Max Weber and the First World War: Protestant and Catholic living standards in Germany, 1915-1919

We assess informal institutions of Protestants and Catholics by investigating their economic
resilience in a natural experiment. The First World War constitutes an exogenous shock to living standards since the duration and intensity of the war exceeded all expectations. We assess the ability of Protestant and Catholic communities to cope with increasing food prices and wartime black markets. Literature based on Weber (1904, 1905) suggests that Protestants must be more resilient than their Catholic peers. Using individual height data on some 2,800 Germans to assess levels of malnutrition during the war, we find that living standards for both Protestants and Catholics declined; however, the decrease of Catholics’ height was disproportionately large. Our empirical analysis finds a large statistically significant difference between Protestants and Catholics for the 1914-19 birth cohort, and we argue that this height gap cannot be attributed to socioeconomic background and fertility alone.

Max Weber and the First World War: Protestant and Catholic living standards in Germany, 1914-1919

We assess informal institutions of Protestants and Catholics by investigating their economic resilience in a natural experiment. The First World War constitutes an exogenous shock to living standards since the duration and intensity of the war exceeded all expectations. We assess the ability of Protestant and Catholic communities to cope with increasing food prices and wartime black markets. Literature based on Weber (1904, 1905) suggests that Protestants must be more resilient than their Catholic peers. Using individual height data on some 2,800 Germans to assess levels of malnutrition during the war, we find that living standards for both Protestants and Catholics declined; however, the decrease of Catholics’ height was disproportionately large. Our empirical analysis finds a large statistically significant difference between Protestants and Catholics for the 1915–19 birth cohort, and we argue that this height gap cannot be attributed to socioeconomic background and fertility alone.

Improving f(MAX)/f(T) ratio in FinFETs using source/drain extension region engineering

A design methodology to optimise the ratio of maximum oscillation frequency to cutoff frequency, f(MAX)/f(T), in 60 nm FinFETs is presented. Results show that 25 to 60% improvement in f(MAX)/f(T) at drain currents of 20-300 mu A/mu m can be achieved in a non-overlap gate-source/drain architecture. The reported work provides new insights into the design and optimisation of nanoscale FinFETs for RF applications.

Max separation clustering for feature extraction from Optical Emission Spectroscopy Data

This paper proposes max separation clustering (MSC), a new non-hierarchical clustering method used for feature extraction from optical emission spectroscopy (OES) data for plasma etch process control applications. OES data is high dimensional and inherently highly redundant with the result that it is difficult if not impossible to recognize useful features and key variables by direct visualization. MSC is developed for clustering variables with distinctive patterns and providing effective pattern representation by a small number of representative variables. The relationship between signal-to-noise ratio (SNR) and clustering performance is highlighted, leading to a requirement that low SNR signals be removed before applying MSC. Experimental results on industrial OES data show that MSC with low SNR signal removal produces effective summarization of the dominant patterns in the data.

Min-BDeu and Max-BDeu Scores for Learning Bayesian Networks

This work presents two new score functions based on the Bayesian Dirichlet equivalent uniform (BDeu) score for learning Bayesian network structures. They consider the sensitivity of BDeu to varying parameters of the Dirichlet prior. The scores take on the most adversary and the most beneficial priors among those within a contamination set around the symmetric one. We build these scores in such way that they are decomposable and can be computed efficiently. Because of that, they can be integrated into any state-of-the-art structure learning method that explores the space of directed acyclic graphs and allows decomposable scores. Empirical results suggest that our scores outperform the standard BDeu score in terms of the likelihood of unseen data and in terms of edge discovery with respect to the true network, at least when the training sample size is small. We discuss the relation between these new scores and the accuracy of inferred models. Moreover, our new criteria can be used to identify the amount of data after which learning is saturated, that is, additional data are of little help to improve the resulting model.