Green's function coupled cluster formulations utilizing extended inner excitations

In this paper we analyze new approximations of the Green's function coupled cluster (GFCC) method where locations of poles are improved by extending the excitation level of inner auxiliary operators. These new GFCC approximations can be categorized as GFCC-i($n,m$) method, where the excitation level of the inner auxiliary operators ($m$) used to describe the ionization potentials and electron affinities effects in the $N$$-$1 and $N$+1 particle spaces is higher than the excitation level ($n$) used to correlate the ground-state coupled cluster wave function for the $N$-electron system. Furthermore, we reveal the so-called "$n$+1" rule in this category (or the GFCC-i($n$,$n$+1) method), which states that in order to maintain size-extensivity of the Green's function matrix elements, the excitation level of inner auxiliary operators $X_p(\omega)$ and $Y_q(\omega)$ cannot exceed $n$+1. We also discuss the role of the moments of coupled cluster equations that in a natural way assures these properties. Our implementation in the present study is focused on the first approximation in this GFCC category, i.e. the GFCC-i(2,3) method. As our first practice, we use the GFCC-i(2,3) method to compute the spectral functions for the N$_2$ and CO molecules in the inner and outer valence regimes. In comparison with the GFCCSD results, the computed spectral functions from the GFCC-i(2,3) method exhibit better agreement with the experimental results and other theoretical results, particularly in terms of providing higher resolution of satellite peaks and more accurate relative positions of these satellite peaks with respect to the main peak positions.Comment: 27 pagers, 5 figure

Detection of facial feature points in three-dimensional space for meal support equipment

学位記番号：理工博乙6

Automatic Image Segmentation by Dynamic Region Merging

This paper addresses the automatic image segmentation problem in a region merging style. With an initially over-segmented image, in which the many regions (or super-pixels) with homogeneous color are detected, image segmentation is performed by iteratively merging the regions according to a statistical test. There are two essential issues in a region merging algorithm: order of merging and the stopping criterion. In the proposed algorithm, these two issues are solved by a novel predicate, which is defined by the sequential probability ratio test (SPRT) and the maximum likelihood criterion. Starting from an over-segmented image, neighboring regions are progressively merged if there is an evidence for merging according to this predicate. We show that the merging order follows the principle of dynamic programming. This formulates image segmentation as an inference problem, where the final segmentation is established based on the observed image. We also prove that the produced segmentation satisfies certain global properties. In addition, a faster algorithm is developed to accelerate the region merging process, which maintains a nearest neighbor graph in each iteration. Experiments on real natural images are conducted to demonstrate the performance of the proposed dynamic region merging algorithm.Comment: 28 pages. This paper is under review in IEEE TI

Elliptic Algebra and Integrable Models for Solitons on Noncummutative Torus T{\cal T}

We study the algebra ${\cal A}_n$ and the basis of the Hilbert space ${\cal H}_n$ in terms of the $\theta$ functions of the positions of $n$ solitons. Then we embed the Heisenberg group as the quantum operator factors in the representation of the transfer matrice of various integrable models. Finally we generalize our result to the generic $\theta$ case.Comment: Talk given by Bo-Yu Hou at the Joint APCTP-Nankai Symposium. Tianjin (PRC), Oct. 2001. To appear in the proceedings, to be published by Int. J. Mod. Phys. B. 7 pages, latex, no figure