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.

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,...

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.

Linear perturbation renormalization group for the two-dimensional Ising model with nearest- and next-nearest-neighbor interactions in a field

Science.gov (United States)

Sznajd, J.

2016-12-01

The linear perturbation renormalization group (LPRG) is used to study the phase transition of the weakly coupled Ising chains with intrachain (J ) and interchain nearest-neighbor (J1) and next-nearest-neighbor (J2) interactions forming the triangular and rectangular lattices in a field. The phase diagrams with the frustration point at J2=-J1/2 for a rectangular lattice and J2=-J1 for a triangular lattice have been found. The LPRG calculations support the idea that the phase transition is always continuous except for the frustration point and is accompanied by a divergence of the specific heat. For the antiferromagnetic chains, the external field does not change substantially the shape of the phase diagram. The critical temperature is suppressed to zero according to the power law when approaching the frustration point with an exponent dependent on the value of the field.

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

Directory of Open Access Journals (Sweden)

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.

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

Directory of Open Access Journals (Sweden)

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.

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

Science.gov (United States)

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

2017-02-01

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

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...

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

Science.gov (United States)

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

2017-07-01

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

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.

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...

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

Hole motion in the t-J and Hubbard models: Effect of a next-nearest-neighbor hopping

International Nuclear Information System (INIS)

Gagliano, E.; Bacci, S.; Dagotto, E.

1990-01-01

Using exact diagonalization techniques, we study one dynamical hole in the two-dimensional t-J and Hubbard models on a square lattice including a next-nearest-neighbor hopping t'. We present the phase diagram in the parameter space (J/t,t'/t), discussing the ground-state properties of the hole. At J=0, a crossing of levels exists at some value of t' separating a ferromagnetic from an antiferromagnetic ground state. For nonzero J, at least four different regions appear where the system behaves like an antiferromagnet or a (not fully saturated) ferromagnet. We study the quasiparticle behavior of the hole, showing that for small values of |t'| the previously presented string picture is still valid. We also find that, for a realistic set of parameters derived from the Cu-O Hamiltonian, the hole has momentum (π/2,π/2), suggesting an enhancement of the p-wave superconducting mode due to the second-neighbor interactions in the spin-bag picture. Results for the t-t'-U model are also discussed with conclusions similar to those of the t-t'-J model. In general we found that t'=0 is not a singular point of these models

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.

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

Science.gov (United States)

Joshi, Nalini; Nakazono, Nobutaka

2017-07-01

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

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...

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.

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

Directory of Open Access Journals (Sweden)

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.

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.

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.

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.

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...

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

The spectrum and the quantum Hall effect on the square lattice with next-nearest-neighbor hopping: Statistics of holons and spinons in the t-J model

International Nuclear Information System (INIS)

Hatsugai, Y.; Kohmoto, M.

1992-01-01

We investigate the energy spectrum and the Hall effect of electrons on the square lattice with next-nearest-neighbor (NNN) hopping as well as nearest-neighbor hopping. General rational values of magnetic flux per unit cell φ=p/q are considered. In the absence of NNN hopping, the two bands at the center touch for q even, thus the Hall conductance is not well defined at half filling. An energy gap opens there by introducing NNN hoping. When φ=1/2, the NNN model coincides with the mean field Hamiltonian for the chiral spin state proposed by Wen, Wilczek and Zee (WWZ). The Hall conductance is calculated from the Diophantine equation and the E-φ diagram. We find that gaps close for other fillings at certain values of NNN hopping strength. The quantized value of the Hall conductance changes once this phenomenon occurs. In a mean field treatment of the t-J model, the effective Hamiltonian is the same as our NNN model. From this point of view, the statistics of the quasi-particles is not always semion and depends on the filling and the strength of the mean field. (orig.)

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.

Polymers with nearest- and next nearest-neighbor interactions on the Husimi lattice

Science.gov (United States)

Oliveira, Tiago J.

2016-04-01

The exact grand-canonical solution of a generalized interacting self-avoid walk (ISAW) model, placed on a Husimi lattice built with squares, is presented. In this model, beyond the traditional interaction {ω }1={{{e}}}{ɛ 1/{k}BT} between (nonconsecutive) monomers on nearest-neighbor (NN) sites, an additional energy {ɛ }2 is associated to next-NN (NNN) monomers. Three definitions of NNN sites/interactions are considered, where each monomer can have, effectively, at most two, four, or six NNN monomers on the Husimi lattice. The phase diagrams found in all cases have (qualitatively) the same thermodynamic properties: a non-polymerized (NP) and a polymerized (P) phase separated by a critical and a coexistence surface that meet at a tricritical (θ-) line. This θ-line is found even when one of the interactions is repulsive, existing for {ω }1 in the range [0,∞ ), i.e., for {ɛ }1/{k}BT in the range [-∞ ,∞ ). Thus, counterintuitively, a θ-point exists even for an infinite repulsion between NN monomers ({ω }1=0), being associated to a coil-‘soft globule’ transition. In the limit of an infinite repulsive force between NNN monomers, however, the coil-globule transition disappears, and only NP-P continuous transition is observed. This particular case, with {ω }2=0, is also solved exactly on the square lattice, using a transfer matrix calculation where a discontinuous NP-P transition is found. For attractive and repulsive forces between NN and NNN monomers, respectively, the model becomes quite similar to the semiflexible-ISAW one, whose crystalline phase is not observed here, as a consequence of the frustration due to competing NN and NNN forces. The mapping of the phase diagrams in canonical ones is discussed and compared with recent results from Monte Carlo simulations on the square lattice.

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.

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.

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.

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.

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.

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.

Reentrant behavior in the nearest-neighbor Ising antiferromagnet in a magnetic field

Science.gov (United States)

Neto, Minos A.; de Sousa, J. Ricardo

2004-12-01

Motived by the H-T phase diagram in the bcc Ising antiferromagnetic with nearest-neighbor interactions obtained by Monte Carlo simulation [Landau, Phys. Rev. B 16, 4164 (1977)] that shows a reentrant behavior at low temperature, with two critical temperatures in magnetic field about 2% greater than the critical value Hc=8J , we apply the effective field renormalization group (EFRG) approach in this model on three-dimensional lattices (simple cubic-sc and body centered cubic-bcc). We find that the critical curve TN(H) exhibits a maximum point around of H≃Hc only in the bcc lattice case. We also discuss the critical behavior by the effective field theory in clusters with one (EFT-1) and two (EFT-2) spins, and a reentrant behavior is observed for the sc and bcc lattices. We have compared our results of EFRG in the bcc lattice with Monte Carlo and series expansion, and we observe a good accordance between the methods.

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.

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).

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.

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

Science.gov (United States)

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

2009-04-01

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

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.

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 ...

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.

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.

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

Directory of Open Access Journals (Sweden)

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

Directory of Open Access Journals (Sweden)

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

Directory of Open Access Journals (Sweden)

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.

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

Self-consistent-field calculations of proteinlike incorporations in polyelectrolyte complex micelles

NARCIS (Netherlands)

Lindhoud, S.; Cohen Stuart, M.A.; Norde, W.; Leermakers, F.A.M.

2009-01-01

Self-consistent field theory is applied to model the structure and stability of polyelectrolyte complex micelles with incorporated protein (molten globule) molecules in the core. The electrostatic interactions that drive the micelle formation are mimicked by nearest-neighbor interactions using

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.

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.

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.

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.

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

Directory of Open Access Journals (Sweden)

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.

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

The square Ising model with second-neighbor interactions and the Ising chain in a transverse field

International Nuclear Information System (INIS)

Grynberg, M.D.; Tanatar, B.

1991-06-01

We consider the thermal and critical behaviour of the square Ising lattice with frustrated first - and second-neighbor interactions. A low-temperature domain wall analysis including kinks and dislocations shows that there is a close relation between this classical model and the Hamiltonian of an Ising chain in a transverse field provided that the ratio of the next-nearest to nearest-neighbor coupling, is close to 1/2. Due to the field inversion symmetry of the Ising chain Hamiltonian, the thermal properties of the classical system are symmetrical with respect to this coupling ratio. In the neighborhood of this regime critical exponents of the model turn out to belong to the Ising universality class. Our results are compared with previous Monte Carlo simulations. (author). 23 refs, 6 figs

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.

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.

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.

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.

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.

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

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

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.

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.

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

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.

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.

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.

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.

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.

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

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

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.

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

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.

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%.

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.

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.

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.

Incorporating dominant environment into individual fitness promotes cooperation in the spatial prisoners' dilemma game

International Nuclear Information System (INIS)

Jin, Jiahua; Shen, Chen; Chu, Chen; Shi, Lei

2017-01-01

Highlights: • In spatial games, each player incorporates its environment into fitness only when its environment is greater than or equal to its payoff. • The mechanism of incorporating dominant environment promotes evolution of cooperation. • The robustness of such a mechanism to promote cooperation is verified for the snowdrift game and the various interaction networks. - Abstract: In spatial evolutionary games, the fitness of each player is usually measured by its inheritance (i.e. the accumulated payoffs by playing the game with its all nearest neighbors), or by the linear combination of its inheritance and its environment (i.e. the average of its all nearest neighbors’ inheritance). However, a rational individual incorporates environment into its fitness to develop itself only when environment is dominant in real life. Here, we redefine the individual fitness as a linear combination of inheritance and environment when environment performs better than inheritance. Multiple Monte Carlo simulation results show that incorporating dominant environment can improve cooperation comparing with the traditional case, and furthermore increasing the proportion of prevailing environment can enhance cooperative level better. These findings indicate that our mechanism enhances the individual ability to adapt environment, and makes the spatial reciprocity more efficient. Besides, we also verify its robustness against different game models and various topology structures.

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

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.

Fuzzy Case-Based Reasoning in Product Style Acquisition Incorporating Valence-Arousal-Based Emotional Cellular Model

Directory of Open Access Journals (Sweden)

Fuqian Shi

2012-01-01

Full Text Available Emotional cellular (EC, proposed in our previous works, is a kind of semantic cell that contains kernel and shell and the kernel is formalized by a triple- L = , where P denotes a typical set of positive examples relative to word-L, d is a pseudodistance measure on emotional two-dimensional space: valence-arousal, and δ is a probability density function on positive real number field. The basic idea of EC model is to assume that the neighborhood radius of each semantic concept is uncertain, and this uncertainty will be measured by one-dimensional density function δ. In this paper, product form features were evaluated by using ECs and to establish the product style database, fuzzy case based reasoning (FCBR model under a defined similarity measurement based on fuzzy nearest neighbors (FNN incorporating EC was applied to extract product styles. A mathematical formalized inference system for product style was also proposed, and it also includes uncertainty measurement tool emotional cellular. A case study of style acquisition of mobile phones illustrated the effectiveness of the proposed methodology.

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.

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.

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.

Modeling the effect of neighboring grains on twin growth in HCP polycrystals

Science.gov (United States)

Kumar, M. Arul; Beyerlein, I. J.; Lebensohn, R. A.; Tomé, C. N.

2017-09-01

In this paper, we study the dependence of neighboring grain orientation on the local stress state around a deformation twin in a hexagonal close packed (HCP) crystal and its effects on the resistance against twin thickening. We use a recently developed, full-field elasto-visco-plastic formulation based on fast Fourier transforms that account for the twinning shear transformation imposed by the twin lamella. The study is applied to Mg, Zr and Ti, since these HCP metals tend to deform by activation of different types of slip modes. The analysis shows that the local stress along the twin boundary are strongly controlled by the relative orientation of the easiest deformation modes in the neighboring grain with respect to the twin lamella in the parent grain. A geometric expression that captures this parent-neighbor relationship is proposed and incorporated into a larger scale, mean-field visco-plastic self-consistent model to simulate the role of neighboring grain orientation on twin thickening. We demonstrate that the approach improves the prediction of twin area fraction distribution when compared with experimental observations.

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

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.

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.

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.

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

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.

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.

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.

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.

Prototype-Incorporated Emotional Neural Network.

Science.gov (United States)

Oyedotun, Oyebade K; Khashman, Adnan

2017-08-15

Artificial neural networks (ANNs) aim to simulate the biological neural activities. Interestingly, many ''engineering'' prospects in ANN have relied on motivations from cognition and psychology studies. So far, two important learning theories that have been subject of active research are the prototype and adaptive learning theories. The learning rules employed for ANNs can be related to adaptive learning theory, where several examples of the different classes in a task are supplied to the network for adjusting internal parameters. Conversely, the prototype-learning theory uses prototypes (representative examples); usually, one prototype per class of the different classes contained in the task. These prototypes are supplied for systematic matching with new examples so that class association can be achieved. In this paper, we propose and implement a novel neural network algorithm based on modifying the emotional neural network (EmNN) model to unify the prototype- and adaptive-learning theories. We refer to our new model as ``prototype-incorporated EmNN''. Furthermore, we apply the proposed model to two real-life challenging tasks, namely, static hand-gesture recognition and face recognition, and compare the result to those obtained using the popular back-propagation neural network (BPNN), emotional BPNN (EmNN), deep networks, an exemplar classification model, and k-nearest neighbor.

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

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...

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.

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.

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...

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.

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

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.

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.

Improved Model for Predicting the Free Energy Contribution of Dinucleotide Bulges to RNA Duplex Stability.

Science.gov (United States)

Tomcho, Jeremy C; Tillman, Magdalena R; Znosko, Brent M

2015-09-01

Predicting the secondary structure of RNA is an intermediate in predicting RNA three-dimensional structure. Commonly, determining RNA secondary structure from sequence uses free energy minimization and nearest neighbor parameters. Current algorithms utilize a sequence-independent model to predict free energy contributions of dinucleotide bulges. To determine if a sequence-dependent model would be more accurate, short RNA duplexes containing dinucleotide bulges with different sequences and nearest neighbor combinations were optically melted to derive thermodynamic parameters. These data suggested energy contributions of dinucleotide bulges were sequence-dependent, and a sequence-dependent model was derived. This model assigns free energy penalties based on the identity of nucleotides in the bulge (3.06 kcal/mol for two purines, 2.93 kcal/mol for two pyrimidines, 2.71 kcal/mol for 5'-purine-pyrimidine-3', and 2.41 kcal/mol for 5'-pyrimidine-purine-3'). The predictive model also includes a 0.45 kcal/mol penalty for an A-U pair adjacent to the bulge and a -0.28 kcal/mol bonus for a G-U pair adjacent to the bulge. The new sequence-dependent model results in predicted values within, on average, 0.17 kcal/mol of experimental values, a significant improvement over the sequence-independent model. This model and new experimental values can be incorporated into algorithms that predict RNA stability and secondary structure from sequence.

Incorporating the information from direct and indirect neighbors into fitness evaluation enhances the cooperation in the social dilemmas

International Nuclear Information System (INIS)

Hu, Menglong; Wang, Juan; Kong, Lingcong; An, Kang; Bi, Tao; Guo, Baohong; Dong, Enzeng

2015-01-01

Highlights: •A novel fitness evaluation method integrating environmental information is presented. •The introduction of neighbors’ payoff favors the promotion of cooperation in the PDG. •The role of direct neighbors becomes much more prominent. •In the SDG, the cooperative behavior is also improved by this new mechanism. -- Abstract: We propose an improved fitness evaluation method to investigate the evolution of cooperation in the spatial social dilemmas. In our model, a focal player’s fitness is calculated as the linear combination of his own payoff, the average payoffs of direct and indirect neighbors in which two independent selection parameters (α and β) are used to control the proportion of various payoff contribution to the current fitness. Then, the fitness-based strategy update rule is still Fermi-like, and asynchronous update is adopted here. A large plethora of numerical simulations are performed to validate the behaviors of the current model, and the results unambiguously demonstrate that the cooperation level is greatly enhanced by introducing the payoffs from the surrounding players. In particular, the influence of direct neighbors become more evident when compared with indirect neighbors since the correlation between focal players and their direct neighbors is much closer. Current outcomes are significant for us to further illustrate the origin and emergence of cooperation within a wide variety of natural and man-made systems

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.

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.

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.

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.

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.…

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

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.

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.

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 ...

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.

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

Effects of temperature on domain-growth kinetics of fourfold-degenerate (2×1) ordering in Ising models

DEFF Research Database (Denmark)

Høst-Madsen, Anders; Shah, Peter Jivan; Hansen, Torben

1987-01-01

Computer-simulation techniques are used to study the domain-growth kinetics of (2×1) ordering in a two-dimensional Ising model with nonconserved order parameter and with variable ratio α of next-nearest- and nearest-neighbor interactions. At zero temperature, persistent growth characterized...

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.

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.

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

Quantum Lattice-Gas Model for the Diffusion Equation

National Research Council Canada - National Science Library

Yepez, J

2001-01-01

.... It is a minimal model with two qubits per node of a one-dimensional lattice and it is suitable for implementation on a large array of small quantum computers interconnected by nearest-neighbor...

Digital terrain model generalization incorporating scale, semantic and cognitive constraints

Science.gov (United States)

Partsinevelos, Panagiotis; Papadogiorgaki, Maria

2014-05-01

research scheme comprises of the combination of SOM with the variations of other widely used generalization algorithms. For instance, an adaptation of the Douglas-Peucker line simplification method in 3D data is used in order to reduce the initial nodes, while maintaining their actual coordinates. Furthermore, additional methods are deployed, aiming to corroborate and verify the significance of each node, such as mathematical algorithms exploiting the pixel's nearest neighbors. Finally, besides the quantitative evaluation of error vs information preservation in a DTM, cognitive inputs from geoscience experts are incorporated in order to test, fine-tune and advance our algorithm. Under the described strategy that incorporates mechanical, topology, semantic and cognitive restrains, results demonstrate the necessity to integrate these characteristics in describing raster DTM surfaces. Acknowledgements: This work is partially supported under the framework of the "Cooperation 2011" project ATLANTAS (11_SYN_6_1937) funded from the Operational Program "Competitiveness and Entrepreneurship" (co-funded by the European Regional Development Fund (ERDF)) and managed by the Greek General Secretariat for Research and Technology.

Distance-Based Image Classification: Generalizing to New Classes at Near Zero Cost

NARCIS (Netherlands)

Mensink, T.; Verbeek, J.; Perronnin, F.; Csurka, G.

2013-01-01

We study large-scale image classification methods that can incorporate new classes and training images continuously over time at negligible cost. To this end, we consider two distance-based classifiers, the k-nearest neighbor (k-NN) and nearest class mean (NCM) classifiers, and introduce a new

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

Thermodynamic optimization of the (Na2O + SiO2 + NaF + SiF4) reciprocal system using the Modified Quasichemical Model in the Quadruplet Approximation

International Nuclear Information System (INIS)

Lambotte, Guillaume; Chartrand, Patrice

2011-01-01

Highlights: → We model the Na 2 O-SiO 2 -NaF-SiF 4 reciprocal system based on a comprehensive review of all available experimental data. → The assessment includes Na 2 O-SiO 2 and NaF-SiF 4 binary systems. → Improvements to the Modified Quasichemical Model in the Quadruplet Approximation are presented. → The very strong short-range ordering among first-nearest and second-nearest neighbors in this system is reproduced. → This work constitutes the first assessment for all compositions and temperatures of a reciprocal oxyfluoride system. - Abstract: All available thermodynamic and phase diagram data for the condensed phases of the ternary reciprocal system (NaF + SiF 4 + Na 2 O + SiO 2 ) have been critically assessed. Model parameters for the unary (SiF 4 ), the binary systems and the ternary reciprocal system have been found, which permit to reproduce the most reliable experimental data. The Modified Quasichemical Model in the Quadruplet Approximation was used for the oxyfluoride liquid solution, which exhibits strong first-nearest-neighbor and second-nearest-neighbor short-range ordering. This thermodynamic model takes into account both types of short-range ordering as well as the coupling between them. Model parameters have been estimated for the hypothetical high-temperature liquid SiF 4 .

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.

PERBANDINGAN K-NEAREST NEIGHBOR DAN NAIVE BAYES UNTUK KLASIFIKASI TANAH LAYAK TANAM POHON JATI

Directory of Open Access Journals (Sweden)

Didik Srianto

2016-10-01

Full Text Available Data mining adalah proses menganalisa data dari perspektif yang berbeda dan menyimpulkannya menjadi informasi-informasi penting yang dapat dipakai untuk meningkatkan keuntungan, memperkecil biaya pengeluaran, atau bahkan keduanya. Secara teknis, data mining dapat disebut sebagai proses untuk menemukan korelasi atau pola dari ratusan atau ribuan field dari sebuah relasional database yang besar. Pada perum perhutani KPH SEMARANG saat ini masih menggunakan cara manual untuk menentukan jenis tanaman (jati / non jati. K-Nearest Neighbour atau k-NN merupakan algoritma data mining yang dapat digunakan untuk proses klasifikasi dan regresi. Naive bayes Classifier merupakan suatu teknik yang dapat digunakan untuk teknik klasifikasi. Pada penelitian ini k-NN dan Naive Bayes akan digunakan untuk mengklasifikasi data pohon jati dari perum perhutani KPH SEMARANG. Yang mana hasil klasifikasi dari k-NN dan Naive Bayes akan dibandingkan hasilnya. Pengujian dilakukan menggunakan software RapidMiner. Setelah dilakukan pengujian k-NN dianggap lebih baik dari Naife Bayes dengan akurasi 96.66% dan 82.63. Kata kunci -k-NN,Klasifikasi,Naive Bayes,Penanaman Pohon Jati

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

Science.gov (United States)

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

2016-04-01

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

Abelian tensor models on the lattice

Science.gov (United States)

Chaudhuri, Soumyadeep; Giraldo-Rivera, Victor I.; Joseph, Anosh; Loganayagam, R.; Yoon, Junggi

2018-04-01

We consider a chain of Abelian Klebanov-Tarnopolsky fermionic tensor models coupled through quartic nearest-neighbor interactions. We characterize the gauge-singlet spectrum for small chains (L =2 ,3 ,4 ,5 ) and observe that the spectral statistics exhibits strong evidence in favor of quasi-many-body localization.

Road Short-Term Travel Time Prediction Method Based on Flow Spatial Distribution and the Relations

Directory of Open Access Journals (Sweden)

Mingjun Deng

2016-01-01

Full Text Available There are many short-term road travel time forecasting studies based on time series, but indeed, road travel time not only relies on the historical travel time series, but also depends on the road and its adjacent sections history flow. However, few studies have considered that. This paper is based on the correlation of flow spatial distribution and the road travel time series, applying nearest neighbor and nonparametric regression method to build a forecasting model. In aspect of spatial nearest neighbor search, three different space distances are defined. In addition, two forecasting functions are introduced: one combines the forecasting value by mean weight and the other uses the reciprocal of nearest neighbors distance as combined weight. Three different distances are applied in nearest neighbor search, which apply to the two forecasting functions. For travel time series, the nearest neighbor and nonparametric regression are applied too. Then minimizing forecast error variance is utilized as an objective to establish the combination model. The empirical results show that the combination model can improve the forecast performance obviously. Besides, the experimental results of the evaluation for the computational complexity show that the proposed method can satisfy the real-time requirement.

Anti-ferromagnetic Heisenberg model on bilayer honeycomb

International Nuclear Information System (INIS)

Shoja, M.; Shahbazi, F.

2012-01-01

Recent experiment on spin-3/2 bilayer honeycomb lattice antiferromagnet Bi 3 Mn 4 O 12 (NO 3 ) shows a spin liquid behavior down to very low temperatures. This behavior can be ascribed to the frustration effect due to competitions between first and second nearest neighbour's antiferromagnet interaction. Motivated by the experiment, we study J 1 -J 2 Antiferromagnet Heisenberg model, using Mean field Theory. This calculation shows highly degenerate ground state. We also calculate the effect of second nearest neighbor through z direction and show these neighbors also increase frustration in these systems. Because of these degenerate ground state in these systems, spins can't find any ground state to be freeze in low temperatures. This behavior shows a novel spin liquid state down to very low temperatures.

Measurement of near neighbor separations of surface atoms

International Nuclear Information System (INIS)

Cohen, P.I.

Two techniques are being developed to measure the nearest neighbor distances of atoms at the surfaces of solids. Both measures extended fine structure in the excitation probability of core level electrons which are excited by an incident electron beam. This is an important problem because the structures of most surface systems are as yet unknown, even though the location of surface atoms is the basis for any quantitative understanding of the chemistry and physics of surfaces and interfaces. These methods would allow any laboratory to make in situ determinations of surface structure in conjunction with most other laboratory probes of surfaces. Each of these two techniques has different advantages; further, the combination of the two will increase confidence in the results by reducing systematic error in the data analysis

Cultural macroevolution on neighbor graphs : vertical and horizontal transmission among Western North American Indian societies.

Science.gov (United States)

Towner, Mary C; Grote, Mark N; Venti, Jay; Borgerhoff Mulder, Monique

2012-09-01

What are the driving forces of cultural macroevolution, the evolution of cultural traits that characterize societies or populations? This question has engaged anthropologists for more than a century, with little consensus regarding the answer. We develop and fit autologistic models, built upon both spatial and linguistic neighbor graphs, for 44 cultural traits of 172 societies in the Western North American Indian (WNAI) database. For each trait, we compare models including or excluding one or both neighbor graphs, and for the majority of traits we find strong evidence in favor of a model which uses both spatial and linguistic neighbors to predict a trait's distribution. Our results run counter to the assertion that cultural trait distributions can be explained largely by the transmission of traits from parent to daughter populations and are thus best analyzed with phylogenies. In contrast, we show that vertical and horizontal transmission pathways can be incorporated in a single model, that both transmission modes may indeed operate on the same trait, and that for most traits in the WNAI database, accounting for only one mode of transmission would result in a loss of information.

Bacterial genomes lacking long-range correlations may not be modeled by low-order Markov chains: the role of mixing statistics and frame shift of neighboring genes.

Science.gov (United States)

Cocho, Germinal; Miramontes, Pedro; Mansilla, Ricardo; Li, Wentian

2014-12-01

We examine the relationship between exponential correlation functions and Markov models in a bacterial genome in detail. Despite the well known fact that Markov models generate sequences with correlation function that decays exponentially, simply constructed Markov models based on nearest-neighbor dimer (first-order), trimer (second-order), up to hexamer (fifth-order), and treating the DNA sequence as being homogeneous all fail to predict the value of exponential decay rate. Even reading-frame-specific Markov models (both first- and fifth-order) could not explain the fact that the exponential decay is very slow. Starting with the in-phase coding-DNA-sequence (CDS), we investigated correlation within a fixed-codon-position subsequence, and in artificially constructed sequences by packing CDSs with out-of-phase spacers, as well as altering CDS length distribution by imposing an upper limit. From these targeted analyses, we conclude that the correlation in the bacterial genomic sequence is mainly due to a mixing of heterogeneous statistics at different codon positions, and the decay of correlation is due to the possible out-of-phase between neighboring CDSs. There are also small contributions to the correlation from bases at the same codon position, as well as by non-coding sequences. These show that the seemingly simple exponential correlation functions in bacterial genome hide a complexity in correlation structure which is not suitable for a modeling by Markov chain in a homogeneous sequence. Other results include: use of the (absolute value) second largest eigenvalue to represent the 16 correlation functions and the prediction of a 10-11 base periodicity from the hexamer frequencies. Copyright © 2014 Elsevier Ltd. All rights reserved.

