Mt. Pleasant Church, Conewago Township

About two miles south of McSherrystown and a similar distance southwest of Hanover, in Conewago Township, lies the small village of Mt. Pleasant. The community developed at and near the intersection of State Route 194, commonly called the Hanover-Littlestown Pike, and Legislative Route 01005, known in days past as the road from McSherrystown to Gitt\u27s Mill and its segment south of the intersection called in recent times Narrow Drive. In the eastern quadrant of the intersection, a church was built in 1878; nearby and adjacent to the crossroads sat a public school, which had been built sometime before 1858. The school was known by two names, Mt. Pleasant and Schwartz\u27s, and the village itself was also called by some people Schwartz\u27s or Schwartz\u27s Schoolhouse. Further, like the church, the school had religious significance to residents of the vicinity. [excerpt

March into Oblivion

The Whiskey Rebellion often is assigned, even by historians, to an obscurity which belies its significance. Its importance was major not only to the people most affected by its cause and those most intimately involved in the playing out of the events, but also to the young federal government, which had to demonstrate its authority yet not trample its own citizens. The situation held a very real potential for tearing apart the fragile nation. President George Washington felt strongly enough about it to involve himself personally in the beginnings of the military action. In the last few years of the century, rapid improvement in economics, safety, and foreign relations, surely spurred in part by the government\u27s reactions to the insurrection, underscored the importance to the nation as a whole. [excerpt

Linguistics and LIS: A Research Agenda

Linguistics and Library and Information Science (LIS) are both interdisciplinary fields that draws from areas such as languages, psychology, sociology, cognitive science, computer science, anthropology, education, and management. The theories and methods of linguistic research can have significant explanatory power for LIS. This article presents a research agenda for LIS that proposes the use of linguistic analysis methods, including discourse analysis, typology, and genre theory

Algorithms and Speech

One of the central questions in free speech jurisprudence is what activities the First Amendment encompasses. This Article considers that question in the context of an area of increasing importance – algorithm-based decisions. I begin by looking to broadly accepted legal sources, which for the First Amendment means primarily Supreme Court jurisprudence. That jurisprudence provides for very broad First Amendment coverage, and the Court has reinforced that breadth in recent cases. Under the Court’s jurisprudence the First Amendment (and the heightened scrutiny it entails) would apply to many algorithm-based decisions, specifically those entailing substantive communications. We could of course adopt a limiting conception of the First Amendment, but any nonarbitrary exclusion of algorithm-based decisions would require major changes in the Court’s jurisprudence. I believe that First Amendment coverage of algorithm-based decisions is too small a step to justify such changes. But insofar as we are concerned about the expansiveness of First Amendment coverage, we may want to limit it in two areas of genuine uncertainty: editorial decisions that are neither obvious nor communicated to the reader, and laws that single out speakers but do not regulate their speech. Even with those limitations, however, an enormous and growing amount of activity will be subject to heightened scrutiny absent a fundamental reorientation of First Amendment jurisprudence

The rational SPDE approach for Gaussian random fields with general smoothness

A popular approach for modeling and inference in spatial statistics is to represent Gaussian random fields as solutions to stochastic partial differential equations (SPDEs) of the form $L^{\beta}u = \mathcal{W}$, where $\mathcal{W}$ is Gaussian white noise, $L$ is a second-order differential operator, and $\beta>0$ is a parameter that determines the smoothness of $u$. However, this approach has been limited to the case $2\beta\in\mathbb{N}$, which excludes several important models and makes it necessary to keep $\beta$ fixed during inference. We propose a new method, the rational SPDE approach, which in spatial dimension $d\in\mathbb{N}$ is applicable for any $\beta>d/4$, and thus remedies the mentioned limitation. The presented scheme combines a finite element discretization with a rational approximation of the function $x^{-\beta}$ to approximate $u$. For the resulting approximation, an explicit rate of convergence to $u$ in mean-square sense is derived. Furthermore, we show that our method has the same computational benefits as in the restricted case $2\beta\in\mathbb{N}$. Several numerical experiments and a statistical application are used to illustrate the accuracy of the method, and to show that it facilitates likelihood-based inference for all model parameters including $\beta$.Comment: 28 pages, 4 figure

Spatial models generated by nested stochastic partial differential equations, with an application to global ozone mapping

A new class of stochastic field models is constructed using nested stochastic partial differential equations (SPDEs). The model class is computationally efficient, applicable to data on general smooth manifolds, and includes both the Gaussian Mat\'{e}rn fields and a wide family of fields with oscillating covariance functions. Nonstationary covariance models are obtained by spatially varying the parameters in the SPDEs, and the model parameters are estimated using direct numerical optimization, which is more efficient than standard Markov Chain Monte Carlo procedures. The model class is used to estimate daily ozone maps using a large data set of spatially irregular global total column ozone data.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS383 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org