Cultural and Social Cognitive Predictors of Academic Satisfaction in Southeast Asian American College Students

This study examined the predictive utility of Lent’s (2004) social cognitive model of well-being in the context of academic satisfaction with a sample of Southeast Asian American college students using a cross-sectional design. Path analysis was used to examine the role of perceived parental trauma, perceived parental acculturative stress, intergenerational family conflict, and social cognitive predictors to academic satisfaction. Participants were 111 Southeast Asian American and 111 East Asian American college students who completed online measures. Contrary to expectations, none of the contextual cultural variables were significant predictors of academic satisfaction. Also contrary to expectations, academic support and self-efficacy were not directly linked to academic satisfaction and outcome expectation was not linked to goal progress. Other social cognitive predictors were related directly and indirectly to academic satisfaction, consistent with prior research. Limitations and implications for future research and practice are addressed.

Sparse Signal Representation in Digital and Biological Systems

Theories of sparse signal representation, wherein a signal is decomposed as the sum of a small number of constituent elements, play increasing roles in both mathematical signal processing and neuroscience. This happens despite the differences between signal models in the two domains. After reviewing preliminary material on sparse signal models, I use work on compressed sensing for the electron tomography of biological structures as a target for exploring the efficacy of sparse signal reconstruction in a challenging application domain. My research in this area addresses a topic of keen interest to the biological microscopy community, and has resulted in the development of tomographic reconstruction software which is competitive with the state of the art in its field. Moving from the linear signal domain into the nonlinear dynamics of neural encoding, I explain the sparse coding hypothesis in neuroscience and its relationship with olfaction in locusts. I implement a numerical ODE model of the activity of neural populations responsible for sparse odor coding in locusts as part of a project involving offset spiking in the Kenyon cells. I also explain the validation procedures we have devised to help assess the model's similarity to the biology. The thesis concludes with the development of a new, simplified model of locust olfactory network activity, which seeks with some success to explain statistical properties of the sparse coding processes carried out in the network.