Utilizing Astrometric Orbits to Obtain Coronagraphic Images

We present an approach for utilizing astrometric orbit information to improve the yield of planetary images and spectra from a follow-on direct detection mission. This approach is based on the notion-strictly hypothetical-that if a particular star could be observed continuously, the instrument would in time observe all portions of the habitable zone so that no planet residing therein could be missed. This strategy could not be implemented in any realistic mission scenario. But if an exoplanet's orbit is known from astrometric observation, then it may be possible to plan and schedule a sequence of imaging observations that is the equivalent of continuous observation. A series of images-optimally spaced in time-could be recorded to examine contiguous segments of the orbit. In time, all segments would be examined, leading to the inevitable detection of the planet. In this paper, we show how astrometric orbit information can be used to construct such a sequence. Using stars from astrometric and imaging target lists, we find that the number of observations in this sequence typically ranges from 2 to 7, representing the maximum number of observations required to find the planet. The probable number of observations ranges from 1.5 to 3.1. This is a dramatic improvement in efficiency over previous methods proposed for utilizing astrometric orbits. We examine how the implementation of this approach is complicated and limited by operational constraints. We find that it can be fully implemented for internal coronagraph and visual nuller missions, with a success rate approaching 100%. External occulter missions will also benefit, but to a lesser degree.Comment: 28 pages, 14 figures, submitted to PAS

Highly Optimized Tolerance: Robustness and Power Laws in Complex Systems

We introduce highly optimized tolerance (HOT), a mechanism that connects evolving structure and power laws in interconnected systems. HOT systems arise, e.g., in biology and engineering, where design and evolution create complex systems sharing common features, including (1) high efficiency, performance, and robustness to designed-for uncertainties, (2) hypersensitivity to design flaws and unanticipated perturbations, (3) nongeneric, specialized, structured configurations, and (4) power laws. We introduce HOT states in the context of percolation, and contrast properties of the high density HOT states with random configurations near the critical point. While both cases exhibit power laws, only HOT states display properties (1-3) associated with design and evolution.Comment: 4 pages, 2 figure

Highly Optimized Tolerance: Robustness and Design in Complex Systems

Highly optimized tolerance (HOT) is a mechanism that relates evolving structure to power laws in interconnected systems. HOT systems arise where design and evolution create complex systems sharing common features, including (1) high efficiency, performance, and robustness to designed-for uncertainties, (2) hypersensitivity to design flaws and unanticipated perturbations, (3) nongeneric, specialized, structured configurations, and (4) power laws. We study the impact of incorporating increasing levels of design and find that even small amounts of design lead to HOT states in percolation

Power Laws, Highly Optimized Tolerance, and Generalized Source Coding

We introduce a family of robust design problems for complex systems in uncertain environments which are based on tradeoffs between resource allocations and losses. Optimized solutions yield the “robust, yet fragile” features of highly optimized tolerance and exhibit power law tails in the distributions of events for all but the special case of Shannon coding for data compression. In addition to data compression, we construct specific solutions for world wide web traffic and forest fires, and obtain excellent agreement with measured data

Missing Variables in Theories of Strategic Human Resource Management: Time, Cause, and Individuals

Much progress has been made with regard to theory building and application in the field of Strategic Human Resource Management (HRM) since Wright and McMahan’s (1992) critical review. While researchers have increasingly investigated the impact of HR on economic success within the Resource Based view of the firm, and have developed more middle level theories regarding the processes through which HR impacts firm performance, much work still needs to be done. This paper examines how future theorizing in SHRM should explore the concepts of time, cause, and individuals. Such consideration will drive more longitudinal research, more complex causal models, and consideration of multi-level phenomena

Bus Transit Operational Efficiency Resulting from Passenger Boardings at Park-and-Ride Facilities

In order to save time and money by not driving to an ultimate destination, some urban commuters drive themselves a few miles to specially designated parking lots built for transit customers and located where trains or buses stop. The focus of this paper is the effect Park-and-Ride (P&R) lots have on the efficiency of bus transit as measured in five bus transit systems in the western U.S. This study describes a series of probes with models and data to find objective P&R influence measures that, when combined with other readily-available data, permit a quantitative assessment of the significance of P&R on transit efficiency. The authors developed and describe techniques that examine P&R as an influence on transit boardings at bus stops and on bus boardings along an entire route. The regression results reported are based on the two in-depth case studies for which sufficient data were obtained to examine (using econometric techniques) the effects of park-and-ride availability on bus transit productivity. Both Ordinary Least Square (OLS) regression and Poisson regression are employed. The results from the case studies suggest that availability of parking near bus stops is a stronger influence on transit ridership than residential housing near bus stops. Results also suggest that expanding parking facilities near suburban park-and-ride lots increases the productivity of bus operations as measured by ridership per service hour. The authors also illustrate that reasonable daily parking charges (compared to the cost of driving to much more expensive parking downtown) would provide sufficient capital to build and operate new P&R capacity without subsidy from other revenue sources

A New Method for Protecting Interrelated Time Series with Bayesian Prior Distributions and Synthetic Data

Organizations disseminate statistical summaries of administrative data via the Web for unrestricted public use. They balance the trade-off between confidentiality protection and inference quality. Recent developments in disclosure avoidance techniques include the incorporation of synthetic data, which capture the essential features of underlying data by releasing altered data generated from a posterior predictive distribution. The United States Census Bureau collects millions of interrelated time series micro-data that are hierarchical and contain many zeros and suppressions. Rule-based disclosure avoidance techniques often require the suppression of count data for small magnitudes and the modification of data based on a small number of entities. Motivated by this problem, we use zero-inflated extensions of Bayesian Generalized Linear Mixed Models (BGLMM) with privacy-preserving prior distributions to develop methods for protecting and releasing synthetic data from time series about thousands of small groups of entities without suppression based on the of magnitudes or number of entities. We find that as the prior distributions of the variance components in the BGLMM become more precise toward zero, confidentiality protection increases and inference quality deteriorates. We evaluate our methodology using a strict privacy measure, empirical differential privacy, and a newly defined risk measure, Probability of Range Identification (PoRI), which directly measures attribute disclosure risk. We illustrate our results with the U.S. Census Bureau’s Quarterly Workforce Indicators