Spreading Widths of Doorway States

As a function of energy E, the average strength function S(E) of a doorway state is commonly assumed to be Lorentzian in shape and characterized by two parameters, the peak energy E_0 and the spreading width Gamma. The simple picture is modified when the density of background states that couple to the doorway state changes significantly in an energy interval of size Gamma. For that case we derive an approximate analytical expression for S(E). We test our result successfully against numerical simulations. Our result may have important implications for shell--model calculations.Comment: 13 pages, 7 figure

Interaction of Regular and Chaotic States

Modelling the chaotic states in terms of the Gaussian Orthogonal Ensemble of random matrices (GOE), we investigate the interaction of the GOE with regular bound states. The eigenvalues of the latter may or may not be embedded in the GOE spectrum. We derive a generalized form of the Pastur equation for the average Green's function. We use that equation to study the average and the variance of the shift of the regular states, their spreading width, and the deformation of the GOE spectrum non-perturbatively. We compare our results with various perturbative approaches.Comment: 26 pages, 9 figure

Methodology for estimation of total body composition in laboratory mammals

A standardized dissection and chemical analysis procedure was developed for individual animals of several species in the size range mouse to monkey (15 g to 15 kg). The standardized procedure permits rigorous comparisons to be made both interspecifically and intraspecifically of organ weights and gross chemical composition in mammalian species series, and was applied successfully to laboratory mice, hamsters, rats, guinea pigs, and rabbits, as well as to macaque monkeys. The procedure is described in detail

Adaptive statistical pattern classifiers for remotely sensed data

A technique for the adaptive estimation of nonstationary statistics necessary for Bayesian classification is developed. The basic approach to the adaptive estimation procedure consists of two steps: (1) an optimal stochastic approximation of the parameters of interest and (2) a projection of the parameters in time or position. A divergence criterion is developed to monitor algorithm performance. Comparative results of adaptive and nonadaptive classifier tests are presented for simulated four dimensional spectral scan data