ANIMAC: A Multiprocessor Architecture for Real-Time Computer Animation

Advances in integrated circuit technology have been largely responsible for the growth of the computer graphics industry. This technology promises additional growth through the remainder of the century. This dissertation addresses how this future technology can be harnessed and used to construct very high performance real-time computer graphics systems. This thesis proposes a new architecture for real-time animation engines. The ANIMAL architecture achieves high performance by utilizing a two-dimensional array of processors that determine visible surfaces in parallel. An array of sixteen processors with only nearest neighbor interprocessor communications can produce real-time shadowed images of scenes containing 100, 000 triangles. The ANIMAL architecture is based upon analysis and simulations of various parallelization techniques. These simulations suggest that the viewing space be spatially subdivided and that each processor produce a visible surface image for several viewing space subvolumes. Simple assignments of viewing space subvolumes to processors are shown to offer high parallel efficiencies. Simulations of parallel algorithms were driven with data derived from real scenes since analysis of scene composition suggested that using simplistic models of scene composition might lead to incorrect results. The ANIMAL architecture required the development of a shadowing algorithm which was tailored to its parallel environment. This algorithm separates shadowing into local and foreign effects. Its implementation allows individual processors to compute shadowing effects for their image regions utilizing only very local information.The design of the ANIMAL processors makes extensive use of new VLSI architectures. A formerly proposed processor per object architecture is used to determine visible surfaces while new processor per object and processor per pixel architectures are used to determine shadowing effects. It is estimated that the ANIMAL architecture can be realized in the early 1990's. Realizing this architecture will require considerable amounts of hardware and capital yet its cost will not be out of line when compared with today's real time computer graphics systems

A Versatile Ethernet Interface

No Abstract

High-resolution, H band Spectroscopy of Be Stars with SDSS-III/APOGEE: I. New Be Stars, Line Identifications, and Line Profiles

APOGEE has amassed the largest ever collection of multi-epoch, high-resolution (R~22,500), H-band spectra for B-type emission line (Be) stars. The 128/238 APOGEE Be stars for which emission had never previously been reported serve to increase the total number of known Be stars by ~6%. We focus on identification of the H-band lines and analysis of the emission peak velocity separations (v_p) and emission peak intensity ratios (V/R) of the usually double-peaked H I and non-hydrogen emission lines. H I Br11 emission is found to preferentially form in the circumstellar disks at an average distance of ~2.2 stellar radii. Increasing v_p toward the weaker Br12--Br20 lines suggests these lines are formed interior to Br11. By contrast, the observed IR Fe II emission lines present evidence of having significantly larger formation radii; distinctive phase lags between IR Fe II and H I Brackett emission lines further supports that these species arise from different radii in Be disks. Several emission lines have been identified for the first time including ~16895, a prominent feature in the spectra for almost a fifth of the sample and, as inferred from relatively large v_p compared to the Br11-Br20, a tracer of the inner regions of Be disks. Unlike the typical metallic lines observed for Be stars in the optical, the H-band metallic lines, such as Fe II 16878, never exhibit any evidence of shell absorption, even when the H I lines are clearly shell-dominated. The first known example of a quasi-triple-peaked Br11 line profile is reported for HD 253659, one of several stars exhibiting intra- and/or extra-species V/R and radial velocity variation within individual spectra. Br11 profiles are presented for all discussed stars, as are full APOGEE spectra for a portion of the sample.Comment: accepted in A

Profiles of psychological flexibility: A latent class analysis of the Acceptance and Commitment Therapy model

There exists uncertainty for clinicians over how the separate sub-component processes of psychological flexibility, a core construct of the Acceptance and Commitment Therapy model, interact and influence distress experienced. The present study (N = 567) employed latent class analysis to (i) identify potential classes (i.e., subgroups) of psychological flexibility based on responses on measures of key sub-component process, and (ii) to examine whether such classes could reliably differentiate levels of self-reported psychological distress and positive and negative emotionality. We found three distinct classes: (i) High Psychological Flexibility, (ii) Moderate Psychological Flexibility, and (iii) Low Psychological Flexibility. Those in the Low Psychology Flexibility class reported highest levels of psychological distress, whereas those in the High Psychological Flexibility class subgroup reported lowest levels of psychological distress. This study provides a clearer view to clinicians of the profile of the broader spectrum of the psychological flexibility model to facilitate change in clients

The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey : baryon acoustic oscillations in the Data Releases 10 and 11 Galaxy samples

