying rejection-free canonical and microcanonical Monte Carlo algorithms, and we obtain caloric curves and heat capacity plots for square (2D) and face-centred cubic (3D) lattices with periodic boundary conditions. Since the model admits precise adjustment of bulk latent heat and communal entropy, neither of which affect the interface properties, we are able to tune the crystal nucleation barriers at a fixed degree of undercooling and verify a dimension-dependent scaling expected from classical nucleation theory. We also analyse the equilibrium crystal-melt coexistence in the microcanonical ensemble, where we detect negative heat capacities and find that this phenomenon is more pronounced when the interface is the dominant contributor to the total entropy. The negative branch of the heat capacity appears smooth only when the equilibrium interface-area-to-volume ratio is not constant but varies smoothly with the excitation energy. Finally, we simulate microcanonical crystal nucleation and subsequent relaxation to an equilibrium Wulff shape, demonstrating the model's utility in tracking crystal-melt interfaces at the atomistic level.

Degenerate Ising model for atomistic simulation of crystal-melt interfaces

International Nuclear Information System (INIS)

Schebarchov, D.; Schulze, T. P.; Hendy, S. C.

2014-01-01

One of the simplest microscopic models for a thermally driven first-order phase transition is an Ising-type lattice system with nearest-neighbour interactions, an external field, and a degeneracy parameter. The underlying lattice and the interaction coupling constant control the anisotropic energy of the phase boundary, the field strength represents the bulk latent heat, and the degeneracy quantifies the difference in communal entropy between the two phases. We simulate the (stochastic) evolution of this minimal model by applying rejection-free canonical and microcanonical Monte Carlo algorithms, and we obtain caloric curves and heat capacity plots for square (2D) and face-centred cubic (3D) lattices with periodic boundary conditions. Since the model admits precise adjustment of bulk latent heat and communal entropy, neither of which affect the interface properties, we are able to tune the crystal nucleation barriers at a fixed degree of undercooling and verify a dimension-dependent scaling expected from classical nucleation theory. We also analyse the equilibrium crystal-melt coexistence in the microcanonical ensemble, where we detect negative heat capacities and find that this phenomenon is more pronounced when the interface is the dominant contributor to the total entropy. The negative branch of the heat capacity appears smooth only when the equilibrium interface-area-to-volume ratio is not constant but varies smoothly with the excitation energy. Finally, we simulate microcanonical crystal nucleation and subsequent relaxation to an equilibrium Wulff shape, demonstrating the model's utility in tracking crystal-melt interfaces at the atomistic level

Highly optimized simulations on single- and multi-GPU systems of the 3D Ising spin glass model

Science.gov (United States)

Lulli, M.; Bernaschi, M.; Parisi, G.

2015-11-01

We present a highly optimized implementation of a Monte Carlo (MC) simulator for the three-dimensional Ising spin-glass model with bimodal disorder, i.e., the 3D Edwards-Anderson model running on CUDA enabled GPUs. Multi-GPU systems exchange data by means of the Message Passing Interface (MPI). The chosen MC dynamics is the classic Metropolis one, which is purely dissipative, since the aim was the study of the critical off-equilibrium relaxation of the system. We focused on the following issues: (i) the implementation of efficient memory access patterns for nearest neighbours in a cubic stencil and for lagged-Fibonacci-like pseudo-Random Numbers Generators (PRNGs); (ii) a novel implementation of the asynchronous multispin-coding Metropolis MC step allowing to store one spin per bit and (iii) a multi-GPU version based on a combination of MPI and CUDA streams. Cubic stencils and PRNGs are two subjects of very general interest because of their widespread use in many simulation codes.

Physics and financial economics (1776-2014): puzzles, Ising and agent-based models

Science.gov (United States)

Sornette, Didier

2014-06-01

This short review presents a selected history of the mutual fertilization between physics and economics—from Isaac Newton and Adam Smith to the present. The fundamentally different perspectives embraced in theories developed in financial economics compared with physics are dissected with the examples of the volatility smile and of the excess volatility puzzle. The role of the Ising model of phase transitions to model social and financial systems is reviewed, with the concepts of random utilities and the logit model as the analog of the Boltzmann factor in statistical physics. Recent extensions in terms of quantum decision theory are also covered. A wealth of models are discussed briefly that build on the Ising model and generalize it to account for the many stylized facts of financial markets. A summary of the relevance of the Ising model and its extensions is provided to account for financial bubbles and crashes. The review would be incomplete if it did not cover the dynamical field of agent-based models (ABMs), also known as computational economic models, of which the Ising-type models are just special ABM implementations. We formulate the ‘Emerging Intelligence Market Hypothesis’ to reconcile the pervasive presence of ‘noise traders’ with the near efficiency of financial markets. Finally, we note that evolutionary biology, more than physics, is now playing a growing role to inspire models of financial markets.

Physics and financial economics (1776-2014): puzzles, Ising and agent-based models.

Science.gov (United States)

Sornette, Didier

2014-06-01

This short review presents a selected history of the mutual fertilization between physics and economics--from Isaac Newton and Adam Smith to the present. The fundamentally different perspectives embraced in theories developed in financial economics compared with physics are dissected with the examples of the volatility smile and of the excess volatility puzzle. The role of the Ising model of phase transitions to model social and financial systems is reviewed, with the concepts of random utilities and the logit model as the analog of the Boltzmann factor in statistical physics. Recent extensions in terms of quantum decision theory are also covered. A wealth of models are discussed briefly that build on the Ising model and generalize it to account for the many stylized facts of financial markets. A summary of the relevance of the Ising model and its extensions is provided to account for financial bubbles and crashes. The review would be incomplete if it did not cover the dynamical field of agent-based models (ABMs), also known as computational economic models, of which the Ising-type models are just special ABM implementations. We formulate the 'Emerging Intelligence Market Hypothesis' to reconcile the pervasive presence of 'noise traders' with the near efficiency of financial markets. Finally, we note that evolutionary biology, more than physics, is now playing a growing role to inspire models of financial markets.

J{sub 1x}-J{sub 1y}-J{sub 2} square-lattice anisotropic Heisenberg model

Energy Technology Data Exchange (ETDEWEB)

Pires, A.S.T., E-mail: antpires@frisica.ufmg.br

2017-08-01

Highlights: • We use the SU(3) Schwinger boson formalism. • We present the phase diagram at zero temperature. • We calculate the quadrupole structure factor. - Abstract: The spin one Heisenberg model with an easy-plane single-ion anisotropy and spatially anisotropic nearest-neighbor coupling, frustrated by a next-nearest neighbor interaction, is studied at zero temperature using a SU(3) Schwinger boson formalism (sometimes also referred to as flavor wave theory) in a mean field approximation. The local constraint is enforced by introducing a Lagrange multiplier. The enlarged Hilbert space of S = 1 spins lead to a nematic phase that is ubiquitous to S = 1 spins with single ion anisotropy. The phase diagram shows two magnetically ordered phase, separated by a quantum paramagnetic (nematic) phase.

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

Science.gov (United States)

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.

Neighbor-dependent Ramachandran probability distributions of amino acids developed from a hierarchical Dirichlet process model.

Directory of Open Access Journals (Sweden)

Daniel Ting

2010-04-01

Full Text Available Distributions of the backbone dihedral angles of proteins have been studied for over 40 years. While many statistical analyses have been presented, only a handful of probability densities are publicly available for use in structure validation and structure prediction methods. The available distributions differ in a number of important ways, which determine their usefulness for various purposes. These include: 1 input data size and criteria for structure inclusion (resolution, R-factor, etc.; 2 filtering of suspect conformations and outliers using B-factors or other features; 3 secondary structure of input data (e.g., whether helix and sheet are included; whether beta turns are included; 4 the method used for determining probability densities ranging from simple histograms to modern nonparametric density estimation; and 5 whether they include nearest neighbor effects on the distribution of conformations in different regions of the Ramachandran map. In this work, Ramachandran probability distributions are presented for residues in protein loops from a high-resolution data set with filtering based on calculated electron densities. Distributions for all 20 amino acids (with cis and trans proline treated separately have been determined, as well as 420 left-neighbor and 420 right-neighbor dependent distributions. The neighbor-independent and neighbor-dependent probability densities have been accurately estimated using Bayesian nonparametric statistical analysis based on the Dirichlet process. In particular, we used hierarchical Dirichlet process priors, which allow sharing of information between densities for a particular residue type and different neighbor residue types. The resulting distributions are tested in a loop modeling benchmark with the program Rosetta, and are shown to improve protein loop conformation prediction significantly. The distributions are available at http://dunbrack.fccc.edu/hdp.

ReliefSeq: a gene-wise adaptive-K nearest-neighbor feature selection tool for finding gene-gene interactions and main effects in mRNA-Seq gene expression data.

Directory of Open Access Journals (Sweden)

Brett A McKinney

Full Text Available Relief-F is a nonparametric, nearest-neighbor machine learning method that has been successfully used to identify relevant variables that may interact in complex multivariate models to explain phenotypic variation. While several tools have been developed for assessing differential expression in sequence-based transcriptomics, the detection of statistical interactions between transcripts has received less attention in the area of RNA-seq analysis. We describe a new extension and assessment of Relief-F for feature selection in RNA-seq data. The ReliefSeq implementation adapts the number of nearest neighbors (k for each gene to optimize the Relief-F test statistics (importance scores for finding both main effects and interactions. We compare this gene-wise adaptive-k (gwak Relief-F method with standard RNA-seq feature selection tools, such as DESeq and edgeR, and with the popular machine learning method Random Forests. We demonstrate performance on a panel of simulated data that have a range of distributional properties reflected in real mRNA-seq data including multiple transcripts with varying sizes of main effects and interaction effects. For simulated main effects, gwak-Relief-F feature selection performs comparably to standard tools DESeq and edgeR for ranking relevant transcripts. For gene-gene interactions, gwak-Relief-F outperforms all comparison methods at ranking relevant genes in all but the highest fold change/highest signal situations where it performs similarly. The gwak-Relief-F algorithm outperforms Random Forests for detecting relevant genes in all simulation experiments. In addition, Relief-F is comparable to the other methods based on computational time. We also apply ReliefSeq to an RNA-Seq study of smallpox vaccine to identify gene expression changes between vaccinia virus-stimulated and unstimulated samples. ReliefSeq is an attractive tool for inclusion in the suite of tools used for analysis of mRNA-Seq data; it has power to

A Nearest Neighbor Classifier Employing Critical Boundary Vectors for Efficient On-Chip Template Reduction.

Science.gov (United States)

Xia, Wenjun; Mita, Yoshio; Shibata, Tadashi

2016-05-01

Aiming at efficient data condensation and improving accuracy, this paper presents a hardware-friendly template reduction (TR) method for the nearest neighbor (NN) classifiers by introducing the concept of critical boundary vectors. A hardware system is also implemented to demonstrate the feasibility of using an field-programmable gate array (FPGA) to accelerate the proposed method. Initially, k -means centers are used as substitutes for the entire template set. Then, to enhance the classification performance, critical boundary vectors are selected by a novel learning algorithm, which is completed within a single iteration. Moreover, to remove noisy boundary vectors that can mislead the classification in a generalized manner, a global categorization scheme has been explored and applied to the algorithm. The global characterization automatically categorizes each classification problem and rapidly selects the boundary vectors according to the nature of the problem. Finally, only critical boundary vectors and k -means centers are used as the new template set for classification. Experimental results for 24 data sets show that the proposed algorithm can effectively reduce the number of template vectors for classification with a high learning speed. At the same time, it improves the accuracy by an average of 2.17% compared with the traditional NN classifiers and also shows greater accuracy than seven other TR methods. We have shown the feasibility of using a proof-of-concept FPGA system of 256 64-D vectors to accelerate the proposed method on hardware. At a 50-MHz clock frequency, the proposed system achieves a 3.86 times higher learning speed than on a 3.4-GHz PC, while consuming only 1% of the power of that used by the PC.

Precision islands in the Ising and O(N) models

Energy Technology Data Exchange (ETDEWEB)

Kos, Filip [Department of Physics, Yale University, New Haven, CT 06520 (United States); Poland, David [Department of Physics, Yale University, New Haven, CT 06520 (United States); School of Natural Sciences, Institute for Advanced Study, Princeton, New Jersey 08540 (United States); Simmons-Duffin, David [School of Natural Sciences, Institute for Advanced Study, Princeton, New Jersey 08540 (United States); Vichi, Alessandro [Theory Division, CERN, Geneva (Switzerland)

2016-08-04

We make precise determinations of the leading scaling dimensions and operator product expansion (OPE) coefficients in the 3d Ising, O(2), and O(3) models from the conformal bootstrap with mixed correlators. We improve on previous studies by scanning over possible relative values of the leading OPE coefficients, which incorporates the physical information that there is only a single operator at a given scaling dimension. The scaling dimensions and OPE coefficients obtained for the 3d Ising model, (Δ{sub σ},Δ{sub ϵ},λ{sub σσϵ},λ{sub ϵϵϵ})=(0.5181489(10),1.412625(10),1.0518537(41),1.532435(19)), give the most precise determinations of these quantities to date.

Precision Islands in the Ising and $O(N)$ Models

CERN Document Server

Kos, Filip; Simmons-Duffin, David; Vichi, Alessandro

2016-01-01

We make precise determinations of the leading scaling dimensions and operator product expansion (OPE) coefficients in the 3d Ising, $O(2)$, and $O(3)$ models from the conformal bootstrap with mixed correlators. We improve on previous studies by scanning over possible relative values of the leading OPE coefficients, which incorporates the physical information that there is only a single operator at a given scaling dimension. The scaling dimensions and OPE coefficients obtained for the 3d Ising model, $(\\Delta_{\\sigma}, \\Delta_{\\epsilon},\\lambda_{\\sigma\\sigma\\epsilon}, \\lambda_{\\epsilon\\epsilon\\epsilon}) = (0.5181489(10), 1.412625(10), 1.0518537(41), 1.532435(19))$, give the most precise determinations of these quantities to date.

Ising models and soliton equations

International Nuclear Information System (INIS)

Perk, J.H.H.; Au-Yang, H.

1985-01-01

Several new results for the critical point of correlation functions of the Hirota equation are derived within the two-dimensional Ising model. The recent success of the conformal-invariance approach in the determination of a critical two-spin correration function is analyzed. The two-spin correlation function is predicted to be rotationally invariant and to decay with a power law in this approach. In the approach suggested here systematic corrections due to the underlying lattice breaking the rotational invariance are obtained

Estimating Stand Height and Tree Density in Pinus taeda plantations using in-situ data, airborne LiDAR and k-Nearest Neighbor Imputation

Directory of Open Access Journals (Sweden)

CARLOS ALBERTO SILVA

Full Text Available ABSTRACT Accurate forest inventory is of great economic importance to optimize the entire supply chain management in pulp and paper companies. The aim of this study was to estimate stand dominate and mean heights (HD and HM and tree density (TD of Pinus taeda plantations located in South Brazil using in-situ measurements, airborne Light Detection and Ranging (LiDAR data and the non- k-nearest neighbor (k-NN imputation. Forest inventory attributes and LiDAR derived metrics were calculated at 53 regular sample plots and we used imputation models to retrieve the forest attributes at plot and landscape-levels. The best LiDAR-derived metrics to predict HD, HM and TD were H99TH, HSD, SKE and HMIN. The Imputation model using the selected metrics was more effective for retrieving height than tree density. The model coefficients of determination (adj.R2 and a root mean squared difference (RMSD for HD, HM and TD were 0.90, 0.94, 0.38m and 6.99, 5.70, 12.92%, respectively. Our results show that LiDAR and k-NN imputation can be used to predict stand heights with high accuracy in Pinus taeda. However, furthers studies need to be realized to improve the accuracy prediction of TD and to evaluate and compare the cost of acquisition and processing of LiDAR data against the conventional inventory procedures.

Data-driven method based on particle swarm optimization and k-nearest neighbor regression for estimating capacity of lithium-ion battery

International Nuclear Information System (INIS)

Hu, Chao; Jain, Gaurav; Zhang, Puqiang; Schmidt, Craig; Gomadam, Parthasarathy; Gorka, Tom

2014-01-01

Highlights: • We develop a data-driven method for the battery capacity estimation. • Five charge-related features that are indicative of the capacity are defined. • The kNN regression model captures the dependency of the capacity on the features. • Results with 10 years’ continuous cycling data verify the effectiveness of the method. - Abstract: Reliability of lithium-ion (Li-ion) rechargeable batteries used in implantable medical devices has been recognized as of high importance from a broad range of stakeholders, including medical device manufacturers, regulatory agencies, physicians, and patients. To ensure Li-ion batteries in these devices operate reliably, it is important to be able to assess the battery health condition by estimating the battery capacity over the life-time. This paper presents a data-driven method for estimating the capacity of Li-ion battery based on the charge voltage and current curves. The contributions of this paper are three-fold: (i) the definition of five characteristic features of the charge curves that are indicative of the capacity, (ii) the development of a non-linear kernel regression model, based on the k-nearest neighbor (kNN) regression, that captures the complex dependency of the capacity on the five features, and (iii) the adaptation of particle swarm optimization (PSO) to finding the optimal combination of feature weights for creating a kNN regression model that minimizes the cross validation (CV) error in the capacity estimation. Verification with 10 years’ continuous cycling data suggests that the proposed method is able to accurately estimate the capacity of Li-ion battery throughout the whole life-time

Model for the high-temperature oxygen-ordering thermodynamics in YBa2Cu3O6+x - inclusion of electron spin and charge degrees of freedom

DEFF Research Database (Denmark)

Schleger, P.; Hardy, W.N.; Casalta, H.

1994-01-01

A lattice-gas model for the high temperature oxygen-ordering thermodynamics in YBa2Cu3O6+x is presented, which assumes constant effective pair interactions between oxygen atoms and includes in a simple fashion the effect of the electron spin and charge degrees of freedom. This is done using...... a commonly utilized picture relating the creation of mobile electron holes and unpaired spins to the insertion of oxygen into the basal plane. The model is solved using the nearest-neighbor square approximation of the cluster-variation method. In addition, preliminary Monte Carlo results using next......-nearest-neighbor interactions are presented. The model is compared to experimental results for the thermodynamic response function, kT (partial derivative x/partial derivative mu)T (mu is the chemical potential), the number of monovalent copper atoms, and the fractional site occupancies. The model drastically improves...

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

Science.gov (United States)

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.

Exact sampling hardness of Ising spin models

Science.gov (United States)

Fefferman, B.; Foss-Feig, M.; Gorshkov, A. V.

2017-09-01

We study the complexity of classically sampling from the output distribution of an Ising spin model, which can be implemented naturally in a variety of atomic, molecular, and optical systems. In particular, we construct a specific example of an Ising Hamiltonian that, after time evolution starting from a trivial initial state, produces a particular output configuration with probability very nearly proportional to the square of the permanent of a matrix with arbitrary integer entries. In a similar spirit to boson sampling, the ability to sample classically from the probability distribution induced by time evolution under this Hamiltonian would imply unlikely complexity theoretic consequences, suggesting that the dynamics of such a spin model cannot be efficiently simulated with a classical computer. Physical Ising spin systems capable of achieving problem-size instances (i.e., qubit numbers) large enough so that classical sampling of the output distribution is classically difficult in practice may be achievable in the near future. Unlike boson sampling, our current results only imply hardness of exact classical sampling, leaving open the important question of whether a much stronger approximate-sampling hardness result holds in this context. The latter is most likely necessary to enable a convincing experimental demonstration of quantum supremacy. As referenced in a recent paper [A. Bouland, L. Mancinska, and X. Zhang, in Proceedings of the 31st Conference on Computational Complexity (CCC 2016), Leibniz International Proceedings in Informatics (Schloss Dagstuhl-Leibniz-Zentrum für Informatik, Dagstuhl, 2016)], our result completes the sampling hardness classification of two-qubit commuting Hamiltonians.

