Identification of communities is significant in understanding the structures and functions of networks. Since some nodes naturally belong to several communities, the study of overlapping communities has attracted incr...
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Identification of communities is significant in understanding the structures and functions of networks. Since some nodes naturally belong to several communities, the study of overlapping communities has attracted increasing attention recently, and many algorithms have been designed to detect overlapping communities. In this paper, an overlapping communities detecting algorithm is proposed whose main strategies are finding an initial partial community from a node with maximal node strength and adding tight nodes to expand the partial community. Seven real-world complex networks and one synthetic network are used to evaluate the algorithm. Experimental results demonstrate that the algorithm proposed is efficient for detecting overlapping communities in weighted networks. (C) 2010 Elsevier B.V. All rights reserved.
Seven thousand four hundred and twenty-three compound feed samples were used to develop near-infrared (NIR) calibrations for predicting the percentage of each ingredient used in the manufacture of a given compound fee...
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Seven thousand four hundred and twenty-three compound feed samples were used to develop near-infrared (NIR) calibrations for predicting the percentage of each ingredient used in the manufacture of a given compound feedingstuff. Spectra were collected at 2 run increments using a FOSS NIRSystems 5000 monochromator. The reference data used for each ingredient percentage were those declared in the formula for each feedingstuff. Two chemometric tools for developing NIRS prediction models were compared: the so-called GLOBAL MPLS (modified partial least squares), traditionally used in developing NIRS applications, and the more recently developed calibration strategy known as local. The local procedure is designed to select, from a large database, samples with spectra resembling the sample being analyzed. Selected samples are used as calibration sets to develop specific MPLS equations for predicting each unknown sample. For all predicted ingredients, local calibrations resulted in a significant improvement in both standard error of prediction (SEP) and bias values compared with GLOBAL calibrations. Determination coefficient values (r(2)) also improved using the local strategy, exceeding 0.90 for most ingredients. Use of the local algorithm for calibration thus proved valuable in minimizing the errors in NIRS calibration equations for predicting a parameter as complex as the percentage of each ingredient in compound feedingstuffs.
In recent years growing interest in local distributed algorithms has widely been observed. This results from their high resistance to errors and damage, as well as from their good performance, which is independent of ...
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In recent years growing interest in local distributed algorithms has widely been observed. This results from their high resistance to errors and damage, as well as from their good performance, which is independent of the size of the network. A local deterministic distributed algorithm finding an approximation of a Minimum Dominating Set in planar graphs has been presented by Lenzen et al., and they proved that the algorithm returns a 130-approximation of the Minimum Dominating Set. In this article we will show that the algorithm is two times more effective than was previously assumed, and we prove that the algorithm by Lenzen et al. outputs a 52-approximation to a Minimum Dominating Set. Therefore the gap between the lower bound and the approximation ratio of the best yet local deterministic distributed algorithm is reduced by half. (C) 2013 Published by Elsevier B.V.
Remote sensing image fusion is an effective way to extract a large volume of data from multi-source images. However, traditional image fusion methods cannot meet the requirements of applications because they can lose ...
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Remote sensing image fusion is an effective way to extract a large volume of data from multi-source images. However, traditional image fusion methods cannot meet the requirements of applications because they can lose spatial information or distort spectral characteristics. In this paper, a new wavelet method based on a local algorithm is presented. The proposed method fuses multi-spectral (MS) and panchromatic (PAN) images to improve spatial information and preserve spectral characteristics. The main advantage of the new fusion method is the exploitation of the dependency between neighboring pixels. SPOT5 MS and PAN images were employed to execute the fusion methods. To compare with the new method, the principal component analysis (PCA), wavelet transformation, and PCA-based wavelet (PCA-FW) image fusion methods were selected. Qualitative and quantitative analyses and classification accuracy assessment were conducted to evaluate the performance of the fusion methods. The results demonstrate that the new wavelet method based on a local algorithm is better than traditional image fusion methods. The new fusion method can achieve a wide range of balance between high spatial resolution retention and spectral characteristic preservation;thus, the new method is suitable for different applications. Crown Copyright (C) 2013 Published by Elsevier Ltd. All rights reserved.
In developing a new method to measure the phase distribution of a light wave utilizing the adaptive control of the pupil function with a liquid crystal panel, the optimization procedure for the adaptive control is sho...
