Scalable Nearest Neighbor Algorithms for High Dimensional Data.
Muja, Marius; Lowe, David G
2014-11-01
For many computer vision and machine learning problems, large training sets are key for good performance. However, the most computationally expensive part of many computer vision and machine learning algorithms consists of finding nearest neighbor matches to high dimensional vectors that represent the training data. We propose new algorithms for approximate nearest neighbor matching and evaluate and compare them with previous algorithms. For matching high dimensional features, we find two algorithms to be the most efficient: the randomized k-d forest and a new algorithm proposed in this paper, the priority search k-means tree. We also propose a new algorithm for matching binary features by searching multiple hierarchical clustering trees and show it outperforms methods typically used in the literature. We show that the optimal nearest neighbor algorithm and its parameters depend on the data set characteristics and describe an automated configuration procedure for finding the best algorithm to search a particular data set. In order to scale to very large data sets that would otherwise not fit in the memory of a single machine, we propose a distributed nearest neighbor matching framework that can be used with any of the algorithms described in the paper. All this research has been released as an open source library called fast library for approximate nearest neighbors (FLANN), which has been incorporated into OpenCV and is now one of the most popular libraries for nearest neighbor matching.
Takashi, Tonegawa; Makoto, Kaburagi; Takeshi, Nakao; Department of Physics, Faculty of Science, Kobe University; Faculty of Cross-Cultural Studies, Kobe University; Department of Physics, Faculty of Science, Kobe University
1995-01-01
The Haldane to dimer phase transition is studied in the spin-1 Haldane system with bond-alternating nearest-neighbor and uniform next-nearest-neighbor exchange interactions, where both interactions are antiferromagnetic and thus compete with each other. By using a method of exact diagonalization, the ground-state phase diagram on the ratio of the next-nearest-neighbor interaction constant to the nearest-neighbor one versus the bond-alternation parameter of the nearest-neighbor interactions is...
On Competitiveness of Nearest-Neighbor-Based Music Classification: A Methodological Critique
DEFF Research Database (Denmark)
Pálmason, Haukur; Jónsson, Björn Thór; Amsaleg, Laurent
2017-01-01
The traditional role of nearest-neighbor classification in music classification research is that of a straw man opponent for the learning approach of the hour. Recent work in high-dimensional indexing has shown that approximate nearest-neighbor algorithms are extremely scalable, yielding results...... of reasonable quality from billions of high-dimensional features. With such efficient large-scale classifiers, the traditional music classification methodology of aggregating and compressing the audio features is incorrect; instead the approximate nearest-neighbor classifier should be given an extensive data...... collection to work with. We present a case study, using a well-known MIR classification benchmark with well-known music features, which shows that a simple nearest-neighbor classifier performs very competitively when given ample data. In this position paper, we therefore argue that nearest...
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.
Frog sound identification using extended k-nearest neighbor classifier
Mukahar, Nordiana; Affendi Rosdi, Bakhtiar; Athiar Ramli, Dzati; Jaafar, Haryati
2017-09-01
Frog sound identification based on the vocalization becomes important for biological research and environmental monitoring. As a result, different types of feature extractions and classifiers have been employed to evaluate the accuracy of frog sound identification. This paper presents a frog sound identification with Extended k-Nearest Neighbor (EKNN) classifier. The EKNN classifier integrates the nearest neighbors and mutual sharing of neighborhood concepts, with the aims of improving the classification performance. It makes a prediction based on who are the nearest neighbors of the testing sample and who consider the testing sample as their nearest neighbors. In order to evaluate the classification performance in frog sound identification, the EKNN classifier is compared with competing classifier, k -Nearest Neighbor (KNN), Fuzzy k -Nearest Neighbor (FKNN) k - General Nearest Neighbor (KGNN)and Mutual k -Nearest Neighbor (MKNN) on the recorded sounds of 15 frog species obtained in Malaysia forest. The recorded sounds have been segmented using Short Time Energy and Short Time Average Zero Crossing Rate (STE+STAZCR), sinusoidal modeling (SM), manual and the combination of Energy (E) and Zero Crossing Rate (ZCR) (E+ZCR) while the features are extracted by Mel Frequency Cepstrum Coefficient (MFCC). The experimental results have shown that the EKNCN classifier exhibits the best performance in terms of accuracy compared to the competing classifiers, KNN, FKNN, GKNN and MKNN for all cases.
Multiple k Nearest Neighbor Query Processing in Spatial Network Databases
DEFF Research Database (Denmark)
Xuegang, Huang; Jensen, Christian Søndergaard; Saltenis, Simonas
2006-01-01
This paper concerns the efficient processing of multiple k nearest neighbor queries in a road-network setting. The assumed setting covers a range of scenarios such as the one where a large population of mobile service users that are constrained to a road network issue nearest-neighbor queries...... for points of interest that are accessible via the road network. Given multiple k nearest neighbor queries, the paper proposes progressive techniques that selectively cache query results in main memory and subsequently reuse these for query processing. The paper initially proposes techniques for the case...... where an upper bound on k is known a priori and then extends the techniques to the case where this is not so. Based on empirical studies with real-world data, the paper offers insight into the circumstances under which the different proposed techniques can be used with advantage for multiple k nearest...
Zhang, Zhongzhi; Dong, Yuze; Sheng, Yibin
2015-10-01
Random walks including non-nearest-neighbor jumps appear in many real situations such as the diffusion of adatoms and have found numerous applications including PageRank search algorithm; however, related theoretical results are much less for this dynamical process. In this paper, we present a study of mixed random walks in a family of fractal scale-free networks, where both nearest-neighbor and next-nearest-neighbor jumps are included. We focus on trapping problem in the network family, which is a particular case of random walks with a perfect trap fixed at the central high-degree node. We derive analytical expressions for the average trapping time (ATT), a quantitative indicator measuring the efficiency of the trapping process, by using two different methods, the results of which are consistent with each other. Furthermore, we analytically determine all the eigenvalues and their multiplicities for the fundamental matrix characterizing the dynamical process. Our results show that although next-nearest-neighbor jumps have no effect on the leading scaling of the trapping efficiency, they can strongly affect the prefactor of ATT, providing insight into better understanding of random-walk process in complex systems.
Lectures on the nearest neighbor method
Biau, Gérard
2015-01-01
This text presents a wide-ranging and rigorous overview of nearest neighbor methods, one of the most important paradigms in machine learning. Now in one self-contained volume, this book systematically covers key statistical, probabilistic, combinatorial and geometric ideas for understanding, analyzing and developing nearest neighbor methods. Gérard Biau is a professor at Université Pierre et Marie Curie (Paris). Luc Devroye is a professor at the School of Computer Science at McGill University (Montreal). .
Dimensional testing for reverse k-nearest neighbor search
DEFF Research Database (Denmark)
Casanova, Guillaume; Englmeier, Elias; Houle, Michael E.
2017-01-01
Given a query object q, reverse k-nearest neighbor (RkNN) search aims to locate those objects of the database that have q among their k-nearest neighbors. In this paper, we propose an approximation method for solving RkNN queries, where the pruning operations and termination tests are guided...... by a characterization of the intrinsic dimensionality of the data. The method can accommodate any index structure supporting incremental (forward) nearest-neighbor search for the generation and verification of candidates, while avoiding impractically-high preprocessing costs. We also provide experimental evidence...
[Galaxy/quasar classification based on nearest neighbor method].
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.
Diagnostic tools for nearest neighbors techniques when used with satellite imagery
Ronald E. McRoberts
2009-01-01
Nearest neighbors techniques are non-parametric approaches to multivariate prediction that are useful for predicting both continuous and categorical forest attribute variables. Although some assumptions underlying nearest neighbor techniques are common to other prediction techniques such as regression, other assumptions are unique to nearest neighbor techniques....
Nearest neighbors by neighborhood counting.
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.
Secure Nearest Neighbor Query on Crowd-Sensing Data
Directory of Open Access Journals (Sweden)
Ke Cheng
2016-09-01
Full Text Available Nearest neighbor queries are fundamental in location-based services, and secure nearest neighbor queries mainly focus on how to securely and quickly retrieve the nearest neighbor in the outsourced cloud server. However, the previous big data system structure has changed because of the crowd-sensing data. On the one hand, sensing data terminals as the data owner are numerous and mistrustful, while, on the other hand, in most cases, the terminals find it difficult to finish many safety operation due to computation and storage capability constraints. In light of they Multi Owners and Multi Users (MOMU situation in the crowd-sensing data cloud environment, this paper presents a secure nearest neighbor query scheme based on the proxy server architecture, which is constructed by protocols of secure two-party computation and secure Voronoi diagram algorithm. It not only preserves the data confidentiality and query privacy but also effectively resists the collusion between the cloud server and the data owners or users. Finally, extensive theoretical and experimental evaluations are presented to show that our proposed scheme achieves a superior balance between the security and query performance compared to other schemes.
Nearest Neighbor Networks: clustering expression data based on gene neighborhoods
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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
The Islands Approach to Nearest Neighbor Querying in Spatial Networks
DEFF Research Database (Denmark)
Huang, Xuegang; Jensen, Christian Søndergaard; Saltenis, Simonas
2005-01-01
, and versatile approach to k nearest neighbor computation that obviates the need for using several k nearest neighbor approaches for supporting a single service scenario. The experimental comparison with the existing techniques uses real-world road network data and considers both I/O and CPU performance...
Finger vein identification using fuzzy-based k-nearest centroid neighbor classifier
Rosdi, Bakhtiar Affendi; Jaafar, Haryati; Ramli, Dzati Athiar
2015-02-01
In this paper, a new approach for personal identification using finger vein image is presented. Finger vein is an emerging type of biometrics that attracts attention of researchers in biometrics area. As compared to other biometric traits such as face, fingerprint and iris, finger vein is more secured and hard to counterfeit since the features are inside the human body. So far, most of the researchers focus on how to extract robust features from the captured vein images. Not much research was conducted on the classification of the extracted features. In this paper, a new classifier called fuzzy-based k-nearest centroid neighbor (FkNCN) is applied to classify the finger vein image. The proposed FkNCN employs a surrounding rule to obtain the k-nearest centroid neighbors based on the spatial distributions of the training images and their distance to the test image. Then, the fuzzy membership function is utilized to assign the test image to the class which is frequently represented by the k-nearest centroid neighbors. Experimental evaluation using our own database which was collected from 492 fingers shows that the proposed FkNCN has better performance than the k-nearest neighbor, k-nearest-centroid neighbor and fuzzy-based-k-nearest neighbor classifiers. This shows that the proposed classifier is able to identify the finger vein image effectively.
Dimensionality reduction with unsupervised nearest neighbors
Kramer, Oliver
2013-01-01
This book is devoted to a novel approach for dimensionality reduction based on the famous nearest neighbor method that is a powerful classification and regression approach. It starts with an introduction to machine learning concepts and a real-world application from the energy domain. Then, unsupervised nearest neighbors (UNN) is introduced as efficient iterative method for dimensionality reduction. Various UNN models are developed step by step, reaching from a simple iterative strategy for discrete latent spaces to a stochastic kernel-based algorithm for learning submanifolds with independent parameterizations. Extensions that allow the embedding of incomplete and noisy patterns are introduced. Various optimization approaches are compared, from evolutionary to swarm-based heuristics. Experimental comparisons to related methodologies taking into account artificial test data sets and also real-world data demonstrate the behavior of UNN in practical scenarios. The book contains numerous color figures to illustr...
Multi-strategy based quantum cost reduction of linear nearest-neighbor quantum circuit
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.
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.
International Nuclear Information System (INIS)
Gong, Longyan; Feng, Yan; Ding, Yougen
2017-01-01
Highlights: • Quasiperiodic lattice models with next-nearest-neighbor hopping are studied. • Shannon information entropies are used to reflect state localization properties. • Phase diagrams are obtained for the inverse bronze and golden means, respectively. • Our studies present a more complete picture than existing works. - Abstract: We explore the reduced relative Shannon information entropies SR for a quasiperiodic lattice model with nearest- and next-nearest-neighbor hopping, where an irrational number is in the mathematical expression of incommensurate on-site potentials. Based on SR, we respectively unveil the phase diagrams for two irrationalities, i.e., the inverse bronze mean and the inverse golden mean. The corresponding phase diagrams include regions of purely localized phase, purely delocalized phase, pure critical phase, and regions with mobility edges. The boundaries of different regions depend on the values of irrational number. These studies present a more complete picture than existing works.
Energy Technology Data Exchange (ETDEWEB)
Gong, Longyan, E-mail: lygong@njupt.edu.cn [Information Physics Research Center and Department of Applied Physics, Nanjing University of Posts and Telecommunications, Nanjing, 210003 (China); Institute of Signal Processing and Transmission, Nanjing University of Posts and Telecommunications, Nanjing, 210003 (China); National Laboratory of Solid State Microstructures, Nanjing University, Nanjing 210093 (China); Feng, Yan; Ding, Yougen [Information Physics Research Center and Department of Applied Physics, Nanjing University of Posts and Telecommunications, Nanjing, 210003 (China); Institute of Signal Processing and Transmission, Nanjing University of Posts and Telecommunications, Nanjing, 210003 (China)
2017-02-12
Highlights: • Quasiperiodic lattice models with next-nearest-neighbor hopping are studied. • Shannon information entropies are used to reflect state localization properties. • Phase diagrams are obtained for the inverse bronze and golden means, respectively. • Our studies present a more complete picture than existing works. - Abstract: We explore the reduced relative Shannon information entropies SR for a quasiperiodic lattice model with nearest- and next-nearest-neighbor hopping, where an irrational number is in the mathematical expression of incommensurate on-site potentials. Based on SR, we respectively unveil the phase diagrams for two irrationalities, i.e., the inverse bronze mean and the inverse golden mean. The corresponding phase diagrams include regions of purely localized phase, purely delocalized phase, pure critical phase, and regions with mobility edges. The boundaries of different regions depend on the values of irrational number. These studies present a more complete picture than existing works.
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...
Nearest unlike neighbor (NUN): an aid to decision confidence estimation
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.
An Improvement To The k-Nearest Neighbor Classifier For ECG Database
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%.
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...
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.
Estimating forest attribute parameters for small areas using nearest neighbors techniques
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...
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.
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...
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.
Collective Behaviors of Mobile Robots Beyond the Nearest Neighbor Rules With Switching Topology.
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.
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.
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.
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.
Seismic clusters analysis in Northeastern Italy by the nearest-neighbor approach
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
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...
Attribute Weighting Based K-Nearest Neighbor Using Gain Ratio
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%.
Jurčišinová, E.; Jurčišin, M.
2018-02-01
The influence of the next-nearest-neighbor interaction on the properties of the geometrically frustrated antiferromagnetic systems is investigated in the framework of the exactly solvable antiferromagnetic spin- 1 / 2 Ising model in the external magnetic field on the square-kagome recursive lattice, where the next-nearest-neighbor interaction is supposed between sites within each elementary square of the lattice. The thermodynamic properties of the model are investigated in detail and it is shown that the competition between the nearest-neighbor antiferromagnetic interaction and the next-nearest-neighbor ferromagnetic interaction changes properties of the single-point ground states but does not change the frustrated character of the basic model. On the other hand, the presence of the antiferromagnetic next-nearest-neighbor interaction leads to the enhancement of the frustration effects with the formation of additional plateau and single-point ground states at low temperatures. Exact expressions for magnetizations and residual entropies of all ground states of the model are found. It is shown that the model exhibits various ground states with the same value of magnetization but different macroscopic degeneracies as well as the ground states with different values of magnetization but the same value of the residual entropy. The specific heat capacity is investigated and it is shown that the model exhibits the Schottky-type anomaly behavior in the vicinity of each single-point ground state value of the magnetic field. The formation of the field-induced double-peak structure of the specific heat capacity at low temperatures is demonstrated and it is shown that its very existence is directly related to the presence of highly macroscopically degenerated single-point ground states in the model.
The nearest neighbor and the bayes error rates.
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.
Pini, Maria Gloria; Rettori, Angelo
1993-08-01
The thermodynamical properties of an alternating spin (S,s) one-dimensional (1D) Ising model with competing nearest- and next-nearest-neighbor interactions are exactly calculated using a transfer-matrix technique. In contrast to the case S=s=1/2, previously investigated by Harada, the alternation of different spins (S≠s) along the chain is found to give rise to two-peaked static structure factors, signaling the coexistence of different short-range-order configurations. The relevance of our calculations with regard to recent experimental data by Gatteschi et al. in quasi-1D molecular magnetic materials, R (hfac)3 NITEt (R=Gd, Tb, Dy, Ho, Er, . . .), is discussed; hfac is hexafluoro-acetylacetonate and NlTEt is 2-Ethyl-4,4,5,5-tetramethyl-4,5-dihydro-1H-imidazolyl-1-oxyl-3-oxide.
FCNN-MR: A Parallel Instance Selection Method Based on Fast Condensed Nearest Neighbor Rule
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...
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.
Elliptic Painlevé equations from next-nearest-neighbor translations on the E_8^{(1)} lattice
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.
Multidimensional k-nearest neighbor model based on EEMD for financial time series forecasting
Zhang, Ningning; Lin, Aijing; Shang, Pengjian
2017-07-01
In this paper, we propose a new two-stage methodology that combines the ensemble empirical mode decomposition (EEMD) with multidimensional k-nearest neighbor model (MKNN) in order to forecast the closing price and high price of the stocks simultaneously. The modified algorithm of k-nearest neighbors (KNN) has an increasingly wide application in the prediction of all fields. Empirical mode decomposition (EMD) decomposes a nonlinear and non-stationary signal into a series of intrinsic mode functions (IMFs), however, it cannot reveal characteristic information of the signal with much accuracy as a result of mode mixing. So ensemble empirical mode decomposition (EEMD), an improved method of EMD, is presented to resolve the weaknesses of EMD by adding white noise to the original data. With EEMD, the components with true physical meaning can be extracted from the time series. Utilizing the advantage of EEMD and MKNN, the new proposed ensemble empirical mode decomposition combined with multidimensional k-nearest neighbor model (EEMD-MKNN) has high predictive precision for short-term forecasting. Moreover, we extend this methodology to the case of two-dimensions to forecast the closing price and high price of the four stocks (NAS, S&P500, DJI and STI stock indices) at the same time. The results indicate that the proposed EEMD-MKNN model has a higher forecast precision than EMD-KNN, KNN method and ARIMA.
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 (...
Introduction to machine learning: k-nearest neighbors.
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.
Applying an efficient K-nearest neighbor search to forest attribute imputation
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...
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.
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.
Efficient and accurate nearest neighbor and closest pair search in high-dimensional space
Tao, Yufei; Yi, Ke; Sheng, Cheng; Kalnis, Panos
2010-01-01
Nearest Neighbor (NN) search in high-dimensional space is an important problem in many applications. From the database perspective, a good solution needs to have two properties: (i) it can be easily incorporated in a relational database, and (ii
Credit scoring analysis using weighted k nearest neighbor
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.
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
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.
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.
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...
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
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...
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
Rivas, Elena; Lang, Raymond; Eddy, Sean R
2012-02-01
The standard approach for single-sequence RNA secondary structure prediction uses a nearest-neighbor thermodynamic model with several thousand experimentally determined energy parameters. An attractive alternative is to use statistical approaches with parameters estimated from growing databases of structural RNAs. Good results have been reported for discriminative statistical methods using complex nearest-neighbor models, including CONTRAfold, Simfold, and ContextFold. Little work has been reported on generative probabilistic models (stochastic context-free grammars [SCFGs]) of comparable complexity, although probabilistic models are generally easier to train and to use. To explore a range of probabilistic models of increasing complexity, and to directly compare probabilistic, thermodynamic, and discriminative approaches, we created TORNADO, a computational tool that can parse a wide spectrum of RNA grammar architectures (including the standard nearest-neighbor model and more) using a generalized super-grammar that can be parameterized with probabilities, energies, or arbitrary scores. By using TORNADO, we find that probabilistic nearest-neighbor models perform comparably to (but not significantly better than) discriminative methods. We find that complex statistical models are prone to overfitting RNA structure and that evaluations should use structurally nonhomologous training and test data sets. Overfitting has affected at least one published method (ContextFold). The most important barrier to improving statistical approaches for RNA secondary structure prediction is the lack of diversity of well-curated single-sequence RNA secondary structures in current RNA databases.
Mapping change of older forest with nearest-neighbor imputation and Landsat time-series
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
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...
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.
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)...
Morphological type correlation between nearest neighbor pairs of galaxies
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.
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.
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
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.
Predicting Audience Location on the Basis of the k-Nearest Neighbor Multilabel Classification
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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.
Quality and efficiency in high dimensional Nearest neighbor search
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.
Error minimizing algorithms for nearest eighbor classifiers
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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 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.
River Flow Prediction Using the Nearest Neighbor Probabilistic Ensemble Method
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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
Geometric k-nearest neighbor estimation of entropy and mutual information
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.
Nearest neighbor 3D segmentation with context features
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.
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%.
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.
Third nearest neighbor parameterized tight binding model for graphene nano-ribbons
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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.
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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.
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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.
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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.
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,...
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.
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....
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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.
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.
Quantum Algorithm for K-Nearest Neighbors Classification Based on the Metric of Hamming Distance
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).
A Novel Quantum Solution to Privacy-Preserving Nearest Neighbor Query in Location-Based Services
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.
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 .
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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.
Implementation of Nearest Neighbor using HSV to Identify Skin Disease
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.
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.
k-Nearest Neighbors Algorithm in Profiling Power Analysis Attacks
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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.
Fast and Accuracy Control Chart Pattern Recognition using a New cluster-k-Nearest Neighbor
Samir Brahim Belhaouari
2009-01-01
By taking advantage of both k-NN which is highly accurate and K-means cluster which is able to reduce the time of classification, we can introduce Cluster-k-Nearest Neighbor as "variable k"-NN dealing with the centroid or mean point of all subclasses generated by clustering algorithm. In general the algorithm of K-means cluster is not stable, in term of accuracy, for that reason we develop another algorithm for clustering our space which gives a higher accuracy than K-means cluster, less ...
Nearest neighbor spacing distributions of low-lying levels of vibrational nuclei
International Nuclear Information System (INIS)
Abul-Magd, A.Y.; Simbel, M.H.
1996-01-01
Energy-level statistics are considered for nuclei whose Hamiltonian is divided into intrinsic and collective-vibrational terms. The levels are described as a random superposition of independent sequences, each corresponding to a given number of phonons. The intrinsic motion is assumed chaotic. The level spacing distribution is found to be intermediate between the Wigner and Poisson distributions and similar in form to the spacing distribution of a system with classical phase space divided into separate regular and chaotic domains. We have obtained approximate expressions for the nearest neighbor spacing and cumulative spacing distribution valid when the level density is described by a constant-temperature formula and not involving additional free parameters. These expressions have been able to achieve good agreement with the experimental spacing distributions. copyright 1996 The American Physical Society
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.
Common Nearest Neighbor Clustering—A Benchmark
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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.
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
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...
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
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.
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.
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.
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.
Polymers with nearest- and next nearest-neighbor interactions on the Husimi lattice
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.
Improved Fuzzy K-Nearest Neighbor Using Modified Particle Swarm Optimization
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%
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
Reentrant behavior in the nearest-neighbor Ising antiferromagnet in a magnetic field
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.
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.
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
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.
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.
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.
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
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.
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.
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.
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.
Fracton topological order from nearest-neighbor two-spin interactions and dualities
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.
Local Order in the Unfolded State: Conformational Biases and Nearest Neighbor Interactions
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Siobhan Toal
2014-07-01
Full Text Available The discovery of Intrinsically Disordered Proteins, which contain significant levels of disorder yet perform complex biologically functions, as well as unwanted aggregation, has motivated numerous experimental and theoretical studies aimed at describing residue-level conformational ensembles. Multiple lines of evidence gathered over the last 15 years strongly suggest that amino acids residues display unique and restricted conformational preferences in the unfolded state of peptides and proteins, contrary to one of the basic assumptions of the canonical random coil model. To fully understand residue level order/disorder, however, one has to gain a quantitative, experimentally based picture of conformational distributions and to determine the physical basis underlying residue-level conformational biases. Here, we review the experimental, computational and bioinformatic evidence for conformational preferences of amino acid residues in (mostly short peptides that can be utilized as suitable model systems for unfolded states of peptides and proteins. In this context particular attention is paid to the alleged high polyproline II preference of alanine. We discuss how these conformational propensities may be modulated by peptide solvent interactions and so called nearest-neighbor interactions. The relevance of conformational propensities for the protein folding problem and the understanding of IDPs is briefly discussed.
Kinetic Models for Topological Nearest-Neighbor Interactions
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.
Using K-Nearest Neighbor Classification to Diagnose Abnormal Lung Sounds
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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.
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.
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)
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
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.
A Regression-based K nearest neighbor algorithm for gene function prediction from heterogeneous data
Directory of Open Access Journals (Sweden)
Ruzzo Walter L
2006-03-01
Full Text Available Abstract Background As a variety of functional genomic and proteomic techniques become available, there is an increasing need for functional analysis methodologies that integrate heterogeneous data sources. Methods In this paper, we address this issue by proposing a general framework for gene function prediction based on the k-nearest-neighbor (KNN algorithm. The choice of KNN is motivated by its simplicity, flexibility to incorporate different data types and adaptability to irregular feature spaces. A weakness of traditional KNN methods, especially when handling heterogeneous data, is that performance is subject to the often ad hoc choice of similarity metric. To address this weakness, we apply regression methods to infer a similarity metric as a weighted combination of a set of base similarity measures, which helps to locate the neighbors that are most likely to be in the same class as the target gene. We also suggest a novel voting scheme to generate confidence scores that estimate the accuracy of predictions. The method gracefully extends to multi-way classification problems. Results We apply this technique to gene function prediction according to three well-known Escherichia coli classification schemes suggested by biologists, using information derived from microarray and genome sequencing data. We demonstrate that our algorithm dramatically outperforms the naive KNN methods and is competitive with support vector machine (SVM algorithms for integrating heterogenous data. We also show that by combining different data sources, prediction accuracy can improve significantly. Conclusion Our extension of KNN with automatic feature weighting, multi-class prediction, and probabilistic inference, enhance prediction accuracy significantly while remaining efficient, intuitive and flexible. This general framework can also be applied to similar classification problems involving heterogeneous datasets.
CATEGORIZATION OF GELAM, ACACIA AND TUALANG HONEY ODORPROFILE USING K-NEAREST NEIGHBORS
Directory of Open Access Journals (Sweden)
Nurdiyana Zahed
2018-02-01
Full Text Available Honey authenticity refer to honey types is of great importance issue and interest in agriculture. In current research, several documents of specific types of honey have their own usage in medical field. However, it is quite challenging task to classify different types of honey by simply using our naked eye. This work demostrated a successful an electronic nose (E-nose application as an instrument for identifying odor profile pattern of three common honey in Malaysia (Gelam, Acacia and Tualang honey. The applied E-nose has produced signal for odor measurement in form of numeric resistance (Ω. The data reading have been pre-processed using normalization technique for standardized scale of unique features. Mean features is extracted and boxplot used as the statistical tool to present the data pattern according to three types of honey. Mean features that have been extracted were employed into K-Nearest Neighbors classifier as an input features and evaluated using several splitting ratio. Excellent results were obtained by showing 100% rate of accuracy, sensitivity and specificity of classification from KNN using weigh (k=1, ratio 90:10 and Euclidean distance. The findings confirmed the ability of KNN classifier as intelligent classification to classify different honey types from E-nose calibration. Outperform of other classifier, KNN required less parameter optimization and achieved promising result.
