David A. Yuen

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We obtained likelihoods in the lower mantle for long-wavelength models of bulk sound and shear wave speed, density, and boundary topography, compatible with gravity constraints, from normal mode splitting functions and surface wave data. Taking into account the large uncertainties in Earth's thermodynamic reference state and the published range of mineral(More)
It is widely acknowledged that an important aspect in Earth Science research is the ability to visualize huge amounts of data resulting from numerical simulations or data acquisition. Whether the data is a simple two-dimensional plot or a three-dimensional multivariate grid, the ability to visualize the data is imperative for researchers to properly(More)
Data sets from large-scale simulations (up to 501<sup>3</sup> grid points) of mantle convection are analyzed with volume rendering of the temperature field and a new critical point analysis of the velocity field. As the Rayleigh number <i>Ra</i> is increased the thermal field develops increasingly thin plume-like structures along which heat is convected.(More)
We present a novel technique based on a multi-resolutional clustering and nonlinear multi-dimensional scaling of earthquake patterns to investigate observed and synthetic seismic catalogs. The observed data represent seismic activities around the Japanese islands during 1997–2003. The synthetic data were generated by numerical simulations for various cases(More)
Mixing of particles by chaotic jlow fields was simula(ed on (he Connection 14achine. We assigned each cell to the processor and kept [he coordinates ofpar(icles residing on the cell in the local memory of the processor. This approach implies the exchange between the local memories, when a particle moves from one cell (o another. Approximately I@ particles(More)
This paper presents a new method for detecting clusters of microcalcifications in high-resolution digital mammograms. Using cluster analysis, we have designed a descriptive set of mammogram image features which enables precise recognition of microcalcifications. These features are fed into the Support Vector Machine classifier trained to discriminate(More)
The most frequent symptoms of ductal carcinoma recognised by mammography are clusters of microcalcifications. Their detection from mammograms is difficult, especially for glandular breasts. We present a new computer-aided detection system for small field digital mammography in planning of breast biopsy. The system processes the mammograms in several steps.(More)
We have employed two pattern recognition methods used commonly for face recognition in order to analyse digital mammograms. The methods are based on novel classification schemes, the AdaBoost and the support vector machines (SVM). A number of tests have been carried out to evaluate the accuracy of these two algorithms under different circumstances. Results(More)
Dissipative particle dynamics (DPD) and its generalization – the fluid particle model (FPM)-represent the " fluid particle " approach for simulating fluid-like behavior in the mesoscale. Unlike particles from molecular dynamics (MD) method, the " fluid particle " can be viewed as a " droplet " consisting of liquid molecules. In FPM, " fluid particles "(More)
We present the initial architecture and implementation of VLab, a Grid and Web Service-based system for enabling distributed and collaborative computational chemistry and material science applications for the study of planetary materials. The requirements of VLab include job preparation and submission, job monitoring, data storage and analysis, and(More)