An interactive cooperation model for neighboring virtual power plants

International Nuclear Information System (INIS)

Shabanzadeh, Morteza; Sheikh-El-Eslami, Mohammad-Kazem; Haghifam, Mahmoud-Reza

2017-01-01

Highlights: •The trading strategies of a VPP in cooperation with its neighboring VPPs are addressed. •A portfolio of inter-regional contracts is considered to model this cooperation scheme. •A novel mathematical formulation for possible inadvertent transactions is provided. •A two-stage stochastic programming approach is applied to characterize the uncertainty. •Two efficient risk measures, SSD and CVaR, are implemented in the VPP decision-making problem. -- Abstract: Future distribution systems will accommodate an increasing share of distributed energy resources (DERs). Facing with this new reality, virtual power plants (VPPs) play a key role to aggregate DERs with the aim of facilitating their involvement in wholesale electricity markets. In this paper, the trading strategies of a VPP in cooperation with its neighboring VPPs are addressed. Toward this aim, a portfolio of inter-regional contracts is considered to model this cooperation and maximize the energy trade opportunities of the VPP within a medium-term horizon. To hedge against profit variability caused by market price uncertainties, two efficient risk management approaches are also implemented in the VPP decision-making problem based on the concepts of conditional value at risk (CVaR) and second-order stochastic dominance constraints (SSD). The resulting models are formulated as mixed-integer linear programming (MILP) problems that can be solved using off-the-shelf software packages. The efficiency of the proposed risk-hedging models is analyzed through a detailed case study, and thereby relevant conclusions are drawn.

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.

MINIMIZING THE PREPARATION TIME OF A TUBES MACHINE: EXACT SOLUTION AND HEURISTICS

Directory of Open Access Journals (Sweden)

Robinson S.V. Hoto

Full Text Available ABSTRACT In this paper we optimize the preparation time of a tubes machine. Tubes are hard tubes made by gluing strips of paper that are packed in paper reels, and some of them may be reused between the production of one and another tube. We present a mathematical model for the minimization of changing reels and movements and also implementations for the heuristics Nearest Neighbor, an improvement of a nearest neighbor (Best Nearest Neighbor, refinements of the Best Nearest Neighbor heuristic and a heuristic of permutation called Best Configuration using the IDE (integrated development environment WxDev C++. The results obtained by simulations improve the one used by the company.

Tricriticality in the q-neighbor Ising model on a partially duplex clique.

Science.gov (United States)

Chmiel, Anna; Sienkiewicz, Julian; Sznajd-Weron, Katarzyna

2017-12-01

We analyze a modified kinetic Ising model, a so-called q-neighbor Ising model, with Metropolis dynamics [Phys. Rev. E 92, 052105 (2015)PLEEE81539-375510.1103/PhysRevE.92.052105] on a duplex clique and a partially duplex clique. In the q-neighbor Ising model each spin interacts only with q spins randomly chosen from its whole neighborhood. In the case of a duplex clique the change of a spin is allowed only if both levels simultaneously induce this change. Due to the mean-field-like nature of the model we are able to derive the analytic form of transition probabilities and solve the corresponding master equation. The existence of the second level changes dramatically the character of the phase transition. In the case of the monoplex clique, the q-neighbor Ising model exhibits a continuous phase transition for q=3, discontinuous phase transition for q≥4, and for q=1 and q=2 the phase transition is not observed. On the other hand, in the case of the duplex clique continuous phase transitions are observed for all values of q, even for q=1 and q=2. Subsequently we introduce a partially duplex clique, parametrized by r∈[0,1], which allows us to tune the network from monoplex (r=0) to duplex (r=1). Such a generalized topology, in which a fraction r of all nodes appear on both levels, allows us to obtain the critical value of r=r^{*}(q) at which a tricriticality (switch from continuous to discontinuous phase transition) appears.

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.

Knowledgeable Neighbors: a mobile clinic model for disease prevention and screening in underserved communities.

Science.gov (United States)

Hill, Caterina; Zurakowski, David; Bennet, Jennifer; Walker-White, Rainelle; Osman, Jamie L; Quarles, Aaron; Oriol, Nancy

2012-03-01

The Family Van mobile health clinic uses a "Knowledgeable Neighbor" model to deliver cost-effective screening and prevention activities in underserved neighborhoods in Boston, MA. We have described the Knowledgeable Neighbor model and used operational data collected from 2006 to 2009 to evaluate the service. The Family Van successfully reached mainly minority low-income men and women. Of the clients screened, 60% had previously undetected elevated blood pressure, 14% had previously undetected elevated blood glucose, and 38% had previously undetected elevated total cholesterol. This represents an important model for reaching underserved communities to deliver proven cost-effective prevention activities, both to help control health care costs and to reduce health disparities.

Characteristics of the probability function for three random-walk models of reaction--diffusion processes

International Nuclear Information System (INIS)

Musho, M.K.; Kozak, J.J.

1984-01-01

A method is presented for calculating exactly the relative width (sigma 2 )/sup 1/2// , the skewness γ 1 , and the kurtosis γ 2 characterizing the probability distribution function for three random-walk models of diffusion-controlled processes. For processes in which a diffusing coreactant A reacts irreversibly with a target molecule B situated at a reaction center, three models are considered. The first is the traditional one of an unbiased, nearest-neighbor random walk on a d-dimensional periodic/confining lattice with traps; the second involves the consideration of unbiased, non-nearest-neigh bor (i.e., variable-step length) walks on the same d-dimensional lattice; and, the third deals with the case of a biased, nearest-neighbor walk on a d-dimensional lattice (wherein a walker experiences a potential centered at the deep trap site of the lattice). Our method, which has been described in detail elsewhere [P.A. Politowicz and J. J. Kozak, Phys. Rev. B 28, 5549 (1983)] is based on the use of group theoretic arguments within the framework of the theory of finite Markov processes

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

State-space prediction model for chaotic time series

Science.gov (United States)

Alparslan, A. K.; Sayar, M.; Atilgan, A. R.

1998-08-01

A simple method for predicting the continuation of scalar chaotic time series ahead in time is proposed. The false nearest neighbors technique in connection with the time-delayed embedding is employed so as to reconstruct the state space. A local forecasting model based upon the time evolution of the topological neighboring in the reconstructed phase space is suggested. A moving root-mean-square error is utilized in order to monitor the error along the prediction horizon. The model is tested for the convection amplitude of the Lorenz model. The results indicate that for approximately 100 cycles of the training data, the prediction follows the actual continuation very closely about six cycles. The proposed model, like other state-space forecasting models, captures the long-term behavior of the system due to the use of spatial neighbors in the state space.

Eksperimen Seleksi Fitur Pada Parameter Proyek Untuk Software Effort Estimation dengan K-Nearest Neighbor

Directory of Open Access Journals (Sweden)

Fachruddin Fachruddin

2017-07-01

Full Text Available Software Effort Estimation adalah proses estimasi biaya perangkat lunak sebagai suatu proses penting dalam melakukan proyek perangkat lunak. Berbagai penelitian terdahulu telah melakukan estimasi usaha perangkat lunak dengan berbagai metode, baik metode machine learning  maupun non machine learning. Penelitian ini mengadakan set eksperimen seleksi atribut pada parameter proyek menggunakan teknik k-nearest neighbours sebagai estimasinya dengan melakukan seleksi atribut menggunakan information gain dan mutual information serta bagaimana menemukan  parameter proyek yang paling representif pada software effort estimation. Dataset software estimation effort yang digunakan pada eksperimen adalah  yakni albrecht, china, kemerer dan mizayaki94 yang dapat diperoleh dari repositori data khusus Software Effort Estimation melalui url http://openscience.us/repo/effort/. Selanjutnya peneliti melakukan pembangunan aplikasi seleksi atribut untuk menyeleksi parameter proyek. Sistem ini menghasilkan dataset arff yang telah diseleksi. Aplikasi ini dibangun dengan bahasa java menggunakan IDE Netbean. Kemudian dataset yang telah di-generate merupakan parameter hasil seleksi yang akan dibandingkan pada saat melakukan Software Effort Estimation menggunakan tool WEKA . Seleksi Fitur berhasil menurunkan nilai error estimasi (yang diwakilkan oleh nilai RAE dan RMSE. Artinya bahwa semakin rendah nilai error (RAE dan RMSE maka semakin akurat nilai estimasi yang dihasilkan. Estimasi semakin baik setelah di lakukan seleksi fitur baik menggunakan information gain maupun mutual information. Dari nilai error yang dihasilkan maka dapat disimpulkan bahwa dataset yang dihasilkan seleksi fitur dengan metode information gain lebih baik dibanding mutual information namun, perbedaan keduanya tidak terlalu signifikan.

Carbon-hydrogen defects with a neighboring oxygen atom in n-type Si

Science.gov (United States)

Gwozdz, K.; Stübner, R.; Kolkovsky, Vl.; Weber, J.

2017-07-01

We report on the electrical activation of neutral carbon-oxygen complexes in Si by wet-chemical etching at room temperature. Two deep levels, E65 and E75, are observed by deep level transient spectroscopy in n-type Czochralski Si. The activation enthalpies of E65 and E75 are obtained as EC-0.11 eV (E65) and EC-0.13 eV (E75). The electric field dependence of their emission rates relates both levels to single acceptor states. From the analysis of the depth profiles, we conclude that the levels belong to two different defects, which contain only one hydrogen atom. A configuration is proposed, where the CH1BC defect, with hydrogen in the bond-centered position between neighboring C and Si atoms, is disturbed by interstitial oxygen in the second nearest neighbor position to substitutional carbon. The significant reduction of the CH1BC concentration in samples with high oxygen concentrations limits the use of this defect for the determination of low concentrations of substitutional carbon in Si samples.

A sequence-dependent rigid-base model of DNA

Science.gov (United States)

Gonzalez, O.; Petkevičiutė, D.; Maddocks, J. H.

2013-02-01

A novel hierarchy of coarse-grain, sequence-dependent, rigid-base models of B-form DNA in solution is introduced. The hierarchy depends on both the assumed range of energetic couplings, and the extent of sequence dependence of the model parameters. A significant feature of the models is that they exhibit the phenomenon of frustration: each base cannot simultaneously minimize the energy of all of its interactions. As a consequence, an arbitrary DNA oligomer has an intrinsic or pre-existing stress, with the level of this frustration dependent on the particular sequence of the oligomer. Attention is focussed on the particular model in the hierarchy that has nearest-neighbor interactions and dimer sequence dependence of the model parameters. For a Gaussian version of this model, a complete coarse-grain parameter set is estimated. The parameterized model allows, for an oligomer of arbitrary length and sequence, a simple and explicit construction of an approximation to the configuration-space equilibrium probability density function for the oligomer in solution. The training set leading to the coarse-grain parameter set is itself extracted from a recent and extensive database of a large number of independent, atomic-resolution molecular dynamics (MD) simulations of short DNA oligomers immersed in explicit solvent. The Kullback-Leibler divergence between probability density functions is used to make several quantitative assessments of our nearest-neighbor, dimer-dependent model, which is compared against others in the hierarchy to assess various assumptions pertaining both to the locality of the energetic couplings and to the level of sequence dependence of its parameters. It is also compared directly against all-atom MD simulation to assess its predictive capabilities. The results show that the nearest-neighbor, dimer-dependent model can successfully resolve sequence effects both within and between oligomers. For example, due to the presence of frustration, the model can

A sequence-dependent rigid-base model of DNA.

Science.gov (United States)

Gonzalez, O; Petkevičiūtė, D; Maddocks, J H

2013-02-07

A novel hierarchy of coarse-grain, sequence-dependent, rigid-base models of B-form DNA in solution is introduced. The hierarchy depends on both the assumed range of energetic couplings, and the extent of sequence dependence of the model parameters. A significant feature of the models is that they exhibit the phenomenon of frustration: each base cannot simultaneously minimize the energy of all of its interactions. As a consequence, an arbitrary DNA oligomer has an intrinsic or pre-existing stress, with the level of this frustration dependent on the particular sequence of the oligomer. Attention is focussed on the particular model in the hierarchy that has nearest-neighbor interactions and dimer sequence dependence of the model parameters. For a Gaussian version of this model, a complete coarse-grain parameter set is estimated. The parameterized model allows, for an oligomer of arbitrary length and sequence, a simple and explicit construction of an approximation to the configuration-space equilibrium probability density function for the oligomer in solution. The training set leading to the coarse-grain parameter set is itself extracted from a recent and extensive database of a large number of independent, atomic-resolution molecular dynamics (MD) simulations of short DNA oligomers immersed in explicit solvent. The Kullback-Leibler divergence between probability density functions is used to make several quantitative assessments of our nearest-neighbor, dimer-dependent model, which is compared against others in the hierarchy to assess various assumptions pertaining both to the locality of the energetic couplings and to the level of sequence dependence of its parameters. It is also compared directly against all-atom MD simulation to assess its predictive capabilities. The results show that the nearest-neighbor, dimer-dependent model can successfully resolve sequence effects both within and between oligomers. For example, due to the presence of frustration, the model can

An Accurate Estimate of the Free Energy and Phase Diagram of All-DNA Bulk Fluids

Directory of Open Access Journals (Sweden)

Emanuele Locatelli

2018-04-01

Full Text Available We present a numerical study in which large-scale bulk simulations of self-assembled DNA constructs have been carried out with a realistic coarse-grained model. The investigation aims at obtaining a precise, albeit numerically demanding, estimate of the free energy for such systems. We then, in turn, use these accurate results to validate a recently proposed theoretical approach that builds on a liquid-state theory, the Wertheim theory, to compute the phase diagram of all-DNA fluids. This hybrid theoretical/numerical approach, based on the lowest-order virial expansion and on a nearest-neighbor DNA model, can provide, in an undemanding way, a parameter-free thermodynamic description of DNA associating fluids that is in semi-quantitative agreement with experiments. We show that the predictions of the scheme are as accurate as those obtained with more sophisticated methods. We also demonstrate the flexibility of the approach by incorporating non-trivial additional contributions that go beyond the nearest-neighbor model to compute the DNA hybridization free energy.

A local non-parametric model for trade sign inference

Science.gov (United States)

Blazejewski, Adam; Coggins, Richard

2005-03-01

We investigate a regularity in market order submission strategies for 12 stocks with large market capitalization on the Australian Stock Exchange. The regularity is evidenced by a predictable relationship between the trade sign (trade initiator), size of the trade, and the contents of the limit order book before the trade. We demonstrate this predictability by developing an empirical inference model to classify trades into buyer-initiated and seller-initiated. The model employs a local non-parametric method, k-nearest neighbor, which in the past was used successfully for chaotic time series prediction. The k-nearest neighbor with three predictor variables achieves an average out-of-sample classification accuracy of 71.40%, compared to 63.32% for the linear logistic regression with seven predictor variables. The result suggests that a non-linear approach may produce a more parsimonious trade sign inference model with a higher out-of-sample classification accuracy. Furthermore, for most of our stocks the observed regularity in market order submissions seems to have a memory of at least 30 trading days.

A localized navigation algorithm for Radiation Evasion for nuclear facilities. Part II: Optimizing the “Nearest Exit” Criterion

Energy Technology Data Exchange (ETDEWEB)

Khasawneh, Mohammed A., E-mail: mkha@ieee.org [Department of Electrical Engineering, Jordan University of Science and Technology (Jordan); Al-Shboul, Zeina Aman M., E-mail: xeinaaman@gmail.com [Department of Electrical Engineering, Jordan University of Science and Technology (Jordan); Jaradat, Mohammad A., E-mail: majaradat@just.edu.jo [Department of Mechanical Engineering, Jordan University of Science and Technology (Jordan); Malkawi, Mohammad I., E-mail: mmalkawi@aimws.com [College of Engineering, Jadara University, Irbid 221 10 (Jordan)

2013-06-15

Highlights: ► A new navigation algorithm for Radiation Evasion around nuclear facilities. ► An optimization criteria minimized under algorithm operation. ► A man-borne device guiding the occupational worker towards paths that warrant least radiation × time products. ► Benefits of using localized navigation as opposed to global navigation schemas. ► A path discrimination function for finding the navigational paths exhibiting the least amounts of radiation. -- Abstract: In this extension from part I (Khasawneh et al., in press), we modify the navigation algorithm which was presented with the objective of optimizing the “Radiation Evasion” Criterion so that navigation would optimize the criterion of “Nearest Exit”. Under this modification, algorithm would yield navigation paths that would guide occupational workers towards Nearest Exit points. Again, under this optimization criterion, algorithm leverages the use of localized information acquired through a well designed and distributed wireless sensor network, as it averts the need for any long-haul communication links or centralized decision and monitoring facility thereby achieving a more reliable performance under dynamic environments. As was done in part I, the proposed algorithm under the “Nearest Exit” Criterion is designed to leverage nearest neighbor information coming in through the sensory network overhead, in computing successful navigational paths from one point to another. For comparison purposes, the proposed algorithm is tested under the two optimization criteria: “Radiation Evasion” and “Nearest Exit”, for different numbers of step look-ahead. We verify the performance of the algorithm by means of simulations, whereby navigational paths are calculated for different radiation fields. We, via simulations, also, verify the performance of the algorithm in comparison with a well-known global navigation algorithm upon which we draw our conclusions.

A localized navigation algorithm for Radiation Evasion for nuclear facilities. Part II: Optimizing the “Nearest Exit” Criterion

International Nuclear Information System (INIS)

Khasawneh, Mohammed A.; Al-Shboul, Zeina Aman M.; Jaradat, Mohammad A.; Malkawi, Mohammad I.

2013-01-01

Highlights: ► A new navigation algorithm for Radiation Evasion around nuclear facilities. ► An optimization criteria minimized under algorithm operation. ► A man-borne device guiding the occupational worker towards paths that warrant least radiation × time products. ► Benefits of using localized navigation as opposed to global navigation schemas. ► A path discrimination function for finding the navigational paths exhibiting the least amounts of radiation. -- Abstract: In this extension from part I (Khasawneh et al., in press), we modify the navigation algorithm which was presented with the objective of optimizing the “Radiation Evasion” Criterion so that navigation would optimize the criterion of “Nearest Exit”. Under this modification, algorithm would yield navigation paths that would guide occupational workers towards Nearest Exit points. Again, under this optimization criterion, algorithm leverages the use of localized information acquired through a well designed and distributed wireless sensor network, as it averts the need for any long-haul communication links or centralized decision and monitoring facility thereby achieving a more reliable performance under dynamic environments. As was done in part I, the proposed algorithm under the “Nearest Exit” Criterion is designed to leverage nearest neighbor information coming in through the sensory network overhead, in computing successful navigational paths from one point to another. For comparison purposes, the proposed algorithm is tested under the two optimization criteria: “Radiation Evasion” and “Nearest Exit”, for different numbers of step look-ahead. We verify the performance of the algorithm by means of simulations, whereby navigational paths are calculated for different radiation fields. We, via simulations, also, verify the performance of the algorithm in comparison with a well-known global navigation algorithm upon which we draw our conclusions

Incorporating excitation-induced dephasing into the Maxwell-Bloch numerical modeling of photon echoes

International Nuclear Information System (INIS)

Burr, G.W.; Harris, Todd L.; Babbitt, Wm. Randall; Jefferson, C. Michael

2004-01-01

We describe the incorporation of excitation-induced dephasing (EID) into the Maxwell-Bloch numerical simulation of photon echoes. At each time step of the usual numerical integration, stochastic frequency jumps of ions--caused by excitation of neighboring ions--is modeled by convolving each Bloch vector with the Bloch vectors of nearby frequency detunings. The width of this convolution kernel follows the instantaneous change in overall population, integrated over the simulated bandwidth. This approach is validated by extensive comparison against published and original experimental results. The enhanced numerical model is then used to investigate the accuracy of experiments designed to extrapolate to the intrinsic dephasing time T 2 from data taken in the presence of EID. Such a modeling capability offers improved understanding of experimental results, and should allow quantitative analysis of engineering tradeoffs in realistic optical coherent transient applications

Global 30m Height Above the Nearest Drainage

Science.gov (United States)

Donchyts, Gennadii; Winsemius, Hessel; Schellekens, Jaap; Erickson, Tyler; Gao, Hongkai; Savenije, Hubert; van de Giesen, Nick

2016-04-01

Variability of the Earth surface is the primary characteristics affecting the flow of surface and subsurface water. Digital elevation models, usually represented as height maps above some well-defined vertical datum, are used a lot to compute hydrologic parameters such as local flow directions, drainage area, drainage network pattern, and many others. Usually, it requires a significant effort to derive these parameters at a global scale. One hydrological characteristic introduced in the last decade is Height Above the Nearest Drainage (HAND): a digital elevation model normalized using nearest drainage. This parameter has been shown to be useful for many hydrological and more general purpose applications, such as landscape hazard mapping, landform classification, remote sensing and rainfall-runoff modeling. One of the essential characteristics of HAND is its ability to capture heterogeneities in local environments, difficult to measure or model otherwise. While many applications of HAND were published in the academic literature, no studies analyze its variability on a global scale, especially, using higher resolution DEMs, such as the new, one arc-second (approximately 30m) resolution version of SRTM. In this work, we will present the first global version of HAND computed using a mosaic of two DEMS: 30m SRTM and Viewfinderpanorama DEM (90m). The lower resolution DEM was used to cover latitudes above 60 degrees north and below 56 degrees south where SRTM is not available. We compute HAND using the unmodified version of the input DEMs to ensure consistency with the original elevation model. We have parallelized processing by generating a homogenized, equal-area version of HydroBASINS catchments. The resulting catchment boundaries were used to perform processing using 30m resolution DEM. To compute HAND, a new version of D8 local drainage directions as well as flow accumulation were calculated. The latter was used to estimate river head by incorporating fixed and

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).

A simple spatiotemporal chaotic Lotka-Volterra model

International Nuclear Information System (INIS)

Sprott, J.C.; Wildenberg, J.C.; Azizi, Yousef

2005-01-01

A mathematically simple example of a high-dimensional (many-species) Lotka-Volterra model that exhibits spatiotemporal chaos in one spatial dimension is described. The model consists of a closed ring of identical agents, each competing for fixed finite resources with two of its four nearest neighbors. The model is prototypical of more complicated models in its quasiperiodic route to chaos (including attracting 3-tori), bifurcations, spontaneous symmetry breaking, and spatial pattern formation

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...

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.

Combining Fourier and lagged k-nearest neighbor imputation for biomedical time series data.

Science.gov (United States)

Rahman, Shah Atiqur; Huang, Yuxiao; Claassen, Jan; Heintzman, Nathaniel; Kleinberg, Samantha

2015-12-01

Most clinical and biomedical data contain missing values. A patient's record may be split across multiple institutions, devices may fail, and sensors may not be worn at all times. While these missing values are often ignored, this can lead to bias and error when the data are mined. Further, the data are not simply missing at random. Instead the measurement of a variable such as blood glucose may depend on its prior values as well as that of other variables. These dependencies exist across time as well, but current methods have yet to incorporate these temporal relationships as well as multiple types of missingness. To address this, we propose an imputation method (FLk-NN) that incorporates time lagged correlations both within and across variables by combining two imputation methods, based on an extension to k-NN and the Fourier transform. This enables imputation of missing values even when all data at a time point is missing and when there are different types of missingness both within and across variables. In comparison to other approaches on three biological datasets (simulated and actual Type 1 diabetes datasets, and multi-modality neurological ICU monitoring) the proposed method has the highest imputation accuracy. This was true for up to half the data being missing and when consecutive missing values are a significant fraction of the overall time series length. Copyright © 2015 Elsevier Inc. All rights reserved.

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.

Neighbors United for Health

Science.gov (United States)

Westhoff, Wayne W.; Corvin, Jaime; Virella, Irmarie

2009-01-01

Modeled upon the ecclesiastic community group concept of Latin America to unite and strengthen the bond between the Church and neighborhoods, a community-based organization created Vecinos Unidos por la Salud (Neighbors United for Health) to bring health messages into urban Latino neighborhoods. The model is based on five tenants, and incorporates…

Model-based mean square error estimators for k-nearest neighbour predictions and applications using remotely sensed data for forest inventories

Science.gov (United States)

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

2009-01-01

New model-based estimators of the uncertainty of pixel-level and areal k-nearest neighbour (knn) predictions of attribute Y from remotely-sensed ancillary data X are presented. Non-parametric functions predict Y from scalar 'Single Index Model' transformations of X. Variance functions generated...

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.

On the Asymptotic Behavior of the Kernel Function in the Generalized Langevin Equation: A One-Dimensional Lattice Model

Science.gov (United States)

Chu, Weiqi; Li, Xiantao

2018-01-01

We present some estimates for the memory kernel function in the generalized Langevin equation, derived using the Mori-Zwanzig formalism from a one-dimensional lattice model, in which the particles interactions are through nearest and second nearest neighbors. The kernel function can be explicitly expressed in a matrix form. The analysis focuses on the decay properties, both spatially and temporally, revealing a power-law behavior in both cases. The dependence on the level of coarse-graining is also studied.

Air Pollution from Livestock Farms Is Associated with Airway Obstruction in Neighboring Residents.

Science.gov (United States)

Borlée, Floor; Yzermans, C Joris; Aalders, Bernadette; Rooijackers, Jos; Krop, Esmeralda; Maassen, Catharina B M; Schellevis, François; Brunekreef, Bert; Heederik, Dick; Smit, Lidwien A M

2017-11-01

Livestock farm emissions may not only affect respiratory health of farmers but also of neighboring residents. To explore associations between spatial and temporal variation in pollutant emissions from livestock farms and lung function in a general, nonfarming, rural population in the Netherlands. We conducted a cross-sectional study in 2,308 adults (age, 20-72 yr). A pulmonary function test was performed measuring prebronchodilator and post-bronchodilator FEV 1 , FVC, FEV 1 /FVC, and maximum mid-expiratory flow (MMEF). Spatial exposure was assessed as (1) number of farms within 500 m and 1,000 m of the home, (2) distance to the nearest farm, and (3) modeled annual average fine dust emissions from farms within 500 m and 1,000 m of the home address. Temporal exposure was assessed as week-average ambient particulate matter livestock farms within a 1,000-m buffer from the home address and MMEF, which was more pronounced in participants without atopy. No associations were found with other spatial exposure variables. Week-average particulate matter livestock air pollution emissions are associated with lung function deficits in nonfarming residents.

Ground state phase diagram of extended attractive Hubbard model

International Nuclear Information System (INIS)

Robaszkiewicz, S.; Chao, K.A.; Micnas, R.

