High-order harmonic generation and multi-photon ionization of Na-2 in laser fields

In this paper high-order harmonic generation (HHG) spectra and the ionization probabilities of various charge states of small cluster Na-2 in the multiphoton regimes are calculated by using time-dependent local density approximation (TDLDA) for one-colour (1064 nm) and two-colour (1064 nm and 532 nm) ultrashort (25 fs) laser pulses. HHG spectra of Na2 have not the large extent of plateaus due to pronounced collective effects of electron dynamics. In addition, the two-colour laser field can result in the breaking of the symmetry and generation of the even order harmonic such as the second order harmonic. The results of ionization probabilities show that a two-colour laser field can increase the ionization probability of higher charge state.

Angular distribution of characteristic photons after radiative electron capture at strong central fields

We investigate the difference in the angular distribution of Ly-alpha(1) and K alpha(1) photons from hydrogenlike and heliumlike ions of uranium after radiative electron capture to the L shell. The strong anisotropy in the former case is changed to a very small one in the latter case. Our calculations support the observation. The effect takes place even in the limiting case of noninteracting electrons, being caused by the Pauli principle.

The effect of the scalar-isovector meson field on hyperon-rich neutron star matter

We investigate the effect of the calar-isovector delta-meson field on the equation of state (EOS) and composition of hyperonic neutron star matter, and the properties of hyperonic neutron stars within the frame work of the relativistic mean field theory. The influence of the delta-field turns out to be quite different and generally weaker for hyperonic neutron star matter as compared to that for npe mu neutron star matter. We find that inclusion of the delta-field enhances the strangeness content slightly and consequently moderately softens the EOS of neutron star matter in its hyperonic phase. As for the composition of hyperonic star matter, the effect of the delta-field is shown to shift the onset of the negatively-charged (positively-charged) hyperons to slightly lower (higher) densities and to enhance (reduce) their abundances. The influence of the delta-field on the maximum mass of hyperonic neutron stars is found to be fairly weak, where as inclusion of the delta-field turns out to enhance sizably both the radii and the moments of inertia of neutron stars with given masses. It is also shown that the effects of the delta-field on the properties of hyperonic neutron stars remain similar in the case of switching off the Sigma hyperons.

Equivalent-barotropic definition of tropospheric mean temperature

An equivalent-barotropic (EB) description of the tropospheric temperature field is derived from the geostrophic empirical mode (GEM) in the form of a scalar function Gamma(p, phi), where p is pressure and phi is 300-850-mb thickness. Baroclinic parameter phi plays the role of latitude at each longitudinal section. Compared with traditional Eulerian-mean methods, GEM defines a mean field in baroclinic streamfunction space with a time scale much longer than synoptic variability. It prompts an EB concept that is only based on a baroclinic field. Monthly GEM fields are diagnosed from NCEP-NCAR reanalysis data and account for more than 90% of the tropospheric thermal variance. The circumglobal composite of GEM fields exhibits seasonal, zonal, and hemispheric asymmetries, with larger rms errors occurring in winter and in the Northern Hemisphere (NH). Zonally asymmetric features and planetary deviation from EB are seen in the NH winter GEM. Reconstruction of synoptic sections and correlation analysis reveal that the tropospheric temperature field is EB at the leading order and has a 1-day phase lag behind barotropic variations in extratropical regions.

Multi-Scale Vector-Ridge-Detection for Perceptual Organization Without Edges

We present a novel ridge detector that finds ridges on vector fields. It is designed to automatically find the right scale of a ridge even in the presence of noise, multiple steps and narrow valleys. One of the key features of such ridge detector is that it has a zero response at discontinuities. The ridge detector can be applied to scalar and vector quantities such as color. We also present a parallel perceptual organization scheme based on such ridge detector that works without edges; in addition to perceptual groups, the scheme computes potential focus of attention points at which to direct future processing. The relation to human perception and several theoretical findings supporting the scheme are presented. We also show results of a Connection Machine implementation of the scheme for perceptual organization (without edges) using color.