Next neighbors effect along the Ca-Sr-Ba-åkermanite join: Long-range vs. short-range structural features

Science.gov (United States)

Dondi, Michele; Ardit, Matteo; Cruciani, Giuseppe

2013-06-01

An original approach has been developed herein to explore the correlations between short- and long-range structural properties of solid solutions. X-ray diffraction (XRD) and electronic absorption spectroscopy (EAS) data were combined on a (Ca,Sr,Ba)2(Mg0.7Co0.3)Si2O7 join to determine average and local distances, respectively. Instead of varying the EAS-active ion concentration along the join, as has commonly been performed in previous studies, the constant replacement of Mg2+ by a minimal fraction of a similar size cation (Co2+) has been used to assess the effects of varying second-nearest neighbor cations (Ca, Sr, Ba) on the local distances of the first shell. A comparison between doped and un-doped series has shown that, although the overall symmetry of the Co-centered T1-site was retained, greater relaxation occurs at the CoO4 tetrahedra which become increasingly large and more distorted than the MgO4 tetrahedra. This is indicated by an increase in both the quadratic elongation (λT1) and the bond angle variance (σ2T1) distortion indices, as the whole structure expands due to an increase in size in the second-nearest neighbors. This behavior highlights the effect of the different electronic configurations of Co2+ (3d7) and Mg2+ (2p6) in spite of their very similar ionic size. Furthermore, although the overall symmetry of the Co-centered T1-site is retained, relatively limited (Co2+-O occur along the solid solution series and large changes are found in molar absorption coefficients showing that EAS Co2+-bands are highly sensitive to change in the local structure.

Single-file water as a one-dimensional Ising model

Energy Technology Data Exchange (ETDEWEB)

Koefinger, Juergen [Laboratory of Chemical Physics, Bldg 5, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892 (United States); Dellago, Christoph, E-mail: koefingerj@mail.nih.go [Faculty of Physics, University of Vienna, Boltzmanngasse 5, 1090 Vienna (Austria)

2010-09-15

We show that single-file water in nanopores can be viewed as a one-dimensional (1D) Ising model, and we investigate, on the basis of this, the static dielectric response of a chain of hydrogen-bonded water molecules to an external field. To achieve this, we use a recently developed dipole lattice model that accurately captures the free energetics of nanopore water. In this model, the total energy of the system can be expressed as the sum of the effective interactions of chain ends and orientational defects. Neglecting these interactions, we essentially obtain the 1D Ising model, which allows us to derive analytical expressions for the free energy as a function of the total dipole moment and for the dielectric susceptibility. Our expressions, which agree very well with simulation results, provide the basis for the interpretation of future dielectric spectroscopy experiments on water-filled nanopore membranes.

Dynamical quantum phase transitions in extended transverse Ising models

Science.gov (United States)

Bhattacharjee, Sourav; Dutta, Amit

2018-04-01

We study the dynamical quantum phase transitions (DQPTs) manifested in the subsequent unitary dynamics of an extended Ising model with an additional three spin interactions following a sudden quench. Revisiting the equilibrium phase diagram of the model, where different quantum phases are characterized by different winding numbers, we show that in some situations the winding number may not change across a gap closing point in the energy spectrum. Although, usually there exists a one-to-one correspondence between the change in winding number and the number of critical time scales associated with DQPTs, we show that the extended nature of interactions may lead to unusual situations. Importantly, we show that in the limit of the cluster Ising model, three critical modes associated with DQPTs become degenerate, thereby leading to a single critical time scale for a given sector of Fisher zeros.

Long-range transverse Ising model built with dipolar condensates in two-well arrays

International Nuclear Information System (INIS)

Li, Yongyao; Pang, Wei; Xu, Jun; Lee, Chaohong; Malomed, Boris A; Santos, Luis

2017-01-01

Dipolar Bose–Einstein condensates in an array of double-well potentials realize an effective transverse Ising model with peculiar inter-layer interactions, that may result under proper conditions in an anomalous first-order ferromagnetic–antiferromagnetic phase transition, and non-trivial phases due to frustration. The considered setup allows as well for the study of Kibble–Zurek defect formation, whose kink statistics follows that expected from the universality class of the mean-field one-dimensional transverse Ising model. Furthermore, random occupation of each layer of the stack leads to random effective Ising interactions and local transverse fields, that may lead to the Anderson-like localization of imbalance perturbations. (paper)

Physics and financial economics (1776–2014): puzzles, Ising and agent-based models

International Nuclear Information System (INIS)

Sornette, Didier

2014-01-01

This short review presents a selected history of the mutual fertilization between physics and economics—from Isaac Newton and Adam Smith to the present. The fundamentally different perspectives embraced in theories developed in financial economics compared with physics are dissected with the examples of the volatility smile and of the excess volatility puzzle. The role of the Ising model of phase transitions to model social and financial systems is reviewed, with the concepts of random utilities and the logit model as the analog of the Boltzmann factor in statistical physics. Recent extensions in terms of quantum decision theory are also covered. A wealth of models are discussed briefly that build on the Ising model and generalize it to account for the many stylized facts of financial markets. A summary of the relevance of the Ising model and its extensions is provided to account for financial bubbles and crashes. The review would be incomplete if it did not cover the dynamical field of agent-based models (ABMs), also known as computational economic models, of which the Ising-type models are just special ABM implementations. We formulate the ‘Emerging Intelligence Market Hypothesis’ to reconcile the pervasive presence of ‘noise traders’ with the near efficiency of financial markets. Finally, we note that evolutionary biology, more than physics, is now playing a growing role to inspire models of financial markets. (key issues reviews)

Diagonalization of replicated transfer matrices for disordered Ising spin systems

International Nuclear Information System (INIS)

Nikoletopoulos, T; Coolen, A C C

2004-01-01

We present an alternative procedure for solving the eigenvalue problem of replicated transfer matrices describing disordered spin systems with (random) 1D nearest neighbour bonds and/or random fields, possibly in combination with (random) long range bonds. Our method is based on transforming the original eigenvalue problem for a 2 n x 2 n matrix (where n → 0) into an eigenvalue problem for integral operators. We first develop our formalism for the Ising chain with random bonds and fields, where we recover known results. We then apply our methods to models of spins which interact simultaneously via a one-dimensional ring and via more complex long-range connectivity structures, e.g., (1 + ∞)-dimensional neural networks and 'small-world' magnets. Numerical simulations confirm our predictions satisfactorily

d = 2 transverse-field Ising model under the screw-boundary condition: an optimization of the screw pitch

International Nuclear Information System (INIS)

Nishiyama, Yoshihiro

2011-01-01

A length-N spin chain with the √N(=v)th neighbor interaction is identical to a two-dimensional (d = 2) model under the screw-boundary (SB) condition. The SB condition provides a flexible scheme to construct a d ≥ 2 cluster from an arbitrary number of spins; the numerical diagonalization combined with the SB condition admits a potential applicability to a class of systems intractable with the quantum Monte Carlo method due to the negative-sign problem. However, the simulation results suffer from characteristic finite-size corrections inherent in SB. In order to suppress these corrections, we adjust the screw pitch v(N) so as to minimize the excitation gap for each N. This idea is adapted to the transverse-field Ising model on the triangular lattice with N ≤ 32 spins. As a demonstration, the correlation-length critical exponent ν is analyzed in some detail

Monte Carlo Simulations of Compressible Ising Models: Do We Understand Them?

Science.gov (United States)

Landau, D. P.; Dünweg, B.; Laradji, M.; Tavazza, F.; Adler, J.; Cannavaccioulo, L.; Zhu, X.

Extensive Monte Carlo simulations have begun to shed light on our understanding of phase transitions and universality classes for compressible Ising models. A comprehensive analysis of a Landau-Ginsburg-Wilson hamiltonian for systems with elastic degrees of freedom resulted in the prediction that there should be four distinct cases that would have different behavior, depending upon symmetries and thermodynamic constraints. We shall provide an account of the results of careful Monte Carlo simulations for a simple compressible Ising model that can be suitably modified so as to replicate all four cases.

Anisotropic ordering in a two-temperature lattice gas

DEFF Research Database (Denmark)

Szolnoki, Attila; Szabó, György; Mouritsen, Ole G.

1997-01-01

We consider a two-dimensional lattice gas model with repulsive nearest- and next-nearest-neighbor interactions that evolves in time according to anisotropic Kawasaki dynamics. The hopping of particles along the principal directions is governed by two heat baths at different temperatures T-x and T...

Dynamic compensation temperature in the kinetic spin-1 Ising model in an oscillating external magnetic field on alternate layers of a hexagonal lattice

International Nuclear Information System (INIS)

Temizer, Umuet; Keskin, Mustafa; Canko, Osman

2009-01-01

The dynamic behavior of a two-sublattice spin-1 Ising model with a crystal-field interaction (D) in the presence of a time-varying magnetic field on a hexagonal lattice is studied by using the Glauber-type stochastic dynamics. The lattice is formed by alternate layers of spins σ=1 and S=1. For this spin arrangement, any spin at one lattice site has two nearest-neighbor spins on the same sublattice, and four on the other sublattice. The intersublattice interaction is antiferromagnetic. We employ the Glauber transition rates to construct the mean-field dynamical equations. Firstly, we study time variations of the average magnetizations in order to find the phases in the system, and the temperature dependence of the average magnetizations in a period, which is also called the dynamic magnetizations, to obtain the dynamic phase transition (DPT) points as well as to characterize the nature (continuous and discontinuous) of transitions. Then, the behavior of the total dynamic magnetization as a function of the temperature is investigated to find the types of the compensation behavior. Dynamic phase diagrams are calculated for both DPT points and dynamic compensation effect. Phase diagrams contain the paramagnetic (p) and antiferromagnetic (af) phases, the p+af and nm+p mixed phases, nm is the non-magnetic phase, and the compensation temperature or the L-type behavior that strongly depend on the interaction parameters. For D 0 >3.8275, H 0 is the magnetic field amplitude, the compensation effect does not appear in the system.

Dynamic compensation temperature in the kinetic spin-1 Ising model in an oscillating external magnetic field on alternate layers of a hexagonal lattice

Energy Technology Data Exchange (ETDEWEB)

Temizer, Umuet [Department of Physics, Bozok University, 66100 Yozgat (Turkey); Keskin, Mustafa [Department of Physics, Erciyes University, 38039 Kayseri (Turkey)], E-mail: keskin@erciyes.edu.tr; Canko, Osman [Department of Physics, Erciyes University, 38039 Kayseri (Turkey)

2009-10-15

The dynamic behavior of a two-sublattice spin-1 Ising model with a crystal-field interaction (D) in the presence of a time-varying magnetic field on a hexagonal lattice is studied by using the Glauber-type stochastic dynamics. The lattice is formed by alternate layers of spins {sigma}=1 and S=1. For this spin arrangement, any spin at one lattice site has two nearest-neighbor spins on the same sublattice, and four on the other sublattice. The intersublattice interaction is antiferromagnetic. We employ the Glauber transition rates to construct the mean-field dynamical equations. Firstly, we study time variations of the average magnetizations in order to find the phases in the system, and the temperature dependence of the average magnetizations in a period, which is also called the dynamic magnetizations, to obtain the dynamic phase transition (DPT) points as well as to characterize the nature (continuous and discontinuous) of transitions. Then, the behavior of the total dynamic magnetization as a function of the temperature is investigated to find the types of the compensation behavior. Dynamic phase diagrams are calculated for both DPT points and dynamic compensation effect. Phase diagrams contain the paramagnetic (p) and antiferromagnetic (af) phases, the p+af and nm+p mixed phases, nm is the non-magnetic phase, and the compensation temperature or the L-type behavior that strongly depend on the interaction parameters. For D<2.835 and H{sub 0}>3.8275, H{sub 0} is the magnetic field amplitude, the compensation effect does not appear in the system.

High temperature limit of the order parameter correlation functions in the quantum Ising model

Science.gov (United States)

Reyes, S. A.; Tsvelik, A. M.

2006-06-01

In this paper we use the exact results for the anisotropic two-dimensional Ising model obtained by Bugrii and Lisovyy [A.I. Bugrii, O.O. Lisovyy, Theor. Math. Phys. 140 (2004) 987] to derive the expressions for dynamical correlation functions for the quantum Ising model in one dimension at high temperatures.

High temperature limit of the order parameter correlation functions in the quantum Ising model

Energy Technology Data Exchange (ETDEWEB)

Reyes, S.A. [Department of Physics and Astronomy, SUNY at Stony Brook, Stony Brook, NY 11794-3840 (United States); Department of Condensed Matter Physics and Materials Science, Brookhaven National Laboratory, Upton, NY 11973-5000 (United States); Tsvelik, A.M. [Department of Physics and Astronomy, SUNY at Stony Brook, Stony Brook, NY 11794-3840 (United States) and Department of Condensed Matter Physics and Materials Science, Brookhaven National Laboratory, Upton, NY 11973-5000 (United States)]. E-mail tsvelik@bnl.gov

2006-06-12

In this paper we use the exact results for the anisotropic two-dimensional Ising model obtained by Bugrii and Lisovyy [A.I. Bugrii, O.O. Lisovyy, Theor. Math. Phys. 140 (2004) 987] to derive the expressions for dynamical correlation functions for the quantum Ising model in one dimension at high temperatures.

A novel method for the detection of R-peaks in ECG based on K-Nearest Neighbors and Particle Swarm Optimization

Science.gov (United States)

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.

Specific heat of the simple-cubic Ising model

NARCIS (Netherlands)

Feng, X.; Blöte, H.W.J.

2010-01-01

We provide an expression quantitatively describing the specific heat of the Ising model on the simple-cubic lattice in the critical region. This expression is based on finite-size scaling of numerical results obtained by means of a Monte Carlo method. It agrees satisfactorily with series expansions

Model of directed lines for square ice with second-neighbor and third-neighbor interactions

Science.gov (United States)

Kirov, Mikhail V.

2018-02-01

The investigation of the properties of nanoconfined systems is one of the most rapidly developing scientific fields. Recently it has been established that water monolayer between two graphene sheets forms square ice. Because of the energetic disadvantage, in the structure of the square ice there are no longitudinally arranged molecules. The result is that the structure is formed by unidirectional straight-lines of hydrogen bonds only. A simple but accurate discrete model of square ice with second-neighbor and third-neighbor interactions is proposed. According to this model, the ground state includes all configurations which do not contain three neighboring unidirectional chains of hydrogen bonds. Each triplet increases the energy by the same value. This new model differs from an analogous model with long-range interactions where in the ground state all neighboring chains are antiparallel. The new model is suitable for the corresponding system of point electric (and magnetic) dipoles on the square lattice. It allows separately estimating the different contributions to the total binding energy and helps to understand the properties of infinite monolayers and finite nanostructures. Calculations of the binding energy for square ice and for point dipole system are performed using the packages TINKER and LAMMPS.

Genus-two characters of the Ising model

International Nuclear Information System (INIS)

Choi, J.H.; Koh, I.G.

1989-01-01

As a first step in studying conformal theories on a higher-genus Riemann surface, we construct genus-two characters of the Ising model from their behavior in zero- and nonzero-homology pinching limits, the Goddard-Kent-Oliveco set-space construction, and the branching coefficients in the level-two A 1 /sup (1)/ Kac-Moody characters on the higher-genus Riemann surface

Oxygen ordering in YBa2Cu3O6+x using Monte Carlo simulation and analytic theory

DEFF Research Database (Denmark)

Mønster, D.; Lindgård, Per-Anker; Andersen, N.H.

2001-01-01

We have simulated the phase diagram and structural properties of the oxygen ordering in YBa2Cu3O6+x testing simple extensions of the asymmetric next-nearest-neighbor Ising (ASYNNNI) Model. In a preliminary paper [Phys. Rev. B 60, 110 (1999)] we demonstrated that the inclusion of a single further...... on a nano scale into box-like domains and anti-domains of typical average dimension (10a,30b,2c). Theory and model simulations demonstrate that the distribution of such domains causes deviations from Lorentzian line shapes, and not the Porod effect. Analytic theory is used to estimate the effect of a range...... of values of the interaction parameters used, as well as the effect of an extension to include infinite ranged interactions. In the experiments a large cap is found between the onset temperatures of the ortho-I and ortho-II orders at x=0.5. This cannot be fully reproduced in the simulations. The simulations...

Particles and scaling for lattice fields and Ising models

International Nuclear Information System (INIS)

Glimm, J.; Jaffe, A.

1976-01-01

The conjectured inequality GAMMA 6 4 -fields and the scaling limit for d-dimensional Ising models. Assuming GAMMA 6 = 6 these phi 4 fields are free fields unless the field strength renormalization Z -1 diverges. (orig./BJ) [de

Quantum Ising model on hierarchical structures

International Nuclear Information System (INIS)

Lin Zhifang; Tao Ruibao.

1989-11-01

A quantum Ising chain with both the exchange couplings and the transverse fields arranged in a hierarchical way is considered. Exact analytical results for the critical line and energy gap are obtained. It is shown that when R 1 not= R 2 , where R 1 and R 2 are the hierarchical parameters for the exchange couplings and the transverse fields, respectively, the system undergoes a phase transition in a different universality class from the pure quantum Ising chain with R 1 =R 2 =1. On the other hand, when R 1 =R 2 =R, there exists a critical value R c dependent on the furcating number of the hierarchy. In case of R > R c , the system is shown to exhibit as Ising-like critical point with the critical behaviour the same as in the pure case, while for R c the system belongs to another universality class. (author). 19 refs, 2 figs

Dynamics of the Random Field Ising Model

Science.gov (United States)

Xu, Jian

The Random Field Ising Model (RFIM) is a general tool to study disordered systems. Crackling noise is generated when disordered systems are driven by external forces, spanning a broad range of sizes. Systems with different microscopic structures such as disordered mag- nets and Earth's crust have been studied under the RFIM. In this thesis, we investigated the domain dynamics and critical behavior in two dipole-coupled Ising ferromagnets Nd2Fe14B and LiHoxY 1-xF4. With Tc well above room temperature, Nd2Fe14B has shown reversible disorder when exposed to an external transverse field and crosses between two universality classes in the strong and weak disorder limits. Besides tunable disorder, LiHoxY1-xF4 has shown quantum tunneling effects arising from quantum fluctuations, providing another mechanism for domain reversal. Universality within and beyond power law dependence on avalanche size and energy were studied in LiHo0.65Y0.35 F4.

Spotlighting quantum critical points via quantum correlations at finite temperatures

International Nuclear Information System (INIS)

Werlang, T.; Ribeiro, G. A. P.; Rigolin, Gustavo

2011-01-01

We extend the program initiated by T. Werlang et al. [Phys. Rev. Lett. 105, 095702 (2010)] in several directions. Firstly, we investigate how useful quantum correlations, such as entanglement and quantum discord, are in the detection of critical points of quantum phase transitions when the system is at finite temperatures. For that purpose we study several thermalized spin models in the thermodynamic limit, namely, the XXZ model, the XY model, and the Ising model, all of which with an external magnetic field. We compare the ability of quantum discord, entanglement, and some thermodynamic quantities to spotlight the quantum critical points for several different temperatures. Secondly, for some models we go beyond nearest neighbors and also study the behavior of entanglement and quantum discord for second nearest neighbors around the critical point at finite temperature. Finally, we furnish a more quantitative description of how good all these quantities are in spotlighting critical points of quantum phase transitions at finite T, bridging the gap between experimental data and those theoretical descriptions solely based on the unattainable absolute zero assumption.

The ising model on the dynamical triangulated random surface

International Nuclear Information System (INIS)

Aleinov, I.D.; Migelal, A.A.; Zmushkow, U.V.

1990-01-01

The critical properties of Ising model on the dynamical triangulated random surface embedded in D-dimensional Euclidean space are investigated. The strong coupling expansion method is used. The transition to thermodynamical limit is performed by means of continuous fractions

Near Neighbor Distribution in Sets of Fractal Nature

Czech Academy of Sciences Publication Activity Database

Jiřina, Marcel

2013-01-01

Roč. 5, č. 1 (2013), s. 159-166 ISSN 2150-7988 R&D Projects: GA MŠk(CZ) LG12020 Institutional support: RVO:67985807 Keywords : nearest neighbor * fractal set * multifractal * Erlang distribution Subject RIV: BB - Applied Statistics, Operational Research http://www.mirlabs.org/ijcisim/regular_papers_2013/Paper91.pdf

From tricritical Ising to critical Ising by thermodynamic Bethe ansatz

International Nuclear Information System (INIS)

Zamolodchikov, A.B.

1991-01-01

A simple factorized scattering theory is suggested for the massless Goldstone fermions of the trajectory flowing from the tricritical Ising fixed point to the critical Ising one. The thermodynamic Bethe ansatz approach is applied to this scattering theory to support its interpretation both analytically and numerically. As a generalization a sequence of massless TBA systems is proposed which seems relevant for the trajectories interpolating between two successive minimal CFT models M p and M p-1 . (orig.)