An RFID Indoor Positioning Algorithm Based on Bayesian Probability and K-Nearest Neighbor.
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.
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.
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.)
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
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
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.
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.
Clustered K nearest neighbor algorithm for daily inflow forecasting
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
Fast Most Similar Neighbor (MSN) classifiers for Mixed Data
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...
Nearest neighbor imputation using spatial-temporal correlations in wireless sensor networks.
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
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...
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%.
Evidence of codon usage in the nearest neighbor spacing distribution of bases in bacterial genomes
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.
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.
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
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.
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.
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.
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.
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.
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.
K-nearest uphill clustering in the protein structure space
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.
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
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.
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.
What Will the Neighbors Think? Building Large-Scale Science Projects Around the World
International Nuclear Information System (INIS)
Jones, Craig; Mrotzek, Christian; Toge, Nobu; Sarno, Doug
2007-01-01
Public participation is an essential ingredient for turning the International Linear Collider into a reality. Wherever the proposed particle accelerator is sited in the world, its neighbors -- in any country -- will have something to say about hosting a 35-kilometer-long collider in their backyards. When it comes to building large-scale physics projects, almost every laboratory has a story to tell. Three case studies from Japan, Germany and the US will be presented to examine how community relations are handled in different parts of the world. How do particle physics laboratories interact with their local communities? How do neighbors react to building large-scale projects in each region? How can the lessons learned from past experiences help in building the next big project? These and other questions will be discussed to engage the audience in an active dialogue about how a large-scale project like the ILC can be a good neighbor.
Efficient and accurate nearest neighbor and closest pair search in high-dimensional space
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
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.
COMBINED EFFECTS OF GALAXY INTERACTIONS AND LARGE-SCALE ENVIRONMENT ON GALAXY PROPERTIES
International Nuclear Information System (INIS)
Park, Changbom; Choi, Yun-Young
2009-01-01
We inspect the coupled dependence of physical parameters of the Sloan Digital Sky Survey galaxies on the small-scale (distance to and morphology of the nearest neighbor galaxy) and the large-scale (background density smoothed over 20 nearby galaxies) environments. The impacts of interaction on galaxy properties are detected at least out to the neighbor separation corresponding to the virial radius of galaxies, which is typically between 200 and 400 h -1 kpc for the galaxies in our sample. To detect these long-range interaction effects, it is crucial to divide galaxy interactions into four cases dividing the morphology of target and neighbor galaxies into early and late types. We show that there are two characteristic neighbor-separation scales where the galaxy interactions cause abrupt changes in the properties of galaxies. The first scale is the virial radius of the nearest neighbor galaxy r vir,nei . Many physical parameters start to deviate from those of extremely isolated galaxies at the projected neighbor separation r p of about r vir,nei . The second scale is at r p ∼ 0.05r vir,nei = 10-20 h -1 kpc, and is the scale at which the galaxies in pairs start to merge. We find that late-type neighbors enhance the star formation activity of galaxies while early-type neighbors reduce it, and that these effects occur within r vir,nei . The hot halo gas and cold disk gas must be participating in the interactions at separations less than the virial radius of the galaxy plus dark halo system. Our results also show that the role of the large-scale density in determining galaxy properties is minimal once luminosity and morphology are fixed. We propose that the weak residual dependence of galaxy properties on the large-scale density is due to the dependence of the halo gas property on the large-scale density.
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.
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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.
Alpha centauri unveiling the secrets of our nearest stellar neighbor
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...
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.
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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.
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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
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.
Large Scale Visual Recommendations From Street Fashion Images
Jagadeesh, Vignesh; Piramuthu, Robinson; Bhardwaj, Anurag; Di, Wei; Sundaresan, Neel
2014-01-01
We describe a completely automated large scale visual recommendation system for fashion. Our focus is to efficiently harness the availability of large quantities of online fashion images and their rich meta-data. Specifically, we propose four data driven models in the form of Complementary Nearest Neighbor Consensus, Gaussian Mixture Models, Texture Agnostic Retrieval and Markov Chain LDA for solving this problem. We analyze relative merits and pitfalls of these algorithms through extensive e...
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
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.
Fast Tree: Computing Large Minimum-Evolution Trees with Profiles instead of a Distance Matrix
N. Price, Morgan
2009-01-01
Gene families are growing rapidly, but standard methods for inferring phylogenies do not scale to alignments with over 10,000 sequences. We present FastTree, a method for constructing large phylogenies and for estimating their reliability. Instead of storing a distance matrix, FastTree stores sequence profiles of internal nodes in the tree. FastTree uses these profiles to implement neighbor-joining and uses heuristics to quickly identify candidate joins. FastTree then uses nearest-neighbor i...
FastTree: Computing Large Minimum Evolution Trees with Profiles instead of a Distance Matrix
Price, Morgan N.; Dehal, Paramvir S.; Arkin, Adam P.
2009-01-01
Gene families are growing rapidly, but standard methods for inferring phylogenies do not scale to alignments with over 10,000 sequences. We present FastTree, a method for constructing large phylogenies and for estimating their reliability. Instead of storing a distance matrix, FastTree stores sequence profiles of internal nodes in the tree. FastTree uses these profiles to implement Neighbor-Joining and uses heuristics to quickly identify candidate joins. FastTree then uses nearest neighbor in...
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.
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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.
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.
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
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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.
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.
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.
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
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
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
Yin, Lucy; Andrews, Jennifer; Heaton, Thomas
2018-05-01
Earthquake parameter estimations using nearest neighbor searching among a large database of observations can lead to reliable prediction results. However, in the real-time application of Earthquake Early Warning (EEW) systems, the accurate prediction using a large database is penalized by a significant delay in the processing time. We propose to use a multidimensional binary search tree (KD tree) data structure to organize large seismic databases to reduce the processing time in nearest neighbor search for predictions. We evaluated the performance of KD tree on the Gutenberg Algorithm, a database-searching algorithm for EEW. We constructed an offline test to predict peak ground motions using a database with feature sets of waveform filter-bank characteristics, and compare the results with the observed seismic parameters. We concluded that large database provides more accurate predictions of the ground motion information, such as peak ground acceleration, velocity, and displacement (PGA, PGV, PGD), than source parameters, such as hypocenter distance. Application of the KD tree search to organize the database reduced the average searching process by 85% time cost of the exhaustive method, allowing the method to be feasible for real-time implementation. The algorithm is straightforward and the results will reduce the overall time of warning delivery for EEW.
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.
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
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.
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
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 Fast Logdet Divergence Based Metric Learning Algorithm for Large Data Sets Classification
Directory of Open Access Journals (Sweden)
Jiangyuan Mei
2014-01-01
the basis of classifiers, for example, the k-nearest neighbors classifier. Experiments on benchmark data sets demonstrate that the proposed algorithm compares favorably with the state-of-the-art methods.
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.
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...
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.
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
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.
International Nuclear Information System (INIS)
Lian, Jijian; He, Longjun; Ma, Bin; Peng, Wenxiang; Li, Huokun
2013-01-01
Research on optimal sensor placement (OSP) has become very important due to the need to obtain effective testing results with limited testing resources in health monitoring. In this study, a new methodology is proposed to select the best sensor locations for large structures. First, a novel fitness function derived from the nearest neighbour index is proposed to overcome the drawbacks of the effective independence method for OSP for large structures. This method maximizes the contribution of each sensor to modal observability and simultaneously avoids the redundancy of information between the selected degrees of freedom. A hybrid algorithm combining the improved discrete particle swarm optimization (DPSO) with the clonal selection algorithm is then implemented to optimize the proposed fitness function effectively. Finally, the proposed method is applied to an arch dam for performance verification. The results show that the proposed hybrid swarm intelligence algorithm outperforms a genetic algorithm with decimal two-dimension array encoding and DPSO in the capability of global optimization. The new fitness function is advantageous in terms of sensor distribution and ensuring a well-conditioned information matrix and orthogonality of modes, indicating that this method may be used to provide guidance for OSP in various large structures. (paper)
Neighborhood Discriminant Hashing for Large-Scale Image Retrieval.
Tang, Jinhui; Li, Zechao; Wang, Meng; Zhao, Ruizhen
2015-09-01
With the proliferation of large-scale community-contributed images, hashing-based approximate nearest neighbor search in huge databases has aroused considerable interest from the fields of computer vision and multimedia in recent years because of its computational and memory efficiency. In this paper, we propose a novel hashing method named neighborhood discriminant hashing (NDH) (for short) to implement approximate similarity search. Different from the previous work, we propose to learn a discriminant hashing function by exploiting local discriminative information, i.e., the labels of a sample can be inherited from the neighbor samples it selects. The hashing function is expected to be orthogonal to avoid redundancy in the learned hashing bits as much as possible, while an information theoretic regularization is jointly exploited using maximum entropy principle. As a consequence, the learned hashing function is compact and nonredundant among bits, while each bit is highly informative. Extensive experiments are carried out on four publicly available data sets and the comparison results demonstrate the outperforming performance of the proposed NDH method over state-of-the-art hashing techniques.
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.
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
GEMINI: a computationally-efficient search engine for large gene expression datasets.
DeFreitas, Timothy; Saddiki, Hachem; Flaherty, Patrick
2016-02-24
Low-cost DNA sequencing allows organizations to accumulate massive amounts of genomic data and use that data to answer a diverse range of research questions. Presently, users must search for relevant genomic data using a keyword, accession number of meta-data tag. However, in this search paradigm the form of the query - a text-based string - is mismatched with the form of the target - a genomic profile. To improve access to massive genomic data resources, we have developed a fast search engine, GEMINI, that uses a genomic profile as a query to search for similar genomic profiles. GEMINI implements a nearest-neighbor search algorithm using a vantage-point tree to store a database of n profiles and in certain circumstances achieves an [Formula: see text] expected query time in the limit. We tested GEMINI on breast and ovarian cancer gene expression data from The Cancer Genome Atlas project and show that it achieves a query time that scales as the logarithm of the number of records in practice on genomic data. In a database with 10(5) samples, GEMINI identifies the nearest neighbor in 0.05 sec compared to a brute force search time of 0.6 sec. GEMINI is a fast search engine that uses a query genomic profile to search for similar profiles in a very large genomic database. It enables users to identify similar profiles independent of sample label, data origin or other meta-data information.
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.
Constructing a logical, regular axis topology from an irregular topology
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.
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
Highly Anisotropic Magnon Dispersion in Ca_{2}RuO_{4}: Evidence for Strong Spin Orbit Coupling.
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.
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: NaÄ³ve 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.
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.
Distance-Based Image Classification: Generalizing to New Classes at Near Zero Cost
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
Consistency Analysis of Nearest Subspace Classifier
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 ...
Norrie disease and MAO genes: nearest neighbors.
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.
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
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.
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.
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...
Neighboring and Urbanism: Commonality versus Friendship.
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.…
Multiclustered chimeras in large semiconductor laser arrays with nonlocal interactions
Shena, J.; Hizanidis, J.; Hövel, P.; Tsironis, G. P.
2017-09-01
The dynamics of a large array of coupled semiconductor lasers is studied numerically for a nonlocal coupling scheme. Our focus is on chimera states, a self-organized spatiotemporal pattern of coexisting coherence and incoherence. In laser systems, such states have been previously found for global and nearest-neighbor coupling, mainly in small networks. The technological advantage of large arrays has motivated us to study a system of 200 nonlocally coupled lasers with respect to the emerging collective dynamics. Moreover, the nonlocal nature of the coupling allows us to obtain robust chimera states with multiple (in)coherent domains. The crucial parameters are the coupling strength, the coupling phase and the range of the nonlocal interaction. We find that multiclustered chimera states exist in a wide region of the parameter space and we provide quantitative characterization for the obtained spatiotemporal patterns. By proposing two different experimental setups for the realization of the nonlocal coupling scheme, we are confident that our results can be confirmed in the laboratory.
Interactions of galaxies outside clusters and massive groups
Yadav, Jaswant K.; Chen, Xuelei
2018-06-01
We investigate the dependence of physical properties of galaxies on small- and large-scale density environment. The galaxy population consists of mainly passively evolving galaxies in comparatively low-density regions of Sloan Digital Sky Survey (SDSS). We adopt (i) local density, ρ _{20}, derived using adaptive smoothing kernel, (ii) projected distance, r_p, to the nearest neighbor galaxy and (iii) the morphology of the nearest neighbor galaxy as various definitions of environment parameters of every galaxy in our sample. In order to detect long-range interaction effects, we group galaxy interactions into four cases depending on morphology of the target and neighbor galaxies. This study builds upon an earlier study by Park and Choi (2009) by including improved definitions of target and neighbor galaxies, thus enabling us to better understand the effect of "the nearest neighbor" interaction on the galaxy. We report that the impact of interaction on galaxy properties is detectable at least up to the pair separation corresponding to the virial radius of (the neighbor) galaxies. This turns out to be mostly between 210 and 360 h^{-1}kpc for galaxies included in our study. We report that early type fraction for isolated galaxies with r_p > r_{vir,nei} is almost ignorant of the background density and has a very weak density dependence for closed pairs. Star formation activity of a galaxy is found to be crucially dependent on neighbor galaxy morphology. We find star formation activity parameters and structure parameters of galaxies to be independent of the large-scale background density. We also exhibit that changing the absolute magnitude of the neighbor galaxies does not affect significantly the star formation activity of those target galaxies whose morphology and luminosities are fixed.
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.
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.
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.
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.
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.
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...
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.
Avena, L.; Hollander, den W.Th.F.; Redig, F.H.J.
2010-01-01
Consider a one-dimensional shift-invariant attractive spin-flip system in equilibrium, constituting a dynamic random environment, together with a nearest-neighbor random walk that on occupied sites has a local drift to the right but on vacant sites has a local drift to the left. In previous work we
Discriminative Hierarchical K-Means Tree for Large-Scale Image Classification.
Chen, Shizhi; Yang, Xiaodong; Tian, Yingli
2015-09-01
A key challenge in large-scale image classification is how to achieve efficiency in terms of both computation and memory without compromising classification accuracy. The learning-based classifiers achieve the state-of-the-art accuracies, but have been criticized for the computational complexity that grows linearly with the number of classes. The nonparametric nearest neighbor (NN)-based classifiers naturally handle large numbers of categories, but incur prohibitively expensive computation and memory costs. In this brief, we present a novel classification scheme, i.e., discriminative hierarchical K-means tree (D-HKTree), which combines the advantages of both learning-based and NN-based classifiers. The complexity of the D-HKTree only grows sublinearly with the number of categories, which is much better than the recent hierarchical support vector machines-based methods. The memory requirement is the order of magnitude less than the recent Naïve Bayesian NN-based approaches. The proposed D-HKTree classification scheme is evaluated on several challenging benchmark databases and achieves the state-of-the-art accuracies, while with significantly lower computation cost and memory requirement.
Evidence for cultural differences between neighboring chimpanzee communities.
Luncz, Lydia V; Mundry, Roger; Boesch, Christophe
2012-05-22
The majority of evidence for cultural behavior in animals has come from comparisons between populations separated by large geographical distances that often inhabit different environments. The difficulty of excluding ecological and genetic variation as potential explanations for observed behaviors has led some researchers to challenge the idea of animal culture. Chimpanzees (Pan troglodytes verus) in the Taï National Park, Côte d'Ivoire, crack Coula edulis nuts using stone and wooden hammers and tree root anvils. In this study, we compare for the first time hammer selection for nut cracking across three neighboring chimpanzee communities that live in the same forest habitat, which reduces the likelihood of ecological variation. Furthermore, the study communities experience frequent dispersal of females at maturity, which eliminates significant genetic variation. We compared key ecological factors, such as hammer availability and nut hardness, between the three neighboring communities and found striking differences in group-specific hammer selection among communities despite similar ecological conditions. Differences were found in the selection of hammer material and hammer size in response to changes in nut resistance over time. Our findings highlight the subtleties of cultural differences in wild chimpanzees and illustrate how cultural knowledge is able to shape behavior, creating differences among neighboring social groups. Copyright © 2012 Elsevier Ltd. All rights reserved.
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)
Avena, L.; Hollander, den W.Th.F.; Redig, F.H.J.
2009-01-01
Consider a one-dimensional shift-invariant attractive spin-ip system in equilibrium, constituting a dynamic random environment, together with a nearest-neighbor random walk that on occupied sites has a local drift to the right but on vacant sites has a local drift to the left. In [2] we proved a law
Recrafting the neighbor-joining method
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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.
Technique for fast and efficient hierarchical clustering
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.
Fast Tree: Computing Large Minimum-Evolution Trees with Profiles instead of a Distance Matrix
Energy Technology Data Exchange (ETDEWEB)
N. Price, Morgan; S. Dehal, Paramvir; P. Arkin, Adam
2009-07-31
Gene families are growing rapidly, but standard methods for inferring phylogenies do not scale to alignments with over 10,000 sequences. We present FastTree, a method for constructing large phylogenies and for estimating their reliability. Instead of storing a distance matrix, FastTree stores sequence profiles of internal nodes in the tree. FastTree uses these profiles to implement neighbor-joining and uses heuristics to quickly identify candidate joins. FastTree then uses nearest-neighbor interchanges to reduce the length of the tree. For an alignment with N sequences, L sites, and a different characters, a distance matrix requires O(N^2) space and O(N^2 L) time, but FastTree requires just O( NLa + N sqrt(N) ) memory and O( N sqrt(N) log(N) L a ) time. To estimate the tree's reliability, FastTree uses local bootstrapping, which gives another 100-fold speedup over a distance matrix. For example, FastTree computed a tree and support values for 158,022 distinct 16S ribosomal RNAs in 17 hours and 2.4 gigabytes of memory. Just computing pairwise Jukes-Cantor distances and storing them, without inferring a tree or bootstrapping, would require 17 hours and 50 gigabytes of memory. In simulations, FastTree was slightly more accurate than neighbor joining, BIONJ, or FastME; on genuine alignments, FastTree's topologies had higher likelihoods. FastTree is available at http://microbesonline.org/fasttree.
Cariati, Elena; Macchi, Roberto; Roberto, Dominique; Ugo, Renato; Galli, Simona; Casati, Nicola; Macchi, Piero; Sironi, Angelo; Bogani, Lapo; Caneschi, Andrea; Gatteschi, Dante
2007-08-01
Mixed M(II)/M(III) metal oxalates, as "stripes" connected through strong hydrogen bonding by para-dimethylaminobenzaldeide (DAMBA) and water, form an organic-inorganic 2D network that enables segregation in layers of the cationic organic NLO-phore trans-4-(4-dimethylaminostyryl)-1-methylpyridinium, [DAMS+]. The crystalline hybrid materials obtained have the general formula [DAMS]4[M2M'(C2O4)6].2DAMBA.2H2O (M = Rh, Fe, Cr; M' = Mn, Zn), and their overall three-dimensional packing is non-centrosymmetric and polar, therefore suitable for second harmonic generation (SHG). All the compounds investigated are characterized by an exceptional SHG activity, due both to the large molecular quadratic hyperpolarizability of [DAMS+] and to the efficiency of the crystalline network which organizes [DAMS+] into head-to-tail arranged J-type aggregates. The tunability of the pairs of metal ions allows exploiting also the magnetic functionality of the materials. Examples containing antiferro-, ferro-, and ferri-magnetic interactions (mediated by oxalato bridges) are obtained by coupling proper M(III) ions (Fe, Cr, Rh) with M(II) (Mn, Zn). This shed light on the role of weak next-nearest-neighbor interactions and main nearest-neighbor couplings along "stripes" of mixed M(II)/M(III) metal oxalates of the organic-inorganic 2D network, thus suggesting that these hybrid materials may display isotropic 1D magnetic properties along the mixed M(II)/M(III) metal oxalates "stripes".
Radi, Marjan; Dezfouli, Behnam; Abu Bakar, Kamalrulnizam; Abd Razak, Shukor
2014-01-01
Network connectivity and link quality information are the fundamental requirements of wireless sensor network protocols to perform their desired functionality. Most of the existing discovery protocols have only focused on the neighbor discovery problem, while a few number of them provide an integrated neighbor search and link estimation. As these protocols require a careful parameter adjustment before network deployment, they cannot provide scalable and accurate network initialization in large-scale dense wireless sensor networks with random topology. Furthermore, performance of these protocols has not entirely been evaluated yet. In this paper, we perform a comprehensive simulation study on the efficiency of employing adaptive protocols compared to the existing nonadaptive protocols for initializing sensor networks with random topology. In this regard, we propose adaptive network initialization protocols which integrate the initial neighbor discovery with link quality estimation process to initialize large-scale dense wireless sensor networks without requiring any parameter adjustment before network deployment. To the best of our knowledge, this work is the first attempt to provide a detailed simulation study on the performance of integrated neighbor discovery and link quality estimation protocols for initializing sensor networks. This study can help system designers to determine the most appropriate approach for different applications.
Directory of Open Access Journals (Sweden)
Marjan Radi
2014-01-01
Full Text Available Network connectivity and link quality information are the fundamental requirements of wireless sensor network protocols to perform their desired functionality. Most of the existing discovery protocols have only focused on the neighbor discovery problem, while a few number of them provide an integrated neighbor search and link estimation. As these protocols require a careful parameter adjustment before network deployment, they cannot provide scalable and accurate network initialization in large-scale dense wireless sensor networks with random topology. Furthermore, performance of these protocols has not entirely been evaluated yet. In this paper, we perform a comprehensive simulation study on the efficiency of employing adaptive protocols compared to the existing nonadaptive protocols for initializing sensor networks with random topology. In this regard, we propose adaptive network initialization protocols which integrate the initial neighbor discovery with link quality estimation process to initialize large-scale dense wireless sensor networks without requiring any parameter adjustment before network deployment. To the best of our knowledge, this work is the first attempt to provide a detailed simulation study on the performance of integrated neighbor discovery and link quality estimation protocols for initializing sensor networks. This study can help system designers to determine the most appropriate approach for different applications.
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.
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.
EXAFS and XPS Study of Rutile-Type Difluorides of First-Row Transition Metals
International Nuclear Information System (INIS)
Murai, Kei-ichiro; Suzuki, Yohei; Moriga, Toshihiro; Yoshiasa, Akira
2007-01-01
Although most rutile-type difluorides (MnF2, CoF2 and NiF2) have a positive thermal expansion coefficient, FeF2 has a negative thermal expansion (NTE) along the c-axis in the high temperature region. In this study, we give an explanation of that behavior with Extended X-ray Absorption Fine Structure (EXAFS) and X-ray Photoelectron Spectroscopy (XPS) techniques. From EXAFS results, it has become apparent that the length of the share-edge (Fe-Fe) of FeF6 octahedra increased with the rise of temperature in the high temperature region. We have revealed that the force constant between nearest neighbor atoms (Fe-F) was much larger than that between second-nearest neighbor atoms (Fe-Fe) in FeF2. In XPS measurements, it was discovered that the peak of F 1s of FeF2 was located at the lowest binding energy position as compared to that of other difluorides. This means that the charge density around the F atom in FeF2 was higher than that in other difluorides. It follows from this that the share-edge repulsive force in FeF2 is larger than that in other difluorides. On account of the large repulsive force and the large force constant between nearest neighbor atoms, Fe atoms are attracted to share-edge with the rise of temperature
Model of directed lines for square ice with second-neighbor and third-neighbor interactions
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.
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
Air Pollution from Livestock Farms Is Associated with Airway Obstruction in Neighboring Residents.
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.
"Equilibrium structure of monatomic steps on vicinal Si(001)
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
Shock tube measurements of the rate constants for seven large alkanes+OH
Badra, Jihad; Elwardani, Ahmed Elsaid; Farooq, Aamir
2015-01-01
subscripts indicate the number of carbon atoms bonded to the next-nearest-neighbor carbon): S20=1.58×10-11exp(-1550K/T)cm3molecule-1s-1(887-1327K)S30=2.37×10-11exp(-1850K/T)cm3molecule-1s-1(887-1327K)S21=4.5×10-12exp(-793.7K/T)cm3molecule-1s-1(833-1440K)T100=2
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
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...
Resampling nucleotide sequences with closest-neighbor trimming and its comparison to other methods.
Directory of Open Access Journals (Sweden)
Kouki Yonezawa
Full Text Available A large number of nucleotide sequences of various pathogens are available in public databases. The growth of the datasets has resulted in an enormous increase in computational costs. Moreover, due to differences in surveillance activities, the number of sequences found in databases varies from one country to another and from year to year. Therefore, it is important to study resampling methods to reduce the sampling bias. A novel algorithm-called the closest-neighbor trimming method-that resamples a given number of sequences from a large nucleotide sequence dataset was proposed. The performance of the proposed algorithm was compared with other algorithms by using the nucleotide sequences of human H3N2 influenza viruses. We compared the closest-neighbor trimming method with the naive hierarchical clustering algorithm and [Formula: see text]-medoids clustering algorithm. Genetic information accumulated in public databases contains sampling bias. The closest-neighbor trimming method can thin out densely sampled sequences from a given dataset. Since nucleotide sequences are among the most widely used materials for life sciences, we anticipate that our algorithm to various datasets will result in reducing sampling bias.
New Results on the Nearest OB Association: Sco-Cen (Sco OB2)
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
Jennings, Elyse A; Barber, Jennifer S
2013-03-01
Large families can have a negative impact on the health and well-being of women, children, and their communities. Seventy-three percent of the individuals in our rural Nepalese sample report that two children is their ideal number, yet about half of the married women continue childbearing after their second child. Using longitudinal data from the Chitwan Valley Family Study, we explore the influence of women's and neighbors' family size preferences on women's progression to high parity births, comparing this influence across two cohorts. We find that neighbors' family size preferences influence women's fertility, that older cohorts of women are more influenced by their neighbors' preferences than are younger cohorts of women, and that the influence of neighbors' preferences is independent of women's own preferences. © 2013 The Population Council, Inc.
Deep Hashing Based Fusing Index Method for Large-Scale Image Retrieval
Directory of Open Access Journals (Sweden)
Lijuan Duan
2017-01-01
Full Text Available Hashing has been widely deployed to perform the Approximate Nearest Neighbor (ANN search for the large-scale image retrieval to solve the problem of storage and retrieval efficiency. Recently, deep hashing methods have been proposed to perform the simultaneous feature learning and the hash code learning with deep neural networks. Even though deep hashing has shown the better performance than traditional hashing methods with handcrafted features, the learned compact hash code from one deep hashing network may not provide the full representation of an image. In this paper, we propose a novel hashing indexing method, called the Deep Hashing based Fusing Index (DHFI, to generate a more compact hash code which has stronger expression ability and distinction capability. In our method, we train two different architecture’s deep hashing subnetworks and fuse the hash codes generated by the two subnetworks together to unify images. Experiments on two real datasets show that our method can outperform state-of-the-art image retrieval applications.