Learning Object-Independent Modes of Variation with Feature Flow Fields

We present a unifying framework in which "object-independent" modes of variation are learned from continuous-time data such as video sequences. These modes of variation can be used as "generators" to produce a manifold of images of a new object from a single example of that object. We develop the framework in the context of a well-known example: analyzing the modes of spatial deformations of a scene under camera movement. Our method learns a close approximation to the standard affine deformations that are expected from the geometry of the situation, and does so in a completely unsupervised (i.e. ignorant of the geometry of the situation) fashion. We stress that it is learning a "parameterization", not just the parameter values, of the data. We then demonstrate how we have used the same framework to derive a novel data-driven model of joint color change in images due to common lighting variations. The model is superior to previous models of color change in describing non-linear color changes due to lighting.

Contextual models for object detection using boosted random fields

We seek to both detect and segment objects in images. To exploit both local image data as well as contextual information, we introduce Boosted Random Fields (BRFs), which uses Boosting to learn the graph structure and local evidence of a conditional random field (CRF). The graph structure is learned by assembling graph fragments in an additive model. The connections between individual pixels are not very informative, but by using dense graphs, we can pool information from large regions of the image; dense models also support efficient inference. We show how contextual information from other objects can improve detection performance, both in terms of accuracy and speed, by using a computational cascade. We apply our system to detect stuff and things in office and street scenes.

Time-dependent wavepacket approach to the influence of intense fields on the population of molecular excited states

The influence of laser-field parameters, such as intensity and pulse width, on the population of molecular excited state is investigated by using the time-dependent wavepacket method. For a two-state system in intense laser fields, the populations in the upper and lower states are given by the wavefunctions obtained by solving the Schrodinger equation through split-operator scheme. The calculation shows that both the laser intensity and the pulse width have a strong effect on the population in molecular excited state, and that as the common feature of light-matter interaction (LMI), the periodic changing of the population with the evolution time in each state can be interpreted by Rabi oscillation and area-theorem. The results illustrate that by controlling these two parameters, the needed population in excited state of interest can be obtained, which provides the foundation of light manipulation of molecular processes. (C) 2005 Elsevier B.V. All rights reserved.

By-paths to forgotten folks; stories of real life in Baptist home mission fields, by Coe Hayne. Ed. by the Department of missionary education, Board of education of the Northern Baptist convention.

http://www.archive.org/details/bypathstoforgott00haynrich

Missionary heroines in eastern lands : woman's work in mission fields / by E.R. Pitman.

http://www.archive.org/details/missionaryheroin00pitmuoft

Uncertainty visualization in 3D scalar data

One problem in most three-dimensional (3D) scalar data visualization techniques is that they often overlook to depict uncertainty that comes with the 3D scalar data and thus fail to faithfully present the 3D scalar data and have risks which may mislead users’ interpretations, conclusions or even decisions. Therefore this thesis focuses on the study of uncertainty visualization in 3D scalar data and we seek to create better uncertainty visualization techniques, as well as to find out the advantages/disadvantages of those state-of-the-art uncertainty visualization techniques. To do this, we address three specific hypotheses: (1) the proposed Texture uncertainty visualization technique enables users to better identify scalar/error data, and provides reduced visual overload and more appropriate brightness than four state-of-the-art uncertainty visualization techniques, as demonstrated using a perceptual effectiveness user study. (2) The proposed Linked Views and Interactive Specification (LVIS) uncertainty visualization technique enables users to better search max/min scalar and error data than four state-of-the-art uncertainty visualization techniques, as demonstrated using a perceptual effectiveness user study. (3) The proposed Probabilistic Query uncertainty visualization technique, in comparison to traditional Direct Volume Rendering (DVR) methods, enables radiologists/physicians to better identify possible alternative renderings relevant to a diagnosis and the classification probabilities associated to the materials appeared on these renderings; this leads to improved decision support for diagnosis, as demonstrated in the domain of medical imaging. For each hypothesis, we test it by following/implementing a unified framework that consists of three main steps: the first main step is uncertainty data modeling, which clearly defines and generates certainty types of uncertainty associated to given 3D scalar data. The second main step is uncertainty visualization, which transforms the 3D scalar data and their associated uncertainty generated from the first main step into two-dimensional (2D) images for insight, interpretation or communication. The third main step is evaluation, which transforms the 2D images generated from the second main step into quantitative scores according to specific user tasks, and statistically analyzes the scores. As a result, the quality of each uncertainty visualization technique is determined.