Quasi-realistic distribution of interaction fields leading to a variant of Ising spin glass model

International Nuclear Information System (INIS)

Tanasa, Radu; Enachescu, Cristian; Stancu, Alexandru; Linares, Jorge; Varret, Francois

2004-01-01

The distribution of interaction fields of an Ising-like system, obtained by Monte Carlo entropic sampling is used for modeling the hysteretic behavior of patterned media made of magnetic particles with a common anisotropy axis; a variant of the canonical Edwards-Anderson Ising spin glass model is introduced

Performance modeling of neighbor discovery in proactive routing protocols

Directory of Open Access Journals (Sweden)

Andres Medina

2011-07-01

Full Text Available It is well known that neighbor discovery is a critical component of proactive routing protocols in wireless ad hoc networks. However there is no formal study on the performance of proposed neighbor discovery mechanisms. This paper provides a detailed model of key performance metrics of neighbor discovery algorithms, such as node degree and the distribution of the distance to symmetric neighbors. The model accounts for the dynamics of neighbor discovery as well as node density, mobility, radio and interference. The paper demonstrates a method for applying these models to the evaluation of global network metrics. In particular, it describes a model of network connectivity. Validation of the models shows that the degree estimate agrees, within 5% error, with simulations for the considered scenarios. The work presented in this paper serves as a basis for the performance evaluation of remaining performance metrics of routing protocols, vital for large scale deployment of ad hoc networks.

Nonequilibrium relaxations within the ground-state manifold in the antiferromagnetic Ising model on a triangular lattice.

Science.gov (United States)

Kim, Eunhye; Lee, Sung Jong; Kim, Bongsoo

2007-02-01

We present an extensive Monte Carlo simulation study on the nonequilibrium kinetics of triangular antiferromagnetic Ising model within the ground state ensemble which consists of sectors, each of which is characterized by a unique value of the string density p through a dimer covering method. Building upon our recent work [Phys. Rev. E 68, 066127 (2003)] where we considered the nonequilibrium relaxation observed within the dominant sector with p=2/3, we here focus on the nonequilibrium kinetics within the minor sectors with psimple scaling behavior A(t)=A(t/tau(A)(p)), where the time scale tau(A)(p) shows a power-law divergence with vanishing p as tau(A)(p) approximately p(-phi) with phi approximately or equal to 4. These features can be understood in terms of random walk nature of the fluctuations of the strings within the typical separation between neighboring strings.

Mapping of the Bak, Tang, and Wiesenfeld sandpile model on a two-dimensional Ising-correlated percolation lattice to the two-dimensional self-avoiding random walk

Science.gov (United States)

Cheraghalizadeh, J.; Najafi, M. N.; Dashti-Naserabadi, H.; Mohammadzadeh, H.

2017-11-01

The self-organized criticality on the random fractal networks has many motivations, like the movement pattern of fluid in the porous media. In addition to the randomness, introducing correlation between the neighboring portions of the porous media has some nontrivial effects. In this paper, we consider the Ising-like interactions between the active sites as the simplest method to bring correlations in the porous media, and we investigate the statistics of the BTW model in it. These correlations are controlled by the artificial "temperature" T and the sign of the Ising coupling. Based on our numerical results, we propose that at the Ising critical temperature Tc the model is compatible with the universality class of two-dimensional (2D) self-avoiding walk (SAW). Especially the fractal dimension of the loops, which are defined as the external frontier of the avalanches, is very close to DfSAW=4/3 . Also, the corresponding open curves has conformal invariance with the root-mean-square distance Rrms˜t3 /4 (t being the parametrization of the curve) in accordance with the 2D SAW. In the finite-size study, we observe that at T =Tc the model has some aspects compatible with the 2D BTW model (e.g., the 1 /log(L ) -dependence of the exponents of the distribution functions) and some in accordance with the Ising model (e.g., the 1 /L -dependence of the fractal dimensions). The finite-size scaling theory is tested and shown to be fulfilled for all statistical observables in T =Tc . In the off-critical temperatures in the close vicinity of Tc the exponents show some additional power-law behaviors in terms of T -Tc with some exponents that are reported in the text. The spanning cluster probability at the critical temperature also scales with L1/2, which is different from the regular 2D BTW model.

Computational Analysis of 3D Ising Model Using Metropolis Algorithms

International Nuclear Information System (INIS)

Sonsin, A F; Cortes, M R; Nunes, D R; Gomes, J V; Costa, R S

2015-01-01

We simulate the Ising Model with the Monte Carlo method and use the algorithms of Metropolis to update the distribution of spins. We found that, in the specific case of the three-dimensional Ising Model, methods of Metropolis are efficient. Studying the system near the point of phase transition, we observe that the magnetization goes to zero. In our simulations we analyzed the behavior of the magnetization and magnetic susceptibility to verify the phase transition in a paramagnetic to ferromagnetic material. The behavior of the magnetization and of the magnetic susceptibility as a function of the temperature suggest a phase transition around KT/J ≈ 4.5 and was evidenced the problem of finite size of the lattice to work with large lattice. (paper)

Critical Behavior of the Annealed Ising Model on Random Regular Graphs

Science.gov (United States)

Can, Van Hao

2017-11-01

In Giardinà et al. (ALEA Lat Am J Probab Math Stat 13(1):121-161, 2016), the authors have defined an annealed Ising model on random graphs and proved limit theorems for the magnetization of this model on some random graphs including random 2-regular graphs. Then in Can (Annealed limit theorems for the Ising model on random regular graphs, arXiv:1701.08639, 2017), we generalized their results to the class of all random regular graphs. In this paper, we study the critical behavior of this model. In particular, we determine the critical exponents and prove a non standard limit theorem stating that the magnetization scaled by n^{3/4} converges to a specific random variable, with n the number of vertices of random regular graphs.

Efficient and accurate nearest neighbor and closest pair search in high-dimensional space

KAUST Repository

Tao, Yufei

2010-07-01

Nearest Neighbor (NN) search in high-dimensional space is an important problem in many applications. From the database perspective, a good solution needs to have two properties: (i) it can be easily incorporated in a relational database, and (ii) its query cost should increase sublinearly with the dataset size, regardless of the data and query distributions. Locality-Sensitive Hashing (LSH) is a well-known methodology fulfilling both requirements, but its current implementations either incur expensive space and query cost, or abandon its theoretical guarantee on the quality of query results. Motivated by this, we improve LSH by proposing an access method called the Locality-Sensitive B-tree (LSB-tree) to enable fast, accurate, high-dimensional NN search in relational databases. The combination of several LSB-trees forms a LSB-forest that has strong quality guarantees, but improves dramatically the efficiency of the previous LSH implementation having the same guarantees. In practice, the LSB-tree itself is also an effective index which consumes linear space, supports efficient updates, and provides accurate query results. In our experiments, the LSB-tree was faster than: (i) iDistance (a famous technique for exact NN search) by two orders ofmagnitude, and (ii) MedRank (a recent approximate method with nontrivial quality guarantees) by one order of magnitude, and meanwhile returned much better results. As a second step, we extend our LSB technique to solve another classic problem, called Closest Pair (CP) search, in high-dimensional space. The long-term challenge for this problem has been to achieve subquadratic running time at very high dimensionalities, which fails most of the existing solutions. We show that, using a LSB-forest, CP search can be accomplished in (worst-case) time significantly lower than the quadratic complexity, yet still ensuring very good quality. In practice, accurate answers can be found using just two LSB-trees, thus giving a substantial

Multiple Time Series Ising Model for Financial Market Simulations

International Nuclear Information System (INIS)

Takaishi, Tetsuya

2015-01-01

In this paper we propose an Ising model which simulates multiple financial time series. Our model introduces the interaction which couples to spins of other systems. Simulations from our model show that time series exhibit the volatility clustering that is often observed in the real financial markets. Furthermore we also find non-zero cross correlations between the volatilities from our model. Thus our model can simulate stock markets where volatilities of stocks are mutually correlated

Ising model for neural data

DEFF Research Database (Denmark)

Roudi, Yasser; Tyrcha, Joanna; Hertz, John

2009-01-01

(dansk abstrakt findes ikke) We study pairwise Ising models for describing the statistics of multi-neuron spike trains, using data from a simulated cortical network. We explore efficient ways of finding the optimal couplings in these models and examine their statistical properties. To do this, we...... extract the optimal couplings for subsets of size up to $200$ neurons, essentially exactly, using Boltzmann learning. We then study the quality of several approximate methods for finding the couplings by comparing their results with those found from Boltzmann learning. Two of these methods -- inversion...... of the Thouless-Anderson-Palmer equations and an approximation proposed by Sessak and Monasson -- are remarkably accurate. Using these approximations for larger subsets of neurons, we find that extracting couplings using data from a subset smaller than the full network tends systematically to overestimate...

Dynamics of the directed Ising chain

International Nuclear Information System (INIS)

Godrèche, Claude

2011-01-01

The study by Glauber of the time-dependent statistics of the Ising chain is extended to the case where each spin is influenced unequally by its nearest neighbours. The asymmetry of the dynamics implies the failure of the detailed balance condition. The functional form of the rate at which an individual spin changes its state is constrained by the global balance condition with respect to the equilibrium measure of the Ising chain. The local magnetization, the equal-time and two-time correlation functions and the linear response to an external magnetic field obey linear equations which are solved explicitly. The behaviour of these quantities and the relation between the correlation and response functions are analysed both in the stationary state and in the zero-temperature scaling regime. In the stationary state, a transition between two behaviours of the correlation function occurs when the amplitude of the asymmetry crosses a critical value, with the consequence that the limit fluctuation-dissipation ratio decays continuously from the value 1, for the equilibrium state in the absence of asymmetry, to 0 for this critical value. At zero temperature, under asymmetric dynamics, the system loses its critical character, yet keeping many of the characteristic features of a coarsening system

Improved Multiscale Entropy Technique with Nearest-Neighbor Moving-Average Kernel for Nonlinear and Nonstationary Short-Time Biomedical Signal Analysis

Directory of Open Access Journals (Sweden)

S. P. Arunachalam

2018-01-01

Full Text Available Analysis of biomedical signals can yield invaluable information for prognosis, diagnosis, therapy evaluation, risk assessment, and disease prevention which is often recorded as short time series data that challenges existing complexity classification algorithms such as Shannon entropy (SE and other techniques. The purpose of this study was to improve previously developed multiscale entropy (MSE technique by incorporating nearest-neighbor moving-average kernel, which can be used for analysis of nonlinear and non-stationary short time series physiological data. The approach was tested for robustness with respect to noise analysis using simulated sinusoidal and ECG waveforms. Feasibility of MSE to discriminate between normal sinus rhythm (NSR and atrial fibrillation (AF was tested on a single-lead ECG. In addition, the MSE algorithm was applied to identify pivot points of rotors that were induced in ex vivo isolated rabbit hearts. The improved MSE technique robustly estimated the complexity of the signal compared to that of SE with various noises, discriminated NSR and AF on single-lead ECG, and precisely identified the pivot points of ex vivo rotors by providing better contrast between the rotor core and the peripheral region. The improved MSE technique can provide efficient complexity analysis of variety of nonlinear and nonstationary short-time biomedical signals.

Large-Scale Mapping of Carbon Stocks in Riparian Forests with Self-Organizing Maps and the k-Nearest-Neighbor Algorithm

Directory of Open Access Journals (Sweden)

Leonhard Suchenwirth

2014-07-01

Full Text Available Among the machine learning tools being used in recent years for environmental applications such as forestry, self-organizing maps (SOM and the k-nearest neighbor (kNN algorithm have been used successfully. We applied both methods for the mapping of organic carbon (Corg in riparian forests due to their considerably high carbon storage capacity. Despite the importance of floodplains for carbon sequestration, a sufficient scientific foundation for creating large-scale maps showing the spatial Corg distribution is still missing. We estimated organic carbon in a test site in the Danube Floodplain based on RapidEye remote sensing data and additional geodata. Accordingly, carbon distribution maps of vegetation, soil, and total Corg stocks were derived. Results were compared and statistically evaluated with terrestrial survey data for outcomes with pure remote sensing data and for the combination with additional geodata using bias and the Root Mean Square Error (RMSE. Results show that SOM and kNN approaches enable us to reproduce spatial patterns of riparian forest Corg stocks. While vegetation Corg has very high RMSEs, outcomes for soil and total Corg stocks are less biased with a lower RMSE, especially when remote sensing and additional geodata are conjointly applied. SOMs show similar percentages of RMSE to kNN estimations.

Relaxation theory of spin-3/2 Ising system near phase transition temperatures

International Nuclear Information System (INIS)

Canko, Osman; Keskin, Mustafa

2010-01-01

Dynamics of a spin-3/2 Ising system Hamiltonian with bilinear and biquadratic nearest-neighbour exchange interactions is studied by a simple method in which the statistical equilibrium theory is combined with the Onsager's theory of irreversible thermodynamics. First, the equilibrium behaviour of the model in the molecular-field approximation is given briefly in order to obtain the phase transition temperatures, i.e. the first- and second-order and the tricritical points. Then, the Onsager theory is applied to the model and the kinetic or rate equations are obtained. By solving these equations three relaxation times are calculated and their behaviours are examined for temperatures near the phase transition points. Moreover, the z dynamic critical exponent is calculated and compared with the z values obtained for different systems experimentally and theoretically, and they are found to be in good agrement. (general)

Susceptibility and magnetization of a random Ising model

Energy Technology Data Exchange (ETDEWEB)

Kumar, D; Srivastava, V [Roorkee Univ. (India). Dept. of Physics

1977-08-01

The susceptibility of a bond disordered Ising model is calculated by configurationally averaging an Ornstein-Zernike type of equation for the two spin correlation function. The equation for the correlation function is derived using a diagrammatic method due to Englert. The averaging is performed using bond CPA. The magnetization is also calculated by averaging in a similar manner a linearised molecular field equation.

Zero-temperature renormalization of the 2D transverse Ising model

International Nuclear Information System (INIS)

Kamieniarz, G.

1982-08-01

A zero-temperature real-space renormalization-group method is applied to the transverse Ising model on planar hexagonal, triangular and quadratic lattices. The critical fields and the critical exponents describing low-field large-field transition are calculated. (author)

Large Deviations for the Annealed Ising Model on Inhomogeneous Random Graphs: Spins and Degrees

Science.gov (United States)

Dommers, Sander; Giardinà, Cristian; Giberti, Claudio; Hofstad, Remco van der

2018-04-01

We prove a large deviations principle for the total spin and the number of edges under the annealed Ising measure on generalized random graphs. We also give detailed results on how the annealing over the Ising model changes the degrees of the vertices in the graph and show how it gives rise to interesting correlated random graphs.

Anisotropic Heisenberg model for a semi-infinite crystal

International Nuclear Information System (INIS)

Queiroz, C.A.

1985-11-01

A semi-infinite Heisenberg model with exchange interactions between nearest and next-nearest neighbors in a simple cubic lattice. The free surface from the other layers of magnetic ions, by choosing a single ion uniaxial anisotropy in the surface (Ds) different from the anisotropy in the other layers (D). Using the Green function formalism, the behavior of magnetization as a function of the temperature for each layer, as well as the spectrum localized magnons for several values of ratio Ds/D for surface magnetization. Above this critical ratio, a ferromagnetic surface layer is obtained white the other layers are already in the paramagnetic phase. In this situation the critical temperature of surface becomes larger than the critical temperature of the bulk. (Author) [pt

Localized magnetic excitations for a line of magnetic impurities in a transverse Ising thin film ferromagnet

International Nuclear Information System (INIS)

Leite, R.V.; Oliveira Filho, L.O. de; Milton Pereira, J.; Cottam, M.G.; Costa Filho, R.N.

2009-01-01

A Green's function method is used to obtain the spectrum of spin excitations associated with a linear array of magnetic impurities implanted in a ferromagnetic thin film. The equations of motion for the Green's functions of the anisotropic film are written in the framework of the Ising model in a transverse field. The frequencies of localized modes are calculated as a function of the interaction parameters for the exchange coupling between impurity-spin pairs, host-spin pairs, and impurity-host neighbors, as well as the effective field parameter at the impurity sites.

Canonical vs. micro-canonical sampling methods in a 2D Ising model

International Nuclear Information System (INIS)

Kepner, J.

1990-12-01

Canonical and micro-canonical Monte Carlo algorithms were implemented on a 2D Ising model. Expressions for the internal energy, U, inverse temperature, Z, and specific heat, C, are given. These quantities were calculated over a range of temperature, lattice sizes, and time steps. Both algorithms accurately simulate the Ising model. To obtain greater than three decimal accuracy from the micro-canonical method requires that the more complicated expression for Z be used. The overall difference between the algorithms is small. The physics of the problem under study should be the deciding factor in determining which algorithm to use. 13 refs., 6 figs., 2 tabs

Bayesian Modeling of ChIP-chip Data Through a High-Order Ising Model

KAUST Repository

Mo, Qianxing; Liang, Faming

2010-01-01

approach to ChIP-chip data through an Ising model with high-order interactions. The proposed method naturally takes into account the intrinsic spatial structure of the data and can be used to analyze data from multiple platforms with different genomic

An analysis of intergroup rivalry using Ising model and reinforcement learning

Science.gov (United States)

Zhao, Feng-Fei; Qin, Zheng; Shao, Zhuo

2014-01-01

Modeling of intergroup rivalry can help us better understand economic competitions, political elections and other similar activities. The result of intergroup rivalry depends on the co-evolution of individual behavior within one group and the impact from the rival group. In this paper, we model the rivalry behavior using Ising model. Different from other simulation studies using Ising model, the evolution rules of each individual in our model are not static, but have the ability to learn from historical experience using reinforcement learning technique, which makes the simulation more close to real human behavior. We studied the phase transition in intergroup rivalry and focused on the impact of the degree of social freedom, the personality of group members and the social experience of individuals. The results of computer simulation show that a society with a low degree of social freedom and highly educated, experienced individuals is more likely to be one-sided in intergroup rivalry.

Equivalent charge source model based iterative maximum neighbor weight for sparse EEG source localization.

Science.gov (United States)

Xu, Peng; Tian, Yin; Lei, Xu; Hu, Xiao; Yao, Dezhong

2008-12-01

How to localize the neural electric activities within brain effectively and precisely from the scalp electroencephalogram (EEG) recordings is a critical issue for current study in clinical neurology and cognitive neuroscience. In this paper, based on the charge source model and the iterative re-weighted strategy, proposed is a new maximum neighbor weight based iterative sparse source imaging method, termed as CMOSS (Charge source model based Maximum neighbOr weight Sparse Solution). Different from the weight used in focal underdetermined system solver (FOCUSS) where the weight for each point in the discrete solution space is independently updated in iterations, the new designed weight for each point in each iteration is determined by the source solution of the last iteration at both the point and its neighbors. Using such a new weight, the next iteration may have a bigger chance to rectify the local source location bias existed in the previous iteration solution. The simulation studies with comparison to FOCUSS and LORETA for various source configurations were conducted on a realistic 3-shell head model, and the results confirmed the validation of CMOSS for sparse EEG source localization. Finally, CMOSS was applied to localize sources elicited in a visual stimuli experiment, and the result was consistent with those source areas involved in visual processing reported in previous studies.

Tricritical Ising model with a boundary

International Nuclear Information System (INIS)

De Martino, A.; Moriconi, M.

1998-03-01

We study the integrable and supersymmetric massive φ (1,3) deformation of the tricritical Ising model in the presence of a boundary. We use constraints from supersymmetry in order to compute the exact boundary S-matrices, which turn out to depend explicitly on the topological charge of the supersymmetry algebra. We also solve the general boundary Yang-Baxter equation and show that in appropriate limits the general reflection matrices go over the supersymmetry preserving solutions. Finally, we briefly discuss the possible connection between our reflection matrices and boundary perturbations within the framework of perturbed boundary conformal field theory. (author)

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

Science.gov (United States)

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.

The spin S quantum Ising model at T=0

International Nuclear Information System (INIS)

Kamieniarz, G.; Kowalewski, L.; Piechocki, W.