1980-08-01

The ground state phase diagram of the extended Hubbard model with intraatomic attraction has been derived in the Hartree-Fock approximation formulated in terms of the Bogoliubov variational approach. For a given value of electron density, the nature of the ordered ground state depends essentially on the sign and the strength of the nearest neighbor coupling. (author)

Data Mining Learning Models and Algorithms on a Scada System Data Repository

Directory of Open Access Journals (Sweden)

Mircea Rîşteiu

2010-06-01

Full Text Available This paper presents three data mining techniques applied
on a SCADA system data repository: Naijve Bayes, k-Nearest Neighbor and Decision Trees. A conclusion that k-Nearest Neighbor is a suitable method to classify the large amount of data considered is made finally according to the mining result and its reasonable explanation. The experiments are built on the training data set and evaluated using the new test set with machine learning tool WEKA.

Optical phonons in cubic AlxGa1-xN approached by the modified random element isodisplacement model

International Nuclear Information System (INIS)

Liu, M.S.; Bursill, L.A.; Prawer, S.

1998-01-01

The behaviour of longitudinal and transverse optical phonons in cubic Al x Ga l-x N are derived theoretically as a function of the concentration x (0≤x≤1). The calculation is based on a Modified Random Element Isodisplacement model which considers the interactions from the nearest neighbor and second neighbor atoms. We find one-mode behavior in Al x Ga l-x N where the phonon frequency in general varies continuously and approximately linearly with x. (author)

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 lattice model for influenza spreading.

Directory of Open Access Journals (Sweden)

Antonella Liccardo

Full Text Available We construct a stochastic SIR model for influenza spreading on a D-dimensional lattice, which represents the dynamic contact network of individuals. An age distributed population is placed on the lattice and moves on it. The displacement from a site to a nearest neighbor empty site, allows individuals to change the number and identities of their contacts. The dynamics on the lattice is governed by an attractive interaction between individuals belonging to the same age-class. The parameters, which regulate the pattern dynamics, are fixed fitting the data on the age-dependent daily contact numbers, furnished by the Polymod survey. A simple SIR transmission model with a nearest neighbors interaction and some very basic adaptive mobility restrictions complete the model. The model is validated against the age-distributed Italian epidemiological data for the influenza A(H1N1 during the [Formula: see text] season, with sensible predictions for the epidemiological parameters. For an appropriate topology of the lattice, we find that, whenever the accordance between the contact patterns of the model and the Polymod data is satisfactory, there is a good agreement between the numerical and the experimental epidemiological data. This result shows how rich is the information encoded in the average contact patterns of individuals, with respect to the analysis of the epidemic spreading of an infectious disease.

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.

Constructing a logical, regular axis topology from an irregular topology

Science.gov (United States)

Faraj, Daniel A.

2014-07-01

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

Efficient computation of k-Nearest Neighbour Graphs for large high-dimensional data sets on GPU clusters.

Directory of Open Access Journals (Sweden)

Ali Dashti

Full Text Available This paper presents an implementation of the brute-force exact k-Nearest Neighbor Graph (k-NNG construction for ultra-large high-dimensional data cloud. The proposed method uses Graphics Processing Units (GPUs and is scalable with multi-levels of parallelism (between nodes of a cluster, between different GPUs on a single node, and within a GPU. The method is applicable to homogeneous computing clusters with a varying number of nodes and GPUs per node. We achieve a 6-fold speedup in data processing as compared with an optimized method running on a cluster of CPUs and bring a hitherto impossible [Formula: see text]-NNG generation for a dataset of twenty million images with 15 k dimensionality into the realm of practical possibility.

Use of the neighboring orbital model for analysis of electronic coupling in Class III intervalence compounds

International Nuclear Information System (INIS)

Nelsen, Stephen F.; Weaver, Michael N.; Luo Yun; Lockard, Jenny V.; Zink, Jeffrey I.

2006-01-01

Symmetrical charge-delocalized intervalence radical ions should not be described by the traditional two-state model that has been so successful for their localized counterparts. If they lack direct overlap between their charge-bearing units (M), their diabatic orbitals have an equal energy pair of symmetrized M-centered combination orbitals that are symmetric (S) or antisymmetric (A) with respect to a symmetry element at the center of the molecule. The M combination orbitals will mix separately with bridge orbitals of the same symmetry. We call the simplest useful model for this situation the neighboring orbital model, which uses the S and A bridge orbitals of high overlap that lie closest in energy to the M orbital pair, resulting in two two-state models that have a common energy for one pair. This model is developed quantitatively, and examples having 1, 3, 5, and 7 electrons in the neighboring orbitals are illustrated

The media effect in Axelrod's model explained

Science.gov (United States)

Peres, L. R.; Fontanari, J. F.

2011-11-01

We revisit the problem of introducing an external global field —the mass media— in Axelrod's model of social dynamics, where in addition to their nearest neighbors, the agents can interact with a virtual neighbor whose cultural features are fixed from the outset. The finding that this apparently homogenizing field actually increases the cultural diversity has been considered a puzzle since the phenomenon was first reported more than a decade ago. Here we offer a simple explanation for it, which is based on the pedestrian observation that Axelrod's model exhibits more cultural diversity, i.e., more distinct cultural domains, when the agents are allowed to interact solely with the media field than when they can interact with their neighbors as well. In this perspective, it is the local homogenizing interactions that work towards making the absorbing configurations less fragmented as compared with the extreme situation in which the agents interact with the media only.

Norrie disease and MAO genes: nearest neighbors.

Science.gov (United States)

Chen, Z Y; Denney, R M; Breakefield, X O

1995-01-01

The Norrie disease and MAO genes are tandemly arranged in the p11.4-p11.3 region of the human X chromosome in the order tel-MAOA-MAOB-NDP-cent. This relationship is conserved in the mouse in the order tel-MAOB-MAOA-NDP-cent. The MAO genes appear to have arisen by tandem duplication of an ancestral MAO gene, but their positional relationship to NDP appears to be random. Distinctive X-linked syndromes have been described for mutations in the MAOA and NDP genes, and in addition, individuals have been identified with contiguous gene syndromes due to chromosomal deletions which encompass two or three of these genes. Loss of function of the NDP gene causes a syndrome of congenital blindness and progressive hearing loss, sometimes accompanied by signs of CNS dysfunction, including variable mental retardation and psychiatric symptoms. Other mutations in the NDP gene have been found to underlie another X-linked eye disease, exudative vitreo-retinopathy. An MAOA deficiency state has been described in one family to date, with features of altered amine and amine metabolite levels, low normal intelligence, apparent difficulty in impulse control and cardiovascular difficulty in affected males. A contiguous gene syndrome in which all three genes are lacking, as well as other as yet unidentified flanking genes, results in severe mental retardation, small stature, seizures and congenital blindness, as well as altered amine and amine metabolites. Issues that remain to be resolved are the function of the NDP gene product, the frequency and phenotype of the MAOA deficiency state, and the possible occurrence and phenotype of an MAOB deficiency state.

Predicción de fracaso en empresas latinoamericanas utilizando el método del vecino más cercano para predecir efectos aleatorios en modelos mixtos || Prediction of Failure in Latin-American Companies Using the Nearest-Neighbor Method to Predict Random Effects in Mixed Models

Directory of Open Access Journals (Sweden)

Caro, Norma Patricia

2017-12-01

Full Text Available En la presente década, en economías emergentes como las latinoamericanas, se han comenzado a aplicar modelos logísticos mixtos para predecir el fracaso financiero de las empresas. No obstante, existen limitaciones subyacentes a la metodología, vinculadas a la factibilidad de predicción del estado de nuevas empresas que no han formado parte de la muestra de entrenamiento con la que se estimó el modelo. En la literatura se han propuesto diversos métodos de predicción para los efectos aleatorios que forman parte de los modelos mixtos, entre ellos, el del vecino más cercano. Este método es aplicado en una segunda etapa, luego de la estimación de un modelo que explica la situación financiera (en crisis o sana de las empresas mediante la consideración del comportamiento de sus ratios contables. En el presente trabajo, se consideraron empresas de Argentina, Chile y Perú, estimando los efectos aleatorios que resultaron significativos en la estimación del modelo mixto. De este modo, se concluye que la aplicación de este método permite identificar empresas con problemas financieros con una tasa de clasificación correcta superior a 80%, lo cual cobra relevancia en la modelación y predicción de este tipo de riesgo. || In the present decade, in emerging economies such as those in Latin-America, mixed logistic models have been started applying to predict the financial failure of companies. However, there are limitations for the methodology linked to the feasibility of predicting the state of new companies that have not been part of the training sample which was used to estimate the model. In the literature, several methods have been proposed for predicting random effects in the mixed models such as, for example, the nearest neighbor. This method is applied in a second step, after estimating a model that explains the financial situation (in crisis or healthy of companies by considering the behavior of its financial ratios. In this study

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.

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

ENTROPY CHARACTERISTICS IN MODELS FOR COORDINATION OF NEIGHBORING ROAD SECTIONS

Directory of Open Access Journals (Sweden)

N. I. Kulbashnaya

2016-01-01

Full Text Available The paper considers an application of entropy characteristics as criteria to coordinate traffic conditions at neighboring road sections. It has been proved that the entropy characteristics are widely used in the methods that take into account information influence of the environment on drivers and in the mechanisms that create such traffic conditions which ensure preservation of the optimal level of driver’s emotional tension during the drive. Solution of such problem is considered in the aspect of coordination of traffic conditions at neighboring road sections that, in its turn, is directed on exclusion of any driver’s transitional processes. Methodology for coordination of traffic conditions at neighboring road sections is based on the E. V. Gavrilov’s concept on coordination of some parameters of road sections which can be expressed in the entropy characteristics. The paper proposes to execute selection of coordination criteria according to accident rates because while moving along neighboring road sections traffic conditions change drastically that can result in creation of an accident situation. Relative organization of a driver’s perception field and driver’s interaction with the traffic environment has been selected as entropy characteristics. Therefore, the given characteristics are made conditional to the road accidents rate. The investigation results have revealed a strong correlation between the relative organization of the driver’s perception field and the relative organization of the driver’s interaction with the traffic environment and the accident rate. Results of the executed experiment have proved an influence of the accident rate on the investigated entropy characteristics.

Plastic dislocation motion via nonequilibrium molecular and continuum dynamics

International Nuclear Information System (INIS)

Hoover, W.G.; Ladd, A.J.C.; Hoover, N.E.

1980-01-01

The classical two-dimensional close-packed triangular lattice, with nearest-neighbor spring forces, is a convenient standard material for the investigation of dislocation motion and plastic flow. Two kinds of calculations, based on this standard material, are described here: (1) Molecular Dynamics simulations, incorporating adiabatic strains described with the help of Doll's Tensor, and (2) Continuum Dynamics simulations, incorporating periodic boundaries and dislocation interaction through stress-field superposition

Empirical mode decomposition and k-nearest embedding vectors for timely analyses of antibiotic resistance trends.

Science.gov (United States)

Teodoro, Douglas; Lovis, Christian

2013-01-01

Antibiotic resistance is a major worldwide public health concern. In clinical settings, timely antibiotic resistance information is key for care providers as it allows appropriate targeted treatment or improved empirical treatment when the specific results of the patient are not yet available. To improve antibiotic resistance trend analysis algorithms by building a novel, fully data-driven forecasting method from the combination of trend extraction and machine learning models for enhanced biosurveillance systems. We investigate a robust model for extraction and forecasting of antibiotic resistance trends using a decade of microbiology data. Our method consists of breaking down the resistance time series into independent oscillatory components via the empirical mode decomposition technique. The resulting waveforms describing intrinsic resistance trends serve as the input for the forecasting algorithm. The algorithm applies the delay coordinate embedding theorem together with the k-nearest neighbor framework to project mappings from past events into the future dimension and estimate the resistance levels. The algorithms that decompose the resistance time series and filter out high frequency components showed statistically significant performance improvements in comparison with a benchmark random walk model. We present further qualitative use-cases of antibiotic resistance trend extraction, where empirical mode decomposition was applied to highlight the specificities of the resistance trends. The decomposition of the raw signal was found not only to yield valuable insight into the resistance evolution, but also to produce novel models of resistance forecasters with boosted prediction performance, which could be utilized as a complementary method in the analysis of antibiotic resistance trends.

Adsorbate-enhanced chemisorption in the CO/Re(001) system: Experiment and theory

International Nuclear Information System (INIS)

Becker, O.M.; Chacham, I.; Asscher, M.; Ben-Shaul, A.

1989-01-01

The chemisorption kinetics of CO molecules on Re(001) crystal surface was studied by temperature-programmed desorption (TPD) experiments in the crystal temperature range of 80-360 K. Correlation was found between the population of the tightly bound, partially dissociated, β-CO state and the less tightly bound α-CO state; basically, the α-state starts to populate when the β-sites approach saturation. Furthermore, the increase in β-CO coverage is accompanied by an increase in the overall sticking probability, as well as in the nonmonotonic coverage dependence of the peak desorption temperature of the α-state. The chemisorption kinetics was found to be crystal temperature independent. LEED analysis reveals that the CO overlayer is disordered, as reported previously. A theoretical model is proposed to account for the above observations. In this model, the β-sites are treated as traps for mobile α-CO admolecules. Occupied β-sites then serve as nucleation centers for enhanced, extrinsic precursor-mediated, chemisorption and island growth. The nonmonotonic variation of α-CO adsorption energy, and the appearance of a shoulder in the α-CO TPD peak at high coverages, are explained by a lattice gas model, incorporating repulsive nearest-neighbor and attractive next-nearest-neighbor lateral interactions between the chemisorbed molecules

Design ensemble machine learning model for breast cancer diagnosis.

Science.gov (United States)

Hsieh, Sheau-Ling; Hsieh, Sung-Huai; Cheng, Po-Hsun; Chen, Chi-Huang; Hsu, Kai-Ping; Lee, I-Shun; Wang, Zhenyu; Lai, Feipei

2012-10-01

In this paper, we classify the breast cancer of medical diagnostic data. Information gain has been adapted for feature selections. Neural fuzzy (NF), k-nearest neighbor (KNN), quadratic classifier (QC), each single model scheme as well as their associated, ensemble ones have been developed for classifications. In addition, a combined ensemble model with these three schemes has been constructed for further validations. The experimental results indicate that the ensemble learning performs better than individual single ones. Moreover, the combined ensemble model illustrates the highest accuracy of classifications for the breast cancer among all models.

NeighborHood

OpenAIRE

Corominola Ocaña, Víctor

2015-01-01

NeighborHood és una aplicació basada en el núvol, adaptable a qualsevol dispositiu (mòbil, tablet, desktop). L'objectiu d'aquesta aplicació és poder permetre als usuaris introduir a les persones del seu entorn més immediat i que aquestes persones siguin visibles per a la resta d'usuaris. NeighborHood es una aplicación basada en la nube, adaptable a cualquier dispositivo (móvil, tablet, desktop). El objetivo de esta aplicación es poder permitir a los usuarios introducir a las personas de su...

Personalised news filtering and recommendation system using Chi-square statistics-based K-nearest neighbour (χ2SB-KNN) model

Science.gov (United States)

Adeniyi, D. A.; Wei, Z.; Yang, Y.

2017-10-01

Recommendation problem has been extensively studied by researchers in the field of data mining, database and information retrieval. This study presents the design and realisation of an automated, personalised news recommendations system based on Chi-square statistics-based K-nearest neighbour (χ2SB-KNN) model. The proposed χ2SB-KNN model has the potential to overcome computational complexity and information overloading problems, reduces runtime and speeds up execution process through the use of critical value of χ2 distribution. The proposed recommendation engine can alleviate scalability challenges through combined online pattern discovery and pattern matching for real-time recommendations. This work also showcases the development of a novel method of feature selection referred to as Data Discretisation-Based feature selection method. This is used for selecting the best features for the proposed χ2SB-KNN algorithm at the preprocessing stage of the classification procedures. The implementation of the proposed χ2SB-KNN model is achieved through the use of a developed in-house Java program on an experimental website called OUC newsreaders' website. Finally, we compared the performance of our system with two baseline methods which are traditional Euclidean distance K-nearest neighbour and Naive Bayesian techniques. The result shows a significant improvement of our method over the baseline methods studied.

The Sznajd Model with Team Work

Science.gov (United States)

Li, Hong-Jun; Lin, Lu-Zi; Sun, He; He, Ming-Feng

In 2000, Sznajd-weron and Sznajd introduced a model for the simulation of a closed democratic community with a two-party system, and it is found that a closed community has to evolve either to a dictatorship or a stalemate state. In this paper, we continued to study on this model. All the neighboring individuals holding the same opinion is defined as a team, which will influence its nearest neighbor's decision and realize the opinion evolution. After some time-steps, a steady state appeared and the stalemate state in original model is eliminated. Moreover, the demand of time-steps has decreased dramatically. In addition, we also analyzed the effect of the various dispersal degree of the initial opinion on the opinion converging at the probability of one steady state. Finally we analyzed the effect of noise on convergence and found that the ability of anti-noise was increased about 1000 times compared with Sznajd model.

Self-Organized Criticality in an Anisotropic Earthquake Model

Science.gov (United States)

Li, Bin-Quan; Wang, Sheng-Jun

2018-03-01

We have made an extensive numerical study of a modified model proposed by Olami, Feder, and Christensen to describe earthquake behavior. Two situations were considered in this paper. One situation is that the energy of the unstable site is redistributed to its nearest neighbors randomly not averagely and keeps itself to zero. The other situation is that the energy of the unstable site is redistributed to its nearest neighbors randomly and keeps some energy for itself instead of reset to zero. Different boundary conditions were considered as well. By analyzing the distribution of earthquake sizes, we found that self-organized criticality can be excited only in the conservative case or the approximate conservative case in the above situations. Some evidence indicated that the critical exponent of both above situations and the original OFC model tend to the same result in the conservative case. The only difference is that the avalanche size in the original model is bigger. This result may be closer to the real world, after all, every crust plate size is different. Supported by National Natural Science Foundation of China under Grant Nos. 11675096 and 11305098, the Fundamental Research Funds for the Central Universities under Grant No. GK201702001, FPALAB-SNNU under Grant No. 16QNGG007, and Interdisciplinary Incubation Project of SNU under Grant No. 5

A distance weighted-based approach for self-organized aggregation in robot swarms

KAUST Repository

Khaldi, Belkacem

2017-12-14

In this paper, a Distance-Weighted K Nearest Neighboring (DW-KNN) topology is proposed to study self-organized aggregation as an emergent swarming behavior within robot swarms. A virtual physics approach is applied among the proposed neighborhood topology to keep the robots together. A distance-weighted function based on a Smoothed Particle Hydrodynamic (SPH) interpolation approach is used as a key factor to identify the K-Nearest neighbors taken into account when aggregating the robots. The intra virtual physical connectivity among these neighbors is achieved using a virtual viscoelastic-based proximity model. With the ARGoS based-simulator, we model and evaluate the proposed approach showing various self-organized aggregations performed by a swarm of N foot-bot robots.

Charge-spin-orbital dynamics of one-dimensional two-orbital Hubbard model

Energy Technology Data Exchange (ETDEWEB)

Onishi, Hiroaki [Advanced Science Research Center, Japan Atomic Energy Agency, Tokai, Ibaraki 319-1195 (Japan)

2010-01-15

We study the real-time evolution of a charge-excited state in a one-dimensional e{sub g}-orbital degenerate Hubbard model, by a time-dependent density-matrix renormalization group method. Considering a chain along the z direction, electrons hop between adjacent 3z{sup 2}-r{sup 2} orbitals, while x{sup 2}-y{sup 2} orbitals are localized. For the charge-excited state, a holon-doublon pair is introduced into the ground state at quarter filling. At initial time, there is no electron in a holon site, while a pair of electrons occupies 3z{sup 2}-r{sup 2} orbital in a doublon site. As the time evolves, the holon motion is governed by the nearest-neighbor hopping, but the electron pair can transfer between 3z{sup 2}-r{sup 2} orbital and x{sup 2}-y{sup 2} orbital through the pair hopping in addition to the nearest-neighbor hopping. Thus holon and doublon propagate at different speed due to the pair hopping that is characteristic of multi-orbital systems.

Incorporating functional inter-relationships into protein function prediction algorithms

Directory of Open Access Journals (Sweden)

Kumar Vipin

2009-05-01

Full Text Available Abstract Background Functional classification schemes (e.g. the Gene Ontology that serve as the basis for annotation efforts in several organisms are often the source of gold standard information for computational efforts at supervised protein function prediction. While successful function prediction algorithms have been developed, few previous efforts have utilized more than the protein-to-functional class label information provided by such knowledge bases. For instance, the Gene Ontology not only captures protein annotations to a set of functional classes, but it also arranges these classes in a DAG-based hierarchy that captures rich inter-relationships between different classes. These inter-relationships present both opportunities, such as the potential for additional training examples for small classes from larger related classes, and challenges, such as a harder to learn distinction between similar GO terms, for standard classification-based approaches. Results We propose a method to enhance the performance of classification-based protein function prediction algorithms by addressing the issue of using these interrelationships between functional classes constituting functional classification schemes. Using a standard measure for evaluating the semantic similarity between nodes in an ontology, we quantify and incorporate these inter-relationships into the k-nearest neighbor classifier. We present experiments on several large genomic data sets, each of which is used for the modeling and prediction of over hundred classes from the GO Biological Process ontology. The results show that this incorporation produces more accurate predictions for a large number of the functional classes considered, and also that the classes benefitted most by this approach are those containing the fewest members. In addition, we show how our proposed framework can be used for integrating information from the entire GO hierarchy for improving the accuracy of

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

Large-n limit of the Heisenberg model: The decorated lattice and the disordered chain

International Nuclear Information System (INIS)

Khoruzhenko, B.A.; Pastur, L.A.; Shcherbina, M.V.

1989-01-01

The critical temperature of the generalized spherical model (large-component limit of the classical Heisenberg model) on a cubic lattice, whose every bond is decorated by L spins, is found. When L → ∞, the asymptotics of the temperature is T c ∼ aL -1 . The reduction of the number of spherical constraints for the model is found to be fairly large. The free energy of the one-dimensional generalized spherical model with random nearest neighbor interaction is calculated

Does a pear growl? Interference from semantic properties of orthographic neighbors.

Science.gov (United States)

Pecher, Diane; de Rooij, Jimmy; Zeelenberg, René

2009-07-01

In this study, we investigated whether semantic properties of a word's orthographic neighbors are activated during visual word recognition. In two experiments, words were presented with a property that was not true for the word itself. We manipulated whether the property was true for an orthographic neighbor of the word. Our results showed that rejection of the property was slower and less accurate when the property was true for a neighbor than when the property was not true for a neighbor. These findings indicate that semantic information is activated before orthographic processing is finished. The present results are problematic for the links model (Forster, 2006; Forster & Hector, 2002) that was recently proposed in order to bring form-first models of visual word recognition into line with previously reported findings (Forster & Hector, 2002; Pecher, Zeelenberg, & Wagenmakers, 2005; Rodd, 2004).

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.

Uniqueness of Gibbs Measure for Models with Uncountable Set of Spin Values on a Cayley Tree

International Nuclear Information System (INIS)

Eshkabilov, Yu. Kh.; Haydarov, F. H.; Rozikov, U. A.

2013-01-01

We consider models with nearest-neighbor interactions and with the set [0, 1] of spin values, on a Cayley tree of order K ≥ 1. It is known that the ‘splitting Gibbs measures’ of the model can be described by solutions of a nonlinear integral equation. For arbitrary k ≥ 2 we find a sufficient condition under which the integral equation has unique solution, hence under the condition the corresponding model has unique splitting Gibbs measure.

IMPROVING NEAREST NEIGHBOUR SEARCH IN 3D SPATIAL ACCESS METHOD

Directory of Open Access Journals (Sweden)

A. Suhaibaha

2016-10-01

Full Text Available Nearest Neighbour (NN is one of the important queries and analyses for spatial application. In normal practice, spatial access method structure is used during the Nearest Neighbour query execution to retrieve information from the database. However, most of the spatial access method structures are still facing with unresolved issues such as overlapping among nodes and repetitive data entry. This situation will perform an excessive Input/Output (IO operation which is inefficient for data retrieval. The situation will become more crucial while dealing with 3D data. The size of 3D data is usually large due to its detail geometry and other attached information. In this research, a clustered 3D hierarchical structure is introduced as a 3D spatial access method structure. The structure is expected to improve the retrieval of Nearest Neighbour information for 3D objects. Several tests are performed in answering Single Nearest Neighbour search and k Nearest Neighbour (kNN search. The tests indicate that clustered hierarchical structure is efficient in handling Nearest Neighbour query compared to its competitor. From the results, clustered hierarchical structure reduced the repetitive data entry and the accessed page. The proposed structure also produced minimal Input/Output operation. The query response time is also outperformed compared to the other competitor. For future outlook of this research several possible applications are discussed and summarized.

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.

Identifying influential neighbors in animal flocking.

Directory of Open Access Journals (Sweden)

Li Jiang

2017-11-01

Full Text Available Schools of fish and flocks of birds can move together in synchrony and decide on new directions of movement in a seamless way. This is possible because group members constantly share directional information with their neighbors. Although detecting the directionality of other group members is known to be important to maintain cohesion, it is not clear how many neighbors each individual can simultaneously track and pay attention to, and what the spatial distribution of these influential neighbors is. Here, we address these questions on shoals of Hemigrammus rhodostomus, a species of fish exhibiting strong schooling behavior. We adopt a data-driven analysis technique based on the study of short-term directional correlations to identify which neighbors have the strongest influence over the participation of an individual in a collective U-turn event. We find that fish mainly react to one or two neighbors at a time. Moreover, we find no correlation between the distance rank of a neighbor and its likelihood to be influential. We interpret our results in terms of fish allocating sequential and selective attention to their neighbors.

Identifying influential neighbors in animal flocking.

Science.gov (United States)

Jiang, Li; Giuggioli, Luca; Perna, Andrea; Escobedo, Ramón; Lecheval, Valentin; Sire, Clément; Han, Zhangang; Theraulaz, Guy

2017-11-01

Schools of fish and flocks of birds can move together in synchrony and decide on new directions of movement in a seamless way. This is possible because group members constantly share directional information with their neighbors. Although detecting the directionality of other group members is known to be important to maintain cohesion, it is not clear how many neighbors each individual can simultaneously track and pay attention to, and what the spatial distribution of these influential neighbors is. Here, we address these questions on shoals of Hemigrammus rhodostomus, a species of fish exhibiting strong schooling behavior. We adopt a data-driven analysis technique based on the study of short-term directional correlations to identify which neighbors have the strongest influence over the participation of an individual in a collective U-turn event. We find that fish mainly react to one or two neighbors at a time. Moreover, we find no correlation between the distance rank of a neighbor and its likelihood to be influential. We interpret our results in terms of fish allocating sequential and selective attention to their neighbors.

