Nuclear orientation of156Tb in gadolinium matrix

The anisotropy of the angular distribution of gamma-rays from the decay of¹⁵⁶Tb, oriented in a gadolinium matrix at low temperatures, has been measured at the angles of 0 and π/2 with respect to the applied magnetic field direction in the range of temperatures from 14·6 to 68·4 mK. The temperature dependence of anisotropy was measured for the first time. The parameters of hyperfine magnetic dipole and electric quadrupole splittings have been determined and the values of the magnetic dipole moment ¦Μ¹⁵⁶¦=(9·6±1·3)×10^?27 J/T and the electric quadrupole momentQ ¹⁵⁶=(2·9±0·9)×10^?28 m² of the¹⁵⁶Tb ground state have been calculated. Multipole mixing ratios andB(E2) branching ratios of many gamma-ray transitions occurring in¹⁵⁶Gd have been found and the results have been discussed in terms of the rotational-vibration and pairing-plus-quadrupole models.     

一种改进的参考点同步模型多媒体同步控制方法

首先介绍了多媒体同步的分类,并指出了实现多种媒体同步的关键就在于建立适当的同步控制模型;接着分析了现在流行的３ 种单一同步模型以及二级同步模型,评价了它们各自的优点与缺陷． 在此基础上,提出了一种改进的参考点同步模型多媒体同步输出控制方法,这种折衷的同步控制方法,在一定程度上弥补了单一同步模型和二级同步模型各自的缺陷．     

Polynomial-time approximation schemes for packing and piercing fat objects

We consider two problems: given a collection of n fat objects in a fixed dimension, (1) ( packing) find the maximum subcollection of pairwise disjoint objects, and (2) ( piercing) find the minimum point set that intersects every object. Recently, Erlebach, Jansen, and Seidel gave a polynomial-time approximation scheme (PTAS) for the packing problem, based on a shifted hierarchical subdivision method. Using shifted quadtrees, we describe a similar algorithm for packing but with a smaller time bound. Erlebach et al.'s algorithm requires polynomial space. We describe a different algorithm, based on geometric separators, that requires only linear space. This algorithm can also be applied to piercing, yielding the first PTAS for that problem.     

Fastiterative methods for least squares estimations

Least squares estimations have been used extensively in many applications, e.g. system identification and signal prediction. When the stochastic process is stationary, the least squares estimators can be found by solving a Toeplitz or near-Toeplitz matrix system depending on the knowledge of the data statistics. In this paper, we employ the preconditioned conjugate gradient method with circulant preconditioners to solve such systems. Our proposed circulant preconditioners are derived from the spectral property of the given stationary process. In the case where the spectral density functions() of the process is known, we prove that ifs() is a positive continuous function, then the spectrum of the preconditioned system will be clustered around 1 and the method converges superlinearly. However, if the statistics of the process is unknown, then we prove that with probability 1, the spectrum of the preconditioned system is still clustered around 1 provided that large data samples are taken. For finite impulse response (FIR) system identification problems, our numerical results show that annth order least squares estimator can usually be obtained inO(n logn) operations whenO(n) data samples are used. Finally, we remark that our algorithm can be modified to suit the applications of recursive least squares computations with the proper use of sliding window method arising in signal processing applications.Research supported in part by HKRGC grant no. 221600070, ONR contract no. N00014-90-J-1695 and DOE grant no. DE-FG03-87ER25037.