Background Several approaches are available for evaluating heterogeneity in meta-analysis. Sensitivity analyses are often used, but these are often implemented in various non-standardized ways. Methods We developed an...
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Background Several approaches are available for evaluating heterogeneity in meta-analysis. Sensitivity analyses are often used, but these are often implemented in various non-standardized ways. Methods We developed and implemented sequential and combinatorial algorithms that evaluate the change in between-study heterogeneity as one or more studies are excluded from the calculations. The algorithms exclude studies aiming to achieve either the maximum or the minimum final I-2 below a desired pre-set threshold. We applied these algorithms in databases of meta-analyses of binary outcome and >= 4 studies from Cochrane Database of Systematic Reviews (Issue 4, 2005, n = 1011) and meta-analyses of genetic associations (n = 50). Two I-2 thresholds were used (50% and 25%). Results Both algorithms have succeeded in achieving the pre-specified final I-2 thresholds. Differences in the number of excluded studies varied from 0% to 6% depending on the database and the heterogeneity threshold, while it was common to exclude different specific studies. Among meta-analyses with initial I-2 > 50%, in the large majority [19 (90.5%) and 208 (85.9%) in genetic and Cochrane meta-analyses, respectively] exclusion of one or two studies sufficed to decrease I-2 < 50. Similarly, among meta-analyses with initial I-2 25, in most cases [16 (57.1%) and 382 (81.3%), respectively) exclusion of one or two studies sufficed to decrease heterogeneity even < 25%. The number of excluded studies correlated modestly with initial estimated I-2 (correlation coefficients 0.52-0.68 depending on algorithm used). Conclusions The proposed algorithms can be routinely applied in meta-analyses as standardized sensitivity analyses for heterogeneity. Caution is needed evaluating post hoc which specific studies are responsible for the heterogeneity.
This paper presents an algorithm for simultaneous order identification and parameter estimation of linear, discrete, MIMO system with unknown observability indices. It may be considered as a multivariable extension of...
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This paper presents an algorithm for simultaneous order identification and parameter estimation of linear, discrete, MIMO system with unknown observability indices. It may be considered as a multivariable extension of conventional loss function tests used to detect the order of SISO systems. The algorithm identifies the order of the system by checking the linear dependence of rows of the matrix containing the available input-output data, the rows being arranged according to the assumed values of the pseudoobservability indices. Hence, no previous structure identification is required. Feasibility is tested by simulation experiments.
An approach in which manually written task procedures are used is proposed for generating natural and comprehensive task plans. However, semantic inconsistencies may occur in manually programmed procedures. In this pa...
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An approach in which manually written task procedures are used is proposed for generating natural and comprehensive task plans. However, semantic inconsistencies may occur in manually programmed procedures. In this paper, we propose a script management system on the basis of action-relation rules that have been generated from patterns mined by the Prefixspan algorithm. Action-relation rules generated in an interactive manner act as a guide for eliminating or modifying task plans. This paper presents the principle and user interface of the management system and the result of implementing the proposed method.
Centrality is an important measure to identify the most important actors in a network. This paper discusses the various Centrality Measures used in Social Network Analysis. These measures are tested on complex real-wo...
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ISBN:
(纸本)9781509021949
Centrality is an important measure to identify the most important actors in a network. This paper discusses the various Centrality Measures used in Social Network Analysis. These measures are tested on complex real-world social network data sets such as Video Sharing Networks, Social Interaction Network and Co-Authorship Networks to examine their effects on them. We carry out the correlation analysis of these centralities and plot the results to recommend when to use those centrality measures. Additionally, we introduce a new centrality measure - Cohesion Centrality based on the cohesiveness of a graph, develop its sequential algorithm and further devise a parallel algorithm to implement it.
In this paper, we develop the first rigorous distributed algorithm for link scheduling in the SINR model under any length-monotone sub-linear power assignments. Our algorithms give constant factor approximation guaran...
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ISBN:
(纸本)9781467359443
In this paper, we develop the first rigorous distributed algorithm for link scheduling in the SINR model under any length-monotone sub-linear power assignments. Our algorithms give constant factor approximation guarantees, matching the bounds of the sequential algorithms for these problems, with provable bounds on the running time in terms of the graph topology. We also study a related and fundamental problem of local broadcasting for uniform power levels, and obtain similar bounds. These problems are much more challenging in the SINR model than in the more standard graph based interference models, because of the non-locality of the SINR model. Our algorithms are randomized and crucially rely on physical carrier sensing for the distributed communication steps. We find that the specific wireless device capability of duplex/half-duplex communication significantly impacts the performance. Our main technique involves the distributed computation of affectance and a construct called a ruling, which are likely to be useful in other scheduling problems in the SINR model. We also study the empirical performance of our algorithms, and find that the performance depends on the topology, and the approximation ratio is very close to the best sequential algorithm.
An application of fuzzy modeling to the problem of telecommunications data prediction is proposed in this paper. The model building process is a two-stage sequential algorithm, based on the Orthogonal Least Squares (O...
