The continued salience of religious voting in the United States, Germany, and Great Britain

Conventional wisdom on party systems in advanced industrial democracies holds that modern electorates are dealigned and that social cleavages no longer structure party politics. Recent work on class cleavages has challenged this stylized fact. The analysis performed here extends this criticism to the religious-secular cleavage. Using path analysis and comparing the current electorates of the United States, Germany, and Great Britain with the early 1960s, this paper demonstrates that the religious-secular cleavage remains or has become a significant predictor of conservative vote choice. While the effects of the religious-secular cleavage on vote choice have become largely indirect, the total of the direct and indirect effects is substantial and equivalent to the effects of class and status.

What’s information got to do with it? Third-party voting in plurality systems

Although strategic voting theory predicts that the number of parties will not exceed two in single-member district plurality systems, the observed number of parties often does. Previous research suggests that the reason why people vote for third parties is that they possess inaccurate information about the parties’ relative chances of winning. However, research has yet to determine whether third-party voting persists under conditions of accurate information. In this article, we examine whether possessing accurate information prevents individuals from voting for third-placed parties in the 2005 and 2010 British elections. We find that possessing accurate information does not prevent most individuals from voting for third-placed parties and that many voters possess reasonably accurate information regarding the viability of the parties in their constituencies. These findings suggest that arguments emphasizing levels of voter information as a major explanation for why multiparty systems often emerge in plurality systems are exaggerated.

A predictive maintenance system for integral type faults based on support vector machines:An application to ion implantation

In semiconductor fabrication processes, effective management of maintenance operations is fundamental to decrease costs associated with failures and downtime. Predictive Maintenance (PdM) approaches, based on statistical methods and historical data, are becoming popular for their predictive capabilities and low (potentially zero) added costs. We present here a PdM module based on Support Vector Machines for prediction of integral type faults, that is, the kind of failures that happen due to machine usage and stress of equipment parts. The proposed module may also be employed as a health factor indicator. The module has been applied to a frequent maintenance problem in semiconductor manufacturing industry, namely the breaking of the filament in the ion-source of ion-implantation tools. The PdM has been tested on a real production dataset. © 2013 IEEE.

Electronic voting in the French legislative elections of 2012

This chapter presents an analysis of the unprecedented use of electronic voting by expatriates during the French 2012 legislative elections, when they elected their own representatives (referred to here as ‘deputies’), to the National Assembly in Paris for the first time, in 11 newly created overseas constituencies.
The study is presented within the broader perspective of electronic voting in France more generally, and in the historical context of extra-territorial voting by French expatriates. The authors discuss the main issues and controversies that arose during the 2012 elections, and in a final section analyse the results. The authors conclude by drawing attention to recent developments in electronic voting in France since the 2012 elections, which suggest that although there was much criticism expressed by experts of electronic voting as to the security and transparency of the system used, the official discourse that acclaimed the experience as a success, appears to have convinced its target audience.

Emotions and voting in EU referendums

There is an emerging scholarship on the emotional bases of political opinion and behaviour and, in particular, the contrasting implications of two distinct negative emotions - anger and anxiety. I apply the insights in this literature to the previously unresearched realm of the emotional bases of voting in EU referendums. I hypothesise that anxious voters rely on substantive EU issues and angry voters rely on second-order factors relating to domestic politics (partisanship and satisfaction with government). Focusing on the case of Irish voting in the Fiscal Compact referendum, and using data from a representative sample of voters, I find support for the hypotheses and discuss the implications of the findings for our understanding of the emotional conditionality of EU referendum voting. 

Potentially Voting across the Divide in Deeply Divided Places: Ethnic Catch-All Voting in Consociational Northern Ireland

I elaborate a model of cross-bloc party support in deeply divided places. The model expects that the variation in the level of electoral support that citizens in Community A have for parties in Community B is a function of citizens' evaluations of the relative ability of parties in Community B to represent the interests of all communities. This 'ethnic catch-all' model of cross-bloc party support is tested in the context of consociational Northern Ireland, using data from a representative survey conducted directly after the 2010 Westminster general election. The findings are asymmetric: the model explains Protestant support for nationalist parties but not Catholic support for unionist parties. The findings, and their implications, are discussed.

Extreme learning machines for virtual metrology and etch rate prediction

Virtual metrology (VM) aims to predict metrology values using sensor data from production equipment and physical metrology values of preceding samples. VM is a promising technology for the semiconductor manufacturing industry as it can reduce the frequency of in-line metrology operations and provide supportive information for other operations such as fault detection, predictive maintenance and run-to-run control. Methods with minimal user intervention are required to perform VM in a real-time industrial process. In this paper we propose extreme learning machines (ELM) as a competitive alternative to popular methods like lasso and ridge regression for developing VM models. In addition, we propose a new way to choose the hidden layer weights of ELMs that leads to an improvement in its prediction performance.

Defining Democracy: Voting Procedures in Decision-making, Elections and Governance

Book Review: Emerson, Peter, Defining Democracy: Voting Procedures in Decision-making, Elections and
Governance (2nd edn), Springer, London, 2012,

A Fast Algorithm for Sparse Support Vector Machines for Mobile Computing Applications

This research presents a fast algorithm for projected support vector machines (PSVM) by selecting a basis vector set (BVS) for the kernel-induced feature space, the training points are projected onto the subspace spanned by the selected BVS. A standard linear support vector machine (SVM) is then produced in the subspace with the projected training points. As the dimension of the subspace is determined by the size of the selected basis vector set, the size of the produced SVM expansion can be specified. A two-stage algorithm is derived which selects and refines the basis vector set achieving a locally optimal model. The model expansion coefficients and bias are updated recursively for increase and decrease in the basis set and support vector set. The condition for a point to be classed as outside the current basis vector and selected as a new basis vector is derived and embedded in the recursive procedure. This guarantees the linear independence of the produced basis set. The proposed algorithm is tested and compared with an existing sparse primal SVM (SpSVM) and a standard SVM (LibSVM) on seven public benchmark classification problems. Our new algorithm is designed for use in the application area of human activity recognition using smart devices and embedded sensors where their sometimes limited memory and processing resources must be exploited to the full and the more robust and accurate the classification the more satisfied the user. Experimental results demonstrate the effectiveness and efficiency of the proposed algorithm. This work builds upon a previously published algorithm specifically created for activity recognition within mobile applications for the EU Haptimap project [1]. The algorithms detailed in this paper are more memory and resource efficient making them suitable for use with bigger data sets and more easily trained SVMs.

Explaining voting behaviour on free votes: Solely a matter of preference?

While studies examining free votes find MPs’ preferences influence their voting behaviour, most studies also show MPs tend to divide along party lines even after the whips have been withdrawn. Recent work offers a possible alternative explanation for this finding: this sustained party cohesion represents the impact of MPs’ party identification similar to party identification effects in the electorate. This argument is tested using a series of free votes on same-sex relations. Even after controlling for preferences using several direct measures, party continues to shape voting behaviour. Although indirect, this provides evidence in favour of the party-asidentification argument.