The absorpt~on of microw~ves in a normally magnetized YIG disk has been measured and analyzed. The ~bsorptiOn peaks are mterpreted to be caused by magnetostatic waves propagating radially across the disk. The mode numbers of absorption peaks are determined by the aspect ratio of the disk, (W~y)2_ H ~ and BiHi _(~/y)2, where w is the microwave angular frequency, y is the gyromagnetic ratio,. and Hi and Bi are mtern~1 magnetic fields and depend upon the applied magnetic field. The experimental values of the applied magnetic field at which the absorption peaks are caused are in good agreement with the theoretical values. It has been verified both theoretically and experimentally that the new mode absorptions due to the magnetostatic waves exist in the case of wly- [B,(0) >0, where B,(O) and H, (O) are the internal fields at the disk center. These new mode abosrption peaks tend to shift toward the cutoff point of the usual absorption spectra as the incident microwave power is increased.

We present an analysis of charm quark fragmentation at 10.6 GeV, based on a data sample of 103 fb −1 collected by the Belle detector at the KEKB accelerator. We consider fragmentation into the main charmed hadron ground states, namely D 0 , D + , D + s and Λ + c , as well as the excited states D * 0 and D * + . The fragmentation functions are important to measure as they describe processes at a low energy scale, where calculations in perturbation theory lead to large uncertainties. Fragmentation functions can also be used as input distributions for Monte Carlo generators. Additionally, we determine the average number of these charmed hadrons produced per B decay at the Υ(4S) resonance and measure the distribution of their production angle in e + e − annihilation events and in B decays.

In this paper, we consider the problem of discovering interesting substructures from a large collection of semi-structured data in the framework of optimized pattern discovery. We model semi-structured data and patterns with labeled ordered trees, and present an efficient algorithm that discovers the best labeled ordered trees that optimize a given statistical measure, such as the information entropy and the classification accuracy, in a collection of semi-structured data. We give theoretical analyses of the computational complexity of the algorithm for patterns with bounded and unbounded size. Experiments show that the algorithm performs well and discovered interesting patterns on real datasets.