“The Double Feature Evil”: Efforts to Eliminate the American Dual Bill

The essay discusses the actions and motivations of various groups that tried to end the practice of double feature film exhibition in the United States during the 1930s and 1940s. Used as a price-cutting strategy, double features were embraced by marginal exhibitors and low-budget producers, but attacked by most major studios and established theatre chains. Methods employed to control the double feature included voluntary bans, governmental legislation, and legal action. During the depression, Franklin D. Roosevelt's New Deal opposed the double feature as a strategy likely to undermine established admission price levels. But the double feature proved resilient and survived all these efforts, as well as an additional series of assaults, based on conservation of energy and materiel, mounted during the Second World War.

Popular Writers: Women Historians, the Academic Community and National History Writing

The research for this paper formed part of the European Science Foundation project on Representations of the Past: The Writing of National Histories in Europe. Using data generated by the project, the article traces the emergence of professional academic women historians in twentieth-century European universities. It argues that the marginalisation of women historians in academia until the 1980s led women history graduates to develop research-based careers outside the university. In particular, the ambiguous attitude of academic historians towards popular history writing opened up a space for the woman author. The article analyses the careers and writings of five historians who pursued very successful careers as authors of popular history in England, France, Ireland and Scotland. They were among the first 'public' historians.

An efficient feature selection method for mobile devices with application to activity recognition

This paper presents a feature selection method for data classification, which combines a model-based variable selection technique and a fast two-stage subset selection algorithm. The relationship between a specified (and complete) set of candidate features and the class label is modelled using a non-linear full regression model which is linear-in-the-parameters. The performance of a sub-model measured by the sum of the squared-errors (SSE) is used to score the informativeness of the subset of features involved in the sub-model. The two-stage subset selection algorithm approaches a solution sub-model with the SSE being locally minimized. The features involved in the solution sub-model are selected as inputs to support vector machines (SVMs) for classification. The memory requirement of this algorithm is independent of the number of training patterns. This property makes this method suitable for applications executed in mobile devices where physical RAM memory is very limited. An application was developed for activity recognition, which implements the proposed feature selection algorithm and an SVM training procedure. Experiments are carried out with the application running on a PDA for human activity recognition using accelerometer data. A comparison with an information gain based feature selection method demonstrates the effectiveness and efficiency of the proposed algorithm.