We present a one per cent measurement of the cosmic distance scale from the detections of the baryon acoustic oscillations (BAO) in the clustering of galaxies from the Baryon Oscillation Spectroscopic Survey, which is part of the Sloan Digital Sky Survey III. Our results come from the Data Release 11 (DR11) sample, containing nearly one million galaxies and covering approximately 8500 square degrees and the redshift range 0.2 < z < 0.7. We also compare these results with those from the publicly released DR9 and DR10 samples. Assuming a concordance Λ cold dark matter (ΛCDM) cosmological model, the DR11 sample covers a volume of 13 Gpc3 and is the largest region of the Universe ever surveyed at this density. We measure the correlation function and power spectrum, including density-field reconstruction of the BAO feature. The acoustic features are detected at a significance of over 7σ in both the correlation function and power spectrum. Fitting for the position of the acoustic features measures the distance relative to the sound horizon at the drag epoch, rd, which has a value of rd,fid = 149.28 Mpc in our fiducial cosmology. We find DV = (1264 ± 25 Mpc)(rd/rd,fid) at z = 0.32 and DV = (2056 ± 20 Mpc)(rd/rd,fid) at z = 0.57. At 1.0 per cent, this latter measure is the most precise distance constraint ever obtained from a galaxy survey. Separating the clustering along and transverse to the line of sight yields measurements at z = 0.57 of DA = (1421 ± 20 Mpc)(rd/rd,fid) and H = (96.8 ± 3.4 km s−1 Mpc−1)(rd,fid/rd). Our measurements of the distance scale are in good agreement with previous BAO measurements and with the predictions from cosmic microwave background data for a spatially flat CDM model with a cosmological constant.Publisher PDFPeer reviewe

Non-Negative Matrix Factorization for Learning Alignment-Specific Models of Protein Evolution

Models of protein evolution currently come in two flavors: generalist and specialist. Generalist models (e.g. PAM, JTT, WAG) adopt a one-size-fits-all approach, where a single model is estimated from a number of different protein alignments. Specialist models (e.g. mtREV, rtREV, HIVbetween) can be estimated when a large quantity of data are available for a single organism or gene, and are intended for use on that organism or gene only. Unsurprisingly, specialist models outperform generalist models, but in most instances there simply are not enough data available to estimate them. We propose a method for estimating alignment-specific models of protein evolution in which the complexity of the model is adapted to suit the richness of the data. Our method uses non-negative matrix factorization (NNMF) to learn a set of basis matrices from a general dataset containing a large number of alignments of different proteins, thus capturing the dimensions of important variation. It then learns a set of weights that are specific to the organism or gene of interest and for which only a smaller dataset is available. Thus the alignment-specific model is obtained as a weighted sum of the basis matrices. Having been constrained to vary along only as many dimensions as the data justify, the model has far fewer parameters than would be required to estimate a specialist model. We show that our NNMF procedure produces models that outperform existing methods on all but one of 50 test alignments. The basis matrices we obtain confirm the expectation that amino acid properties tend to be conserved, and allow us to quantify, on specific alignments, how the strength of conservation varies across different properties. We also apply our new models to phylogeny inference and show that the resulting phylogenies are different from, and have improved likelihood over, those inferred under standard models

Climate variability and Ross River virus infections in Riverland, South Australia, 1992-2004

Ross River virus (RRV) infection is the most common notifiable vector-borne disease in Australia, with around 6000 cases annually. This study aimed to examine the relationship between climate variability and notified RRV infections in the Riverland region of South Australia in order to set up an early warning system for the disease in temperate-climate regions. Notified data of RRV infections were collected by the South Australian Department of Health. Climatic variables and monthly river flow were provided by the Australian Bureau of Meteorology and South Australian Department of Water, Land and Biodiversity Conservation over the period 1992–2004. Spearman correlation and time-series-adjusted Poisson regression analysis were performed. The results indicate that increases in monthly mean minimum and maximum temperatures, monthly total rainfall, monthly mean Southern Oscillation Index and monthly flow in the Murray River increase the likelihood, but an increase in monthly mean relative humidity decreases the likelihood, of disease transmission in the region, with different time-lag effects. This study demonstrates that a useful early warning system can be developed for local regions based on the statistical analysis of readily available climate data. These early warning systems can be utilized by local public health authorities to develop disease prevention and control activities.P. Bi, J. E. Hiller, A. S. Cameron, Y. Zhang and R. Givne