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ISBN:
(纸本)9781467315074
An application of fuzzy modeling to the problem of telecommunications data prediction is proposed in this paper. The model building process is a two-stage sequential algorithm, based on the Orthogonal Least Squares (OLS) technique. Particularly, the OLS is first employed to partition the input space and determine the number of fuzzy rules and the premise parameters. In the sequel, a second orthogonal estimator determines the input terms which should be included in the consequent part of each fuzzy rule and calculate their parameters. Input selection is automatically performed, given a large input candidate set. Real world telecommunications data are used in order to highlight the characteristics of the proposed forecaster and to provide a comparative analysis with well-established forecasting models.
For Communication of Secret information from one place to another place for different application Cryptography and Steganography are the techniques used most commonly. Usually in cryptography the content of secret mes...
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ISBN:
(纸本)9781509046218
For Communication of Secret information from one place to another place for different application Cryptography and Steganography are the techniques used most commonly. Usually in cryptography the content of secret message is scrambled while in steganography the secret message is embedded into the cover medium. In this paper a high secured model has been developed by combining cryptographic and Steganographic security. In this paper sequential algorithm is used for Steganography and Symmetric XOR algorithm is used for Cryptography.
We propose a primary user (PU) traffic distribution classifier for dynamic spectrum access networks based on multi-hypothesis sequential probability ratio test (MSPRT). In specific, we propose two classifiers: (i) an ...
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ISBN:
(纸本)9781479913510
We propose a primary user (PU) traffic distribution classifier for dynamic spectrum access networks based on multi-hypothesis sequential probability ratio test (MSPRT). In specific, we propose two classifiers: (i) an estimate-then-classify classifier, and (ii) a modified MSPRT classifier based on the average likelihood function considering partial knowledge of the PU traffic parameters. Using the sequential algorithm, we show that our proposed classifiers can achieve higher classification performance compared to the traditional maximum likelihood classifier using constant number of samples.
The Lopsided Lovasz Local Lemma (LLLL) is a powerful probabilistic principle which has been used in a variety of combinatorial constructions. While this principle began as a general statement about probability spaces,...
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ISBN:
(纸本)9781510813311
The Lopsided Lovasz Local Lemma (LLLL) is a powerful probabilistic principle which has been used in a variety of combinatorial constructions. While this principle began as a general statement about probability spaces, it has recently been transformed into a variety of polynomial-time algorithms. The resampling algorithm of Moser & Tardos is the most well-known example of this. A variety of criteria have been shown for the LLLL; the strongest possible criterion was shown by Shearer, and other criteria which are easier to use computationally have been shown by Bissacot et al, Pegden, and Kolipaka & Szegedy. We show a new criterion for the Moser-Tardos algorithm to converge. This criterion is stronger than the LLLL criterion, and in fact can yield better results even than the full Shearer criterion. This is possible because it does not apply in the same generality as the original LLLL; yet, it is strong enough to cover many applications of the LLLL in combinatorics. We show a variety of new bounds and algorithms. A noteworthy application is for k-SAT, with bounded occurences of variables. As shown in Gebauer, Szabo, and Tardos, a k-SAT instance in which every variable appears L ≤ 2~(k+1)/(e(k+1)) times, is satisfiable. Although this bound is asymptotically tight (in k), we improve it to L ≤ (2~(k+1)(1-1/k)~k)/(k-1) - 2/k which can be significantly stronger when k is small. We introduce a new parallel algorithm for the LLLL. While Moser & Tardos described a simple parallel algorithm for the Lovasz Local Lemma, and described a simple sequential algorithm for a form of the Lopsided Lemma, they were not able to combine the two. Our new algorithm applies in nearly all settings in which the sequential algorithm works - this includes settings covered by our new stronger LLLL criterion.
Max-product Belief Propagation (BP) is a popular message-passing algorithm for computing a Maximum-A-Posteriori (MAP) assignment over a distribution represented by a Graphical Model (GM). It has been shown that BP can...
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ISBN:
(纸本)9781510825024
Max-product Belief Propagation (BP) is a popular message-passing algorithm for computing a Maximum-A-Posteriori (MAP) assignment over a distribution represented by a Graphical Model (GM). It has been shown that BP can solve a number of combinatorial optimization problems including minimum weight matching, shortest path, network flow and vertex cover under the following common assumption: the respective Linear Programming (LP) relaxation is tight, i.e., no integrality gap is present. However, when LP shows an integrality gap, no model has been known which can be solved systematically via sequential applications of BP. In this paper, we develop the first such algorithm, coined Blossom-BP, for solving the minimum weight matching problem over arbitrary graphs. Each step of the sequential algorithm requires applying BP over a modified graph constructed by contractions and expansions of blossoms, i.e., odd sets of vertices. Our scheme guarantees termination in O(n~2) of BP runs, where n is the number of vertices in the original graph. In essence, the Blossom-BP offers a distributed version of the celebrated Edmonds' Blossom algorithm by jumping at once over many sub-steps with a single BP. Moreover, our result provides an interpretation of the Edmonds' algorithm as a sequence of LPs.
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