1982-09-01

The Ising model with a transverse field for a general spin S is investigated within the framework of the Green-function method in the paramagnetic region at T=0. The analysis of selfconsistent equations gives a description of softmode phase transition as well as extrapolated values of critical fields and critical energy gap exponents. (author)

NMR evidence of a gapless chiral phase in the S=1 zigzag antiferromagnet CaV2O4

International Nuclear Information System (INIS)

Fukushima, Hiroyuki; Kikuchi, Hikomitsu; Chiba, Meiro; Fujii, Yutaka; Yamamoto, Yoshiyuki; Hori, Hidenobu

2002-01-01

We have performed magnetic susceptibility and 51 V NMR experiments with CaV 2 O 4 , a model substance for a frustrated S=1 spin chain with competing nearest neighbor (NN) and next-nearest neighbor (NNN) antiferromagnetic interactions. We report on the analysis of the magnetic susceptibility and the 51 V NMR experiments suggesting a gapless nature of CaV 2 O 4 . The absence of a spin gap is in clear contrast to the case of a non-frustrated spin chains which usually have a Haldane gap. (author)

On the quantum symmetry of the chiral Ising model

Science.gov (United States)

Vecsernyés, Peter

1994-03-01

We introduce the notion of rational Hopf algebras that we think are able to describe the superselection symmetries of rational quantum field theories. As an example we show that a six-dimensional rational Hopf algebra H can reproduce the fusion rules, the conformal weights, the quantum dimensions and the representation of the modular group of the chiral Ising model. H plays the role of the global symmetry algebra of the chiral Ising model in the following sense: (1) a simple field algebra F and a representation π on Hπ of it is given, which contains the c = {1}/{2} unitary representations of the Virasoro algebra as subrepresentations; (2) the embedding U: H → B( Hπ) is such that the observable algebra π( A) - is the invariant subalgebra of B( Hπ) with respect to the left adjoint action of H and U(H) is the commutant of π( A); (3) there exist H-covariant primary fields in B( Hπ), which obey generalized Cuntz algebra properties and intertwine between the inequivalent sectors of the observables.

Comparison of Two Classifiers; K-Nearest Neighbor and Artificial Neural Network, for Fault Diagnosis on a Main Engine Journal-Bearing

Directory of Open Access Journals (Sweden)

A. Moosavian

2013-01-01

Full Text Available Vibration analysis is an accepted method in condition monitoring of machines, since it can provide useful and reliable information about machine working condition. This paper surveys a new scheme for fault diagnosis of main journal-bearings of internal combustion (IC engine based on power spectral density (PSD technique and two classifiers, namely, K-nearest neighbor (KNN and artificial neural network (ANN. Vibration signals for three different conditions of journal-bearing; normal, with oil starvation condition and extreme wear fault were acquired from an IC engine. PSD was applied to process the vibration signals. Thirty features were extracted from the PSD values of signals as a feature source for fault diagnosis. KNN and ANN were trained by training data set and then used as diagnostic classifiers. Variable K value and hidden neuron count (N were used in the range of 1 to 20, with a step size of 1 for KNN and ANN to gain the best classification results. The roles of PSD, KNN and ANN techniques were studied. From the results, it is shown that the performance of ANN is better than KNN. The experimental results dèmonstrate that the proposed diagnostic method can reliably separate different fault conditions in main journal-bearings of IC engine.

The dilute random field Ising model by finite cluster approximation

International Nuclear Information System (INIS)

Benyoussef, A.; Saber, M.

1987-09-01

Using the finite cluster approximation, phase diagrams of bond and site diluted three-dimensional simple cubic Ising models with a random field have been determined. The resulting phase diagrams have the same general features for both bond and site dilution. (author). 7 refs, 4 figs

Generic Ising trees

DEFF Research Database (Denmark)

Durhuus, Bergfinnur Jøgvan; Napolitano, George Maria

2012-01-01

The Ising model on a class of infinite random trees is defined as a thermodynamiclimit of finite systems. A detailed description of the corresponding distribution of infinite spin configurations is given. As an application, we study the magnetization properties of such systems and prove that they......The Ising model on a class of infinite random trees is defined as a thermodynamiclimit of finite systems. A detailed description of the corresponding distribution of infinite spin configurations is given. As an application, we study the magnetization properties of such systems and prove...... that they exhibit no spontaneous magnetization. Furthermore, the values of the Hausdorff and spectral dimensions of the underlying trees are calculated and found to be, respectively,¯dh =2 and¯ds = 4/3....

Ising model of financial markets with many assets

Science.gov (United States)

Eckrot, A.; Jurczyk, J.; Morgenstern, I.

2016-11-01

Many models of financial markets exist, but most of them simulate single asset markets. We study a multi asset Ising model of a financial market. Each agent has two possible actions (buy/sell) for every asset. The agents dynamically adjust their coupling coefficients according to past market returns and external news. This leads to fat tails and volatility clustering independent of the number of assets. We find that a separation of news into different channels leads to sector structures in the cross correlations, similar to those found in real markets.

Q-deformed Grassmann field and the two-dimensional Ising model

International Nuclear Information System (INIS)

Bugrij, A.I.; Shadura, V.N.

1994-01-01

In this paper we construct the exact representation of the Ising partition function in form of the SL q (2,R)-invariant functional integral for the lattice free q-fermion field theory (q=-1). It is shown that the proposed method of q-fermionization allows one to re-express the partition function of the eight vertex model in external field through the functional integral with four-fermion interaction. For the construction of these representation we define a lattice (l,q,s)-deformed Grassmann bi spinor field and extend the Berezin integration rules for this field. At q = - 1, l = s 1 we obtain the lattice q-fermion field which allows to fermionize the two-dimensional Ising model. We show that Gaussian integral over (q,s)-Grassmann variables is expressed through the (q,s)-deformed Pfaffian which is equal to square root of the determinant of some matrix at q = ± 1, s = ±1. (author). 39 refs

Correlation effects in the Ising model in an external field

International Nuclear Information System (INIS)

Borges, H.E.; Silva, P.R.

1983-01-01

The thermodynamic properties of the spin-1/2 Ising Model in an external field are evaluated through the use of the exponential differential operator method and Callen's exact relations. The correlations effects are treated in a phenomenological approach and the results are compared with other treatments. (Author) [pt

Flocking with discrete symmetry: The two-dimensional active Ising model.

Science.gov (United States)

Solon, A P; Tailleur, J

2015-10-01

We study in detail the active Ising model, a stochastic lattice gas where collective motion emerges from the spontaneous breaking of a discrete symmetry. On a two-dimensional lattice, active particles undergo a diffusion biased in one of two possible directions (left and right) and align ferromagnetically their direction of motion, hence yielding a minimal flocking model with discrete rotational symmetry. We show that the transition to collective motion amounts in this model to a bona fide liquid-gas phase transition in the canonical ensemble. The phase diagram in the density-velocity parameter plane has a critical point at zero velocity which belongs to the Ising universality class. In the density-temperature "canonical" ensemble, the usual critical point of the equilibrium liquid-gas transition is sent to infinite density because the different symmetries between liquid and gas phases preclude a supercritical region. We build a continuum theory which reproduces qualitatively the behavior of the microscopic model. In particular, we predict analytically the shapes of the phase diagrams in the vicinity of the critical points, the binodal and spinodal densities at coexistence, and the speeds and shapes of the phase-separated profiles.

Ising tricriticality in the extended Hubbard model with bond dimerization

Science.gov (United States)

Fehske, Holger; Ejima, Satoshi; Lange, Florian; Essler, Fabian H. L.

We explore the quantum phase transition between Peierls and charge-density-wave insulating states in the one-dimensional, half-filled, extended Hubbard model with explicit bond dimerization. We show that the critical line of the continuous Ising transition terminates at a tricritical point, belonging to the universality class of the tricritical Ising model with central charge c=7/10. Above this point, the quantum phase transition becomes first order. Employing a numerical matrix-product-state based (infinite) density-matrix renormalization group method we determine the ground-state phase diagram, the spin and two-particle charge excitations gaps, and the entanglement properties of the model with high precision. Performing a bosonization analysis we can derive a field description of the transition region in terms of a triple sine-Gordon model. This allows us to derive field theory predictions for the power-law (exponential) decay of the density-density (spin-spin) and bond-order-wave correlation functions, which are found to be in excellent agreement with our numerical results. This work was supported by Deutsche Forschungsgemeinschaft (Germany), SFB 652, project B5, and by the EPSRC under Grant No. EP/N01930X/1 (FHLE).

Phase transitions in the random field Ising model in the presence of a transverse field

Energy Technology Data Exchange (ETDEWEB)

Dutta, A.; Chakrabarti, B.K. [Saha Institute of Nuclear Physics, Bidhannagar, Calcutta (India); Stinchcombe, R.B. [Saha Institute of Nuclear Physics, Bidhannagar, Calcutta (India); Department of Physics, Oxford (United Kingdom)

1996-09-07

We have studied the phase transition behaviour of the random field Ising model in the presence of a transverse (or tunnelling) field. The mean field phase diagram has been studied in detail, and in particular the nature of the transition induced by the tunnelling (transverse) field at zero temperature. Modified hyper-scaling relation for the zero-temperature transition has been derived using the Suzuki-Trotter formalism and a modified 'Harris criterion'. Mapping of the model to a randomly diluted antiferromagnetic Ising model in uniform longitudinal and transverse field is also given. (author)

Nearest Neighbor Queries in Road Networks

DEFF Research Database (Denmark)

Jensen, Christian Søndergaard; Kolar, Jan; Pedersen, Torben Bach

2003-01-01

in road networks. Such queries may be of use in many services. Specifically, we present an easily implementable data model that serves well as a foundation for such queries. We also present the design of a prototype system that implements the queries based on the data model. The algorithm used...

Beyond Proxima b: Characterizing the X-ray Environments of the Next Three Nearest Potentially Habitable Exoplanets

Science.gov (United States)

Guinan, Edward

2017-09-01

The discovery that Prox Cen hosts a potentially habitable Earth-size planet motivated the study of the next three nearest HZ planets, whose host stars all lie within 4.5 pc. X-ray observations are requested of Kapteyn s Star = KS (M1 V; 3.9 pc), Wolf 1061 (M3V; 4.3 pc), and a third not yet publicly announced star discussed in the proposal. KS hosts a HZ super-earth: Kapteyn b ( 4.8 ME). Wolf 1061 hosts three super-earths - one in the HZ: Wolf 1061c ( 4.3 ME). Unlike Prox Cen s extensive X-ray observations (> 400 ks), the X-ray properties of these stars are poorly constrained. In our study of Prox b (Ribas et al. 2016), Prox Cen s X-ray (& UV) radiation strongly affect (via photoionization/photo-dissociation) the planet s atmosphere, water inventory & ultimate habitability.

Quantum-information approach to the Ising model: Entanglement in chains of qubits

International Nuclear Information System (INIS)

Stelmachovic, Peter; Buzek, Vladimir

2004-01-01

Simple physical interactions between spin-1/2 particles may result in quantum states that exhibit exotic correlations that are difficult to find if one simply explores state spaces of multipartite systems. In particular, we present a detailed investigation of the well-known Ising model of a chain (ring) of spin-1/2 particles (qubits) in a transverse magnetic field. We present explicit expressions for eigenstates of the model Hamiltonian for arbitrary number of spin-1/2 particles in the chain in the standard (computer) basis, and we investigate quantum entanglement between individual qubits. We analyze bipartite as well as multipartite entanglement in the ground state of the model. In particular, we show that bipartite entanglement between pairs of qubits of the Ising chain (measured in terms of a concurrence) as a function of the parameter λ has a maximum around the point λ=1, and it monotonically decreases for large values of λ. We prove that in the limit λ→∞ this state is locally unitary equivalent to an N-partite Greenberger-Horn-Zeilinger state. We also analyze a very specific eigenstate of the Ising Hamiltonian with a zero eigenenergy (we denote this eigenstate as the X-state). This X-state exhibits the 'extreme' entanglement in a sense that an arbitrary subset A of k≤n qubits in the Ising chain composed of N=2n+1 qubits is maximally entangled with the remaining qubits (set B) in the chain. In addition, we prove that by performing a local operation just on the subset B, one can transform the X-state into a direct product of k singlets shared by the parties A and B. This property of the X-state can be utilized for new secure multipartite communication protocols

Observation and analysis of nanodomain textures in dielectric relaxor lead magnesium niobate

International Nuclear Information System (INIS)

Bursill, L.A.; Qian, Hua; Peng, Julin; Fan, X.D.

1995-01-01

High-resolution (0.2nm) images are used to locate chemical domains occurring with length scales of 1-5nm in the dielectric relaxor lead magnesium niobate (PMN). The experimental HRTEM images are analysed using computer-simulations and image matching in order to clarify and characterize the nature of the chemical ordering. Madelung electrostatic energy calculations are used to rank a set of structural models for possible ordered and disordered distributions of Nb and Mg over the B-sites of perovskite ABO 3 . Next, the chemical domain textures are modelled using next-nearest-neighbour Ising (NNNI) models and Monte Carlo methods. This results in a preferred model for the B-site distribution (the extended NNN-Ising model), which is used for image simulations. Both HRTEM many-beam bright-and dark-field and single-beam dark-field TEM images are obtained and compared with the experimental images. The final result is a realistic atomic model for the Nb, Mg distribution of PMN. 42 refs., 2 tabs., 10 figs

Strong coupling expansion for scattering phases in hamiltonian lattice field theories. Pt. 1. The (d+1)-dimensional Ising model

International Nuclear Information System (INIS)

Dahmen, Bernd

1994-01-01

A systematic method to obtain strong coupling expansions for scattering quantities in hamiltonian lattice field theories is presented. I develop the conceptual ideas for the case of the hamiltonian field theory analogue of the Ising model, in d space and one time dimension. The main result is a convergent series representation for the scattering states and the transition matrix. To be explicit, the special cases of d=1 and d=3 spatial dimensions are discussed in detail. I compute the next-to-leading order approximation for the phase shifts. The application of the method to investigate low-energy scattering phenomena in lattice gauge theory and QCD is proposed. ((orig.))

Tightness of the Ising-Kac Model on the Two-Dimensional Torus

Science.gov (United States)

Hairer, Martin; Iberti, Massimo

2018-05-01

We consider the sequence of Gibbs measures of Ising models with Kac interaction defined on a periodic two-dimensional discrete torus near criticality. Using the convergence of the Glauber dynamic proven by Mourrat and Weber (Commun Pure Appl Math 70:717-812, 2017) and a method by Tsatsoulis and Weber employed in (arXiv:1609.08447 2016), we show tightness for the sequence of Gibbs measures of the Ising-Kac model near criticality and characterise the law of the limit as the Φ ^4_2 measure on the torus. Our result is very similar to the one obtained by Cassandro et al. (J Stat Phys 78(3):1131-1138, 1995) on Z^2, but our strategy takes advantage of the dynamic, instead of correlation inequalities. In particular, our result covers the whole critical regime and does not require the large temperature/large mass/small coupling assumption present in earlier results.

Ising model on tangled chain - 1: Free energy and entropy

International Nuclear Information System (INIS)

Mejdani, R.

1993-04-01

In this paper we have considered an Ising model defined on tangled chain, in which more bonds have been added to those of pure Ising chain. to understand their competition, particularly between ferromagnetic and antiferromagnetic bonds, we have studied, using the transfer matrix method, some simple analytical calculations and an iterative algorithm, the behaviour of the free energy and entropy, particularly in the zero-field and zero temperature limit, for different configurations of the ferromagnetic tangled chain and different types of addition interaction (ferromagnetic or antiferromagnetic). We found that the condition J=J' between the ferromagnetic interaction J along the chain and the antiferromagnetic interaction J' across the chain is somewhat as a ''transition-region'' condition for this behaviour. Our results indicate also the existence of non-zero entropy at zero temperature. (author). 17 refs, 8 figs

Modeling of the financial market using the two-dimensional anisotropic Ising model

Science.gov (United States)

Lima, L. S.

2017-09-01

We have used the two-dimensional classical anisotropic Ising model in an external field and with an ion single anisotropy term as a mathematical model for the price dynamics of the financial market. The model presented allows us to test within the same framework the comparative explanatory power of rational agents versus irrational agents with respect to the facts of financial markets. We have obtained the mean price in terms of the strong of the site anisotropy term Δ which reinforces the sensitivity of the agent's sentiment to external news.

Semi-local invariance in Ising models with multi-spin interaction

International Nuclear Information System (INIS)

Lipowski, A.

1996-08-01

We examine implications of semi-local invariance in Ising models with multispin interaction. In ergodic models all spin-spin correlation functions vanish and the local symmetry is the same as in locally gauge-invariant models. The d = 3 model with four-spin interaction is nonergodic at low temperature but the magnetic symmetry remains unbroken. The d = 3 model with eight-spin interaction is ergodic but undergoes the phase transition and most likely its low-temperature phase is characterized by a nonlocal order parameter. (author). 7 refs, 1 fig

Ground state properties of a spin chain within Heisenberg model with a single lacking spin site

International Nuclear Information System (INIS)

Mebrouki, M.

2011-01-01

The ground state and first excited state energies of an antiferromagnetic spin-1/2 chain with and without a single lacking spin site are computed using exact diagonalization method, within the Heisenberg model. In order to keep both parts of a spin chain with a lacking site connected, next nearest neighbors interactions are then introduced. Also, the Density Matrix Renormalization Group (DMRG) method is used, to investigate ground state energies of large system sizes; which permits us to inquire about the effect of large system sizes on energies. Other quantum quantities such as fidelity and correlation functions are also studied and compared in both cases. - Research highlights: → In this paper we compute ground state and first excited state energies of a spin chain with and without a lacking spin site. The next nearest neighbors are introduced with the antiferromagnetic Heisenberg spin-half. → Exact diagonalization is used for small systems, where DMRG method is used to compute energies for large systems. Other quantities like quantum fidelity and correlation are also computed. → Results are presented in figures with comments. → E 0 /N is computed in a function of N for several values of J 2 and for both systems. First excited energies are also investigated.

Ising formulations of many NP problems

OpenAIRE

Lucas, Andrew

2013-01-01

We provide Ising formulations for many NP-complete and NP-hard problems, including all of Karp's 21 NP-complete problems. This collects and extends mappings to the Ising model from partitioning, covering and satisfiability. In each case, the required number of spins is at most cubic in the size of the problem. This work may be useful in designing adiabatic quantum optimization algorithms.

Ising and Potts models: binding disorder-and dimension effects

International Nuclear Information System (INIS)

Curado, E.M.F.

1983-01-01

Within the real space renormalization group framework, some thermal equilibrium properties of pure and disordered insulating systems are calculated. In the pure hypercubic lattice system, the Ising model surface tension and the correlation length of the q-state Potts model, which generalizes the former are analyzed. Several asymptotic behaviors are obtained (for the first time as far as we know) for both functions and the influence of dimension over them can be observed. Accurate numerical proposals for the surface tension are made in several dimensions, and the effect of the number of states (q) on the correlation lenght is shown. In disordered systems, attention is focused essentiall on those which can be theoretically represented by pure sistem Hamiltonians where probability distributions are assumed for the coupling constants (disorder in the bonds). It is obtained with high precision several approximate critical surfaces for the quenched square-lattice Ising model, whose probability distribution can assume two positive values (hence there is no frustration). These aproximate surfaces contain all the exact known points. In the cases where the coupling constant probability distribution can also assume negative values (allowing disordered and frustrated systems), a theoretical treatment which distinguishes the frustration effect from the dilution one is proposed. This distinction can be seen by the different ways in which the bonds of any series-parallel topological array combine. (Author) [pt

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

Science.gov (United States)

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.

The Ising model and its applications to a phase transition of biological interest

International Nuclear Information System (INIS)

Cabrera, G.G.; Stein-Barana, A.M.; Zuckermann, M.J.

1984-01-01

It is investigated a gel-liquid crystal phase transition employing a two-state model equivalent to the Spin 1/2 Ising Model with applied magnetic field. The model is studied from the standpoint of the cluster variational method of Kikuchi for cooperative phenomena. (M.W.O.) [pt

Stability and replica symmetry in the ising spin glass: a toy model

International Nuclear Information System (INIS)

De Dominicis, C.; Mottishaw, P.

1986-01-01

Searching for possible replica symmetric solutions in an Ising spin glass (in the tree approximation) we investigate a toy model whose bond distribution has two non vanishing cumulants (instead of one only as in a gaussian distribution)

Hyperscaling breakdown and Ising spin glasses: The Binder cumulant

Science.gov (United States)

Lundow, P. H.; Campbell, I. A.

