Doctor of Philosophy

dissertationThis study aimed to examine ambulatory blood pressure (ABP) differences between men and women who make larger appraisal biases of their spouse using the dimensions of the interpersonal circumplex (IPC), and to observe whether these differences depend or are attenuated based on whether the ABP readings took place during a stressor, with the spouse, or others. Appraisal biases have been associated with individual differences in negative affect, but few studies have examined the relationship between appraisal biases on the IPC and blood presssure during normal daily activities. A sample of 263 middle aged and older married couples who were part of a larger study were asked to fill out a questionnaire that included demographic information, as well as participate in a laboratory conflict task with their spouse and then rate how controlling, hostile, friendly, and submissive they viewed their spouse. These interactions were also coded by objective observers, and the discrepancy calculated the bias. The participants underwent simultaneous 1-day monitoring of ambulatory BP, at the same time keeping a diary that included a number of situational variables. Significant linear results were present for systolic blood pressure differences for those who make controlling apprasisal biases and curvilinear effects for both systolic and diastolic blood pressure for those who make hostile appraisal biases. These results suggest that booth dimensions of social behavior on the IPC demonstrate association with ABP, demonstrating the IPC's usefulness as an integrative framework to understand psychological factor that confers risk for coronary heart disease. The causal relationship is not understood

The new college: latin & english dictionary

x, 502 p

Working draft submitted to CHI 2006 Robust Reputations for Peer-to-peer Marketplaces ∗

We have developed a suite of algorithms to address two large problems confronting reputation systems for large peer-topeer markets: data sparseness and inaccurate feedback. To handle sparse data, we propose a Bayesian version of the well-known Percent Positive Feedback system. To mitigate the effect of inaccurate feedback – particularly retaliatory negative feedback – we propose EM-trust, which uses a latent variable statistical model of the feedback process. Using a marketplace simulator, we demonstrate that both of these algorithms provide more accurate reputations than standard Percent Positive Feedback. Finally, we show that even better performance can be obtained by combining the two approaches into a Bayesian EM-trust reputation system