Mathematical models of electric activity in cardiac tissue are becoming increasingly powerful tools in the study of cardiac arrhythmias. Considered here are mathematical models based on ordinary differential equations (ODEs) and partial differential equations (PDEs) that describe the behaviour of this electrical activity. Generating an efficient numerical solution of these models is a challenging task, and in fact the physiological accuracy of tissue-scale models is often limited by the efficiency of the numerical solution process. In this thesis, we discuss two sets of experiments that test ideas for making the numerical solution process more efficient. In the first set of experiments, we examine the numerical solution of four single cell cardiac electrophysiological models, which consist solely of ODEs. We study the efficiency of using implicit-explicit Runge-Kutta (IMEX-RK) splitting methods to solve these models. We find that variable step-size implementations of IMEX-RK methods (ARK3 and ARK5) that take advantage of Jacobian structure clearly outperform most methods commonly used in practice for two of the models, and they outperform all methods commonly used in practice for the remaining models. In the second set of experiments, we examine the solution of the bidomain model, a model consisting of both ODEs and PDEs that are typically solved separately. We focus these experiments on numerical methods for the solution of the two PDEs in the bidomain model. The most popular method for this task, the Crank-Nicolson method, produces unphysical oscillations; we propose a method based on a second-order L-stable singly diagonally implicit Runge-Kutta (SDIRK) method to eliminate these oscillations.<p> We find that although the SDIRK method is able to eliminate these unphysical oscillations, it is only more efficient for crude error tolerances.

Automated facial expression analysis for Emotion Recognition (ER) is an active research area towards creating socially intelligent systems. The eye region, often considered integral for ER by psychologists and neuroscientists, has received very little attention in engineering and computer sciences. Using eye region as an input signal presents several bene ts for low-cost, non-intrusive ER applications. This work proposes two frameworks towards ER from eye region images. The first framework uses Local Binary Patterns (LBP) as the feature extractor on grayscale eye region images. The results validate the eye region as a signi cant contributor towards communicating the emotion in the face by achieving high person-dependent accuracy. The system is also able to generalize well across di erent environment conditions. In the second proposed framework, a color-based approach to ER from the eye region is explored using Local Color Vector Binary Patterns (LCVBP). LCVBP extend the traditional LBP by incorporating color information extracting a rich and a highly discriminative feature set, thereby providing promising results.

Automated facial expression analysis for Emotion Recognition (ER) is an active research area towards creating socially intelligent systems. The eye region, often considered integral for ER by psychologists and neuroscientists, has received very little attention in engineering and computer sciences. Using eye region as an input signal presents several bene ts for low-cost, non-intrusive ER applications. This work proposes two frameworks towards ER from eye region images. The first framework uses Local Binary Patterns (LBP) as the feature extractor on grayscale eye region images. The results validate the eye region as a signi cant contributor towards communicating the emotion in the face by achieving high person-dependent accuracy. The system is also able to generalize well across di erent environment conditions. In the second proposed framework, a color-based approach to ER from the eye region is explored using Local Color Vector Binary Patterns (LCVBP). LCVBP extend the traditional LBP by incorporating color information extracting a rich and a highly discriminative feature set, thereby providing promising results.

Visual implicit propositioning suggests that exemplars can activate stereotypes regarding social group members, affecting how individuals may perceive issues presented in a news format. This experiment sought to test the main tenet of visual implicit propositioning by examining how gay exemplars affect social estimate perceptions associated with the mortgage crisis and support for programs that would help homeowners refinance their mortgages. One hundred and ninety heterosexual college students read a news story featuring recent the mortgage crisis with a gay male couple, a heterosexual couple or a house and reported their perceptions related to various social groups. Gay exemplars did not affect social estimate perceptions, but did affect support for programs that would help homeowners refinance their homes. Attitudes towards gay males was examined as a moderating variable, however, this study did not find that these attitudes moderated the relationship between the exemplar presented and social estimates or support for programs.

Surface reconstruction of anatomical structures is an integral part of medical modeling. Contour information is extracted from serial cross-sections of tissue data and is stored as "slice" files. Although there are several reasonably efficient triangulation algorithms that reconstruct surfaces from slice data, the models generated from them have a jagged or faceted appearance due to the large inter-slice distance created by the sectioning process. Moreover, inconsistencies in user input aggravate the problem. So, we created a method that reduces inter-slice distance, as well as ignores the inconsistencies in the user input. Our method called the piecewise weighted implicit functions, is based on the approach of weighting smaller implicit functions. It takes only a few slices at a time to construct the implicit function. This method is based on a technique called variational interpolation. <p> Other approaches based on variational interpolation have the disadvantage of becoming unstable when the model is quite large with more than a few thousand constraint points. Furthermore, tracing the intermediate contours becomes expensive for large models. Even though some fast fitting methods handle such instability problems, there is no apparent improvement in contour tracing time, because, the value of each data point on the contour boundary is evaluated using a single large implicit function that essentially uses all constraint points. Our method handles both these problems using a sliding window approach. As our method uses only a local domain to construct each implicit function, it achieves a considerable run-time saving over the other methods. The resulting software produces interpolated models from large data sets in a few minutes on an ordinary desktop computer.