2018-02-01

Among the Renormalization Group Theory scaling rules relating critical exponents, there are hyperscaling rules involving the dimension of the system. It is well known that in Ising models hyperscaling breaks down above the upper critical dimension. It was shown by Schwartz (1991) that the standard Josephson hyperscaling rule can also break down in Ising systems with quenched random interactions. A related Renormalization Group Theory hyperscaling rule links the critical exponents for the normalized Binder cumulant and the correlation length in the thermodynamic limit. An appropriate scaling approach for analyzing measurements from criticality to infinite temperature is first outlined. Numerical data on the scaling of the normalized correlation length and the normalized Binder cumulant are shown for the canonical Ising ferromagnet model in dimension three where hyperscaling holds, for the Ising ferromagnet in dimension five (so above the upper critical dimension) where hyperscaling breaks down, and then for Ising spin glass models in dimension three where the quenched interactions are random. For the Ising spin glasses there is a breakdown of the normalized Binder cumulant hyperscaling relation in the thermodynamic limit regime, with a return to size independent Binder cumulant values in the finite-size scaling regime around the critical region.

Oxygen-ordering phenomena in YBa2Cu3O6+x studied by Monte Carlo simulation

DEFF Research Database (Denmark)

Fiig, T.; Andersen, J.V.; Andersen, N.H.

1993-01-01

The oxygen order in YBa2Cu3O6+x has been investigated by Monte Carlo simulation with the two-dimensional anisotropic next-nearest-neighbor lattice gas model, the ASYNNNI model. For a specific set of interaction parameters we have calculated the structural phase diagram, the chemical potential...

Annealed central limit theorems for the ising model on random graphs

NARCIS (Netherlands)

Giardinà, C.; Giberti, C.; van der Hofstad, R.W.; Prioriello, M.L.

2016-01-01

The aim of this paper is to prove central limit theorems with respect to the annealed measure for the magnetization rescaled by √N of Ising models on random graphs. More precisely, we consider the general rank-1 inhomogeneous random graph (or generalized random graph), the 2-regular configuration

Study on non-universal critical behaviour in Ising model with defects

International Nuclear Information System (INIS)

Guimaraes, L.G.

1986-01-01

One-dimensional quantum analogous of two-dimensional Ising models with line and step type linear defects are studied. The phenomenological renormalization group was approached using conformal invariance for relating critical exponent N sup(*) sub(H). Aiming to obtain the Hamiltonian diagonal, Lanczos tridiagonal method was used. (H.C.K.)

Ising formulations of many NP problems

Directory of Open Access Journals (Sweden)

Andrew eLucas

2014-02-01

Full Text Available We provide Ising formulations for many NP-complete and NP-hard problems, including all of Karp's 21 NP-complete problems. This collects and extends mappings to the Ising model from partitioning, covering and satisfiability. In each case, the required number of spins is at most cubic in the size of the problem. This work may be useful in designing adiabatic quantum optimization algorithms.

Monte Carlo Studies of Phase Separation in Compressible 2-dim Ising Models

Science.gov (United States)

Mitchell, S. J.; Landau, D. P.

2006-03-01

Using high resolution Monte Carlo simulations, we study time-dependent domain growth in compressible 2-dim ferromagnetic (s=1/2) Ising models with continuous spin positions and spin-exchange moves [1]. Spins interact with slightly modified Lennard-Jones potentials, and we consider a model with no lattice mismatch and one with 4% mismatch. For comparison, we repeat calculations for the rigid Ising model [2]. For all models, large systems (512^2) and long times (10^ 6 MCS) are examined over multiple runs, and the growth exponent is measured in the asymptotic scaling regime. For the rigid model and the compressible model with no lattice mismatch, the growth exponent is consistent with the theoretically expected value of 1/3 [1] for Model B type growth. However, we find that non-zero lattice mismatch has a significant and unexpected effect on the growth behavior.Supported by the NSF.[1] D.P. Landau and K. Binder, A Guide to Monte Carlo Simulations in Statistical Physics, second ed. (Cambridge University Press, New York, 2005).[2] J. Amar, F. Sullivan, and R.D. Mountain, Phys. Rev. B 37, 196 (1988).

Singular ferromagnetic susceptibility of the transverse-field Ising antiferromagnet on the triangular lattice

Science.gov (United States)

Biswas, Sounak; Damle, Kedar

2018-02-01

A transverse magnetic field Γ is known to induce antiferromagnetic three-sublattice order of the Ising spins σz in the triangular lattice Ising antiferromagnet at low enough temperature. This low-temperature order is known to melt on heating in a two-step manner, with a power-law ordered intermediate temperature phase characterized by power-law correlations at the three-sublattice wave vector Q : ˜cos(Q .R ⃗) /|R⃗| η (T ) with the temperature-dependent power-law exponent η (T )∈(1 /9 ,1 /4 ) . Here, we use a quantum cluster algorithm to study the ferromagnetic easy-axis susceptibility χu(L ) of an L ×L sample in this power-law ordered phase. Our numerical results are consistent with a recent prediction of a singular L dependence χu(L ) ˜L2 -9 η when η (T ) is in the range (1 /9 ,2 /9 ) . This finite-size result implies, via standard scaling arguments, that the ferromagnetic susceptibility χu(B ) to a uniform field B along the easy axis is singular at intermediate temperatures in the small B limit, χu(B ) ˜|B| -4/-18 η 4 -9 η for η (T )∈(1 /9 ,2 /9 ) , although there is no ferromagnetic long-range order in the low temperature state. Additionally we establish similar two-step melting behavior (via a study of the order parameter susceptibility χQ) in the case of the ferrimagnetic three-sublattice ordered phase which is stabilized by ferromagnetic next-neighbor couplings (J2) and confirm that the ferromagnetic susceptibility obeys the predicted singular form in the associated power-law ordered phase.

Monte Carlo technique for very large ising models

Science.gov (United States)

Kalle, C.; Winkelmann, V.

1982-08-01

Rebbi's multispin coding technique is improved and applied to the kinetic Ising model with size 600*600*600. We give the central part of our computer program (for a CDC Cyber 76), which will be helpful also in a simulation of smaller systems, and describe the other tricks necessary to go to large lattices. The magnetization M at T=1.4* T c is found to decay asymptotically as exp(-t/2.90) if t is measured in Monte Carlo steps per spin, and M( t = 0) = 1 initially.

Statistically interacting quasiparticles in Ising chains

International Nuclear Information System (INIS)

Lu Ping; Vanasse, Jared; Piecuch, Christopher; Karbach, Michael; Mueller, Gerhard

2008-01-01

The exclusion statistics of two complementary sets of quasiparticles, generated from opposite ends of the spectrum, are identified for Ising chains with spin s = 1/2, 1. In the s = 1/2 case the two sets are antiferromagnetic domain walls (solitons) and ferromagnetic domains (strings). In the s = 1 case they are soliton pairs and nested strings, respectively. The Ising model is equivalent to a system of two species of solitons for s = 1/2 and to a system of six species of soliton pairs for s = 1. Solitons exist on single bonds but soliton pairs may be spread across many bonds. The thermodynamics of a system of domains spanning up to M lattice sites is amenable to exact analysis and shown to become equivalent, in the limit M → ∞, to the thermodynamics of the s = 1/2 Ising chain. A relation is presented between the solitons in the Ising limit and the spinons in the XX limit of the s = 1/2 XXZ chain

Theoretical studies of unconventional superconductors

Energy Technology Data Exchange (ETDEWEB)

Groensleth, Martin Sigurd

2008-07-01

This thesis presents four research papers. In the first three papers we have derived analytical results for the transport properties in unconventional superconductors and ferromagnetic systems with multiple broken symmetries. In Paper I and parts of Paper II we have studied tunneling transport between two non-unitary ferromagnetic spin-triplet superconductors, and found a novel interplay between ferromagnetism and superconductivity manifested in the Josephson effect as a spin- and charge-current in the absence of an applied voltage across the junction. The critical amplitudes of these currents can be adjusted by the relative magnetization direction on each side of the junction. Furthermore, in Paper II, we have found a way of controlling a spin-current between two ferromagnets with spin-orbit coupling. Paper III considers a junction consisting of a ferromagnet and a non-unitary ferromagnetic superconductor, and we show that the conductance spectra contains detailed information about the superconducting gaps and pairing symmetry of the Cooper-pairs. In the last paper we present a Monte Carlo study of an effective Hamiltonian describing orbital currents in the CuO2 layers of high-temperature superconductive cuprates. The model features two intrinsically anisotropic Ising models, coupled through an anisotropic next-nearest neighbor interaction, and an Ashkin-Teller nearest neighbor fourth order coupling. We have studied the specific heat anomaly, as well as the anomaly in the staggered magnetization associated with the orbital currents and its susceptibility. We have found that in a limited parameter regime, the specific heat anomaly is substantially suppressed, while the susceptibility has a non-analytical peak across the order-disorder transition. The model is therefore a candidate for describing the breakup of hidden order when crossing the pseudo-gap line on the under-doped side in the phase diagram of high-temperature superconductors. (Author) 64 refs., figs

Shielding property for thermal equilibrium states in the quantum Ising model

Science.gov (United States)

Móller, N. S.; de Paula, A. L.; Drumond, R. C.

2018-03-01

We show that Gibbs states of nonhomogeneous transverse Ising chains satisfy a shielding property. Namely, whatever the fields on each spin and exchange couplings between neighboring spins are, if the field in one particular site is null, then the reduced states of the subchains to the right and to the left of this site are exactly the Gibbs states of each subchain alone. Therefore, even if there is a strong exchange coupling between the extremal sites of each subchain, the Gibbs states of the each subchain behave as if there is no interaction between them. In general, if a lattice can be divided into two disconnected regions separated by an interface of sites with zero applied field, then we can guarantee a similar result only if the surface contains a single site. Already for an interface with two sites we show an example where the property does not hold. When it holds, however, we show that if a perturbation of the Hamiltonian parameters is done in one side of the lattice, then the other side is completely unchanged, with regard to both its equilibrium state and dynamics.

Algorithmic modeling of the irrelevant sound effect (ISE) by the hearing sensation fluctuation strength.

Science.gov (United States)

Schlittmeier, Sabine J; Weissgerber, Tobias; Kerber, Stefan; Fastl, Hugo; Hellbrück, Jürgen

2012-01-01

Background sounds, such as narration, music with prominent staccato passages, and office noise impair verbal short-term memory even when these sounds are irrelevant. This irrelevant sound effect (ISE) is evoked by so-called changing-state sounds that are characterized by a distinct temporal structure with varying successive auditory-perceptive tokens. However, because of the absence of an appropriate psychoacoustically based instrumental measure, the disturbing impact of a given speech or nonspeech sound could not be predicted until now, but necessitated behavioral testing. Our database for parametric modeling of the ISE included approximately 40 background sounds (e.g., speech, music, tone sequences, office noise, traffic noise) and corresponding performance data that was collected from 70 behavioral measurements of verbal short-term memory. The hearing sensation fluctuation strength was chosen to model the ISE and describes the percept of fluctuations when listening to slowly modulated sounds (f(mod) background sounds, the algorithm estimated behavioral performance data in 63 of 70 cases within the interquartile ranges. In particular, all real-world sounds were modeled adequately, whereas the algorithm overestimated the (non-)disturbance impact of synthetic steady-state sounds that were constituted by a repeated vowel or tone. Implications of the algorithm's strengths and prediction errors are discussed.

Fast Most Similar Neighbor (MSN) classifiers for Mixed Data

OpenAIRE

Hernández Rodríguez, Selene

2010-01-01

The k nearest neighbor (k-NN) classifier has been extensively used in Pattern Recognition because of its simplicity and its good performance. However, in large datasets applications, the exhaustive k-NN classifier becomes impractical. Therefore, many fast k-NN classifiers have been developed; most of them rely on metric properties (usually the triangle inequality) to reduce the number of prototype comparisons. Hence, the existing fast k-NN classifiers are applicable only when the comparison f...

Antiferromagnetic Ising model with transverse and longitudinal field

International Nuclear Information System (INIS)

Kischinhevsky, M.

1985-01-01

We study the quantum hamiltonian version of the Ising Model in one spacial dimension under an external longitudinal (uniform) field at zero temperature. A phenomenological renormalization group procedure is used to obtain the phase diagram; the transverse and longitudinal zero field limits are studied and we verify the validity of universality at non zero transverse fields, where two-dimensional critical behaviour is obtained. To perform the numerical calculations we use the Lanczos scheme, which gives highly precise results with rather short processing times. We also analyse the possibility of using these techniques to extend the present work to the quantum hamiltonian version of the q-state Potts Model (q>2) in larger system. (author) [pt

Some Observations about the Nearest-Neighbor Model of the Error Threshold

International Nuclear Information System (INIS)

Gerrish, Philip J.

2009-01-01

I explore some aspects of the 'error threshold' - a critical mutation rate above which a population is nonviable. The phase transition that occurs as mutation rate crosses this threshold has been shown to be mathematically equivalent to the loss of ferromagnetism that occurs as temperature exceeds the Curie point. I will describe some refinements and new results based on the simplest of these mutation models, will discuss the commonly unperceived robustness of this simple model, and I will show some preliminary results comparing qualitative predictions with simulations of finite populations adapting at high mutation rates. I will talk about how these qualitative predictions are relevant to biomedical science and will discuss how my colleagues and I are looking for phase-transition signatures in real populations of Escherichia coli that go extinct as a result of excessive mutation.

Golden mean renormalization for a generalized Harper equation: The Ketoja-Satija orchid

International Nuclear Information System (INIS)

Mestel, B.D.; Osbaldestin, A.H.

2004-01-01

We provide a rigorous analysis of the fluctuations of localized eigenstates in a generalized Harper equation with golden mean flux and with next-nearest-neighbor interactions. For next-nearest-neighbor interaction above a critical threshold, these self-similar fluctuations are characterized by orbits of a renormalization operator on a universal strange attractor, whose projection was dubbed the ''orchid'' by Ketoja and Satija [Phys. Rev. Lett. 75, 2762 (1995)]. We show that the attractor is given essentially by an embedding of a subshift of finite type, and give a description of its periodic orbits

Solving the 3d Ising Model with the Conformal Bootstrap II. c-Minimization and Precise Critical Exponents

CERN Document Server

El-Showk, Sheer; Poland, David; Rychkov, Slava; Simmons-Duffin, David; Vichi, Alessandro

2014-01-01

We use the conformal bootstrap to perform a precision study of the operator spectrum of the critical 3d Ising model. We conjecture that the 3d Ising spectrum minimizes the central charge c in the space of unitary solutions to crossing symmetry. Because extremal solutions to crossing symmetry are uniquely determined, we are able to precisely reconstruct the first several Z2-even operator dimensions and their OPE coefficients. We observe that a sharp transition in the operator spectrum occurs at the 3d Ising dimension Delta_sigma=0.518154(15), and find strong numerical evidence that operators decouple from the spectrum as one approaches the 3d Ising point. We compare this behavior to the analogous situation in 2d, where the disappearance of operators can be understood in terms of degenerate Virasoro representations.

Chiral-glass transition and replica symmetry breaking of a three-dimensional Heisenberg spin glass

OpenAIRE

Hukushima, K.; Kawamura, H.

2000-01-01

Extensive equilibrium Monte Carlo simulations are performed for a three-dimensional Heisenberg spin glass with the nearest-neighbor Gaussian coupling to investigate its spin-glass and chiral-glass orderings. The occurrence of a finite-temperature chiral-glass transition without the conventional spin-glass order is established. Critical exponents characterizing the transition are different from those of the standard Ising spin glass. The calculated overlap distribution suggests the appearance ...

Monte Carlo method for critical systems in infinite volume: The planar Ising model.

Science.gov (United States)

Herdeiro, Victor; Doyon, Benjamin

2016-10-01

In this paper we propose a Monte Carlo method for generating finite-domain marginals of critical distributions of statistical models in infinite volume. The algorithm corrects the problem of the long-range effects of boundaries associated to generating critical distributions on finite lattices. It uses the advantage of scale invariance combined with ideas of the renormalization group in order to construct a type of "holographic" boundary condition that encodes the presence of an infinite volume beyond it. We check the quality of the distribution obtained in the case of the planar Ising model by comparing various observables with their infinite-plane prediction. We accurately reproduce planar two-, three-, and four-point of spin and energy operators. We also define a lattice stress-energy tensor, and numerically obtain the associated conformal Ward identities and the Ising central charge.

Observation and analysis of nanodomain textures in dielectric relaxor lead magnesium niobate

Energy Technology Data Exchange (ETDEWEB)

Bursill, L A; Qian, Hua; Peng, Julin; Fan, X D

1995-10-01

High-resolution (0.2nm) images are used to locate chemical domains occurring with length scales of 1-5nm in the dielectric relaxor lead magnesium niobate (PMN). The experimental HRTEM images are analysed using computer-simulations and image matching in order to clarify and characterize the nature of the chemical ordering. Madelung electrostatic energy calculations are used to rank a set of structural models for possible ordered and disordered distributions of Nb and Mg over the B-sites of perovskite ABO{sub 3}. Next, the chemical domain textures are modelled using next-nearest-neighbour Ising (NNNI) models and Monte Carlo methods. This results in a preferred model for the B-site distribution (the extended NNN-Ising model), which is used for image simulations. Both HRTEM many-beam bright-and dark-field and single-beam dark-field TEM images are obtained and compared with the experimental images. The final result is a realistic atomic model for the Nb, Mg distribution of PMN. 42 refs., 2 tabs., 10 figs.

Recurrence relations in the three-dimensional Ising model

International Nuclear Information System (INIS)

Yukhnovskij, I.R.; Kozlovskij, M.P.

1977-01-01

Recurrence relations between the coefficients asub(2)sup((i)), asub(4)sup((i)) and Psub(2)sup((i)), Psub(4)sup((i)) which characterize the probabilities of distribution for the three-dimensional Ising model are studied. It is shown that for large arguments z of the Makdonald functions Ksub(ν)(z) the recurrence relations correspond to the known Wilson relations. But near the critical point for small values of the transfer momentum k this limit case does not take place. In the pointed region the argument z tends to zero, and new recurrence relations take place

The Landau-Lifshitz equation describes the Ising spin correlation function in the free-fermion model

CERN Document Server

Rutkevich, S B

1998-01-01

We consider time and space dependence of the Ising spin correlation function in a continuous one-dimensional free-fermion model. By the Ising spin we imply the 'sign' variable, which takes alternating +-1 values in adjacent domains bounded by domain walls (fermionic world paths). The two-point correlation function is expressed in terms of the solution of the Cauchy problem for a nonlinear partial differential equation, which is proved to be equivalent to the exactly solvable Landau-Lifshitz equation. A new zero-curvature representation for this equation is presented. In turn, the initial condition for the Cauchy problem is given by the solution of a nonlinear ordinary differential equation, which has also been derived. In the Ising limit the above-mentioned partial and ordinary differential equations reduce to the sine-Gordon and Painleve III equations, respectively. (author)

Simulation of glioblastoma multiforme (GBM) tumor cells using ising model on the Creutz Cellular Automaton

Science.gov (United States)

Züleyha, Artuç; Ziya, Merdan; Selçuk, Yeşiltaş; Kemal, Öztürk M.; Mesut, Tez

2017-11-01

Computational models for tumors have difficulties due to complexity of tumor nature and capacities of computational tools, however, these models provide visions to understand interactions between tumor and its micro environment. Moreover computational models have potential to develop strategies for individualized treatments for cancer. To observe a solid brain tumor, glioblastoma multiforme (GBM), we present a two dimensional Ising Model applied on Creutz cellular automaton (CCA). The aim of this study is to analyze avascular spherical solid tumor growth, considering transitions between non tumor cells and cancer cells are like phase transitions in physical system. Ising model on CCA algorithm provides a deterministic approach with discrete time steps and local interactions in position space to view tumor growth as a function of time. Our simulation results are given for fixed tumor radius and they are compatible with theoretical and clinic data.

A hidden Ising model for ChIP-chip data analysis

KAUST Repository

Mo, Q.

