The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative. Results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis and a "real-time" experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. A long-term tracking subchallenge has been introduced to the set of standard VOT sub-challenges. The new subchallenge focuses on long-term tracking properties, namely coping with target disappearance and reappearance. A new dataset has been compiled and a performance evaluation methodology that focuses on long-term tracking capabilities has been adopted. The VOT toolkit has been updated to support both standard short-term and the new longterm tracking subchallenges. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website 60 .

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We study the exclusive semileptonic and nonleptonic decays of the first radial excited heavy-light pseudoscalars 𝐷𝑞(2𝑆) and 𝐵𝑞(2𝑆) (𝑞 = 𝑢, 𝑑, 𝑠) with the improved Bethe-Salpeter (B-S) method which takes into account the relativistic effects in wave functions and transition matrix elements.

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Contributions to the design and analysis of statistical experiments are grouped into five chapters. The minimisation of mean squared error over a design region has been given as a criterion for designing an experiment by Box and Draper. Their fundamental theorem is extended to the case of the general linear hypothesis. Some numerical results are given for polynomial regression designs. The bordering method of inverting matrices is used to solve the usual least squares linear algebra equations. It is found that this method will in theory provide accurate orthogonal functions and tests for linear dependence and the rank of the matrix. Rounding errors are measured in a numerical example solved on a computer. Chapter three is an investigation of the usefulness of order statistics obtained from the analysis of variance, in the case where we test "treatment" sums of squares all having the same number of degrees of freedom. Considerable use is made of the idea that the ordered data should be plotted on the relevant probability paper as a pictorial aid. This assists the investigator to avoid the pitfalls of carrying out standard tests such as F tests under conditions where it is disadvantageous to do so. Even in unroplicated experiments one may see immediately that the usual hypothesis of normality is wrong, 4 or that the assumption of additivity may not hold. In chapter four a solution is given to the problem of fitting a curve subject to the constraint that the slope should be non-negative throughout the region of interest, This is treated as a problem in quadratic programming with a single linear parametric constraint. Chapter five contains a brief investigation into the problems of fitting a model which is non-linear overall but which consists of separate linear models joined at location parameters which have to be estimated. 5 Acknowledgments