Neighbor Rupture Degree of Some Middle Graphs

Directory of Open Access Journals (Sweden)

Gökşen BACAK-TURAN

2017-12-01

Full Text Available Networks have an important place in our daily lives. Internet networks, electricity networks, water networks, transportation networks, social networks and biological networks are some of the networks we run into every aspects of our lives. A network consists of centers connected by links. A network is represented when centers and connections modelled by vertices and edges, respectively. In consequence of the failure of some centers or connection lines, measurement of the resistance of the network until the communication interrupted is called vulnerability of the network. In this study, neighbor rupture degree which is a parameter that explores the vulnerability values of the resulting graphs due to the failure of some centers of a communication network and its neighboring centers becoming nonfunctional were applied to some middle graphs and neighbor rupture degree of the $M(C_{n},$ $M(P_{n},$ $M(K_{1,n},$ $M(W_{n},$ $M(P_{n}\\times K_{2}$ and $M(C_{n}\\times K_{2}$ have been found.

The mass media destabilizes the cultural homogenous regime in Axelrod's model

Science.gov (United States)

Peres, Lucas R.; Fontanari, José F.

2010-02-01

An important feature of Axelrod's model for culture dissemination or social influence is the emergence of many multicultural absorbing states, despite the fact that the local rules that specify the agents interactions are explicitly designed to decrease the cultural differences between agents. Here we re-examine the problem of introducing an external, global interaction—the mass media—in the rules of Axelrod's model: in addition to their nearest neighbors, each agent has a certain probability p to interact with a virtual neighbor whose cultural features are fixed from the outset. Most surprisingly, this apparently homogenizing effect actually increases the cultural diversity of the population. We show that, contrary to previous claims in the literature, even a vanishingly small value of p is sufficient to destabilize the homogeneous regime for very large lattice sizes.

The mass media destabilizes the cultural homogenous regime in Axelrod's model

International Nuclear Information System (INIS)

Peres, Lucas R; Fontanari, Jose F

2010-01-01

An important feature of Axelrod's model for culture dissemination or social influence is the emergence of many multicultural absorbing states, despite the fact that the local rules that specify the agents interactions are explicitly designed to decrease the cultural differences between agents. Here we re-examine the problem of introducing an external, global interaction-the mass media-in the rules of Axelrod's model: in addition to their nearest neighbors, each agent has a certain probability p to interact with a virtual neighbor whose cultural features are fixed from the outset. Most surprisingly, this apparently homogenizing effect actually increases the cultural diversity of the population. We show that, contrary to previous claims in the literature, even a vanishingly small value of p is sufficient to destabilize the homogeneous regime for very large lattice sizes.

Self-avoiding trails with nearest-neighbour interactions on the square lattice

International Nuclear Information System (INIS)

Bedini, A; Owczarek, A L; Prellberg, T

2013-01-01

Self-avoiding walks and self-avoiding trails, two models of a polymer coil in dilute solution, have been shown to be governed by the same universality class. On the other hand, self-avoiding walks interacting via nearest-neighbour contacts (ISAW) and self-avoiding trails interacting via multiply visited sites (ISAT) are two models of the coil-globule, or collapse transition of a polymer in dilute solution. On the square lattice it has been established numerically that the collapse transition of each model lies in a different universality class. The models differ in two substantial ways. They differ in the types of subsets of random walk configurations utilized (site self-avoidance versus bond self-avoidance) and in the type of attractive interaction. It is therefore of some interest to consider self-avoiding trails interacting via nearest-neighbour attraction (INNSAT) in order to ascertain the source of the difference in the collapse universality class. Using the flatPERM algorithm, we have performed computer simulations of this model. We present numerical evidence that the singularity in the free energy of INNSAT at the collapse transition has a similar exponent to that of the ISAW model rather than the ISAT model. This would indicate that the type of interaction used in ISAW and ISAT is the source of the difference in the universality class. (paper)

Analytical results for entanglement in the five-qubit anisotropic Heisenberg model

International Nuclear Information System (INIS)

Wang Xiaoguang

2004-01-01

We solve the eigenvalue problem of the five-qubit anisotropic Heisenberg model, without use of Bethe's ansatz, and give analytical results for entanglement and mixedness of two nearest-neighbor qubits. The entanglement takes its maximum at Δ=1 (Δ>1) for the case of zero (finite) temperature with Δ being the anisotropic parameter. In contrast, the mixedness takes its minimum at Δ=1 (Δ>1) for the case of zero (finite) temperature

Acoustic modeling for emotion recognition

CERN Document Server

Anne, Koteswara Rao; Vankayalapati, Hima Deepthi

2015-01-01

 This book presents state of art research in speech emotion recognition. Readers are first presented with basic research and applications – gradually more advance information is provided, giving readers comprehensive guidance for classify emotions through speech. Simulated databases are used and results extensively compared, with the features and the algorithms implemented using MATLAB. Various emotion recognition models like Linear Discriminant Analysis (LDA), Regularized Discriminant Analysis (RDA), Support Vector Machines (SVM) and K-Nearest neighbor (KNN) and are explored in detail using prosody and spectral features, and feature fusion techniques.

Boosting nearest-neighbour to long-range integrable spin chains

International Nuclear Information System (INIS)

Bargheer, Till; Beisert, Niklas; Loebbert, Florian

2008-01-01

We present an integrability-preserving recursion relation for the explicit construction of long-range spin chain Hamiltonians. These chains are generalizations of the Haldane–Shastry and Inozemtsev models and they play an important role in recent advances in string/gauge duality. The method is based on arbitrary nearest-neighbour integrable spin chains and it sheds light on the moduli space of deformation parameters. We also derive the closed chain asymptotic Bethe equations. (letter)

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...

Predicted Mobility Edges in One-Dimensional Incommensurate Optical Lattices: An Exactly Solvable Model of Anderson Localization

International Nuclear Information System (INIS)

Biddle, J.; Das Sarma, S.

2010-01-01

Localization properties of noninteracting quantum particles in one-dimensional incommensurate lattices are investigated with an exponential short-range hopping that is beyond the minimal nearest-neighbor tight-binding model. Energy dependent mobility edges are analytically predicted in this model and verified with numerical calculations. The results are then mapped to the continuum Schroedinger equation, and an approximate analytical expression for the localization phase diagram and the energy dependent mobility edges in the ground band is obtained.

Quantum entanglement and criticality of the antiferromagnetic Heisenberg model in an external field

International Nuclear Information System (INIS)

Liu Guanghua; Li Ruoyan; Tian Guangshan

2012-01-01

By Lanczos exact diagonalization and the infinite time-evolving block decimation (iTEBD) technique, the two-site entanglement as well as the bipartite entanglement, the ground state energy, the nearest-neighbor correlations, and the magnetization in the antiferromagnetic Heisenberg (AFH) model under an external field are investigated. With increasing external field, the small size system shows some distinct upward magnetization stairsteps, accompanied synchronously with some downward two-site entanglement stairsteps. In the thermodynamic limit, the two-site entanglement, as well as the bipartite entanglement, the ground state energy, the nearest-neighbor correlations, and the magnetization are calculated, and the critical magnetic field h c = 2.0 is determined exactly. Our numerical results show that the quantum entanglement is sensitive to the subtle changing of the ground state, and can be used to describe the magnetization and quantum phase transition. Based on the discontinuous behavior of the first-order derivative of the entanglement entropy and fidelity per site, we think that the quantum phase transition in this model should belong to the second-order category. Furthermore, in the magnon existence region (h < 2.0), a logarithmically divergent behavior of block entanglement which can be described by a free bosonic field theory is observed, and the central charge c is determined to be 1. (paper)

Predictive modeling without notion of time

NARCIS (Netherlands)

Hoogendoorn, Mark; Funk, Burkhardt

2018-01-01

Supervised learning approaches that do not explicitly take the time component into account are briefly discussed in this chapter. The approaches explained include feedforward neural networks, support vector machines, k-nearest neighbor, decision trees, naïve bayes and ensembles. Guidelines are

Topological order in an exactly solvable 3D spin model

International Nuclear Information System (INIS)

Bravyi, Sergey; Leemhuis, Bernhard; Terhal, Barbara M.

2011-01-01

Research highlights: RHtriangle We study exactly solvable spin model with six-qubit nearest neighbor interactions on a 3D face centered cubic lattice. RHtriangle The ground space of the model exhibits topological quantum order. RHtriangle Elementary excitations can be geometrically described as the corners of rectangular-shaped membranes. RHtriangle The ground space can encode 4g qubits where g is the greatest common divisor of the lattice dimensions. RHtriangle Logical operators acting on the encoded qubits are described in terms of closed strings and closed membranes. - Abstract: We study a 3D generalization of the toric code model introduced recently by Chamon. This is an exactly solvable spin model with six-qubit nearest-neighbor interactions on an FCC lattice whose ground space exhibits topological quantum order. The elementary excitations of this model which we call monopoles can be geometrically described as the corners of rectangular-shaped membranes. We prove that the creation of an isolated monopole separated from other monopoles by a distance R requires an operator acting on Ω(R 2 ) qubits. Composite particles that consist of two monopoles (dipoles) and four monopoles (quadrupoles) can be described as end-points of strings. The peculiar feature of the model is that dipole-type strings are rigid, that is, such strings must be aligned with face-diagonals of the lattice. For periodic boundary conditions the ground space can encode 4g qubits where g is the greatest common divisor of the lattice dimensions. We describe a complete set of logical operators acting on the encoded qubits in terms of closed strings and closed membranes.

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

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

Science.gov (United States)

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

2016-01-01

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

Using recurrent neural network models for early detection of heart failure onset.

Science.gov (United States)

Choi, Edward; Schuetz, Andy; Stewart, Walter F; Sun, Jimeng

2017-03-01

We explored whether use of deep learning to model temporal relations among events in electronic health records (EHRs) would improve model performance in predicting initial diagnosis of heart failure (HF) compared to conventional methods that ignore temporality. Data were from a health system's EHR on 3884 incident HF cases and 28 903 controls, identified as primary care patients, between May 16, 2000, and May 23, 2013. Recurrent neural network (RNN) models using gated recurrent units (GRUs) were adapted to detect relations among time-stamped events (eg, disease diagnosis, medication orders, procedure orders, etc.) with a 12- to 18-month observation window of cases and controls. Model performance metrics were compared to regularized logistic regression, neural network, support vector machine, and K-nearest neighbor classifier approaches. Using a 12-month observation window, the area under the curve (AUC) for the RNN model was 0.777, compared to AUCs for logistic regression (0.747), multilayer perceptron (MLP) with 1 hidden layer (0.765), support vector machine (SVM) (0.743), and K-nearest neighbor (KNN) (0.730). When using an 18-month observation window, the AUC for the RNN model increased to 0.883 and was significantly higher than the 0.834 AUC for the best of the baseline methods (MLP). Deep learning models adapted to leverage temporal relations appear to improve performance of models for detection of incident heart failure with a short observation window of 12-18 months. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association.

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.

Extended Bose Hubbard model of interacting bosonic atoms in optical lattices: From superfluidity to density waves

International Nuclear Information System (INIS)

Mazzarella, G.; Giampaolo, S. M.; Illuminati, F.

2006-01-01

For systems of interacting, ultracold spin-zero neutral bosonic atoms, harmonically trapped and subject to an optical lattice potential, we derive an Extended Bose Hubbard (EBH) model by developing a systematic expansion for the Hamiltonian of the system in powers of the lattice parameters and of a scale parameter, the lattice attenuation factor. We identify the dominant terms that need to be retained in realistic experimental conditions, up to nearest-neighbor interactions and nearest-neighbor hoppings conditioned by the on-site occupation numbers. In the mean field approximation, we determine the free energy of the system and study the phase diagram both at zero and at finite temperature. At variance with the standard on site Bose Hubbard model, the zero-temperature phase diagram of the EBH model possesses a dual structure in the Mott insulating regime. Namely, for specific ranges of the lattice parameters, a density wave phase characterizes the system at integer fillings, with domains of alternating mean occupation numbers that are the atomic counterparts of the domains of staggered magnetizations in an antiferromagnetic phase. We show as well that in the EBH model, a zero-temperature quantum phase transition to pair superfluidity is, in principle, possible, but completely suppressed at the lowest order in the lattice attenuation factor. Finally, we determine the possible occurrence of the different phases as a function of the experimentally controllable lattice parameters

Modeling of the shape of infrared stimulated luminescence signals in feldspars

DEFF Research Database (Denmark)

Pagonis, Vasilis; Jain, Mayank; Murray, Andrew S.

2012-01-01

This paper presents a new empirical model describing infrared (IR) stimulation phenomena in feldspars. In the model electrons from the ground state of an electron trap are raised by infrared optical stimulation to the excited state, and subsequently recombine with a nearest-neighbor hole via...... corresponds to a fast rate of recombination processes taking place along the infrared stimulated luminescence (IRSL) curves. The subsequent decay of the simulated IRSL signal is characterized by a much slower recombination rate, which can be described by a power-law type of equation.Several simulations...

Estimation and Mapping Forest Attributes Using “k Nearest Neighbor” Method on IRS-P6 LISS III Satellite Image Data

Directory of Open Access Journals (Sweden)

Amir Eslam Bonyad

2015-06-01

Full Text Available In this study, we explored the utility of k Nearest Neighbor (kNN algorithm to integrate IRS-P6 LISS III satellite imagery data and ground inventory data for application in forest attributes (DBH, trees height, volume, basal area, density and forest cover type estimation and mapping. The ground inventory data was based on a systematic-random sampling grid and the numbers of sampling plots were 408 circular plots in a plantation in Guilan province, north of Iran. We concluded that kNN method was useful tool for mapping at a fine accuracy between 80% and 93.94%. Values of k between 5 and 8 seemed appropriate. The best distance metrics were found Euclidean, Fuzzy and Mahalanobis. Results showed that kNN was accurate enough for practical applicability for mapping forest areas.

Stochastic epidemic-type model with enhanced connectivity: exact solution

International Nuclear Information System (INIS)

Williams, H T; Mazilu, I; Mazilu, D A

2012-01-01

We present an exact analytical solution to a one-dimensional model of the susceptible–infected–recovered (SIR) epidemic type, with infection rates dependent on nearest-neighbor occupations. We use a quantum mechanical approach, transforming the master equation via a quantum spin operator formulation. We calculate exactly the time-dependent density of infected, recovered and susceptible populations for random initial conditions. Our results compare well with those of previous work, validating the model as a useful tool for additional and extended studies in this important area. Our model also provides exact solutions for the n-point correlation functions, and can be extended to more complex epidemic-type models

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)

ACTION RECOGNITION USING SALIENT NEIGHBORING HISTOGRAMS

DEFF Research Database (Denmark)

Ren, Huamin; Moeslund, Thomas B.

2013-01-01

Combining spatio-temporal interest points with Bag-of-Words models achieves state-of-the-art performance in action recognition. However, existing methods based on “bag-ofwords” models either are too local to capture the variance in space/time or fail to solve the ambiguity problem in spatial...... and temporal dimensions. Instead, we propose a salient vocabulary construction algorithm to select visual words from a global point of view, and form compact descriptors to represent discriminative histograms in the neighborhoods. Those salient neighboring histograms are then trained to model different actions...

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.

Incorporating pushing in exclusion-process models of cell migration.

Science.gov (United States)

Yates, Christian A; Parker, Andrew; Baker, Ruth E

2015-05-01

The macroscale movement behavior of a wide range of isolated migrating cells has been well characterized experimentally. Recently, attention has turned to understanding the behavior of cells in crowded environments. In such scenarios it is possible for cells to interact, inducing neighboring cells to move in order to make room for their own movements or progeny. Although the behavior of interacting cells has been modeled extensively through volume-exclusion processes, few models, thus far, have explicitly accounted for the ability of cells to actively displace each other in order to create space for themselves. In this work we consider both on- and off-lattice volume-exclusion position-jump processes in which cells are explicitly allowed to induce movements in their near neighbors in order to create space for themselves to move or proliferate into. We refer to this behavior as pushing. From these simple individual-level representations we derive continuum partial differential equations for the average occupancy of the domain. We find that, for limited amounts of pushing, comparison between the averaged individual-level simulations and the population-level model is nearly as good as in the scenario without pushing. Interestingly, we find that, in the on-lattice case, the diffusion coefficient of the population-level model is increased by pushing, whereas, for the particular off-lattice model that we investigate, the diffusion coefficient is reduced. We conclude, therefore, that it is important to consider carefully the appropriate individual-level model to use when representing complex cell-cell interactions such as pushing.

Grain price spikes and beggar-thy-neighbor policy responses

DEFF Research Database (Denmark)

Boysen, Ole; Jensen, Hans Grinsted

on the agenda of various international policy fora, including the annual meetings of G20 countries in recent years. For that reason, recent studies have attempted to quantify the extent to which such policy actions contributed to the rise in food prices. A study by Jensen & Anderson (2014) uses the global AGE...... model GTAP and the corresponding database to quantify the global policy actions contributions to the raise in food prices by modeling the changes in distortions to agricultural incentives in the period 2006 to 2008. We link the results from this global model into a national AGE model, highlighting how...... global "Beggar-thy-Neighbor Policy Responses" impacted on poor households in Uganda. More specifically we examine the following research questions: What were the Ugandan economy-wide and poverty impacts of the price spikes? What was the impact of other countries "Beggar-thy-Neighbor Policy Responses...

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

Science.gov (United States)

Drzewiecki, Wojciech

2016-12-01

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. The results proved that in case of sub-pixel evaluation the most accurate prediction of change may not necessarily be based on the most accurate individual assessments. When single methods are considered, based on obtained results Cubist algorithm may be advised for Landsat based mapping of imperviousness for single dates. However, Random Forest may be endorsed when the most reliable evaluation of imperviousness change is the primary goal. It gave lower accuracies for individual assessments, but better prediction of change due to more correlated errors of individual predictions. Heterogeneous model ensembles performed for individual time points assessments at least as well as the best individual models. In case of imperviousness change assessment the ensembles always outperformed single model approaches. It means that it is possible to improve the accuracy of sub-pixel imperviousness change assessment using ensembles of heterogeneous non-linear regression models.

Modeling ready biodegradability of fragrance materials.

Science.gov (United States)

Ceriani, Lidia; Papa, Ester; Kovarich, Simona; Boethling, Robert; Gramatica, Paola

2015-06-01

In the present study, quantitative structure activity relationships were developed for predicting ready biodegradability of approximately 200 heterogeneous fragrance materials. Two classification methods, classification and regression tree (CART) and k-nearest neighbors (kNN), were applied to perform the modeling. The models were validated with multiple external prediction sets, and the structural applicability domain was verified by the leverage approach. The best models had good sensitivity (internal ≥80%; external ≥68%), specificity (internal ≥80%; external 73%), and overall accuracy (≥75%). Results from the comparison with BIOWIN global models, based on group contribution method, show that specific models developed in the present study perform better in prediction than BIOWIN6, in particular for the correct classification of not readily biodegradable fragrance materials. © 2015 SETAC.

A tight binding model study of tunneling conductance spectra of spin and orbitally ordered CMR manganites

Science.gov (United States)

Panda, Saswati; Sahoo, D. D.; Rout, G. C.

2018-04-01

We report here a tight binding model for colossal magnetoresistive (CMR) manganites to study the pseudo gap (PG) behavior near Fermi level. In the Kubo-Ohata type DE model, we consider first and second nearest neighbor interactions for transverse spin fluctuations in core band and hopping integrals in conduction band, in the presence of static band Jahn-Teller distortion. The model Hamiltonian is solved using Zubarev's Green's function technique. The electron density of states (DOS) is found out from the Green's functions. We observe clear PG near Fermi level in the electron DOS.

Nonlocal synchronization in nearest neighbour coupled oscillators

International Nuclear Information System (INIS)

El-Nashar, H.F.; Elgazzar, A.S.; Cerdeira, H.A.

2002-02-01

We investigate a system of nearest neighbour coupled oscillators. We show that the nonlocal frequency synchronization, that might appear in such a system, occurs as a consequence of the nearest neighbour coupling. The power spectra of nonadjacent oscillators shows that there is no complete coincidence between all frequency peaks of the oscillators in the nonlocal cluster, while the peaks for neighbouring oscillators approximately coincide even if they are not yet in a cluster. It is shown that nonadjacent oscillators closer in frequencies, share slow modes with their adjacent oscillators which are neighbours in space. It is also shown that when a direct coupling between non-neighbours oscillators is introduced explicitly, the peaks of the spectra of the frequencies of those non-neighbours coincide. (author)

The mass media destabilizes the cultural homogenous regime in Axelrod's model

Energy Technology Data Exchange (ETDEWEB)

Peres, Lucas R; Fontanari, Jose F [Instituto de Fisica de Sao Carlos, Universidade de Sao Paulo, Caixa Postal 369, 13560-970 Sao Carlos SP (Brazil)

2010-02-05

An important feature of Axelrod's model for culture dissemination or social influence is the emergence of many multicultural absorbing states, despite the fact that the local rules that specify the agents interactions are explicitly designed to decrease the cultural differences between agents. Here we re-examine the problem of introducing an external, global interaction-the mass media-in the rules of Axelrod's model: in addition to their nearest neighbors, each agent has a certain probability p to interact with a virtual neighbor whose cultural features are fixed from the outset. Most surprisingly, this apparently homogenizing effect actually increases the cultural diversity of the population. We show that, contrary to previous claims in the literature, even a vanishingly small value of p is sufficient to destabilize the homogeneous regime for very large lattice sizes.

Detecting PM2.5's Correlations between Neighboring Cities Using a Time-Lagged Cross-Correlation Coefficient.

Science.gov (United States)

Wang, Fang; Wang, Lin; Chen, Yuming

2017-08-31

In order to investigate the time-dependent cross-correlations of fine particulate (PM2.5) series among neighboring cities in Northern China, in this paper, we propose a new cross-correlation coefficient, the time-lagged q-L dependent height crosscorrelation coefficient (denoted by p q (τ, L)), which incorporates the time-lag factor and the fluctuation amplitude information into the analogous height cross-correlation analysis coefficient. Numerical tests are performed to illustrate that the newly proposed coefficient ρ q (τ, L) can be used to detect cross-correlations between two series with time lags and to identify different range of fluctuations at which two series possess cross-correlations. Applying the new coefficient to analyze the time-dependent cross-correlations of PM2.5 series between Beijing and the three neighboring cities of Tianjin, Zhangjiakou, and Baoding, we find that time lags between the PM2.5 series with larger fluctuations are longer than those between PM2.5 series withsmaller fluctuations. Our analysis also shows that cross-correlations between the PM2.5 series of two neighboring cities are significant and the time lags between two PM2.5 series of neighboring cities are significantly non-zero. These findings providenew scientific support on the view that air pollution in neighboring cities can affect one another not simultaneously but with a time lag.

Recrafting the neighbor-joining method

Directory of Open Access Journals (Sweden)

Pedersen Christian NS

2006-01-01

Full Text Available Abstract Background The neighbor-joining method by Saitou and Nei is a widely used method for constructing phylogenetic trees. The formulation of the method gives rise to a canonical Θ(n3 algorithm upon which all existing implementations are based. Results In this paper we present techniques for speeding up the canonical neighbor-joining method. Our algorithms construct the same phylogenetic trees as the canonical neighbor-joining method. The best-case running time of our algorithms are O(n2 but the worst-case remains O(n3. We empirically evaluate the performance of our algoritms on distance matrices obtained from the Pfam collection of alignments. The experiments indicate that the running time of our algorithms evolve as Θ(n2 on the examined instance collection. We also compare the running time with that of the QuickTree tool, a widely used efficient implementation of the canonical neighbor-joining method. Conclusion The experiments show that our algorithms also yield a significant speed-up, already for medium sized instances.

Beyond mean-field approximations for accurate and computationally efficient models of on-lattice chemical kinetics

Science.gov (United States)

Pineda, M.; Stamatakis, M.

2017-07-01

Modeling the kinetics of surface catalyzed reactions is essential for the design of reactors and chemical processes. The majority of microkinetic models employ mean-field approximations, which lead to an approximate description of catalytic kinetics by assuming spatially uncorrelated adsorbates. On the other hand, kinetic Monte Carlo (KMC) methods provide a discrete-space continuous-time stochastic formulation that enables an accurate treatment of spatial correlations in the adlayer, but at a significant computation cost. In this work, we use the so-called cluster mean-field approach to develop higher order approximations that systematically increase the accuracy of kinetic models by treating spatial correlations at a progressively higher level of detail. We further demonstrate our approach on a reduced model for NO oxidation incorporating first nearest-neighbor lateral interactions and construct a sequence of approximations of increasingly higher accuracy, which we compare with KMC and mean-field. The latter is found to perform rather poorly, overestimating the turnover frequency by several orders of magnitude for this system. On the other hand, our approximations, while more computationally intense than the traditional mean-field treatment, still achieve tremendous computational savings compared to KMC simulations, thereby opening the way for employing them in multiscale modeling frameworks.

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.

Technique for fast and efficient hierarchical clustering

Science.gov (United States)

Stork, Christopher

2013-10-08

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

An Evaluation of Parametric and Nonparametric Models of Fish Population Response.

Energy Technology Data Exchange (ETDEWEB)

Haas, Timothy C.; Peterson, James T.; Lee, Danny C.

1999-11-01

Predicting the distribution or status of animal populations at large scales often requires the use of broad-scale information describing landforms, climate, vegetation, etc. These data, however, often consist of mixtures of continuous and categorical covariates and nonmultiplicative interactions among covariates, complicating statistical analyses. Using data from the interior Columbia River Basin, USA, we compared four methods for predicting the distribution of seven salmonid taxa using landscape information. Subwatersheds (mean size, 7800 ha) were characterized using a set of 12 covariates describing physiography, vegetation, and current land-use. The techniques included generalized logit modeling, classification trees, a nearest neighbor technique, and a modular neural network. We evaluated model performance using out-of-sample prediction accuracy via leave-one-out cross-validation and introduce a computer-intensive Monte Carlo hypothesis testing approach for examining the statistical significance of landscape covariates with the non-parametric methods. We found the modular neural network and the nearest-neighbor techniques to be the most accurate, but were difficult to summarize in ways that provided ecological insight. The modular neural network also required the most extensive computer resources for model fitting and hypothesis testing. The generalized logit models were readily interpretable, but were the least accurate, possibly due to nonlinear relationships and nonmultiplicative interactions among covariates. Substantial overlap among the statistically significant (P<0.05) covariates for each method suggested that each is capable of detecting similar relationships between responses and covariates. Consequently, we believe that employing one or more methods may provide greater biological insight without sacrificing prediction accuracy.