2010-01-28

Motivation: Chromatin immunoprecipitation (ChIP) coupled with tiling microarray (chip) experiments have been used in a wide range of biological studies such as identification of transcription factor binding sites and investigation of DNA methylation and histone modification. Hidden Markov models are widely used to model the spatial dependency of ChIP-chip data. However, parameter estimation for these models is typically either heuristic or suboptimal, leading to inconsistencies in their applications. To overcome this limitation and to develop an efficient software, we propose a hidden ferromagnetic Ising model for ChIP-chip data analysis. Results: We have developed a simple, but powerful Bayesian hierarchical model for ChIP-chip data via a hidden Ising model. Metropolis within Gibbs sampling algorithm is used to simulate from the posterior distribution of the model parameters. The proposed model naturally incorporates the spatial dependency of the data, and can be used to analyze data with various genomic resolutions and sample sizes. We illustrate the method using three publicly available datasets and various simulated datasets, and compare it with three closely related methods, namely TileMap HMM, tileHMM and BAC. We find that our method performs as well as TileMap HMM and BAC for the high-resolution data from Affymetrix platform, but significantly outperforms the other three methods for the low-resolution data from Agilent platform. Compared with the BAC method which also involves MCMC simulations, our method is computationally much more efficient. Availability: A software called iChip is freely available at http://www.bioconductor.org/. Contact: moq@mskcc.org. © The Author 2010. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org.

Monte Carlo simulation of Ising models by multispin coding on a vector computer

Science.gov (United States)

Wansleben, Stephan; Zabolitzky, John G.; Kalle, Claus

1984-11-01

Rebbi's efficient multispin coding algorithm for Ising models is combined with the use of the vector computer CDC Cyber 205. A speed of 21.2 million updates per second is reached. This is comparable to that obtained by special- purpose computers.

Fisher zeros in the Kallen-Lehmann approach to 3D Ising model

International Nuclear Information System (INIS)

Astorino, Marco; Canfora, Fabrizio; Giribet, Gaston

2009-01-01

The distribution of the Fisher zeros in the Kallen-Lehmann approach to three-dimensional Ising model is studied. It is argued that the presence of a non-trivial angle (a cusp) in the distribution of zeros in the complex temperatures plane near the physical singularity is realized through a strong breaking of the 2D Ising self-duality. Remarkably, the realization of the cusp in the Fisher distribution ultimately leads to an improvement of the results of the Kallen-Lehmann ansatz. In fact, excellent agreement with Monte Carlo predictions both at high and at low temperatures is observed. Besides, agreement between both approaches is found for the predictions of the critical exponent α and of the universal amplitude ratio Δ=A + /A - , within the 3.5% and 7% of the Monte Carlo predictions, respectively

Frustrated lattices of Ising chains

International Nuclear Information System (INIS)

Kudasov, Yurii B; Korshunov, Aleksei S; Pavlov, V N; Maslov, Dmitrii A

2012-01-01

The magnetic structure and magnetization dynamics of systems of plane frustrated Ising chain lattices are reviewed for three groups of compounds: Ca 3 Co 2 O 6 , CsCoCl 3 , and Sr 5 Rh 4 O 12 . The available experimental data are analyzed and compared in detail. It is shown that a high-temperature magnetic phase on a triangle lattice is normally and universally a partially disordered antiferromagnetic (PDA) structure. The diversity of low-temperature phases results from weak interactions that lift the degeneracy of a 2D antiferromagnetic Ising model on the triangle lattice. Mean-field models, Monte Carlo simulation results on the static magnetization curve, and results on slow magnetization dynamics obtained with Glauber's theory are discussed in detail. (reviews of topical problems)

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

Science.gov (United States)

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.

Exotic lagomorph may influence eagle abundances and breeding spatial aggregations: a field study and meta-analysis on the nearest neighbor distance

Directory of Open Access Journals (Sweden)

Facundo Barbar

2018-05-01

Full Text Available The introduction of alien species could be changing food source composition, ultimately restructuring demography and spatial distribution of native communities. In Argentine Patagonia, the exotic European hare has one of the highest numbers recorded worldwide and is now a widely consumed prey for many predators. We examine the potential relationship between abundance of this relatively new prey and the abundance and breeding spacing of one of its main consumers, the Black-chested Buzzard-Eagle (Geranoaetus melanoleucus. First we analyze the abundance of individuals of a raptor guild in relation to hare abundance through a correspondence analysis. We then estimated the Nearest Neighbor Distance (NND of the Black-chested Buzzard-eagle abundances in the two areas with high hare abundances. Finally, we performed a meta-regression between the NND and the body masses of Accipitridae raptors, to evaluate if Black-chested Buzzard-eagle NND deviates from the expected according to their mass. We found that eagle abundance was highly associated with hare abundance, more than with any other raptor species in the study area. Their NND deviates from the value expected, which was significantly lower than expected for a raptor species of this size in two areas with high hare abundance. Our results support the hypothesis that high local abundance of prey leads to a reduction of the breeding spacing of its main predator, which could potentially alter other interspecific interactions, and thus the entire community.

An Ising spin state explanation for financial asset allocation

Science.gov (United States)

Horvath, Philip A.; Roos, Kelly R.; Sinha, Amit

2016-03-01

We build on the developments in the application of statistical mechanics, notably the identity of the spin degree of freedom in the Ising model, to explain asset price dynamics in financial markets with a representative agent. Specifically, we consider the value of an individual spin to represent the proportional holdings in various assets. We use partial moment arguments to identify asymmetric reactions to information and develop an extension of a plunging and dumping model. This unique identification of the spin is a relaxation of the conventional discrete state limitation on an Ising spin to accommodate a new archetype in Ising model-finance applications wherein spin states may take on continuous values, and may evolve in time continuously, or discretely, depending on the values of the partial moments.

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

Science.gov (United States)

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.

Ising game: Nonequilibrium steady states of resource-allocation systems

Science.gov (United States)

Xin, C.; Yang, G.; Huang, J. P.

2017-04-01

Resource-allocation systems are ubiquitous in the human society. But how external fields affect the state of such systems remains poorly explored due to the lack of a suitable model. Because the behavior of spins pursuing energy minimization required by physical laws is similar to that of humans chasing payoff maximization studied in game theory, here we combine the Ising model with the market-directed resource-allocation game, yielding an Ising game. Based on the Ising game, we show theoretical, simulative and experimental evidences for a formula, which offers a clear expression of nonequilibrium steady states (NESSs). Interestingly, the formula also reveals a convertible relationship between the external field (exogenous factor) and resource ratio (endogenous factor), and a class of saturation as the external field exceeds certain limits. This work suggests that the Ising game could be a suitable model for studying external-field effects on resource-allocation systems, and it could provide guidance both for seeking more relations between NESSs and equilibrium states and for regulating human systems by choosing NESSs appropriately.

Pushing the limits of Monte Carlo simulations for the three-dimensional Ising model

Science.gov (United States)

Ferrenberg, Alan M.; Xu, Jiahao; Landau, David P.

2018-04-01

While the three-dimensional Ising model has defied analytic solution, various numerical methods like Monte Carlo, Monte Carlo renormalization group, and series expansion have provided precise information about the phase transition. Using Monte Carlo simulation that employs the Wolff cluster flipping algorithm with both 32-bit and 53-bit random number generators and data analysis with histogram reweighting and quadruple precision arithmetic, we have investigated the critical behavior of the simple cubic Ising Model, with lattice sizes ranging from 163 to 10243. By analyzing data with cross correlations between various thermodynamic quantities obtained from the same data pool, e.g., logarithmic derivatives of magnetization and derivatives of magnetization cumulants, we have obtained the critical inverse temperature Kc=0.221 654 626 (5 ) and the critical exponent of the correlation length ν =0.629 912 (86 ) with precision that exceeds all previous Monte Carlo estimates.

First steps towards a state classification in the random-field Ising model

International Nuclear Information System (INIS)

Basso, Vittorio; Magni, Alessandro; Bertotti, Giorgio

2006-01-01

The properties of locally stable states of the random-field Ising model are studied. A map is defined for the dynamics driven by the field starting from a locally stable state. The fixed points of the map are connected with the limit hysteresis loops that appear in the classification of the states

Phase diagram of the Kondo-Heisenberg model on honeycomb lattice with geometrical frustration

Science.gov (United States)

Li, Huan; Song, Hai-Feng; Liu, Yu

2016-11-01

We calculated the phase diagram of the Kondo-Heisenberg model on a two-dimensional honeycomb lattice with both nearest-neighbor and next-nearest-neighbor antiferromagnetic spin exchanges, to investigate the interplay between RKKY and Kondo interactions in the presence of magnetic frustration. Within a mean-field decoupling technology in slave-fermion representation, we derived the zero-temperature phase diagram as a function of Kondo coupling J k and frustration strength Q. The geometrical frustration can destroy the magnetic order, driving the original antiferromagnetic (AF) phase to non-magnetic valence bond solids (VBS). In addition, we found two distinct VBS. As J k is increased, a phase transition from AF to Kondo paramagnetic (KP) phase occurs, without the intermediate phase coexisting AF order with Kondo screening found in square lattice systems. In the KP phase, the enhancement of frustration weakens the Kondo screening effect, resulting in a phase transition from KP to VBS. We also found a process to recover the AF order from VBS by increasing J k in a wide range of frustration strength. Our work may provide predictions for future experimental observation of new processes of quantum phase transitions in frustrated heavy-fermion compounds.

Pengembangan Indentation Size Effect (ISE Dalam Penentuan Koefisien Pengerasan Regang Baja

Directory of Open Access Journals (Sweden)

I Nyoman Budiarsa

2016-07-01

Full Text Available Abstrak: Hubungan antara sifat material konstitutif dengan indentasi kekerasan (Hardness Indentation termasuk ISE (Indentation Size Effect telah dikembangkan dan dievaluasi dengan indentasi Vickers, hal Ini akan menjadi alat yang berguna dalam mengevaluasi kelayakan penggunaan nilai kekerasan dalam memprediksi parameter bahan konstitutif dengan mengacu pada syarat akurasi pada rentang semua potensi bahan. ISE dapat konsisten diukur dan dapat berpotensi dihubungkan dengan H/E rasio. Skala ISE dari sampel yang diuji menunjukkan pengulangan yang konsisten dan berhubungan kuat dengan sifat material secara signifikan. Hal Ini berpotensi memberikan set data eksperimen yang mencerminkan sifat material yang terkait dengan ketegangan gradien dan kerapatan dislokasi selama proses indentasi Konsep untuk menggunakan data ukuran indentasi Vickers telah dikembangkan untuk meningkatkan akurasi sifat invers pemodelan berdasarkan kekerasan menggunakan baja sebagai sistem bahan. Penelitian ini menunjukkan bahwa ada ISE signifikan dalam tes kekerasan Vickers dimana skala dan reliabilitas ISE dianalisis dengan fitting data mengikuti Power law and proportional resistance model Sebuah konsep baru menggunakan data ISE untuk memperkirakan Koefisien Pengerasan Regang (n nilai-nilai dari baja telah dievaluasi dan menunjukkan hasil yang baik untuk mempersempit kisaran sifat material yang diprediksi berdasarkan nilai-nilai kekerasan. . Kata kunci: ISE, H/E rasio, Koefisien Pengerasan Regang (n Abstract: The relationship between the constitutive material properties with Hardness indentation including ISE (indentation Size Effect has been developed and evaluated by Vickers indentation. This provided a useful tool in evaluating the feasibility of using of hardness value in predicting the constitutive material parameters with reference to the terms of accuracy in the all the potential materials range. ISE can be consistently measured and may potentially be associated with H

Structure and Bonding in Noncrystalline Solids Abstracts

Science.gov (United States)

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

Anomalous magnon Nernst effect of topological magnonic materials

OpenAIRE

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

2017-01-01

The magnon transport driven by 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 (DMI) 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 effe...

Phase diagram and re-entrant fermionic entanglement in a hybrid Ising-Hubbard ladder

Science.gov (United States)

Sousa, H. S.; Pereira, M. S. S.; de Oliveira, I. N.; Strečka, J.; Lyra, M. L.

2018-05-01

The degree of fermionic entanglement is examined in an exactly solvable Ising-Hubbard ladder, which involves interacting electrons on the ladder's rungs described by Hubbard dimers at half-filling on each rung, accounting for intrarung hopping and Coulomb terms. The coupling between neighboring Hubbard dimers is assumed to have an Ising-like nature. The ground-state phase diagram consists of four distinct regions corresponding to the saturated paramagnetic, the classical antiferromagnetic, the quantum antiferromagnetic, and the mixed classical-quantum phase. We have exactly computed the fermionic concurrence, which measures the degree of quantum entanglement between the pair of electrons on the ladder rungs. The effects of the hopping amplitude, the Coulomb term, temperature, and magnetic fields on the fermionic entanglement are explored in detail. It is shown that the fermionic concurrence displays a re-entrant behavior when quantum entanglement is being generated at moderate temperatures above the classical saturated paramagnetic ground state.

The dilute spin-one Ising model with both bilinear and biquadratic exchange interactions

International Nuclear Information System (INIS)

Saber, M.

1987-08-01

The influence of bond and site dilution on the two-dimensional spin-one Ising model on a honeycomb lattice is investigated. Temperature-concentration phase diagrams for fixed values of the ratio of bilinear and biquadratic exchange interactions are determined. (author). 7 refs, 3 figs

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

Science.gov (United States)

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.

Criticality of the random-site Ising model: Metropolis, Swendsen-Wang and Wolff Monte Carlo algorithms

Directory of Open Access Journals (Sweden)

D.Ivaneyko

2005-01-01

Full Text Available We apply numerical simulations to study of the criticality of the 3D Ising model with random site quenched dilution. The emphasis is given to the issues not being discussed in detail before. In particular, we attempt a comparison of different Monte Carlo techniques, discussing regions of their applicability and advantages/disadvantages depending on the aim of a particular simulation set. Moreover, besides evaluation of the critical indices we estimate the universal ratio Γ+/Γ- for the magnetic susceptibility critical amplitudes. Our estimate Γ+/Γ- = 1.67 ± 0.15 is in a good agreement with the recent MC analysis of the random-bond Ising model giving further support that both random-site and random-bond dilutions lead to the same universality class.

Experimental Validation of an Efficient Fan-Beam Calibration Procedure for k-Nearest Neighbor Position Estimation in Monolithic Scintillator Detectors

Science.gov (United States)

Borghi, Giacomo; Tabacchini, Valerio; Seifert, Stefan; Schaart, Dennis R.

2015-02-01

Monolithic scintillator detectors can achieve excellent spatial resolution and coincidence resolving time. However, their practical use for positron emission tomography (PET) and other applications in the medical imaging field is still limited due to drawbacks of the different methods used to estimate the position of interaction. Common statistical methods for example require the collection of an extensive dataset of reference events with a narrow pencil beam aimed at a fine grid of reference positions. Such procedures are time consuming and not straightforwardly implemented in systems composed of many detectors. Here, we experimentally demonstrate for the first time a new calibration procedure for k-nearest neighbor ( k-NN) position estimation that utilizes reference data acquired with a fan beam. The procedure is tested on two detectors consisting of 16 mm ×16 mm ×10 mm and 16 mm ×16 mm ×20 mm monolithic, Ca-codoped LSO:Ce crystals and digital photon counter (DPC) arrays. For both detectors, the spatial resolution and the bias obtained with the new method are found to be practically the same as those obtained with the previously used method based on pencil-beam irradiation, while the calibration time is reduced by a factor of 20. Specifically, a FWHM of 1.1 mm and a FWTM of 2.7 mm were obtained using the fan-beam method with the 10 mm crystal, whereas a FWHM of 1.5 mm and a FWTM of 6 mm were achieved with the 20 mm crystal. Using a fan beam made with a 4.5 MBq 22Na point-source and a tungsten slit collimator with 0.5 mm aperture, the total measurement time needed to acquire the reference dataset was 3 hours for the thinner crystal and 2 hours for the thicker one.

Finite cluster renormalization and new two step renormalization group for Ising model

International Nuclear Information System (INIS)

Benyoussef, A.; El Kenz, A.

1989-09-01

New types of renormalization group theory using the generalized Callen identities are exploited in the study of the Ising model. Another type of two-step renormalization is proposed. Critical couplings and critical exponents y T and y H are calculated by these methods for square and simple cubic lattices, using different size clusters. (author). 17 refs, 2 tabs

Magnetic properties of the three-dimensional Ising model with an interface amorphization

International Nuclear Information System (INIS)

Benyoussef, A.; El Kenz, A.; Saber, M.

1993-09-01

A three-dimensional ferromagnetic Ising model with an interface amorphization is investigated with the use of the effective field theory. Phase diagrams and reduced magnetization curves of interface and bulks are studied. We obtain a number of characteristic behaviour such as the possibility of the reentrant phenomena and a large depression of interface magnetization. (author). 21 refs, 5 figs

Matrix-valued Boltzmann equation for the nonintegrable Hubbard chain.

Science.gov (United States)

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.

Stimulated polarization wave process in spin 3/2 chains

International Nuclear Information System (INIS)

Furman, G. B.

2007-01-01

Stimulated wave of polarization, triggered by a flip of a single spin, presents a simple model of quantum amplification. Recently, it has been demonstrated that, in an idealized one-dimensional Ising spin 1/2 chain with nearest-neighbor interactions and realistic spin 1/2 chain including the natural dipole-dipole interactions, irradiated by a weak resonant transverse field, a wave of flipped spins can be triggered by a single spin flip. Here we focuse on control of polarization wave in chain of spin 3/2, where the nuclear quadrupole interaction is dominant. Results of simulations for 1D spin chains and rings with up to five spins are presented.

Nuclear hyperfine structure of muonium in CuCl resolved by means of avoided level crossing

International Nuclear Information System (INIS)

Schneider, J.W.; Celio, M.; Keller, H.; Kuendig, W.; Odermatt, W.; Puempin, B.; Savic, I.M.; Simmler, H.; Estle, T.L.; Schwab, C.; Kiefl, R.F.; Renker, D.

1990-01-01

We report detailed avoided-level-crossing spectra of a muonium center (Mu II ) in single-crystal CuCl in a magnetic field range of 4--5 T and at a temperature of 100 K. The hyperfine parameters of the muon and the closest two shells of nuclei indicate that this center consists of muonium at a tetrahedral interstice with four Cu nearest neighbors and six Cl next-nearest neighbors and that the spin density is appreciable on the muon and on the ten neighboring nuclei but negligible elsewhere

Theoretical study of the electronic and magnetic properties of β-TeVO4

Science.gov (United States)

Saul, Andres; Radtke, Guillaume

2014-03-01

The β phase of this compound can be described by zigzag chains formed by VO5 distorted square pyramids sharing corners. This oxide, with V4+ ions as magnetic centers, can be thus seen as a realization of a quasi-one-dimensional Heisenberg S=1/2 Hamiltonian. The corner-sharing of the VO5 pyramids could lead to the prediction of AFM nearest neighbor interactions mediated by a weak super-exchange mechanism opening the possibility of complex magnetic properties due to competing next nearest-neighbors or inter-chain interactions. In this work we have studied its electronic and magnetic properties using density functional calculations. In particular, we evaluated the magnetic couplings on the basis of broken-symmetry formalism. We have performed extensive calculations comparing the results of the standard GGA (PBE) functional to the hybrid PBE0 functional and two different GGA+U implementations (SIC and AMF). The overall picture that arises from our calculations is of a frustrated AFM system with small FM nearest neigbors interactions but larger AFM nearest neighbors couplings. We discuss our results in the framework of the Kugel-Khomskii model using a projection of the electronic structure in localized Wannier functions.

K-nearest uphill clustering in the protein structure space

KAUST Repository

Cui, Xuefeng

2016-08-26

The protein structure classification problem, which is to assign a protein structure to a cluster of similar proteins, is one of the most fundamental problems in the construction and application of the protein structure space. Early manually curated protein structure classifications (e.g., SCOP and CATH) are very successful, but recently suffer the slow updating problem because of the increased throughput of newly solved protein structures. Thus, fully automatic methods to cluster proteins in the protein structure space have been designed and developed. In this study, we observed that the SCOP superfamilies are highly consistent with clustering trees representing hierarchical clustering procedures, but the tree cutting is very challenging and becomes the bottleneck of clustering accuracy. To overcome this challenge, we proposed a novel density-based K-nearest uphill clustering method that effectively eliminates noisy pairwise protein structure similarities and identifies density peaks as cluster centers. Specifically, the density peaks are identified based on K-nearest uphills (i.e., proteins with higher densities) and K-nearest neighbors. To our knowledge, this is the first attempt to apply and develop density-based clustering methods in the protein structure space. Our results show that our density-based clustering method outperforms the state-of-the-art clustering methods previously applied to the problem. Moreover, we observed that computational methods and human experts could produce highly similar clusters at high precision values, while computational methods also suggest to split some large superfamilies into smaller clusters. © 2016 Elsevier B.V.