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...
Energetics and Dynamics of Cu(001)-c(2x2)Cl steps
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
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
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...
Patel, Mohak; Leggett, Susan E; Landauer, Alexander K; Wong, Ian Y; Franck, Christian
2018-04-03
Spatiotemporal tracking of tracer particles or objects of interest can reveal localized behaviors in biological and physical systems. However, existing tracking algorithms are most effective for relatively low numbers of particles that undergo displacements smaller than their typical interparticle separation distance. Here, we demonstrate a single particle tracking algorithm to reconstruct large complex motion fields with large particle numbers, orders of magnitude larger than previously tractably resolvable, thus opening the door for attaining very high Nyquist spatial frequency motion recovery in the images. Our key innovations are feature vectors that encode nearest neighbor positions, a rigorous outlier removal scheme, and an iterative deformation warping scheme. We test this technique for its accuracy and computational efficacy using synthetically and experimentally generated 3D particle images, including non-affine deformation fields in soft materials, complex fluid flows, and cell-generated deformations. We augment this algorithm with additional particle information (e.g., color, size, or shape) to further enhance tracking accuracy for high gradient and large displacement fields. These applications demonstrate that this versatile technique can rapidly track unprecedented numbers of particles to resolve large and complex motion fields in 2D and 3D images, particularly when spatial correlations exist.
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
Arabic Text Categorization Using Improved k-Nearest neighbour Algorithm
Directory of Open Access Journals (Sweden)
Wail Hamood KHALED
2014-10-01
Full Text Available The quantity of text information published in Arabic language on the net requires the implementation of effective techniques for the extraction and classifying of relevant information contained in large corpus of texts. In this paper we presented an implementation of an enhanced k-NN Arabic text classifier. We apply the traditional k-NN and Naive Bayes from Weka Toolkit for comparison purpose. Our proposed modified k-NN algorithm features an improved decision rule to skip the classes that are less similar and identify the right class from k nearest neighbours which increases the accuracy. The study evaluates the improved decision rule technique using the standard of recall, precision and f-measure as the basis of comparison. We concluded that the effectiveness of the proposed classifier is promising and outperforms the classical k-NN classifier.
Distribution of Steps with Finite-Range Interactions: Analytic Approximations and Numerical Results
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.
Cluster Analysis of Flow Cytometric List Mode Data on a Personal Computer
Bakker Schut, Tom C.; Bakker schut, T.C.; de Grooth, B.G.; Greve, Jan
1993-01-01
A cluster analysis algorithm, dedicated to analysis of flow cytometric data is described. The algorithm is written in Pascal and implemented on an MS-DOS personal computer. It uses k-means, initialized with a large number of seed points, followed by a modified nearest neighbor technique to reduce
Directory of Open Access Journals (Sweden)
Christoph eSchmid
2013-08-01
Full Text Available Root-root interactions are much more sophisticated than previously thought, yet the mechanisms of belowground neighbor perception remain largely obscure. Genome-wide transcriptome analyses allow detailed insight into plant reactions to environmental cues.A root interaction trial was set up to explore both morphological and whole genome transcriptional responses in roots of Arabidopsis thaliana in the presence or absence of an inferior competitor, Hieracium pilosella.Neighbor perception was indicated by Arabidopsis roots predominantly growing away from the neighbor (segregation, while solitary plants placed more roots towards the middle of the pot. Total biomass remained unaffected. Database comparisons in transcriptome analysis revealed considerable similarity between Arabidopsis root reactions to neighbors and reactions to pathogens. Detailed analyses of the functional category ‘biotic stress’ using MapMan tools found the sub-category ‘pathogenesis-related proteins’ highly significantly induced. A comparison to a study on intraspecific competition brought forward a core of genes consistently involved in reactions to neighbor roots.We conclude that beyond resource depletion roots perceive neighboring roots or their associated microorganisms by a relatively uniform mechanism that involves the strong induction of pathogenesis-related proteins. In an ecological context the findings reveal that belowground neighbor detection may occur independently of resource depletion, allowing for a time advantage for the root to prepare for potential interactions.
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).
Ma, Xiaolei; Dai, Zhuang; He, Zhengbing; Ma, Jihui; Wang, Yong; Wang, Yunpeng
2017-04-10
This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network. The results show that the proposed method outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time. The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks.
Matrix-valued Boltzmann equation for the nonintegrable Hubbard chain.
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.
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)
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
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
Radiative energy loss of neighboring subjets arXiv
Mehtar-Tani, Yacine
We compute the in-medium energy loss probability distribution of two neighboring subjets at leading order, in the large-$N_c$ approximation. Our result exhibits a gradual onset of color decoherence of the system and accounts for two expected limiting cases. When the angular separation is smaller than the characteristic angle for medium-induced radiation, the two-pronged substructure lose energy coherently as a single color charge, namely that of the parent parton. At large angular separation the two subjets lose energy independently. Our result is a first step towards quantifying effects of energy loss as a result of the fluctuation of the multi-parton jet substructure and therefore goes beyond the standard approach to jet quenching based on single parton energy loss. We briefly discuss applications to jet observables in heavy-ion collisions.
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
Protein function prediction using neighbor relativity in protein-protein interaction network.
Moosavi, Sobhan; Rahgozar, Masoud; Rahimi, Amir
2013-04-01
There is a large gap between the number of discovered proteins and the number of functionally annotated ones. Due to the high cost of determining protein function by wet-lab research, function prediction has become a major task for computational biology and bioinformatics. Some researches utilize the proteins interaction information to predict function for un-annotated proteins. In this paper, we propose a novel approach called "Neighbor Relativity Coefficient" (NRC) based on interaction network topology which estimates the functional similarity between two proteins. NRC is calculated for each pair of proteins based on their graph-based features including distance, common neighbors and the number of paths between them. In order to ascribe function to an un-annotated protein, NRC estimates a weight for each neighbor to transfer its annotation to the unknown protein. Finally, the unknown protein will be annotated by the top score transferred functions. We also investigate the effect of using different coefficients for various types of functions. The proposed method has been evaluated on Saccharomyces cerevisiae and Homo sapiens interaction networks. The performance analysis demonstrates that NRC yields better results in comparison with previous protein function prediction approaches that utilize interaction network. Copyright © 2012 Elsevier Ltd. All rights reserved.
Ghinita, Gabriel; Kalnis, Panos; Kantarcioǧlu, Murâ t; Bertino, Elisa
2010-01-01
Mobile devices with global positioning capabilities allow users to retrieve points of interest (POI) in their proximity. To protect user privacy, it is important not to disclose exact user coordinates to un-trusted entities that provide location-based services. Currently, there are two main approaches to protect the location privacy of users: (i) hiding locations inside cloaking regions (CRs) and (ii) encrypting location data using private information retrieval (PIR) protocols. Previous work focused on finding good trade-offs between privacy and performance of user protection techniques, but disregarded the important issue of protecting the POI dataset D. For instance, location cloaking requires large-sized CRs, leading to excessive disclosure of POIs (O({pipe}D{pipe}) in the worst case). PIR, on the other hand, reduces this bound to O(√{pipe}D{pipe}), but at the expense of high processing and communication overhead. We propose hybrid, two-step approaches for private location-based queries which provide protection for both the users and the database. In the first step, user locations are generalized to coarse-grained CRs which provide strong privacy. Next, a PIR protocol is applied with respect to the obtained query CR. To protect against excessive disclosure of POI locations, we devise two cryptographic protocols that privately evaluate whether a point is enclosed inside a rectangular region or a convex polygon. We also introduce algorithms to efficiently support PIR on dynamic POI sub-sets. We provide solutions for both approximate and exact NN queries. In the approximate case, our method discloses O(1) POI, orders of magnitude fewer than CR- or PIR-based techniques. For the exact case, we obtain optimal disclosure of a single POI, although with slightly higher computational overhead. Experimental results show that the hybrid approaches are scalable in practice, and outperform the pure-PIR approach in terms of computational and communication overhead. © 2010
Ghinita, Gabriel
2010-12-15
Mobile devices with global positioning capabilities allow users to retrieve points of interest (POI) in their proximity. To protect user privacy, it is important not to disclose exact user coordinates to un-trusted entities that provide location-based services. Currently, there are two main approaches to protect the location privacy of users: (i) hiding locations inside cloaking regions (CRs) and (ii) encrypting location data using private information retrieval (PIR) protocols. Previous work focused on finding good trade-offs between privacy and performance of user protection techniques, but disregarded the important issue of protecting the POI dataset D. For instance, location cloaking requires large-sized CRs, leading to excessive disclosure of POIs (O({pipe}D{pipe}) in the worst case). PIR, on the other hand, reduces this bound to O(√{pipe}D{pipe}), but at the expense of high processing and communication overhead. We propose hybrid, two-step approaches for private location-based queries which provide protection for both the users and the database. In the first step, user locations are generalized to coarse-grained CRs which provide strong privacy. Next, a PIR protocol is applied with respect to the obtained query CR. To protect against excessive disclosure of POI locations, we devise two cryptographic protocols that privately evaluate whether a point is enclosed inside a rectangular region or a convex polygon. We also introduce algorithms to efficiently support PIR on dynamic POI sub-sets. We provide solutions for both approximate and exact NN queries. In the approximate case, our method discloses O(1) POI, orders of magnitude fewer than CR- or PIR-based techniques. For the exact case, we obtain optimal disclosure of a single POI, although with slightly higher computational overhead. Experimental results show that the hybrid approaches are scalable in practice, and outperform the pure-PIR approach in terms of computational and communication overhead. © 2010
A Coupled k-Nearest Neighbor Algorithm for Multi-Label Classification
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
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
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%
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
Datta, A.; Banerjee, S.; Finley, A.O.; Hamm, N.A.S.; Schaap, M.
2016-01-01
Particulate matter (PM) is a class of malicious environmental pollutants known to be detrimental to human health. Regulatory efforts aimed at curbing PM levels in different countries often require high resolution space–time maps that can identify red-flag regions exceeding statutory concentration
Fidelity study of superconductivity in extended Hubbard models
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.
Bees do not use nearest-neighbour rules for optimization of multi-location routes.
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.
Shortest triplet clustering: reconstructing large phylogenies using representative sets
Directory of Open Access Journals (Sweden)
Sy Vinh Le
2005-04-01
Full Text Available Abstract Background Understanding the evolutionary relationships among species based on their genetic information is one of the primary objectives in phylogenetic analysis. Reconstructing phylogenies for large data sets is still a challenging task in Bioinformatics. Results We propose a new distance-based clustering method, the shortest triplet clustering algorithm (STC, to reconstruct phylogenies. The main idea is the introduction of a natural definition of so-called k-representative sets. Based on k-representative sets, shortest triplets are reconstructed and serve as building blocks for the STC algorithm to agglomerate sequences for tree reconstruction in O(n2 time for n sequences. Simulations show that STC gives better topological accuracy than other tested methods that also build a first starting tree. STC appears as a very good method to start the tree reconstruction. However, all tested methods give similar results if balanced nearest neighbor interchange (BNNI is applied as a post-processing step. BNNI leads to an improvement in all instances. The program is available at http://www.bi.uni-duesseldorf.de/software/stc/. Conclusion The results demonstrate that the new approach efficiently reconstructs phylogenies for large data sets. We found that BNNI boosts the topological accuracy of all methods including STC, therefore, one should use BNNI as a post-processing step to get better topological accuracy.
Large Scale Experiments of Multihop Networks in Mobile Scenarios
Directory of Open Access Journals (Sweden)
Yacine Benchaïb
2016-03-01
Full Text Available This paper presents the latest advances in our research work focused on VIRMANEL and SILUMOD, a couple of tools developed for research in wireless mobile multihop networks. SILUMOD is a domain specific language dedicated to the definition of mobility models. This language contains key- words and special operators that make it easy to define a mobility model and calculate the positions of a trajectory. These positions are sent to VIRMANEL, a tool that man- ages virtual machines corresponding to mobile nodes, emu- lates their movements and the resulting connections and dis- connections, and displays the network evolution to the user, thanks to its graphical user interface. The virtualization ap- proach we take here allows to run real code and to test real protocol implementations without deploying an important experimental platform. For the experimentation of a large number of virtual mobile nodes, we defined and implemented a new algorithm for the nearest neighbor search to find the nodes that are within communication range. We then car- ried out a considerable measurement campaign in order to evaluate the performance of this algorithm. The results show that even with an experiment using a large number of mobile nodes, our algorithm make it possible to evaluate the state of connectivity between mobile nodes within a reasonable time and number of operations.
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
A dumbed-down approach to unite Fermilab, its neighbors
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).
A scalable PC-based parallel computer for lattice QCD
International Nuclear Information System (INIS)
Fodor, Z.; Katz, S.D.; Pappa, G.
2003-01-01
A PC-based parallel computer for medium/large scale lattice QCD simulations is suggested. The Eoetvoes Univ., Inst. Theor. Phys. cluster consists of 137 Intel P4-1.7GHz nodes. Gigabit Ethernet cards are used for nearest neighbor communication in a two-dimensional mesh. The sustained performance for dynamical staggered (wilson) quarks on large lattices is around 70(110) GFlops. The exceptional price/performance ratio is below $1/Mflop
A scalable PC-based parallel computer for lattice QCD
International Nuclear Information System (INIS)
Fodor, Z.; Papp, G.
2002-09-01
A PC-based parallel computer for medium/large scale lattice QCD simulations is suggested. The Eoetvoes Univ., Inst. Theor. Phys. cluster consists of 137 Intel P4-1.7 GHz nodes. Gigabit Ethernet cards are used for nearest neighbor communication in a two-dimensional mesh. The sustained performance for dynamical staggered(wilson) quarks on large lattices is around 70(110) GFlops. The exceptional price/performance ratio is below $1/Mflop. (orig.)
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.
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…
Pollinator-mediated interactions in experimental arrays vary with neighbor identity.
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.
Energy localization and molecular dissociation
International Nuclear Information System (INIS)
Takeno, S.; Tsironis, G.P.
2005-01-01
We study analytically as well as numerically the role that large-amplitude vibrations play during the process of molecular dissociation. Our model consists of a linear three-atom molecule composed of identical atoms interacting with their nearest neighbors by Morse potentials. We find a close relation between energy localization and bond breaking and evaluate numerically the corresponding reaction paths
The clinic as a good corporate neighbor.
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.
Unwanted Behaviors and Nuisance Behaviors Among Neighbors in a Belgian Community Sample.
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.
Structure and Bonding in Noncrystalline Solids Abstracts
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
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...
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.
Fusion yield rate recovery by escaping hot-spot fast ions in the neighboring fuel layer
Tang, Xian-Zhu; McDevitt, C. J.; Guo, Zehua; Berk, H. L.
2014-02-01
Free-streaming loss by fast ions can deplete the tail population in the hot spot of an inertial confinement fusion (ICF) target. Escaping fast ions in the neighboring fuel layer of a cryogenic target can produce a surplus of fast ions locally. In contrast to the Knudsen layer effect that reduces hot-spot fusion reactivity due to tail ion depletion, the inverse Knudsen layer effect increases fusion reactivity in the neighboring fuel layer. In the case of a burning ICF target in the presence of significant hydrodynamic mix which aggravates the Knudsen layer effect, the yield recovery largely compensates for the yield reduction. For mix-dominated sub-ignition targets, the yield reduction is the dominant process.
Computer Simulation of Energy Parameters and Magnetic Effects in Fe-Si-C Ternary Alloys
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.
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.
Quantitative diagnosis of bladder cancer by morphometric analysis of HE images
Wu, Binlin; Nebylitsa, Samantha V.; Mukherjee, Sushmita; Jain, Manu
2015-02-01
In clinical practice, histopathological analysis of biopsied tissue is the main method for bladder cancer diagnosis and prognosis. The diagnosis is performed by a pathologist based on the morphological features in the image of a hematoxylin and eosin (HE) stained tissue sample. This manuscript proposes algorithms to perform morphometric analysis on the HE images, quantify the features in the images, and discriminate bladder cancers with different grades, i.e. high grade and low grade. The nuclei are separated from the background and other types of cells such as red blood cells (RBCs) and immune cells using manual outlining, color deconvolution and image segmentation. A mask of nuclei is generated for each image for quantitative morphometric analysis. The features of the nuclei in the mask image including size, shape, orientation, and their spatial distributions are measured. To quantify local clustering and alignment of nuclei, we propose a 1-nearest-neighbor (1-NN) algorithm which measures nearest neighbor distance and nearest neighbor parallelism. The global distributions of the features are measured using statistics of the proposed parameters. A linear support vector machine (SVM) algorithm is used to classify the high grade and low grade bladder cancers. The results show using a particular group of nuclei such as large ones, and combining multiple parameters can achieve better discrimination. This study shows the proposed approach can potentially help expedite pathological diagnosis by triaging potentially suspicious biopsies.
Clustering, randomness, and regularity in cloud fields: 2. Cumulus cloud fields
Zhu, T.; Lee, J.; Weger, R. C.; Welch, R. M.
1992-12-01
During the last decade a major controversy has been brewing concerning the proper characterization of cumulus convection. The prevailing view has been that cumulus clouds form in clusters, in which cloud spacing is closer than that found for the overall cloud field and which maintains its identity over many cloud lifetimes. This "mutual protection hypothesis" of Randall and Huffman (1980) has been challenged by the "inhibition hypothesis" of Ramirez et al. (1990) which strongly suggests that the spatial distribution of cumuli must tend toward a regular distribution. A dilemma has resulted because observations have been reported to support both hypotheses. The present work reports a detailed analysis of cumulus cloud field spatial distributions based upon Landsat, Advanced Very High Resolution Radiometer, and Skylab data. Both nearest-neighbor and point-to-cloud cumulative distribution function statistics are investigated. The results show unequivocally that when both large and small clouds are included in the cloud field distribution, the cloud field always has a strong clustering signal. The strength of clustering is largest at cloud diameters of about 200-300 m, diminishing with increasing cloud diameter. In many cases, clusters of small clouds are found which are not closely associated with large clouds. As the small clouds are eliminated from consideration, the cloud field typically tends towards regularity. Thus it would appear that the "inhibition hypothesis" of Ramirez and Bras (1990) has been verified for the large clouds. However, these results are based upon the analysis of point processes. A more exact analysis also is made which takes into account the cloud size distributions. Since distinct clouds are by definition nonoverlapping, cloud size effects place a restriction upon the possible locations of clouds in the cloud field. The net effect of this analysis is that the large clouds appear to be randomly distributed, with only weak tendencies towards
New Sliding Puzzle with Neighbors Swap Motion
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...
The advantages of the surface Laplacian in brain-computer interface research.
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 role of orthography in the semantic activation of neighbors.
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.
Chimera states in bursting neurons
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...
Spin-Ice Thin Films: Large-N Theory and Monte Carlo Simulations
Lantagne-Hurtubise, Étienne; Rau, Jeffrey G.; Gingras, Michel J. P.
2018-04-01
We explore the physics of highly frustrated magnets in confined geometries, focusing on the Coulomb phase of pyrochlore spin ices. As a specific example, we investigate thin films of nearest-neighbor spin ice, using a combination of analytic large-N techniques and Monte Carlo simulations. In the simplest film geometry, with surfaces perpendicular to the [001] crystallographic direction, we observe pinch points in the spin-spin correlations characteristic of a two-dimensional Coulomb phase. We then consider the consequences of crystal symmetry breaking on the surfaces of the film through the inclusion of orphan bonds. We find that when these bonds are ferromagnetic, the Coulomb phase is destroyed by the presence of fluctuating surface magnetic charges, leading to a classical Z2 spin liquid. Building on this understanding, we discuss other film geometries with surfaces perpendicular to the [110] or the [111] direction. We generically predict the appearance of surface magnetic charges and discuss their implications for the physics of such films, including the possibility of an unusual Z3 classical spin liquid. Finally, we comment on open questions and promising avenues for future research.
Punchi-Manage, Ruwan; Wiegand, Thorsten; Wiegand, Kerstin; Getzin, Stephan; Huth, Andreas; Gunatilleke, C V Savitri; Gunatilleke, I A U Nimal
2015-07-01
Interactions among neighboring individuals influence plant performance and should create spatial patterns in local community structure. In order to assess the role of large trees in generating spatial patterns in local species richness, we used the individual species-area relationship (ISAR) to evaluate the species richness of trees of different size classes (and dead trees) in circular neighborhoods with varying radius around large trees of different focal species. To reveal signals of species interactions, we compared the ISAR function of the individuals of focal species with that of randomly selected nearby locations. We expected that large trees should strongly affect the community structure of smaller trees in their neighborhood, but that these effects should fade away with increasing size class. Unexpectedly, we found that only few focal species showed signals of species interactions with trees of the different size classes and that this was less likely for less abundant focal species. However, the few and relatively weak departures from independence were consistent with expectations of the effect of competition for space and the dispersal syndrome on spatial patterns. A noisy signal of competition for space found for large trees built up gradually with increasing life stage; it was not yet present for large saplings but detectable for intermediates. Additionally, focal species with animal-dispersed seeds showed higher species richness in their neighborhood than those with gravity- and gyration-dispersed seeds. Our analysis across the entire ontogeny from recruits to large trees supports the hypothesis that stochastic effects dilute deterministic species interactions in highly diverse communities. Stochastic dilution is a consequence of the stochastic geometry of biodiversity in species-rich communities where the identities of the nearest neighbors of a given plant are largely unpredictable. While the outcome of local species interactions is governed for each
Effect of external noise on the current response in superlattices
International Nuclear Information System (INIS)
Suqing Duan; Wu Binyi; Wang Zhigang; Zhao Xiangeng
2004-01-01
Within the single-band model and the nearest-neighbor approximation we investigate the effect of external noise on the current response driven by dc-ac field which has a fluctuating component. We find the external noise can destroy the current resonant peaks. When the strength of the external noise becomes enough large, all the current resonant peaks will completely disappear
Duality transformations for general abelian systems
International Nuclear Information System (INIS)
Savit, R.
1982-01-01
We describe the general structure of duality transformations for a very broad set of abelian statistical and field theoretic systems. This includes theories with many different types of fields and a large variety of kinds of interactions including, but not limited to nearest neighbor, next nearest neighbor, multi-spin interactions, etc. We find that the dual form of a theory does not depend directly on the dimensionality of the theory, but rather on the number of fields and number of different kinds of interactions. The dual forms we find have a generalized gauge symmetry and posses the usual property of having a temperature (or coupling constant) which is inverted from that of the original theory. Our results reduce to the well-known results in those particular cases that have heretofore been studied. Our procedure also suggests variations capable of generating other forms of the dual theory which may be useful in various specific cases. (orig.)
Infrared polarimetry of the nucleus of Centaurus A: the nearest blazar
Energy Technology Data Exchange (ETDEWEB)
Bailey, J; Sparks, W B; Hough, J H; Axon, D J
1986-07-10
As one of the nearest examples of an active galaxy, NGC5128 (Centaurus A) has been studied in detail over a wide range of wavelengths. The authors have made polarization observations of the infrared nucleus at wavelengths from 1.2 to 3.8 ..mu..m. The nucleus is found to have a large intrinsic polarization of approx.=9% at position angle 147/sup 0/. This position angle is perpendicular to the direction of the X-ray and radio jet. The polarized emission from the nucleus is interpreted as synchrotron radiation from a region whose magnetic field is parallel to the jet direction. The properties of the Cen A nucleus are essentially identical to those of the much more luminous blazars. This suggest that blazar-type activity extends over a very wide range in luminosity, and low-luminosity blazars may be common in elliptical galaxies.
Phase transition of the FCC Ising ferromagnet with competing interactions
International Nuclear Information System (INIS)
Oh, J.H.; Lee, J.Y.; Kim, D.C.
1984-01-01
A molecular field theory with correlation and Monte Carlo simulations are utilized to determine the zero field phase diagram of a fcc Ising model with ferromagnetic nearest neighbor(-J) and antiferromagnetic next neighbor (*aJ) interactions. The correlated molecular field theory predicts a fluctuation induced first order phase transition for 0.87<*a<1.31. Monte Carlo analysis indicates that the first order transition occurs for a somewhat wider range of *a. The transition temperatures obtained by the two methods are in good agreement especially near *a=1 where the fluctuation effect is expected to be large. (Author)
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.
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.
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
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.
SUPERFLUID VORTEX UNPINNING AS A COHERENT NOISE PROCESS, AND THE SCALE INVARIANCE OF PULSAR GLITCHES
International Nuclear Information System (INIS)
Melatos, A.; Warszawski, L.
2009-01-01
The scale-invariant glitch statistics observed in individual pulsars (exponential waiting-time and power-law size distributions) are consistent with a critical self-organization process, wherein superfluid vortices pin metastably in macroscopic domains and unpin collectively via nearest-neighbor avalanches. Macroscopic inhomogeneity emerges naturally if pinning occurs at crustal faults. If, instead, pinning occurs at lattice sites and defects, which are macroscopically homogeneous, we show that an alternative, noncritical self-organization process operates, termed coherent noise, wherein the global Magnus force acts uniformly on vortices trapped in a range of pinning potentials and undergoing thermal creep. It is found that vortices again unpin collectively, but not via nearest-neighbor avalanches, and that, counterintuitively, the resulting glitch sizes are scale invariant, in accord with observational data. A mean-field analytic theory of the coherent noise process, supported by Monte Carlo simulations, yields a power-law size distribution, between the smallest and largest glitch, with exponent a in the range -2 ≤ a ≤ 0. When the theory is fitted to data from the nine most active pulsars, including the two quasi-periodic glitchers PSR J0537-6910 and PSR J0835-4510, it directly constrains the distribution of pinning potentials in the star, leading to two conclusions: (1) the potentials are broadly distributed, with the mean comparable to the standard deviation; and (2) the mean potential decreases with characteristic age. Fitting the theory to the data also constrains the pinned vortex fraction and the rate of thermal creep. An observational test is proposed to discriminate between nearest-neighbor avalanches and coherent noise: the latter process predicts a statistical excess of large glitches ('aftershocks') following a large glitch, whereas the former process does not. Its discriminatory power is discussed under various microphysical scenarios.
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.
Badra, Jihad; Elwardany, Ahmed E; Farooq, Aamir
2014-06-28
Reaction rate constants of the reaction of four large ketones with hydroxyl (OH) are investigated behind reflected shock waves using OH laser absorption. The studied ketones are isomers of hexanone and include 2-hexanone, 3-hexanone, 3-methyl-2-pentanone, and 4-methl-2-pentanone. Rate constants are measured under pseudo-first-order kinetics at temperatures ranging from 866 K to 1375 K and pressures near 1.5 atm. The reported high-temperature rate constant measurements are the first direct measurements for these ketones under combustion-relevant conditions. The effects of the position of the carbonyl group (C=O) and methyl (CH3) branching on the overall rate constant with OH are examined. Using previously published data, rate constant expressions covering, low-to-high temperatures, are developed for acetone, 2-butanone, 3-pentanone, and the hexanone isomers studied here. These Arrhenius expressions are used to devise rate rules for H-abstraction from various sites. Specifically, the current scheme is applied with good success to H-abstraction by OH from a series of n-ketones. Finally, general expressions for primary and secondary site-specific H-abstraction by OH from ketones are proposed as follows (the subscript numbers indicate the number of carbon atoms bonded to the next-nearest-neighbor carbon atom, the subscript CO indicates that the abstraction is from a site next to the carbonyl group (C=O), and the prime is used to differentiate different neighboring environments of a methylene group):
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.