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In developing a new method to measure the phase distribution of a light wave utilizing the adaptive control of the pupil function with a liquid crystal panel, the optimization procedure for the adaptive control is shown to improve when a local algorithm is adopted. The feasibility of the proposed system is confirmed by computer simulation as well as by some basic experiments.
Bisimilarity is one of the most important relations for comparing the behaviour of formal systems in concurrency theory. Decision algorithms for bisimilarity in finite state systems are usually classified into two kin...
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ISBN:
(纸本)9780769539324
Bisimilarity is one of the most important relations for comparing the behaviour of formal systems in concurrency theory. Decision algorithms for bisimilarity in finite state systems are usually classified into two kinds: global algorithms are generally efficient but require to generate the whole state spaces in advance, and local algorithms combine the verification of a system's behaviour with the generation of the system's state space, which is often more effective to determine that one system fails to be related to another. Although local algorithms are well established in the classical concurrency theory, the study of local algorithms in probabilistic concurrency theory is not mature. In this paper we propose a polynomial time local algorithm for checking probabilistic bisimilarity. With mild modification, the algorithm can be easily adapted to decide probabilistic similarity with the same time complexity.
Background: algorithms of sequence alignment are the key instruments for computer-assisted studies of biopolymers. Obviously, it is important to take into account the "quality" of the obtained alignments, i....
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Background: algorithms of sequence alignment are the key instruments for computer-assisted studies of biopolymers. Obviously, it is important to take into account the "quality" of the obtained alignments, i.e. how closely the algorithms manage to restore the "gold standard" alignment (GS-alignment), which superimposes positions originating from the same position in the common ancestor of the compared sequences. As an approximation of the GS-alignment, a 3D-alignment is commonly used not quite reasonably. Among the currently used algorithms of a pair-wise alignment, the best quality is achieved by using the algorithm of optimal alignment based on affine penalties for deletions (the Smith-Waterman algorithm). Nevertheless, the expedience of using local or global versions of the algorithm has not been studied. Results: Using model series of amino acid sequence pairs, we studied the relative "quality" of results produced by local and global alignments versus (1) the relative length of similar parts of the sequences (their "cores") and their nonhomologous parts, and (2) relative positions of the core regions in the compared sequences. We obtained numerical values of the average quality (measured as accuracy and confidence) of the global alignment method and the local alignment method for evolutionary distances between homologous sequence parts from 30 to 240 PAM and for the core length making from 10% to 70% of the total length of the sequences for all possible positions of homologous sequence parts relative to the centers of the sequences. Conclusion: We revealed criteria allowing to specify conditions of preferred applicability for the local and the global alignment algorithms depending on positions and relative lengths of the cores and nonhomologous parts of the sequences to be aligned. It was demonstrated that when the core part of one sequence was positioned above the core of the other sequence, the global algorithm was more stable at longer evolutionary distances a
A dominating set in a graph is a subset of vertices such that every vertex in V belongs to D or has at least one neighbour in D. In this paper we deal with the problem of finding an approximation of the dominating set...
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A dominating set in a graph is a subset of vertices such that every vertex in V belongs to D or has at least one neighbour in D. In this paper we deal with the problem of finding an approximation of the dominating set of minimum size, i.e., the approximation of the minimum dominating set problem (MDS) in a distributed setting. A distributed algorithm that runs in a constant number of rounds, independent of the size of the network, is called local. In research on distributed local algorithms it is commonly assumed that each vertex has an unique identifier. However, as was shown by Goos et al., for certain classes of graphs (for example, lift-closed bounded degree graphs) identifiers are unnecessary and only a port numbering is needed. We confirm that the same remains true for the MDS up to a constant factor in the class of planar graphs. Namely, we present a local deterministic 694-approximation algorithm for the MDS in planar graphs in a model with a port numbering only. Moreover, our algorithm uses only short messages, i.e., in each round each node can send only a -bit message to each of its neighbours.
We show that the largest density of factor of i.i.d. independent sets in the d-regular tree is asymptotically at most (log d)/d as d -> infinity. This matches the lower bound given by previous constructions. It fol...
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We show that the largest density of factor of i.i.d. independent sets in the d-regular tree is asymptotically at most (log d)/d as d -> infinity. This matches the lower bound given by previous constructions. It follows that the largest independent sets given by local algorithms on random d-regular graphs have the same asymptotic density. In contrast, the density of the largest independent sets in these graphs is asymptotically 2(log d)/d. We prove analogous results for Poisson-Galton-Watson trees, which yield bounds for local algorithms on sparse Erdos-Renyi graphs.
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