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The traditional Self-Organize Map (SOM) method is used for the arrangement of seabed nodes in this paper. If the distance between the nodes and the events is long, these nodes cannot be victory nodes and they will be abandoned, because they cannot move to the direction of events, and as a result they are not being fully utilized and are destroying the balance of energy consumption in the network. Aiming at this problem, this paper proposes an improved self-organize map algorithm with the introduction of the probability-selection mechanism in Gibbs sampling to select victory nodes, thus optimizing the selection strategy for victory nodes. The simulation results show that the Improved Self-Organize Map (ISOM) algorithm can balance the energy consumption in the network and prolong the network lifetime. Compared with the traditional self-organize map algorithm, the adopting of the improved self-organize map algorithm can make the event driven coverage rate increase about 3%.

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We present a work in progress aimed at extracting translation pairs of source and target dependency treelets to be used in a dependency-based machine translation system. We introduce a novel unsupervised method for parallel tree segmentation based on Gibbs sampling. Using the data from a Czech-English parallel treebank, we show that the procedure converges to a dictionary containing reasonably sized treelets; in some cases, the segmentation seems to have interesting linguistic interpretations.


Spatial environmental heterogeneity are well known characteristics of field forest genetic trials, even in small experiments (<1ha) established under seemingly uniform conditions and intensive site management. In such trials, it is commonly assumed that any simple type of experimental field design based on randomization theory, as a completely randomized design (CRD), should account for any of the minor site variability. However, most published results indicate that in these types of trials harbor a large component of the spatial variation which commonly resides in the error term. Here we applied a two-dimensional smoothed surface in an individual-tree mixed model, using tensor product of linear, quadratic and cubic B-spline bases with different and equal number of knots for rows and columns, to account for the environmental spatial variability in two relatively small (i.e., 576 m2 and 5,705 m2) forest genetic trials, with large multiple-tree contiguous plot configurations. In general, models accounting for site variability with a two-dimensional surface displayed a lower value of the deviance information criterion than the classical RCD. Linear B-spline bases may yield a reasonable description of the environmental variability, when a relatively small amount of information available. The mixed models fitting a smoothed surface resulted in a reduction in the posterior means of the error variance (σ2 e), an increase in the posterior means of the additive genetic variance (σ2 a) and heritability (h 2 HT), and an increase of 16.05% and 46.03% (for parents) or 11.86% and 44.68% (for offspring) in the accuracy of breeding values, respectively in the two experiments.


Based on progressively Type-II censored samples, this paper deals with the estimation of R = P(X < Y) when X and Y come from two independent inverted exponentiated rayleigh distributions with different shape parameters, but having the same scale parameter. The maximum likelihood estimator and UMVUE of R is obtained. Different confidence intervals are presented. The Bayes estimator of R and the corresponding credible interval using the Gibbs sampling technique are also proposed. Monte Carlo simulations are performed to compare the performances of the different methods. One illustrative example is provided to demonstrate the application of the proposed method.

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: Proc. 6th world congress on genetics applied to livestock production, Armidale. Univ. of New England, NSW. pp. 509-512. SEARLE, S. R. and ROUNSAVILLE, T. R. (1974): A note on estimating covariance components. Am. Stat. 28: 67-68. VAN TASSEL, C. P., CASELLA, G. and POLLAK, E. J. (1995): Effects of selection on estimates of variance components using Gibbs sampling and restricted maximum likelihood. J. Dairy Sci. 78: 678-692. VAN VLECK, L. D. (1968): Selection bias in estimation of the genetic correlation. Biometrics 24: 951-962. VISSCHER, P. M. (1998): On the sampling

Bayesian Treatment of the IBM Alignment Models. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Stroudsburg, PA, USA, 2013. Association for Computational Linguistics. Gelfand, Alan E. and Adrian F. M. Smith. Gibbs Sampling for Marginal Posterior Expectations. Technical report, Department of Statistics, Stanford University, 1991. Heafield, Kenneth, Ivan Pouzyrevsky, Jonathan H. Clark, and Philipp Koehn. Scalable Modified Kneser-Ney Language Model Estimation. In Proceedings