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
Unexpected magnetism, and transport properties in mixed lanthanide compound
Pathak, Arjun; Gschneidner, Karl, Jr.; Pecharsky, Vitalij; Ames Laboratory Team
For intelligent materials design it is desirable to have compounds which have multiple functionalities such as a large magnetoresistance, ferromagnetic and ferrimagnetic states, and field-induced first-order metamagnetic transitions. Here, we discuss one such example where we have combined two lanthanide elements Pr and Er in Pr0.6Er0.4Al2. This compound exhibits multiple functionalities in magnetic fields between 1 and 40 kOe. It undergoes only a trivial ferrimagnetism to paramagnetism transition in a zero magnetic field, but Pr0.6Er0.4Al2 exhibits a large positive magnetoresistance (MR) for H >=40 kOe, a small but non negligible negative MR for H field cooling from the paramagnetic state. These phenomena are attributed to the competition between single-ion anisotropies of Pr and Er ions coupled with the opposite nearest-neighbor and next-nearest-neighbor exchange interactions. This work was supported by the US Department of Energy, Office of Basic Energy Science, Division of Material Sciences and Engineering. The research was performed at the Ames Laboratory. The Ames Laboratory is operated by Iowa State University for the US D.
Hybrid Genetic Algorithm Optimization for Case Based Reasoning Systems
International Nuclear Information System (INIS)
Mohamed, A.H.
2008-01-01
The success of a CBR system largely depen ds on an effective retrieval of useful prior case for the problem. Nearest neighbor and induction are the main CBR retrieval algorithms. Each of them can be more suitable in different situations. Integrated the two retrieval algorithms can catch the advantages of both of them. But, they still have some limitations facing the induction retrieval algorithm when dealing with a noisy data, a large number of irrelevant features, and different types of data. This research utilizes a hybrid approach using genetic algorithms (GAs) to case-based induction retrieval of the integrated nearest neighbor - induction algorithm in an attempt to overcome these limitations and increase the overall classification accuracy. GAs can be used to optimize the search space of all the possible subsets of the features set. It can deal with the irrelevant and noisy features while still achieving a significant improvement of the retrieval accuracy. Therefore, the proposed CBR-GA introduces an effective general purpose retrieval algorithm that can improve the performance of CBR systems. It can be applied in many application areas. CBR-GA has proven its success when applied for different problems in real-life
Karaoulanis, D; Bacalis, N C
2000-01-01
We have performed Monte Carlo simulations of magnetic semiconductors above and below the nearest-neighbour percolation threshold (NNPT) using a classical Heisenberg Hamiltonian with up to third nearest-neighbour (nn) interactions. Large clusters were created allowing use of realistically low magnetic fields (10 G). Above NNPT our results, apart from confirming the existing picture of this class of materials, also show that the inclusion of the second and third (nn) interactions increases the frustration, thus making the transition temperature smaller and closer to experiment than calculated via the first nn interactions only. A physically plausible explanation is given. Below NNPT our results strongly support the validity of the hypothesis (D. Karaoulanis, J.P. Xanthakis, C. Papatriantafillou, J. Magn. Magn. Mater. 161 (1996) 231), that the experimentally observed susceptibility is the sum of two contributions: a paramagnetic one due to isolated magnetic clusters, and a spin-glass contribution due to an 'infi...
Neighboring Genes Show Correlated Evolution in Gene Expression
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
Suhaibah, A.; Uznir, U.; Anton, F.; Mioc, D.; Rahman, A. A.
2016-06-01
Locating and analysing the location of new stores or outlets is one of the common issues facing retailers and franchisers. This is due to assure that new opening stores are at their strategic location to attract the highest possible number of customers. Spatial information is used to manage, maintain and analyse these store locations. However, since the business of franchising and chain stores in urban areas runs within high rise multi-level buildings, a three-dimensional (3D) method is prominently required in order to locate and identify the surrounding information such as at which level of the franchise unit will be located or is the franchise unit located is at the best level for visibility purposes. One of the common used analyses used for retrieving the surrounding information is Nearest Neighbour (NN) analysis. It uses a point location and identifies the surrounding neighbours. However, with the immense number of urban datasets, the retrieval and analysis of nearest neighbour information and their efficiency will become more complex and crucial. In this paper, we present a technique to retrieve nearest neighbour information in 3D space using a clustered hierarchical tree structure. Based on our findings, the proposed approach substantially showed an improvement of response time analysis compared to existing approaches of spatial access methods in databases. The query performance was tested using a dataset consisting of 500,000 point locations building and franchising unit. The results are presented in this paper. Another advantage of this structure is that it also offers a minimal overlap and coverage among nodes which can reduce repetitive data entry.
Reasons patients leave their nearest healthcare service to attend Karen Park Clinic, Pretoria North
Directory of Open Access Journals (Sweden)
Agnes T. Masango- Makgobela
2013-10-01
Conclusion: The majority of patients who had attended their nearest clinic were adamant that they would not return. It is necessary to reduce waiting times, thus reducing long queues. This can be achieved by having adequate, satisfied healthcare providers to render a quality service and by organising training for management. Patients can thus be redirected to their nearest clinic and the health centre’s capacity can be increased by procuring adequate drugs. There is a need to follow up on patients’ complaints about staff attitudes.
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.
Local randomization in neighbor selection improves PRM roadmap quality
McMahon, Troy; Jacobs, Sam; Boyd, Bryan; Tapia, Lydia; Amato, Nancy M.
2012-01-01
Probabilistic Roadmap Methods (PRMs) are one of the most used classes of motion planning methods. These sampling-based methods generate robot configurations (nodes) and then connect them to form a graph (roadmap) containing representative feasible pathways. A key step in PRM roadmap construction involves identifying a set of candidate neighbors for each node. Traditionally, these candidates are chosen to be the k-closest nodes based on a given distance metric. In this paper, we propose a new neighbor selection policy called LocalRand(k,K'), that first computes the K' closest nodes to a specified node and then selects k of those nodes at random. Intuitively, LocalRand attempts to benefit from random sampling while maintaining the higher levels of local planner success inherent to selecting more local neighbors. We provide a methodology for selecting the parameters k and K'. We perform an experimental comparison which shows that for both rigid and articulated robots, LocalRand results in roadmaps that are better connected than the traditional k-closest policy or a purely random neighbor selection policy. The cost required to achieve these results is shown to be comparable to k-closest. © 2012 IEEE.
Local randomization in neighbor selection improves PRM roadmap quality
McMahon, Troy
2012-10-01
Probabilistic Roadmap Methods (PRMs) are one of the most used classes of motion planning methods. These sampling-based methods generate robot configurations (nodes) and then connect them to form a graph (roadmap) containing representative feasible pathways. A key step in PRM roadmap construction involves identifying a set of candidate neighbors for each node. Traditionally, these candidates are chosen to be the k-closest nodes based on a given distance metric. In this paper, we propose a new neighbor selection policy called LocalRand(k,K\\'), that first computes the K\\' closest nodes to a specified node and then selects k of those nodes at random. Intuitively, LocalRand attempts to benefit from random sampling while maintaining the higher levels of local planner success inherent to selecting more local neighbors. We provide a methodology for selecting the parameters k and K\\'. We perform an experimental comparison which shows that for both rigid and articulated robots, LocalRand results in roadmaps that are better connected than the traditional k-closest policy or a purely random neighbor selection policy. The cost required to achieve these results is shown to be comparable to k-closest. © 2012 IEEE.
Dynamics of Nearest-Neighbour Competitions on Graphs
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.
Handling Neighbor Discovery and Rendezvous Consistency with Weighted Quorum-Based Approach.
Own, Chung-Ming; Meng, Zhaopeng; Liu, Kehan
2015-09-03
Neighbor discovery and the power of sensors play an important role in the formation of Wireless Sensor Networks (WSNs) and mobile networks. Many asynchronous protocols based on wake-up time scheduling have been proposed to enable neighbor discovery among neighboring nodes for the energy saving, especially in the difficulty of clock synchronization. However, existing researches are divided two parts with the neighbor-discovery methods, one is the quorum-based protocols and the other is co-primality based protocols. Their distinction is on the arrangements of time slots, the former uses the quorums in the matrix, the latter adopts the numerical analysis. In our study, we propose the weighted heuristic quorum system (WQS), which is based on the quorum algorithm to eliminate redundant paths of active slots. We demonstrate the specification of our system: fewer active slots are required, the referring rate is balanced, and remaining power is considered particularly when a device maintains rendezvous with discovered neighbors. The evaluation results showed that our proposed method can effectively reschedule the active slots and save the computing time of the network system.
Handling Neighbor Discovery and Rendezvous Consistency with Weighted Quorum-Based Approach
Directory of Open Access Journals (Sweden)
Chung-Ming Own
2015-09-01
Full Text Available Neighbor discovery and the power of sensors play an important role in the formation of Wireless Sensor Networks (WSNs and mobile networks. Many asynchronous protocols based on wake-up time scheduling have been proposed to enable neighbor discovery among neighboring nodes for the energy saving, especially in the difficulty of clock synchronization. However, existing researches are divided two parts with the neighbor-discovery methods, one is the quorum-based protocols and the other is co-primality based protocols. Their distinction is on the arrangements of time slots, the former uses the quorums in the matrix, the latter adopts the numerical analysis. In our study, we propose the weighted heuristic quorum system (WQS, which is based on the quorum algorithm to eliminate redundant paths of active slots. We demonstrate the specification of our system: fewer active slots are required, the referring rate is balanced, and remaining power is considered particularly when a device maintains rendezvous with discovered neighbors. The evaluation results showed that our proposed method can effectively reschedule the active slots and save the computing time of the network system.
Accelerating distributed average consensus by exploring the information of second-order neighbors
Energy Technology Data Exchange (ETDEWEB)
Yuan Deming [School of Automation, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu (China); Xu Shengyuan, E-mail: syxu02@yahoo.com.c [School of Automation, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu (China); Zhao Huanyu [School of Automation, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu (China); Chu Yuming [Department of Mathematics, Huzhou Teacher' s College, Huzhou 313000, Zhejiang (China)
2010-05-17
The problem of accelerating distributed average consensus by using the information of second-order neighbors in both the discrete- and continuous-time cases is addressed in this Letter. In both two cases, when the information of second-order neighbors is used in each iteration, the network will converge with a speed faster than the algorithm only using the information of first-order neighbors. Moreover, the problem of using partial information of second-order neighbors is considered, and the edges are not chosen randomly from second-order neighbors. In the continuous-time case, the edges are chosen by solving a convex optimization problem which is formed by using the convex relaxation method. In the discrete-time case, for small network the edges are chosen optimally via the brute force method. Finally, simulation examples are provided to demonstrate the effectiveness of the proposed algorithm.
Liu, Dapeng
2017-01-10
Reaction rate coefficients for the reaction of hydroxyl (OH) radicals with nine large branched alkanes (i.e., 2-methyl-3-ethyl-pentane, 2,3-dimethyl-pentane, 2,2,3-trimethylbutane, 2,2,3-trimethyl-pentane, 2,3,4-trimethyl-pentane, 3-ethyl-pentane, 2,2,3,4-tetramethyl-pentane, 2,2-dimethyl-3-ethyl-pentane, and 2,4-dimethyl-3-ethyl-pentane) are measured at high temperatures (900-1300 K) using a shock tube and narrow-line-width OH absorption diagnostic in the UV region. In addition, room-temperature measurements of six out of these nine rate coefficients are performed in a photolysis cell using high repetition laser-induced fluorescence of OH radicals. Our experimental results are combined with previous literature measurements to obtain three-parameter Arrhenius expressions valid over a wide temperature range (300-1300 K). The rate coefficients are analyzed using the next-nearest-neighbor (N-N-N) methodology to derive nine tertiary (T003, T012, T013, T022, T023, T111, T112, T113, and T122) site-specific rate coefficients for the abstraction of H atoms by OH radicals from branched alkanes. Derived Arrhenius expressions, valid over 950-1300 K, are given as (the subscripts denote the number of carbon atoms connected to the next-nearest-neighbor carbon): T003 = 1.80 × 10-10 exp(-2971 K/T) cm3 molecule-1 s-1; T012 = 9.36 × 10-11 exp(-3024 K/T) cm3 molecule-1 s-1; T013 = 4.40 × 10-10 exp(-4162 K/T) cm3 molecule-1 s-1; T022 = 1.47 × 10-10 exp(-3587 K/T) cm3 molecule-1 s-1; T023 = 6.06 × 10-11 exp(-3010 K/T) cm3 molecule-1 s-1; T111 = 3.98 × 10-11 exp(-1617 K/T) cm3 molecule-1 s-1; T112 = 9.08 × 10-12 exp(-3661 K/T) cm3 molecule-1 s-1; T113 = 6.74 × 10-9 exp(-7547 K/T) cm3 molecule-1 s-1; T122 = 3.47 × 10-11 exp(-1802 K/T) cm3 molecule-1 s-1.
Theoretical study of the electronic and magnetic properties of β-TeVO4
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.
The electronic structures and ferromagnetism of Fe-doped GaSb: The first-principle calculation study
Lin, Xue-ling; Niu, Cao-ping; Pan, Feng-chun; Chen, Huan-ming; Wang, Xu-ming
2017-09-01
The electronic structures and the magnetic properties of Fe doped GaSb have been investigated by the first-principles calculation based on the framework of the generalized gradient approximation (GGA) and GGA+U schemes. The calculated results indicated that Fe atoms tend to form the anti-ferromagnetic (AFM) coupling with the nearest-neighbor positions preferentially. Compared with the anti-ferromagnetic coupling, the ferromagnetic interactions occurred at the second nearest-neighbor and third nearest-neighbor sites have a bigger superiority energetically. The effect of strong electron correlation at Fe-d orbit taking on the magnetic properties predicted by GGA+U approach demonstrated that the ferromagnetic (FM) coupling between the Fe ions is even stronger in consideration of the strong electron correlation effect. The ferromagnetism in Fe doped GaSb system predicted by our investigation implied that the doping of Fe into GaSb can be as a vital routine for manufacturing the FM semiconductors with higher Curie temperature.
Beyond formal groups: neighboring acts and watershed protection in Appalachia
Directory of Open Access Journals (Sweden)
Heather Lukacs
2016-09-01
Full Text Available This paper explores how watershed organizations in Appalachia have persisted in addressing water quality issues in areas with a history of coal mining. We identified two watershed groups that have taken responsibility for restoring local creeks that were previously highly degraded and sporadically managed. These watershed groups represent cases of self-organized commons governance in resource-rich, economically poor Appalachian communities. We describe the extent and characteristics of links between watershed group volunteers and watershed residents who are not group members. Through surveys, participant observation, and key-informant consultation, we found that neighbors – group members as well as non-group-members – supported the group's function through informal neighboring acts. Past research has shown that local commons governance institutions benefit from being nested in supportive external structures. We found that the persistence and success of community watershed organizations depends on the informal participation of local residents, affirming the necessity of looking beyond formal, organized groups to understand the resources, expertise, and information needed to address complex water pollution at the watershed level. Our findings augment the concept of nestedness in commons governance to include that of a formal organization acting as a neighbor that exchanges informal neighboring acts with local residents. In this way, we extend the concept of neighboring to include interactions between individuals and a group operating in the same geographic area.
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.
Low-spin identical bands in neighboring odd-A and even-even nuclei
International Nuclear Information System (INIS)
Baktash, C.; Winchell, D.F.; Garrett, J.D.; Smith, A.
1992-01-01
A comprehensive study of odd-A rotational bands in normally deformed rare-earth nuclei indicates that a large number of seniority-one configurations (21% for odd-Z nuclei) at low spin have moments of inertia nearly identical to that of the seniority-zero configuration of the neighboring even-even nucleus with one less nucleon. It is difficult to reconcile these results with conventional models of nuclear pair correlation, which predict variations of about 15% in the moments of inertia of configurations differing by one unit in seniority
Large scale simulations of lattice QCD thermodynamics on Columbia Parallel Supercomputers
International Nuclear Information System (INIS)
Ohta, Shigemi
1989-01-01
The Columbia Parallel Supercomputer project aims at the construction of a parallel processing, multi-gigaflop computer optimized for numerical simulations of lattice QCD. The project has three stages; 16-node, 1/4GF machine completed in April 1985, 64-node, 1GF machine completed in August 1987, and 256-node, 16GF machine now under construction. The machines all share a common architecture; a two dimensional torus formed from a rectangular array of N 1 x N 2 independent and identical processors. A processor is capable of operating in a multi-instruction multi-data mode, except for periods of synchronous interprocessor communication with its four nearest neighbors. Here the thermodynamics simulations on the two working machines are reported. (orig./HSI)
Plant neighbor identity influences plant biochemistry and physiology related to defense.
Broz, Amanda K; Broeckling, Corey D; De-la-Peña, Clelia; Lewis, Matthew R; Greene, Erick; Callaway, Ragan M; Sumner, Lloyd W; Vivanco, Jorge M
2010-06-17
Chemical and biological processes dictate an individual organism's ability to recognize and respond to other organisms. A small but growing body of evidence suggests that plants may be capable of recognizing and responding to neighboring plants in a species specific fashion. Here we tested whether or not individuals of the invasive exotic weed, Centaurea maculosa, would modulate their defensive strategy in response to different plant neighbors. In the greenhouse, C. maculosa individuals were paired with either conspecific (C. maculosa) or heterospecific (Festuca idahoensis) plant neighbors and elicited with the plant defense signaling molecule methyl jasmonate to mimic insect herbivory. We found that elicited C. maculosa plants grown with conspecific neighbors exhibited increased levels of total phenolics, whereas those grown with heterospecific neighbors allocated more resources towards growth. To further investigate these results in the field, we conducted a metabolomics analysis to explore chemical differences between individuals of C. maculosa growing in naturally occurring conspecific and heterospecific field stands. Similar to the greenhouse results, C. maculosa individuals accumulated higher levels of defense-related secondary metabolites and lower levels of primary metabolites when growing in conspecific versus heterospecific field stands. Leaf herbivory was similar in both stand types; however, a separate field study positively correlated specialist herbivore load with higher densities of C. maculosa conspecifics. Our results suggest that an individual C. maculosa plant can change its defensive strategy based on the identity of its plant neighbors. This is likely to have important consequences for individual and community success.
Does a pear growl? Interference from semantic properties of orthographic neighbors.
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).
LOCAL TEXTURE DESCRIPTION FRAMEWORK FOR TEXTURE BASED FACE RECOGNITION
Directory of Open Access Journals (Sweden)
R. Reena Rose
2014-02-01
Full Text Available Texture descriptors have an important role in recognizing face images. However, almost all the existing local texture descriptors use nearest neighbors to encode a texture pattern around a pixel. But in face images, most of the pixels have similar characteristics with that of its nearest neighbors because the skin covers large area in a face and the skin tone at neighboring regions are same. Therefore this paper presents a general framework called Local Texture Description Framework that uses only eight pixels which are at certain distance apart either circular or elliptical from the referenced pixel. Local texture description can be done using the foundation of any existing local texture descriptors. In this paper, the performance of the proposed framework is verified with three existing local texture descriptors Local Binary Pattern (LBP, Local Texture Pattern (LTP and Local Tetra Patterns (LTrPs for the five issues viz. facial expression, partial occlusion, illumination variation, pose variation and general recognition. Five benchmark databases JAFFE, Essex, Indian faces, AT&T and Georgia Tech are used for the experiments. Experimental results demonstrate that even with less number of patterns, the proposed framework could achieve higher recognition accuracy than that of their base models.
A distance weighted-based approach for self-organized aggregation in robot swarms
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.
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)
Detect thy neighbor: Identity recognition at the root level in plants
Chen, B.J.W.; During, H.J.; Anten, N.P.R.
2012-01-01
Some plant species increase root allocation at the expense of reproduction in the presence of non-self and non-kin neighbors, indicating the capacity of neighbor-identityrecognition at the rootlevel. Yet in spite of the potential consequences of rootidentityrecognition for the relationship between
Low-spin identical bands in neighboring odd-A and even-even nuclei
International Nuclear Information System (INIS)
Baktash, C.; Winchell, D.F.; Garrett, J.D.; Smith, A.
1993-01-01
A comprehensive study of odd-A rotational bands in normally deformed rare-earth nuclei indicates that a large number of seniority-one configurations (21 % for odd-Z nuclei) at low spin have moments of inertia nearly identical to that of the seniority-zero configuration of the neighboring even-even nucleus with one less nucleon. It is difficult to reconcile these results with conventional models of nuclear pair correlation, which predict variations of about 15% in the moments of inertia of configurations differing by one unit in seniority. (orig.)
Neighboring trees affect ectomycorrhizal fungal community composition in a woodland-forest ecotone.
Hubert, Nathaniel A; Gehring, Catherine A
2008-09-01
Ectomycorrhizal fungi (EMF) are frequently species rich and functionally diverse; yet, our knowledge of the environmental factors that influence local EMF diversity and species composition remains poor. In particular, little is known about the influence of neighboring plants on EMF community structure. We tested the hypothesis that the EMF of plants with heterospecific neighbors would differ in species richness and community composition from the EMF of plants with conspecific neighbors. We conducted our study at the ecotone between pinyon (Pinus edulis)-juniper (Juniperus monosperma) woodland and ponderosa pine (Pinus ponderosa) forest in northern Arizona, USA where the dominant trees formed associations with either EMF (P. edulis and P. ponderosa) or arbuscular mycorrhizal fungi (AMF; J. monosperma). We also compared the EMF communities of pinyon and ponderosa pines where their rhizospheres overlapped. The EMF community composition, but not species richness of pinyon pines was significantly influenced by neighboring AM juniper, but not by neighboring EM ponderosa pine. Ponderosa pine EMF communities were different in species composition when growing in association with pinyon pine than when growing in association with a conspecific. The EMF communities of pinyon and ponderosa pines were similar where their rhizospheres overlapped consisting of primarily the same species in similar relative abundance. Our findings suggest that neighboring tree species identity shaped EMF community structure, but that these effects were specific to host-neighbor combinations. The overlap in community composition between pinyon pine and ponderosa pine suggests that these tree species may serve as reservoirs of EMF inoculum for one another.
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
Plant neighbor identity influences plant biochemistry and physiology related to defense
Directory of Open Access Journals (Sweden)
Callaway Ragan M
2010-06-01
Full Text Available Abstract Background Chemical and biological processes dictate an individual organism's ability to recognize and respond to other organisms. A small but growing body of evidence suggests that plants may be capable of recognizing and responding to neighboring plants in a species specific fashion. Here we tested whether or not individuals of the invasive exotic weed, Centaurea maculosa, would modulate their defensive strategy in response to different plant neighbors. Results In the greenhouse, C. maculosa individuals were paired with either conspecific (C. maculosa or heterospecific (Festuca idahoensis plant neighbors and elicited with the plant defense signaling molecule methyl jasmonate to mimic insect herbivory. We found that elicited C. maculosa plants grown with conspecific neighbors exhibited increased levels of total phenolics, whereas those grown with heterospecific neighbors allocated more resources towards growth. To further investigate these results in the field, we conducted a metabolomics analysis to explore chemical differences between individuals of C. maculosa growing in naturally occurring conspecific and heterospecific field stands. Similar to the greenhouse results, C. maculosa individuals accumulated higher levels of defense-related secondary metabolites and lower levels of primary metabolites when growing in conspecific versus heterospecific field stands. Leaf herbivory was similar in both stand types; however, a separate field study positively correlated specialist herbivore load with higher densities of C. maculosa conspecifics. Conclusions Our results suggest that an individual C. maculosa plant can change its defensive strategy based on the identity of its plant neighbors. This is likely to have important consequences for individual and community success.
3D NEAREST NEIGHBOUR SEARCH USING A CLUSTERED HIERARCHICAL TREE STRUCTURE
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A. Suhaibah
2016-06-01
Full Text Available Locating and analysing the location of new stores or outlets is one of the common issues facing retailers and franchisers. This is due to assure that new opening stores are at their strategic location to attract the highest possible number of customers. Spatial information is used to manage, maintain and analyse these store locations. However, since the business of franchising and chain stores in urban areas runs within high rise multi-level buildings, a three-dimensional (3D method is prominently required in order to locate and identify the surrounding information such as at which level of the franchise unit will be located or is the franchise unit located is at the best level for visibility purposes. One of the common used analyses used for retrieving the surrounding information is Nearest Neighbour (NN analysis. It uses a point location and identifies the surrounding neighbours. However, with the immense number of urban datasets, the retrieval and analysis of nearest neighbour information and their efficiency will become more complex and crucial. In this paper, we present a technique to retrieve nearest neighbour information in 3D space using a clustered hierarchical tree structure. Based on our findings, the proposed approach substantially showed an improvement of response time analysis compared to existing approaches of spatial access methods in databases. The query performance was tested using a dataset consisting of 500,000 point locations building and franchising unit. The results are presented in this paper. Another advantage of this structure is that it also offers a minimal overlap and coverage among nodes which can reduce repetitive data entry.
Probability Machines: Consistent Probability Estimation Using Nonparametric Learning Machines
Malley, J. D.; Kruppa, J.; Dasgupta, A.; Malley, K. G.; Ziegler, A.
2011-01-01
Summary Background Most machine learning approaches only provide a classification for binary responses. However, probabilities are required for risk estimation using individual patient characteristics. It has been shown recently that every statistical learning machine known to be consistent for a nonparametric regression problem is a probability machine that is provably consistent for this estimation problem. Objectives The aim of this paper is to show how random forests and nearest neighbors can be used for consistent estimation of individual probabilities. Methods Two random forest algorithms and two nearest neighbor algorithms are described in detail for estimation of individual probabilities. We discuss the consistency of random forests, nearest neighbors and other learning machines in detail. We conduct a simulation study to illustrate the validity of the methods. We exemplify the algorithms by analyzing two well-known data sets on the diagnosis of appendicitis and the diagnosis of diabetes in Pima Indians. Results Simulations demonstrate the validity of the method. With the real data application, we show the accuracy and practicality of this approach. We provide sample code from R packages in which the probability estimation is already available. This means that all calculations can be performed using existing software. Conclusions Random forest algorithms as well as nearest neighbor approaches are valid machine learning methods for estimating individual probabilities for binary responses. Freely available implementations are available in R and may be used for applications. PMID:21915433
Quantum Correlation Properties in Composite Parity-Conserved Matrix Product States
Zhu, Jing-Min
2016-09-01
We give a new thought for constructing long-range quantum correlation in quantum many-body systems. Our proposed composite parity-conserved matrix product state has long-range quantum correlation only for two spin blocks where their spin-block length larger than 1 compared to any subsystem only having short-range quantum correlation, and we investigate quantum correlation properties of two spin blocks varying with environment parameter and spacing spin number. We also find that the geometry quantum discords of two nearest-neighbor spin blocks and two next-nearest-neighbor spin blocks become smaller and for other conditions the geometry quantum discord becomes larger than that in any subcomponent, i.e., the increase or the production of the long-range quantum correlation is at the cost of reducing the short-range quantum correlation compared to the corresponding classical correlation and total correlation having no any characteristic of regulation. For nearest-neighbor and next-nearest-neighbor all the correlations take their maximal values at the same points, while for other conditions no whether for spacing same spin number or for different spacing spin numbers all the correlations taking their maximal values are respectively at different points which are very close. We believe that our work is helpful to comprehensively and deeply understand the organization and structure of quantum correlation especially for long-range quantum correlation of quantum many-body systems; and further helpful for the classification, the depiction and the measure of quantum correlation of quantum many-body systems.