Low-field susceptibility of classical Heisenberg chains with arbitrary and different nearest-neighbour exchange

International Nuclear Information System (INIS)

Cregg, P J; Murphy, K; Garcia-Palacios, J L; Svedlindh, P

2008-01-01

Interest in molecular magnets continues to grow, offering a link between the atomic and nanoscale properties. The classical Heisenberg model has been effective in modelling exchange interactions in such systems. In this, the magnetization and susceptibility are calculated through the partition function, where the Hamiltonian contains both Zeeman and exchange energy. For an ensemble of N spins, this requires integrals in 2N dimensions. For two, three and four spin nearest-neighbour chains these integrals reduce to sums of known functions. For the case of the three and four spin chains, the sums are equivalent to results of Joyce. Expanding these sums, the effect of the exchange on the linear susceptibility appears as Langevin functions with exchange term arguments. These expressions are generalized here to describe an N spin nearest-neighbour chain, where the exchange between each pair of nearest neighbours is different and arbitrary. For a common exchange constant, this reduces to the result of Fisher. The high-temperature expansion of the Langevin functions for the different exchange constants leads to agreement with the appropriate high-temperature quantum formula of Schmidt et al, when the spin number is large. Simulations are presented for open linear chains of three, four and five spins with up to four different exchange constants, illustrating how the exchange constants can be retrieved successfully

A Classification Framework Applied to Cancer Gene Expression Profiles

Directory of Open Access Journals (Sweden)

Hussein Hijazi

2013-01-01

Full Text Available Classification of cancer based on gene expression has provided insight into possible treatment strategies. Thus, developing machine learning methods that can successfully distinguish among cancer subtypes or normal versus cancer samples is important. This work discusses supervised learning techniques that have been employed to classify cancers. Furthermore, a two-step feature selection method based on an attribute estimation method (e.g., ReliefF and a genetic algorithm was employed to find a set of genes that can best differentiate between cancer subtypes or normal versus cancer samples. The application of different classification methods (e.g., decision tree, k-nearest neighbor, support vector machine (SVM, bagging, and random forest on 5 cancer datasets shows that no classification method universally outperforms all the others. However, k-nearest neighbor and linear SVM generally improve the classification performance over other classifiers. Finally, incorporating diverse types of genomic data (e.g., protein-protein interaction data and gene expression increase the prediction accuracy as compared to using gene expression alone.

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.

Neighbor discovery in multi-hop wireless networks: evaluation and dimensioning with interferences considerations

Directory of Open Access Journals (Sweden)

Elyes Ben Hamida

2008-04-01

Full Text Available In this paper, we study the impact of collisions and interferences on a neighbor discovery process in the context of multi-hop wireless networks. We consider three models in which interferences and collisions are handled in very different ways. From an ideal channel where simultaneous transmissions do not interfere, we derive an alternate channel where simultaneous transmissions are considered two-by-two under the form of collisions, to finally reach a more realistic channel where simultaneous transmissions are handled under the form of shot-noise interferences. In these models, we analytically compute the link probability success between two neighbors as well as the expected number of nodes that correctly receive a Hello packet. Using this analysis, we show that if the neighbor discovery process is asymptotically equivalent in the three models, it offers very different behaviors locally in time. In particular, the scalability of the process is not the same depending on the way interferences are handled. Finally, we apply our results to the dimensioning of a Hello protocol parameters. We propose a method to adapt the protocol parameters to meet application constraints on the neighbor discovery process and to minimize the protocol energy consumption.

Incorporating parametric uncertainty into population viability analysis models

Science.gov (United States)

McGowan, Conor P.; Runge, Michael C.; Larson, Michael A.

2011-01-01

Uncertainty in parameter estimates from sampling variation or expert judgment can introduce substantial uncertainty into ecological predictions based on those estimates. However, in standard population viability analyses, one of the most widely used tools for managing plant, fish and wildlife populations, parametric uncertainty is often ignored in or discarded from model projections. We present a method for explicitly incorporating this source of uncertainty into population models to fully account for risk in management and decision contexts. Our method involves a two-step simulation process where parametric uncertainty is incorporated into the replication loop of the model and temporal variance is incorporated into the loop for time steps in the model. Using the piping plover, a federally threatened shorebird in the USA and Canada, as an example, we compare abundance projections and extinction probabilities from simulations that exclude and include parametric uncertainty. Although final abundance was very low for all sets of simulations, estimated extinction risk was much greater for the simulation that incorporated parametric uncertainty in the replication loop. Decisions about species conservation (e.g., listing, delisting, and jeopardy) might differ greatly depending on the treatment of parametric uncertainty in population models.

Evaluation of potential novel variations and their interactions related to bipolar disorders: analysis of genome-wide association study data.

Science.gov (United States)

Acikel, Cengizhan; Aydin Son, Yesim; Celik, Cemil; Gul, Husamettin

2016-01-01

Multifactor dimensionality reduction (MDR) is a nonparametric approach that can be used to detect relevant interactions between single-nucleotide polymorphisms (SNPs). The aim of this study was to build the best genomic model based on SNP associations and to identify candidate polymorphisms that are the underlying molecular basis of the bipolar disorders. This study was performed on Whole-Genome Association Study of Bipolar Disorder (dbGaP [database of Genotypes and Phenotypes] study accession number: phs000017.v3.p1) data. After preprocessing of the genotyping data, three classification-based data mining methods (ie, random forest, naïve Bayes, and k-nearest neighbor) were performed. Additionally, as a nonparametric, model-free approach, the MDR method was used to evaluate the SNP profiles. The validity of these methods was evaluated using true classification rate, recall (sensitivity), precision (positive predictive value), and F-measure. Random forests, naïve Bayes, and k-nearest neighbors identified 16, 13, and ten candidate SNPs, respectively. Surprisingly, the top six SNPs were reported by all three methods. Random forests and k-nearest neighbors were more successful than naïve Bayes, with recall values >0.95. On the other hand, MDR generated a model with comparable predictive performance based on five SNPs. Although different SNP profiles were identified in MDR compared to the classification-based models, all models mapped SNPs to the DOCK10 gene. Three classification-based data mining approaches, random forests, naïve Bayes, and k-nearest neighbors, have prioritized similar SNP profiles as predictors of bipolar disorders, in contrast to MDR, which has found different SNPs through analysis of two-way and three-way interactions. The reduced number of associated SNPs discovered by MDR, without loss in the classification performance, would facilitate validation studies and decision support models, and would reduce the cost to develop predictive and

Density functional approach for the magnetism of β-TeVO4

Science.gov (United States)

Saúl, A.; Radtke, G.

2014-03-01

Density functional calculations have been carried out to investigate the microscopic origin of the magnetic properties of β-TeVO4. Two different approaches, based either on a perturbative treatment of the multiorbital Hubbard model in the strongly correlated limit or on the calculation of supercell total energy differences, have been employed to evaluate magnetic couplings in this compound. The picture provided by these two approaches is that of weakly coupled frustrated chains with ferromagnetic nearest-neighbor and antiferromagnetic second-nearest-neighbor couplings. These results, differing substantially from previous reports, should motivate further experimental investigations of the magnetic properties of this compound.

"Equilibrium structure of monatomic steps on vicinal Si(001)

NARCIS (Netherlands)

Zandvliet, Henricus J.W.; Elswijk, H.B.; van Loenen, E.J.; Dijkkamp, D.

1992-01-01

The equilibrium structure of monatomic steps on vicinal Si(001) is described in terms of anisotropic nearest-neighbor and isotropic second-nearest-neighbor interactions between dimers. By comparing scanning-tunneling-microscopy data and this equilibrium structure, we obtained interaction energies of

Micromechanical Modeling of Fiber-Reinforced Composites with Statistically Equivalent Random Fiber Distribution

Directory of Open Access Journals (Sweden)

Wenzhi Wang

2016-07-01

Full Text Available Modeling the random fiber distribution of a fiber-reinforced composite is of great importance for studying the progressive failure behavior of the material on the micro scale. In this paper, we develop a new algorithm for generating random representative volume elements (RVEs with statistical equivalent fiber distribution against the actual material microstructure. The realistic statistical data is utilized as inputs of the new method, which is archived through implementation of the probability equations. Extensive statistical analysis is conducted to examine the capability of the proposed method and to compare it with existing methods. It is found that the proposed method presents a good match with experimental results in all aspects including the nearest neighbor distance, nearest neighbor orientation, Ripley’s K function, and the radial distribution function. Finite element analysis is presented to predict the effective elastic properties of a carbon/epoxy composite, to validate the generated random representative volume elements, and to provide insights of the effect of fiber distribution on the elastic properties. The present algorithm is shown to be highly accurate and can be used to generate statistically equivalent RVEs for not only fiber-reinforced composites but also other materials such as foam materials and particle-reinforced composites.

Science and Technology Text Mining Basic Concepts

National Research Council Canada - National Science Library

Losiewicz, Paul

2003-01-01

...). It then presents some of the most widely used data and text mining techniques, including clustering and classification methods, such as nearest neighbor, relational learning models, and genetic...

Utilization of Singularity Exponent in Nearest Neighbor Based Classifier

Czech Academy of Sciences Publication Activity Database

Jiřina, Marcel; Jiřina jr., M.

2013-01-01

Roč. 30, č. 1 (2013), s. 3-29 ISSN 0176-4268 Grant - others:Czech Technical University(CZ) CZ68407700 Institutional support: RVO:67985807 Keywords : multivariate data * probability density estimation * classification * probability distribution mapping function * probability density mapping function * power approximation Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.571, year: 2013

SpaceTwist

DEFF Research Database (Denmark)

Yiu, Man Lung; Jensen, Christian Søndergaard; Xuegang, Huang

2008-01-01

-based matching generally fall short in offering practical query accuracy guarantees. Our proposed framework, called SpaceTwist, rectifies these shortcomings for k nearest neighbor (kNN) queries. Starting with a location different from the user's actual location, nearest neighbors are retrieved incrementally...

On factor associated with the unordered phase of λ-model on a Cayley tree

International Nuclear Information System (INIS)

Mukhamedov, Farruh

2002-11-01

In this paper we consider nearest neighbors models where the spin takes values in the set Φ={η 1 , η 2 ,..., η q } and is assigned to the vertices of the Cayley tree Γ k . A configuration σ on Γ k is then defined as a function x is element of Γ k →σ(x) is element of Φ; the set of all configurations coincides with Ω=Φ Γ k . The Hamiltonian is of an λ-model form: H λ (σ)=Σ λ(σ(x), σ(y); J), where J is element of R n is a coupling constant and the sum is taken over all pairs of neighboring vertices , σ is element of Ω. Here λ:ΦxΦxR n →R is some given function. We find a condition for the function λ to determine a type of von Neumann algebra generated by the GNS-construction associated with the unordered phase of the λ-model. (author)

On Models with Uncountable Set of Spin Values on a Cayley Tree: Integral Equations

International Nuclear Information System (INIS)

Rozikov, Utkir A.; Eshkobilov, Yusup Kh.

2010-01-01

We consider models with nearest-neighbor interactions and with the set [0, 1] of spin values, on a Cayley tree of order k ≥ 1. We reduce the problem of describing the 'splitting Gibbs measures' of the model to the description of the solutions of some nonlinear integral equation. For k = 1 we show that the integral equation has a unique solution. In case k ≥ 2 some models (with the set [0, 1] of spin values) which have a unique splitting Gibbs measure are constructed. Also for the Potts model with uncountable set of spin values it is proven that there is unique splitting Gibbs measure.

A Novel Hybrid Similarity Calculation Model

Directory of Open Access Journals (Sweden)

Xiaoping Fan

2017-01-01

Full Text Available This paper addresses the problems of similarity calculation in the traditional recommendation algorithms of nearest neighbor collaborative filtering, especially the failure in describing dynamic user preference. Proceeding from the perspective of solving the problem of user interest drift, a new hybrid similarity calculation model is proposed in this paper. This model consists of two parts, on the one hand the model uses the function fitting to describe users’ rating behaviors and their rating preferences, and on the other hand it employs the Random Forest algorithm to take user attribute features into account. Furthermore, the paper combines the two parts to build a new hybrid similarity calculation model for user recommendation. Experimental results show that, for data sets of different size, the model’s prediction precision is higher than the traditional recommendation algorithms.

New Results on the Nearest OB Association: Sco-Cen (Sco OB2)

Science.gov (United States)

Mamajek, Eric E.

2013-01-01

The Scorpius-Centaurus OB association (Sco OB2) is the nearest site of recent massive star formation to the Sun. The primary stellar groups in the Sco-Cen complex (including OB subgroups Upper Sco, Upper Cen Lup, and Lower Cen Cru, the neighboring molecular cloud complexes Lup, Cha, CrA, Oph, and dispersed young groups Eta Cha, Epsilon Cha, TW Hya, and Beta Pic) have been participants in a complex episode of stellar birth (and some stellar death) over the past ~20 Myr. Here I summarize some recent results on the Sco-Cen complex from the U. Rochester group: (1) isochronal analysis of the HR diagram positions for >1 Msun stars in the Upper Scorpius subgroup shows it to be twice as old as previously thought (11 Myr vs. 5 Myr), (2) analysis of high resolution optical echelle spectra show that the subgroups are approximately solar in composition, (3) surveys for lower mass members are showing that the complex shows more substructure than previously recognized, including at least one new subgroup ("Lower Sco"), and the velocity and age data for the nearest OB subgroup Lower Cen Cru argue for a bifurcation into a younger 10 Myr) southern part ("Crux") and an older 20 Myr) northern part ("Lower Centaurus"), (4) an eclipsing, multi-ring dust disk system was serendipitously discovered in the SuperWASP and ASAS light curve for the newly discovered K5-type Sco-Cen member 1SWASP J140747.93-394542.6. With regard to some recent results by other investigators, we find that (1) attempts by some authors to subsume the Sco-Cen subgroups into a single sample of a single age are unnecessarily mixing samples with a wide range in ages, and (2) I have been unable to replicate the expansion age determinations claimed by some investigators for the TW Hya and Beta Pic groups (both purported to have expansion ages of 8 and 12 Myr, respectively), which have been used by some investigators to independently age-date the Sco-Cen subgroups. We acknowledge support from NSF grant AST-1008908 and the

Theory of lithium islands and monolayers: Electronic structure and stability

International Nuclear Information System (INIS)

Quassowski, S.; Hermann, K.

1995-01-01

Systematic calculations on planar clusters and monolayers of lithium are performed to study geometries and stabilities of the clusters as well as their convergence behavior with increasing cluster size. The calculations are based on ab initio methods using density-functional theory within the local-spin-density approximation for exchange and correlation. The optimized nearest-neighbor distances d NN of the Li n clusters, n=1,...,25, of both hexagonal and square geometry increase with cluster size, converging quite rapidly towards the monolayer results. Further, the cluster cohesive energies E c increase with cluster size and converge towards the respective monolayer values that form upper bounds. Clusters of hexagonal geometry are found to be more stable than square clusters of comparable size, consistent with the monolayer results. The size dependence of the cluster cohesive energies can be described approximately by a coordination model based on the concept of pairwise additive nearest-neighbor binding. This indicates that the average binding in the Li n clusters and their relative stabilities can be explained by simple geometric effects which derive from the nearest-neighbor coordination

Incorporating groundwater flow into the WEPP model

Science.gov (United States)

William Elliot; Erin Brooks; Tim Link; Sue Miller

2010-01-01

The water erosion prediction project (WEPP) model is a physically-based hydrology and erosion model. In recent years, the hydrology prediction within the model has been improved for forest watershed modeling by incorporating shallow lateral flow into watershed runoff prediction. This has greatly improved WEPP's hydrologic performance on small watersheds with...

A LITERATURE SURVEY ON VARIOUS ILLUMINATION NORMALIZATION TECHNIQUES FOR FACE RECOGNITION WITH FUZZY K NEAREST NEIGHBOUR CLASSIFIER

Directory of Open Access Journals (Sweden)

A. Thamizharasi

2015-05-01

Full Text Available The face recognition is popular in video surveillance, social networks and criminal identifications nowadays. The performance of face recognition would be affected by variations in illumination, pose, aging and partial occlusion of face by Wearing Hats, scarves and glasses etc. The illumination variations are still the challenging problem in face recognition. The aim is to compare the various illumination normalization techniques. The illumination normalization techniques include: Log transformations, Power Law transformations, Histogram equalization, Adaptive histogram equalization, Contrast stretching, Retinex, Multi scale Retinex, Difference of Gaussian, DCT, DCT Normalization, DWT, Gradient face, Self Quotient, Multi scale Self Quotient and Homomorphic filter. The proposed work consists of three steps. First step is to preprocess the face image with the above illumination normalization techniques; second step is to create the train and test database from the preprocessed face images and third step is to recognize the face images using Fuzzy K nearest neighbor classifier. The face recognition accuracy of all preprocessing techniques is compared using the AR face database of color images.

Recrafting the Neighbor-Joining Method

DEFF Research Database (Denmark)

Mailund; Brodal, Gerth Stølting; Fagerberg, Rolf

2006-01-01

Background: The neighbor-joining method by Saitou and Nei is a widely used method for constructing phylogenetic trees. The formulation of the method gives rise to a canonical Θ(n3) algorithm upon which all existing implementations are based. Methods: In this paper we present techniques for speeding...... up the canonical neighbor-joining method. Our algorithms construct the same phylogenetic trees as the canonical neighbor-joining method. The best-case running time of our algorithms are O(n2) but the worst-case remains O(n3). We empirically evaluate the performance of our algoritms on distance...... matrices obtained from the Pfam collection of alignments. Results: The experiments indicate that the running time of our algorithms evolve as Θ(n2) on the examined instance collection. We also compare the running time with that of the QuickTree tool, a widely used efficient implementation of the canonical...

Modelo digital do terreno através de diferentes interpolações do programa Surfer 12 | Digital terrain model through different interpolations in the surfer 12 software

Directory of Open Access Journals (Sweden)

José Machado

2016-04-01

the MDT interpolation of measured points is required. The use of TDM, 3D surfaces and contours in moving fast computer programs and can create some problems, such as the type of interpolation used. This work aims to analyze the interpolation methods in points quoted from an irregular geometric figure generated by the Surfer program. They used 12 interpolations available (Data Metrics, Inverse Distance, Kriging, Local Polynomial, Minimum Curvature, Modified Shepard Method, Moving Average, Natural Neighbor, Nearest Neighbor, Polynomial Regression, Radial fuction and Triangulation with Linear Interpolation and analyzed the generated topographic maps. The relief was generated graphical representation via the MDT. They were awarded the excellent concepts, excellent, good, average and bad representation of relief and discussed according Relief representations to the listed geometric image. Data Metrics, Polynomial Regression, Moving Average e Local Polynomial (bad; Moving Average e Modified Shepard Method (regular; Nearest Neighbor (media; Inverse Distance (good; Kriging e Radial Function (great e Triangulation With Linear Interpolation e Natural Neighbor (excellent conditions to representation presented dates.

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

Prediction of hot spots in protein interfaces using a random forest model with hybrid features.

Science.gov (United States)

Wang, Lin; Liu, Zhi-Ping; Zhang, Xiang-Sun; Chen, Luonan

2012-03-01

Prediction of hot spots in protein interfaces provides crucial information for the research on protein-protein interaction and drug design. Existing machine learning methods generally judge whether a given residue is likely to be a hot spot by extracting features only from the target residue. However, hot spots usually form a small cluster of residues which are tightly packed together at the center of protein interface. With this in mind, we present a novel method to extract hybrid features which incorporate a wide range of information of the target residue and its spatially neighboring residues, i.e. the nearest contact residue in the other face (mirror-contact residue) and the nearest contact residue in the same face (intra-contact residue). We provide a novel random forest (RF) model to effectively integrate these hybrid features for predicting hot spots in protein interfaces. Our method can achieve accuracy (ACC) of 82.4% and Matthew's correlation coefficient (MCC) of 0.482 in Alanine Scanning Energetics Database, and ACC of 77.6% and MCC of 0.429 in Binding Interface Database. In a comparison study, performance of our RF model exceeds other existing methods, such as Robetta, FOLDEF, KFC, KFC2, MINERVA and HotPoint. Of our hybrid features, three physicochemical features of target residues (mass, polarizability and isoelectric point), the relative side-chain accessible surface area and the average depth index of mirror-contact residues are found to be the main discriminative features in hot spots prediction. We also confirm that hot spots tend to form large contact surface areas between two interacting proteins. Source data and code are available at: http://www.aporc.org/doc/wiki/HotSpot.

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

Energy Technology Data Exchange (ETDEWEB)

Majidpour, Mostafa; Qiu, Charlie; Chung, Ching-Yen; Chu, Peter; Gadh, Rajit; Pota, Hemanshu R.

2014-07-31

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

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...

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

A Markov chain Monte Carlo Expectation Maximization Algorithm for Statistical Analysis of DNA Sequence Evolution with Neighbor-Dependent Substitution Rates

DEFF Research Database (Denmark)

Hobolth, Asger

2008-01-01

-dimensional integrals required in the EM algorithm are estimated using MCMC sampling. The MCMC sampler requires simulation of sample paths from a continuous time Markov process, conditional on the beginning and ending states and the paths of the neighboring sites. An exact path sampling algorithm is developed......The evolution of DNA sequences can be described by discrete state continuous time Markov processes on a phylogenetic tree. We consider neighbor-dependent evolutionary models where the instantaneous rate of substitution at a site depends on the states of the neighboring sites. Neighbor......-dependent substitution models are analytically intractable and must be analyzed using either approximate or simulation-based methods. We describe statistical inference of neighbor-dependent models using a Markov chain Monte Carlo expectation maximization (MCMC-EM) algorithm. In the MCMC-EM algorithm, the high...

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

NARCIS (Netherlands)

Wit, Hero P.; van Dijk, Pim

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

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

Science.gov (United States)

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

2017-10-01

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

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.

Reduction in predator defense in the presence of neighbors in a colonial fish.

Directory of Open Access Journals (Sweden)

Franziska C Schädelin

Full Text Available Predation pressure has long been considered a leading explanation of colonies, where close neighbors may reduce predation via dilution, alarming or group predator attacks. Attacking predators may be costly in terms of energy and survival, leading to the question of how neighbors contribute to predator deterrence in relationship to each other. Two hypotheses explaining the relative efforts made by neighbors are byproduct-mutualism, which occurs when breeders inadvertently attack predators by defending their nests, and reciprocity, which occurs when breeders deliberately exchange predator defense efforts with neighbors. Most studies investigating group nest defense have been performed with birds. However, colonial fish may constitute a more practical model system for an experimental approach because of the greater ability of researchers to manipulate their environment. We investigated in the colonial fish, Neolamprologus caudopunctatus, whether prospecting pairs preferred to breed near conspecifics or solitarily, and how breeders invested in anti-predator defense in relation to neighbors. In a simple choice test, prospecting pairs selected breeding sites close to neighbors versus a solitary site. Predators were then sequentially presented to the newly established test pairs, the previously established stimulus pairs or in between the two pairs. Test pairs attacked the predator eight times more frequently when they were presented on their non-neighbor side compared to between the two breeding sites, where stimulus pairs maintained high attack rates. Thus, by joining an established pair, test pairs were able to reduce their anti-predator efforts near neighbors, at no apparent cost to the stimulus pairs. These findings are unlikely to be explained by reciprocity or byproduct-mutualism. Our results instead suggest a commensal relationship in which new pairs exploit the high anti-predator efforts of established pairs, which invest similarly with or

Uniqueness of Gibbs measure for Potts model with countable set of spin values

International Nuclear Information System (INIS)

Ganikhodjaev, N.N.; Rozikov, U.A.

2004-11-01

We consider a nearest-neighbor Potts model with countable spin values 0,1,..., and non zero external field, on a Cayley tree of order k (with k+1 neighbors). We study translation-invariant 'splitting' Gibbs measures. We reduce the problem to the description of the solutions of some infinite system of equations. For any k≥1 and any fixed probability measure ν with ν(i)>0 on the set of all non negative integer numbers Φ={0,1,...} we show that the set of translation-invariant splitting Gibbs measures contains at most one point, independently on parameters of the Potts model with countable set of spin values on Cayley tree. Also we give a full description of the class of measures ν on Φ such that wit respect to each element of this class our infinite system of equations has unique solution {a i =1,2,...}, where a is an element of (0,1). (author)

From localized to extended states in a time-dependent quantum model

International Nuclear Information System (INIS)

Jose, J.V.

1986-01-01

The problem of a particle inside a rigid box with one of the walls oscillating periodically in time is studied quantum mechanically. In the classical limit, this model was introduced by Fermi in the context of cosmic ray physics. The classical solutions can go from being quasiperiodic to chaotic, as a function of the amplitude of the wall oscillation. In the quantum case, the authors calculate the spectral properties of the corresponding evolution operator, i.e.: the quasi-energy eigenvalues and eigenvectors. The specific form of the wall oscillation, e.g. iota(t) = √ 1 + 2δabsolute value of t, with absolute value of t ≤ 1/2, and iota(t + 1) = iota(t), is essential to the solutions presented here. It is found that as h increases with δ fixed, the nearest neighbor separation between quasi-energy eigenvalues changes from showing no energy level repulsion to energy level repulsion. This transition, from Poisson-like statistics to Gaussian-Orthogonal-Ensemble-like statistics is tested by looking at the distribution of quasi-energy level nearest neighbor separations and the Δ/sub e/(L) statistics. these results are also correlated to a transition between localized to extended states in energy space. The possible relevance of the results presented here to experiments in quasi-one-dimensional atoms is also discussed

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

Science.gov (United States)

Wit, Hero P; van Dijk, Pim

2012-08-01

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

Numerical study of the t-J model: Exact ground state and flux phases

International Nuclear Information System (INIS)

Hasegawa, Y.; Poilblanc, D.

1990-01-01

Strongly correlated 2D electrons described by the t-J model are investigated numerically. Exact ground state for one and two holes in a finite cluster with periodic boundary conditions are obtained by using the Lanczos algorithm. The effects of Coulomb repulsion of the holes on the nearest neighbor sites are taken into account. Commensurate flux phases are investigated for the same size of clusters. They are shown to be a good approximation for the ground state specially in the intermediate value of J/t. (author). 21 refs, 3 figs

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.

Dynamical mean field study of the Mott transition in the half-filled Hubbard model on a triangular lattice

OpenAIRE

Aryanpour, K.; Pickett, W. E.; Scalettar, R. T.

2006-01-01

We employ dynamical mean field theory (DMFT) with a Quantum Monte Carlo (QMC) atomic solver to investigate the finite temperature Mott transition in the Hubbard model with the nearest neighbor hopping on a triangular lattice at half-filling. We estimate the value of the critical interaction to be $U_c=12.0 \\pm 0.5$ in units of the hopping amplitude $t$ through the evolution of the magnetic moment, spectral function, internal energy and specific heat as the interaction $U$ and temperature $T$ ...

Finite temperature magnon spectra in yttrium iron garnet from a mean field approach in a tight-binding model

Science.gov (United States)

Shen, Ka

2018-04-01

We study magnon spectra at finite temperature in yttrium iron garnet using a tight-binding model with nearest-neighbor exchange interaction. The spin reduction due to thermal magnon excitation is taken into account via the mean field approximation to the local spin and is found to be different at two sets of iron atoms. The resulting temperature dependence of the spin wave gap shows good agreement with experiment. We find that only two magnon modes are relevant to the ferromagnetic resonance.

