Hierarchical spatial models for predicting tree species assemblages across large domains

Spatially explicit data layers of tree species assemblages, referred to as forest types or forest type groups, are a key component in large-scale assessments of forest sustainability, biodiversity, timber biomass, carbon sinks and forest health monitoring. This paper explores the utility of coupling georeferenced national forest inventory (NFI) data with readily available and spatially complete environmental predictor variables through spatially-varying multinomial logistic regression models to predict forest type groups across large forested landscapes. These models exploit underlying spatial associations within the NFI plot array and the spatially-varying impact of predictor variables to improve the accuracy of forest type group predictions. The richness of these models incurs onerous computational burdens and we discuss dimension reducing spatial processes that retain the richness in modeling. We illustrate using NFI data from Michigan, USA, where we provide a comprehensive analysis of this large study area and demonstrate improved prediction with associated measures of uncertainty.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS250 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Lasing in Strong Coupling

An almost ideal thresholdless laser can be realized in the strong-coupling regime of light-matter interaction, with Poissonian fluctuations of the field at all pumping powers and all intensities of the field. This ideal scenario is thwarted by quantum nonlinearities when crossing from the linear to the stimulated emission regime, resulting in a universal jump in the second order coherence, which measurement could however be used to establish a standard of lasing in strong coupling.Comment: 5 pages, 2 figure

Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets

Spatial process models for analyzing geostatistical data entail computations that become prohibitive as the number of spatial locations become large. This manuscript develops a class of highly scalable Nearest Neighbor Gaussian Process (NNGP) models to provide fully model-based inference for large geostatistical datasets. We establish that the NNGP is a well-defined spatial process providing legitimate finite-dimensional Gaussian densities with sparse precision matrices. We embed the NNGP as a sparsity-inducing prior within a rich hierarchical modeling framework and outline how computationally efficient Markov chain Monte Carlo (MCMC) algorithms can be executed without storing or decomposing large matrices. The floating point operations (flops) per iteration of this algorithm is linear in the number of spatial locations, thereby rendering substantial scalability. We illustrate the computational and inferential benefits of the NNGP over competing methods using simulation studies and also analyze forest biomass from a massive United States Forest Inventory dataset at a scale that precludes alternative dimension-reducing methods

Observational Constraints on Multi-messenger Sources of Gravitational Waves and High-energy Neutrinos

It remains an open question to what extent many of the astronomical sources of intense bursts of electromagnetic radiation are also strong emitters of non-photon messengers, in particular gravitational waves (GWs) and high-energy neutrinos (HENs). Such emission would provide unique insights into the physics of the bursts; moreover some suspected classes, e.g. choked gamma-ray bursts, may in fact only be identifiable via these alternative channels. Here we explore the reach of current and planned experiments to address this question. We derive constraints on the rate of GW and HEN bursts per Milky Way equivalent (MWE) galaxy based on independent observations by the initial LIGO and Virgo GW detectors and the partially completed IceCube (40-string) HEN detector. We take into account the blue-luminosity-weighted distribution of nearby galaxies, assuming that source distribution follows the blue-luminosity distribution. We then estimate the reach of joint GW+HEN searches using advanced GW detectors and the completed cubic-km IceCube detector to probe the joint parameter space. We show that searches undertaken by advanced detectors will be capable of detecting, constraining or excluding, several existing models with one year of observation

Special Education Fiscal Effort and Juvenile Adjudication: A Financial Relationship Exploration

Educational leaders stretch pennies, often in futile attempts, to provide equitable educational opportunities for all students. An educated populous, or lack thereof, heavily impacts not only students, but also local, state, and national economies, job markets, and public safety. Knowing where to exert public school fiscal effort to reduce juvenile crime is an issue with implications that reach far beyond the field of education. By employing a quantitative approach and analyses examining slopes of fiscal effort for special education and juvenile adjudications, this study explores the relationship between the two factors. The research points out several connections between special education and the social and economic costs of juvenile crime. Findings support placing additional fiscal effort in special education and lower juvenile adjudication; subsequently, creating substantial savings and creating a paradigm shift in public school finance

Space acceleration measurement system triaxial sensor head error budget

The objective of the Space Acceleration Measurement System (SAMS) is to measure and record the microgravity environment for a given experiment aboard the Space Shuttle. To accomplish this, SAMS uses remote triaxial sensor heads (TSH) that can be mounted directly on or near an experiment. The errors of the TSH are reduced by calibrating it before and after each flight. The associated error budget for the calibration procedure is discussed here