Nearest-cell: a fast and easy tool for locating crystal matches in the PDB
International Nuclear Information System (INIS)
Ramraj, V.; Evans, G.; Diprose, J. M.; Esnouf, R. M.
2012-01-01
A fast and easy tool to locate unit-cell matches in the PDB is described. When embarking upon X-ray diffraction data collection from a potentially novel macromolecular crystal form, it can be useful to ascertain whether the measured data reflect a crystal form that is already recorded in the Protein Data Bank and, if so, whether it is part of a large family of related structures. Providing such information to crystallographers conveniently and quickly, as soon as the first images have been recorded and the unit cell characterized at an X-ray beamline, has the potential to save time and effort as well as pointing to possible search models for molecular replacement. Given an input unit cell, and optionally a space group, Nearest-cell rapidly scans the Protein Data Bank and retrieves near-matches
A multilevel-skin neighbor list algorithm for molecular dynamics simulation
Zhang, Chenglong; Zhao, Mingcan; Hou, Chaofeng; Ge, Wei
2018-01-01
Searching of the interaction pairs and organization of the interaction processes are important steps in molecular dynamics (MD) algorithms and are critical to the overall efficiency of the simulation. Neighbor lists are widely used for these steps, where thicker skin can reduce the frequency of list updating but is discounted by more computation in distance check for the particle pairs. In this paper, we propose a new neighbor-list-based algorithm with a precisely designed multilevel skin which can reduce unnecessary computation on inter-particle distances. The performance advantages over traditional methods are then analyzed against the main simulation parameters on Intel CPUs and MICs (many integrated cores), and are clearly demonstrated. The algorithm can be generalized for various discrete simulations using neighbor lists.
Effects of second neighbor interactions on skyrmion lattices in chiral magnets
International Nuclear Information System (INIS)
Oliveira, E A S; Silva, R L; Silva, R C; Pereira, A R
2017-01-01
In this paper we investigate the influences of the second neighbor interactions on a skyrmion lattice in two-dimensional chiral magnets. Such a system contains the exchange and the Dzyaloshinskii–Moriya for the spin interactions and therefore, we analyse three situations: firstly, the second neighbor interaction is present only in the exchange coupling; secondly, it is present only in the Dzyaloshinskii–Moriya coupling. Finally, the second neighbor interactions are present in both exchange and Dzyaloshinskii–Moriya couplings. We show that such effects cause important modifications to the helical and skyrmion phases when an external magnetic field is applied. (paper)
Improving Fraudster Detection in Online Auctions by Using Neighbor-Driven Attributes
Directory of Open Access Journals (Sweden)
Jun-Lin Lin
2015-12-01
Full Text Available Online auction websites use a simple reputation system to help their users to evaluate the trustworthiness of sellers and buyers. However, to improve their reputation in the reputation system, fraudulent users can easily deceive the reputation system by creating fake transactions. This inflated-reputation fraud poses a major problem for online auction websites because it can lead legitimate users into scams. Numerous approaches have been proposed in the literature to address this problem, most of which involve using social network analysis (SNA to derive critical features (e.g., k-core, center weight, and neighbor diversity for distinguishing fraudsters from legitimate users. This paper discusses the limitations of these SNA features and proposes a class of SNA features referred to as neighbor-driven attributes (NDAs. The NDAs of users are calculated from the features of their neighbors. Because fraudsters require collusive neighbors to provide them with positive ratings in the reputation system, using NDAs can be helpful for detecting fraudsters. Although the idea of NDAs is not entirely new, experimental results on a real-world dataset showed that using NDAs improves classification accuracy compared with state-of-the-art methods that use the k-core, center weight, and neighbor diversity.
Interacting steps with finite-range interactions: Analytical approximation and numerical results
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.
Molecular dynamics study of Pb-substituted Cu(1 0 0) surface layers
Energy Technology Data Exchange (ETDEWEB)
Evangelakis, G.A. [Department of Physics, University of Ioannina, PO Box 1186, Ioannina 45110 (Greece); Pontikis, V., E-mail: Vassilis.pontikis@cea.f [Laboratoire des Solides Irradies, CEA-DRECAM, 91191 Gif-sur-Yvette Cedex (France)
2009-08-26
Using molecular dynamics (MD) and phenomenological n-body potentials from the literature, we have studied the structure of the uppermost layers of low-index surfaces in copper after partial substitution of copper by lead atoms at randomly selected sites. We found that lead atoms substituting copper strongly perturb the positions of nearest and of next-nearest neighbors thus triggering the setup of a disordered, nanometer-thick amorphous-like surface layer. Equilibrium atomic density profiles, computed along the [1 0 0] crystallographic direction, show that amorphous overlayers are largely metastable whereas the system displays a structured compositional profile of lead segregating at the interfaces. Similarities between our results and experimental findings are briefly discussed.
Molecular dynamics study of Pb-substituted Cu(1 0 0) surface layers
International Nuclear Information System (INIS)
Evangelakis, G.A.; Pontikis, V.
2009-01-01
Using molecular dynamics (MD) and phenomenological n-body potentials from the literature, we have studied the structure of the uppermost layers of low-index surfaces in copper after partial substitution of copper by lead atoms at randomly selected sites. We found that lead atoms substituting copper strongly perturb the positions of nearest and of next-nearest neighbors thus triggering the setup of a disordered, nanometer-thick amorphous-like surface layer. Equilibrium atomic density profiles, computed along the [1 0 0] crystallographic direction, show that amorphous overlayers are largely metastable whereas the system displays a structured compositional profile of lead segregating at the interfaces. Similarities between our results and experimental findings are briefly discussed.
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
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.
Alvarez, Jose; Massey, Steven; Kalitsov, Alan; Velev, Julian
Nanopore sequencing via transverse current has emerged as a competitive candidate for mapping DNA methylation without needed bisulfite-treatment, fluorescent tag, or PCR amplification. By eliminating the error producing amplification step, long read lengths become feasible, which greatly simplifies the assembly process and reduces the time and the cost inherent in current technologies. However, due to the large error rates of nanopore sequencing, single base resolution has not been reached. A very important source of noise is the intrinsic structural noise in the electric signature of the nucleotide arising from the influence of neighboring nucleotides. In this work we perform calculations of the tunneling current through DNA molecules in nanopores using the non-equilibrium electron transport method within an effective multi-orbital tight-binding model derived from first-principles calculations. We develop a base-calling algorithm accounting for the correlations of the current through neighboring bases, which in principle can reduce the error rate below any desired precision. Using this method we show that we can clearly distinguish DNA methylation and other base modifications based on the reading of the tunneling current.
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...
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.
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
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)
DEFF Research Database (Denmark)
Gieseke, Fabian Cristian; Oancea, Cosmin Eugen; Igel, Christian
2017-01-01
The bufferkdtree package is an open-source software that provides an efficient implementation for processing huge amounts of nearest neighbor queries in Euclidean spaces of moderate dimensionality. Its underlying implementation resorts to a variant of the classical k-d tree data structure, called...... buffer k-d tree, which can be used to efficiently perform bulk nearest neighbor searches on modern many-core devices. The package, which is based on Python, C, and OpenCL, is made publicly available online at https://github.com/gieseke/bufferkdtree under the GPLv2 license....
Golden mean renormalization for a generalized Harper equation: The Ketoja-Satija orchid
International Nuclear Information System (INIS)
Mestel, B.D.; Osbaldestin, A.H.
2004-01-01
We provide a rigorous analysis of the fluctuations of localized eigenstates in a generalized Harper equation with golden mean flux and with next-nearest-neighbor interactions. For next-nearest-neighbor interaction above a critical threshold, these self-similar fluctuations are characterized by orbits of a renormalization operator on a universal strange attractor, whose projection was dubbed the ''orchid'' by Ketoja and Satija [Phys. Rev. Lett. 75, 2762 (1995)]. We show that the attractor is given essentially by an embedding of a subshift of finite type, and give a description of its periodic orbits
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
Feature selection and nearest centroid classification for protein mass spectrometry
Directory of Open Access Journals (Sweden)
Levner Ilya
2005-03-01
Full Text Available Abstract Background The use of mass spectrometry as a proteomics tool is poised to revolutionize early disease diagnosis and biomarker identification. Unfortunately, before standard supervised classification algorithms can be employed, the "curse of dimensionality" needs to be solved. Due to the sheer amount of information contained within the mass spectra, most standard machine learning techniques cannot be directly applied. Instead, feature selection techniques are used to first reduce the dimensionality of the input space and thus enable the subsequent use of classification algorithms. This paper examines feature selection techniques for proteomic mass spectrometry. Results This study examines the performance of the nearest centroid classifier coupled with the following feature selection algorithms. Student-t test, Kolmogorov-Smirnov test, and the P-test are univariate statistics used for filter-based feature ranking. From the wrapper approaches we tested sequential forward selection and a modified version of sequential backward selection. Embedded approaches included shrunken nearest centroid and a novel version of boosting based feature selection we developed. In addition, we tested several dimensionality reduction approaches, namely principal component analysis and principal component analysis coupled with linear discriminant analysis. To fairly assess each algorithm, evaluation was done using stratified cross validation with an internal leave-one-out cross-validation loop for automated feature selection. Comprehensive experiments, conducted on five popular cancer data sets, revealed that the less advocated sequential forward selection and boosted feature selection algorithms produce the most consistent results across all data sets. In contrast, the state-of-the-art performance reported on isolated data sets for several of the studied algorithms, does not hold across all data sets. Conclusion This study tested a number of popular feature
Use of Acoustic Emission and Pattern Recognition for Crack Detection of a Large Carbide Anvil.
Chen, Bin; Wang, Yanan; Yan, Zhaoli
2018-01-29
Large-volume cubic high-pressure apparatus is commonly used to produce synthetic diamond. Due to the high pressure, high temperature and alternative stresses in practical production, cracks often occur in the carbide anvil, thereby resulting in significant economic losses or even casualties. Conventional methods are unsuitable for crack detection of the carbide anvil. This paper is concerned with acoustic emission-based crack detection of carbide anvils, regarded as a pattern recognition problem; this is achieved using a microphone, with methods including sound pulse detection, feature extraction, feature optimization and classifier design. Through analyzing the characteristics of background noise, the cracked sound pulses are separated accurately from the originally continuous signal. Subsequently, three different kinds of features including a zero-crossing rate, sound pressure levels, and linear prediction cepstrum coefficients are presented for characterizing the cracked sound pulses. The original high-dimensional features are adaptively optimized using principal component analysis. A hybrid framework of a support vector machine with k nearest neighbors is designed to recognize the cracked sound pulses. Finally, experiments are conducted in a practical diamond workshop to validate the feasibility and efficiency of the proposed method.
Sánchez-Piñero, Francisco; Urbano-Tenorio, Fernando
The distribution and behavior of foraging animals usually imply a balance between resource availability and predation risk. In some predators such as scorpions, cannibalism constitutes an important mortality factor determining their ecology and behavior. Climbing on vegetation by scorpions has been related both to prey availability and to predation (cannibalism) risk. We tested different hypotheses proposed to explain climbing on vegetation by scorpions. We analyzed shrub climbing in Buthus cf. occitanus with regard to the following: a) better suitability of prey size for scorpions foraging on shrubs than on the ground, b) selection of shrub species with higher prey load, c) seasonal variations in prey availability on shrubs, and d) whether or not cannibalism risk on the ground increases the frequency of shrub climbing. Prey availability on shrubs was compared by estimating prey abundance in sticky traps placed in shrubs. A prey sample from shrubs was measured to compare prey size. Scorpions were sampled in six plots (50 m x 10 m) to estimate the proportion of individuals climbing on shrubs. Size difference and distance between individuals and their closest scorpion neighbor were measured to assess cannibalism risk. The results showed that mean prey size was two-fold larger on the ground. Selection of particular shrub species was not related to prey availability. Seasonal variations in the number of scorpions on shrubs were related to the number of active scorpions, but not with fluctuations in prey availability. Size differences between a scorpion and its nearest neighbor were positively related with a higher probability for a scorpion to climb onto a shrub when at a disadvantage, but distance was not significantly related. These results do not support hypotheses explaining shrub climbing based on resource availability. By contrast, our results provide evidence that shrub climbing is related to cannibalism risk.
Classification in medical image analysis using adaptive metric k-NN
DEFF Research Database (Denmark)
Chen, Chen; Chernoff, Konstantin; Karemore, Gopal
2010-01-01
The performance of the k-nearest neighborhoods (k-NN) classifier is highly dependent on the distance metric used to identify the k nearest neighbors of the query points. The standard Euclidean distance is commonly used in practice. This paper investigates the performance of k-NN classifier...
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...
Sondergaard, Kathrine B; Hansen, Steen Moller; Pallisgaard, Jannik L; Gerds, Thomas Alexander; Wissenberg, Mads; Karlsson, Lena; Lippert, Freddy K; Gislason, Gunnar H; Torp-Pedersen, Christian; Folke, Fredrik
2018-03-01
Despite wide dissemination of automated external defibrillators (AEDs), bystander defibrillation rates remain low. We aimed to investigate how route distance to the nearest accessible AED was associated with probability of bystander defibrillation in public and residential locations. We used data from the nationwide Danish Cardiac Arrest Registry and the Danish AED Network to identify out-of-hospital cardiac arrests and route distances to nearest accessible registered AED during 2008-2013. The association between route distance and bystander defibrillation was described using restricted cubic spline logistic regression. We included 6971 out-of-hospital cardiac arrest cases. The proportion of arrests according to distance in meters (≤100, 101-200, >200) to the nearest accessible AED was: 4.6% (n=320), 5.3% (n=370), and 90.1% (n=6281), respectively. For cardiac arrests in public locations, the probability of bystander defibrillation at 0, 100 and 200m from the nearest AED was 35.7% (95% confidence interval 28.0%-43.5%), 21.3% (95% confidence interval 17.4%-25.2%), and 13.7% (95% confidence interval 10.1%-16.8%), respectively. The corresponding numbers for cardiac arrests in residential locations were 7.0% (95% confidence interval -2.1%-16.1%), 1.5% (95% confidence interval 0.002%-2.8%), and 0.9% (95% confidence interval 0.0005%-1.7%), respectively. In public locations, the probability of bystander defibrillation decreased rapidly within the first 100m route distance from cardiac arrest to nearest accessible AED whereas the probability of bystander defibrillation was low for all distances in residential areas. Copyright © 2017 Elsevier B.V. All rights reserved.
International Nuclear Information System (INIS)
Thompson, A.P.; Swiler, L.P.; Trott, C.R.; Foiles, S.M.; Tucker, G.J.
2015-01-01
We present a new interatomic potential for solids and liquids called Spectral Neighbor Analysis Potential (SNAP). The SNAP potential has a very general form and uses machine-learning techniques to reproduce the energies, forces, and stress tensors of a large set of small configurations of atoms, which are obtained using high-accuracy quantum electronic structure (QM) calculations. The local environment of each atom is characterized by a set of bispectrum components of the local neighbor density projected onto a basis of hyperspherical harmonics in four dimensions. The bispectrum components are the same bond-orientational order parameters employed by the GAP potential [1]. The SNAP potential, unlike GAP, assumes a linear relationship between atom energy and bispectrum components. The linear SNAP coefficients are determined using weighted least-squares linear regression against the full QM training set. This allows the SNAP potential to be fit in a robust, automated manner to large QM data sets using many bispectrum components. The calculation of the bispectrum components and the SNAP potential are implemented in the LAMMPS parallel molecular dynamics code. We demonstrate that a previously unnoticed symmetry property can be exploited to reduce the computational cost of the force calculations by more than one order of magnitude. We present results for a SNAP potential for tantalum, showing that it accurately reproduces a range of commonly calculated properties of both the crystalline solid and the liquid phases. In addition, unlike simpler existing potentials, SNAP correctly predicts the energy barrier for screw dislocation migration in BCC tantalum
Energy Technology Data Exchange (ETDEWEB)
Thompson, Aidan P. [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States). Multiscale Science Dept.; Swiler, Laura P. [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States). Optimization and Uncertainty Quantification Dept.; Trott, Christian R. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Scalable Algorithms Dept.; Foiles, Stephen M. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Computational Materials and Data Science Dept.; Tucker, Garritt J. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Computational Materials and Data Science Dept.; Drexel Univ., Philadelphia, PA (United States). Dept. of Materials Science and Engineering
2015-03-15
Here, we present a new interatomic potential for solids and liquids called Spectral Neighbor Analysis Potential (SNAP). The SNAP potential has a very general form and uses machine-learning techniques to reproduce the energies, forces, and stress tensors of a large set of small configurations of atoms, which are obtained using high-accuracy quantum electronic structure (QM) calculations. The local environment of each atom is characterized by a set of bispectrum components of the local neighbor density projected onto a basis of hyperspherical harmonics in four dimensions. The bispectrum components are the same bond-orientational order parameters employed by the GAP potential [1]. The SNAP potential, unlike GAP, assumes a linear relationship between atom energy and bispectrum components. The linear SNAP coefficients are determined using weighted least-squares linear regression against the full QM training set. This allows the SNAP potential to be fit in a robust, automated manner to large QM data sets using many bispectrum components. The calculation of the bispectrum components and the SNAP potential are implemented in the LAMMPS parallel molecular dynamics code. We demonstrate that a previously unnoticed symmetry property can be exploited to reduce the computational cost of the force calculations by more than one order of magnitude. We present results for a SNAP potential for tantalum, showing that it accurately reproduces a range of commonly calculated properties of both the crystalline solid and the liquid phases. In addition, unlike simpler existing potentials, SNAP correctly predicts the energy barrier for screw dislocation migration in BCC tantalum.
Energy Technology Data Exchange (ETDEWEB)
Thompson, A.P., E-mail: athomps@sandia.gov [Multiscale Science Department, Sandia National Laboratories, PO Box 5800, MS 1322, Albuquerque, NM 87185 (United States); Swiler, L.P., E-mail: lpswile@sandia.gov [Optimization and Uncertainty Quantification Department, Sandia National Laboratories, PO Box 5800, MS 1318, Albuquerque, NM 87185 (United States); Trott, C.R., E-mail: crtrott@sandia.gov [Scalable Algorithms Department, Sandia National Laboratories, PO Box 5800, MS 1322, Albuquerque, NM 87185 (United States); Foiles, S.M., E-mail: foiles@sandia.gov [Computational Materials and Data Science Department, Sandia National Laboratories, PO Box 5800, MS 1411, Albuquerque, NM 87185 (United States); Tucker, G.J., E-mail: gtucker@coe.drexel.edu [Computational Materials and Data Science Department, Sandia National Laboratories, PO Box 5800, MS 1411, Albuquerque, NM 87185 (United States); Department of Materials Science and Engineering, Drexel University, Philadelphia, PA 19104 (United States)
2015-03-15
We present a new interatomic potential for solids and liquids called Spectral Neighbor Analysis Potential (SNAP). The SNAP potential has a very general form and uses machine-learning techniques to reproduce the energies, forces, and stress tensors of a large set of small configurations of atoms, which are obtained using high-accuracy quantum electronic structure (QM) calculations. The local environment of each atom is characterized by a set of bispectrum components of the local neighbor density projected onto a basis of hyperspherical harmonics in four dimensions. The bispectrum components are the same bond-orientational order parameters employed by the GAP potential [1]. The SNAP potential, unlike GAP, assumes a linear relationship between atom energy and bispectrum components. The linear SNAP coefficients are determined using weighted least-squares linear regression against the full QM training set. This allows the SNAP potential to be fit in a robust, automated manner to large QM data sets using many bispectrum components. The calculation of the bispectrum components and the SNAP potential are implemented in the LAMMPS parallel molecular dynamics code. We demonstrate that a previously unnoticed symmetry property can be exploited to reduce the computational cost of the force calculations by more than one order of magnitude. We present results for a SNAP potential for tantalum, showing that it accurately reproduces a range of commonly calculated properties of both the crystalline solid and the liquid phases. In addition, unlike simpler existing potentials, SNAP correctly predicts the energy barrier for screw dislocation migration in BCC tantalum.
Incidence and Prevalence of Tuberculosis in Iran and Neighboring Countries
Directory of Open Access Journals (Sweden)
Arezoo Tavakoli
2017-07-01
Full Text Available Background Tuberculosis is one of the major public health concerns in many countries, however the available and effective treatment is known. Tuberculosis typically determined with socio-economic problems such as war, malnutrition and HIV prevalence. In Iran, many progresses are carried to control tuberculosis but, different factors such as immigration from neighboring countries are affective to tuberculosis infection. Objectives In this paper, the incidence and prevalence of tuberculosis is evaluated in different regions of Iran and neighboring countries. Methods The data are collected from different and valid sources such as Scopus, Pubmed and also many reports from world health organization (WHO and center of disease control and prevention (CDC for a period of 25 years (1990 - 2015 evaluated for Iran and neighboring countries. Results This study as a descriptive- analytical research is conducted cross- sectional among Iran and neighboring countries since 1990. The information is obtained from exact and valid informative data from web of sciences. The east and west border countries of Iran which are faced with war and immigration in Afghanistan, Pakistan and Iraq are source of tuberculosis infection that effect on tuberculosis prevalence in Iran. The data were analyzed by SPSS 22 and Excel 2013. Conclusions The incidence of tuberculosis in Iran has been decreased because of many controlling actions such as BCG vaccination, electronic reporting system for tuberculosis and free access to tuberculosis medication. Some of Iran neighboring countries such as Tajikistan and Pakistan have the highest incidence of tuberculosis which known as a challenge for tuberculosis control in Iran while Saudi Arabia and Turkey have the lowest incidence.
Examining change detection approaches for tropical mangrove monitoring
Myint, Soe W.; Franklin, Janet; Buenemann, Michaela; Kim, Won; Giri, Chandra
2014-01-01
This study evaluated the effectiveness of different band combinations and classifiers (unsupervised, supervised, object-oriented nearest neighbor, and object-oriented decision rule) for quantifying mangrove forest change using multitemporal Landsat data. A discriminant analysis using spectra of different vegetation types determined that bands 2 (0.52 to 0.6 μm), 5 (1.55 to 1.75 μm), and 7 (2.08 to 2.35 μm) were the most effective bands for differentiating mangrove forests from surrounding land cover types. A ranking of thirty-six change maps, produced by comparing the classification accuracy of twelve change detection approaches, was used. The object-based Nearest Neighbor classifier produced the highest mean overall accuracy (84 percent) regardless of band combinations. The automated decision rule-based approach (mean overall accuracy of 88 percent) as well as a composite of bands 2, 5, and 7 used with the unsupervised classifier and the same composite or all band difference with the object-oriented Nearest Neighbor classifier were the most effective approaches.
Computerized index for teaching files
International Nuclear Information System (INIS)
Bramble, J.M.
1989-01-01
A computerized index can be used to retrieve cases from a teaching file that have radiographic findings similar to an unknown case. The probability that a user will review cases with a correct diagnosis was estimated with use of radiographic findings of arthritis in hand radiographs of 110 cases from a teaching file. The nearest-neighbor classification algorithm was used as a computer index to 110 cases of arthritis. Each case was treated as an unknown and inputted to the computer index. The accuracy of the computer index in retrieving cases with the same diagnosis (including rheumatoid arthritis, gout, psoriatic arthritis, inflammatory osteoarthritis, and pyrophosphate arthropathy) was measured. A Bayes classifier algorithm was also tested on the same database. Results are presented. The nearest-neighbor algorithm was 83%. By comparison, the estimated accuracy of the Bayes classifier algorithm was 78%. Conclusions: A computerized index to a teaching file based on the nearest-neighbor algorithm should allow the user to review cases with the correct diagnosis of an unknown case, by entering the findings of the unknown case
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
Missing value imputation in DNA microarrays based on conjugate gradient method.
Dorri, Fatemeh; Azmi, Paeiz; Dorri, Faezeh
2012-02-01
Analysis of gene expression profiles needs a complete matrix of gene array values; consequently, imputation methods have been suggested. In this paper, an algorithm that is based on conjugate gradient (CG) method is proposed to estimate missing values. k-nearest neighbors of the missed entry are first selected based on absolute values of their Pearson correlation coefficient. Then a subset of genes among the k-nearest neighbors is labeled as the best similar ones. CG algorithm with this subset as its input is then used to estimate the missing values. Our proposed CG based algorithm (CGimpute) is evaluated on different data sets. The results are compared with sequential local least squares (SLLSimpute), Bayesian principle component analysis (BPCAimpute), local least squares imputation (LLSimpute), iterated local least squares imputation (ILLSimpute) and adaptive k-nearest neighbors imputation (KNNKimpute) methods. The average of normalized root mean squares error (NRMSE) and relative NRMSE in different data sets with various missing rates shows CGimpute outperforms other methods. Copyright © 2011 Elsevier Ltd. All rights reserved.
Mapping growing stock volume and forest live biomass: a case study of the Polissya region of Ukraine
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.
Nearest-Neighbor Interactions and Their Influence on the Structural Aspects of Dipeptides
Directory of Open Access Journals (Sweden)
Gunajyoti Das
2013-01-01
Full Text Available In this theoretical study, the role of the side chain moiety of C-terminal residue in influencing the structural and molecular properties of dipeptides is analyzed by considering a series of seven dipeptides. The C-terminal positions of the dipeptides are varied with seven different amino acid residues, namely. Val, Leu, Asp, Ser, Gln, His, and Pyl while their N-terminal positions are kept constant with Sec residues. Full geometry optimization and vibrational frequency calculations are carried out at B3LYP/6-311++G(d,p level in gas and aqueous phase. The stereo-electronic effects of the side chain moieties of C-terminal residues are found to influence the values of Φ and Ω dihedrals, planarity of the peptide planes, and geometry around the C7 α-carbon atoms of the dipeptides. The gas phase intramolecular H-bond combinations of the dipeptides are similar to those in aqueous phase. The theoretical vibrational spectra of the dipeptides reflect the nature of intramolecular H-bonds existing in the dipeptide structures. Solvation effects of aqueous environment are evident on the geometrical parameters related to the amide planes, dipole moments, HOMOLUMO energy gaps as well as thermodynamic stability of the dipeptides.
