Detector and Event Visualization with SketchUp at the CMS Experiment

We have created 3D models of the CMS detector and particle collision events in SketchUp, a 3D modelling program. SketchUp provides a Ruby API which we use to interface with the CMS Detector Description to create 3D models of the CMS detector. With the Ruby API, we also have created an interface to the JSON-based event format used for the iSpy event display to create 3D models of CMS events. These models have many applications related to 3D representation of the CMS detector and events. Figures produced based on these models were used in conference presentations, journal publications, technical design reports for the detector upgrades, art projects, outreach programs, and other presentations.Comment: 5 pages, 6 figures, Proceedings for CHEP 2013, 20th International Conference on Computing in High Energy and Nuclear Physic

Chern-Weil techniques on loop spaces and the Maslov index in partial differential equations

This dissertation consists of two distinct parts, the first concerning S^1-equivariant cohomology of loop spaces and the second concerning stability in partial differential equations. In the first part of this dissertation, we study the existence of S^1-equivariant characteristic classes on certain natural infinite rank bundles over the loop space LM of a manifold M. We discuss the different S^1-equivariant cohomology theories in the literature and clarify their relationships. We attempt to use S^1-equivariant Chern-Weil techniques to construct S^1-equivariant characteristic classes. The main result is the construction of a sequence of S^1-equivariant characteristic classes on the total space of the bundles, but these classes do not descend to the base LM. In addition, we identify a class of bundles for which a single S^1-equivariant characteristic class does admit an S^1-equivariant Chern-Weil construction. In the second part of this dissertation, we study the Maslov index as a tool to analyze stability of steady state solutions to a reaction-diffusion equation in one spatial dimension. We show that the path of unstable subspaces associated to this equation is governed by a matrix Riccati equation whose solution S develops singularities when changes in the Maslov index occur. Our main result proves that at these singularities the change in Maslov index equals the number of eigenvalues of S that increase to +∞ minus the number of eigenvalues that decrease to -∞

Computational Structure of Evolved Forgiveness Systems.

Researchers have recently suggested that humans possess dedicated cognitive systems for forgiveness, which evolved to repair valuable cooperative relationships with transgressors and stave off harmful revenge behaviors. These putative systems are computational in nature, utilizing information pertaining to the relationship value, exploitation risk, and genetic relatedness of a transgressor in determining whether or not to employ forgiveness. While a few studies have provided empirical support for this conjecture, surprisingly little empirical research has been conducted to determine if forgiveness systems actually have such a computational structure. The aim of this thesis was to fill this gap in the literature by testing hypotheses related to evolved systems for forgiveness. Using a sample of undergraduate participants, we tested hypotheses related to the computational structure of forgiveness, focusing on the role of internal regulatory variables (IRVs) including relationship value, exploitation risk, and genetic relatedness. Seven separate predictions were all empirically supported, providing verisimilitude to evolved accounts of forgiveness, and offering new insights into the form and function of forgiveness systems

Searching for Prosociality in Qualitative Data: Comparing Manual, Closed-Vocabulary, and Open-Vocabulary Methods

Although most people present themselves as possessing prosocial traits, people differ in the extent to which they actually act prosocially in everyday life. Qualitative data that were not ostensibly collected to measure prosociality might contain information about prosocial dispositions that is not distorted by self–presentation concerns. This paper seeks to characterise charitable donors from qualitative data. We compared a manual approach of extracting predictors from participants’ self–described personal strivings to two automated approaches: A summation of words predefined as prosocial and a support vector machine classifier. Although variables extracted by the support vector machine predicted donation behaviour well in the training sample ( N = 984), virtually, no variables from any method significantly predicted donations in a holdout sample ( N = 496). Raters’ attempts to predict donations to charity based on reading participants’ personal strivings were also unsuccessful. However, raters’ predictions were associated with past charitable involvement. In sum, predictors derived from personal strivings did not robustly explain variation in charitable behaviour, but personal strivings may nevertheless contain some information about trait prosociality. The sparseness of personal strivings data, rather than the irrelevance of open–ended text or individual differences in goal pursuit, likely explains their limited value in predicting prosocial behaviour. © 2020 European Association of Personality Psycholog

Autonomic physiological data associated with simulator discomfort

The development of a physiological monitoring capability for the Army's advanced helicopter simulator facility is reported. Additionally, preliminary physiological data is presented. Our objective was to demonstrate the sensitivity of physiological measures in this simulator to self-reported simulator sickness. The data suggested that heart period, hypergastria, and skin conductance level were more sensitive to simulator sickness than were vagal tone and normal electrogastric activity