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

Science.gov (United States)

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.

Effective model with strong Kitaev interactions for α -RuCl3

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Chaotic and stable perturbed maps: 2-cycles and spatial models

Science.gov (United States)

Braverman, E.; Haroutunian, J.

2010-06-01

As the growth rate parameter increases in the Ricker, logistic and some other maps, the models exhibit an irreversible period doubling route to chaos. If a constant positive perturbation is introduced, then the Ricker model (but not the classical logistic map) experiences period doubling reversals; the break of chaos finally gives birth to a stable two-cycle. We outline the maps which demonstrate a similar behavior and also study relevant discrete spatial models where the value in each cell at the next step is defined only by the values at the cell and its nearest neighbors. The stable 2-cycle in a scalar map does not necessarily imply 2-cyclic-type behavior in each cell for the spatial generalization of the map.

Exact solution of an Ising model with competing interactions on a Cayley tree

CERN Document Server

Ganikhodjaev, N N; Wahiddin, M R B

2003-01-01

The exact solution of an Ising model with competing restricted interactions on the Cayley tree, and in the absence of an external field is presented. A critical curve is defined where it is possible to get phase transitions above it, and a single Gibbs state is obtained elsewhere.

Phase diagram of ZnCr2pA12-2pS(Se)4 and Zn1-pCdpCr2S(Se)4

International Nuclear Information System (INIS)

Afif, K.; Benyoussef, A.; Hamedoun, M.; Hourmatallah, A.

1999-06-01

We compute the phase diagram of the nonmetallic compounds ZnCr2 p A1 2-2p S(Se) 4 (I[S,Se]) and Zn 1-p Cd p Cr 2 S(Se) 4 (II[S,Se]). We consider the bond-diluted Ising model on the spinel B site (S.B.S.) lattice with competitive exchange interactions, i.e. the ferromagnetic exchange interaction J 1 between nearest neighbours (n.n.) and the antiferromagnetic superexchange interaction J 2 between next-nearest neighbours' (n.n.n.) (and/or the more distant superexchange interactions J i (i > 1). Dilution and competition are found to be responsible for the spill glass phase and the percolation behaviour. (author)

Critical percolation in the slow cooling of the bi-dimensional ferromagnetic Ising model

Science.gov (United States)

Ricateau, Hugo; Cugliandolo, Leticia F.; Picco, Marco

2018-01-01

We study, with numerical methods, the fractal properties of the domain walls found in slow quenches of the kinetic Ising model to its critical temperature. We show that the equilibrium interfaces in the disordered phase have critical percolation fractal dimension over a wide range of length scales. We confirm that the system falls out of equilibrium at a temperature that depends on the cooling rate as predicted by the Kibble-Zurek argument and we prove that the dynamic growing length once the cooling reaches the critical point satisfies the same scaling. We determine the dynamic scaling properties of the interface winding angle variance and we show that the crossover between critical Ising and critical percolation properties is determined by the growing length reached when the system fell out of equilibrium.

3D-Ising model as a string theory in three-dimensional euclidean space

International Nuclear Information System (INIS)

Sedrakyan, A.

1992-11-01

A three-dimensional string model is analyzed in the strong coupling regime. The contribution of surfaces with different topology to the partition function is essential. A set of corresponding models is discovered. Their critical indices, which depend on two integers (m,n) are calculated analytically. The critical indices of the three-dimensional Ising model should belong to this set. A possible connection with the chain of three dimensional lattice Pott's models is pointed out. (author) 22 refs.; 2 figs

Neighboring and Urbanism: Commonality versus Friendship.

Science.gov (United States)

Silverman, Carol J.

1986-01-01

Examines a dimension of neighboring that need not assume friendship as the role model. When the model assumes only a sense of connectedness as defining neighboring, then the residential correlation, shown in many studies between urbanism and neighboring, disappears. Theories of neighboring, study variables, methods, and analysis are discussed.…

Magneto-structural correlations in trinuclear Cu(II) complexes: a density functional study

CERN Document Server

Rodríguez-Forteá, A; Alvarez, S; Centre-De Recera-En-Quimica-Teorica; Alemany, P A; Centre-De Recera-En-Quimica-Teorica

2003-01-01

Density functional theoretical methods have been used to study magneto-structural correlations for linear trinuclear hydroxo-bridged copper(II) complexes. The nearest-neighbor exchange coupling constant shows very similar trends to those found earlier for dinuclear compounds for which the Cu-O-Cu angle and the out of plane displacement of the hydrogen atoms at the bridge are the two key structural factors that determine the nature of their magnetic behavior. Changes in these two parameters can induce variations of over 1000 cm sup - sup 1 in the value of the nearest-neighbor coupling constant. On the contrary, coupling between next-nearest neighbors is found to be practically independent of structural changes with a value for the coupling constant of about -60 cm sup - sup 1. The magnitude calculated for this coupling constant indicates that considering its value to be negligible, as usually done in experimental studies, can lead to considerable errors, especially for compounds in which the nearest-neighbor c...

Commuting quantum circuits and complexity of Ising partition functions

International Nuclear Information System (INIS)

Fujii, Keisuke; Morimae, Tomoyuki

2017-01-01

Instantaneous quantum polynomial-time (IQP) computation is a class of quantum computation consisting only of commuting two-qubit gates and is not universal. Nevertheless, it has been shown that if there is a classical algorithm that can simulate IQP efficiently, the polynomial hierarchy collapses to the third level, which is highly implausible. However, the origin of the classical intractability is still less understood. Here we establish a relationship between IQP and computational complexity of calculating the imaginary-valued partition functions of Ising models. We apply the established relationship in two opposite directions. One direction is to find subclasses of IQP that are classically efficiently simulatable by using exact solvability of certain types of Ising models. Another direction is applying quantum computational complexity of IQP to investigate (im)possibility of efficient classical approximations of Ising partition functions with imaginary coupling constants. Specifically, we show that a multiplicative approximation of Ising partition functions is #P-hard for almost all imaginary coupling constants even on planar lattices of a bounded degree. (paper)

On P-adic λ-model on the Cayley tree

International Nuclear Information System (INIS)

Khamaraev, M.; Mukhamedov, F.

2004-04-01

We consider a nearest-neighbour p-adic A-model with spin values ±1 on the Cayley tree of order k ≥ 1. We prove that a p-adic Gibbs measure is unique for p≥ 3. If p 2 then we find a condition which guarantees uniqueness of p-adic Gibbs measure. Besides, the results are applied to the p-adic Ising model. (author)

Correspondence between spanning trees and the Ising model on a square lattice

Science.gov (United States)

Viswanathan, G. M.

2017-06-01

An important problem in statistical physics concerns the fascinating connections between partition functions of lattice models studied in equilibrium statistical mechanics on the one hand and graph theoretical enumeration problems on the other hand. We investigate the nature of the relationship between the number of spanning trees and the partition function of the Ising model on the square lattice. The spanning tree generating function T (z ) gives the spanning tree constant when evaluated at z =1 , while giving the lattice green function when differentiated. It is known that for the infinite square lattice the partition function Z (K ) of the Ising model evaluated at the critical temperature K =Kc is related to T (1 ) . Here we show that this idea in fact generalizes to all real temperatures. We prove that [Z(K ) s e c h 2 K ] 2=k exp[T (k )] , where k =2 tanh(2 K )s e c h (2 K ) . The identical Mahler measure connects the two seemingly disparate quantities T (z ) and Z (K ) . In turn, the Mahler measure is determined by the random walk structure function. Finally, we show that the the above correspondence does not generalize in a straightforward manner to nonplanar lattices.

75 FR 62412 - Notice of Proposed Information Collection: Comment Request; HUD-Owned Real Estate-Good Neighbor...

Science.gov (United States)

2010-10-08

... DEPARTMENT OF HOUSING AND URBAN DEVELOPMENT [Docket No. FR-5380-N-36] Notice of Proposed Information Collection: Comment Request; HUD- Owned Real Estate-Good Neighbor Next Door Program AGENCY: Office... information: Title of Proposal: HUD-Owned Real Estate-Good Neighbor Next Door Program. OMB Control Number, if...

A lattice gas model on a tangled chain

International Nuclear Information System (INIS)

Mejdani, R.

1993-04-01

We have used a model of a lattice gas defined on a tangled chain to study the enzyme kinetics by a modified transfer matrix method. By using a simple iterative algorithm we have obtained different kinds of saturation curves for different configurations of the tangled chain and different types of the additional interactions. In some special cases of configurations and interactions we have found the same equations for the saturation curves, which we have obtained before studying the lattice gas model with nearest neighbor interactions or the lattice gas model with alternate nearest neighbor interactions, using different techniques as the correlated walks' theory, the partition point technique or the transfer matrix model. This more general model and the new results could be useful for the experimental investigations. (author). 20 refs, 6 figs

Ising model of a randomly triangulated random surface as a definition of fermionic string theory

International Nuclear Information System (INIS)

Bershadsky, M.A.; Migdal, A.A.

1986-01-01

Fermionic degrees of freedom are added to randomly triangulated planar random surfaces. It is shown that the Ising model on a fixed graph is equivalent to a certain Majorana fermion theory on the dual graph. (orig.)

Chirality dependence of dipole matrix element of carbon nanotubes in axial magnetic field: A third neighbor tight binding approach

Science.gov (United States)

Chegel, Raad; Behzad, Somayeh

2014-02-01

We have studied the electronic structure and dipole matrix element, D, of carbon nanotubes (CNTs) under magnetic field, using the third nearest neighbor tight binding model. It is shown that the 1NN and 3NN-TB band structures show differences such as the spacing and mixing of neighbor subbands. Applying the magnetic field leads to breaking the degeneracy behavior in the D transitions and creates new allowed transitions corresponding to the band modifications. It is found that |D| is proportional to the inverse tube radius and chiral angle. Our numerical results show that amount of filed induced splitting for the first optical peak is proportional to the magnetic field by the splitting rate ν11. It is shown that ν11 changes linearly and parabolicly with the chiral angle and radius, respectively.

Improving sub-pixel imperviousness change prediction by ensembling heterogeneous non-linear regression models

Directory of Open Access Journals (Sweden)

Drzewiecki Wojciech

2016-12-01

Full Text Available In this work nine non-linear regression models were compared for sub-pixel impervious surface area mapping from Landsat images. The comparison was done in three study areas both for accuracy of imperviousness coverage evaluation in individual points in time and accuracy of imperviousness change assessment. The performance of individual machine learning algorithms (Cubist, Random Forest, stochastic gradient boosting of regression trees, k-nearest neighbors regression, random k-nearest neighbors regression, Multivariate Adaptive Regression Splines, averaged neural networks, and support vector machines with polynomial and radial kernels was also compared with the performance of heterogeneous model ensembles constructed from the best models trained using particular techniques.

On the phase transition nature in compressible Ising models

International Nuclear Information System (INIS)

Ota, A.T.

1985-01-01

The phase transition phenomenon is analysed in a compressible ferromagnetic Ising model at null field, through the mean-field approximation. The model studied is d-dimensional under the magnetic point of view and one-dimensional under the elastic point of view. This is achieved keeping the compressive interactions among the ions and rejecting annealing forces completely. The exchange parameter J is linear and the elastic potential quadratic in relation to the microscopic shifts of the lattice. In the one-dimensional case, this model shows no phase transition. In the two-dimensional case, the role of the S i spin of the i-the ion is crucial: a) for spin 1/2 the transitions are of second order; b) for spin 1, desides the second order transitions there is a three-critical point and a first-order transitions line. (L.C.) [pt

Testing ground for fluctuation theorems: The one-dimensional Ising model

Science.gov (United States)

Lemos, C. G. O.; Santos, M.; Ferreira, A. L.; Figueiredo, W.

2018-04-01

In this paper we determine the nonequilibrium magnetic work performed on a Ising model and relate it to the fluctuation theorem derived some years ago by Jarzynski. The basic idea behind this theorem is the relationship connecting the free energy difference between two thermodynamic states of a system and the average work performed by an external agent, in a finite time, through nonequilibrium paths between the same thermodynamic states. We test the validity of this theorem by considering the one-dimensional Ising model where the free energy is exactly determined as a function of temperature and magnetic field. We have found that the Jarzynski theorem remains valid for all the values of the rate of variation of the magnetic field applied to the system. We have also determined the probability distribution function for the work performed on the system for the forward and reverse processes and verified that predictions based on the Crooks relation are equally correct. We also propose a method to calculate the lag between the current state of the system and that of the equilibrium based on macroscopic variables. We have shown that the lag increases with the sweeping rate of the field at its final value for the reverse process, while it decreases in the case of the forward process. The lag increases linearly with the size of the chain and with a slope decreasing with the inverse of the rate of variation of the field.

Spin-waves in Antiferromagnetic Single-crystal LiFePO4

International Nuclear Information System (INIS)

Li, Jiying; Garlea, Vasile O.; Zarestky, Jarel; Vaknin, D.

2006-01-01

Spin-wave dispersions in the antiferromagnetic state of single-crystal LiFePO 4 were determined by inelastic neutron scattering measurements. The dispersion curves measured from the (0,1,0) reflection along both a* and b* reciprocal-space directions reflect the anisotropic coupling of the layered Fe 2+ (S=2) spin system. The spin-wave dispersion curves were theoretically modeled using linear spin-wave theory by including in the spin Hamiltonian in-plane nearest- and next-nearest-neighbor interactions (J 1 and J 2 ), inter-plane nearest-neighbor interactions (J(perpendicular)) and a single-ion anisotropy (D). A weak (0,1,0) magnetic peak was observed in elastic neutron scattering studies of the same crystal indicating that the ground state of the staggered iron moments is not along the (0,1,0) direction, as previously reported from polycrystalline samples studies, but slightly rotated away from this axis.

ISE System Development Methodology Manual

Energy Technology Data Exchange (ETDEWEB)

Hayhoe, G.F.

1992-02-17

The Information Systems Engineering (ISE) System Development Methodology Manual (SDM) is a framework of life cycle management guidelines that provide ISE personnel with direction, organization, consistency, and improved communication when developing and maintaining systems. These guide-lines were designed to allow ISE to build and deliver Total Quality products, and to meet the goals and requirements of the US Department of Energy (DOE), Westinghouse Savannah River Company, and Westinghouse Electric Corporation.

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

Science.gov (United States)

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.

Effective-field theory on the kinetic Ising model

International Nuclear Information System (INIS)

Shi Xiaoling; Wei Guozhu; Li Lin

2008-01-01

As an analytical method, the effective-field theory (EFT) is used to study the dynamical response of the kinetic Ising model in the presence of a sinusoidal oscillating field. The effective-field equations of motion of the average magnetization are given for the square lattice (Z=4) and the simple cubic lattice (Z=6), respectively. The dynamic order parameter, the hysteresis loop area and the dynamic correlation are calculated. In the field amplitude h 0 /ZJ-temperature T/ZJ plane, the phase boundary separating the dynamic ordered and the disordered phase has been drawn, and the dynamical tricritical point has been observed. We also make the compare results of EFT with that given by using the mean field theory (MFT)

The role of nano-contacts in electrical transport through a molecular wire

International Nuclear Information System (INIS)

Shokri, Ali A.; Mardaani, M.

2006-01-01

Theoretical studies on electrical transport in a nano-device which consisting of two semi-infinite cubic leads with finite cross-sections separated by a typical molecular wire (MW) are carried out by including the effect of single and multiple contacts. The calculations are based on the tight-binding model and Green's function method in the coherent regime. In order to calculate the effect of contact coupling on molecular wire transport, we derive a theoretical formula based on the nearest and next nearest neighbor coupling strengths between the MW and the surface atoms in the simple cubic leads. This approach can be generalized to other leads with different lattice structure. The results show small changes in the transport properties with changing next nearest neighbor coupling strength. Some asymmetry is noted in the strong multiple contact limit. Also, we observe that with enlarging the cross-section size of leads, the current density increases and then leads to the quantum unit of conductance. Hence, our derived formalism can be used for devices attached to macroscopic surfaces. The theoretical results obtained, can be a base for developments in designing nano-electronic devices

Decorated Ising models with competing interactions and modulated structures

International Nuclear Information System (INIS)

Tragtenberg, M.H.R.; Yokoi, C.S.O.; Salinas, S.R.A.

1988-01-01

The phase diagrams of a variety of decorated Ising lattices are calculated. The competing interactions among the decorating spins may induce different types of modulated orderings. In particular, the effect of an applied field on the phase diagram of the two-dimensional mock ANNNI model is considered, where only the original horizontal bonds on a square lattice are decorated. Some Bravais lattices and Cayley trees where all bonds are equally decorated are then studied. The Bravais lattices display a few stable modulated structures. The Cayley trees, on the other hand, display a large number of modulated phases, which increases with the lattice coordination number. (authors) [pt

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

Science.gov (United States)

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.

Inverse Ising problem in continuous time: A latent variable approach

Science.gov (United States)

Donner, Christian; Opper, Manfred

2017-12-01

We consider the inverse Ising problem: the inference of network couplings from observed spin trajectories for a model with continuous time Glauber dynamics. By introducing two sets of auxiliary latent random variables we render the likelihood into a form which allows for simple iterative inference algorithms with analytical updates. The variables are (1) Poisson variables to linearize an exponential term which is typical for point process likelihoods and (2) Pólya-Gamma variables, which make the likelihood quadratic in the coupling parameters. Using the augmented likelihood, we derive an expectation-maximization (EM) algorithm to obtain the maximum likelihood estimate of network parameters. Using a third set of latent variables we extend the EM algorithm to sparse couplings via L1 regularization. Finally, we develop an efficient approximate Bayesian inference algorithm using a variational approach. We demonstrate the performance of our algorithms on data simulated from an Ising model. For data which are simulated from a more biologically plausible network with spiking neurons, we show that the Ising model captures well the low order statistics of the data and how the Ising couplings are related to the underlying synaptic structure of the simulated network.

Ising formulation of associative memory models and quantum annealing recall

Science.gov (United States)

Santra, Siddhartha; Shehab, Omar; Balu, Radhakrishnan

2017-12-01

Associative memory models, in theoretical neuro- and computer sciences, can generally store at most a linear number of memories. Recalling memories in these models can be understood as retrieval of the energy minimizing configuration of classical Ising spins, closest in Hamming distance to an imperfect input memory, where the energy landscape is determined by the set of stored memories. We present an Ising formulation for associative memory models and consider the problem of memory recall using quantum annealing. We show that allowing for input-dependent energy landscapes allows storage of up to an exponential number of memories (in terms of the number of neurons). Further, we show how quantum annealing may naturally be used for recall tasks in such input-dependent energy landscapes, although the recall time may increase with the number of stored memories. Theoretically, we obtain the radius of attractor basins R (N ) and the capacity C (N ) of such a scheme and their tradeoffs. Our calculations establish that for randomly chosen memories the capacity of our model using the Hebbian learning rule as a function of problem size can be expressed as C (N ) =O (eC1N) , C1≥0 , and succeeds on randomly chosen memory sets with a probability of (1 -e-C2N) , C2≥0 with C1+C2=(0.5-f ) 2/(1 -f ) , where f =R (N )/N , 0 ≤f ≤0.5 , is the radius of attraction in terms of the Hamming distance of an input probe from a stored memory as a fraction of the problem size. We demonstrate the application of this scheme on a programmable quantum annealing device, the D-wave processor.

String effects in the 3d gauge Ising model

International Nuclear Information System (INIS)

Caselle, Michele; Panero, Marco; Hasenbusch, Martin

2003-01-01

We compare the predictions of the effective string description of confinement with a set of Monte Carlo data for the 3d gauge Ising model at finite temperature. Thanks to a new algorithm which makes use of the dual symmetry of the model we can reach very high precisions even for large quark-antiquark distances. We are thus able to explore the large R regime of the effective string. We find that for large enough distances and low enough temperature the data are well described by a pure bosonic string. As the temperature increases higher order corrections become important and cannot be neglected even at large distances. These higher order corrections seem to be well described by the Nambu-Goto action truncated at the first perturbative order. (author)

Consistency Analysis of Nearest Subspace Classifier

OpenAIRE

Wang, Yi

2015-01-01

The Nearest subspace classifier (NSS) finds an estimation of the underlying subspace within each class and assigns data points to the class that corresponds to its nearest subspace. This paper mainly studies how well NSS can be generalized to new samples. It is proved that NSS is strongly consistent under certain assumptions. For completeness, NSS is evaluated through experiments on various simulated and real data sets, in comparison with some other linear model based classifiers. It is also ...

