Indenture, Marshall County, 17 December 1840

https://egrove.olemiss.edu/aldrichcorr_b/1019/thumbnail.jp

Feedback-enhanced algorithm for aberration correction of holographic atom traps

We show that a phase-only spatial light modulator can be used to generate non-trivial light distributions suitable for trapping ultracold atoms, when the hologram calculation is included within a simple and robust feedback loop that corrects for imperfect device response and optical aberrations. This correction reduces the discrepancy between target and experimental light distribution to the level of a few percent (RMS error). We prove the generality of this algorithm by applying it to a variety of target light distributions of relevance for cold atomic physics.Comment: 5 pages, 4 figure

Discovery of Resolved Debris Disk Around HD 131835

We report the discovery of the resolved disk around HD 131835 and present the analysis and modeling of its thermal emission. HD 131835 is a ~15 Myr A2 star in the Scorpius-Centaurus OB association at a distance of 122.7 +16.2 -12.8 parsec. The extended disk has been detected to ~1.5" (200 AU) at 11.7 {\mu}m and 18.3 {\mu}m with T-ReCS on Gemini South. The disk is inclined at an angle of ~75{\deg} with the position angle of ~61{\deg}. The flux of HD 131835 system is 49.3+-7.6 mJy and 84+-45 mJy at 11.7 {\mu}m and 18.3 {\mu}m respectively. A model with three grain populations gives a satisfactory fit to both the spectral energy distribution and the images simultaneously. This best-fit model is composed of a hot continuous power-law disk and two rings. We characterized the grain temperature profile and found that the grains in all three populations are emitting at temperatures higher than blackbodies. In particular, the grains in the continuous disk are unusually warm; even when considering small graphite particles as the composition.Comment: 11 pages, 5 figures, Accepted for Publication in Ap

Dynamic Pattern Matching Using Temporal Data Mining for Demand Forecasting

Traditional time series methods are designed to analyze historical data and develop models to explain the observed behaviors and then predict future value(s) through the extrapolation from the models. The underlying premise is that the future values should follow the path of the historical data analyzed by the time series methods, and as such, these methods necessitate a significant amount of historical data to validate the model. However, this assumption may not make sense for applications, such as demand forecasting, where the characteristics of the time series may alter frequently because of the changes of consumers’ behavior and/or cooperate strategies such as promotions. As the product life cycle gets shorter as it tends to be in today’s e-business, it becomes increasingly difficult to make a forecast using traditional time series methods. In response to this challenge, this paper proposes a novel pattern matching procedure to decide whether one or combination of several patterns actually represents the development of the time series and then to use the patterns in forecasting. Several pattern transformation algorithms are also proposed to facilitate a flexible match. Rematching through dynamic reevaluation of the new data may be needed until the true development of the time series is discovered. Initial evaluation indicates superior performance in predicting the demand of a new product