PENINGKATAN KECERDASAN COMPUTER PLAYER PADA GAME PERTARUNGAN BERBASIS K-NEAREST NEIGHBOR BERBOBOT
Directory of Open Access Journals (Sweden)
M Ihsan Alfani Putera
2018-02-01
Full Text Available Salah satu teknologi komputer yang berkembang dan perubahannya cukup pesat adalah game. Tujuan dibuatnya game adalah sebagai sarana hiburan dan memberikan kesenangan bagi penggunanya. Contoh elemen dalam pembuatan game yang penting adalah adanya tantangan yang seimbang sesuai level. Dalam hal ini, adanya kecerdasan buatan atau AI merupakan salah satu unsur yang diperlukan dalam pembentukan game. Penggunaan AI yang tidak beradaptasi ke strategi lawan akan mudah diprediksi dan repetitif. Jika AI terlalu pintar maka player akan kesulitan dalam memainkan game tersebut. Dengan keadaan seperti itu akan menurunkan tingkat enjoyment dari pemain. Oleh karena itu, dibutuhkan suatu metode AI yang dapat beradaptasi dengan kemampuan dari player yang bermain. Sehingga tingkat kesulitan yang dihadapi dapat mengikuti kemampuan pemainnya dan pengalaman enjoyment ketika bermain game terus terjaga. Pada penelitian sebelumnya, metode AI yang sering digunakan pada game berjenis pertarungan adalah K-NN. Namun metode tersebut menganggap semua atribut dalam game adalah sama sehingga hal ini mempengaruhi hasil learning AI menjadi kurang optimal.Penelitian ini mengusulkan metode untuk AI dengan menggunakan metode K-NN berbobot pada game berjenis pertarungan. Dimana, pembobotan tersebut dilakukan untuk memberikan pengaruh setiap atribut dengan bobot disesuaikan dengan aksi player. Dari hasil evaluasi yang dilakukan terhadap 50 kali pertandingan pada 3 skenario uji coba, metode yang diusulkan yaitu K-NN berbobot mampu menghasilkan tingkat kecerdasan AI dengan akurasi sebesar 51%. Sedangkan, metode sebelumnya yaitu K-NN tanpa bobot hanya menghasilkan tingkat kecerdasan AI sebesar 38% dan metode random menghasilkan tingkat kecerdasan AI sebesar 25%.
PENINGKATAN KECERDASAN COMPUTER PLAYER PADA GAME PERTARUNGAN BERBASIS K-NEAREST NEIGHBOR BERBOBOT
M Ihsan Alfani Putera; Darlis Heru Murti
2018-01-01
Salah satu teknologi komputer yang berkembang dan perubahannya cukup pesat adalah game. Tujuan dibuatnya game adalah sebagai sarana hiburan dan memberikan kesenangan bagi penggunanya. Contoh elemen dalam pembuatan game yang penting adalah adanya tantangan yang seimbang sesuai level. Dalam hal ini, adanya kecerdasan buatan atau AI merupakan salah satu unsur yang diperlukan dalam pembentukan game. Penggunaan AI yang tidak beradaptasi ke strategi lawan akan mudah diprediksi dan repetitif. Jika ...
Sitek, M.; Szymański, M. K.; Udalski, A.; Skowron, D. M.; Kostrzewa-Rutkowska, Z.; Skowron, J.; Karczmarek, P.; Cieślar, M.; Wyrzykowski, Ł.; Kozłowski, S.; Pietrukowicz, P.; Soszyński, I.; Mróz, P.; Pawlak, M.; Poleski, R.
2018-01-01
The Magellanic System (MS) encompasses the nearest neighbors of the Milky Way, the Large (LMC) and Small (SMC) Magellanic Clouds, and the Magellanic Bridge (MBR). This system contains a diverse sample of star clusters. Their parameters, such as the spatial distribution, chemical composition and age distribution yield important information about the formation scenario of the whole Magellanic System. Using deep photometric maps compiled in the fourth phase of the Optical Gravitational Lensing E...
First-principles lattice-gas Hamiltonian revisited: O-Pd(100)
Kappus, Wolfgang
2016-01-01
The methodology of deriving an adatom lattice-gas Hamiltonian (LGH) from first principles (FP) calculations is revisited. Such LGH cluster expansions compute a large set of lateral pair-, trio-, quarto interactions by solving a set of linear equations modelling regular adatom configurations and their FP energies. The basic assumption of truncating interaction terms beyond fifth nearest neighbors does not hold when adatoms show longer range interactions, e.g. substrate mediated elastic interac...
Bulk ordering and surface segregation in Ni50Pt50
DEFF Research Database (Denmark)
Pourovskii, L.P.; Ruban, Andrei; Abrikosov, I.A.
2001-01-01
in the bulk compare well with experimental data. The surface-alloy compositions for the (111) and (110) facets above the ordering transition temperature are also found to be in a good agreement with experiments. It is demonstrated that the segregation profile at the (110) surface of NiPt is mainly caused...... by the unusually strong segregation of Pt into the second layer and the interlayer ordering due to large chemical nearest-neighbor interactions....
Segmenting Multiple Sclerosis Lesions using a Spatially Constrained K-Nearest Neighbour approach
DEFF Research Database (Denmark)
Lyksborg, Mark; Larsen, Rasmus; Sørensen, Per Soelberg
2012-01-01
We propose a method for the segmentation of Multiple Sclerosis lesions. The method is based on probability maps derived from a K-Nearest Neighbours classication. These are used as a non parametric likelihood in a Bayesian formulation with a prior that assumes connectivity of neighbouring voxels. ...
Fast Localization in Large-Scale Environments Using Supervised Indexing of Binary Features.
Youji Feng; Lixin Fan; Yihong Wu
2016-01-01
The essence of image-based localization lies in matching 2D key points in the query image and 3D points in the database. State-of-the-art methods mostly employ sophisticated key point detectors and feature descriptors, e.g., Difference of Gaussian (DoG) and Scale Invariant Feature Transform (SIFT), to ensure robust matching. While a high registration rate is attained, the registration speed is impeded by the expensive key point detection and the descriptor extraction. In this paper, we propose to use efficient key point detectors along with binary feature descriptors, since the extraction of such binary features is extremely fast. The naive usage of binary features, however, does not lend itself to significant speedup of localization, since existing indexing approaches, such as hierarchical clustering trees and locality sensitive hashing, are not efficient enough in indexing binary features and matching binary features turns out to be much slower than matching SIFT features. To overcome this, we propose a much more efficient indexing approach for approximate nearest neighbor search of binary features. This approach resorts to randomized trees that are constructed in a supervised training process by exploiting the label information derived from that multiple features correspond to a common 3D point. In the tree construction process, node tests are selected in a way such that trees have uniform leaf sizes and low error rates, which are two desired properties for efficient approximate nearest neighbor search. To further improve the search efficiency, a probabilistic priority search strategy is adopted. Apart from the label information, this strategy also uses non-binary pixel intensity differences available in descriptor extraction. By using the proposed indexing approach, matching binary features is no longer much slower but slightly faster than matching SIFT features. Consequently, the overall localization speed is significantly improved due to the much faster key
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.
Improving Recommendations in Tag-based Systems with Spectral Clustering of Tag Neighbors
DEFF Research Database (Denmark)
Pan, Rong; Xu, Guandong; Dolog, Peter
2012-01-01
Tag as a useful metadata reflects the collaborative and conceptual features of documents in social collaborative annotation systems. In this paper, we propose a collaborative approach for expanding tag neighbors and investigate the spectral clustering algorithm to filter out noisy tag neighbors...... in order to get appropriate recommendation for users. The preliminary experiments have been conducted on MovieLens dataset to compare our proposed approach with the traditional collaborative filtering recommendation approach and naive tag neighbors expansion approach in terms of precision, and the result...... demonstrates that our approach could considerably improve the performance of recommendations....
Local biotic adaptation of trees and shrubs to plant neighbors
Grady, Kevin C.; Wood, Troy E.; Kolb, Thomas E.; Hersch-Green, Erika; Shuster, Stephen M.; Gehring, Catherine A.; Hart, Stephen C.; Allan, Gerard J.; Whitham, Thomas G.
2017-01-01
Natural selection as a result of plant–plant interactions can lead to local biotic adaptation. This may occur where species frequently interact and compete intensely for resources limiting growth, survival, and reproduction. Selection is demonstrated by comparing a genotype interacting with con- or hetero-specific sympatric neighbor genotypes with a shared site-level history (derived from the same source location), to the same genotype interacting with foreign neighbor genotypes (from different sources). Better genotype performance in sympatric than allopatric neighborhoods provides evidence of local biotic adaptation. This pattern might be explained by selection to avoid competition by shifting resource niches (differentiation) or by interactions benefitting one or more members (facilitation). We tested for local biotic adaptation among two riparian trees, Populus fremontii and Salix gooddingii, and the shrub Salix exigua by transplanting replicated genotypes from multiple source locations to a 17 000 tree common garden with sympatric and allopatric treatments along the Colorado River in California. Three major patterns were observed: 1) across species, 62 of 88 genotypes grew faster with sympatric neighbors than allopatric neighbors; 2) these growth rates, on an individual tree basis, were 44, 15 and 33% higher in sympatric than allopatric treatments for P. fremontii, S. exigua and S. gooddingii, respectively, and; 3) survivorship was higher in sympatric treatments for P. fremontiiand S. exigua. These results support the view that fitness of foundation species supporting diverse communities and dominating ecosystem processes is determined by adaptive interactions among multiple plant species with the outcome that performance depends on the genetic identity of plant neighbors. The occurrence of evolution in a plant-community context for trees and shrubs builds on ecological evolutionary research that has demonstrated co-evolution among herbaceous taxa, and
Correlations in a chain of three oscillators with nearest neighbour coupling
Idrus, B.; Konstadopoulou, A.; Spiller, T.; Vourdas, A.
2010-04-01
A chain of three oscillators A, B, C with nearest neighbour coupling, is considered. It is shown that the correlations between A, C (which are not coupled directly) can be stronger than the correlations between A, B. Also in some cases various witnesses of entanglement show that A, C are entangled but they cannot lead to any conclusion about A, B.
Density functional approach for the magnetism of β-TeVO4
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.
Identification of the interstitial Mn site in ferromagnetic (Ga,Mn)As
AUTHOR|(CDS)2093111; Wahl, Ulrich; Augustyns, Valerie; Silva, Daniel; Granadeiro Costa, Angelo Rafael; Houben, K; Edmonds, Kevin W; Gallagher, BL; Campion, RP; Van Bael, MJ; Castro Ribeiro Da Silva, Manuel; Martins Correia, Joao; Esteves De Araujo, Araujo Joao Pedro; Temst, Kristiaan; Vantomme, André; Da Costa Pereira, Lino Miguel
2015-01-01
We determined the lattice location of Mn in ferromagnetic (Ga,Mn)As using the electron emission channeling technique. We show that interstitial Mn occupies the tetrahedral site with As nearest neighbors (TAs) both before and after thermal annealing at 200 °C, whereas the occupancy of the tetrahedral site with Ga nearest neighbors (TGa) is negligible. TAs is therefore the energetically favorable site for interstitial Mn in isolated form as well as when forming complexes with substitutional Mn. These results shed new light on the long standing controversy regarding TAs versus TGa occupancy of interstitial Mn in (Ga,Mn)As.
Wildflower Plantings Do Not Compete With Neighboring Almond Orchards for Pollinator Visits.
Lundin, Ola; Ward, Kimiora L; Artz, Derek R; Boyle, Natalie K; Pitts-Singer, Theresa L; Williams, Neal M
2017-06-01
The engineering of flowering agricultural field borders has emerged as a research and policy priority to mitigate threats to pollinators. Studies have, however, rarely addressed the potential that flowering field borders might compete with neighboring crops for pollinator visits if they both are in bloom at the same time, despite this being a concern expressed by growers. We evaluated how wildflower plantings added to orchard borders in a large (512 ha) commercial almond orchard affected honey bee and wild bee visitation to orchard borders and the crop. The study was conducted over two consecutive seasons using three large (0.48 ha) wildflower plantings paired with control orchard borders in a highly simplified agricultural landscape in California. Honey bee (Apis mellifera L.) and wild bee visitation to wildflower plots were at least an order of magnitude higher than to control plots, but increased honey bee visitation to wildflower plots did not lead to any detectable shifts in honey bee visitation to almond flowers in the neighboring orchard. Wild bees were rarely observed visiting almond flowers irrespective of border treatment, indicating a limited short-term potential for augmenting crop pollination using wild bees in highly simplified agricultural landscapes. Although further studies are warranted on bee visitation and crop yield from spatially independent orchards, this study indicates that growers can support bees with alternative forage in almond orchards without risking competition between the wildflower plantings and the crop. © The Authors 2017. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
Working with Family, Friend, and Neighbor Caregivers: Lessons from Four Diverse Communities
Powell, Douglas R.
2011-01-01
This article is excerpted from "Who's Watching the Babies? Improving the Quality of Family, Friend, and Neighbor Care" by Douglas R. Powell ("ZERO TO THREE," 2008). The article explores questions about program development and implementation strategies for supporting Family, Friend, and Neighbor (FFN) caregivers: How do programs and their host…
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 .
Thyroid monitoring for residents of disaster-affected and neighboring areas
International Nuclear Information System (INIS)
Ito, Shigeki
2014-01-01
The devastating environmental contamination caused by the nuclear disaster at the Fukushima Daiichi Nuclear Power Station of The Tokyo Electric Power Company is exposing the residents of the disaster-affected areas to health risks attributable to radiation exposure, and fear of the development of 131 I-induced thyroid cancer, which is a stochastic effect of radiation and is particularly high. As part of the response to nuclear disasters by the government of the municipality where the nuclear power station is located and in operation and by the governments of neighboring municipalities, it is necessary to conduct thyroid monitoring for the purpose of alleviating the fears of residents of the disaster-affected areas as well as those living in the contaminated, even if only slightly, neighboring areas (local residents). This health monitoring needs to be implemented without delay in the case of a disaster along with dissemination of a portable type thyroid monitoring system available at evacuation centers, etc. for assessing thyroid exposure doses. The establishment of a system for developing personnel ready to perform monitoring is also essential. Assessing thyroid exposure doses is indispensable as a means of assuring local residents not only of safety but also of security from the risks of radiation. To date, contamination has not been detected in people, except for residents contaminated by a large amount of iodine, by employing the mobile type of thyroid monitoring system. However, when local residents seeking security desire thyroid monitoring, it is preferable that a portable type simplified thyroid monitoring system be used as a means of ensuring security against radiation. (author)
Modeling the effect of neighboring grains on twin growth in HCP polycrystals
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.
Nearest Neighbour Corner Points Matching Detection Algorithm
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Zhang Changlong
2015-01-01
Full Text Available Accurate detection towards the corners plays an important part in camera calibration. To deal with the instability and inaccuracies of present corner detection algorithm, the nearest neighbour corners match-ing detection algorithms was brought forward. First, it dilates the binary image of the photographed pictures, searches and reserves quadrilateral outline of the image. Second, the blocks which accord with chess-board-corners are classified into a class. If too many blocks in class, it will be deleted; if not, it will be added, and then let the midpoint of the two vertex coordinates be the rough position of corner. At last, it precisely locates the position of the corners. The Experimental results have shown that the algorithm has obvious advantages on accuracy and validity in corner detection, and it can give security for camera calibration in traffic accident measurement.
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.
Intermittent search strategies revisited: effect of the jump length and biased motion
Energy Technology Data Exchange (ETDEWEB)
Rojo, F; Budde, C E [Fa.M.A.F., Universidad Nacional de Cordoba, Ciudad Universitaria, X5000HUA Cordoba (Argentina); Revelli, J; Wio, H S [Instituto de Fisica de Cantabria, Universidad de Cantabria and CSIC, E-39005 Santander (Spain); Oshanin, G [Laboratoire de Physique Theorique de la Matiere Condensee, Universite Pierre et Marie Curie, 4 place Jussieu, 75252 Paris Cedex 5 (France); Lindenberg, Katja [Department of Chemistry and Biochemistry and BioCircuits Institute, University of California, San Diego, La Jolla, CA 92093-0340 (United States)
2010-08-27
We study the kinetics of a search of a single fixed target by a large number of searchers performing an intermittent biased random walk in a homogeneous medium. Our searchers carry out their walks in one of two states between which they switch randomly. One of these states (search phase) is a nearest-neighbor walk characterized by the probability of stepping in a given direction (i.e. the walks in this state are not necessarily isotropic). The other (relocation phase) is characterized by the length of the jumps (i.e. when in this state a walker does not perform a nearest-neighbor walk). Within such a framework, we propose a model to describe the searchers' dynamics, generalizing results of our previous work. We have obtained, and numerically evaluated, analytic results for the mean number of distinct sites visited up to a maximum evolution time. We have studied the dependence of this quantity on both the transition probability between the states and the parameters that characterize each state. In addition to our theoretical approach, we have implemented Monte Carlo simulations, finding excellent agreement between the theoretical-numerical and simulations results.
Intermittent search strategies revisited: effect of the jump length and biased motion
International Nuclear Information System (INIS)
Rojo, F; Budde, C E; Revelli, J; Wio, H S; Oshanin, G; Lindenberg, Katja
2010-01-01
We study the kinetics of a search of a single fixed target by a large number of searchers performing an intermittent biased random walk in a homogeneous medium. Our searchers carry out their walks in one of two states between which they switch randomly. One of these states (search phase) is a nearest-neighbor walk characterized by the probability of stepping in a given direction (i.e. the walks in this state are not necessarily isotropic). The other (relocation phase) is characterized by the length of the jumps (i.e. when in this state a walker does not perform a nearest-neighbor walk). Within such a framework, we propose a model to describe the searchers' dynamics, generalizing results of our previous work. We have obtained, and numerically evaluated, analytic results for the mean number of distinct sites visited up to a maximum evolution time. We have studied the dependence of this quantity on both the transition probability between the states and the parameters that characterize each state. In addition to our theoretical approach, we have implemented Monte Carlo simulations, finding excellent agreement between the theoretical-numerical and simulations results.
Feature Selection and Predictors of Falls with Foot Force Sensors Using KNN-Based Algorithms
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Shengyun Liang
2015-11-01
Full Text Available The aging process may lead to the degradation of lower extremity function in the elderly population, which can restrict their daily quality of life and gradually increase the fall risk. We aimed to determine whether objective measures of physical function could predict subsequent falls. Ground reaction force (GRF data, which was quantified by sample entropy, was collected by foot force sensors. Thirty eight subjects (23 fallers and 15 non-fallers participated in functional movement tests, including walking and sit-to-stand (STS. A feature selection algorithm was used to select relevant features to classify the elderly into two groups: at risk and not at risk of falling down, for three KNN-based classifiers: local mean-based k-nearest neighbor (LMKNN, pseudo nearest neighbor (PNN, local mean pseudo nearest neighbor (LMPNN classification. We compared classification performances, and achieved the best results with LMPNN, with sensitivity, specificity and accuracy all 100%. Moreover, a subset of GRFs was significantly different between the two groups via Wilcoxon rank sum test, which is compatible with the classification results. This method could potentially be used by non-experts to monitor balance and the risk of falling down in the elderly population.
Dispersion of a layered electron gas with nearest neighbour-tunneling
International Nuclear Information System (INIS)
Miesenboeck, H.M.
1988-09-01
The dispersion of the first plasmon band is calculated within the Random Phase Approximation for a superlattice of two-dimensional electron-gases, mutually interacting, and with nearest neighbour hopping between the planes. It is further shown that the deviations of this dispersion from the one in systems with zero interplane motion are very small in commonly realized experimental situations and that they are expected to be observable only in samples with plane distances of 100A and less. (author). 15 refs, 3 figs, 1 tab
SibRank: Signed bipartite network analysis for neighbor-based collaborative ranking
Shams, Bita; Haratizadeh, Saman
2016-09-01
Collaborative ranking is an emerging field of recommender systems that utilizes users' preference data rather than rating values. Unfortunately, neighbor-based collaborative ranking has gained little attention despite its more flexibility and justifiability. This paper proposes a novel framework, called SibRank that seeks to improve the state of the art neighbor-based collaborative ranking methods. SibRank represents users' preferences as a signed bipartite network, and finds similar users, through a novel personalized ranking algorithm in signed networks.
ENTROPY CHARACTERISTICS IN MODELS FOR COORDINATION OF NEIGHBORING ROAD SECTIONS
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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.
Do alcohol compliance checks decrease underage sales at neighboring establishments?
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.
Fe magnetic moment formation and exchange interaction in Fe{sub 2}P: A first-principles study
Energy Technology Data Exchange (ETDEWEB)
Liu, X.B., E-mail: liuxubo@uta.edu [Department of Physics, University of Texas at Arlington, Arlington, TX 76019 (United States); Ping Liu, J.; Zhang, Qiming [Department of Physics, University of Texas at Arlington, Arlington, TX 76019 (United States); Altounian, Z. [Center for the Physics of Materials and Department of Physics, McGill University, 3600 University Street, Montreal, Quebec, H3A 2T8 (Canada)
2013-03-15
Electronic structure and magnetic properties of Fe{sub 2}P have been studied by a first-principles density functional theory calculation. The ground state is ferromagnetic and the calculated magnetic moments for Fe{sub 1} (3f) and Fe{sub 2} (3g) are 0.83 and 2.30μ{sub B}, respectively. The nearest neighbor inter-site magnetic exchange coupling parameter at the Fe{sub 1} layer (0.02 mRy) is much smaller than that at the Fe{sub 2} layer (1.29 mRy). The Fe moment at the 3f site is metastable and sensitive to the inter-site exchange interaction with its magnetic neighbors, which is responsible for the first order magnetic transition and large magneto-caloric effect around T{sub C}.
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.
Large margin classification with indefinite similarities
Alabdulmohsin, Ibrahim
2016-01-07
Classification with indefinite similarities has attracted attention in the machine learning community. This is partly due to the fact that many similarity functions that arise in practice are not symmetric positive semidefinite, i.e. the Mercer condition is not satisfied, or the Mercer condition is difficult to verify. Examples of such indefinite similarities in machine learning applications are ample including, for instance, the BLAST similarity score between protein sequences, human-judged similarities between concepts and words, and the tangent distance or the shape matching distance in computer vision. Nevertheless, previous works on classification with indefinite similarities are not fully satisfactory. They have either introduced sources of inconsistency in handling past and future examples using kernel approximation, settled for local-minimum solutions using non-convex optimization, or produced non-sparse solutions by learning in Krein spaces. Despite the large volume of research devoted to this subject lately, we demonstrate in this paper how an old idea, namely the 1-norm support vector machine (SVM) proposed more than 15 years ago, has several advantages over more recent work. In particular, the 1-norm SVM method is conceptually simpler, which makes it easier to implement and maintain. It is competitive, if not superior to, all other methods in terms of predictive accuracy. Moreover, it produces solutions that are often sparser than more recent methods by several orders of magnitude. In addition, we provide various theoretical justifications by relating 1-norm SVM to well-established learning algorithms such as neural networks, SVM, and nearest neighbor classifiers. Finally, we conduct a thorough experimental evaluation, which reveals that the evidence in favor of 1-norm SVM is statistically significant.
Simulation of the diffusion equation on a type-II quantum computer
International Nuclear Information System (INIS)
Berman, G.P.; Kamenev, D.I.; Ezhov, A.A.; Yepez, J.
2002-01-01
A lattice-gas algorithm for the one-dimensional diffusion equation is realized using radio frequency pulses in a one-dimensional spin system. The model is a large array of quantum two-qubit nodes interconnected by the nearest-neighbor classical communication channels. We present a quantum protocol for implementation of the quantum collision operator and a method for initialization and reinitialization of quantum states. Numerical simulations of the quantum-classical dynamics are in good agreement with the analytic solution for the diffusion equation
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.
Ye, Xue-Hua; Zhang, Ya-Lin; Liu, Zhi-Lan; Gao, Shu-Qin; Song, Yao-Bin; Liu, Feng-Hong; Dong, Ming
2016-01-01
Resources such as water taken up by plants can be released into soils through hydraulic redistribution and can also be translocated by clonal integration within a plant clonal network. We hypothesized that the resources from one (donor) microsite could be translocated within a clonal network, released into different (recipient) microsites and subsequently used by neighbor plants in the recipient microsite. To test these hypotheses, we conducted two experiments in which connected and disconnected ramet pairs of Potentilla anserina were grown under both homogeneous and heterogeneous water regimes, with seedlings of Artemisia ordosica as neighbors. The isotopes [(15)N] and deuterium were used to trace the translocation of nitrogen and water, respectively, within the clonal network. The water and nitrogen taken up by P. anserina ramets in the donor microsite were translocated into the connected ramets in the recipient microsites. Most notably, portions of the translocated water and nitrogen were released into the recipient microsite and were used by the neighboring A. ordosica, which increased growth of the neighboring A. ordosica significantly. Therefore, our hypotheses were supported, and plant clonal integration mediated the horizontal hydraulic redistribution of resources, thus benefiting neighboring plants. Such a plant clonal integration-mediated resource redistribution in horizontal space may have substantial effects on the interspecific relations and composition of the community and consequently on ecosystem processes.
Penentuan Kematangan Buah Salak Pondoh Di Pohon Berbasis Pengolahan Citra Digital
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Pawit Rianto
2017-07-01
Full Text Available Because there is no a system based on Digital Image Processing to determine the degree of ripeness of Salak Pondoh (Salacca zalacca Gaertner Voss. on tree, then this study has attempted to implement such a system. System was built with consists of several sub-processes. First, the segmentation process, the system will perform a search of pixels alleged pixels salak pondoh, by utilizing the features of color components r, g, b, and gray of each pixel salak pondoh then calculated large the dissimilarity ( Euclidean Distance against values of data features , , , and comparison. If the value of dissimilarity less than the threshold value and is also supported by the neighboring pixels from different directions has a value of dissimilarity is less than a threshold value, the pixel is set as an object pixel, for the other condition set as background pixels. For the next, improvements through an elimination noise stage and filling in the pixels to get a perfect binary image segmentation. Second, classification, by knowning the mean value of R and V of the entire pixel object, then the level of ripeness salak pondoh can be determined by using the method of classification backpropagation or k -Nearest Neighbor. From the test results indicate that the success of the system by 92% when using a backpropagatioan classification algorithm and 93% with k-Nearest Neighbor algorithm.
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.
Neighbor Discovery Algorithm in Wireless Local Area Networks Using Multi-beam Directional Antennas
Wang, Jin; Peng, Wei; Liu, Song
2017-10-01
Neighbor discovery is an important step for Wireless Local Area Networks (WLAN) and the use of multi-beam directional antennas can greatly improve the network performance. However, most neighbor discovery algorithms in WLAN, based on multi-beam directional antennas, can only work effectively in synchronous system but not in asynchro-nous system. And collisions at AP remain a bottleneck for neighbor discovery. In this paper, we propose two asynchrono-us neighbor discovery algorithms: asynchronous hierarchical scanning (AHS) and asynchronous directional scanning (ADS) algorithm. Both of them are based on three-way handshaking mechanism. AHS and ADS reduce collisions at AP to have a good performance in a hierarchical way and directional way respectively. In the end, the performance of the AHS and ADS are tested on OMNeT++. Moreover, it is analyzed that different application scenarios and the factors how to affect the performance of these algorithms. The simulation results show that AHS is suitable for the densely populated scenes around AP while ADS is suitable for that most of the neighborhood nodes are far from AP.