Low-temperature specific heat of the degenerate supersymmetric t-J model in one dimension

International Nuclear Information System (INIS)

Lee, K.; Schlottmann, P.

1996-01-01

We consider the one-dimensional SU(N)-invariant t-J model, which consists of electrons with N spin components on a lattice with nearest-neighbor hopping t constrained by the excluded multiple occupancy of the sites and spin-exchange J between neighboring lattice sites. The model is integrable and has been diagonalized in terms of nested Bethe ansatze at the supersymmetric point t=J. The low-T specific heat is proportional to T. The γ-coefficient is extracted from the thermodynamic Bethe-ansatz equations and is expressed in terms of the spin wave velocities and the group velocity of the charges for arbitrary N, band filling, and splitting of the levels (magnetic and crystalline fields). Our results contain the following special cases: (i) For N=2 the traditional spin-1/2 supersymmetric t-J model, (ii) for exactly one electron per site the SU(N)-Heisenberg chain, and (iii) for N=4 the two-band supersymmetric t-J model with crystalline field splitting. copyright 1996 American Institute of Physics

Neighboring Optimal Aircraft Guidance in a General Wind Environment

Science.gov (United States)

Jardin, Matthew R. (Inventor)

2003-01-01

Method and system for determining an optimal route for an aircraft moving between first and second waypoints in a general wind environment. A selected first wind environment is analyzed for which a nominal solution can be determined. A second wind environment is then incorporated; and a neighboring optimal control (NOC) analysis is performed to estimate an optimal route for the second wind environment. In particular examples with flight distances of 2500 and 6000 nautical miles in the presence of constant or piecewise linearly varying winds, the difference in flight time between a nominal solution and an optimal solution is 3.4 to 5 percent. Constant or variable winds and aircraft speeds can be used. Updated second wind environment information can be provided and used to obtain an updated optimal route.

Model-based segmentation of abdominal aortic aneurysms in CTA images

Science.gov (United States)

de Bruijne, Marleen; van Ginneken, Bram; Niessen, Wiro J.; Loog, Marco; Viergever, Max A.

2003-05-01

Segmentation of thrombus in abdominal aortic aneurysms is complicated by regions of low boundary contrast and by the presence of many neighboring structures in close proximity to the aneurysm wall. We present an automated method that is similar to the well known Active Shape Models (ASM), combining a three-dimensional shape model with a one-dimensional boundary appearance model. Our contribution is twofold: we developed a non-parametric appearance modeling scheme that effectively deals with a highly varying background, and we propose a way of generalizing models of curvilinear structures from small training sets. In contrast with the conventional ASM approach, the new appearance model trains on both true and false examples of boundary profiles. The probability that a given image profile belongs to the boundary is obtained using k nearest neighbor (kNN) probability density estimation. The performance of this scheme is compared to that of original ASMs, which minimize the Mahalanobis distance to the average true profile in the training set. The generalizability of the shape model is improved by modeling the objects axis deformation independent of its cross-sectional deformation. A leave-one-out experiment was performed on 23 datasets. Segmentation using the kNN appearance model significantly outperformed the original ASM scheme; average volume errors were 5.9% and 46% respectively.

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.

An expanded Notch-Delta model exhibiting long-range patterning and incorporating MicroRNA regulation.

Directory of Open Access Journals (Sweden)

Jerry S Chen

2014-06-01

Full Text Available Notch-Delta signaling is a fundamental cell-cell communication mechanism that governs the differentiation of many cell types. Most existing mathematical models of Notch-Delta signaling are based on a feedback loop between Notch and Delta leading to lateral inhibition of neighboring cells. These models result in a checkerboard spatial pattern whereby adjacent cells express opposing levels of Notch and Delta, leading to alternate cell fates. However, a growing body of biological evidence suggests that Notch-Delta signaling produces other patterns that are not checkerboard, and therefore a new model is needed. Here, we present an expanded Notch-Delta model that builds upon previous models, adding a local Notch activity gradient, which affects long-range patterning, and the activity of a regulatory microRNA. This model is motivated by our experiments in the ascidian Ciona intestinalis showing that the peripheral sensory neurons, whose specification is in part regulated by the coordinate activity of Notch-Delta signaling and the microRNA miR-124, exhibit a sparse spatial pattern whereby consecutive neurons may be spaced over a dozen cells apart. We perform rigorous stability and bifurcation analyses, and demonstrate that our model is able to accurately explain and reproduce the neuronal pattern in Ciona. Using Monte Carlo simulations of our model along with miR-124 transgene over-expression assays, we demonstrate that the activity of miR-124 can be incorporated into the Notch decay rate parameter of our model. Finally, we motivate the general applicability of our model to Notch-Delta signaling in other animals by providing evidence that microRNAs regulate Notch-Delta signaling in analogous cell types in other organisms, and by discussing evidence in other organisms of sparse spatial patterns in tissues where Notch-Delta signaling is active.

Intersite electron correlations in a Hubbard model on inhomogeneous lattices

International Nuclear Information System (INIS)

Takemori, Nayuta; Koga, Akihisa; Hafermann, Hartmut

2016-01-01

We study intersite electron correlations in the half-filled Hubbard model on square lattices with periodic and open boundary conditions by means of a real-space dual fermion approach. By calculating renormalization factors, we clarify that nearest-neighbor intersite correlations already significantly reduce the critical interaction. The Mott transition occurs at U/t ∼ 6.4, where U is the interaction strength and t is the hopping integral. This value is consistent with quantum Monte Carlo results. It shows the importance of short-range intersite correlations, which are taken into account in the framework of the real-space dual fermion approach. (paper)

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

Browse Title Index

African Journals Online (AJOL)

Items 701 - 750 of 985 ... Vol 16 (2010), On Typical Elastic Problem of Green's Function For Rectangular ... of Transfer Function Models using Genetic Algorithms, Abstract ... the Traveling Salesman And the Nearest Neighbors Algorithms, Abstract.

Diagnostics of nitrogen deficiency in mini-cucumber plant by near ...

African Journals Online (AJOL)

K-nearest neighbors (KNN) and artificial neural network (ANN) were applied to build diagnostics models, respectively. Some parameters of the model were optimized by cross-validation. The performance of the KNN model and the ANN model based on NIRS data was compared. Experiment results showed that the ANN ...

The distribution of the number of node neighbors in random hypergraphs

International Nuclear Information System (INIS)

López, Eduardo

2013-01-01

Hypergraphs, the generalization of graphs in which edges become conglomerates of r nodes called hyperedges of rank r ⩾ 2, are excellent models to study systems with interactions that are beyond the pairwise level. For hypergraphs, the node degree ℓ (number of hyperedges connected to a node) and the number of neighbors k of a node differ from each other in contrast to the case of graphs, where counting the number of edges is equivalent to counting the number of neighbors. In this paper, I calculate the distribution of the number of node neighbors in random hypergraphs in which hyperedges of uniform rank r have a homogeneous (equal for all hyperedges) probability p to appear. This distribution is equivalent to the degree distribution of ensembles of graphs created as projections of hypergraph or bipartite network ensembles, where the projection connects any two nodes in the projected graph when they are also connected in the hypergraph or bipartite network. The calculation is non-trivial due to the possibility that neighbor nodes belong simultaneously to multiple hyperedges (node overlaps). From the exact results, the traditional asymptotic approximation to the distribution in the sparse regime (small p) where overlaps are ignored is rederived and improved; the approximation exhibits Poisson-like behavior accompanied by strong fluctuations modulated by power-law decays in the system size N with decay exponents equal to the minimum number of overlapping nodes possible for a given number of neighbors. It is shown that the dense limit cannot be explained if overlaps are ignored, and the correct asymptotic distribution is provided. The neighbor distribution requires the calculation of a new combinatorial coefficient Q r−1 (k, ℓ), which counts the number of distinct labeled hypergraphs of k nodes, ℓ hyperedges of rank r − 1, and where every node is connected to at least one hyperedge. Some identities of Q r−1 (k, ℓ) are derived and applied to the

A Markov chain Monte Carlo Expectation Maximization Algorithm for Statistical Analysis of DNA Sequence Evolution with Neighbor-Dependent Substitution Rates

DEFF Research Database (Denmark)

Hobolth, Asger

2008-01-01

The evolution of DNA sequences can be described by discrete state continuous time Markov processes on a phylogenetic tree. We consider neighbor-dependent evolutionary models where the instantaneous rate of substitution at a site depends on the states of the neighboring sites. Neighbor...

Incorporating direct marketing activity into latent attrition models

NARCIS (Netherlands)

Schweidel, David A.; Knox, George

2013-01-01

When defection is unobserved, latent attrition models provide useful insights about customer behavior and accurate forecasts of customer value. Yet extant models ignore direct marketing efforts. Response models incorporate the effects of direct marketing, but because they ignore latent attrition,

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.

Enlarged symmetry algebras of spin chains, loop models, and S-matrices

International Nuclear Information System (INIS)

Read, N.; Saleur, H.

2007-01-01

The symmetry algebras of certain families of quantum spin chains are considered in detail. The simplest examples possess m states per site (m>=2), with nearest-neighbor interactions with U(m) symmetry, under which the sites transform alternately along the chain in the fundamental m and its conjugate representation m-bar. We find that these spin chains, even with arbitrary coefficients of these interactions, have a symmetry algebra A m much larger than U(m), which implies that the energy eigenstates fall into sectors that for open chains (i.e., free boundary conditions) can be labeled by j=0,1,...,L, for the 2L-site chain such that the degeneracies of all eigenvalues in the jth sector are generically the same and increase rapidly with j. For large j, these degeneracies are much larger than those that would be expected from the U(m) symmetry alone. The enlarged symmetry algebra A m (2L) consists of operators that commute in this space of states with the Temperley-Lieb algebra that is generated by the set of nearest-neighbor interaction terms; A m (2L) is not a Yangian. There are similar results for supersymmetric chains with gl(m+n|n) symmetry of nearest-neighbor interactions, and a richer representation structure for closed chains (i.e., periodic boundary conditions). The symmetries also apply to the loop models that can be obtained from the spin chains in a spacetime or transfer matrix picture. In the loop language, the symmetries arise because the loops cannot cross. We further define tensor products of representations (for the open chains) by joining chains end to end. The fusion rules for decomposing the tensor product of representations labeled j 1 and j 2 take the same form as the Clebsch-Gordan series for SU(2). This and other structures turn the symmetry algebra A m into a ribbon Hopf algebra, and we show that this is 'Morita equivalent' to the quantum group U q (sl 2 ) for m=q+q -1 . The open-chain results are extended to the cases vertical bar m vertical

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.

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.

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

Science.gov (United States)

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

2018-03-01

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

Opinion Dynamics on Networks with Inference of Unobservable States of Others

Science.gov (United States)

Fujie, Ryo

In most opinion formation models which have been proposed, the agents decide their states (i.e. opinions) by referring to the states of others. However, the referred states of others are not necessarily observable and may be inferred. To investigate the effect of an inference of the states of others on opinion dynamics, I propose an extended voter model on networks where observable and referable node sets are different. These sets for a node defined as the nearest to the mo-th neighbors for observable nodes and the nearest to the mr-th neighbors for referable nodes. The state of referable but unobservable node which is the m-th neighbor (mo pagerank'' is conserved. This conserved quantity coincides with the fixation probability. On the other hand, in the case of mo =mr = 1 , the model comes down to the standard voter model on networks and the conserved quantity is a degree-weighted superposition of the states. Thus, the introduction of the inference changes the important opinion spreaders from the high-degree nodes to the high-betweenness pagerank nodes. This work is supported by the Collaboration Research Program of IDEAS, Chubu University IDEAS2016233.

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.

Satelite structure in 59Co NMR spectrum of magnetically ordered Dysub(1-x)Ysub(x)Co2 intermetallic compound

International Nuclear Information System (INIS)

Yoshimura, Kazuyoshi; Hirosawa, Satoshi; Nakamura, Yoji

1984-01-01

The magnetic environment effect of cobalt in Dysub(1-x)Ysub(x)Co 2 has been studied by means of bulk magnetization and 59 Co spin-echo NMR measurements at 4.2K. Clearly resolved satellite structures of the NMR spectra have been observed. The hyperfine field distributions of 59 Co are decomposed into contributions of Co atoms in various nearest neighbor configurations of rare earth atoms. In this analysis the dipole field due to nearest neighbor rare earth moments plays an important role. The result indicates that the magnetic moment of Co in the RCo 2 cubic Laves phase pseudobinary compounds is quite sensitive to the nearest neighbor rare earth environment. (author)

An integrated modeling approach to age invariant face recognition

Science.gov (United States)

Alvi, Fahad Bashir; Pears, Russel

2015-03-01

This Research study proposes a novel method for face recognition based on Anthropometric features that make use of an integrated approach comprising of a global and personalized models. The system is aimed to at situations where lighting, illumination, and pose variations cause problems in face recognition. A Personalized model covers the individual aging patterns while a Global model captures general aging patterns in the database. We introduced a de-aging factor that de-ages each individual in the database test and training sets. We used the k nearest neighbor approach for building a personalized model and global model. Regression analysis was applied to build the models. During the test phase, we resort to voting on different features. We used FG-Net database for checking the results of our technique and achieved 65 percent Rank 1 identification rate.

Valence behavior of Eu-ions in intermetallic compound Ce{sub 0.5}Eu{sub 0.5}Pd{sub 3}B{sub 0.5}

Energy Technology Data Exchange (ETDEWEB)

Pandey, Abhishek, E-mail: apandey@ameslab.gov [Experimental Condensed Matter Physics Division, Saha Institute of Nuclear Physics, 1/AF, Bidhannagar, Kolkata 700064 (India); S.N. Bose National Centre for Basic Sciences, Block-JD, Sector-III, Salt Lake, Kolkata 700098 (India); Mazumdar, Chandan, E-mail: chandan.mazumdar@saha.ac.in [S.N. Bose National Centre for Basic Sciences, Block-JD, Sector-III, Salt Lake, Kolkata 700098 (India); Ranganathan, R. [S.N. Bose National Centre for Basic Sciences, Block-JD, Sector-III, Salt Lake, Kolkata 700098 (India); Raghavendra Reddy, V.; Gupta, Ajay [UGC-DAE Consortium for Scientific Research, University Campus, Khandawa Road, Indore (India)

2011-12-15

We have studied the valence behavior of rare-earth ions, in particular Eu-ions, in a cubic intermetallic compound Ce{sub 0.5}Eu{sub 0.5}Pd{sub 3}B{sub 0.5} which is a homogeneous solid solution of two mixed-valent compounds CePd{sub 3} and EuPd{sub 3}B. Results of {sup 151}Eu Moessbauer spectroscopic measurements show that two different valence states, i.e., divalent- and trivalent-like states of Eu-ions exist in the compound. The possible reason for the observed heterogeneous valency vis-a-vis the variation in the chemical environment and the number of nearest-neighbor B atoms surrounding the Eu-ions has been discussed. Our results demonstrate that B incorporation in such Eu-based cubic intermetallic compounds leads to a situation where heterogeneous-valence state of Eu-ions is an energetically favorable ground state. - Highlights: > Intermetallic compound Ce{sub 0.5}Eu{sub 0.5}Pd{sub 3}B{sub 0.5} crystallizes in a single phase. > Eu-ions in Ce{sub 0.5}Eu{sub 0.5}Pd{sub 3}B{sub 0.5} are charge-ordered compared to +2.3 valency in Ce{sub 0.5}Eu{sub 0.5}Pd{sub 3}. > B incorporation makes charge-ordered state of Eu-ions energetically more favorable. > Nearest-neighbor chemical environment affects the Eu valency.

Constrained parameter estimation for semi-supervised learning : The case of the nearest mean classifier

NARCIS (Netherlands)

Loog, M.

2011-01-01

A rather simple semi-supervised version of the equally simple nearest mean classifier is presented. However simple, the proposed approach is of practical interest as the nearest mean classifier remains a relevant tool in biomedical applications or other areas dealing with relatively high-dimensional

Diagnostic radiology in the nearest future

International Nuclear Information System (INIS)

Lindenbraten, L.D.

1984-01-01

Basic trends of diagnostic radiology (DR) development in the nearest future are formulated. Possibilities of perspective ways and means of DR studies are described. The prohlems of strategy, tactics, organization of diagnostic radiological service are considered. An attempt has been made to outline the professional image of a specialist in the DR of the future. It is shown that prediction of the DR future development is the planning stage of the present, the choice of a right way of development

Research Article Special Issue

African Journals Online (AJOL)

pc

2018-03-22

Mar 22, 2018 ... Python-based split train and k-fold model evaluation to evaluate the performance of the transformation .... split train and k-fold. Both of these validation methods have been widely .... cross-correlation and k-nearest neighbor.

A nearest-neighbour discretisation of the regularized stokeslet boundary integral equation

Science.gov (United States)

Smith, David J.

2018-04-01

The method of regularized stokeslets is extensively used in biological fluid dynamics due to its conceptual simplicity and meshlessness. This simplicity carries a degree of cost in computational expense and accuracy because the number of degrees of freedom used to discretise the unknown surface traction is generally significantly higher than that required by boundary element methods. We describe a meshless method based on nearest-neighbour interpolation that significantly reduces the number of degrees of freedom required to discretise the unknown traction, increasing the range of problems that can be practically solved, without excessively complicating the task of the modeller. The nearest-neighbour technique is tested against the classical problem of rigid body motion of a sphere immersed in very viscous fluid, then applied to the more complex biophysical problem of calculating the rotational diffusion timescales of a macromolecular structure modelled by three closely-spaced non-slender rods. A heuristic for finding the required density of force and quadrature points by numerical refinement is suggested. Matlab/GNU Octave code for the key steps of the algorithm is provided, which predominantly use basic linear algebra operations, with a full implementation being provided on github. Compared with the standard Nyström discretisation, more accurate and substantially more efficient results can be obtained by de-refining the force discretisation relative to the quadrature discretisation: a cost reduction of over 10 times with improved accuracy is observed. This improvement comes at minimal additional technical complexity. Future avenues to develop the algorithm are then discussed.

A Coupled k-Nearest Neighbor Algorithm for Multi-Label Classification

Science.gov (United States)

2015-05-22

classification, an image may contain several concepts simultaneously, such as beach, sunset and kangaroo . Such tasks are usually denoted as multi-label...informatics, a gene can belong to both metabolism and transcription classes; and in music categorization, a song may labeled as Mozart and sad. In the

MOST OBSERVATIONS OF OUR NEAREST NEIGHBOR: FLARES ON PROXIMA CENTAURI

Energy Technology Data Exchange (ETDEWEB)

Davenport, James R. A. [Department of Physics and Astronomy, Western Washington University, 516 High Street, Bellingham, WA 98225 (United States); Kipping, David M. [Department of Astronomy, Columbia University, 550 West 120th Street, New York, NY 10027 (United States); Sasselov, Dimitar [Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States); Matthews, Jaymie M. [Department of Physics and Astronomy, University of British Columbia, 6224 Agricultural Road, Vancouver, BC V6T 1Z1 (Canada); Cameron, Chris [Department of Mathematics, Physics and Geology, Cape Breton University, 1250 Grand Lake Road, Sydney, NS B1P 6L2 (Canada)

2016-10-01

We present a study of white-light flares from the active M5.5 dwarf Proxima Centauri using the Canadian microsatellite Microvariability and Oscillations of STars . Using 37.6 days of monitoring data from 2014 to 2015, we have detected 66 individual flare events, the largest number of white-light flares observed to date on Proxima Cen. Flare energies in our sample range from 10{sup 29} to 10{sup 31.5} erg. The flare rate is lower than that of other classic flare stars of a similar spectral type, such as UV Ceti, which may indicate Proxima Cen had a higher flare rate in its youth. Proxima Cen does have an unusually high flare rate given its slow rotation period, however. Extending the observed power-law occurrence distribution down to 10{sup 28} erg, we show that flares with flux amplitudes of 0.5% occur 63 times per day, while superflares with energies of 10{sup 33} erg occur ∼8 times per year. Small flares may therefore pose a great difficulty in searches for transits from the recently announced 1.27 M {sub ⊕} Proxima b, while frequent large flares could have significant impact on the planetary atmosphere.

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.

Monte Carlo simulation of atomic short range order and cluster formation in two dimensional model alloys

International Nuclear Information System (INIS)

Rojas T, J.; Instituto Peruano de Energia Nuclear, Lima; Manrique C, E.; Torres T, E.

2002-01-01

Using monte Carlo simulation have been carried out an atomistic description of the structure and ordering processes in the system Cu-Au in a two-dimensional model. The ABV model of the alloy is a system of N atoms A and B, located in rigid lattice with some vacant sites. In the model we assume pair wise interactions between nearest neighbors with constant ordering energy J = 0,03 eV. The dynamics was introduced by means of a vacancy that exchanges of place with any atom of its neighbors. The simulations were carried out in a square lattice with 1024 and 4096 particles, using periodic boundary conditions to avoid border effects. We calculate the first two parameters of short range order of Warren-Cowley as function of the concentration and temperature. It was also studied the probabilities of formation of different atomic clusters that consist of 9 atoms as function of the concentration of the alloy and temperatures in a wide range of values. In some regions of temperature and concentration it was observed compositional and thermal polymorphism

Dynamics of Nearest-Neighbour Competitions on Graphs

Science.gov (United States)

Rador, Tonguç

2017-10-01

Considering a collection of agents representing the vertices of a graph endowed with integer points, we study the asymptotic dynamics of the rate of the increase of their points according to a very simple rule: we randomly pick an an edge from the graph which unambiguously defines two agents we give a point the the agent with larger point with probability p and to the lagger with probability q such that p+q=1. The model we present is the most general version of the nearest-neighbour competition model introduced by Ben-Naim, Vazquez and Redner. We show that the model combines aspects of hyperbolic partial differential equations—as that of a conservation law—graph colouring and hyperplane arrangements. We discuss the properties of the model for general graphs but we confine in depth study to d-dimensional tori. We present a detailed study for the ring graph, which includes a chemical potential approximation to calculate all its statistics that gives rather accurate results. The two-dimensional torus, not studied in depth as the ring, is shown to possess critical behaviour in that the asymptotic speeds arrange themselves in two-coloured islands separated by borders of three other colours and the size of the islands obey power law distribution. We also show that in the large d limit the d-dimensional torus shows inverse sine law for the distribution of asymptotic speeds.

Numerical Simulation of the Diffusion Processes in Nanoelectrode Arrays Using an Axial Neighbor Symmetry Approximation.

Science.gov (United States)

Peinetti, Ana Sol; Gilardoni, Rodrigo S; Mizrahi, Martín; Requejo, Felix G; González, Graciela A; Battaglini, Fernando

2016-06-07

Nanoelectrode arrays have introduced a complete new battery of devices with fascinating electrocatalytic, sensitivity, and selectivity properties. To understand and predict the electrochemical response of these arrays, a theoretical framework is needed. Cyclic voltammetry is a well-fitted experimental technique to understand the undergoing diffusion and kinetics processes. Previous works describing microelectrode arrays have exploited the interelectrode distance to simulate its behavior as the summation of individual electrodes. This approach becomes limited when the size of the electrodes decreases to the nanometer scale due to their strong radial effect with the consequent overlapping of the diffusional fields. In this work, we present a computational model able to simulate the electrochemical behavior of arrays working either as the summation of individual electrodes or being affected by the overlapping of the diffusional fields without previous considerations. Our computational model relays in dividing a regular electrode array in cells. In each of them, there is a central electrode surrounded by neighbor electrodes; these neighbor electrodes are transformed in a ring maintaining the same active electrode area than the summation of the closest neighbor electrodes. Using this axial neighbor symmetry approximation, the problem acquires a cylindrical symmetry, being applicable to any diffusion pattern. The model is validated against micro- and nanoelectrode arrays showing its ability to predict their behavior and therefore to be used as a designing tool.

Is a reduction in distance to nearest supermarket associated with BMI change among type 2 diabetes patients?

Science.gov (United States)

Zhang, Y Tara; Laraia, Barbara A; Mujahid, Mahasin S; Blanchard, Samuel D; Warton, E Margaret; Moffet, Howard H; Karter, Andrew J

2016-07-01

We examined whether residing within 2 miles of a new supermarket opening was longitudinally associated with a change in body mass index (BMI). We identified 12 new supermarkets that opened between 2009 and 2010 in 8 neighborhoods. Using the Kaiser Permanente Northern California Diabetes Registry, we identified members with type 2 diabetes residing continuously in any of these neighborhoods 12 months prior to the first supermarket opening until 10 months following the opening of the last supermarket. Exposure was defined as a reduction (yes/no) in travel distance to the nearest supermarket as a result of a new supermarket opening. First difference regression models were used to estimate the impact of reduced supermarket distance on BMI, adjusting for longitudinal changes in patient and neighborhood characteristics. Among patients in the exposed group, new supermarket openings reduced travel distance to the nearest supermarket by 0.7 miles on average. However, reduced distance to nearest supermarket was not associated with BMI changes. Overall, we found no evidence that reduced supermarket distance was associated with reduced levels of obesity for residents with type 2 diabetes. Published by Elsevier Ltd.

Case-Based Reasoning untuk Diagnosis Penyakit Jantung

Directory of Open Access Journals (Sweden)

Eka Wahyudi

2017-01-01

            The test results using medical records data validated by expert indicate that the system is able to recognize diseases heart using nearest neighbor similarity method, minskowski distance similarity and euclidean distance similarity correctly respectively of 100%. Using nearest neighbor get accuracy of 86.21%, minkowski 100%, and euclidean 94.83%

True dose from incorporated activities. Models for internal dosimetry

International Nuclear Information System (INIS)

Breustedt, B.; Eschner, W.; Nosske, D.

2012-01-01

The assessment of doses after incorporation of radionuclides cannot use direct measurements of the doses, as for example dosimetry in external radiation fields. The only observables are activities in the body or in excretions. Models are used to calculate the doses based on the measured activities. The incorporated activities and the resulting doses can vary by more than seven orders of magnitude between occupational and medical exposures. Nevertheless the models and calculations applied in both cases are similar. Since the models for the different applications have been developed independently by ICRP and MIRD different terminologies have been used. A unified terminology is being developed. (orig.)

Do alcohol compliance checks decrease underage sales at neighboring establishments?

Science.gov (United States)

Erickson, Darin J; Smolenski, Derek J; Toomey, Traci L; Carlin, Bradley P; Wagenaar, Alexander C

2013-11-01

Underage alcohol compliance checks conducted by law enforcement agencies can reduce the likelihood of illegal alcohol sales at checked alcohol establishments, and theory suggests that an alcohol establishment that is checked may warn nearby establishments that compliance checks are being conducted in the area. In this study, we examined whether the effects of compliance checks diffuse to neighboring establishments. We used data from the Complying with the Minimum Drinking Age trial, which included more than 2,000 compliance checks conducted at more than 900 alcohol establishments. The primary outcome was the sale of alcohol to a pseudo-underage buyer without the need for age identification. A multilevel logistic regression was used to model the effect of a compliance check at each establishment as well as the effect of compliance checks at neighboring establishments within 500 m (stratified into four equal-radius concentric rings), after buyer, license, establishment, and community-level variables were controlled for. We observed a decrease in the likelihood of establishments selling alcohol to underage youth after they had been checked by law enforcement, but these effects quickly decayed over time. Establishments that had a close neighbor (within 125 m) checked in the past 90 days were also less likely to sell alcohol to young-appearing buyers. The spatial effect of compliance checks on other establishments decayed rapidly with increasing distance. Results confirm the hypothesis that the effects of police compliance checks do spill over to neighboring establishments. These findings have implications for the development of an optimal schedule of police compliance checks.

