The Inner Magnetospheric Imager (IMI): Instrument heritage and orbit viewing analysis

For the last two years an engineering team in the Program Development Office at MSFC has been doing design studies for the proposed Inner Magnetospheric Imager (IMI) mission. This team had a need for more information about the instruments that this mission would carry so that they could get a better handle on instrument volume, mass, power, and telemetry needs as well as information to help assess the possible cost of such instruments and what technology development they would need. To get this information, an extensive literature search was conducted as well as interviews with several members of the IMI science working group. The results of this heritage survey are summarized below. There was also a need to evaluate the orbits proposed for this mission from the stand point of their suitability for viewing the various magnetospheric features that are planned for this mission. This was accomplished by first, identifying the factors which need to be considered in selecting an orbit, second, translating these considerations into specific criteria, and third, evaluating the proposed orbits against these criteria. The specifics of these criteria and the results of the orbit analysis are contained in the last section of this report

Copula Processes

We define a copula process which describes the dependencies between arbitrarily many random variables independently of their marginal distributions. As an example, we develop a stochastic volatility model, Gaussian Copula Process Volatility (GCPV), to predict the latent standard deviations of a sequence of random variables. To make predictions we use Bayesian inference, with the Laplace approximation, and with Markov chain Monte Carlo as an alternative. We find both methods comparable. We also find our model can outperform GARCH on simulated and financial data. And unlike GARCH, GCPV can easily handle missing data, incorporate covariates other than time, and model a rich class of covariance structures.Comment: 11 pages, 1 table, 1 figure. Submitted for publication. Since last version: minor edits and reformattin

Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP)

We introduce a new structured kernel interpolation (SKI) framework, which generalises and unifies inducing point methods for scalable Gaussian processes (GPs). SKI methods produce kernel approximations for fast computations through kernel interpolation. The SKI framework clarifies how the quality of an inducing point approach depends on the number of inducing (aka interpolation) points, interpolation strategy, and GP covariance kernel. SKI also provides a mechanism to create new scalable kernel methods, through choosing different kernel interpolation strategies. Using SKI, with local cubic kernel interpolation, we introduce KISS-GP, which is 1) more scalable than inducing point alternatives, 2) naturally enables Kronecker and Toeplitz algebra for substantial additional gains in scalability, without requiring any grid data, and 3) can be used for fast and expressive kernel learning. KISS-GP costs O(n) time and storage for GP inference. We evaluate KISS-GP for kernel matrix approximation, kernel learning, and natural sound modelling.Comment: 19 pages, 4 figure

Fire Retardancy in 2001

Fire is a world-wide problem which claims lives and causes significant loss of property. Some of the problems are discussed and the solution delineated. This peer-reviewed volume is designed to be as the state-of-the-art. This chapter provides a perspective for current work