Social cognition is a fundamental aspect of human experience that enables us to have relationships with and understanding of other people. Social relationships have been shown to mitigate cognitive decline in old age and benefit cognitive functioning, and the social interaction on which these relationships rely requires an extensive network of cognitive processes, and by extension neural systems, that have not, as of yet, been widely studied in older adults. Nor has the function of these systems been tied to social relationships in the real world. Here, I will compare self-reports of real-world quality and extent of social networks with behavioral and neural measures of other-regard in the laboratory. It is hoped that by so doing we will be able to link social neuroscientific measures in the laboratory with persons’ perceptions of the quality and extent of their social relationships. In this study, other-regard in older adults was operationalized with a reaction-time measure in an implicit turn-taking task, neural measures were provided by dense array EEG, and all participants also completed self-report measures of empathy subscales and of the quality and extent of their social networks. I found that measures of empathic personal distress decreased with increased other-regard (r = -0.36, p = 0.01, beta = 0.47), while increased quality and extent of social networks associated marginally with increased other-regard (r = 0.20, p = 0.11, beta = 0.39). Neural analyses are ongoing and are expected to show differential activation consistent with cognitive processes such as theory of mind, empathy, joint attention, and executive control.

In this study, the relationship between essentialism, religious beliefs, and views of change was investigated. Participants were given surveys containing three sets of items and a demographic questionnaire. Item sets included the Intrinsic/Extrinsic-Revised Scale of Religiosity, the Essentialist Belief Scales, and the Change Vignettes. Results indicated those with gradualist religious views were not more likely to endorse essentialist views when compared to those with conversionist views. Those who essentialized at high levels were not less likely to endorse the possibility of change in comparison to those who essentialized at lower levels. Participants with high levels of extrinsic religiosity were not more likely to demonstrate essentialist beliefs as compared to those with low levels of extrinsic religiosity. In addition, individuals did not view change as more plausible as they were determined to be more intrinsically religious. No relationship was found between religious affiliation and views of change or measures of essentialist thought. Those belonging to Fundamentalists and Liberalist denominational groups were found to be similar in regard to beliefs about change, and essentialism, as well as intrinsic and extrinsic religiosity. Those classified as Others were significantly different from Fundamentalist and Liberalists, excluding ratings of the importance of good deeds.

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Researchers have raised concerns about measurement equivalence in comparing personalities across cultures using personality assessments. The self-reported personality measurements often do not assess the same construct, trigger different response styles (i.e., extreme response style), or use behavioral exemplars that are inappropriate across cultures (Byrne&Watkins, 2003; Chen, 2008; Poortinga, van de Vijber,&van Hermert, 2002, van de Vijver&Leung, 1997). James et al. (2005) developed a new measurement system for aggression that is different from traditional personality assessment. It is referred to as the Conditional Reasoning Test for Aggression (CRT-A). The CRT-A is an indirect measure for assessing unconscious motives to be aggressive that was developed in the USA. It has not been studied with people from different cultures. Study 1 investigated the equivalences of the Aggression Questionnaire (AQ) and the CRT-A by administering both to groups of Americans (n=432) and Koreans (n=363). Results based on the exploratory and confirmatory factor analyses and DIF analyses showed that the AQ and CRT-A are not invariant across these cultures. Study 2 replicated LeBreton et al.(2007) study regarding faking issues of the CRT-A with the Korean population. Study 2 found that on the CRT-A, Koreans were able to identify aggressive alternatives when they were told to do so, and Korean students and employees did not score differently on the CRT-A. Implications and future directions of the study are discussed herein.

Recent studies show that when words are correlated with the colours they are printed in (e.g., MOVE is presented 75% of the time in blue), colour identification is faster when the word is presented in its expected colour (MOVE in blue) than in an unexpected colour (MOVE in green). The present series of experiments explored the possible mechanisms involved in this colour-word contingency learning effect. Experiment 1 demonstrated that the effect was already present after 18 learning trials. During subsequent unlearning, the effect extinguished equally rapidly, suggesting that only a handful of the most recently encountered trials are used to predict responses. Two reanalyses of data from Schmidt, Crump, Cheesman, and Besner (2007) ruled out an account of the effect in terms of stimulus repetitions. Experiments 2 and 3 demonstrated that participants who carry a memory load do not show a contingency effect, supporting the hypothesis that limited-capacity resources are used to retrieve a small number of trial memories in order to prepare a response. Experiment 4 demonstrated that memory resources are required for both storage and retrieval processes.