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...

Homogenization of heterogeneously coupled bistable ODE's - applied to excitation waves in pancreatic islets of Langerhans

DEFF Research Database (Denmark)

Pedersen, Morten Gram

2004-01-01

We consider a lattice of coupled identical differential equations. The coupling is between nearest neighbors and of resistance type, but the strength of coupling varies from site to site. Such a lattice can, for example, model an islet of Langerhans, where the sites in the lattice model individua...

Cerebellum segmentation in MRI using atlas registration and local multi-scale image descriptors

DEFF Research Database (Denmark)

van der Lijn, F.; de Bruijne, M.; Hoogendam, Y.Y.

2009-01-01

We propose a novel cerebellum segmentation method for MRI, based on a combination of statistical models of the structure's expected location in the brain and its local appearance. The appearance model is obtained from a k-nearest-neighbor classifier, which uses a set of multi-scale local image...

The tight binding model study of the role of band filling on the charge gap in graphene-on-substrate in paramagnetic state

Science.gov (United States)

Panda, Rudrashish; Sahu, Sivabrata; Rout, G. C.

2017-05-01

We communicate here a tight binding theoretical model study of the band filling effect on the charge gap in graphene-on-substrate. The Hamiltonian consists of nearest neighbor electron hopping and substrate induced gap. Besides this the Coulomb interaction is considered here within mean-field approximation in the paramagnetic limit. The electron occupancies at two sublattices are calculated by Green's function technique and are solved self consistently. Finally the charge gap i.e. Δ ¯=U [ - ] is calculated and computed numerically. The results are reported.

Percolation transitions in two dimensions

NARCIS (Netherlands)

Feng, X.; Deng, Y.; Blöte, H.W.J.

2008-01-01

We investigate bond- and site-percolation models on several two-dimensional lattices numerically, by means of transfer-matrix calculations and Monte Carlo simulations. The lattices include the square, triangular, honeycomb kagome, and diced lattices with nearest-neighbor bonds, and the square

Effect of Floquet engineering on the p-wave superconductor with second-neighbor couplings

Science.gov (United States)

Li, X. P.; Li, C. F.; Wang, L. C.; Zhou, L.

2018-06-01

The influence of the Floquet engineering on a particular one-dimensional p-wave superconductor, Kitaev model, with second-neighbor couplings is investigated in this paper. The effective Hamiltonians in the rotated reference frames have been obtained, and the convergent regions of the approximated Hamiltonian as well as the topological phase diagrams have been analyzed and discussed. We show that by modulating the external driving field amplitude, frequency as well as the second-neighbor hopping amplitude, the rich phase diagrams and transitions between different topological phases can be obtained.

Incorporating interfacial phenomena in solidification models

Science.gov (United States)

Beckermann, Christoph; Wang, Chao Yang

1994-01-01

A general methodology is available for the incorporation of microscopic interfacial phenomena in macroscopic solidification models that include diffusion and convection. The method is derived from a formal averaging procedure and a multiphase approach, and relies on the presence of interfacial integrals in the macroscopic transport equations. In a wider engineering context, these techniques are not new, but their application in the analysis and modeling of solidification processes has largely been overlooked. This article describes the techniques and demonstrates their utility in two examples in which microscopic interfacial phenomena are of great importance.

High-Strain Rate Failure Modeling Incorporating Shear Banding and Fracture

Science.gov (United States)

2017-11-22

High Strain Rate Failure Modeling Incorporating Shear Banding and Fracture The views, opinions and/or findings contained in this report are those of...SECURITY CLASSIFICATION OF: 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND SUBTITLE 13. SUPPLEMENTARY NOTES 12. DISTRIBUTION AVAILIBILITY STATEMENT 6. AUTHORS...Report as of 05-Dec-2017 Agreement Number: W911NF-13-1-0238 Organization: Columbia University Title: High Strain Rate Failure Modeling Incorporating

Prediction of monthly electric energy consumption using pattern-based fuzzy nearest neighbour regression

Directory of Open Access Journals (Sweden)

Pełka Paweł

2017-01-01

Full Text Available Electricity demand forecasting is of important role in power system planning and operation. In this work, fuzzy nearest neighbour regression has been utilised to estimate monthly electricity demands. The forecasting model was based on the pre-processed energy consumption time series, where input and output variables were defined as patterns representing unified fragments of the time series. Relationships between inputs and outputs, which were simplified due to patterns, were modelled using nonparametric regression with weighting function defined as a fuzzy membership of learning points to the neighbourhood of a query point. In an experimental part of the work the model was evaluated using real-world data. The results are encouraging and show high performances of the model and its competitiveness compared to other forecasting models.

Bees do not use nearest-neighbour rules for optimization of multi-location routes.

Science.gov (United States)

Lihoreau, Mathieu; Chittka, Lars; Le Comber, Steven C; Raine, Nigel E

2012-02-23

Animals collecting patchily distributed resources are faced with complex multi-location routing problems. Rather than comparing all possible routes, they often find reasonably short solutions by simply moving to the nearest unvisited resources when foraging. Here, we report the travel optimization performance of bumble-bees (Bombus terrestris) foraging in a flight cage containing six artificial flowers arranged such that movements between nearest-neighbour locations would lead to a long suboptimal route. After extensive training (80 foraging bouts and at least 640 flower visits), bees reduced their flight distances and prioritized shortest possible routes, while almost never following nearest-neighbour solutions. We discuss possible strategies used during the establishment of stable multi-location routes (or traplines), and how these could allow bees and other animals to solve complex routing problems through experience, without necessarily requiring a sophisticated cognitive representation of space.

Evaluation of novel candidate variations and their interactions related to bipolar disorders: Analysis of GWAS data

Directory of Open Access Journals (Sweden)

Acikel C

2016-11-01

Full Text Available Cengizhan Acikel,1 Yesim Aydin Son,2 Cemil Celik,3 Husamettin Gul4 1Department of Biostatistics, Gulhane Military Medical Academy, 2Department of Health Informatics, Graduate School of Informatics, Middle East Technical University, 3Department of Medical Psychiatry, 4Department of Medical Informatics, Gulhane Military Medical Academy, Ankara, Turkey Background: Multifactor dimensionality reduction (MDR is a nonparametric approach that can be used to detect relevant interactions between single-nucleotide polymorphisms (SNPs. The aim of this study was to build the best genomic model based on SNP associations and to identify candidate polymorphisms that are the underlying molecular basis of the bipolar disorders. Methods: This study was performed on Whole-Genome Association Study of Bipolar Disorder (dbGaP [database of Genotypes and Phenotypes] study accession number: phs000017.v3.p1 data. After preprocessing of the genotyping data, three classification-based data mining methods (ie, random forest, naïve Bayes, and k-nearest neighbor were performed. Additionally, as a nonparametric, model-free approach, the MDR method was used to evaluate the SNP profiles. The validity of these methods was evaluated using true classification rate, recall (sensitivity, precision (positive predictive value, and F-measure. Results: Random forests, naïve Bayes, and k-nearest neighbors identified 16, 13, and ten candidate SNPs, respectively. Surprisingly, the top six SNPs were reported by all three methods. Random forests and k-nearest neighbors were more successful than naïve Bayes, with recall values >0.95. On the other hand, MDR generated a model with comparable predictive performance based on five SNPs. Although different SNP profiles were identified in MDR compared to the classification-based models, all models mapped SNPs to the DOCK10 gene. Conclusion: Three classification-based data mining approaches, random forests, naïve Bayes, and k-nearest neighbors

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

Evaluation of clustering statistics with N-body simulations

International Nuclear Information System (INIS)

Quinn, T.R.

1986-01-01

Two series of N-body simulations are used to determine the effectiveness of various clustering statistics in revealing initial conditions from evolved models. All the simulations contained 16384 particles and were integrated with the PPPM code. One series is a family of models with power at only one wavelength. The family contains five models with the wavelength of the power separated by factors of √2. The second series is a family of all equal power combinations of two wavelengths taken from the first series. The clustering statistics examined are the two point correlation function, the multiplicity function, the nearest neighbor distribution, the void probability distribution, the distribution of counts in cells, and the peculiar velocity distribution. It is found that the covariance function, the nearest neighbor distribution, and the void probability distribution are relatively insensitive to the initial conditions. The distribution of counts in cells show a little more sensitivity, but the multiplicity function is the best of the statistics considered for revealing the initial conditions

Geospatial tools effectively estimate nonexceedance probabilities of daily streamflow at ungauged and intermittently gauged locations in Ohio

Directory of Open Access Journals (Sweden)

William H. Farmer

2017-10-01

New hydrological insights for the region: Several methods for estimating nonexceedance probabilities of daily mean streamflows are explored, including single-index methodologies (nearest-neighboring index and geospatial tools (kriging and topological kriging. These methods were evaluated by conducting leave-one-out cross-validations based on analyses of nearly 7 years of daily streamflow data from 79 unregulated streamgages in Ohio and neighboring states. The pooled, ordinary kriging model, with a median Nash–Sutcliffe performance of 0.87, was superior to the single-site index methods, though there was some bias in the tails of the probability distribution. Incorporating network structure through topological kriging did not improve performance. The pooled, ordinary kriging model was applied to 118 locations without systematic streamgaging across Ohio where instantaneous streamflow measurements had been made concurrent with water-quality sampling on at least 3 separate days. Spearman rank correlations between estimated nonexceedance probabilities and measured streamflows were high, with a median value of 0.76. In consideration of application, the degree of regulation in a set of sample sites helped to specify the streamgages required to implement kriging approaches successfully.

Electron tunneling in carbon nanotube composites

International Nuclear Information System (INIS)

Gau, C; Kuo, Cheng-Yung; Ko, H S

2009-01-01

Nanocomposites, such as polymer blending with carbon nanotubes (CNTs), have been shown to have a drastic reduction in the resistivity and become conductive when the CNTs concentration has reached a certain percolation threshold. The reduction could be more than a millionth of the original polymer material. This has been realized as the formation of an infinite cluster of connected CNTs or pathways. Therefore, the conductivity of a nanocomposite should follow that of CNTs. Here we show that the resistivity of a nanocomposite is not governed by the interconnected CNTs, but the polymer between neighboring CNTs. That is, polymer-CNTs exhibit the nature of a conducting polymer, which can be explained as the tunneling of electrons one by one from the first CNT electrode to the next-nearest CNT electrode, forming a CNT/polymer pathway. A conduction model based on the tunneling of electrons passing, one by one, through the polymer gap between two neighboring CNT electrodes is formulated and derived. This model can accurately predict the significant reduction of the polymer-CNTs' resistivity with the addition of CNTs. The temperature effect can be readily incorporated to account for resistivity variation with the temperature of this nanocomposites.

Error Analysis for RADAR Neighbor Matching Localization in Linear Logarithmic Strength Varying Wi-Fi Environment

Directory of Open Access Journals (Sweden)

Mu Zhou

2014-01-01

Full Text Available This paper studies the statistical errors for the fingerprint-based RADAR neighbor matching localization with the linearly calibrated reference points (RPs in logarithmic received signal strength (RSS varying Wi-Fi environment. To the best of our knowledge, little comprehensive analysis work has appeared on the error performance of neighbor matching localization with respect to the deployment of RPs. However, in order to achieve the efficient and reliable location-based services (LBSs as well as the ubiquitous context-awareness in Wi-Fi environment, much attention has to be paid to the highly accurate and cost-efficient localization systems. To this end, the statistical errors by the widely used neighbor matching localization are significantly discussed in this paper to examine the inherent mathematical relations between the localization errors and the locations of RPs by using a basic linear logarithmic strength varying model. Furthermore, based on the mathematical demonstrations and some testing results, the closed-form solutions to the statistical errors by RADAR neighbor matching localization can be an effective tool to explore alternative deployment of fingerprint-based neighbor matching localization systems in the future.

Error Analysis for RADAR Neighbor Matching Localization in Linear Logarithmic Strength Varying Wi-Fi Environment

Science.gov (United States)

Tian, Zengshan; Xu, Kunjie; Yu, Xiang

2014-01-01

This paper studies the statistical errors for the fingerprint-based RADAR neighbor matching localization with the linearly calibrated reference points (RPs) in logarithmic received signal strength (RSS) varying Wi-Fi environment. To the best of our knowledge, little comprehensive analysis work has appeared on the error performance of neighbor matching localization with respect to the deployment of RPs. However, in order to achieve the efficient and reliable location-based services (LBSs) as well as the ubiquitous context-awareness in Wi-Fi environment, much attention has to be paid to the highly accurate and cost-efficient localization systems. To this end, the statistical errors by the widely used neighbor matching localization are significantly discussed in this paper to examine the inherent mathematical relations between the localization errors and the locations of RPs by using a basic linear logarithmic strength varying model. Furthermore, based on the mathematical demonstrations and some testing results, the closed-form solutions to the statistical errors by RADAR neighbor matching localization can be an effective tool to explore alternative deployment of fingerprint-based neighbor matching localization systems in the future. PMID:24683349

A dumbed-down approach to unite Fermilab, its neighbors

CERN Multimedia

Constable, B

2004-01-01

"...Fermilab is reaching out to its suburban neighbors...With the nation on orange alert, Fermilab scientists no longer can sit on the front porch and invite neighbors in for coffee and quasars" (1 page).

Emotion detection model of Filipino music

Science.gov (United States)

Noblejas, Kathleen Alexis; Isidro, Daryl Arvin; Samonte, Mary Jane C.

2017-02-01

This research explored the creation of a model to detect emotion from Filipino songs. The emotion model used was based from Paul Ekman's six basic emotions. The songs were classified into the following genres: kundiman, novelty, pop, and rock. The songs were annotated by a group of music experts based on the emotion the song induces to the listener. Musical features of the songs were extracted using jAudio while the lyric features were extracted by Bag-of- Words feature representation. The audio and lyric features of the Filipino songs were extracted for classification by the chosen three classifiers, Naïve Bayes, Support Vector Machines, and k-Nearest Neighbors. The goal of the research was to know which classifier would work best for Filipino music. Evaluation was done by 10-fold cross validation and accuracy, precision, recall, and F-measure results were compared. Models were also tested with unknown test data to further determine the models' accuracy through the prediction results.

Atomic state and characterization of nitrogen at the SiC/SiO2 interface

International Nuclear Information System (INIS)

Xu, Y.; Garfunkel, E. L.; Zhu, X.; Lee, H. D.; Xu, C.; Shubeita, S. M.; Gustafsson, T.; Ahyi, A. C.; Sharma, Y.; Williams, J. R.; Lu, W.; Ceesay, S.; Tuttle, B. R.; Pantelides, S. T.; Wan, A.; Feldman, L. C.

2014-01-01

We report on the concentration, chemical bonding, and etching behavior of N at the SiC(0001)/SiO 2 interface using photoemission, ion scattering, and computational modeling. For standard NO processing of a SiC MOSFET, a sub-monolayer of nitrogen is found in a thin inter-layer between the substrate and the gate oxide (SiO 2 ). Photoemission shows one main nitrogen related core-level peak with two broad, higher energy satellites. Comparison to theory indicates that the main peak is assigned to nitrogen bound with three silicon neighbors, with second nearest neighbors including carbon, nitrogen, and oxygen atoms. Surprisingly, N remains at the surface after the oxide was completely etched by a buffered HF solution. This is in striking contrast to the behavior of Si(100) undergoing the same etching process. We conclude that N is bound directly to the substrate SiC, or incorporated within the first layers of SiC, as opposed to bonding within the oxide network. These observations provide insights into the chemistry and function of N as an interface passivating additive in SiC MOSFETs

Pollinator-mediated interactions in experimental arrays vary with neighbor identity.

Science.gov (United States)

Ha, Melissa K; Ivey, Christopher T

2017-02-01

Local ecological conditions influence the impact of species interactions on evolution and community structure. We investigated whether pollinator-mediated interactions between coflowering plants vary with plant density, coflowering neighbor identity, and flowering season. We conducted a field experiment in which flowering time and floral neighborhood were manipulated in a factorial design. Early- and late-flowering Clarkia unguiculata plants were placed into arrays with C. biloba neighbors, noncongeneric neighbors, additional conspecific plants, or no additional plants as a density control. We compared whole-plant pollen limitation of seed set, pollinator behavior, and pollen deposition among treatments. Interactions mediated by shared pollinators depended on the identity of the neighbor and possibly changed through time, although flowering-season comparisons were compromised by low early-season plant survival. Interactions with conspecific neighbors were likely competitive late in the season. Interactions with C. biloba appeared to involve facilitation or neutral interactions. Interactions with noncongeners were more consistently competitive. The community composition of pollinators varied among treatment combinations. Pollinator-mediated interactions involved competition and likely facilitation, depending on coflowering neighbor. Experimental manipulation helped to reveal context-dependent variation in indirect biotic interactions. © 2017 Botanical Society of America.

The clinic as a good corporate neighbor.

Science.gov (United States)

Sass, Hans-Martin

2013-02-01

Clinics today specialize in health repair services similar to car repair shops; procedures and prices are standardized, regulated, and inflexibly uniform. Clinics of the future have to become Health Care Centers in order to be more respected and more effective corporate neighbors in offering outreach services in health education and preventive health care. The traditional concept of care for health is much broader than repair management and includes the promotion of lay health competence and responsibility in healthy social and natural environments. The corporate profile and ethics of the clinic as a good and competitive local neighbor will have to focus on [a] better personalized care, [b] education and services in preventive care, [c] direct or web-based information and advice for general, seasonal, or age related health risks, and on developing and improving trustworthy character traits of the clinic as a corporate person and a good neighbor.

Photo-Activated Localization Microscopy of Single Carbohydrate Binding Modules on Cellulose Nanofibers

Science.gov (United States)

Hor, Amy; Dagel, Daryl; Luu, Quocanh; Savaikar, Madhusudan; Ding, Shi-You; Smith, Steve

2015-03-01

Photo Activated Localization Microscopy (PALM) is used to conduct an in vivo study of the binding affinity of polysaccharide-specific Carbohydrate Binding Modules (CBMs) to insoluble cellulose substrates. Two families of CBMs, namely TrCBM1 and CtCBM3, were modified to incorporate photo-activatable mCherry fluorescent protein (PAmCherry), and exposed to highly crystalline Valonia cellulose nano-fibrils. The resulting PALM images show CBMs binding along the nano-fibril long axis in a punctuated linear array, localized with, on average, 10 nm precision. Statistical analysis of the binding events results in nearest neighbor distributions between CBMs. A comparison between TrCBM1 and CtCBM3 reveals a similarity in the nearest neighbor distribution peaks but differences in the overall binding density. The former is attributed to steric hindrance among the CBMs on the nano-fibril whereas the latter is attributed to differences in the CBMs' binding strength. These results are compared to similar distributions derived from TEM measurements of dried samples of CtCBM3-CdSs quantum dot bioconjugates and AFM images of CtCBM3-GFP bound to similar Valonia nano-fibrils. Funding provided by NSF MPS/DMR/BMAT Award # 1206908.

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}.

Incorporating the life course model into MCH nutrition leadership education and training programs.

Science.gov (United States)

Haughton, Betsy; Eppig, Kristen; Looney, Shannon M; Cunningham-Sabo, Leslie; Spear, Bonnie A; Spence, Marsha; Stang, Jamie S

2013-01-01

Life course perspective, social determinants of health, and health equity have been combined into one comprehensive model, the life course model (LCM), for strategic planning by US Health Resources and Services Administration's Maternal and Child Health Bureau. The purpose of this project was to describe a faculty development process; identify strategies for incorporation of the LCM into nutrition leadership education and training at the graduate and professional levels; and suggest broader implications for training, research, and practice. Nineteen representatives from 6 MCHB-funded nutrition leadership education and training programs and 10 federal partners participated in a one-day session that began with an overview of the models and concluded with guided small group discussions on how to incorporate them into maternal and child health (MCH) leadership training using obesity as an example. Written notes from group discussions were compiled and coded emergently. Content analysis determined the most salient themes about incorporating the models into training. Four major LCM-related themes emerged, three of which were about training: (1) incorporation by training grants through LCM-framed coursework and experiences for trainees, and similarly framed continuing education and skills development for professionals; (2) incorporation through collaboration with other training programs and state and community partners, and through advocacy; and (3) incorporation by others at the federal and local levels through policy, political, and prevention efforts. The fourth theme focused on anticipated challenges of incorporating the model in training. Multiple methods for incorporating the LCM into MCH training and practice are warranted. Challenges to incorporating include the need for research and related policy development.

A molecular-thermodynamic model for polyelectrolyte solutions

Energy Technology Data Exchange (ETDEWEB)

Jiang, J.; Liu, H.; Hu, Y. [Thermodynamics Research Laboratory, East China University of Science and Technology, Shanghai 200237 (China); Prausnitz, J.M. [Department of Chemical Engineering, University of California, Berkeley, and Chemical Sciences Division, Lawrence Berkeley Laboratory, University of California, Berkeley, California 94720 (United States)

1998-01-01

Polyelectrolyte solutions are modeled as freely tangent-jointed, charged hard-sphere chains and corresponding counterions in a continuum medium with permitivity {var_epsilon}. By adopting the sticky-point model, the Helmholtz function for polyelectrolyte solutions is derived through the r-particle cavity-correlation function (CCF) for chains of sticky, charged hard spheres. The r-CCF is approximated by a product of effective nearest-neighbor two-particle CCFs; these are determined from the hypernetted-chain and mean-spherical closures (HNC/MSA) inside and outside the hard core, respectively, for the integral equation theory for electrolytes. The colligative properties are given as explicit functions of a scaling parameter {Gamma} that can be estimated by a simple iteration procedure. Osmotic pressures, osmotic coefficients, and activity coefficients are calculated for model solutions with various chain lengths. They are in good agreement with molecular simulation and experimental results. {copyright} {ital 1998 American Institute of Physics.}

Linear perturbation renormalization group method for Ising-like spin systems

Directory of Open Access Journals (Sweden)

J. Sznajd

2013-03-01

Full Text Available The linear perturbation group transformation (LPRG is used to study the thermodynamics of the axial next-nearest-neighbor Ising model with four spin interactions (extended ANNNI in a field. The LPRG for weakly interacting Ising chains is presented. The method is used to study finite field para-ferrimagnetic phase transitions observed in layered uranium compounds, UAs1-xSex, UPd2Si2 or UNi2Si2. The above-mentioned systems are made of ferromagnetic layers and the spins from the nearest-neighbor and next-nearest-neighbor layers are coupled by the antiferromagnetic interactions J121-xSex the para-ferri phase transition is of the first order as expected from the symmetry reason, in UT2Si2 (T=Pd, Ni this transition seems to be a continuous one, at least in the vicinity of the multicritical point. Within the MFA, the critical character of the finite field para-ferrimagnetic transition at least at one isolated point can be described by the ANNNI model supplemented by an additional, e.g., four-spin interaction. However, in LPRG approximation for the ratio κ = J2/J1 around 0.5 there is a critical value of the field for which an isolated critical point also exists in the original ANNNI model. The positive four-spin interaction shifts the critical point towards higher fields and changes the shape of the specific heat curve. In the latter case for the fields small enough, the specific heat exhibits two-peak structure in the paramagnetic phase.

Orchestrated structure evolution: modeling growth-regulated nanomanufacturing

Energy Technology Data Exchange (ETDEWEB)

Abbasi, Shaghayegh; Boehringer, Karl F [Department of Electrical Engineering, University of Washington, Seattle, WA 98195-2500 (United States); Kitayaporn, Sathana; Schwartz, Daniel T, E-mail: karlb@washington.edu [Department of Chemical Engineering, University of Washington, Seattle, WA 98195-2500 (United States)

2011-04-22

Orchestrated structure evolution (OSE) is a scalable manufacturing method that combines the advantages of top-down (tool-directed) and bottom-up (self-propagating) approaches. The method consists of a seed patterning step that defines where material nucleates, followed by a growth step that merges seeded islands into the final patterned thin film. We develop a model to predict the completed pattern based on a computationally efficient approximate Green's function solution of the diffusion equation plus a Voronoi diagram based approach that defines the final grain boundary structure. Experimental results rely on electron beam lithography to pattern the seeds, followed by the mass transfer limited growth of copper via electrodeposition. The seed growth model is compared with experimental results to quantify nearest neighbor seed-to-seed interactions as well as how seeds interact with the pattern boundary to impact the local growth rate. Seed-to-seed and seed-to-pattern interactions are shown to result in overgrowth of seeds on edges and corners of the shape, where seeds have fewer neighbors. We explore how local changes to the seed location can be used to improve the patterning quality without increasing the manufacturing cost. OSE is shown to enable a unique set of trade-offs between the cost, time, and quality of thin film patterning.

Monolayer Iron Carbide Films on Au(111) as a Fischer–Tropsch Model Catalyst

DEFF Research Database (Denmark)

Mannie, Gilbère; Lammich, Lutz; Li, Yong-Wang

2014-01-01

-temperature exposure of Fe islands gas to C2H4 deposited on the clean Au(111) surface results in partly converted Fe/FexCy islands. Multistep flash-heating treatment of the partly converted Fe/FexCy islands at 523 and 773 K results in pure highly crystalline FexCy islands with in-plane nearest-neighbor distances of 0...

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

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.

75 FR 20265 - Airworthiness Directives; Liberty Aerospace Incorporated Model XL-2 Airplanes

Science.gov (United States)

2010-04-19

... Office, 1701 Columbia Avenue, College Park, Georgia 30337; telephone: (404) 474-5524; facsimile: (404... Airworthiness Directives; Liberty Aerospace Incorporated Model XL-2 Airplanes AGENCY: Federal Aviation...-08- 05, which applies to certain Liberty Aerospace Incorporated Model XL-2 airplanes. AD 2009-08-05...

Unwanted Behaviors and Nuisance Behaviors Among Neighbors in a Belgian Community Sample.