Probability-neighbor method of accelerating geometry treatment in reactor Monte Carlo code RMC
International Nuclear Information System (INIS)
She, Ding; Li, Zeguang; Xu, Qi; Wang, Kan; Yu, Ganglin
2011-01-01
Probability neighbor method (PNM) is proposed in this paper to accelerate geometry treatment of Monte Carlo (MC) simulation and validated in self-developed reactor Monte Carlo code RMC. During MC simulation by either ray-tracking or delta-tracking method, large amounts of time are spent in finding out which cell one particle is located in. The traditional way is to search cells one by one with certain sequence defined previously. However, this procedure becomes very time-consuming when the system contains a large number of cells. Considering that particles have different probability to enter different cells, PNM method optimizes the searching sequence, i.e., the cells with larger probability are searched preferentially. The PNM method is implemented in RMC code and the numerical results show that the considerable time of geometry treatment in MC calculation for complicated systems is saved, especially effective in delta-tracking simulation. (author)
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.
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.
Nearest neighbor affects G:C to A:T transitions induced by alkylating agents.
Glickman, B W; Horsfall, M J; Gordon, A J; Burns, P A
1987-01-01
The influence of local DNA sequence on the distribution of G:C to A:T transitions induced in the lacI gene of E. coli by a series of alkylating agents has been analyzed. In the case of nitrosoguanidine, two nitrosoureas and a nitrosamine, a strong preference for mutation at sites proceeded 5' by a purine base was noted. This preference was observed with both methyl and ethyl donors where the predicted common ultimate alkylating species is the alkyl diazonium ion. In contrast, this preference ...
Nearest neighbor affects G:C to A:T transitions induced by alkylating agents.
Glickman, B W; Horsfall, M J; Gordon, A J; Burns, P A
1987-01-01
The influence of local DNA sequence on the distribution of G:C to A:T transitions induced in the lacI gene of E. coli by a series of alkylating agents has been analyzed. In the case of nitrosoguanidine, two nitrosoureas and a nitrosamine, a strong preference for mutation at sites proceeded 5' by a purine base was noted. This preference was observed with both methyl and ethyl donors where the predicted common ultimate alkylating species is the alkyl diazonium ion. In contrast, this preference was not seen following treatment with ethylmethanesulfonate. The observed preference for 5'PuG-3' site over 5'-PyG-3' sites corresponds well with alterations observed in the Ha-ras oncogene recovered after treatment with NMU. This indicates that the mutations recovered in the oncogenes are likely the direct consequence of the alkylation treatment and that the local sequence effects seen in E. coli also appear to occur in mammalian cells. PMID:3329097
Nearest neighbor affects G:C to A:T transitions induced by alkylating agents
Energy Technology Data Exchange (ETDEWEB)
Glickman, B.W.; Horsfall, M.J.; Gordon, A.J.E.; Burns, P.A.
1987-12-01
The influence of local DNA sequence on the distribution of G:C to A:T transitions induced in the lacI gene of E. coli by a series of alkylating agents has been analyzed. In the case of nitrosoguanidine, two nitrosoureas and a nitrosamine, a strong preference for mutation at sites proceeded 5' by a purine base was noted. This preferences was observed with both methyl and ethyl donors where the predicted common ultimate alkylating species in the alkyl diazonium ion. In contrast, this preferences was not seen following treatment with ethylmethanesulfonate. The observed preference for 5'PuG-3' site over 5'-PyG-3' sites corresponds well with alterations observed in the Ha-ras oncogene recovered after treatment with NMU. This indicates that the mutations recovered in the oncogenes are likely the direct consequence of the alkylation treatment and that the local sequence effects seen in E. coli also appear to occur in mammalian cells.
Quasi-phases and pseudo-transitions in one-dimensional models with nearest neighbor interactions
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.
Combining Fourier and lagged k-nearest neighbor imputation for biomedical time series data.
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.
Combining Fourier and lagged k-nearest neighbor imputation for biomedical time series data
Rahman, Shah Atiqur; Huang, Yuxiao; Claassen, Jan; Heintzman, Nathaniel; Kleinberg, Samantha
2015-01-01
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 tim...
Hurricane-Induced Stage-Frequency Relationships for the Territory of American Samoa
National Research Council Canada - National Science Library
Militello, Adele
1998-01-01
.... The statistical approach taken to calculated frequency-of-occurrence relationships was the Empirical Simulation Technique, which applies historical wave information and a nearest neighbor technique...
Hurricane-Induced Stage-Frequency Relationships for the Territory of American Samoa
National Research Council Canada - National Science Library
Militello, Adele
2003-01-01
.... The statistical approach taken to calculate frequency-of-occurrence relationships was the Empirical Simulation Technique, which applies historical wave information and a nearest neighbor technique...
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)
International Nuclear Information System (INIS)
Dhawan, Sahil; Vedeshwar, Agnikumar G; Tandon, R P
2011-01-01
The optical and structural properties of well characterized vacuum-evaporated amorphous V 2 O 5 films were studied in the thickness range 5-500 nm. The structural analyses show that V-O, O-O and V-V nearest neighbour distances defining the short range order vary nonlinearly with film thickness. The optical absorption shows thickness-dependent energy gap (E g ) and the nonlinear behaviour of thickness-dependent E g is similar to that of nearest neighbour distance with film thickness. The E g correlates linearly very well with all the three nearest neighbour distances. The variation of E g with film thickness is attributed to the residual stress in the film which causes the changes in short range order. The change in E g corresponding to the change in V-O distance was found to be 35 eV nm -1 . This change is almost three times of that with V-V distance.
Effective model with strong Kitaev interactions for α -RuCl3
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.
Rumor has it...: relay communication of stress cues in plants.
Falik, Omer; Mordoch, Yonat; Quansah, Lydia; Fait, Aaron; Novoplansky, Ariel
2011-01-01
Recent evidence demonstrates that plants are able not only to perceive and adaptively respond to external information but also to anticipate forthcoming hazards and stresses. Here, we tested the hypothesis that unstressed plants are able to respond to stress cues emitted from their abiotically-stressed neighbors and in turn induce stress responses in additional unstressed plants located further away from the stressed plants. Pisum sativum plants were subjected to drought while neighboring rows of five unstressed plants on both sides, with which they could exchange different cue combinations. On one side, the stressed plant and its unstressed neighbors did not share their rooting volumes (UNSHARED) and thus were limited to shoot communication. On its other side, the stressed plant shared one of its rooting volumes with its nearest unstressed neighbor and all plants shared their rooting volumes with their immediate neighbors (SHARED), allowing both root and shoot communication. Fifteen minutes following drought induction, significant stomatal closure was observed in both the stressed plants and their nearest unstressed SHARED neighbors, and within one hour, all SHARED neighbors closed their stomata. Stomatal closure was not observed in the UNSHARED neighbors. The results demonstrate that unstressed plants are able to perceive and respond to stress cues emitted by the roots of their drought-stressed neighbors and, via 'relay cuing', elicit stress responses in further unstressed plants. Further work is underway to study the underlying mechanisms of this new mode of plant communication and its possible adaptive implications for the anticipation of forthcoming abiotic stresses by plants.
Towards Building a High Performance Spatial Query System for Large Scale Medical Imaging Data.
Aji, Ablimit; Wang, Fusheng; Saltz, Joel H
2012-11-06
Support of high performance queries on large volumes of scientific spatial data is becoming increasingly important in many applications. This growth is driven by not only geospatial problems in numerous fields, but also emerging scientific applications that are increasingly data- and compute-intensive. For example, digital pathology imaging has become an emerging field during the past decade, where examination of high resolution images of human tissue specimens enables more effective diagnosis, prediction and treatment of diseases. Systematic analysis of large-scale pathology images generates tremendous amounts of spatially derived quantifications of micro-anatomic objects, such as nuclei, blood vessels, and tissue regions. Analytical pathology imaging provides high potential to support image based computer aided diagnosis. One major requirement for this is effective querying of such enormous amount of data with fast response, which is faced with two major challenges: the "big data" challenge and the high computation complexity. In this paper, we present our work towards building a high performance spatial query system for querying massive spatial data on MapReduce. Our framework takes an on demand index building approach for processing spatial queries and a partition-merge approach for building parallel spatial query pipelines, which fits nicely with the computing model of MapReduce. We demonstrate our framework on supporting multi-way spatial joins for algorithm evaluation and nearest neighbor queries for microanatomic objects. To reduce query response time, we propose cost based query optimization to mitigate the effect of data skew. Our experiments show that the framework can efficiently support complex analytical spatial queries on MapReduce.
Gapless Spin-Liquid Ground State in the S =1 /2 Kagome Antiferromagnet
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.
Transferable tight-binding model for strained group IV and III-V materials and heterostructures
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.
The Green Function cellular method and its relation to multiple scattering theory
International Nuclear Information System (INIS)
Butler, W.H.; Zhang, X.G.; Gonis, A.
1992-01-01
This paper investigates techniques for solving the wave equation which are based on the idea of obtaining exact local solutions within each potential cell, which are then joined to form a global solution. The authors derive full potential multiple scattering theory (MST) from the Lippmann-Schwinger equation and show that it as well as a closely related cellular method are techniques of this type. This cellular method appears to have all of the advantages of MST and the added advantage of having a secular matrix with only nearest neighbor interactions. Since this cellular method is easily linearized one can rigorously reduce electronic structure calculation to the problem of solving a nearest neighbor tight-binding problem
Anomalous magnon Nernst effect of topological magnonic materials
Wang, X. S.; Wang, X. R.
2018-05-01
The magnon transport driven by a thermal gradient in a perpendicularly magnetized honeycomb lattice is studied. The system with the nearest-neighbor pseudodipolar interaction and the next-nearest-neighbor Dzyaloshinskii–Moriya interaction has various topologically nontrivial phases. When an in-plane thermal gradient is applied, a transverse in-plane magnon current is generated. This phenomenon is termed as the anomalous magnon Nernst effect that closely resembles the anomalous Nernst effect for an electronic system. The anomalous magnon Nernst coefficient and its sign are determined by the magnon Berry curvature distributions in the momentum space and magnon populations in the magnon bands. We predict a temperature-induced sign reversal in anomalous magnon Nernst effect under certain conditions.
Directory of Open Access Journals (Sweden)
Ibrahim Avgin
2017-01-01
Full Text Available Using the coherent potential approximation, we investigate the effects of disorder on the optical absorption and the density of states of Frenkel exciton systems on square, rectangular, and triangular lattices with nearest-neighbor interactions and a Gaussian distribution of transition energies. The analysis is based on an elliptic integral approach that gives results over the entire spectrum. The results for the square lattice are in good agreement with the finite-array calculations of Schreiber and Toyozawa. Our findings suggest that the coherent potential approximation can be useful in interpreting the optical properties of two-dimensional systems with dominant nearest-neighbor interactions and Gaussian diagonal disorder provided the optically active states are Frenkel excitons.
Learning Euclidean Embeddings for Indexing and Classification
National Research Council Canada - National Science Library
Athitsos, Vassilis; Alon, Joni; Sclaroff, Stan; Kollios, George
2004-01-01
BoostMap is a recently proposed method for efficient approximate nearest neighbor retrieval in arbitrary non-Euclidean spaces with computationally expensive and possibly non-metric distance measures...
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.
Real-time interpolation for true 3-dimensional ultrasound image volumes.
Ji, Songbai; Roberts, David W; Hartov, Alex; Paulsen, Keith D
2011-02-01
We compared trilinear interpolation to voxel nearest neighbor and distance-weighted algorithms for fast and accurate processing of true 3-dimensional ultrasound (3DUS) image volumes. In this study, the computational efficiency and interpolation accuracy of the 3 methods were compared on the basis of a simulated 3DUS image volume, 34 clinical 3DUS image volumes from 5 patients, and 2 experimental phantom image volumes. We show that trilinear interpolation improves interpolation accuracy over both the voxel nearest neighbor and distance-weighted algorithms yet achieves real-time computational performance that is comparable to the voxel nearest neighbor algrorithm (1-2 orders of magnitude faster than the distance-weighted algorithm) as well as the fastest pixel-based algorithms for processing tracked 2-dimensional ultrasound images (0.035 seconds per 2-dimesional cross-sectional image [76,800 pixels interpolated, or 0.46 ms/1000 pixels] and 1.05 seconds per full volume with a 1-mm(3) voxel size [4.6 million voxels interpolated, or 0.23 ms/1000 voxels]). On the basis of these results, trilinear interpolation is recommended as a fast and accurate interpolation method for rectilinear sampling of 3DUS image acquisitions, which is required to facilitate subsequent processing and display during operating room procedures such as image-guided neurosurgery.
The mass media destabilizes the cultural homogenous regime in Axelrod's model
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.
Extending GIS Technology to Study Karst Features of Southeastern Minnesota
Gao, Y.; Tipping, R. G.; Alexander, E. C.; Alexander, S. C.
2001-12-01
This paper summarizes ongoing research on karst feature distribution of southeastern Minnesota. The main goals of this interdisciplinary research are: 1) to look for large-scale patterns in the rate and distribution of sinkhole development; 2) to conduct statistical tests of hypotheses about the formation of sinkholes; 3) to create management tools for land-use managers and planners; and 4) to deliver geomorphic and hydrogeologic criteria for making scientifically valid land-use policies and ethical decisions in karst areas of southeastern Minnesota. Existing county and sub-county karst feature datasets of southeastern Minnesota have been assembled into a large GIS-based database capable of analyzing the entire data set. The central database management system (DBMS) is a relational GIS-based system interacting with three modules: GIS, statistical and hydrogeologic modules. ArcInfo and ArcView were used to generate a series of 2D and 3D maps depicting karst feature distributions in southeastern Minnesota. IRIS ExplorerTM was used to produce satisfying 3D maps and animations using data exported from GIS-based database. Nearest-neighbor analysis has been used to test sinkhole distributions in different topographic and geologic settings. All current nearest-neighbor analyses testify that sinkholes in southeastern Minnesota are not evenly distributed in this area (i.e., they tend to be clustered). More detailed statistical methods such as cluster analysis, histograms, probability estimation, correlation and regression have been used to study the spatial distributions of some mapped karst features of southeastern Minnesota. A sinkhole probability map for Goodhue County has been constructed based on sinkhole distribution, bedrock geology, depth to bedrock, GIS buffer analysis and nearest-neighbor analysis. A series of karst features for Winona County including sinkholes, springs, seeps, stream sinks and outcrop has been mapped and entered into the Karst Feature Database
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.
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
Energy Technology Data Exchange (ETDEWEB)
Wang, H.; Zhang, G.; Hui, G.; Li, Y.; Hu, Y.; Zhao, Z.
2016-07-01
Aim of study: Neighborhood-based stand spatial structure parameters can quantify and characterize forest spatial structure effectively. How these neighborhood-based structure parameters are influenced by the selection of different numbers of nearest-neighbor trees is unclear, and there is some disagreement in the literature regarding the appropriate number of nearest-neighbor trees to sample around reference trees. Understanding how to efficiently characterize forest structure is critical for forest management. Area of study: Multi-species uneven-aged forests of Northern China. Material and methods: We simulated stands with different spatial structural characteristics and systematically compared their structure parameters when two to eight neighboring trees were selected. Main results: Results showed that values of uniform angle index calculated in the same stand were different with different sizes of structure unit. When tree species and sizes were completely randomly interspersed, different numbers of neighbors had little influence on mingling and dominance indices. Changes of mingling or dominance indices caused by different numbers of neighbors occurred when the tree species or size classes were not randomly interspersed and their changing characteristics can be detected according to the spatial arrangement patterns of tree species and sizes. Research highlights: The number of neighboring trees selected for analyzing stand spatial structure parameters should be fixed. We proposed that the four-tree structure unit is the best compromise between sampling accuracy and costs for practical forest management. (Author)
Mapping regional patterns of large forest fires in Wildland-Urban Interface areas in Europe.
Modugno, Sirio; Balzter, Heiko; Cole, Beth; Borrelli, Pasquale
2016-05-01
Over recent decades, Land Use and Cover Change (LUCC) trends in many regions of Europe have reconfigured the landscape structures around many urban areas. In these areas, the proximity to landscape elements with high forest fuels has increased the fire risk to people and property. These Wildland-Urban Interface areas (WUI) can be defined as landscapes where anthropogenic urban land use and forest fuel mass come into contact. Mapping their extent is needed to prioritize fire risk control and inform local forest fire risk management strategies. This study proposes a method to map the extent and spatial patterns of the European WUI areas at continental scale. Using the European map of WUI areas, the hypothesis is tested that the distance from the nearest WUI area is related to the forest fire probability. Statistical relationships between the distance from the nearest WUI area, and large forest fire incidents from satellite remote sensing were subsequently modelled by logistic regression analysis. The first European scale map of the WUI extent and locations is presented. Country-specific positive and negative relationships of large fires and the proximity to the nearest WUI area are found. A regional-scale analysis shows a strong influence of the WUI zones on large fires in parts of the Mediterranean regions. Results indicate that the probability of large burned surfaces increases with diminishing WUI distance in touristic regions like Sardinia, Provence-Alpes-Côte d'Azur, or in regions with a strong peri-urban component as Catalunya, Comunidad de Madrid, Comunidad Valenciana. For the above regions, probability curves of large burned surfaces show statistical relationships (ROC value > 0.5) inside a 5000 m buffer of the nearest WUI. Wise land management can provide a valuable ecosystem service of fire risk reduction that is currently not explicitly included in ecosystem service valuations. The results re-emphasise the importance of including this ecosystem service
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...
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...
A Novel AMR-WB Speech Steganography Based on Diameter-Neighbor Codebook Partition
Directory of Open Access Journals (Sweden)
Junhui He
2018-01-01
Full Text Available Steganography is a means of covert communication without revealing the occurrence and the real purpose of communication. The adaptive multirate wideband (AMR-WB is a widely adapted format in mobile handsets and is also the recommended speech codec for VoLTE. In this paper, a novel AMR-WB speech steganography is proposed based on diameter-neighbor codebook partition algorithm. Different embedding capacity may be achieved by adjusting the iterative parameters during codebook division. The experimental results prove that the presented AMR-WB steganography may provide higher and flexible embedding capacity without inducing perceptible distortion compared with the state-of-the-art methods. With 48 iterations of cluster merging, twice the embedding capacity of complementary-neighbor-vertices-based embedding method may be obtained with a decrease of only around 2% in speech quality and much the same undetectability. Moreover, both the quality of stego speech and the security regarding statistical steganalysis are better than the recent speech steganography based on neighbor-index-division codebook partition.
Low-frequency noise from large wind turbines.
Møller, Henrik; Pedersen, Christian Sejer
2011-06-01
As wind turbines get larger, worries have emerged that the turbine noise would move down in frequency and that the low-frequency noise would cause annoyance for the neighbors. The noise emission from 48 wind turbines with nominal electric power up to 3.6 MW is analyzed and discussed. The relative amount of low-frequency noise is higher for large turbines (2.3-3.6 MW) than for small turbines (≤ 2 MW), and the difference is statistically significant. The difference can also be expressed as a downward shift of the spectrum of approximately one-third of an octave. A further shift of similar size is suggested for future turbines in the 10-MW range. Due to the air absorption, the higher low-frequency content becomes even more pronounced, when sound pressure levels in relevant neighbor distances are considered. Even when A-weighted levels are considered, a substantial part of the noise is at low frequencies, and for several of the investigated large turbines, the one-third-octave band with the highest level is at or below 250 Hz. It is thus beyond any doubt that the low-frequency part of the spectrum plays an important role in the noise at the neighbors. © 2011 Acoustical Society of America
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...
A flexible spatial scan statistic with a restricted likelihood ratio for detecting disease clusters.
Tango, Toshiro; Takahashi, Kunihiko
2012-12-30
Spatial scan statistics are widely used tools for detection of disease clusters. Especially, the circular spatial scan statistic proposed by Kulldorff (1997) has been utilized in a wide variety of epidemiological studies and disease surveillance. However, as it cannot detect noncircular, irregularly shaped clusters, many authors have proposed different spatial scan statistics, including the elliptic version of Kulldorff's scan statistic. The flexible spatial scan statistic proposed by Tango and Takahashi (2005) has also been used for detecting irregularly shaped clusters. However, this method sets a feasible limitation of a maximum of 30 nearest neighbors for searching candidate clusters because of heavy computational load. In this paper, we show a flexible spatial scan statistic implemented with a restricted likelihood ratio proposed by Tango (2008) to (1) eliminate the limitation of 30 nearest neighbors and (2) to have surprisingly much less computational time than the original flexible spatial scan statistic. As a side effect, it is shown to be able to detect clusters with any shape reasonably well as the relative risk of the cluster becomes large via Monte Carlo simulation. We illustrate the proposed spatial scan statistic with data on mortality from cerebrovascular disease in the Tokyo Metropolitan area, Japan. Copyright © 2012 John Wiley & Sons, Ltd.
Evaluating a k-nearest neighbours-based classifier for locating faulty areas in power systems
Directory of Open Access Journals (Sweden)
Juan José Mora Flórez
2008-09-01
Full Text Available This paper reports a strategy for identifying and locating faults in a power distribution system. The strategy was based on the K-nearest neighbours technique. This technique simply helps to estimate a distance from the features used for describing a particu-lar fault being classified to the faults presented during the training stage. If new data is presented to the proposed fault locator, it is classified according to the nearest example recovered. A characterisation of the voltage and current measurements obtained at one single line end is also presented in this document for assigning the area in the case of a fault in a power system. The pro-posed strategy was tested in a real power distribution system, average 93% confidence indexes being obtained which gives a good indicator of the proposal’s high performance. The results showed how a fault could be located by using features obtained from voltage and current, improving utility response and thereby improving system continuity indexes in power distribution sys-tems.
Discovery of Nearest Known Brown Dwarf
2003-01-01
perfectly suited to the search for objects with large proper motions and extreme colours, such as brown dwarfs in the Solar vicinity. Everything is moving - a question of perspective In astronomy, the `proper motion' of a star signifies its apparent motion on the celestial sphere; it is usually expressed in arcseconds per year [4]. The corresponding, real velocity of a star (in kilometres per second) can only be estimated if the distance is known. A star with a large proper motion may indicate a real large velocity or simply that the star is close to us. By analogy, an airplane just after takeoff has a much lower true speed than when it's cruising at high altitude, but to an observer watching near an airport, the departing airplane seems to be moving much more quickly across the sky. Proxima Centauri, our nearest stellar neighbour, is just 4.2 light-years away (cf. ESO PR 22/02) and has a proper motion of 3.8 arcsec/year (corresponding to 23 km/sec relative to the Sun, in the direction perpendicular to the line-of-sight). The highest known proper motion star is Barnard's Star at 6 light-years distance and moving 10 arcsec/year (87 km/sec relative to the Sun). All known stars within 30 light-years are high-proper-motion objects and move at least 0.2 arcsec/year. Trawling for fast moving objects For some time, astronomers at the Astrophysical Institute in Potsdam have been making a systematic computerised search for high-proper-motion objects which appear on red photographic sky plates, but not on the equivalent blue plates. Their goal is to identify hitherto unknown cool objects in the Solar neighbourhood. They had previously found a handful of new objects within 30 light-years in this way, but nothing as red or moving remotely as fast as the one they have now snared in the constellation of Indus in the southern sky. This object was only seen on the very longest-wavelength plates in the SuperCOSMOS Sky Survey database. It was moving so quickly that on plates taken just two
The Influence of Neighbor Effect and Urbanization Toward Organ Donation in Thailand.
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.
Electronic structure of B-doped diamond: A first-principles study
Directory of Open Access Journals (Sweden)
T. Oguchi
2006-01-01
Full Text Available Electronic structure of B-doped diamond is studied based on first-principles calculations with supercell models for substitutional and interstitial doping at 1.5–3.1 at.% B concentrations. Substitutional doping induces holes around the valence-band maximum in a rigid-band fashion. The nearest neighbor C site to B shows a large energy shift of 1s core state, which may explain reasonably experimental features in recent photoemission and X-ray absorption spectra. Doping at interstitial Td site is found to be unstable compared with that at the substitutional site
Magnetic-field dependence of impurity-induced muon depolarization in noble metals
International Nuclear Information System (INIS)
Schillaci, M.E.; Heffner, R.H.; Hutson, R.L.; Leon, M.; Cooke, D.W.; Dodds, S.A.; Richards, P.M.; MacLaughlin, D.E.; Boekema, C.
1983-01-01
We have measured the magnetic-field dependence of the muon depolarization rate up to 5 kOe in AuGd (350 ppM), AgGd (340 ppM) and AgEr (300 ppM). A simple model which includes both dipolar and nearest-neighbor contact interactions between the muon and the magnetic impurity does not fit the data. An axial crystal-field interaction, arising from the electric-field gradient induced by the muon at the site of the impurity, is found to dominate the Hamiltonian, and may have a large effect on the field dependence
Magnetic field dependence of impurity-induced muon depolarization in noble metals
Energy Technology Data Exchange (ETDEWEB)
Schillaci, M.E.; Heffner, R.H.; Hutson, R.L.; Leon, M.; Cooke, D.W.; Yaouanc, A. (Los Alamos National Lab., NM (USA)); Dodds, S.A. (Rice Univ., Houston, TX (USA). Dept. of Physics); Richards, P.M. (Sandia National Labs., Albuquerque, NM (USA)); MacLaughlin, D.E. (California Univ., Riverside (USA)); Boekema, C. (Texas Tech Univ., Lubbock (USA))
1984-01-01
The authors have measured the magnetic field dependence of the muon depolarization rate up to 5 kOe in AuGd (350 ppm), AgGd (340 ppm) and AgEr (300 ppm). A simple model which includes both dipolar and nearest-neighbor contact interactions between the muon and the magnetic impurity does not fit the data. An axial crystal-field interaction, arising from the electric field gradient induced by the muon at the site of the impurity, is found to dominate the Hamiltonian, and may have a large effect on the field dependence.
Exploring neighborhoods in the metagenome universe.
