The neutron total cross sections for Th 232 and U 238 have been measured from about 100 eV to 4 keV with ^0.5 nsec/m resolution at the highest energies. The mean level spacings for these nuclei have been observed to be (17.5±0.7) eV and (17.7±0.7) eV. For U 238 about 17% of the observed levels probably belong to / = 1 and are omitted from the statistical analysis of the data. S-wave strength functions of (0.69±0.07)X10~4 and (0.90=b0.10)X10~~4 are obtained for Th 232 and U 238 , respectively. Detailed comparisons of the observed statistical aspects of the level spacings and neutron reduced width distributions have been made with the predictions of various theoretical models. The distributions of r n° for both nuclei seem to be consistent with a single-channel Porter-Thomas distribution. The observed distributions of nearest-and next-nearest-neighbor level spacings agree quite well with the expected distributions for real symmetric Hamiltonian matrices with randomly distributed matrix elements. The correlation coefficients for Th 232 and U 238 are, respectively: (a) (-0.21db0.07) and (-0.26±0.08) for adjacent level spacings, (b) (-0.03 ±0.07) and (-0.17i0.07) for adjacent reduced neutron widths, and (c) (0.12dz0.07) and (0.15±0.07) for the neutron reduced width of a level and the average of its spacing from adjacent levels.

Abstract. Many potential applications exist where a fast and robust detection of human faces is required. Different cues can be used for this purpose. Since each cue has its own pros and cons we, in this paper, suggest to combine several complimentary cues in order to gain more robustness in face detection. Concretely, we apply skin-color, shape, and texture to build a robust detector. We define the face detection problem in a state-space spanned by position, scale, and rotation. The statespace is searched using a Particle Filter where 80% of the particles are predicted from the past frame, 10% are chosen randomly and 10% are from a texture-based detector. The likelihood of each selected particle is evaluated using the skin-color and shape cues. We evaluate the different cues separately as well as in combination. An improvement in both detection rates and false positives is obtained when combining them.

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