Science.gov (United States)

Michaux, Emilie; Groenen, Anne; Uzieblo, Katarzyna

2015-06-30

Unwanted behaviors between (ex-)intimates have been extensively studied, while those behaviors within other contexts such as neighbors have received much less scientific consideration. Research indicates that residents are likely to encounter problem behaviors from their neighbors. Besides the lack of clarity in the conceptualization of problem behaviors among neighbors, little is known on which types of behaviors characterize neighbor problems. In this study, the occurrence of two types of problem behaviors encountered by neighbors was explored within a Belgian community sample: unwanted behaviors such as threats and neighbor nuisance issues such as noise nuisance. By clearly distinguishing those two types of behaviors, this study aimed at contributing to the conceptualization of neighbor problems. Next, the coping strategies used to deal with the neighbor problems were investigated. Our results indicated that unwanted behaviors were more frequently encountered by residents compared with nuisance problems. Four out of 10 respondents reported both unwanted pursuit behavior and nuisance problems. It was especially unlikely to encounter nuisance problems in isolation of unwanted pursuit behaviors. While different coping styles (avoiding the neighbor, confronting the neighbor, and enlisting help from others) were equally used by the stalked participants, none of them was perceived as being more effective in reducing the stalking behaviors. Strikingly, despite being aware of specialized help services such as community mediation services, only a very small subgroup enlisted this kind of professional help. © The Author(s) 2015.

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)

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...

Optical properties of helical cylindrical molecular aggregates : the homogeneous limit

NARCIS (Netherlands)

Didraga, C.; Klugkist, J.A.; Knoester, J.

2002-01-01

Using a Frenkel exciton model, we study the optical absorption spectrum and linear and circular dichroism (CD) spectra of cylindrical molecular aggregates. We demonstrate that such aggregates can always be described as a stack of molecular rings with nearest-neighbor rings rotated relative to each

Optical Properties of Helical Cylindrical Molecular Aggregates : The Homogeneous Limit

NARCIS (Netherlands)

Didraga, Cătălin; Klugkist, Joost A.; Knoester, Jasper

2002-01-01

Using a Frenkel exciton model, we study the optical absorption spectrum and linear and circular dichroism (CD) spectra of cylindrical molecular aggregates. We demonstrate that such aggregates can always be described as a stack of molecular rings with nearest-neighbor rings rotated relative to each

Modeling and knowledge acquisition processes using case-based inference

Directory of Open Access Journals (Sweden)

Ameneh Khadivar

2017-03-01

Full Text Available The method of acquisition and presentation of the organizational Process Knowledge has considered by many KM researches. In this research a model for process knowledge acquisition and presentation has been presented by using the approach of Case Base Reasoning. The validation of the presented model was evaluated by conducting an expert panel. Then a software has been developed based on the presented model and implemented in Eghtesad Novin Bank of Iran. In this company, based on the stages of the presented model, first the knowledge intensive processes has been identified, then the Process Knowledge was stored in a knowledge base in the format of problem/solution/consequent .The retrieval of the knowledge was done based on the similarity of the nearest neighbor algorithm. For validating of the implemented system, results of the system has compared by the results of the decision making of the expert of the process.

ALIGNMENTS OF GROUP GALAXIES WITH NEIGHBORING GROUPS

International Nuclear Information System (INIS)

Wang Yougang; Chen Xuelei; Park, Changbom; Yang Xiaohu; Choi, Yun-Young

2009-01-01

Using a sample of galaxy groups found in the Sloan Digital Sky Survey Data Release 4, we measure the following four types of alignment signals: (1) the alignment between the distributions of the satellites of each group relative to the direction of the nearest neighbor group (NNG); (2) the alignment between the major axis direction of the central galaxy of the host group (HG) and the direction of the NNG; (3) the alignment between the major axes of the central galaxies of the HG and the NNG; and (4) the alignment between the major axes of the satellites of the HG and the direction of the NNG. We find strong signal of alignment between the satellite distribution and the orientation of central galaxy relative to the direction of the NNG, even when the NNG is located beyond 3r vir of the host group. The major axis of the central galaxy of the HG is aligned with the direction of the NNG. The alignment signals are more prominent for groups that are more massive and with early-type central galaxies. We also find that there is a preference for the two major axes of the central galaxies of the HG and NNG to be parallel for the system with both early central galaxies, however, not for the systems with both late-type central galaxies. For the orientation of satellite galaxies, we do not find any significant alignment signals relative to the direction of the NNG. From these four types of alignment measurements, we conclude that the large-scale environment traced by the nearby group affects primarily the shape of the host dark matter halo, and hence also affects the distribution of satellite galaxies and the orientation of central galaxies. In addition, the NNG directly affects the distribution of the satellite galaxies by inducing asymmetric alignment signals, and the NNG at very small separation may also contribute a second-order impact on the orientation of the central galaxy in the HG.

Interaction of a single mode field cavity with the 1D XY model: Energy spectrum

International Nuclear Information System (INIS)

Tonchev, H; Donkov, A A; Chamati, H

2016-01-01

In this work we use the fundamental in quantum optics Jaynes-Cummings model to study the response of spin 1/2chain to a single mode of a laser light falling on one of the spins, a focused interaction model between the light and the spin chain. For the spin-spin interaction along the chain we use the XY model. We report here the exact analytical results, obtained with the help of a computer algebra system, for the energy spectrum in this model for chains of up to 4 spins with nearest neighbors interactions, either for open or cyclic chain configurations. Varying the sign and magnitude of the spin exchange coupling relative to the light-spin interaction we have investigated both cases of ferromagnetic or antiferromagnetic spin chains. (paper)

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.

Monte Carlo algorithms with absorbing Markov chains: Fast local algorithms for slow dynamics

International Nuclear Information System (INIS)

Novotny, M.A.

1995-01-01

A class of Monte Carlo algorithms which incorporate absorbing Markov chains is presented. In a particular limit, the lowest order of these algorithms reduces to the n-fold way algorithm. These algorithms are applied to study the escape from the metastable state in the two-dimensional square-lattice nearest-neighbor Ising ferromagnet in an unfavorable applied field, and the agreement with theoretical predictions is very good. It is demonstrated that the higher-order algorithms can be many orders of magnitude faster than either the traditional Monte Carlo or n-fold way algorithms

Co-Expression of Neighboring Genes in the Zebrafish (Danio rerio Genome

Directory of Open Access Journals (Sweden)

Daryi Wang

2009-08-01

Full Text Available Neighboring genes in the eukaryotic genome have a tendency to express concurrently, and the proximity of two adjacent genes is often considered a possible explanation for their co-expression behavior. However, the actual contribution of the physical distance between two genes to their co-expression behavior has yet to be defined. To further investigate this issue, we studied the co-expression of neighboring genes in zebrafish, which has a compact genome and has experienced a whole genome duplication event. Our analysis shows that the proportion of highly co-expressed neighboring pairs (Pearson’s correlation coefficient R>0.7 is low (0.24% ~ 0.67%; however, it is still significantly higher than that of random pairs. In particular, the statistical result implies that the co-expression tendency of neighboring pairs is negatively correlated with their physical distance. Our findings therefore suggest that physical distance may play an important role in the co-expression of neighboring genes. Possible mechanisms related to the neighboring genes’ co-expression are also discussed.

New Sliding Puzzle with Neighbors Swap Motion

OpenAIRE

Prihardono, Ariyanto; Kawagoe, Kenichi

2015-01-01

The sliding puzzles (15-puzzle, 8-puzzle, 5-puzzle) are known to have 2 kind of puz-zle: solvable puzzle and unsolvable puzzle. In this thesis, we make a new puzzle with only 1 kind of it, solvable puzzle. This new puzzle is made by adopting sliding puzzle with several additional rules from M13 puzzle; the puzzle that is formed form The Mathieu group M13. This puzzle has a movement that called a neighbors swap motion, a rule of movement that enables every neighboring points to swap. This extr...

Two-particle approach to the electronic structure of solids

International Nuclear Information System (INIS)

Gonis, A.

2007-01-01

Based on an extension of Hubbard's treatment of the electronic structure of correlated electrons in matter we propose a methodology that incorporates the scattering off the Coulomb interaction through the determination of a two-particle propagator. The Green function equations of motion are then used to obtain single-particle Green functions and related properties such as densities of states. The solutions of the equations of motion in two- and single-particle spaces are accomplished through applications of the coherent potential approximation. The formalism is illustrated by means of calculations for a single-band model system representing a linear arrangement of sites with nearest neighbor hopping and an one-site repulsion when two electrons of opposite spin occupy the same site in the lattice in the manner described by the so-called Hubbard Hamiltonian

Time evolution of a quenched binary alloy: computer simulation of a three-dimensional model system

International Nuclear Information System (INIS)

Marro, J.; Bortz, A.B.; Kalos, M.H.; Lebowitz, J.L.; Sur, A.

1976-01-01

Results are presented of computer simulation of the time evolution for a model of a binary alloy, such as ZnAl, following quenching. The model system is a simple cubic lattice the sites of which are occupied either by A or B particles. There is a nearest neighbor interaction favoring segregation into an A rich and a B rich phase at low temperatures, T less than T/sub c/. Starting from a random configuration, T much greater than T/sub c/, the system is quenched to and evolves at a temperature T less than T/sub c/. The evolution takes place through exchanges between A and B atoms on nearest neighbor sites. The probability of such an exchange is assumed proportional to e/sup -βΔU/ [1 + e/sup -βΔU/] -1 where β = (k/sub B/T) -1 and ΔU is the change in energy resulting from the exchange. In the simulations either a 30 x 30 x 30 or a 50 x 50 x 50 lattice is used with various fractions of the sites occupied by A particles. The evolution of the Fourier transform of the spherically averaged structure function S(k,t), the energy, and the cluster distribution were computed. Comparison is made with various theories of this process and with some experiments. It is found in particular that the results disagree with the predictions of the linearized Cahn-Hilliard theory of spinodal decomposition. The qualitative form of the results appear to be unaffected if the change in the positions of the atoms takes place via a vacancy mechanism rather than through direct exchanges

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.

The Influence of Neighbor Effect and Urbanization Toward Organ Donation in Thailand.

Science.gov (United States)

Wongboonsin, Kua; Jindahra, Pavitra; Teerakapibal, Surat

2018-03-01

Toward population wellness, an extreme scarcity of organ supply is proven to be an enormous hindrance. Preferences toward organ donation are vital to raise the organ donation rate. Notably, the area people live in can address the social influence on individual preference toward organ donation. This article studies the impact of the neighbor effect on organ donation decisions, addressing the social influence of urbanization on preferences. How neighborhood-specific variables, population density, and socioeconomic status drive the neighbor effect is investigated. The pursuit of organ donor traits is to be answered. The study uses organ donation interview survey data and neighborhood-specific data from Thailand to estimate a series of logistic regression models. Individuals residing in urban areas exhibit a greater likelihood to sign the donor card than those in rural areas. The neighborhood socioeconomic status is the key driver. An individual is more willing to be an organ donor when having neighbors with higher socioeconomic statuses. Results also reveal positive influences of males and education on the organ donation rate. This article documents the "neighbor effect" on the organ donation decision via living area type, offering an alternative exposition in raising the organ donation rate. In shifting the society norm toward organ donation consent, policy-makers should acknowledge the benefit of urbanization on organ donation decision derived from resourceful urban areas. Moreover, raising education levels does improve not only citizens' well-being but also their tendency to exhibit an altruistic act toward others.

The role of orthography in the semantic activation of neighbors.

Science.gov (United States)

Hino, Yasushi; Lupker, Stephen J; Taylor, Tamsen E

2012-09-01

There is now considerable evidence that a letter string can activate semantic information appropriate to its orthographic neighbors (e.g., Forster & Hector's, 2002, TURPLE effect). This phenomenon is the focus of the present research. Using Japanese words, we examined whether semantic activation of neighbors is driven directly by orthographic similarity alone or whether there is also a role for phonological similarity. In Experiment 1, using a relatedness judgment task in which a Kanji word-Katakana word pair was presented on each trial, an inhibitory effect was observed when the initial Kanji word was related to an orthographic and phonological neighbor of the Katakana word target but not when the initial Kanji word was related to a phonological but not orthographic neighbor of the Katakana word target. This result suggests that phonology plays little, if any, role in the activation of neighbors' semantics when reading familiar words. In Experiment 2, the targets were transcribed into Hiragana, a script they are typically not written in, requiring readers to engage in phonological coding. In that experiment, inhibitory effects were observed in both conditions. This result indicates that phonologically mediated semantic activation of neighbors will emerge when phonological processing is necessary in order to understand a written word (e.g., when that word is transcribed into an unfamiliar script). PsycINFO Database Record (c) 2012 APA, all rights reserved.

Role of spin-orbit coupling in the Kugel-Khomskii model on the honeycomb lattice

Science.gov (United States)

Koga, Akihisa; Nakauchi, Shiryu; Nasu, Joji

2018-03-01

We study the effective spin-orbital model for honeycomb-layered transition metal compounds, applying the second-order perturbation theory to the three-orbital Hubbard model with the anisotropic hoppings. This model is reduced to the Kitaev model in the strong spin-orbit coupling limit. Combining the cluster mean-field approximations with the exact diagonalization, we treat the Kugel-Khomskii type superexchange interaction and spin-orbit coupling on an equal footing to discuss ground-state properties. We find that a zigzag ordered state is realized in the model within nearest-neighbor interactions. We clarify how the ordered state competes with the nonmagnetic state, which is adiabatically connected to the quantum spin liquid state realized in a strong spin-orbit coupling limit. Thermodynamic properties are also addressed. The present paper should provide another route to account for the Kitaev-based magnetic properties in candidate materials.

Chimera states in bursting neurons

OpenAIRE

Bera, Bidesh K.; Ghosh, Dibakar; Lakshmanan, M.

2015-01-01

We study the existence of chimera states in pulse-coupled networks of bursting Hindmarsh-Rose neurons with nonlocal, global and local (nearest neighbor) couplings. Through a linear stability analysis, we discuss the behavior of stability function in the incoherent (i.e. disorder), coherent, chimera and multi-chimera states. Surprisingly, we find that chimera and multi-chimera states occur even using local nearest neighbor interaction in a network of identical bursting neurons alone. This is i...

Emergent organization of oscillator clusters in coupled self ...

Indian Academy of Sciences (India)

Additionally, the maps are coupled sequentially and unidirectionally, to their nearest neighbor, through the difference of their parametric variations. Interestingly we find that this model asymptotically yields clusters of superstable oscillators with different periods. We observe that the sizes of these oscillator clusters have a ...

Time series forecasting based on deep extreme learning machine

NARCIS (Netherlands)

Guo, Xuqi; Pang, Y.; Yan, Gaowei; Qiao, Tiezhu; Yang, Guang-Hong; Yang, Dan

2017-01-01

Multi-layer Artificial Neural Networks (ANN) has caught widespread attention as a new method for time series forecasting due to the ability of approximating any nonlinear function. In this paper, a new local time series prediction model is established with the nearest neighbor domain theory, in

Sum rules for four-spinon dynamic structure factor in XXX model

International Nuclear Information System (INIS)

Si Lakhal, B.; Abada, A.

2005-01-01

In the context of the antiferromagnetic spin 12 Heisenberg quantum spin chain (XXX model), we estimate the contribution of the exact four-spinon dynamic structure factor S 4 by calculating a number of sum rules the total dynamic structure factor S is known to satisfy exactly. These sum rules are: the static susceptibility, the integrated intensity, the total integrated intensity, the first frequency moment and the nearest-neighbor correlation function. We find that the contribution of S 4 is between 1% and 2.5%, depending on the sum rule, whereas the contribution of the exact two-spinon dynamic structure factor S 2 is between 70% and 75%. The calculations are numerical and Monte Carlo based. Good statistics are obtained

Speech emotion recognition based on statistical pitch model

Institute of Scientific and Technical Information of China (English)

WANG Zhiping; ZHAO Li; ZOU Cairong

2006-01-01

A modified Parzen-window method, which keep high resolution in low frequencies and keep smoothness in high frequencies, is proposed to obtain statistical model. Then, a gender classification method utilizing the statistical model is proposed, which have a 98% accuracy of gender classification while long sentence is dealt with. By separation the male voice and female voice, the mean and standard deviation of speech training samples with different emotion are used to create the corresponding emotion models. Then the Bhattacharyya distance between the test sample and statistical models of pitch, are utilized for emotion recognition in speech.The normalization of pitch for the male voice and female voice are also considered, in order to illustrate them into a uniform space. Finally, the speech emotion recognition experiment based on K Nearest Neighbor shows that, the correct rate of 81% is achieved, where it is only 73.85%if the traditional parameters are utilized.

Social dilemma alleviated by sharing the gains with immediate neighbors

Science.gov (United States)

Wu, Zhi-Xi; Yang, Han-Xin

2014-01-01

We study the evolution of cooperation in the evolutionary spatial prisoner's dilemma game (PDG) and snowdrift game (SG), within which a fraction α of the payoffs of each player gained from direct game interactions is shared equally by the immediate neighbors. The magnitude of the parameter α therefore characterizes the degree of the relatedness among the neighboring players. By means of extensive Monte Carlo simulations as well as an extended mean-field approximation method, we trace the frequency of cooperation in the stationary state. We find that plugging into relatedness can significantly promote the evolution of cooperation in the context of both studied games. Unexpectedly, cooperation can be more readily established in the spatial PDG than that in the spatial SG, given that the degree of relatedness and the cost-to-benefit ratio of mutual cooperation are properly formulated. The relevance of our model with the stakeholder theory is also briefly discussed.

THE SOLAR NEIGHBORHOOD XXIX: THE HABITABLE REAL ESTATE OF OUR NEAREST STELLAR NEIGHBORS

Energy Technology Data Exchange (ETDEWEB)

Cantrell, Justin R.; Henry, Todd J.; White, Russel J., E-mail: cantrell@chara.gsu.edu, E-mail: thenry@chara.gsu.edu, E-mail: white@chara.gsu.edu [Georgia State University, Atlanta, GA 30302-4106 (United States)

2013-10-01

We use the sample of known stars and brown dwarfs within 5 pc of the Sun, supplemented with AFGK stars within 10 pc, to determine which stellar spectral types provide the most habitable real estate—defined as locations where liquid water could be present on Earth-like planets. Stellar temperatures and radii are determined by fitting model spectra to spatially resolved broadband photometric energy distributions for stars in the sample. Using these values, the locations of the habitable zones are calculated using an empirical formula for planetary surface temperature and assuming the condition of liquid water, called here the empirical habitable zone (EHZ). Systems that have dynamically disruptive companions are considered not habitable. We consider companions to be disruptive if the separation ratio of the companion to the habitable zone is less than 5:1. We use the results of these calculations to derive a simple formula for predicting the location of the EHZ for main sequence stars based on V – K color. We consider EHZ widths as more useful measures of the habitable real estate around stars than areas because multiple planets are not expected to orbit stars at identical stellar distances. This EHZ provides a qualitative guide on where to expect the largest population of planets in the habitable zones of main sequence stars. Because of their large numbers and lower frequency of short-period companions, M stars provide more EHZ real estate than other spectral types, possessing 36.5% of the habitable real estate en masse. K stars are second with 21.5%, while A, F, and G stars offer 18.5%, 6.9%, and 16.6%, respectively. Our calculations show that three M dwarfs within 10 pc harbor planets in their EHZs—GJ 581 may have two planets (d with msin i = 6.1 M {sub ⊕}; g with msin i = 3.1 M {sub ⊕}), GJ 667 C has one (c with msin i = 4.5 M {sub ⊕}), and GJ 876 has two (b with msin i = 1.89 M {sub Jup} and c with msin i = 0.56 M {sub Jup}). If Earth-like planets

THE SOLAR NEIGHBORHOOD XXIX: THE HABITABLE REAL ESTATE OF OUR NEAREST STELLAR NEIGHBORS

International Nuclear Information System (INIS)

Cantrell, Justin R.; Henry, Todd J.; White, Russel J.

2013-01-01

We use the sample of known stars and brown dwarfs within 5 pc of the Sun, supplemented with AFGK stars within 10 pc, to determine which stellar spectral types provide the most habitable real estate—defined as locations where liquid water could be present on Earth-like planets. Stellar temperatures and radii are determined by fitting model spectra to spatially resolved broadband photometric energy distributions for stars in the sample. Using these values, the locations of the habitable zones are calculated using an empirical formula for planetary surface temperature and assuming the condition of liquid water, called here the empirical habitable zone (EHZ). Systems that have dynamically disruptive companions are considered not habitable. We consider companions to be disruptive if the separation ratio of the companion to the habitable zone is less than 5:1. We use the results of these calculations to derive a simple formula for predicting the location of the EHZ for main sequence stars based on V – K color. We consider EHZ widths as more useful measures of the habitable real estate around stars than areas because multiple planets are not expected to orbit stars at identical stellar distances. This EHZ provides a qualitative guide on where to expect the largest population of planets in the habitable zones of main sequence stars. Because of their large numbers and lower frequency of short-period companions, M stars provide more EHZ real estate than other spectral types, possessing 36.5% of the habitable real estate en masse. K stars are second with 21.5%, while A, F, and G stars offer 18.5%, 6.9%, and 16.6%, respectively. Our calculations show that three M dwarfs within 10 pc harbor planets in their EHZs—GJ 581 may have two planets (d with msin i = 6.1 M ⊕ ; g with msin i = 3.1 M ⊕ ), GJ 667 C has one (c with msin i = 4.5 M ⊕ ), and GJ 876 has two (b with msin i = 1.89 M Jup and c with msin i = 0.56 M Jup ). If Earth-like planets are as common around low

Depolarization of diffusing spins by paramagnetic impurities

International Nuclear Information System (INIS)

Schillaci, M.E.; Hutson, R.L.; Heffner, R.H.; Leon, M.; Dodds, S.A.; Estle, T.L.

1981-01-01

We study the depolarization of diffusing spins (muons) interacting with dilute paramagnetic impurities in a solid using a simple computational model which properly treats the muon motion and preserves correct muon-impurity distances. Long-range (dipolar) and nearest-neighbor (contact) interactions are treated together. Diffusion parameters are deduced and model comparisons made for AuGd (300 ppm). (orig.)

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.

Color and neighbor edge directional difference feature for image retrieval

Institute of Scientific and Technical Information of China (English)

Chaobing Huang; Shengsheng Yu; Jingli Zhou; Hongwei Lu

2005-01-01

@@ A novel image feature termed neighbor edge directional difference unit histogram is proposed, in which the neighbor edge directional difference unit is defined and computed for every pixel in the image, and is used to generate the neighbor edge directional difference unit histogram. This histogram and color histogram are used as feature indexes to retrieve color image. The feature is invariant to image scaling and translation and has more powerful descriptive for the natural color images. Experimental results show that the feature can achieve better retrieval performance than other color-spatial features.

Research Article Special Issue

African Journals Online (AJOL)

2018-02-01

Feb 1, 2018 ... Institute of Informatics & Computing in Energy; Universiti Tenaga ... easily develop a machine learning model from scratch with minimum practical knowledge. ... with experience, and what are the fundamental laws that govern all ..... [15] M. Muja and D. G. Lowe, “Scalable nearest neighbor algorithms for ...

Phonon and thermal properties of achiral single wall carbon ...

Indian Academy of Sciences (India)

A detailed theoretical study of the phonon and thermal properties of achiral single wall carbon nanotubes has been carried out using force constant model considering up to third nearest-neighbor interactions. We have calculated the phonon dispersions, density of states, radial breathing modes (RBM) and the specific heats ...

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

Stability of the car-following model on two lanes

Science.gov (United States)

Tang, Tie-Qiao; Huang, Hai-Jun; Gao, Zi-You

2005-12-01

In the case of two-lane traffic, vehicle drivers always worry about the lane changing actions from neighbor lane. This paper studies the stability of a car-following model on two lanes which incorporates the lateral effects in traffic. The stability condition of the model is obtained by using the linear stability theory. The modified Korteweg-de Vries equation is constructed and solved, and three types of traffic flows in the headway-sensitivity space—stable, metastable, and unstable—are classified. Both analytical and simulation results show that the anxiousness about lane changing from neighbor lane indeed has influence upon people’s driving behavior and the consideration of lateral effects could stabilize the traffic flows on both lanes.

Stripe order from the perspective of the Hubbard model

Energy Technology Data Exchange (ETDEWEB)

Devereaux, Thomas Peter

2018-03-01

A microscopic understanding of the strongly correlated physics of the cuprates must account for the translational and rotational symmetry breaking that is present across all cuprate families, commonly in the form of stripes. Here we investigate emergence of stripes in the Hubbard model, a minimal model believed to be relevant to the cuprate superconductors, using determinant quantum Monte Carlo (DQMC) simulations at finite temperatures and density matrix renormalization group (DMRG) ground state calculations. By varying temperature, doping, and model parameters, we characterize the extent of stripes throughout the phase diagram of the Hubbard model. Our results show that including the often neglected next-nearest-neighbor hopping leads to the absence of spin incommensurability upon electron-doping and nearly half-filled stripes upon hole-doping. The similarities of these findings to experimental results on both electron and hole-doped cuprate families support a unified description across a large portion of the cuprate phase diagram.

An Extended Non-Lane-Based Optimal Velocity Model with Dynamic Collaboration

Directory of Open Access Journals (Sweden)

Zhipeng Li

2013-01-01

Full Text Available Incorporating the effects of the lane width in traffic, in this paper, we propose a dynamical model based on the strategy of three-vehicle cooperation driving. We obtain the smoother acceleration distribution in the new model through considering the dynamic collaboration with the nearest preceding vehicle and the nearest following vehicle. It is proved that the stability of the new model is greatly improved compared to the early non-lane-based car following model by using the linear stability theory. We find that when the parameter of lateral separation distance is identified, the amplitude of traffic congestion decreases with increasing the strength of dynamic collaboration in the simulation experiments. In addition, we apply the new extended model to simulate the motions of cars starting from a traffic signal and the dissipating of the traffic congestion; it is found that our new model can predict realistic delay time and kinematic wave speed and obtained a faster dissipation speed of traffic congestion than the traffic flow model without considering the dynamic collaboration.

Neighboring Genes Show Correlated Evolution in Gene Expression

Science.gov (United States)

Ghanbarian, Avazeh T.; Hurst, Laurence D.

2015-01-01

When considering the evolution of a gene’s expression profile, we commonly assume that this is unaffected by its genomic neighborhood. This is, however, in contrast to what we know about the lack of autonomy between neighboring genes in gene expression profiles in extant taxa. Indeed, in all eukaryotic genomes genes of similar expression-profile tend to cluster, reflecting chromatin level dynamics. Does it follow that if a gene increases expression in a particular lineage then the genomic neighbors will also increase in their expression or is gene expression evolution autonomous? To address this here we consider evolution of human gene expression since the human-chimp common ancestor, allowing for both variation in estimation of current expression level and error in Bayesian estimation of the ancestral state. We find that in all tissues and both sexes, the change in gene expression of a focal gene on average predicts the change in gene expression of neighbors. The effect is highly pronounced in the immediate vicinity (genes increasing their expression in humans tend to avoid nuclear lamina domains and be enriched for the gene activator 5-hydroxymethylcytosine, we conclude that, most probably owing to chromatin level control of gene expression, a change in gene expression of one gene likely affects the expression evolution of neighbors, what we term expression piggybacking, an analog of hitchhiking. PMID:25743543

D Nearest Neighbour Search Using a Clustered Hierarchical Tree Structure

Science.gov (United States)