New tools in comparative political economy: The database of political institutions.

[Dataset available: http://hdl.handle.net/10411/15987]

The Ursinus Weekly, October 4, 1937

Jing Johnson exhorts students to wake up and cheer • Seagrave, Stoudt given Anderson drama leads • 17 students attend SCM conference at Penn • Mrs. John Lentz passes, college is sorrowed • NYA still needed, Pa. meeting told • 28 seniors commence teaching careers • Dr. McClure attends Muhlenberg ceremony • Antics from catfight to prizefight lay spectators in aisles • Ehlers introduced as frosh president at annual class banquet • Registrar\u27s report shows men gaining on women in enrollment • New standards of IRC make admission difficult • Grizzly Gridder makes first appearance • South to entertain debaters at first fall meeting • Women athletes meet to greet frosh girls at party • Co-eds again to receive equestrian tutoring • Sophomores shine as grizzlies bury Mud Hens; Baker\u27s booters shatter last year\u27s jinx, 1-0 • Co-ed stick squad is almost all veteran • Bears will be first to beat Red Devils • Brodbeck, Freeland win as dorm football starts • Seniors to defend hockey title in stiff co-ed competition • Hows and whats of using the library • Ursinus purchases 7-acre plot adjoining west campus • Mrs. Barnard to speak to college women • Upperclass co-eds to take Little Sisters to movies • Lecrone elected to head business administration group • First forum on October 17 to discuss Far East • Chess players plan tournament, intercollegiate schedule • Manuscript Club invites writers to organizehttps://digitalcommons.ursinus.edu/weekly/1876/thumbnail.jp

A Local Convergence Proof for the Minvar Algorithm for Computing Continuous Piecewise Linear Approximations

The class of continuous piecewise linear (PL) functions represents a useful family of approximants because invertibility can be readily imposed, and if a PL function is invertible, then it can be inverted in closed form. Many applications, arising, for example, in control systems and robotics, involve the simultaneous construction of a forward and inverse system model from data. Most approximation techniques require that separate forward and inverse models be trained, whereas an invertible continuous PL affords, simultaneously, the forward and inverse system model in a single representation. The minvar algorithm computes a continuous PL approximation to data. Local convergence of minvar is proven for the case when the data generating function is itself a PL function and available directly rather than through data

Invertible Piecewise Linear Approximations for Color Reproduction

We consider the use of linear splines with variable knots for the approximation of unknown functions from data, motivated by control and estimation problems arising in color systems management. Unlike most popular nonlinear-in-parameters representations, piecewise linear (PL) functions can be simply inverted in a closed form. For the one-dimensional case, we present a study comparing PL and neural network (NN) approximations for several function families. Preliminary results suggest that PL, in addition to their analytical benefits, are at least competitive with NN in terms of sum square error, computational effort and training time

Piecewise Linear Homeomorphisms: The Scalar Case

The class of piecewise linear homeomorphisms (PLH) provides a convenient functional representation for many applications wherein an approximation to data is required that is invertible in closed form. In this paper we introduce the graph intersection (GI) algorithm for learning piecewise linear scalar functions in two settings: approximation, where an oracle outputs accurate functional values in response to input queries; and estimation, where only a fixed discrete data base of input-output pairs is available. We provide a local convergence result for the approximation version of the GI algorithm as well as a study of its numerical performance in the estimation setting. We conclude that PLH offers accuracy closed to that of a neural net while requiring, via our GI algorithm, far shorter training time and preserving desired invariant properties unlike any other presently popular basis family

Representation of Color Space Transformations for Effective Calibration and Control

We propose the minvar algorithm for computing continuous, continuously invertible, piecewise linear (PL) approximations of color space transformations that can serve as functional replacements wherever look-up tables are presently used. After motivating the importance of invertible approximants in color space management applications, we review the parameterization and computational implementation of PL functions as representing one useful instance of this notion. Finally, we describe the present version of the minvar algorithm and compare the approximations it yields with standard industrial practice — interpolation of look-up table data

Phase transition curves for mesoscopic superconducting samples

We compute the phase transition curves for mesoscopic superconductors. Special emphasis is given to the limiting shape of the curve when the magnetic flux is large. We derive an asymptotic formula for the ground state of the Schr\"odinger equation in the presence of large applied flux. The expansion is shown to be sensitive to the smoothness of the domain. The theoretical results are compared to recent experiments.Comment: 8 pages, 1 figur

Ensemble Sales Forecasting Study in Semiconductor Industry

Sales forecasting plays a prominent role in business planning and business strategy. The value and importance of advance information is a cornerstone of planning activity, and a well-set forecast goal can guide sale-force more efficiently. In this paper CPU sales forecasting of Intel Corporation, a multinational semiconductor industry, was considered. Past sale, future booking, exchange rates, Gross domestic product (GDP) forecasting, seasonality and other indicators were innovatively incorporated into the quantitative modeling. Benefit from the recent advances in computation power and software development, millions of models built upon multiple regressions, time series analysis, random forest and boosting tree were executed in parallel. The models with smaller validation errors were selected to form the ensemble model. To better capture the distinct characteristics, forecasting models were implemented at lead time and lines of business level. The moving windows validation process automatically selected the models which closely represent current market condition. The weekly cadence forecasting schema allowed the model to response effectively to market fluctuation. Generic variable importance analysis was also developed to increase the model interpretability. Rather than assuming fixed distribution, this non-parametric permutation variable importance analysis provided a general framework across methods to evaluate the variable importance. This variable importance framework can further extend to classification problem by modifying the mean absolute percentage error(MAPE) into misclassify error. Please find the demo code at : https://github.com/qx0731/ensemble_forecast_methodsComment: 14 pages, Industrial Conference on Data Mining 2017 (ICDM 2017

An Introduction to the SMARTRISK Heroes Program: Positive Social Marketing for Adolescent Injury Prevention

This article provides background on the SMARTRISK Heroes Program, a mobile stage production that introduces young people to the prevalence of unintentional injury for their age group and presents them with a series of strategies that will reduce the likelihood that they will be unintentionally injured or killed. The program logic is consistent with theoretical work from the area of health promotion including the Protection Motivation Theory and the Transtheoretical Model of Stages of Change. The SMARTRISK Heroes Program has been the subject of a number of past evaluations that are briefly described. The program logic model was included in this article. Additional information on the program and its evaluations can be found at http://www.smartrisk.ca by selecting SMARTRISK Heroes, under the “Youth Tab.