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Regime fatigue : a cognitive-psychological model for identifying a socialized negativity effect in U.S. Senatorial and Gubernatorial elections from 1960-2008Giles, Clark Andrew 11 July 2014 (has links)
Indiana University-Purdue University Indianapolis (IUPUI) / This research project proposes to try to isolate and measure the influence of “regime fatigue” on gubernatorial elections and senatorial elections in the United States where there is no incumbent running. The research begins with a review of the negativity effect and its potential influence on schema-based impression forming by voters. Applicable literature on the topics of social clustering and homophily is then highlighted as it provides the vehicle through which the negativity effect disseminates across collections of socially-clustered individuals and ultimately contributes to changing tides of public opinion despite the fact that the political party identification can remain relatively fixed in the aggregate.
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Revision of an artificial neural network enabling industrial sortingMalmgren, Henrik January 2019 (has links)
Convolutional artificial neural networks can be applied for image-based object classification to inform automated actions, such as handling of objects on a production line. The present thesis describes theoretical background for creating a classifier and explores the effects of introducing a set of relatively recent techniques to an existing ensemble of classifiers in use for an industrial sorting system.The findings indicate that it's important to use spatial variety dropout regularization for high resolution image inputs, and use an optimizer configuration with good convergence properties. The findings also demonstrate examples of ensemble classifiers being effectively consolidated into unified models using the distillation technique. An analogue arrangement with optimization against multiple output targets, incorporating additional information, showed accuracy gains comparable to ensembling. For use of the classifier on test data with statistics different than those of the dataset, results indicate that augmentation of the input data during classifier creation helps performance, but would, in the current case, likely need to be guided by information about the distribution shift to have sufficiently positive impact to enable a practical application. I suggest, for future development, updated architectures, automated hyperparameter search and leveraging the bountiful unlabeled data potentially available from production lines.
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