Aßhauer, Kathrin P; Klingenberg, Heiner; Lingner, Thomas; Meinicke, Peter
2014-07-14
The variety of metagenomes in current databases provides a rapidly growing source of information for comparative studies. However, the quantity and quality of supplementary metadata is still lagging behind. It is therefore important to be able to identify related metagenomes by means of the available sequence data alone. We have studied efficient sequence-based methods for large-scale identification of similar metagenomes within a database retrieval context. In a broad comparison of different profiling methods we found that vector-based distance measures are well-suitable for the detection of metagenomic neighbors. Our evaluation on more than 1700 publicly available metagenomes indicates that for a query metagenome from a particular habitat on average nine out of ten nearest neighbors represent the same habitat category independent of the utilized profiling method or distance measure. While for well-defined labels a neighborhood accuracy of 100% can be achieved, in general the neighbor detection is severely affected by a natural overlap of manually annotated categories. In addition, we present results of a novel visualization method that is able to reflect the similarity of metagenomes in a 2D scatter plot. The visualization method shows a similarly high accuracy in the reduced space as compared with the high-dimensional profile space. Our study suggests that for inspection of metagenome neighborhoods the profiling methods and distance measures can be chosen to provide a convenient interpretation of results in terms of the underlying features. Furthermore, supplementary metadata of metagenome samples in the future needs to comply with readily available ontologies for fine-grained and standardized annotation. To make profile-based k-nearest-neighbor search and the 2D-visualization of the metagenome universe available to the research community, we included the proposed methods in our CoMet-Universe server for comparative metagenome analysis.
Contrasting demographic histories of the neighboring bonobo and chimpanzee
DEFF Research Database (Denmark)
Hvilsom, Christina; Carlsen, Frands; Heller, Rasmus
2014-01-01
of the neighboring bonobo remained constant. The changes in population size are likely linked to changes in habitat area due to climate oscillations during the late Pleistocene. Furthermore, the timing of population expansion for the rainforest-adapted chimpanzee is concurrent with the expansion of the savanna...
Dependence centrality similarity: Measuring the diversity of profession levels of interests
Yan, Deng-Cheng; Li, Ming; Wang, Bing-Hong
2017-08-01
To understand the relations between developers and software, we study a collaborative coding platform from the perspective of networks, including follower networks, dependence networks and developer-project bipartite networks. Through the analyzing of degree distribution, PageRank and degree-dependent nearest neighbors' centrality, we find that the degree distributions of all networks have a power-law form except the out-degree distributions of dependence networks. The nearest neighbors' centrality is negatively correlated with degree for developers but fluctuates around the average for projects. In order to measure the diversity of profession levels of interests, a new index called dependence centrality similarity is proposed and the correlation between dependence centrality similarity and degree is investigated. The result shows an obvious negative correlations between dependence centrality similarity and degree.
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.
A nearest-neighbour discretisation of the regularized stokeslet boundary integral equation
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.
2017-09-01
KEY MANAGEMENT FOR SECURE NEIGHBOR DISCOVERY IN A DECENTRALIZED WIRELESS SENSOR NETWORK by Kelvin T. Chew September 2017 Thesis Advisor...and to the Office of Management and Budget, Paperwork Reduction Project (0704-0188) Washington, DC 20503. 1. AGENCY USE ONLY (Leave blank) 2. REPORT...DATE September 2017 3. REPORT TYPE AND DATES COVERED Master’s thesis 4. TITLE AND SUBTITLE SYMMETRIC LINK KEY MANAGEMENT FOR SECURE NEIGHBOR
Efficient protein structure search using indexing methods.
Kim, Sungchul; Sael, Lee; Yu, Hwanjo
2013-01-01
Understanding functions of proteins is one of the most important challenges in many studies of biological processes. The function of a protein can be predicted by analyzing the functions of structurally similar proteins, thus finding structurally similar proteins accurately and efficiently from a large set of proteins is crucial. A protein structure can be represented as a vector by 3D-Zernike Descriptor (3DZD) which compactly represents the surface shape of the protein tertiary structure. This simplified representation accelerates the searching process. However, computing the similarity of two protein structures is still computationally expensive, thus it is hard to efficiently process many simultaneous requests of structurally similar protein search. This paper proposes indexing techniques which substantially reduce the search time to find structurally similar proteins. In particular, we first exploit two indexing techniques, i.e., iDistance and iKernel, on the 3DZDs. After that, we extend the techniques to further improve the search speed for protein structures. The extended indexing techniques build and utilize an reduced index constructed from the first few attributes of 3DZDs of protein structures. To retrieve top-k similar structures, top-10 × k similar structures are first found using the reduced index, and top-k structures are selected among them. We also modify the indexing techniques to support θ-based nearest neighbor search, which returns data points less than θ to the query point. The results show that both iDistance and iKernel significantly enhance the searching speed. In top-k nearest neighbor search, the searching time is reduced 69.6%, 77%, 77.4% and 87.9%, respectively using iDistance, iKernel, the extended iDistance, and the extended iKernel. In θ-based nearest neighbor serach, the searching time is reduced 80%, 81%, 95.6% and 95.6% using iDistance, iKernel, the extended iDistance, and the extended iKernel, respectively.
A sequence-dependent rigid-base model of DNA
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.
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 initialization method for the k-means using the concept of useful nearest centers
Ismkhan, Hassan
2017-01-01
The aim of the k-means is to minimize squared sum of Euclidean distance from the mean (SSEDM) of each cluster. The k-means can effectively optimize this function, but it is too sensitive for initial centers (seeds). This paper proposed a method for initialization of the k-means using the concept of useful nearest center for each data point.
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.
Practical characterization of large networks using neighborhood information
Wang, Pinghui; Zhao, Junzhou; Ribeiro, Bruno; Lui, John C. S.; Towsley, Don; Guan, Xiaohong
2018-01-01
querying a node also reveals partial structural information about its neighbors. Our methods are optimized for NoSQL graph databases (if the database can be accessed directly), or utilize Web APIs available on most major large networks for graph sampling
International Nuclear Information System (INIS)
Miyajima, Takao; Kudo, Yoshihiro; Wakahara, Akihiro; Yamaguchi, Tomohiro; Araki, Tsutomu; Nanishi, Yasushi
2006-01-01
We compared the local structure around In atoms in microwave-excited MOCVD- and MBE-grown InN film which indicates an absorption edge at 1.9 and 0.8 eV, respectively. The co-ordination numbers of the 1st-nearest neighbor N atoms and the 2nd-nearest neighbor In atoms for MBE-grown InN were n(N)=3.9±0.5 and n(In)=12.4±0.9, which are close to the ideal value of n(N)=4 and n(In)=12 for InN without defects, respectively. By thermal annealing, the structure of MBE-grown InN was changed from InN to In 2 O 3 , and the absorption edge was changed from 0.8 to 3.5 eV. However, the microwave-excited MOCVD-grown InN had no structure of In 2 O 3 , and had the reduced co-ordination numbers of the 2nd-nearest neighbor In atoms of n(In)=10.6-11.7. From these results, we conclude that the origin of the 1.9-eV absorption edge of InN is the imperfections (defects) of the In lattice sites of InN, rather than the generation of In 2 O 3 , which has a bandgap energy of 3.5 eV. (copyright 2006 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim) (orig.)
Directory of Open Access Journals (Sweden)
Abdullah Abdullah
2017-12-01
The classification of copra quality with the help of computer by using image processing can help to speed up human work. Data mining techniques can be utilized for copra quality classification based on RGB color (red, green, blue and texture (energy, contrast, correlation, homogeneity. The problem is the difficulty in predicting the quality of copra in grade of A (80-85%, grade of B (70-75% and grade of C (60-65%. The purpose of this study is to develope an application for the classification of copra quality based on color and texture. The method used is the nearest mean classifier (NMC. Preprocessing is done before the classification process for background subtraction by using pixel subtraction method to separate the image of object against the background. The benefits of this research are it can save time in classifying the quality of copra and can facilitate the determination of copra price. Based on the evaluation result by using cross validation method obtained the average accuracy is 80.67% with standard deviation is 1.17%. Keywords: classification, image, copra, nearest mean classifier, pixel subtraction, RGB color, texture
Fringe field interference of neighbor magnets in China spallation neutron source
International Nuclear Information System (INIS)
Li, L.; Kang, W.; Wu, X.; Deng, C.D.; Li, S.; Yang, M.; Zhou, J.X.; Liu, Y.Q.; Wu, Y.W.
2016-01-01
In CSNS accelerator construction, the field measurement of all RCS magnets have been finished and the magnets have been installed in the tunnel before the end of 2015. The electromagnetic quadrupoles have a large aperture and the core-to-core distance between magnets is rather short in some places. The corrector magnet or the sextupole magnet is closer to one of the quadrupole magnets which caused certain interference. The interference caused by magnetic fringe field has been appeared and it becomes a significant issue in beam dynamics for beam loss control in this high-intensity proton accelerator. We have performed 3D computing simulations to study integral field distributions between the quadrupole and the corrector magnets, and the sextupole and the other quadrupole magnets. The effect of the magnetic fringe field and the interference has been investigated with different distances of the neighbor magnets. The simulation and the field measurement results will be introduced in this paper.
RINGED SUBSTRUCTURE AND A GAP AT 1 au IN THE NEAREST PROTOPLANETARY DISK
Energy Technology Data Exchange (ETDEWEB)
Andrews, Sean M.; Wilner, David J.; Bai, Xue-Ning; Öberg, Karin I.; Ricci, Luca [Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States); Zhu, Zhaohuan [Department of Astrophysical Sciences, Princeton University, 4 Ivy Lane, Peyton Hall, Princeton, NJ 08544 (United States); Birnstiel, Tilman [Max-Planck-Institut für Astronomie, Königstuhl 17, D-69117 Heidelberg (Germany); Carpenter, John M. [Joint ALMA Observatory (JAO), Alonso de Cordova 3107, Vitacura-Santiago de Chile (Chile); Pérez, Laura M. [Max-Planck-Institut für Radioastronomie, Auf dem Hügel 69, D-53121 Bonn (Germany); Hughes, A. Meredith [Department of Astronomy, Wesleyan University, Van Vleck Observatory, 96 Foss Hill Drive, Middletown, CT 06457 (United States); Isella, Andrea, E-mail: sandrews@cfa.harvard.edu [Department of Physics and Astronomy, Rice University, 6100 Main Street, Houston, TX 77005 (United States)
2016-04-01
We present long baseline Atacama Large Millimeter/submillimeter Array (ALMA) observations of the 870 μm continuum emission from the nearest gas-rich protoplanetary disk, around TW Hya, that trace millimeter-sized particles down to spatial scales as small as 1 au (20 mas). These data reveal a series of concentric ring-shaped substructures in the form of bright zones and narrow dark annuli (1–6 au) with modest contrasts (5%–30%). We associate these features with concentrations of solids that have had their inward radial drift slowed or stopped, presumably at local gas pressure maxima. No significant non-axisymmetric structures are detected. Some of the observed features occur near temperatures that may be associated with the condensation fronts of major volatile species, but the relatively small brightness contrasts may also be a consequence of magnetized disk evolution (the so-called zonal flows). Other features, particularly a narrow dark annulus located only 1 au from the star, could indicate interactions between the disk and young planets. These data signal that ordered substructures on ∼au scales can be common, fundamental factors in disk evolution and that high-resolution microwave imaging can help characterize them during the epoch of planet formation.
Weremijewicz, Joanna; Sternberg, Leonel da Silveira Lobo O'Reilly; Janos, David P
2016-10-01
Arbuscular mycorrhizal (AM) fungi interconnect plants in common mycorrhizal networks (CMNs) which can amplify competition among neighbors. Amplified competition might result from the fungi supplying mineral nutrients preferentially to hosts that abundantly provide fixed carbon, as suggested by research with organ-cultured roots. We examined whether CMNs supplied (15) N preferentially to large, nonshaded, whole plants. We conducted an intraspecific target-neighbor pot experiment with Andropogon gerardii and several AM fungi in intact, severed or prevented CMNs. Neighbors were supplied (15) N, and half of the target plants were shaded. Intact CMNs increased target dry weight (DW), intensified competition and increased size inequality. Shading decreased target weight, but shaded plants in intact CMNs had mycorrhizal colonization similar to that of sunlit plants. AM fungi in intact CMNs acquired (15) N from the substrate of neighbors and preferentially allocated it to sunlit, large, target plants. Sunlit, intact CMN, target plants acquired as much as 27% of their nitrogen from the vicinity of their neighbors, but shaded targets did not. These results suggest that AM fungi in CMNs preferentially provide mineral nutrients to those conspecific host individuals best able to provide them with fixed carbon or representing the strongest sinks, thereby potentially amplifying asymmetric competition below ground. © 2016 The Authors. New Phytologist © 2016 New Phytologist Trust.
Applying cost-sensitive classification for financial fraud detection under high class-imbalance
CSIR Research Space (South Africa)
Moepya, SO
2014-12-01
Full Text Available , sensitivity, specificity, recall and precision using PCA and Factor Analysis. Weighted Support Vector Machines (SVM) were shown superior to the cost-sensitive Naive Bayes (NB) and K-Nearest Neighbors classifiers....
Optimizing Neighbor Discovery for Ad hoc Networks based on the Bluetooth PAN Profile
DEFF Research Database (Denmark)
Kuijpers, Gerben; Nielsen, Thomas Toftegaard; Prasad, Ramjee
2002-01-01
IP layer neighbor discovery mechanisms rely highly on broadcast/multicast capabilities of the underlying link layer. The Bluetooth personal area network (PAN) profile has no native link layer broadcast/multicast capabilities and can only emulate this by repeatedly unicast link layer frames....... This paper introduces a neighbor discovery mechanism that utilizes the resources in the Bluetooth PAN profile more efficient. The performance of the new mechanism is investigated using a IPv6 network simulator and compared with emulated broadcasting. It is shown that the signaling overhead can...
Global 30m Height Above the Nearest Drainage
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
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
Transfer-Efficient Face Routing Using the Planar Graphs of Neighbors in High Density WSNs
Directory of Open Access Journals (Sweden)
Eun-Seok Cho
2017-10-01
Full Text Available Face routing has been adopted in wireless sensor networks (WSNs where topological changes occur frequently or maintaining full network information is difficult. For message forwarding in networks, a planar graph is used to prevent looping, and because long edges are removed by planarization and the resulting planar graph is composed of short edges, and messages are forwarded along multiple nodes connected by them even though they can be forwarded directly. To solve this, face routing using information on all nodes within 2-hop range was adopted to forward messages directly to the farthest node within radio range. However, as the density of the nodes increases, network performance plunges because message transfer nodes receive and process increased node information. To deal with this problem, we propose a new face routing using the planar graphs of neighboring nodes to improve transfer efficiency. It forwards a message directly to the farthest neighbor and reduces loads and processing time by distributing network graph construction and planarization to the neighbors. It also decreases the amount of location information to be transmitted by sending information on the planar graph nodes rather than on all neighboring nodes. Simulation results show that it significantly improves transfer efficiency.
Directory of Open Access Journals (Sweden)
Yunwei Tang
2016-11-01
Full Text Available This study explores the ability of WorldView-2 (WV-2 imagery for bamboo mapping in a mountainous region in Sichuan Province, China. A large area of this place is covered by shadows in the image, and only a few sampled points derived were useful. In order to identify bamboos based on sparse training data, the sample size was expanded according to the reflectance of multispectral bands selected using the principal component analysis (PCA. Then, class separability based on the training data was calculated using a feature space optimization method to select the features for classification. Four regular object-based classification methods were applied based on both sets of training data. The results show that the k-nearest neighbor (k-NN method produced the greatest accuracy. A geostatistically-weighted k-NN classifier, accounting for the spatial correlation between classes, was then applied to further increase the accuracy. It achieved 82.65% and 93.10% of the producer’s and user’s accuracies respectively for the bamboo class. The canopy densities were estimated to explain the result. This study demonstrates that the WV-2 image can be used to identify small patches of understory bamboos given limited known samples, and the resulting bamboo distribution facilitates the assessments of the habitats of giant pandas.
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...
2010-10-08
... DEPARTMENT OF HOUSING AND URBAN DEVELOPMENT [Docket No. FR-5380-N-36] Notice of Proposed Information Collection: Comment Request; HUD- Owned Real Estate-Good Neighbor Next Door Program AGENCY: Office... information: Title of Proposal: HUD-Owned Real Estate-Good Neighbor Next Door Program. OMB Control Number, if...
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
Simulation studies for optimizing the trigger generation criteria for the TACTIC telescope
International Nuclear Information System (INIS)
Koul, M.K.; Tickoo, A.K.; Dhar, V.K.; Venugopal, K.; Chanchalani, K.; Rannot, R.C.; Yadav, K.K.; Chandra, P.; Kothari, M.; Koul, R.
2011-01-01
In this paper, we present the results of Monte Carlo simulations of γ-ray and cosmic-ray proton induced extensive air showers as detected by the TACTIC atmospheric Cherenkov imaging telescope for optimizing its trigger field of view and topological trigger generation scheme. The simulation study has been carried out at several zenith angles. The topological trigger generation uses a coincidence of two or three nearest neighbor pixels for producing an event trigger. The results of this study suggest that a trigger field of 11x11 pixels (∼3.4 0 x3.4 0 ) is quite optimum for achieving maximum effective collection area for γ-rays from a point source. With regard to optimization of topological trigger generation, it is found that both two and three nearest neighbor pixels yield nearly similar results up to a zenith angle of 25 0 with a threshold energy of ∼1.5TeV for γ-rays. Beyond zenith angle of 25 0 , the results suggest that a two-pixel nearest neighbor trigger should be preferred. Comparison of the simulated integral rates has also been made with corresponding measured values for validating the predictions of the Monte Carlo simulations, especially the effective collection area, so that energy spectra of sources (or flux upper limits in case of no detection) can be determined reliably. Reasonably good matching of the measured trigger rates (on the basis of ∼207h of data collected with the telescope in NN-2 and NN-3 trigger configurations) with that obtained from simulations reassures that the procedure followed by us in estimating the threshold energy and detection rates is quite reliable. - Highlights: → Optimization of the trigger field of view and topological trigger generation for the TACTIC telescope. → Monte Carlo simulations of extensive air showers carried out using CORSIKA code. → Trigger generation with two or three nearest neighbor pixels yield similar results up to a zenith angle of 25 deg. → Reasonably good matching of measured trigger
Local and neighboring patch conditions alter sex-specific movement in banana weevils.
Carval, Dominique; Perrin, Benjamin; Duyck, Pierre-François; Tixier, Philippe
2015-12-01
Understanding the mechanisms underlying the movements and spread of a species over time and space is a major concern of ecology. Here, we assessed the effects of an individual's sex and the density and sex ratio of conspecifics in the local and neighboring environment on the movement probability of the banana weevil, Cosmopolites sordidus. In a "two patches" experiment, we used radiofrequency identification tags to study the C. sordidus movement response to patch conditions. We showed that local and neighboring densities of conspecifics affect the movement rates of individuals but that the density-dependent effect can be either positive or negative depending on the relative densities of conspecifics in local and neighboring patches. We demonstrated that sex ratio also influences the movement of C. sordidus, that is, the weevil exhibits nonfixed sex-biased movement strategies. Sex-biased movement may be the consequence of intrasexual competition for resources (i.e., oviposition sites) in females and for mates in males. We also detected a high individual variability in the propensity to move. Finally, we discuss the role of demographic stochasticity, sex-biased movement, and individual heterogeneity in movement on the colonization process.
Jørs, Erik; Konradsen, Flemming; Huici, Omar; Morant, Rafael C; Volk, Julie; Lander, Flemming
2016-01-01
Teaching farmers integrated pest management (IPM) in farmer field schools (FFS) has led to reduced pesticide use and safer handling. This article evaluates the long-term impact of training farmers on IPM and the diffusion of knowledge from trained farmers to neighboring farmers, a subject of importance to justify training costs and to promote a healthy and sustainable agriculture. Training on IPM of farmers took place from 2002 to 2004 in their villages in La Paz County, Bolivia, whereas dissemination of knowledge from trained farmer to neighboring farmer took place until 2009. To evaluate the impact of the intervention, self-reported knowledge and practice on pesticide handling and IPM among trained farmers (n = 23) and their neighboring farmers (n = 47) were analyzed in a follow-up study and compared in a cross-sectional analysis with a control group of farmers (n = 138) introduced in 2009. Variables were analyzed using χ2 test and analysis of variance (ANOVA). Trained farmers improved and performed significantly better in all tested variables than their neighboring farmers, although the latter also improved their performance from 2002 to 2009. Including a control group showed an increasing trend in all variables, with the control farmers having the poorest performance and trained farmers the best. The same was seen in an aggregated variable where trained farmers had a mean score of 16.55 (95% confidence interval [CI]: 15.45-17.65), neighboring farmers a mean score of 11.97 (95% CI: 10.56-13.38), and control farmers a mean score of 9.18 (95% CI: 8.55-9.80). Controlling for age and living altitude did not change these results. Trained farmers and their neighboring farmers improved and maintained knowledge and practice on IPM and pesticide handling. Diffusion of knowledge from trained farmers might explain the better performance of the neighboring farmers compared with the control farmers. Dissemination of knowledge can contribute to justify the cost and convince
Cryptosporidiosis in Saudi Arabia and neighboring countries
International Nuclear Information System (INIS)
Areeshi, Mohammed Y.; Hart, C.A.; Beeching, N.J.
2007-01-01
Cryptosporidium is a coccidian protozoan parasite of the intestinal tract that causes severe and sometimes fatal watery diarrhea in immunocompromised patients and self-limiting but prolonged diarrheal disease in immunocompetent individuals. It exists naturally in animals and can be zoonotic. Although cryptosporidiosis is a significant cause of diarrheal disease in both developing and developed countries, it is more prevalent in developing countries and in tropical environments. We examined the epidemiology and disease burden of Cryptosporidium in Saudi Arabia and neighboring countries by reviewing 23 published studies of Cryptosporidium and etiology of diarrhea in between 1986 and 2006. The prevalence of Cryptosporidium infection in human's ranged from 1% to 37% with a median of 4%, while in animals it was for different species of animals and geographic locations of the studies. Most cases of cryptosporidiosis occurred among children less than 7 years of age and particularly in the first two years of life. The seasonality of Cryptosporidium varied depending on the geographic locations of the studies but it generally most prevalent in the rainy season. The most commonly identified species was Cryptosporidium parvum while C.hominis was detected only in one study from Kuwait. The cumulative experience from Saudi Arabia and four neighboring countries (Kuwait, Oman, Jordan and Iraq) suggest that Cryptosporidium is an important cause of diarrhea in human and cattle. However, the findings of this review also demonstrate the limitations of the available data regarding Cryptosporidium species and strains in circulation in these countries. (author)
Directory of Open Access Journals (Sweden)
Y. Erfanifard
2014-06-01
Full Text Available Distance methods and their estimators of density may have biased measurements unless the studied stand of trees has a random spatial pattern. This study aimed at assessing the effect of spatial arrangement of wild pistachio trees on the results of density estimation by using the nearest individual sampling method in Zagros woodlands, Iran, and applying a correction factor based on the spatial pattern of trees. A 45 ha clumped stand of wild pistachio trees was selected in Zagros woodlands and two random and dispersed stands with similar density and area were simulated. Distances from the nearest individual and neighbour at 40 sample points in a 100 × 100 m grid were measured in the three stands. The results showed that the nearest individual method with Batcheler estimator could not calculate density correctly in all stands. However, applying the correction factor based on the spatial pattern of the trees, density was measured with no significant difference in terms of the real density of the stands. This study showed that considering the spatial arrangement of trees can improve the results of the nearest individual method with Batcheler estimator in density measurement.
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.
Effect of Floquet engineering on the p-wave superconductor with second-neighbor couplings
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.
Random close packing of hard spheres and disks
International Nuclear Information System (INIS)
Berryman, J.G.
1983-01-01
A simple definition of random close packing of hard spheres is presented, and the consequences of this definition are explored. According to this definition, random close packing occurs at the minimum packing fraction eta for which the median nearest-neighbor radius equals the diameter of the spheres. Using the radial distribution function at more dilute concentrations to estimate median nearest-neighbor radii, lower bounds on the critical packing fraction eta/sub RCP/ are obtained and the value of eta/sub RCP/ is estimated by extrapolation. Random close packing is predicted to occur for eta/sub RCP/ = 0.64 +- 0.02 in three dimensions and eta/sub RCP/ = 0.82 +- 0.02 in two dimensions. Both of these predictions are shown to be consistent with the available experimental data
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
Quantum Correlation in Matrix Product States of One-Dimensional Spin Chains
International Nuclear Information System (INIS)
Zhu Jing-Min
2015-01-01
For our proposed composite parity-conserved matrix product state (MPS), if only a spin block length is larger than 1, any two such spin blocks have correlation including classical correlation and quantum correlation. Both the total correlation and the classical correlation become larger than that in any subcomponent; while the quantum correlations of the two nearest-neighbor spin blocks and the two next-nearest-neighbor spin blocks become smaller and for other conditions the quantum correlation becomes larger, i.e., the increase or the production of the long-range quantum correlation is at the cost of reducing the short-range quantum correlation, which deserves to be investigated in the future; and the ration of the quantum correlation to the total correlation monotonically decreases to a steady value as the spacing spin length increasing. (paper)
Meat and fish freshness inspection system based on odor sensing.
Najam ul Hasan; Ejaz, Naveed; Ejaz, Waleed; Kim, Hyung Seok
2012-11-09
We propose a method for building a simple electronic nose based on commercially available sensors used to sniff in the market and identify spoiled/contaminated meat stocked for sale in butcher shops. Using a metal oxide semiconductor-based electronic nose, we measured the smell signature from two of the most common meat foods (beef and fish) stored at room temperature. Food samples were divided into two groups: fresh beef with decayed fish and fresh fish with decayed beef. The prime objective was to identify the decayed item using the developed electronic nose. Additionally, we tested the electronic nose using three pattern classification algorithms (artificial neural network, support vector machine and k-nearest neighbor), and compared them based on accuracy, sensitivity, and specificity. The results demonstrate that the k-nearest neighbor algorithm has the highest accuracy.
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.
Raman scattering mediated by neighboring molecules
Williams, Mathew D.; Bradshaw, David S.; Andrews, David L.
2016-05-01
Raman scattering is most commonly associated with a change in vibrational state within individual molecules, the corresponding frequency shift in the scattered light affording a key way of identifying material structures. In theories where both matter and light are treated quantum mechanically, the fundamental scattering process is represented as the concurrent annihilation of a photon from one radiation mode and creation of another in a different mode. Developing this quantum electrodynamical formulation, the focus of the present work is on the spectroscopic consequences of electrodynamic coupling between neighboring molecules or other kinds of optical center. To encompass these nanoscale interactions, through which the molecular states evolve under the dual influence of the input light and local fields, this work identifies and determines two major mechanisms for each of which different selection rules apply. The constituent optical centers are considered to be chemically different and held in a fixed orientation with respect to each other, either as two components of a larger molecule or a molecular assembly that can undergo free rotation in a fluid medium or as parts of a larger, solid material. The two centers are considered to be separated beyond wavefunction overlap but close enough together to fall within an optical near-field limit, which leads to high inverse power dependences on their local separation. In this investigation, individual centers undergo a Stokes transition, whilst each neighbor of a different species remains in its original electronic and vibrational state. Analogous principles are applicable for the anti-Stokes case. The analysis concludes by considering the experimental consequences of applying this spectroscopic interpretation to fluid media; explicitly, the selection rules and the impact of pressure on the radiant intensity of this process.