Possibility of the field-induced spin-nematic phase in LiCuVO4

International Nuclear Information System (INIS)

Hagiwara, M; Fujita, T; Yamaguchi, H; Kimura, S; Omura, K; Svistov, L E; Smirnov, A I; Prokofiev, A; Honda, Z

2011-01-01

We report on the magnetization of the frustrated S = 1/2 chain compound LiCuVO 4 . In addition to the transition from a planar spiral to a spin modulated structure observed recently by NMR, another transition was observed just below the saturation field. This magnetic phase could be a spin nematic, namely a condensation of two magnon bound states, phase which was predicted theoretically in the S = 1/2 linear chain model with the nearest neighbor ferromagnetic and the next nearest neighbor antiferromagnetic exchange interactions. The slope of magnetization in this phase is in good agreement with a calculated one in a realistic quasi 2-dimensional model (M. E. Zhitomirsky and H. Tsunetsugu, Europhys. Lett. 92 37001 (2010)). We compare the observed phase diagram with a numerically calculated one and discuss the possibility of the spin nematic phase.

Antiferromagnetic Ising model decorated with D-vector spins: Transversal and longitudinal local fields effects

International Nuclear Information System (INIS)

Vasconcelos Dos Santos, R.J.; Coutinho, S.

1995-01-01

The effect of a local field acting on decorating classical D-vector bond spins of an antiferromagnetic Ising model on the square lattice is studied for both the annealed isotropic and the axial decorated cases. In both models the effect on the phase diagrams of the transversal and the longitudinal components of the local field acting on the decorating spins are fully analyzed and discussed

Atomistic simulation of the point defects in B2-type MoTa alloy

International Nuclear Information System (INIS)

Zhang Jianmin; Wang Fang; Xu Kewei; Ji, Vincent

2009-01-01

The formation and migration mechanisms of three different point defects (mono-vacancy, anti-site defect and interstitial atom) in B 2 -type MoTa alloy have been investigated by combining molecular dynamics (MD) simulation with modified analytic embedded-atom method (MAEAM). From minimization of the formation energy, we find that the anti-site defects Mo Ta and Ta Mo are easier to form than Mo and Ta mono-vacancies, while Mo and Ta interstitial atoms are difficult to form in the alloy. In six migration mechanisms of Mo and Ta mono-vacancies, one nearest-neighbor jump (1NNJ) is the most favorable due to its lowest activation and migration energies, but it will cause a disorder in the alloy. One next-nearest-neighbor jump (1NNNJ) and one third-nearest-neighbor jump (1TNNJ) can maintain the ordered property of the alloy but require higher activation and migration energies, so the 1NNNJ and 1TNNJ should be replaced by straight [1 0 0] six nearest-neighbor cyclic jumps (S[1 0 0]6NNCJ) or bent [1 0 0] six nearest-neighbor cyclic jumps (B[1 0 0]6NNCJ) and [1 1 0] six nearest-neighbor cyclic jumps ([1 1 0]6NNCJ), respectively. Although the migrations of Mo and Ta interstitial atoms need much lower energy than Mo and Ta mono-vacancies, they are not main migration mechanisms due to difficult to form in the alloy.

On an Algebraic Property of the Disordered Phase of the Ising Model with Competing Interactions on a Cayley Tree

Energy Technology Data Exchange (ETDEWEB)

Mukhamedov, Farrukh, E-mail: far75m@yandex.ru, E-mail: farrukh.m@uaeu.ac.ae [International Islamic University Malaysia, Department of Computational and Theoretical Sciences, Faculty of Science (Malaysia); Barhoumi, Abdessatar, E-mail: abdessatar.barhoumi@ipein.rnu.tn [Carthage University, Department of Mathematics, Nabeul Preparatory Engineering Institute (Tunisia); Souissi, Abdessatar, E-mail: s.abdessatar@hotmail.fr [Carthage University, Department of Mathematics, Marsa Preparatory Institute for Scientific and Technical Studies (Tunisia)

2016-12-15

It is known that the disordered phase of the classical Ising model on the Caley tree is extreme in some region of the temperature. If one considers the Ising model with competing interactions on the same tree, then about the extremity of the disordered phase there is no any information. In the present paper, we first aiming to analyze the correspondence between Gibbs measures and QMC’s on trees. Namely, we establish that states associated with translation invariant Gibbs measures of the model can be seen as diagonal quantum Markov chains on some quasi local algebra. Then as an application of the established correspondence, we study some algebraic property of the disordered phase of the Ising model with competing interactions on the Cayley tree of order two. More exactly, we prove that a state corresponding to the disordered phase is not quasi-equivalent to other states associated with translation invariant Gibbs measures. This result shows how the translation invariant states relate to each other, which is even a new phenomena in the classical setting. To establish the main result we basically employ methods of quantum Markov chains.

Effect of grain refinement by severe plastic deformation on the next-neighbor misorientation distribution

International Nuclear Information System (INIS)

Toth, L.S.; Beausir, B.; Gu, C.F.; Estrin, Y.; Scheerbaum, N.; Davies, C.H.J.

2010-01-01

Next-neighbor misorientation distributions (NNMD) in severely deformed polycrystalline materials are commonly measured by orientation imaging. A procedure is proposed which enables the separation of NNMD of ultrafine-grained materials into two parts: the distribution of misorientations between newly emerged grains within the original ('parent') grain interior ('internal daughter grains') and the distribution of misorientations between grains adjacent to an original grain boundary on its opposite sides ('grain boundary daughter grains'). The procedure is based on electron backscatter diffraction orientation map analyses carried out on different planes of deformed samples considering the evolution of the grain size and shape during severe plastic deformation. It was applied to copper processed by up to three passes of equal-channel angular pressing. A characteristic feature of the measured NNMD is the occurrence of a double peak, which is clearly due to the differences between the NNMD of the two distinct populations of new grains defined above. The peak at low angles represents mainly the continual grain subdivision process in the interior of a parent grain (and is associated with internal daughter grains), while the peak at large angles is due to the high angle misorientations of the grain boundary daughter grains.

A simple model for the magnetoelectric interaction in multiferroics

International Nuclear Information System (INIS)

Filho, Cesar J Calderon; Barberis, Gaston E

2011-01-01

The (anti)ferromagnetic and ferroelectric transitions in some multiferroic compounds seem to be strongly correlated. Even for systems that do not show spontaneous ferroelectricity such as the LiMPO 4 (M = Mn, Fe, Co, Ni) compounds, the coupling between magnetic and electric degrees of freedom is evident experimentally. Here, we present a simple numerical calculation to simulate this coupling that leads to the two transitions. We assume a magnetic sublattice consisting of classical magnetic moments coupled to a separated nonmagnetic sublattice consisting of classical electric dipoles. The coupling between them is realized through a phenomenological spin-lattice Hamiltonian, and the solution is obtained using the Monte Carlo technique. In the simplest version, the magnetic system is 2D Ising (anti)ferromagnetic lattice, with nearest neighbors interactions only, and the electric moments are permanent moments, coupled electrically. Within this approximation, the second order magnetic transition induces ferroelectricity in the electric dipoles. We show that these calculations can be extended to other magnetic systems, (x-y model and 3D Heisenberg) and to systems where the electric moments are created by strains, generated via spin-lattice coupling, so the model can be applied to model realistic systems such as the olivines mentioned above.

Hyperplane distance neighbor clustering based on local discriminant analysis for complex chemical processes monitoring

Energy Technology Data Exchange (ETDEWEB)

Lu, Chunhong; Xiao, Shaoqing; Gu, Xiaofeng [Jiangnan University, Wuxi (China)

2014-11-15

The collected training data often include both normal and faulty samples for complex chemical processes. However, some monitoring methods, such as partial least squares (PLS), principal component analysis (PCA), independent component analysis (ICA) and Fisher discriminant analysis (FDA), require fault-free data to build the normal operation model. These techniques are applicable after the preliminary step of data clustering is applied. We here propose a novel hyperplane distance neighbor clustering (HDNC) based on the local discriminant analysis (LDA) for chemical process monitoring. First, faulty samples are separated from normal ones using the HDNC method. Then, the optimal subspace for fault detection and classification can be obtained using the LDA approach. The proposed method takes the multimodality within the faulty data into account, and thus improves the capability of process monitoring significantly. The HDNC-LDA monitoring approach is applied to two simulation processes and then compared with the conventional FDA based on the K-nearest neighbor (KNN-FDA) method. The results obtained in two different scenarios demonstrate the superiority of the HDNC-LDA approach in terms of fault detection and classification accuracy.

Hyperplane distance neighbor clustering based on local discriminant analysis for complex chemical processes monitoring

International Nuclear Information System (INIS)

Lu, Chunhong; Xiao, Shaoqing; Gu, Xiaofeng

2014-01-01

The collected training data often include both normal and faulty samples for complex chemical processes. However, some monitoring methods, such as partial least squares (PLS), principal component analysis (PCA), independent component analysis (ICA) and Fisher discriminant analysis (FDA), require fault-free data to build the normal operation model. These techniques are applicable after the preliminary step of data clustering is applied. We here propose a novel hyperplane distance neighbor clustering (HDNC) based on the local discriminant analysis (LDA) for chemical process monitoring. First, faulty samples are separated from normal ones using the HDNC method. Then, the optimal subspace for fault detection and classification can be obtained using the LDA approach. The proposed method takes the multimodality within the faulty data into account, and thus improves the capability of process monitoring significantly. The HDNC-LDA monitoring approach is applied to two simulation processes and then compared with the conventional FDA based on the K-nearest neighbor (KNN-FDA) method. The results obtained in two different scenarios demonstrate the superiority of the HDNC-LDA approach in terms of fault detection and classification accuracy

Monte Carlo study of the magnetic properties of GdSb alloys

Energy Technology Data Exchange (ETDEWEB)

Masrour, R., E-mail: rachidmasrour@hotmail.com [Laboratory of Materials, Processes, Environment and Quality, Cady Ayyed University, National School of Applied Sciences, Sidi Bouzid, Safi, BP, 46000 63 (Morocco); LMPHE (URAC 12), Faculté des Sciences, Université Mohammed V-Agdal, Av. Ibn Batouta, B.P. 1014, Rabat (Morocco); Bahmad, L. [LMPHE (URAC 12), Faculté des Sciences, Université Mohammed V-Agdal, Av. Ibn Batouta, B.P. 1014, Rabat (Morocco); Hlil, E.K. [Institut Néel, CNRS et Université Joseph Fourier, BP 166, F-38042 Grenoble cedex 9 (France); Hamedoun, M. [Institute of Layers and Nanotechnologies, MAScIR, Rabat (Morocco); Benyoussef, A. [LMPHE (URAC 12), Faculté des Sciences, Université Mohammed V-Agdal, Av. Ibn Batouta, B.P. 1014, Rabat (Morocco); Institute of Layers and Nanotechnologies, MAScIR, Rabat (Morocco); Hassan II Academy of Science and Technology, Rabat (Morocco)

2014-03-15

The magnetic properties of antiferromagnetic GdSb layers have been studied using Monte Carlo simulations within the Ising model framework. The considered Hamiltonian includes first nearest-neighbor, an external magnetic field and a crystal field. The thermal magnetizations and magnetic susceptibilities are computed for a fixed size. In addition, the Néel temperature is deduced. The magnetization versus the exchange interactions and crystal field are studied for a fixed system size, N=5, 7 and 9 sites. The magnetic hysteresis cycle versus temperature is also established. - Highlights: • Determination of the Néel temperature of GdSb by MC simulations. • Magnetic hysteresis cycle of GdSb. • Determination of saturation magnetization and field coercive in GdSb.

An efficient architecture for LVQ-SLM for PAPR reduction

International Nuclear Information System (INIS)

Khalid, S.; Yasin, M.

2010-01-01

In this paper we propose an efficient architecture for the implementation of a LVQ (Learning Vector Quantization)NN (Neural Network), used as a classifier, for PAPR (Peak to Average Power Ratio) reduction. A special feature of the implementation is a combinatorial module for nearest neighbor search that allows online execution of this important operation during classification. The LVQ classifier is programmed in Verilog and the entire circuit is synthesized on FPGAs (Field Programmable Gate Arrays) using Xilinx at the rate ISE (Integrated Software Environment) 8.1i. The model is implemented with 64 sub carriers, considering the parametric values of WLANs standard IEEE 802.11a. Using the architecture, efficient on-line classification is achieved. (author)

History of the Lenz–Ising model 1965–1971

DEFF Research Database (Denmark)

Niss, Martin

2011-01-01

when it was realized that the Lenz–Ising model is actually relevant for the understanding of phase transitions. In this article, which is self-contained, I study how this realization affected attempts to understand critical phenomena, which can be understood as limiting cases of (first-order) phase...... of critical phenomena, for example that diverse physical systems exhibit similar behavior close to a critical point. Later, a more systematic program of understanding critical phenomena emerged that involved an explicit formulation of what it means to understand critical phenomena, namely, the elucidation...... of what features of the Hamiltonian of models lead to what kinds of behavior close to critical points. Attempts to accomplish this program culminated with the so-called hypothesis of universality, put forward independently by Robert B. Griffiths and Leo P. Kadanoff in 1970. They divided critical phenomena...

Spiral correlations in frustrated one-dimensional spin-1/2 Heisenberg J1-J2-J3 ferromagnets

International Nuclear Information System (INIS)

Zinke, R; Richter, J; Drechsler, S-L

2010-01-01

We use the coupled cluster method for infinite chains complemented by exact diagonalization of finite periodic chains to discuss the influence of a third-neighbor exchange J 3 on the ground state of the spin- 1/2 Heisenberg chain with ferromagnetic nearest-neighbor interaction J 1 and frustrating antiferromagnetic next-nearest-neighbor interaction J 2 . A third-neighbor exchange J 3 might be relevant to describe the magnetic properties of the quasi-one-dimensional edge-shared cuprates, such as LiVCuO 4 or LiCu 2 O 2 . In particular, we calculate the critical point J 2 c as a function of J 3 , where the ferromagnetic ground state gives way for a ground state with incommensurate spiral correlations. For antiferromagnetic J 3 the ferro-spiral transition is always continuous and the critical values J 2 c of the classical and the quantum model coincide. On the other hand, for ferromagnetic J 3 ∼ 1 | the critical value J 2 c of the quantum model is smaller than that of the classical model. Moreover, the transition becomes discontinuous, i.e. the model exhibits a quantum tricritical point. We also calculate the height of the jump of the spiral pitch angle at the discontinuous ferro-spiral transition.

Ecological risk assessment of TBT in Ise Bay.

Science.gov (United States)

Yamamoto, Joji; Yonezawa, Yoshitaka; Nakata, Kisaburo; Horiguchi, Fumio

2009-02-01

An ecological risk assessment of tributyltin (TBT) in Ise Bay was conducted using the margin of exposure (MOE) method. The assessment endpoint was defined to protect the survival, growth and reproduction of marine organisms. Sources of TBT in this study were assumed to be commercial vessels in harbors and navigation routes. Concentrations of TBT in Ise Bay were estimated using a three-dimensional hydrodynamic model, an ecosystem model and a chemical fate model. Estimated MOEs for marine organisms for 1990 and 2008 were approximately 0.1-2.0 and over 100 respectively, indicating a declining temporal trend in the probability of adverse effects. The chemical fate model predicts a much longer persistence of TBT in sediments than in the water column. Therefore, it is necessary to monitor the harmful effects of TBT on benthic organisms.

Regional Calibration of SCS-CN L-THIA Model: Application for Ungauged Basins

Directory of Open Access Journals (Sweden)

Ji-Hong Jeon

2014-05-01

Full Text Available Estimating surface runoff for ungauged watershed is an important issue. The Soil Conservation Service Curve Number (SCS-CN method developed from long-term experimental data is widely used to estimate surface runoff from gaged or ungauged watersheds. Many modelers have used the documented SCS-CN parameters without calibration, sometimes resulting in significant errors in estimating surface runoff. Several methods for regionalization of SCS-CN parameters were evaluated. The regionalization methods include: (1 average; (2 land use area weighted average; (3 hydrologic soil group area weighted average; (4 area combined land use and hydrologic soil group weighted average; (5 spatial nearest neighbor; (6 inverse distance weighted average; and (7 global calibration method, and model performance for each method was evaluated with application to 14 watersheds located in Indiana. Eight watersheds were used for calibration and six watersheds for validation. For the validation results, the spatial nearest neighbor method provided the highest average Nash-Sutcliffe (NS value at 0.58 for six watersheds but it included the lowest NS value and variance of NS values of this method was the highest. The global calibration method provided the second highest average NS value at 0.56 with low variation of NS values. Although the spatial nearest neighbor method provided the highest average NS value, this method was not statistically different than other methods. However, the global calibration method was significantly different than other methods except the spatial nearest neighbor method. Therefore, we conclude that the global calibration method is appropriate to regionalize SCS-CN parameters for ungauged watersheds.

Search for the Heisenberg spin glass on rewired cubic lattices with antiferromagnetic interaction

International Nuclear Information System (INIS)

Surungan, Tasrief

2016-01-01

Spin glass (SG) is a typical magnetic system which is mainly characterized by a frozen random spin orientation at low temperatures. Frustration and randomness are considered to be the key ingredients for the existence of SGs. Previously, Bartolozzi et al . [Phys. Rev. B73, 224419 (2006)] found that the antiferromagnetic (AF) Ising spins on scale free network (SFN) exhibited SG behavior. This is purely AF system, a new type of SG different from the canonical one which requires the presence of both FM and AF couplings. In this new system, frustration is purely due to a topological factor and its randomness is brought by irregular connectivity. Recently, it was reported that the AF Heisenberg model on SFN exhibited SG behavior [Surungan et al ., JPCS, 640, 012005 (2015)/doi:10.1088/1742-6596/640/1/012005]. In order to accommodate the notion of spatial dimension, we further investigated this type of system by studying an AF Heisenberg model on rewired cubic lattices, constructed by adding one extra bond randomly connecting each spin to one of its next-nearest neighbors. We used Replica Exchange algorithm of Monte Carlo Method and calculated the SG order parameter to search for the existence of SG phase. (paper)

Monte Carlo study of radiation-induced demagnetization using the two-dimensional Ising model

International Nuclear Information System (INIS)

Samin, Adib; Cao, Lei

2015-01-01

A simple radiation-damage model based on the Ising model for magnets is proposed to study the effects of radiation on the magnetism of permanent magnets. The model is studied in two dimensions using a Monte Carlo simulation, and it accounts for the radiation through the introduction of a localized heat pulse. The model exhibits qualitative agreement with experimental results, and it clearly elucidates the role that the coercivity and the radiation particle’s energy play in the process. A more quantitative agreement with experiment will entail accounting for the long-range dipole–dipole interactions and the crystalline anisotropy.

Monte Carlo study of radiation-induced demagnetization using the two-dimensional Ising model

Energy Technology Data Exchange (ETDEWEB)

Samin, Adib; Cao, Lei

2015-10-01

A simple radiation-damage model based on the Ising model for magnets is proposed to study the effects of radiation on the magnetism of permanent magnets. The model is studied in two dimensions using a Monte Carlo simulation, and it accounts for the radiation through the introduction of a localized heat pulse. The model exhibits qualitative agreement with experimental results, and it clearly elucidates the role that the coercivity and the radiation particle’s energy play in the process. A more quantitative agreement with experiment will entail accounting for the long-range dipole–dipole interactions and the crystalline anisotropy.

ISEE : An Intuitive Sound Editing Environment

NARCIS (Netherlands)

Vertegaal, R.P.H.; Bonis, E.

1994-01-01

This article presents ISEE, an intuitive sound editing environment, as a general sound synthesis model based on expert auditory perception and cognition of musical instruments. It discusses the backgrounds of current synthesizer user interface design and related timbre space research. Of the three